Merge branch 'develop' into webdataset

README.md

+
 ([简体中文](./README_cn.md)|English)
 <p align="center">
   <img src="./docs/images/PaddleSpeech_logo.png" />
@@ -494,6 +495,14 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
       <a href = "./examples/aishell3/vc1">ge2e-fastspeech2-aishell3</a>
       </td>
     </tr>
+     <tr>
+      <td rowspan="3">End-to-End</td>
+      <td>VITS</td>
+      <td >CSMSC</td>
+      <td>
+      <a href = "./examples/csmsc/vits">VITS-csmsc</a>
+      </td>
+    </tr>
   </tbody>
 </table>
 

README_cn.md

+
 (简体中文|[English](./README.md))
 <p align="center">
   <img src="./docs/images/PaddleSpeech_logo.png" />
@@ -481,6 +482,15 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
       <a href = "./examples/aishell3/vc1">ge2e-fastspeech2-aishell3</a>
       </td>
     </tr>
+    </tr>
+     <tr>
+      <td rowspan="3">端到端</td>
+      <td>VITS</td>
+      <td >CSMSC</td>
+      <td>
+      <a href = "./examples/csmsc/vits">VITS-csmsc</a>
+      </td>
+    </tr>
   </tbody>
 </table>
 

dataset/aidatatang_200zh/README.md

-# [Aidatatang_200zh](http://www.openslr.org/62/)
+# [Aidatatang_200zh](http://openslr.elda.org/62/)
 
 Aidatatang_200zh is a free Chinese Mandarin speech corpus provided by Beijing DataTang Technology Co., Ltd under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License.
 The contents and the corresponding descriptions of the corpus include:

dataset/aishell/README.md

-# [Aishell1](http://www.openslr.org/33/)
+# [Aishell1](http://openslr.elda.org/33/)
 
 This Open Source Mandarin Speech Corpus, AISHELL-ASR0009-OS1, is 178 hours long. It is a part of AISHELL-ASR0009, of which utterance contains 11 domains, including smart home, autonomous driving, and industrial production. The whole recording was put in quiet indoor environment, using 3 different devices at the same time: high fidelity microphone (44.1kHz, 16-bit,); Android-system mobile phone (16kHz, 16-bit), iOS-system mobile phone (16kHz, 16-bit). Audios in high fidelity were re-sampled to 16kHz to build AISHELL- ASR0009-OS1. 400 speakers from different accent areas in China were invited to participate in the recording. The manual transcription accuracy rate is above 95%, through professional speech annotation and strict quality inspection. The corpus is divided into training, development and testing sets. ( This database is free for academic research, not in the commerce, if without permission. )

dataset/aishell/aishell.py

@@ -31,7 +31,7 @@ from utils.utility import unpack
 
 DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech')
 
-URL_ROOT = 'http://www.openslr.org/resources/33'
+URL_ROOT = 'http://openslr.elda.org/resources/33'
 # URL_ROOT = 'https://openslr.magicdatatech.com/resources/33'
 DATA_URL = URL_ROOT + '/data_aishell.tgz'
 MD5_DATA = '2f494334227864a8a8fec932999db9d8'

dataset/librispeech/librispeech.py

@@ -31,7 +31,7 @@ import soundfile
 from utils.utility import download
 from utils.utility import unpack
 
-URL_ROOT = "http://www.openslr.org/resources/12"
+URL_ROOT = "http://openslr.elda.org/resources/12"
 #URL_ROOT = "https://openslr.magicdatatech.com/resources/12"
 URL_TEST_CLEAN = URL_ROOT + "/test-clean.tar.gz"
 URL_TEST_OTHER = URL_ROOT + "/test-other.tar.gz"

dataset/magicdata/README.md

-# [MagicData](http://www.openslr.org/68/)
+# [MagicData](http://openslr.elda.org/68/)
 
 MAGICDATA Mandarin Chinese Read Speech Corpus was developed by MAGIC DATA Technology Co., Ltd. and freely published for non-commercial use.
 The contents and the corresponding descriptions of the corpus include:

dataset/mini_librispeech/mini_librispeech.py

@@ -30,7 +30,7 @@ import soundfile
 from utils.utility import download
 from utils.utility import unpack
 
-URL_ROOT = "http://www.openslr.org/resources/31"
+URL_ROOT = "http://openslr.elda.org/resources/31"
 URL_TRAIN_CLEAN = URL_ROOT + "/train-clean-5.tar.gz"
 URL_DEV_CLEAN = URL_ROOT + "/dev-clean-2.tar.gz"
 

dataset/musan/musan.py

@@ -34,7 +34,7 @@ from utils.utility import unpack
 
 DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech')
 
-URL_ROOT = 'https://www.openslr.org/resources/17'
+URL_ROOT = 'https://openslr.elda.org/resources/17'
 DATA_URL = URL_ROOT + '/musan.tar.gz'
 MD5_DATA = '0c472d4fc0c5141eca47ad1ffeb2a7df'
 

dataset/primewords/README.md

-# [Primewords](http://www.openslr.org/47/)
+# [Primewords](http://openslr.elda.org/47/)
 
 This free Chinese Mandarin speech corpus set is released by Shanghai Primewords Information Technology Co., Ltd.
 The corpus is recorded by smart mobile phones from 296 native Chinese speakers. The transcription accuracy is larger than 98%, at the confidence level of 95%. It is free for academic use.

dataset/rir_noise/rir_noise.py

@@ -34,7 +34,7 @@ from utils.utility import unzip
 
 DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech')
 
-URL_ROOT = '--no-check-certificate http://www.openslr.org/resources/28'
+URL_ROOT = '--no-check-certificate https://us.openslr.org/resources/28/rirs_noises.zip'
 DATA_URL = URL_ROOT + '/rirs_noises.zip'
 MD5_DATA = 'e6f48e257286e05de56413b4779d8ffb'
 

dataset/st-cmds/README.md

-# [FreeST](http://www.openslr.org/38/)
+# [FreeST](http://openslr.elda.org/38/)

dataset/thchs30/README.md

-# [THCHS30](http://www.openslr.org/18/)
+# [THCHS30](http://openslr.elda.org/18/)
 
 This is the *data part* of the `THCHS30 2015` acoustic data
 & scripts dataset.

dataset/thchs30/thchs30.py

@@ -32,7 +32,7 @@ from utils.utility import unpack
 
 DATA_HOME = os.path.expanduser('~/.cache/paddle/dataset/speech')
 
-URL_ROOT = 'http://www.openslr.org/resources/18'
+URL_ROOT = 'http://openslr.elda.org/resources/18'
 # URL_ROOT = 'https://openslr.magicdatatech.com/resources/18'
 DATA_URL = URL_ROOT + '/data_thchs30.tgz'
 TEST_NOISE_URL = URL_ROOT + '/test-noise.tgz'

demos/streaming_asr_server/web/app.py

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright 2021 Mobvoi Inc. All Rights Reserved.
-# Author: zhendong.peng@mobvoi.com (Zhendong Peng)
-import argparse
-
-from flask import Flask
-from flask import render_template
-
-parser = argparse.ArgumentParser(description='training your network')
-parser.add_argument('--port', default=19999, type=int, help='port id')
-args = parser.parse_args()
-
-app = Flask(__name__)
-
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=args.port, debug=True)

demos/streaming_asr_server/web/readme.md

 # paddlespeech serving 网页Demo
 
-- 感谢[wenet](https://github.com/wenet-e2e/wenet)团队的前端demo代码.
+![图片](./paddle_web_demo.png)
 
+step1: 开启流式语音识别服务器端
 
-## 使用方法
-### 1. 在本地电脑启动网页服务
-   ```
-   python app.py
+```
+# 开启流式语音识别服务
+cd PaddleSpeech/demos/streaming_asr_server
+paddlespeech_server start --config_file conf/ws_conformer_wenetspeech_application_faster.yaml
+```
 
-   ```
+step2: 谷歌游览器打开 `web`目录下`index.html`
 
-### 2. 本地电脑浏览器
+step3: 点击`连接`，验证WebSocket是否成功连接
+
+step4：点击开始录音(弹窗询问，允许录音)
 
-在浏览器中输入127.0.0.1:19999 即可看到相关网页Demo。
 
-![图片](./paddle_web_demo.png)
  

demos/streaming_asr_server/web/static/css/font-awesome.min.css

-/*!
- *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
- *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
- */@font-face{font-family:'FontAwesome';src:url('../fonts/fontawesome-webfont.eot?v=4.7.0');src:url('../fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'),url('../fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'),url('../fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'),url('../fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'),url('../fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');font-weight:normal;font-style:normal}.fa{display:inline-block;font:normal normal normal 14px/1 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demos/streaming_asr_server/web/static/css/style.css

-/*
-* @Author: baipengxia
-* @Date:   2021-03-12 11:44:28
-* @Last Modified by:   baipengxia
-* @Last Modified time: 2021-03-12 15:14:24
-*/
-
-/** COMMON RESET **/
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-td {
-  margin: 0;
-  padding: 0;
-  color: #000;
-}
-
-body {
-  font-size: 14px;
-}
-html, body {
-  min-width: 1200px;
-}
-
-button,
-input,
-select,
-textarea {
-  font-size: 14px;
-}
-
-h1 {
-  font-size: 18px;
-}
-
-h2 {
-  font-size: 14px;
-}
-
-h3 {
-  font-size: 14px;
-}
-
-ul,
-ol,
-li {
-  list-style: none;
-}
-
-a {
-  text-decoration: none;
-}
-
-a:hover {
-  text-decoration: none;
-}
-
-fieldset,
-img {
-  border: none;
-}
-
-table {
-  border-collapse: collapse;
-  border-spacing: 0;
-}
-
-i {
-  font-style: normal;
-}
-
-label {
-  position: inherit;
-}
-
-.clearfix:after {
-  content: ".";
-  display: block;
-  height: 0;
-  clear: both;
-  visibility: hidden;
-}
-
-.clearfix {
-  zoom: 1;
-  display: block;
-}
-
-html,
-body {
-  font-family: Tahoma, Arial, 'microsoft yahei', 'Roboto', 'Droid Sans', 'Helvetica Neue', 'Droid Sans Fallback', 'Heiti SC', 'Hiragino Sans GB', 'Simsun', 'sans-self';
-}
-
-
-
-.audio-banner {
-  width: 100%;
-  overflow: auto;
-  padding: 0;
-  background: url('../image/voice-dictation.svg');
-  background-size: cover;
-}
-.weaper {
-  width: 1200px;
-  height: 155px;
-  margin: 72px auto;
-}
-.text-content {
-  width: 670px;
-  height: 100%;
-  float: left;
-}
-.text-content .title {
-  font-size: 34px;
-  font-family: 'PingFangSC-Medium';
-  font-weight: 500;
-  color: rgba(255, 255, 255, 1);
-  line-height: 48px;
-}
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\ No newline at end of file

demos/streaming_asr_server/web/static/js/SoundRecognizer.js

-SoundRecognizer = {
-    rec: null,
-    wave: null,
-    SampleRate: 16000,
-    testBitRate: 16,
-    isCloseRecorder: false,
-    SendInterval: 300,
-    realTimeSendTryType: 'pcm',
-    realTimeSendTryEncBusy: 0,
-    realTimeSendTryTime: 0,
-    realTimeSendTryNumber: 0,
-    transferUploadNumberMax: 0,
-    realTimeSendTryChunk: null,
-    soundType: "pcm",
-    init: function (config) {
-        this.soundType = config.soundType || 'pcm';
-        this.SampleRate = config.sampleRate || 16000;
-        this.recwaveElm = config.recwaveElm || '';
-        this.TransferUpload = config.translerCallBack || this.TransferProcess;
-        this.initRecorder();
-    },
-    RealTimeSendTryReset: function (type) {
-        this.realTimeSendTryType = type;
-        this.realTimeSendTryTime = 0;
-    },
-    RealTimeSendTry: function (rec, isClose) {
-        var that = this;
-        var t1 = Date.now(), endT = 0, recImpl = Recorder.prototype;
-        if (this.realTimeSendTryTime == 0) {
-            this.realTimeSendTryTime = t1;
-            this.realTimeSendTryEncBusy = 0;
-            this.realTimeSendTryNumber = 0;
-            this.transferUploadNumberMax = 0;
-            this.realTimeSendTryChunk = null;
-        }
-        if (!isClose && t1 - this.realTimeSendTryTime < this.SendInterval) {
-            return;//控制缓冲达到指定间隔才进行传输
-        }
-        this.realTimeSendTryTime = t1;
-        var number = ++this.realTimeSendTryNumber;
-
-        //借用SampleData函数进行数据的连续处理，采样率转换是顺带的
-        var chunk = Recorder.SampleData(rec.buffers, rec.srcSampleRate, this.SampleRate, this.realTimeSendTryChunk, { frameType: isClose ? "" : this.realTimeSendTryType });
-
-        //清理已处理完的缓冲数据，释放内存以支持长时间录音，最后完成录音时不能调用stop，因为数据已经被清掉了
-        for (var i = this.realTimeSendTryChunk ? this.realTimeSendTryChunk.index : 0; i < chunk.index; i++) {
-            rec.buffers[i] = null;
-        }
-        this.realTimeSendTryChunk = chunk;
-
-        //没有新数据，或结束时的数据量太小，不能进行mock转码
-        if (chunk.data.length == 0 || isClose && chunk.data.length < 2000) {
-            this.TransferUpload(number, null, 0, null, isClose);
-            return;
-        }
-        //实时编码队列阻塞处理
-        if (!isClose) {
-            if (this.realTimeSendTryEncBusy >= 2) {
-                console.log("编码队列阻塞，已丢弃一帧", 1);
-                return;
-            }
-        }
-        this.realTimeSendTryEncBusy++;
-
-        //通过mock方法实时转码成mp3、wav
-        var encStartTime = Date.now();
-        var recMock = Recorder({
-            type: this.realTimeSendTryType
-            , sampleRate: this.SampleRate //采样率
-            , bitRate: this.testBitRate //比特率
-        });
-        recMock.mock(chunk.data, chunk.sampleRate);
-        recMock.stop(function (blob, duration) {
-            that.realTimeSendTryEncBusy && (that.realTimeSendTryEncBusy--);
-            blob.encTime = Date.now() - encStartTime;
-
-            //转码好就推入传输
-            that.TransferUpload(number, blob, duration, recMock, isClose);
-        }, function (msg) {
-            that.realTimeSendTryEncBusy && (that.realTimeSendTryEncBusy--);
-            //转码错误？没想到什么时候会产生错误！
-            console.log("不应该出现的错误:" + msg, 1);
-        });
-    },
-    recordClose: function () {
-        try {
-            this.rec.close(function () {
-                this.isCloseRecorder = true;
-            });
-            this.RealTimeSendTry(this.rec, true);//最后一次发送
-        } catch (ex) {
-            // recordClose();
-        }
-    },
-    recordEnd: function () {
-        try {
-            this.rec.stop(function (blob, time) {
-                this.recordClose();
-            }, function (s) {
-                this.recordClose();
-            });
-        } catch (ex) {
-        }
-    },
-    initRecorder: function () {
-        var that = this;
-        var rec = Recorder({
-            type: that.soundType
-            , bitRate: that.testBitRate
-            , sampleRate: that.SampleRate
-            , onProcess: function (buffers, level, time, sampleRate) {
-                that.wave.input(buffers[buffers.length - 1], level, sampleRate);
-                that.RealTimeSendTry(rec, false);//推入实时处理，因为是unknown格式，这里简化函数调用，没有用到buffers和bufferSampleRate，因为这些数据和rec.buffers是完全相同的。
-            }
-        });
-
-        rec.open(function () {
-            that.wave = Recorder.FrequencyHistogramView({
-                elem: that.recwaveElm, lineCount: 90
-                , position: 0
-                , minHeight: 1
-                , stripeEnable: false
-            });
-            rec.start();
-            that.isCloseRecorder = false;
-            that.RealTimeSendTryReset(that.soundType);//重置
-        });
-        this.rec = rec;
-    },
-    TransferProcess: function (number, blobOrNull, duration, blobRec, isClose) {
-
-    }
-}
\ No newline at end of file

demos/streaming_asr_server/web/static/js/jquery-3.2.1.min.js

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demos/streaming_asr_server/web/static/js/recorder/engine/mp3.js

-/*
-录音
-https://github.com/xiangyuecn/Recorder
-src: engine/mp3.js,engine/mp3-engine.js
-*/
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s=Recorder.lamejs.toString(),n=(window.URL||webkitURL).createObjectURL(new Blob(["var wk_lame=(",s,a],{type:"text/javascript"}));t=new Worker(n),setTimeout(function(){(window.URL||webkitURL).revokeObjectURL(n)},1e4),t.onmessage=function(e){var t=e.data,a=_[t.id];a&&("takeoff"==t.action?a.set.takeoffEncodeChunk(new Uint8Array(t.chunk.buffer)):(a.call&&a.call(t),a.call=null))}}var r={worker:t,set:e,takeoffQueue:[]};return e?(r.id=++_.id,_[r.id]=r,t.postMessage({action:"init",id:r.id,sampleRate:e.sampleRate,bitRate:e.bitRate,takeoff:!!e.takeoffEncodeChunk,x:new Int16Array(5)})):t.postMessage({x:new Int16Array(5)}),i=t,r}catch(e){return t&&t.terminate(),console.error(e),null}};Recorder.prototype.mp3_stop=function(e){if(e&&e.worker){e.worker.postMessage({action:"stop",id:e.id}),e.worker=null,delete _[e.id];var t=-1;for(var a in _)t++;t&&console.warn("mp3 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s.bits+=n,1}(e,t,a,s);case 2:case 3:return function(e,t,a,s,n){var r,i,_=0,o=C.ht[s].xlen;i=2==s?C.table23:C.table56;do{var l=e[t+0]*o+e[t+1];t+=2,_+=i[l]}while(t<a);return(r=65535&_)<(_>>=16)&&(_=r,s++),n.bits+=_,s}(e,t,a,_[n-1],s);case 4:case 5:case 6:case 7:case 8:case 9:case 10:case 11:case 12:case 13:case 14:case 15:return function(e,t,a,s,n){var r=0,i=0,_=0,o=C.ht[s].xlen,l=C.ht[s].hlen,f=C.ht[s+1].hlen,c=C.ht[s+2].hlen;do{var h=e[t+0]*o+e[t+1];t+=2,r+=l[h],i+=f[h],_+=c[h]}while(t<a);var u=s;return i<r&&(r=i,u++),_<r&&(r=_,u=s+2),n.bits+=r,u}(e,t,a,_[n-1],s);default:if(y.IXMAX_VAL<n)return s.bits=y.LARGE_BITS,-1;var r,i;for(n-=15,r=24;r<32&&!(C.ht[r].linmax>=n);r++);for(i=r-8;i<24&&!(C.ht[i].linmax>=n);i++);return function(e,t,a,s,n,r){var i,_=65536*C.ht[s].xlen+C.ht[n].xlen,o=0;do{var l=e[t++],f=e[t++];0!=l&&(14<l&&(l=15,o+=_),l*=16),0!=f&&(14<f&&(f=15,o+=_),l+=f),o+=C.largetbl[l]}while(t<a);return(i=65535&o)<(o>>=16)&&(o=i,s=n),r.bits+=o,s}(e,t,a,i,r,s)}}function u(e,t,a,s,n,r,i,_){for(var o=t.big_values,l=2;l<Pe.SBMAX_l+1;l++){var f=e.scalefac_band.l[l];if(o<=f)break;var c=n[l-2]+t.count1bits;if(a.part2_3_length<=c)break;var h=new v(c),u=d(s,f,o,h);c=h.bits,a.part2_3_length<=c||(a.assign(t),a.part2_3_length=c,a.region0_count=r[l-2],a.region1_count=l-2-r[l-2],a.table_select[0]=i[l-2],a.table_select[1]=_[l-2],a.table_select[2]=u)}}this.noquant_count_bits=function(e,t,a){var s=t.l3_enc,n=Math.min(576,t.max_nonzero_coeff+2>>1<<1);for(null!=a&&(a.sfb_count1=0);1<n&&0==(s[n-1]|s[n-2]);n-=2);t.count1=n;for(var r=0,i=0;3<n;n-=4){var _;if(1<(2147483647&(s[n-1]|s[n-2]|s[n-3]|s[n-4])))break;_=2*(2*(2*s[n-4]+s[n-3])+s[n-2])+s[n-1],r+=C.t32l[_],i+=C.t33l[_]}var o=r;if(t.count1table_select=0,i<r&&(o=i,t.count1table_select=1),t.count1bits=o,0==(t.big_values=n))return o;if(t.block_type==Pe.SHORT_TYPE)(r=3*e.scalefac_band.s[3])>t.big_values&&(r=t.big_values),i=t.big_values;else if(t.block_type==Pe.NORM_TYPE){if(r=t.region0_count=e.bv_scf[n-2],i=t.region1_count=e.bv_scf[n-1],i=e.scalefac_band.l[r+i+2],r=e.scalefac_band.l[r+1],i<n){var l=new v(o);t.table_select[2]=d(s,i,n,l),o=l.bits}}else t.region0_count=7,t.region1_count=Pe.SBMAX_l-1-7-1,(i=n)<(r=e.scalefac_band.l[8])&&(r=i);if(r=Math.min(r,n),i=Math.min(i,n),0<r){l=new v(o);t.table_select[0]=d(s,0,r,l),o=l.bits}if(r<i){l=new v(o);t.table_select[1]=d(s,r,i,l),o=l.bits}if(2==e.use_best_huffman&&(t.part2_3_length=o,best_huffman_divide(e,t),o=t.part2_3_length),null!=a&&t.block_type==Pe.NORM_TYPE){for(var f=0;e.scalefac_band.l[f]<t.big_values;)f++;a.sfb_count1=f}return o},this.count_bits=function(e,t,a,s){var n=a.l3_enc,r=y.IXMAX_VAL/M.IPOW20(a.global_gain);if(a.xrpow_max>r)return y.LARGE_BITS;if(function(e,t,a,s,n){var r,i,_,o=0,l=0,f=0,c=0,h=t,u=0,b=h,m=0,p=e,v=0;for(_=null!=n&&s.global_gain==n.global_gain,i=s.block_type==Pe.SHORT_TYPE?38:21,r=0;r<=i;r++){var d=-1;if((_||s.block_type==Pe.NORM_TYPE)&&(d=s.global_gain-(s.scalefac[r]+(0!=s.preflag?M.pretab[r]:0)<<s.scalefac_scale+1)-8*s.subblock_gain[s.window[r]]),_&&n.step[r]==d)0!=l&&(R(l,a,p,v,b,m),l=0),0!=f&&(w(f,a,p,v,b,m),f=0);else{var g,S=s.width[r];if(o+s.width[r]>s.max_nonzero_coeff&&(g=s.max_nonzero_coeff-o+1,Te.fill(t,s.max_nonzero_coeff,576,0),(S=g)<0&&(S=0),r=i+1),0==l&&0==f&&(b=h,m=u,p=e,v=c),null!=n&&0<n.sfb_count1&&r>=n.sfb_count1&&0<n.step[r]&&d>=n.step[r]?(0!=l&&(R(l,a,p,v,b,m),l=0,b=h,m=u,p=e,v=c),f+=S):(0!=f&&(w(f,a,p,v,b,m),f=0,b=h,m=u,p=e,v=c),l+=S),S<=0){0!=f&&(w(f,a,p,v,b,m),f=0),0!=l&&(R(l,a,p,v,b,m),l=0);break}}r<=i&&(u+=s.width[r],c+=s.width[r],o+=s.width[r])}0!=l&&(R(l,a,p,v,b,m),l=0),0!=f&&(w(f,a,p,v,b,m),f=0)}(t,n,M.IPOW20(a.global_gain),a,s),0!=(2&e.substep_shaping))for(var i=0,_=a.global_gain+a.scalefac_scale,o=.634521682242439/M.IPOW20(_),l=0;l<a.sfbmax;l++){var f,c=a.width[l];if(0==e.pseudohalf[l])i+=c;else for(f=i,i+=c;f<i;++f)n[f]=t[f]>=o?n[f]:0}return this.noquant_count_bits(e,a,s)},this.best_huffman_divide=function(e,t){var a=new x,s=t.l3_enc,n=Be(23),r=Be(23),i=Be(23),_=Be(23);if(t.block_type!=Pe.SHORT_TYPE||1!=e.mode_gr){a.assign(t),t.block_type==Pe.NORM_TYPE&&(!function(e,t,a,s,n,r,i){for(var _=t.big_values,o=0;o<=22;o++)s[o]=y.LARGE_BITS;for(o=0;o<16;o++){var l=e.scalefac_band.l[o+1];if(_<=l)break;var f=0,c=new v(f),h=d(a,0,l,c);f=c.bits;for(var u=0;u<8;u++){var b=e.scalefac_band.l[o+u+2];if(_<=b)break;var m=f,p=d(a,l,b,c=new v(m));m=c.bits,s[o+u]>m&&(s[o+u]=m,r[(n[o+u]=o)+u]=h,i[o+u]=p)}}}(e,t,s,n,r,i,_),u(e,a,t,s,n,r,i,_));var o=a.big_values;if(!(0==o||1<(s[o-2]|s[o-1])||576<(o=t.count1+2))){a.assign(t),a.count1=o;for(var l=0,f=0;o>a.big_values;o-=4){var c=2*(2*(2*s[o-4]+s[o-3])+s[o-2])+s[o-1];l+=C.t32l[c],f+=C.t33l[c]}if(a.big_values=o,a.count1table_select=0,f<l&&(l=f,a.count1table_select=1),a.count1bits=l,a.block_type==Pe.NORM_TYPE)u(e,a,t,s,n,r,i,_);else{if(a.part2_3_length=l,o<(l=e.scalefac_band.l[8])&&(l=o),0<l){var h=new v(a.part2_3_length);a.table_select[0]=d(s,0,l,h),a.part2_3_length=h.bits}if(l<o){h=new v(a.part2_3_length);a.table_select[1]=d(s,l,o,h),a.part2_3_length=h.bits}t.part2_3_length>a.part2_3_length&&t.assign(a)}}}};var h=[1,1,1,1,8,2,2,2,4,4,4,8,8,8,16,16],b=[1,2,4,8,1,2,4,8,2,4,8,2,4,8,4,8],m=[0,0,0,0,3,1,1,1,2,2,2,3,3,3,4,4],p=[0,1,2,3,0,1,2,3,1,2,3,1,2,3,2,3];k.slen1_tab=m,k.slen2_tab=p,this.best_scalefac_store=function(e,t,a,s){var n,r,i,_,o=s.tt[t][a],l=0;for(n=i=0;n<o.sfbmax;n++){var f=o.width[n];for(i+=f,_=-f;_<0&&0==o.l3_enc[_+i];_++);0==_&&(o.scalefac[n]=l=-2)}if(0==o.scalefac_scale&&0==o.preflag){var c=0;for(n=0;n<o.sfbmax;n++)0<o.scalefac[n]&&(c|=o.scalefac[n]);if(0==(1&c)&&0!=c){for(n=0;n<o.sfbmax;n++)0<o.scalefac[n]&&(o.scalefac[n]>>=1);o.scalefac_scale=l=1}}if(0==o.preflag&&o.block_type!=Pe.SHORT_TYPE&&2==e.mode_gr){for(n=11;n<Pe.SBPSY_l&&!(o.scalefac[n]<M.pretab[n]&&-2!=o.scalefac[n]);n++);if(n==Pe.SBPSY_l){for(n=11;n<Pe.SBPSY_l;n++)0<o.scalefac[n]&&(o.scalefac[n]-=M.pretab[n]);o.preflag=l=1}}for(r=0;r<4;r++)s.scfsi[a][r]=0;for(2==e.mode_gr&&1==t&&s.tt[0][a].block_type!=Pe.SHORT_TYPE&&s.tt[1][a].block_type!=Pe.SHORT_TYPE&&(!function(e,t){for(var a,s=t.tt[1][e],n=t.tt[0][e],r=0;r<C.scfsi_band.length-1;r++){for(a=C.scfsi_band[r];a<C.scfsi_band[r+1]&&!(n.scalefac[a]!=s.scalefac[a]&&0<=s.scalefac[a]);a++);if(a==C.scfsi_band[r+1]){for(a=C.scfsi_band[r];a<C.scfsi_band[r+1];a++)s.scalefac[a]=-1;t.scfsi[e][r]=1}}var i=0,_=0;for(a=0;a<11;a++)-1!=s.scalefac[a]&&(_++,i<s.scalefac[a]&&(i=s.scalefac[a]));for(var o=0,l=0;a<Pe.SBPSY_l;a++)-1!=s.scalefac[a]&&(l++,o<s.scalefac[a]&&(o=s.scalefac[a]));for(r=0;r<16;r++)if(i<h[r]&&o<b[r]){var f=m[r]*_+p[r]*l;s.part2_length>f&&(s.part2_length=f,s.scalefac_compress=r)}}(a,s),l=0),n=0;n<o.sfbmax;n++)-2==o.scalefac[n]&&(o.scalefac[n]=0);0!=l&&(2==e.mode_gr?this.scale_bitcount(o):this.scale_bitcount_lsf(e,o))};var o=[0,18,36,54,54,36,54,72,54,72,90,72,90,108,108,126],l=[0,18,36,54,51,35,53,71,52,70,88,69,87,105,104,122],f=[0,10,20,30,33,21,31,41,32,42,52,43,53,63,64,74];this.scale_bitcount=function(e){var t,a,s,n=0,r=0,i=e.scalefac;if(e.block_type==Pe.SHORT_TYPE)s=o,0!=e.mixed_block_flag&&(s=l);else if(s=f,0==e.preflag){for(a=11;a<Pe.SBPSY_l&&!(i[a]<M.pretab[a]);a++);if(a==Pe.SBPSY_l)for(e.preflag=1,a=11;a<Pe.SBPSY_l;a++)i[a]-=M.pretab[a]}for(a=0;a<e.sfbdivide;a++)n<i[a]&&(n=i[a]);for(;a<e.sfbmax;a++)r<i[a]&&(r=i[a]);for(e.part2_length=y.LARGE_BITS,t=0;t<16;t++)n<h[t]&&r<b[t]&&e.part2_length>s[t]&&(e.part2_length=s[t],e.scalefac_compress=t);return e.part2_length==y.LARGE_BITS};var g=[[15,15,7,7],[15,15,7,0],[7,3,0,0],[15,31,31,0],[7,7,7,0],[3,3,0,0]];this.scale_bitcount_lsf=function(e,t){var a,s,n,r,i,_,o,l,f=Be(4),c=t.scalefac;for(a=0!=t.preflag?2:0,o=0;o<4;o++)f[o]=0;if(t.block_type==Pe.SHORT_TYPE){s=1;var h=M.nr_of_sfb_block[a][s];for(n=l=0;n<4;n++)for(r=h[n]/3,o=0;o<r;o++,l++)for(i=0;i<3;i++)c[3*l+i]>f[n]&&(f[n]=c[3*l+i])}else{s=0;h=M.nr_of_sfb_block[a][s];for(n=l=0;n<4;n++)for(r=h[n],o=0;o<r;o++,l++)c[l]>f[n]&&(f[n]=c[l])}for(_=!1,n=0;n<4;n++)f[n]>g[a][n]&&(_=!0);if(!_){var u,b,m,p;for(t.sfb_partition_table=M.nr_of_sfb_block[a][s],n=0;n<4;n++)t.slen[n]=S[f[n]];switch(u=t.slen[0],b=t.slen[1],m=t.slen[2],p=t.slen[3],a){case 0:t.scalefac_compress=(5*u+b<<4)+(m<<2)+p;break;case 1:t.scalefac_compress=400+(5*u+b<<2)+m;break;case 2:t.scalefac_compress=500+3*u+b;break;default:$.err.printf("intensity stereo not implemented yet\n")}}if(!_)for(n=t.part2_length=0;n<4;n++)t.part2_length+=t.slen[n]*t.sfb_partition_table[n];return _};var S=[0,1,2,2,3,3,3,3,4,4,4,4,4,4,4,4];this.huffman_init=function(e){for(var t=2;t<=576;t+=2){for(var a,s=0;e.scalefac_band.l[++s]<t;);for(a=n[s][0];e.scalefac_band.l[a+1]>t;)a--;for(a<0&&(a=n[s][0]),e.bv_scf[t-2]=a,a=n[s][1];e.scalefac_band.l[a+e.bv_scf[t-2]+2]>t;)a--;a<0&&(a=n[s][1]),e.bv_scf[t-1]=a}}}function q(){}function M(){this.setModules=function(e,t,a){e,t,a};var _=[0,49345,49537,320,49921,960,640,49729,50689,1728,1920,51009,1280,50625,50305,1088,52225,3264,3456,52545,3840,53185,52865,3648,2560,51905,52097,2880,51457,2496,2176,51265,55297,6336,6528,55617,6912,56257,55937,6720,7680,57025,57217,8e3,56577,7616,7296,56385,5120,54465,54657,5440,55041,6080,5760,54849,53761,4800,4992,54081,4352,53697,53377,4160,61441,12480,12672,61761,13056,62401,62081,12864,13824,63169,63361,14144,62721,13760,13440,62529,15360,64705,64897,15680,65281,16320,16e3,65089,64001,15040,15232,64321,14592,63937,63617,14400,10240,59585,59777,10560,60161,11200,10880,59969,60929,11968,12160,61249,11520,60865,60545,11328,58369,9408,9600,58689,9984,59329,59009,9792,8704,58049,58241,9024,57601,8640,8320,57409,40961,24768,24960,41281,25344,41921,41601,25152,26112,42689,42881,26432,42241,26048,25728,42049,27648,44225,44417,27968,44801,28608,28288,44609,43521,27328,27520,43841,26880,43457,43137,26688,30720,47297,47489,31040,47873,31680,31360,47681,48641,32448,32640,48961,32e3,48577,48257,31808,46081,29888,30080,46401,30464,47041,46721,30272,29184,45761,45953,29504,45313,29120,28800,45121,20480,37057,37249,20800,37633,21440,21120,37441,38401,22208,22400,38721,21760,38337,38017,21568,39937,23744,23936,40257,24320,40897,40577,24128,23040,39617,39809,23360,39169,22976,22656,38977,34817,18624,18816,35137,19200,35777,35457,19008,19968,36545,36737,20288,36097,19904,19584,35905,17408,33985,34177,17728,34561,18368,18048,34369,33281,17088,17280,33601,16640,33217,32897,16448];this.updateMusicCRC=function(e,t,a,s){for(var n=0;n<s;++n)e[0]=(r=t[a+n],i=(i=e[0])>>8^_[255&(i^r)]);var r,i}}function j(){var o=this,s=32773,c=null,h=null,r=null,u=null;this.setModules=function(e,t,a,s){c=e,h=t,r=a,u=s};var b=null,l=0,m=0,p=0;function v(e,t,a){for(;0<a;){var s;0==p&&(p=8,m++,e.header[e.w_ptr].write_timing==l&&(n=e,$.arraycopy(n.header[n.w_ptr].buf,0,b,m,n.sideinfo_len),m+=n.sideinfo_len,l+=8*n.sideinfo_len,n.w_ptr=n.w_ptr+1&Z.MAX_HEADER_BUF-1),b[m]=0),a-=s=Math.min(a,p),p-=s,b[m]|=t>>a<<p,l+=s}var n}function i(e,t,a){for(;0<a;){var s;0==p&&(p=8,b[++m]=0),a-=s=Math.min(a,p),p-=s,b[m]|=t>>a<<p,l+=s}}function _(e,t){var a,s=e.internal_flags;if(8<=t&&(v(s,76,8),t-=8),8<=t&&(v(s,65,8),t-=8),8<=t&&(v(s,77,8),t-=8),8<=t&&(v(s,69,8),t-=8),32<=t){var n=r.getLameShortVersion();if(32<=t)for(a=0;a<n.length&&8<=t;++a)t-=8,v(s,n.charCodeAt(a),8)}for(;1<=t;t-=1)v(s,s.ancillary_flag,1),s.ancillary_flag^=e.disable_reservoir?0:1}function f(e,t,a){for(var s=e.header[e.h_ptr].ptr;0<a;){var n=Math.min(a,8-(7&s));a-=n,e.header[e.h_ptr].buf[s>>3]|=t>>a<<8-(7&s)-n,s+=n}e.header[e.h_ptr].ptr=s}function n(e,t){e<<=8;for(var a=0;a<8;a++)0!=(65536&((t<<=1)^(e<<=1)))&&(t^=s);return t}function d(e,t){var a,s=C.ht[t.count1table_select+32],n=0,r=t.big_values,i=t.big_values;for(a=(t.count1-t.big_values)/4;0<a;--a){var _=0,o=0;0!=t.l3_enc[r+0]&&(o+=8,t.xr[i+0]<0&&_++),0!=t.l3_enc[r+1]&&(o+=4,_*=2,t.xr[i+1]<0&&_++),0!=t.l3_enc[r+2]&&(o+=2,_*=2,t.xr[i+2]<0&&_++),0!=t.l3_enc[r+3]&&(o++,_*=2,t.xr[i+3]<0&&_++),r+=4,i+=4,v(e,_+s.table[o],s.hlen[o]),n+=s.hlen[o]}return n}function g(e,t,a,s,n){var r=C.ht[t],i=0;if(0==t)return i;for(var _=a;_<s;_+=2){var o=0,l=0,f=r.xlen,c=r.xlen,h=0,u=n.l3_enc[_],b=n.l3_enc[_+1];if(0!=u&&(n.xr[_]<0&&h++,o--),15<t){if(14<u)h|=u-15<<1,l=f,u=15;if(14<b)h<<=f,h|=b-15,l+=f,b=15;c=16}0!=b&&(h<<=1,n.xr[_+1]<0&&h++,o--),u=u*c+b,l-=o,o+=r.hlen[u],v(e,r.table[u],o),v(e,h,l),i+=o+l}return i}function S(e,t){var a=3*e.scalefac_band.s[3];a>t.big_values&&(a=t.big_values);var s=g(e,t.table_select[0],0,a,t);return s+=g(e,t.table_select[1],a,t.big_values,t)}function M(e,t){var a,s,n,r;a=t.big_values;var i=t.region0_count+1;return n=e.scalefac_band.l[i],i+=t.region1_count+1,a<n&&(n=a),a<(r=e.scalefac_band.l[i])&&(r=a),s=g(e,t.table_select[0],0,n,t),s+=g(e,t.table_select[1],n,r,t),s+=g(e,t.table_select[2],r,a,t)}function w(){this.total=0}function R(e,t){var a,s,n,r,i,_=e.internal_flags;return i=_.w_ptr,-1==(r=_.h_ptr-1)&&(r=Z.MAX_HEADER_BUF-1),a=_.header[r].write_timing-l,0<=(t.total=a)&&(s=1+r-i,r<i&&(s=1+r-i+Z.MAX_HEADER_BUF),a-=8*s*_.sideinfo_len),a+=n=o.getframebits(e),t.total+=n,t.total%8!=0?t.total=1+t.total/8:t.total=t.total/8,t.total+=m+1,a<0&&$.err.println("strange error flushing buffer ... \n"),a}this.getframebits=function(e){var t,a=e.internal_flags;return t=0!=a.bitrate_index?C.bitrate_table[e.version][a.bitrate_index]:e.brate,8*(0|72e3*(e.version+1)*t/e.out_samplerate+a.padding)},this.CRC_writeheader=function(e,t){var a=65535;a=n(255&t[2],a),a=n(255&t[3],a);for(var s=6;s<e.sideinfo_len;s++)a=n(255&t[s],a);t[4]=byte(a>>8),t[5]=byte(255&a)},this.flush_bitstream=function(e){var t,a,s=e.internal_flags,n=s.h_ptr-1;if(-1==n&&(n=Z.MAX_HEADER_BUF-1),t=s.l3_side,!((a=R(e,new w))<0)){if(_(e,a),s.ResvSize=0,t.main_data_begin=0,s.findReplayGain){var r=c.GetTitleGain(s.rgdata);s.RadioGain=0|Math.floor(10*r+.5)}s.findPeakSample&&(s.noclipGainChange=0|Math.ceil(20*A(s.PeakSample/32767)*10),0<s.noclipGainChange&&(EQ(e.scale,1)||EQ(e.scale,0))?s.noclipScale=Math.floor(32767/s.PeakSample*100)/100:s.noclipScale=-1)}},this.add_dummy_byte=function(e,t,a){for(var s,n=e.internal_flags;0<a--;)for(i(0,t,8),s=0;s<Z.MAX_HEADER_BUF;++s)n.header[s].write_timing+=8},this.format_bitstream=function(e){var t,a=e.internal_flags;t=a.l3_side;var s=this.getframebits(e);_(e,t.resvDrain_pre),function(e,t){var a,s,n,r=e.internal_flags;if(a=r.l3_side,r.header[r.h_ptr].ptr=0,Te.fill(r.header[r.h_ptr].buf,0,r.sideinfo_len,0),e.out_samplerate<16e3?f(r,4094,12):f(r,4095,12),f(r,e.version,1),f(r,1,2),f(r,e.error_protection?0:1,1),f(r,r.bitrate_index,4),f(r,r.samplerate_index,2),f(r,r.padding,1),f(r,e.extension,1),f(r,e.mode.ordinal(),2),f(r,r.mode_ext,2),f(r,e.copyright,1),f(r,e.original,1),f(r,e.emphasis,2),e.error_protection&&f(r,0,16),1==e.version){for(f(r,a.main_data_begin,9),2==r.channels_out?f(r,a.private_bits,3):f(r,a.private_bits,5),n=0;n<r.channels_out;n++){var i;for(i=0;i<4;i++)f(r,a.scfsi[n][i],1)}for(s=0;s<2;s++)for(n=0;n<r.channels_out;n++)f(r,(_=a.tt[s][n]).part2_3_length+_.part2_length,12),f(r,_.big_values/2,9),f(r,_.global_gain,8),f(r,_.scalefac_compress,4),_.block_type!=Pe.NORM_TYPE?(f(r,1,1),f(r,_.block_type,2),f(r,_.mixed_block_flag,1),14==_.table_select[0]&&(_.table_select[0]=16),f(r,_.table_select[0],5),14==_.table_select[1]&&(_.table_select[1]=16),f(r,_.table_select[1],5),f(r,_.subblock_gain[0],3),f(r,_.subblock_gain[1],3),f(r,_.subblock_gain[2],3)):(f(r,0,1),14==_.table_select[0]&&(_.table_select[0]=16),f(r,_.table_select[0],5),14==_.table_select[1]&&(_.table_select[1]=16),f(r,_.table_select[1],5),14==_.table_select[2]&&(_.table_select[2]=16),f(r,_.table_select[2],5),f(r,_.region0_count,4),f(r,_.region1_count,3)),f(r,_.preflag,1),f(r,_.scalefac_scale,1),f(r,_.count1table_select,1)}else for(f(r,a.main_data_begin,8),f(r,a.private_bits,r.channels_out),n=s=0;n<r.channels_out;n++){var _;f(r,(_=a.tt[s][n]).part2_3_length+_.part2_length,12),f(r,_.big_values/2,9),f(r,_.global_gain,8),f(r,_.scalefac_compress,9),_.block_type!=Pe.NORM_TYPE?(f(r,1,1),f(r,_.block_type,2),f(r,_.mixed_block_flag,1),14==_.table_select[0]&&(_.table_select[0]=16),f(r,_.table_select[0],5),14==_.table_select[1]&&(_.table_select[1]=16),f(r,_.table_select[1],5),f(r,_.subblock_gain[0],3),f(r,_.subblock_gain[1],3),f(r,_.subblock_gain[2],3)):(f(r,0,1),14==_.table_select[0]&&(_.table_select[0]=16),f(r,_.table_select[0],5),14==_.table_select[1]&&(_.table_select[1]=16),f(r,_.table_select[1],5),14==_.table_select[2]&&(_.table_select[2]=16),f(r,_.table_select[2],5),f(r,_.region0_count,4),f(r,_.region1_count,3)),f(r,_.scalefac_scale,1),f(r,_.count1table_select,1)}e.error_protection&&CRC_writeheader(r,r.header[r.h_ptr].buf);var o=r.h_ptr;r.h_ptr=o+1&Z.MAX_HEADER_BUF-1,r.header[r.h_ptr].write_timing=r.header[o].write_timing+t,r.h_ptr==r.w_ptr&&$.err.println("Error: MAX_HEADER_BUF too small in bitstream.c \n")}(e,s);var n=8*a.sideinfo_len;if(n+=function(e){var t,a,s,n,r=0,i=e.internal_flags,_=i.l3_side;if(1==e.version)for(t=0;t<2;t++)for(a=0;a<i.channels_out;a++){var o=_.tt[t][a],l=k.slen1_tab[o.scalefac_compress],f=k.slen2_tab[o.scalefac_compress];for(s=n=0;s<o.sfbdivide;s++)-1!=o.scalefac[s]&&(v(i,o.scalefac[s],l),n+=l);for(;s<o.sfbmax;s++)-1!=o.scalefac[s]&&(v(i,o.scalefac[s],f),n+=f);o.block_type==Pe.SHORT_TYPE?n+=S(i,o):n+=M(i,o),r+=n+=d(i,o)}else for(a=t=0;a<i.channels_out;a++){var c,h,u=0;if(h=s=n=0,(o=_.tt[t][a]).block_type==Pe.SHORT_TYPE){for(;h<4;h++){var b=o.sfb_partition_table[h]/3,m=o.slen[h];for(c=0;c<b;c++,s++)v(i,Math.max(o.scalefac[3*s+0],0),m),v(i,Math.max(o.scalefac[3*s+1],0),m),v(i,Math.max(o.scalefac[3*s+2],0),m),u+=3*m}n+=S(i,o)}else{for(;h<4;h++)for(b=o.sfb_partition_table[h],m=o.slen[h],c=0;c<b;c++,s++)v(i,Math.max(o.scalefac[s],0),m),u+=m;n+=M(i,o)}r+=u+(n+=d(i,o))}return r}(e),_(e,t.resvDrain_post),n+=t.resvDrain_post,t.main_data_begin+=(s-n)/8,R(e,new w)!=a.ResvSize&&$.err.println("Internal buffer inconsistency. flushbits <> ResvSize"),8*t.main_data_begin!=a.ResvSize&&($.err.printf("bit reservoir error: \nl3_side.main_data_begin: %d \nResvoir size:             %d \nresv drain (post)         %d \nresv drain (pre)          %d \nheader and sideinfo:      %d \ndata bits:                %d \ntotal bits:               %d (remainder: %d) \nbitsperframe:             %d \n",8*t.main_data_begin,a.ResvSize,t.resvDrain_post,t.resvDrain_pre,8*a.sideinfo_len,n-t.resvDrain_post-8*a.sideinfo_len,n,n%8,s),$.err.println("This is a fatal error.  It has several possible causes:"),$.err.println("90%%  LAME compiled with buggy version of gcc using advanced optimizations"),$.err.println(" 9%%  Your system is overclocked"),$.err.println(" 1%%  bug in LAME encoding library"),a.ResvSize=8*t.main_data_begin),1e9<l){var r;for(r=0;r<Z.MAX_HEADER_BUF;++r)a.header[r].write_timing-=l;l=0}return 0},this.copy_buffer=function(e,t,a,s,n){var r=m+1;if(r<=0)return 0;if(0!=s&&s<r)return-1;if($.arraycopy(b,0,t,a,r),m=-1,(p=0)!=n){var i=Be(1);if(i[0]=e.nMusicCRC,u.updateMusicCRC(i,t,a,r),e.nMusicCRC=i[0],0<r&&(e.VBR_seek_table.nBytesWritten+=r),e.decode_on_the_fly)for(var _,o=ke([2,1152]),l=r,f=-1;0!=f;)if(f=h.hip_decode1_unclipped(e.hip,t,a,l,o[0],o[1]),l=0,-1==f&&(f=0),0<f){if(e.findPeakSample){for(_=0;_<f;_++)o[0][_]>e.PeakSample?e.PeakSample=o[0][_]:-o[0][_]>e.PeakSample&&(e.PeakSample=-o[0][_]);if(1<e.channels_out)for(_=0;_<f;_++)o[1][_]>e.PeakSample?e.PeakSample=o[1][_]:-o[1][_]>e.PeakSample&&(e.PeakSample=-o[1][_])}if(e.findReplayGain&&c.AnalyzeSamples(e.rgdata,o[0],0,o[1],0,f,e.channels_out)==q.GAIN_ANALYSIS_ERROR)return-6}}return r},this.init_bit_stream_w=function(e){b=B(Q.LAME_MAXMP3BUFFER),e.h_ptr=e.w_ptr=0,e.header[e.h_ptr].write_timing=0,m=-1,l=p=0}}function e(e,t,a,s){this.xlen=e,this.linmax=t,this.table=a,this.hlen=s}Ee.STEREO=new Ee(0),Ee.JOINT_STEREO=new Ee(1),Ee.DUAL_CHANNEL=new Ee(2),Ee.MONO=new Ee(3),Ee.NOT_SET=new Ee(4),q.STEPS_per_dB=100,q.MAX_dB=120,q.GAIN_NOT_ENOUGH_SAMPLES=-24601,q.GAIN_ANALYSIS_ERROR=0,q.GAIN_ANALYSIS_OK=1,q.INIT_GAIN_ANALYSIS_ERROR=0,q.INIT_GAIN_ANALYSIS_OK=1,q.MAX_ORDER=q.YULE_ORDER=10,q.MAX_SAMPLES_PER_WINDOW=(q.MAX_SAMP_FREQ=48e3)*(q.RMS_WINDOW_TIME_NUMERATOR=1)/(q.RMS_WINDOW_TIME_DENOMINATOR=20)+1,M.NUMTOCENTRIES=100,M.MAXFRAMESIZE=2880,j.EQ=function(e,t){return Math.abs(e)>Math.abs(t)?Math.abs(e-t)<=1e-6*Math.abs(e):Math.abs(e-t)<=1e-6*Math.abs(t)},j.NEQ=function(e,t){return!j.EQ(e,t)};var C={};function F(e){this.bits=e}function T(){this.over_noise=0,this.tot_noise=0,this.max_noise=0,this.over_count=0,this.over_SSD=0,this.bits=0}function r(e,t,a,s){this.l=Be(1+Pe.SBMAX_l),this.s=Be(1+Pe.SBMAX_s),this.psfb21=Be(1+Pe.PSFB21),this.psfb12=Be(1+Pe.PSFB12);var n=this.l,r=this.s;4==arguments.length&&(this.arrL=e,this.arrS=t,this.arr21=a,this.arr12=s,$.arraycopy(this.arrL,0,n,0,Math.min(this.arrL.length,this.l.length)),$.arraycopy(this.arrS,0,r,0,Math.min(this.arrS.length,this.s.length)),$.arraycopy(this.arr21,0,this.psfb21,0,Math.min(this.arr21.length,this.psfb21.length)),$.arraycopy(this.arr12,0,this.psfb12,0,Math.min(this.arr12.length,this.psfb12.length)))}function y(){var l=null,b=null,s=null;this.setModules=function(e,t,a){l=e,b=t,s=a},this.IPOW20=function(e){return u[e]};var x=2.220446049250313e-16,f=y.IXMAX_VAL+2,c=y.Q_MAX,h=y.Q_MAX2,n=100;this.nr_of_sfb_block=[[[6,5,5,5],[9,9,9,9],[6,9,9,9]],[[6,5,7,3],[9,9,12,6],[6,9,12,6]],[[11,10,0,0],[18,18,0,0],[15,18,0,0]],[[7,7,7,0],[12,12,12,0],[6,15,12,0]],[[6,6,6,3],[12,9,9,6],[6,12,9,6]],[[8,8,5,0],[15,12,9,0],[6,18,9,0]]];var w=[0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,2,2,3,3,3,2,0];this.pretab=w,this.sfBandIndex=[new r([0,6,12,18,24,30,36,44,54,66,80,96,116,140,168,200,238,284,336,396,464,522,576],[0,4,8,12,18,24,32,42,56,74,100,132,174,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new r([0,6,12,18,24,30,36,44,54,66,80,96,114,136,162,194,232,278,332,394,464,540,576],[0,4,8,12,18,26,36,48,62,80,104,136,180,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new r([0,6,12,18,24,30,36,44,54,66,80,96,116,140,168,200,238,284,336,396,464,522,576],[0,4,8,12,18,26,36,48,62,80,104,134,174,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new r([0,4,8,12,16,20,24,30,36,44,52,62,74,90,110,134,162,196,238,288,342,418,576],[0,4,8,12,16,22,30,40,52,66,84,106,136,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new r([0,4,8,12,16,20,24,30,36,42,50,60,72,88,106,128,156,190,230,276,330,384,576],[0,4,8,12,16,22,28,38,50,64,80,100,126,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new r([0,4,8,12,16,20,24,30,36,44,54,66,82,102,126,156,194,240,296,364,448,550,576],[0,4,8,12,16,22,30,42,58,78,104,138,180,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new r([0,6,12,18,24,30,36,44,54,66,80,96,116,140,168,200,238,284,336,396,464,522,576],[0,4,8,12,18,26,36,48,62,80,104,134,174,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new r([0,6,12,18,24,30,36,44,54,66,80,96,116,140,168,200,238,284,336,396,464,522,576],[0,4,8,12,18,26,36,48,62,80,104,134,174,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0]),new r([0,12,24,36,48,60,72,88,108,132,160,192,232,280,336,400,476,566,568,570,572,574,576],[0,8,16,24,36,52,72,96,124,160,162,164,166,192],[0,0,0,0,0,0,0],[0,0,0,0,0,0,0])];var R=Ae(c+h+1),u=Ae(c),m=Ae(f),p=Ae(f);function v(e,t){var a=s.ATHformula(t,e);return a-=n,a=Math.pow(10,a/10+e.ATHlower)}function B(e){this.s=e}this.adj43=p,this.iteration_init=function(e){var t,a=e.internal_flags,s=a.l3_side;if(0==a.iteration_init_init){for(a.iteration_init_init=1,s.main_data_begin=0,function(e){for(var t=e.internal_flags.ATH.l,a=e.internal_flags.ATH.psfb21,s=e.internal_flags.ATH.s,n=e.internal_flags.ATH.psfb12,r=e.internal_flags,i=e.out_samplerate,_=0;_<Pe.SBMAX_l;_++){var o=r.scalefac_band.l[_],l=r.scalefac_band.l[_+1];t[_]=K.MAX_VALUE;for(var f=o;f<l;f++){var c=v(e,f*i/1152);t[_]=Math.min(t[_],c)}}for(_=0;_<Pe.PSFB21;_++)for(o=r.scalefac_band.psfb21[_],l=r.scalefac_band.psfb21[_+1],a[_]=K.MAX_VALUE,f=o;f<l;f++)c=v(e,f*i/1152),a[_]=Math.min(a[_],c);for(_=0;_<Pe.SBMAX_s;_++){for(o=r.scalefac_band.s[_],l=r.scalefac_band.s[_+1],s[_]=K.MAX_VALUE,f=o;f<l;f++)c=v(e,f*i/384),s[_]=Math.min(s[_],c);s[_]*=r.scalefac_band.s[_+1]-r.scalefac_band.s[_]}for(_=0;_<Pe.PSFB12;_++){for(o=r.scalefac_band.psfb12[_],l=r.scalefac_band.psfb12[_+1],n[_]=K.MAX_VALUE,f=o;f<l;f++)c=v(e,f*i/384),n[_]=Math.min(n[_],c);n[_]*=r.scalefac_band.s[13]-r.scalefac_band.s[12]}if(e.noATH){for(_=0;_<Pe.SBMAX_l;_++)t[_]=1e-20;for(_=0;_<Pe.PSFB21;_++)a[_]=1e-20;for(_=0;_<Pe.SBMAX_s;_++)s[_]=1e-20;for(_=0;_<Pe.PSFB12;_++)n[_]=1e-20}r.ATH.floor=10*A(v(e,-1))}(e),m[0]=0,t=1;t<f;t++)m[t]=Math.pow(t,4/3);for(t=0;t<f-1;t++)p[t]=t+1-Math.pow(.5*(m[t]+m[t+1]),.75);for(p[t]=.5,t=0;t<c;t++)u[t]=Math.pow(2,-.1875*(t-210));for(t=0;t<=c+h;t++)R[t]=Math.pow(2,.25*(t-210-h));var n,r,i,_;for(l.huffman_init(a),32<=(t=e.exp_nspsytune>>2&63)&&(t-=64),n=Math.pow(10,t/4/10),32<=(t=e.exp_nspsytune>>8&63)&&(t-=64),r=Math.pow(10,t/4/10),32<=(t=e.exp_nspsytune>>14&63)&&(t-=64),i=Math.pow(10,t/4/10),32<=(t=e.exp_nspsytune>>20&63)&&(t-=64),_=i*Math.pow(10,t/4/10),t=0;t<Pe.SBMAX_l;t++){o=t<=6?n:t<=13?r:t<=20?i:_,a.nsPsy.longfact[t]=o}for(t=0;t<Pe.SBMAX_s;t++){var o;o=t<=5?n:t<=10?r:t<=11?i:_,a.nsPsy.shortfact[t]=o}}},this.on_pe=function(e,t,a,s,n,r){var i,_,o=e.internal_flags,l=0,f=Be(2),c=new F(l),h=b.ResvMaxBits(e,s,c,r),u=(l=c.bits)+h;for(Z.MAX_BITS_PER_GRANULE<u&&(u=Z.MAX_BITS_PER_GRANULE),_=i=0;_<o.channels_out;++_)a[_]=Math.min(Z.MAX_BITS_PER_CHANNEL,l/o.channels_out),f[_]=0|a[_]*t[n][_]/700-a[_],f[_]>3*s/4&&(f[_]=3*s/4),f[_]<0&&(f[_]=0),f[_]+a[_]>Z.MAX_BITS_PER_CHANNEL&&(f[_]=Math.max(0,Z.MAX_BITS_PER_CHANNEL-a[_])),i+=f[_];if(h<i)for(_=0;_<o.channels_out;++_)f[_]=h*f[_]/i;for(_=0;_<o.channels_out;++_)a[_]+=f[_],h-=f[_];for(_=i=0;_<o.channels_out;++_)i+=a[_];if(Z.MAX_BITS_PER_GRANULE<i){for(_=0;_<o.channels_out;++_)a[_]*=Z.MAX_BITS_PER_GRANULE,a[_]/=i,a[_]}return u},this.reduce_side=function(e,t,a,s){var n=.33*(.5-t)/.5;n<0&&(n=0),.5<n&&(n=.5);var r=0|.5*n*(e[0]+e[1]);r>Z.MAX_BITS_PER_CHANNEL-e[0]&&(r=Z.MAX_BITS_PER_CHANNEL-e[0]),r<0&&(r=0),125<=e[1]&&(125<e[1]-r?(e[0]<a&&(e[0]+=r),e[1]-=r):(e[0]+=e[1]-125,e[1]=125)),s<(r=e[0]+e[1])&&(e[0]=s*e[0]/r,e[1]=s*e[1]/r)},this.athAdjust=function(e,t,a){var s=90.30873362,n=ee.FAST_LOG10_X(t,10),r=e*e,i=0;return n-=a,1e-20<r&&(i=1+ee.FAST_LOG10_X(r,10/s)),i<0&&(i=0),n*=i,n+=a+s-94.82444863,Math.pow(10,.1*n)},this.calc_xmin=function(e,t,a,s){var n,r=0,i=e.internal_flags,_=0,o=0,l=i.ATH,f=a.xr,c=e.VBR==ye.vbr_mtrh?1:0,h=i.masking_lower;for(e.VBR!=ye.vbr_mtrh&&e.VBR!=ye.vbr_mt||(h=1),n=0;n<a.psy_lmax;n++){S=(g=e.VBR==ye.vbr_rh||e.VBR==ye.vbr_mtrh?athAdjust(l.adjust,l.l[n],l.floor):l.adjust*l.l[n])/(p=a.width[n]),M=x,A=p>>1,B=0;do{B+=k=f[_]*f[_],M+=k<S?k:S,B+=T=f[++_]*f[_],M+=T<S?T:S,_++}while(0<--A);if(g<B&&o++,n==Pe.SBPSY_l)M<(R=g*i.nsPsy.longfact[n])&&(M=R);if(0!=c&&(g=M),!e.ATHonly)if(0<(w=t.en.l[n]))R=B*t.thm.l[n]*h/w,0!=c&&(R*=i.nsPsy.longfact[n]),g<R&&(g=R);s[r++]=0!=c?g:g*i.nsPsy.longfact[n]}var u=575;if(a.block_type!=Pe.SHORT_TYPE)for(var b=576;0!=b--&&j.EQ(f[b],0);)u=b;a.max_nonzero_coeff=u;for(var m=a.sfb_smin;n<a.psymax;m++,n+=3){var p,v,d;for(d=e.VBR==ye.vbr_rh||e.VBR==ye.vbr_mtrh?athAdjust(l.adjust,l.s[m],l.floor):l.adjust*l.s[m],p=a.width[n],v=0;v<3;v++){var g,S,M,w,R,B=0,A=p>>1;S=d/p,M=x;do{var k,T;B+=k=f[_]*f[_],M+=k<S?k:S,B+=T=f[++_]*f[_],M+=T<S?T:S,_++}while(0<--A);if(d<B&&o++,m==Pe.SBPSY_s)M<(R=d*i.nsPsy.shortfact[m])&&(M=R);if(g=0!=c?M:d,!e.ATHonly&&!e.ATHshort)if(0<(w=t.en.s[m][v]))R=B*t.thm.s[m][v]*h/w,0!=c&&(R*=i.nsPsy.shortfact[m]),g<R&&(g=R);s[r++]=0!=c?g:g*i.nsPsy.shortfact[m]}e.useTemporal&&(s[r-3]>s[r-3+1]&&(s[r-3+1]+=(s[r-3]-s[r-3+1])*i.decay),s[r-3+1]>s[r-3+2]&&(s[r-3+2]+=(s[r-3+1]-s[r-3+2])*i.decay))}return o},this.calc_noise_core=function(e,t,a,s){var n=0,r=t.s,i=e.l3_enc;if(r>e.count1)for(;0!=a--;){o=e.xr[r],r++,n+=o*o,o=e.xr[r],r++,n+=o*o}else if(r>e.big_values){var _=Ae(2);for(_[0]=0,_[1]=s;0!=a--;){o=Math.abs(e.xr[r])-_[i[r]],r++,n+=o*o,o=Math.abs(e.xr[r])-_[i[r]],r++,n+=o*o}}else for(;0!=a--;){var o;o=Math.abs(e.xr[r])-m[i[r]]*s,r++,n+=o*o,o=Math.abs(e.xr[r])-m[i[r]]*s,r++,n+=o*o}return t.s=r,n},this.calc_noise=function(e,t,a,s,n){var r,i,_=0,o=0,l=0,f=0,c=0,h=-20,u=0,b=e.scalefac,m=0;for(r=s.over_SSD=0;r<e.psymax;r++){var p,v=e.global_gain-(b[m++]+(0!=e.preflag?w[r]:0)<<e.scalefac_scale+1)-8*e.subblock_gain[e.window[r]],d=0;if(null!=n&&n.step[r]==v)d=n.noise[r],u+=e.width[r],a[_++]=d/t[o++],d=n.noise_log[r];else{var g,S=R[v+y.Q_MAX2];if(i=e.width[r]>>1,u+e.width[r]>e.max_nonzero_coeff)i=0<(g=e.max_nonzero_coeff-u+1)?g>>1:0;var M=new B(u);d=this.calc_noise_core(e,M,i,S),u=M.s,null!=n&&(n.step[r]=v,n.noise[r]=d),d=a[_++]=d/t[o++],d=ee.FAST_LOG10(Math.max(d,1e-20)),null!=n&&(n.noise_log[r]=d)}if(null!=n&&(n.global_gain=e.global_gain),c+=d,0<d)p=Math.max(0|10*d+.5,1),s.over_SSD+=p*p,l++,f+=d;h=Math.max(h,d)}return s.over_count=l,s.tot_noise=c,s.over_noise=f,s.max_noise=h,l},this.set_pinfo=function(e,t,a,s,n){var r,i,_,o,l,f=e.internal_flags,c=0==t.scalefac_scale?.5:1,h=t.scalefac,u=Ae(z.SFBMAX),b=Ae(z.SFBMAX),m=new T;calc_xmin(e,a,t,u),calc_noise(t,u,b,m,null);var p=0;for(i=t.sfb_lmax,t.block_type!=Pe.SHORT_TYPE&&0==t.mixed_block_flag&&(i=22),r=0;r<i;r++){var v=f.scalefac_band.l[r],d=(g=f.scalefac_band.l[r+1])-v;for(o=0;p<g;p++)o+=t.xr[p]*t.xr[p];o/=d,l=1e15,f.pinfo.en[s][n][r]=l*o,f.pinfo.xfsf[s][n][r]=l*u[r]*b[r]/d,0<a.en.l[r]&&!e.ATHonly?o/=a.en.l[r]:o=0,f.pinfo.thr[s][n][r]=l*Math.max(o*a.thm.l[r],f.ATH.l[r]),(f.pinfo.LAMEsfb[s][n][r]=0)!=t.preflag&&11<=r&&(f.pinfo.LAMEsfb[s][n][r]=-c*w[r]),r<Pe.SBPSY_l&&(f.pinfo.LAMEsfb[s][n][r]-=c*h[r])}if(t.block_type==Pe.SHORT_TYPE)for(i=r,r=t.sfb_smin;r<Pe.SBMAX_s;r++){v=f.scalefac_band.s[r],d=(g=f.scalefac_band.s[r+1])-v;for(var g,S=0;S<3;S++){for(o=0,_=v;_<g;_++)o+=t.xr[p]*t.xr[p],p++;o=Math.max(o/d,1e-20),l=1e15,f.pinfo.en_s[s][n][3*r+S]=l*o,f.pinfo.xfsf_s[s][n][3*r+S]=l*u[i]*b[i]/d,0<a.en.s[r][S]?o/=a.en.s[r][S]:o=0,(e.ATHonly||e.ATHshort)&&(o=0),f.pinfo.thr_s[s][n][3*r+S]=l*Math.max(o*a.thm.s[r][S],f.ATH.s[r]),f.pinfo.LAMEsfb_s[s][n][3*r+S]=-2*t.subblock_gain[S],r<Pe.SBPSY_s&&(f.pinfo.LAMEsfb_s[s][n][3*r+S]-=c*h[i]),i++}}f.pinfo.LAMEqss[s][n]=t.global_gain,f.pinfo.LAMEmainbits[s][n]=t.part2_3_length+t.part2_length,f.pinfo.LAMEsfbits[s][n]=t.part2_length,f.pinfo.over[s][n]=m.over_count,f.pinfo.max_noise[s][n]=10*m.max_noise,f.pinfo.over_noise[s][n]=10*m.over_noise,f.pinfo.tot_noise[s][n]=10*m.tot_noise,f.pinfo.over_SSD[s][n]=m.over_SSD}}function x(){this.xr=Ae(576),this.l3_enc=Be(576),this.scalefac=Be(z.SFBMAX),this.xrpow_max=0,this.part2_3_length=0,this.big_values=0,this.count1=0,this.global_gain=0,this.scalefac_compress=0,this.block_type=0,this.mixed_block_flag=0,this.table_select=Be(3),this.subblock_gain=Be(4),this.region0_count=0,this.region1_count=0,this.preflag=0,this.scalefac_scale=0,this.count1table_select=0,this.part2_length=0,this.sfb_lmax=0,this.sfb_smin=0,this.psy_lmax=0,this.sfbmax=0,this.psymax=0,this.sfbdivide=0,this.width=Be(z.SFBMAX),this.window=Be(z.SFBMAX),this.count1bits=0,this.sfb_partition_table=null,this.slen=Be(4),this.max_nonzero_coeff=0;var a=this;function s(e){return new Int32Array(e)}this.assign=function(e){var t;a.xr=(t=e.xr,new Float32Array(t)),a.l3_enc=s(e.l3_enc),a.scalefac=s(e.scalefac),a.xrpow_max=e.xrpow_max,a.part2_3_length=e.part2_3_length,a.big_values=e.big_values,a.count1=e.count1,a.global_gain=e.global_gain,a.scalefac_compress=e.scalefac_compress,a.block_type=e.block_type,a.mixed_block_flag=e.mixed_block_flag,a.table_select=s(e.table_select),a.subblock_gain=s(e.subblock_gain),a.region0_count=e.region0_count,a.region1_count=e.region1_count,a.preflag=e.preflag,a.scalefac_scale=e.scalefac_scale,a.count1table_select=e.count1table_select,a.part2_length=e.part2_length,a.sfb_lmax=e.sfb_lmax,a.sfb_smin=e.sfb_smin,a.psy_lmax=e.psy_lmax,a.sfbmax=e.sfbmax,a.psymax=e.psymax,a.sfbdivide=e.sfbdivide,a.width=s(e.width),a.window=s(e.window),a.count1bits=e.count1bits,a.sfb_partition_table=e.sfb_partition_table.slice(0),a.slen=s(e.slen),a.max_nonzero_coeff=e.max_nonzero_coeff}}C.t1HB=[1,1,1,0],C.t2HB=[1,2,1,3,1,1,3,2,0],C.t3HB=[3,2,1,1,1,1,3,2,0],C.t5HB=[1,2,6,5,3,1,4,4,7,5,7,1,6,1,1,0],C.t6HB=[7,3,5,1,6,2,3,2,5,4,4,1,3,3,2,0],C.t7HB=[1,2,10,19,16,10,3,3,7,10,5,3,11,4,13,17,8,4,12,11,18,15,11,2,7,6,9,14,3,1,6,4,5,3,2,0],C.t8HB=[3,4,6,18,12,5,5,1,2,16,9,3,7,3,5,14,7,3,19,17,15,13,10,4,13,5,8,11,5,1,12,4,4,1,1,0],C.t9HB=[7,5,9,14,15,7,6,4,5,5,6,7,7,6,8,8,8,5,15,6,9,10,5,1,11,7,9,6,4,1,14,4,6,2,6,0],C.t10HB=[1,2,10,23,35,30,12,17,3,3,8,12,18,21,12,7,11,9,15,21,32,40,19,6,14,13,22,34,46,23,18,7,20,19,33,47,27,22,9,3,31,22,41,26,21,20,5,3,14,13,10,11,16,6,5,1,9,8,7,8,4,4,2,0],C.t11HB=[3,4,10,24,34,33,21,15,5,3,4,10,32,17,11,10,11,7,13,18,30,31,20,5,25,11,19,59,27,18,12,5,35,33,31,58,30,16,7,5,28,26,32,19,17,15,8,14,14,12,9,13,14,9,4,1,11,4,6,6,6,3,2,0],C.t12HB=[9,6,16,33,41,39,38,26,7,5,6,9,23,16,26,11,17,7,11,14,21,30,10,7,17,10,15,12,18,28,14,5,32,13,22,19,18,16,9,5,40,17,31,29,17,13,4,2,27,12,11,15,10,7,4,1,27,12,8,12,6,3,1,0],C.t13HB=[1,5,14,21,34,51,46,71,42,52,68,52,67,44,43,19,3,4,12,19,31,26,44,33,31,24,32,24,31,35,22,14,15,13,23,36,59,49,77,65,29,40,30,40,27,33,42,16,22,20,37,61,56,79,73,64,43,76,56,37,26,31,25,14,35,16,60,57,97,75,114,91,54,73,55,41,48,53,23,24,58,27,50,96,76,70,93,84,77,58,79,29,74,49,41,17,47,45,78,74,115,94,90,79,69,83,71,50,59,38,36,15,72,34,56,95,92,85,91,90,86,73,77,65,51,44,43,42,43,20,30,44,55,78,72,87,78,61,46,54,37,30,20,16,53,25,41,37,44,59,54,81,66,76,57,54,37,18,39,11,35,33,31,57,42,82,72,80,47,58,55,21,22,26,38,22,53,25,23,38,70,60,51,36,55,26,34,23,27,14,9,7,34,32,28,39,49,75,30,52,48,40,52,28,18,17,9,5,45,21,34,64,56,50,49,45,31,19,12,15,10,7,6,3,48,23,20,39,36,35,53,21,16,23,13,10,6,1,4,2,16,15,17,27,25,20,29,11,17,12,16,8,1,1,0,1],C.t15HB=[7,12,18,53,47,76,124,108,89,123,108,119,107,81,122,63,13,5,16,27,46,36,61,51,42,70,52,83,65,41,59,36,19,17,15,24,41,34,59,48,40,64,50,78,62,80,56,33,29,28,25,43,39,63,55,93,76,59,93,72,54,75,50,29,52,22,42,40,67,57,95,79,72,57,89,69,49,66,46,27,77,37,35,66,58,52,91,74,62,48,79,63,90,62,40,38,125,32,60,56,50,92,78,65,55,87,71,51,73,51,70,30,109,53,49,94,88,75,66,122,91,73,56,42,64,44,21,25,90,43,41,77,73,63,56,92,77,66,47,67,48,53,36,20,71,34,67,60,58,49,88,76,67,106,71,54,38,39,23,15,109,53,51,47,90,82,58,57,48,72,57,41,23,27,62,9,86,42,40,37,70,64,52,43,70,55,42,25,29,18,11,11,118,68,30,55,50,46,74,65,49,39,24,16,22,13,14,7,91,44,39,38,34,63,52,45,31,52,28,19,14,8,9,3,123,60,58,53,47,43,32,22,37,24,17,12,15,10,2,1,71,37,34,30,28,20,17,26,21,16,10,6,8,6,2,0],C.t16HB=[1,5,14,44,74,63,110,93,172,149,138,242,225,195,376,17,3,4,12,20,35,62,53,47,83,75,68,119,201,107,207,9,15,13,23,38,67,58,103,90,161,72,127,117,110,209,206,16,45,21,39,69,64,114,99,87,158,140,252,212,199,387,365,26,75,36,68,65,115,101,179,164,155,264,246,226,395,382,362,9,66,30,59,56,102,185,173,265,142,253,232,400,388,378,445,16,111,54,52,100,184,178,160,133,257,244,228,217,385,366,715,10,98,48,91,88,165,157,148,261,248,407,397,372,380,889,884,8,85,84,81,159,156,143,260,249,427,401,392,383,727,713,708,7,154,76,73,141,131,256,245,426,406,394,384,735,359,710,352,11,139,129,67,125,247,233,229,219,393,743,737,720,885,882,439,4,243,120,118,115,227,223,396,746,742,736,721,712,706,223,436,6,202,224,222,218,216,389,386,381,364,888,443,707,440,437,1728,4,747,211,210,208,370,379,734,723,714,1735,883,877,876,3459,865,2,377,369,102,187,726,722,358,711,709,866,1734,871,3458,870,434,0,12,10,7,11,10,17,11,9,13,12,10,7,5,3,1,3],C.t24HB=[15,13,46,80,146,262,248,434,426,669,653,649,621,517,1032,88,14,12,21,38,71,130,122,216,209,198,327,345,319,297,279,42,47,22,41,74,68,128,120,221,207,194,182,340,315,295,541,18,81,39,75,70,134,125,116,220,204,190,178,325,311,293,271,16,147,72,69,135,127,118,112,210,200,188,352,323,306,285,540,14,263,66,129,126,119,114,214,202,192,180,341,317,301,281,262,12,249,123,121,117,113,215,206,195,185,347,330,308,291,272,520,10,435,115,111,109,211,203,196,187,353,332,313,298,283,531,381,17,427,212,208,205,201,193,186,177,169,320,303,286,268,514,377,16,335,199,197,191,189,181,174,333,321,305,289,275,521,379,371,11,668,184,183,179,175,344,331,314,304,290,277,530,383,373,366,10,652,346,171,168,164,318,309,299,287,276,263,513,375,368,362,6,648,322,316,312,307,302,292,284,269,261,512,376,370,364,359,4,620,300,296,294,288,282,273,266,515,380,374,369,365,361,357,2,1033,280,278,274,267,264,259,382,378,372,367,363,360,358,356,0,43,20,19,17,15,13,11,9,7,6,4,7,5,3,1,3],C.t32HB=[1,10,8,20,12,20,16,32,14,12,24,0,28,16,24,16],C.t33HB=[15,28,26,48,22,40,36,64,14,24,20,32,12,16,8,0],C.t1l=[1,4,3,5],C.t2l=[1,4,7,4,5,7,6,7,8],C.t3l=[2,3,7,4,4,7,6,7,8],C.t5l=[1,4,7,8,4,5,8,9,7,8,9,10,8,8,9,10],C.t6l=[3,4,6,8,4,4,6,7,5,6,7,8,7,7,8,9],C.t7l=[1,4,7,9,9,10,4,6,8,9,9,10,7,7,9,10,10,11,8,9,10,11,11,11,8,9,10,11,11,12,9,10,11,12,12,12],C.t8l=[2,4,7,9,9,10,4,4,6,10,10,10,7,6,8,10,10,11,9,10,10,11,11,12,9,9,10,11,12,12,10,10,11,11,13,13],C.t9l=[3,4,6,7,9,10,4,5,6,7,8,10,5,6,7,8,9,10,7,7,8,9,9,10,8,8,9,9,10,11,9,9,10,10,11,11],C.t10l=[1,4,7,9,10,10,10,11,4,6,8,9,10,11,10,10,7,8,9,10,11,12,11,11,8,9,10,11,12,12,11,12,9,10,11,12,12,12,12,12,10,11,12,12,13,13,12,13,9,10,11,12,12,12,13,13,10,10,11,12,12,13,13,13],C.t11l=[2,4,6,8,9,10,9,10,4,5,6,8,10,10,9,10,6,7,8,9,10,11,10,10,8,8,9,11,10,12,10,11,9,10,10,11,11,12,11,12,9,10,11,12,12,13,12,13,9,9,9,10,11,12,12,12,9,9,10,11,12,12,12,12],C.t12l=[4,4,6,8,9,10,10,10,4,5,6,7,9,9,10,10,6,6,7,8,9,10,9,10,7,7,8,8,9,10,10,10,8,8,9,9,10,10,10,11,9,9,10,10,10,11,10,11,9,9,9,10,10,11,11,12,10,10,10,11,11,11,11,12],C.t13l=[1,5,7,8,9,10,10,11,10,11,12,12,13,13,14,14,4,6,8,9,10,10,11,11,11,11,12,12,13,14,14,14,7,8,9,10,11,11,12,12,11,12,12,13,13,14,15,15,8,9,10,11,11,12,12,12,12,13,13,13,13,14,15,15,9,9,11,11,12,12,13,13,12,13,13,14,14,15,15,16,10,10,11,12,12,12,13,13,13,13,14,13,15,15,16,16,10,11,12,12,13,13,13,13,13,14,14,14,15,15,16,16,11,11,12,13,13,13,14,14,14,14,15,15,15,16,18,18,10,10,11,12,12,13,13,14,14,14,14,15,15,16,17,17,11,11,12,12,13,13,13,15,14,15,15,16,16,16,18,17,11,12,12,13,13,14,14,15,14,15,16,15,16,17,18,19,12,12,12,13,14,14,14,14,15,15,15,16,17,17,17,18,12,13,13,14,14,15,14,15,16,16,17,17,17,18,18,18,13,13,14,15,15,15,16,16,16,16,16,17,18,17,18,18,14,14,14,15,15,15,17,16,16,19,17,17,17,19,18,18,13,14,15,16,16,16,17,16,17,17,18,18,21,20,21,18],C.t15l=[3,5,6,8,8,9,10,10,10,11,11,12,12,12,13,14,5,5,7,8,9,9,10,10,10,11,11,12,12,12,13,13,6,7,7,8,9,9,10,10,10,11,11,12,12,13,13,13,7,8,8,9,9,10,10,11,11,11,12,12,12,13,13,13,8,8,9,9,10,10,11,11,11,11,12,12,12,13,13,13,9,9,9,10,10,10,11,11,11,11,12,12,13,13,13,14,10,9,10,10,10,11,11,11,11,12,12,12,13,13,14,14,10,10,10,11,11,11,11,12,12,12,12,12,13,13,13,14,10,10,10,11,11,11,11,12,12,12,12,13,13,14,14,14,10,10,11,11,11,11,12,12,12,13,13,13,13,14,14,14,11,11,11,11,12,12,12,12,12,13,13,13,13,14,15,14,11,11,11,11,12,12,12,12,13,13,13,13,14,14,14,15,12,12,11,12,12,12,13,13,13,13,13,13,14,14,15,15,12,12,12,12,12,13,13,13,13,14,14,14,14,14,15,15,13,13,13,13,13,13,13,13,14,14,14,14,15,15,14,15,13,13,13,13,13,13,13,14,14,14,14,14,15,15,15,15],C.t16_5l=[1,5,7,9,10,10,11,11,12,12,12,13,13,13,14,11,4,6,8,9,10,11,11,11,12,12,12,13,14,13,14,11,7,8,9,10,11,11,12,12,13,12,13,13,13,14,14,12,9,9,10,11,11,12,12,12,13,13,14,14,14,15,15,13,10,10,11,11,12,12,13,13,13,14,14,14,15,15,15,12,10,10,11,11,12,13,13,14,13,14,14,15,15,15,16,13,11,11,11,12,13,13,13,13,14,14,14,14,15,15,16,13,11,11,12,12,13,13,13,14,14,15,15,15,15,17,17,13,11,12,12,13,13,13,14,14,15,15,15,15,16,16,16,13,12,12,12,13,13,14,14,15,15,15,15,16,15,16,15,14,12,13,12,13,14,14,14,14,15,16,16,16,17,17,16,13,13,13,13,13,14,14,15,16,16,16,16,16,16,15,16,14,13,14,14,14,14,15,15,15,15,17,16,16,16,16,18,14,15,14,14,14,15,15,16,16,16,18,17,17,17,19,17,14,14,15,13,14,16,16,15,16,16,17,18,17,19,17,16,14,11,11,11,12,12,13,13,13,14,14,14,14,14,14,14,12],C.t16l=[1,5,7,9,10,10,11,11,12,12,12,13,13,13,14,10,4,6,8,9,10,11,11,11,12,12,12,13,14,13,14,10,7,8,9,10,11,11,12,12,13,12,13,13,13,14,14,11,9,9,10,11,11,12,12,12,13,13,14,14,14,15,15,12,10,10,11,11,12,12,13,13,13,14,14,14,15,15,15,11,10,10,11,11,12,13,13,14,13,14,14,15,15,15,16,12,11,11,11,12,13,13,13,13,14,14,14,14,15,15,16,12,11,11,12,12,13,13,13,14,14,15,15,15,15,17,17,12,11,12,12,13,13,13,14,14,15,15,15,15,16,16,16,12,12,12,12,13,13,14,14,15,15,15,15,16,15,16,15,13,12,13,12,13,14,14,14,14,15,16,16,16,17,17,16,12,13,13,13,13,14,14,15,16,16,16,16,16,16,15,16,13,13,14,14,14,14,15,15,15,15,17,16,16,16,16,18,13,15,14,14,14,15,15,16,16,16,18,17,17,17,19,17,13,14,15,13,14,16,16,15,16,16,17,18,17,19,17,16,13,10,10,10,11,11,12,12,12,13,13,13,13,13,13,13,10],C.t24l=[4,5,7,8,9,10,10,11,11,12,12,12,12,12,13,10,5,6,7,8,9,10,10,11,11,11,12,12,12,12,12,10,7,7,8,9,9,10,10,11,11,11,11,12,12,12,13,9,8,8,9,9,10,10,10,11,11,11,11,12,12,12,12,9,9,9,9,10,10,10,10,11,11,11,12,12,12,12,13,9,10,9,10,10,10,10,11,11,11,11,12,12,12,12,12,9,10,10,10,10,10,11,11,11,11,12,12,12,12,12,13,9,11,10,10,10,11,11,11,11,12,12,12,12,12,13,13,10,11,11,11,11,11,11,11,11,11,12,12,12,12,13,13,10,11,11,11,11,11,11,11,12,12,12,12,12,13,13,13,10,12,11,11,11,11,12,12,12,12,12,12,13,13,13,13,10,12,12,11,11,11,12,12,12,12,12,12,13,13,13,13,10,12,12,12,12,12,12,12,12,12,12,13,13,13,13,13,10,12,12,12,12,12,12,12,12,13,13,13,13,13,13,13,10,13,12,12,12,12,12,12,13,13,13,13,13,13,13,13,10,9,9,9,9,9,9,9,9,9,9,9,10,10,10,10,6],C.t32l=[1,5,5,7,5,8,7,9,5,7,7,9,7,9,9,10],C.t33l=[4,5,5,6,5,6,6,7,5,6,6,7,6,7,7,8],C.ht=[new e(0,0,null,null),new e(2,0,C.t1HB,C.t1l),new e(3,0,C.t2HB,C.t2l),new e(3,0,C.t3HB,C.t3l),new e(0,0,null,null),new e(4,0,C.t5HB,C.t5l),new e(4,0,C.t6HB,C.t6l),new e(6,0,C.t7HB,C.t7l),new e(6,0,C.t8HB,C.t8l),new e(6,0,C.t9HB,C.t9l),new e(8,0,C.t10HB,C.t10l),new e(8,0,C.t11HB,C.t11l),new e(8,0,C.t12HB,C.t12l),new e(16,0,C.t13HB,C.t13l),new e(0,0,null,C.t16_5l),new e(16,0,C.t15HB,C.t15l),new e(1,1,C.t16HB,C.t16l),new e(2,3,C.t16HB,C.t16l),new e(3,7,C.t16HB,C.t16l),new e(4,15,C.t16HB,C.t16l),new e(6,63,C.t16HB,C.t16l),new e(8,255,C.t16HB,C.t16l),new e(10,1023,C.t16HB,C.t16l),new e(13,8191,C.t16HB,C.t16l),new e(4,15,C.t24HB,C.t24l),new e(5,31,C.t24HB,C.t24l),new e(6,63,C.t24HB,C.t24l),new e(7,127,C.t24HB,C.t24l),new e(8,255,C.t24HB,C.t24l),new e(9,511,C.t24HB,C.t24l),new e(11,2047,C.t24HB,C.t24l),new e(13,8191,C.t24HB,C.t24l),new e(0,0,C.t32HB,C.t32l),new e(0,0,C.t33HB,C.t33l)],C.largetbl=[65540,327685,458759,589832,655369,655370,720906,720907,786443,786444,786444,851980,851980,851980,917517,655370,262149,393222,524295,589832,655369,720906,720906,720907,786443,786443,786444,851980,917516,851980,917516,655370,458759,524295,589832,655369,720905,720906,786442,786443,851979,786443,851979,851980,851980,917516,917517,720905,589832,589832,655369,720905,720906,786442,786442,786443,851979,851979,917515,917516,917516,983052,983052,786441,655369,655369,720905,720906,786442,786442,851978,851979,851979,917515,917516,917516,983052,983052,983053,720905,655370,655369,720906,720906,786442,851978,851979,917515,851979,917515,917516,983052,983052,983052,1048588,786441,720906,720906,720906,786442,851978,851979,851979,851979,917515,917516,917516,917516,983052,983052,1048589,786441,720907,720906,786442,786442,851979,851979,851979,917515,917516,983052,983052,983052,983052,1114125,1114125,786442,720907,786443,786443,851979,851979,851979,917515,917515,983051,983052,983052,983052,1048588,1048589,1048589,786442,786443,786443,786443,851979,851979,917515,917515,983052,983052,983052,983052,1048588,983053,1048589,983053,851978,786444,851979,786443,851979,917515,917516,917516,917516,983052,1048588,1048588,1048589,1114125,1114125,1048589,786442,851980,851980,851979,851979,917515,917516,983052,1048588,1048588,1048588,1048588,1048589,1048589,983053,1048589,851978,851980,917516,917516,917516,917516,983052,983052,983052,983052,1114124,1048589,1048589,1048589,1048589,1179661,851978,983052,917516,917516,917516,983052,983052,1048588,1048588,1048589,1179661,1114125,1114125,1114125,1245197,1114125,851978,917517,983052,851980,917516,1048588,1048588,983052,1048589,1048589,1114125,1179661,1114125,1245197,1114125,1048589,851978,655369,655369,655369,720905,720905,786441,786441,786441,851977,851977,851977,851978,851978,851978,851978,655366],C.table23=[65538,262147,458759,262148,327684,458759,393222,458759,524296],C.table56=[65539,262148,458758,524296,262148,327684,524294,589831,458757,524294,589831,655368,524295,524295,589832,655369],C.bitrate_table=[[0,8,16,24,32,40,48,56,64,80,96,112,128,144,160,-1],[0,32,40,48,56,64,80,96,112,128,160,192,224,256,320,-1],[0,8,16,24,32,40,48,56,64,-1,-1,-1,-1,-1,-1,-1]],C.samplerate_table=[[22050,24e3,16e3,-1],[44100,48e3,32e3,-1],[11025,12e3,8e3,-1]],C.scfsi_band=[0,6,11,16,21],y.Q_MAX=257,y.Q_MAX2=116,y.LARGE_BITS=1e5,y.IXMAX_VAL=8206;var z={};function w(){var v,g,M;this.rv=null,this.qupvt=null;var w,n=new function(){this.setModules=function(e,t){}};function R(e){this.ordinal=e}function _(e){for(var t=0;t<e.sfbmax;t++)if(e.scalefac[t]+e.subblock_gain[e.window[t]]==0)return!1;return!0}function B(e,t,a,s,n){var r;switch(e){default:case 9:0<t.over_count?(r=a.over_SSD<=t.over_SSD,a.over_SSD==t.over_SSD&&(r=a.bits<t.bits)):r=a.max_noise<0&&10*a.max_noise+a.bits<=10*t.max_noise+t.bits;break;case 0:r=a.over_count<t.over_count||a.over_count==t.over_count&&a.over_noise<t.over_noise||a.over_count==t.over_count&&j.EQ(a.over_noise,t.over_noise)&&a.tot_noise<t.tot_noise;break;case 8:a.max_noise=function(e,t){for(var a,s=1e-37,n=0;n<t.psymax;n++)s+=(a=e[n],ee.FAST_LOG10(.368+.632*a*a*a));return Math.max(1e-20,s)}(n,s);case 1:r=a.max_noise<t.max_noise;break;case 2:r=a.tot_noise<t.tot_noise;break;case 3:r=a.tot_noise<t.tot_noise&&a.max_noise<t.max_noise;break;case 4:r=a.max_noise<=0&&.2<t.max_noise||a.max_noise<=0&&t.max_noise<0&&t.max_noise>a.max_noise-.2&&a.tot_noise<t.tot_noise||a.max_noise<=0&&0<t.max_noise&&t.max_noise>a.max_noise-.2&&a.tot_noise<t.tot_noise+t.over_noise||0<a.max_noise&&-.05<t.max_noise&&t.max_noise>a.max_noise-.1&&a.tot_noise+a.over_noise<t.tot_noise+t.over_noise||0<a.max_noise&&-.1<t.max_noise&&t.max_noise>a.max_noise-.15&&a.tot_noise+a.over_noise+a.over_noise<t.tot_noise+t.over_noise+t.over_noise;break;case 5:r=a.over_noise<t.over_noise||j.EQ(a.over_noise,t.over_noise)&&a.tot_noise<t.tot_noise;break;case 6:r=a.over_noise<t.over_noise||j.EQ(a.over_noise,t.over_noise)&&(a.max_noise<t.max_noise||j.EQ(a.max_noise,t.max_noise)&&a.tot_noise<=t.tot_noise);break;case 7:r=a.over_count<t.over_count||a.over_noise<t.over_noise}return 0==t.over_count&&(r=r&&a.bits<t.bits),r}function A(e,t,a,s,n){var r=e.internal_flags;!function(e,t,a,s,n){var r,i=e.internal_flags;r=0==t.scalefac_scale?1.2968395546510096:1.6817928305074292;for(var _=0,o=0;o<t.sfbmax;o++)_<a[o]&&(_=a[o]);var l=i.noise_shaping_amp;switch(3==l&&(l=n?2:1),l){case 2:break;case 1:1<_?_=Math.pow(_,.5):_*=.95;break;case 0:default:1<_?_=1:_*=.95}var f=0;for(o=0;o<t.sfbmax;o++){var c,h=t.width[o];if(f+=h,!(a[o]<_)){if(0!=(2&i.substep_shaping)&&(i.pseudohalf[o]=0==i.pseudohalf[o]?1:0,0==i.pseudohalf[o]&&2==i.noise_shaping_amp))return;for(t.scalefac[o]++,c=-h;c<0;c++)s[f+c]*=r,s[f+c]>t.xrpow_max&&(t.xrpow_max=s[f+c]);if(2==i.noise_shaping_amp)return}}}(e,t,a,s,n);var i=_(t);return!i&&(!(i=2==r.mode_gr?w.scale_bitcount(t):w.scale_bitcount_lsf(r,t))||(1<r.noise_shaping&&(Te.fill(r.pseudohalf,0),0==t.scalefac_scale?(!function(e,t){for(var a=0,s=0;s<e.sfbmax;s++){var n=e.width[s],r=e.scalefac[s];if(0!=e.preflag&&(r+=M.pretab[s]),a+=n,0!=(1&r)){r++;for(var i=-n;i<0;i++)t[a+i]*=1.2968395546510096,t[a+i]>e.xrpow_max&&(e.xrpow_max=t[a+i])}e.scalefac[s]=r>>1}e.preflag=0,e.scalefac_scale=1}(t,s),i=!1):t.block_type==Pe.SHORT_TYPE&&0<r.subblock_gain&&(i=function(e,t,a){var s,n=t.scalefac;for(s=0;s<t.sfb_lmax;s++)if(16<=n[s])return!0;for(var r=0;r<3;r++){var i=0,_=0;for(s=t.sfb_lmax+r;s<t.sfbdivide;s+=3)i<n[s]&&(i=n[s]);for(;s<t.sfbmax;s+=3)_<n[s]&&(_=n[s]);if(!(i<16&&_<8)){if(7<=t.subblock_gain[r])return!0;t.subblock_gain[r]++;var o=e.scalefac_band.l[t.sfb_lmax];for(s=t.sfb_lmax+r;s<t.sfbmax;s+=3){var l=t.width[s],f=n[s];if(0<=(f-=4>>t.scalefac_scale))n[s]=f,o+=3*l;else{n[s]=0;var c=210+(f<<t.scalefac_scale+1);u=M.IPOW20(c),o+=l*(r+1);for(var h=-l;h<0;h++)a[o+h]*=u,a[o+h]>t.xrpow_max&&(t.xrpow_max=a[o+h]);o+=l*(3-r-1)}}var u=M.IPOW20(202);for(o+=t.width[s]*(r+1),h=-t.width[s];h<0;h++)a[o+h]*=u,a[o+h]>t.xrpow_max&&(t.xrpow_max=a[o+h])}}return!1}(r,t,s)||_(t))),i||(i=2==r.mode_gr?w.scale_bitcount(t):w.scale_bitcount_lsf(r,t)),!i))}this.setModules=function(e,t,a,s){v=e,g=t,this.rv=t,M=a,this.qupvt=a,w=s,n.setModules(M,w)},this.ms_convert=function(e,t){for(var a=0;a<576;++a){var s=e.tt[t][0].xr[a],n=e.tt[t][1].xr[a];e.tt[t][0].xr[a]=(s+n)*(.5*ee.SQRT2),e.tt[t][1].xr[a]=(s-n)*(.5*ee.SQRT2)}},this.init_xrpow=function(e,t,a){var s=0,n=0|t.max_nonzero_coeff;if(t.xrpow_max=0,Te.fill(a,n,576,0),1e-20<(s=function(e,t,a,s){for(var n=s=0;n<=a;++n){var r=Math.abs(e.xr[n]);s+=r,t[n]=Math.sqrt(r*Math.sqrt(r)),t[n]>e.xrpow_max&&(e.xrpow_max=t[n])}return s}(t,a,n,s))){var r=0;0!=(2&e.substep_shaping)&&(r=1);for(var i=0;i<t.psymax;i++)e.pseudohalf[i]=r;return!0}return Te.fill(t.l3_enc,0,576,0),!1},this.init_outer_loop=function(e,t){t.part2_3_length=0,t.big_values=0,t.count1=0,t.global_gain=210,t.scalefac_compress=0,t.table_select[0]=0,t.table_select[1]=0,t.table_select[2]=0,t.subblock_gain[0]=0,t.subblock_gain[1]=0,t.subblock_gain[2]=0,t.subblock_gain[3]=0,t.region0_count=0,t.region1_count=0,t.preflag=0,t.scalefac_scale=0,t.count1table_select=0,t.part2_length=0,t.sfb_lmax=Pe.SBPSY_l,t.sfb_smin=Pe.SBPSY_s,t.psy_lmax=e.sfb21_extra?Pe.SBMAX_l:Pe.SBPSY_l,t.psymax=t.psy_lmax,t.sfbmax=t.sfb_lmax,t.sfbdivide=11;for(var a=0;a<Pe.SBMAX_l;a++)t.width[a]=e.scalefac_band.l[a+1]-e.scalefac_band.l[a],t.window[a]=3;if(t.block_type==Pe.SHORT_TYPE){var s=Ae(576);t.sfb_smin=0,(t.sfb_lmax=0)!=t.mixed_block_flag&&(t.sfb_smin=3,t.sfb_lmax=2*e.mode_gr+4),t.psymax=t.sfb_lmax+3*((e.sfb21_extra?Pe.SBMAX_s:Pe.SBPSY_s)-t.sfb_smin),t.sfbmax=t.sfb_lmax+3*(Pe.SBPSY_s-t.sfb_smin),t.sfbdivide=t.sfbmax-18,t.psy_lmax=t.sfb_lmax;var n=e.scalefac_band.l[t.sfb_lmax];$.arraycopy(t.xr,0,s,0,576);for(a=t.sfb_smin;a<Pe.SBMAX_s;a++)for(var r=e.scalefac_band.s[a],i=e.scalefac_band.s[a+1],_=0;_<3;_++)for(var o=r;o<i;o++)t.xr[n++]=s[3*o+_];var l=t.sfb_lmax;for(a=t.sfb_smin;a<Pe.SBMAX_s;a++)t.width[l]=t.width[l+1]=t.width[l+2]=e.scalefac_band.s[a+1]-e.scalefac_band.s[a],t.window[l]=0,t.window[l+1]=1,t.window[l+2]=2,l+=3}t.count1bits=0,t.sfb_partition_table=M.nr_of_sfb_block[0][0],t.slen[0]=0,t.slen[1]=0,t.slen[2]=0,t.slen[3]=0,t.max_nonzero_coeff=575,Te.fill(t.scalefac,0),function(e,t){var a=e.ATH,s=t.xr;if(t.block_type!=Pe.SHORT_TYPE)for(var n=!1,r=Pe.PSFB21-1;0<=r&&!n;r--){var i=e.scalefac_band.psfb21[r],_=e.scalefac_band.psfb21[r+1],o=M.athAdjust(a.adjust,a.psfb21[r],a.floor);1e-12<e.nsPsy.longfact[21]&&(o*=e.nsPsy.longfact[21]);for(var l=_-1;i<=l;l--){if(!(Math.abs(s[l])<o)){n=!0;break}s[l]=0}}else for(var f=0;f<3;f++)for(n=!1,r=Pe.PSFB12-1;0<=r&&!n;r--){_=(i=3*e.scalefac_band.s[12]+(e.scalefac_band.s[13]-e.scalefac_band.s[12])*f+(e.scalefac_band.psfb12[r]-e.scalefac_band.psfb12[0]))+(e.scalefac_band.psfb12[r+1]-e.scalefac_band.psfb12[r]);var c=M.athAdjust(a.adjust,a.psfb12[r],a.floor);for(1e-12<e.nsPsy.shortfact[12]&&(c*=e.nsPsy.shortfact[12]),l=_-1;i<=l;l--){if(!(Math.abs(s[l])<c)){n=!0;break}s[l]=0}}}(e,t)},R.BINSEARCH_NONE=new R(0),R.BINSEARCH_UP=new R(1),R.BINSEARCH_DOWN=new R(2),this.trancate_smallspectrums=function(e,t,a,s){var n=Ae(z.SFBMAX);if((0!=(4&e.substep_shaping)||t.block_type!=Pe.SHORT_TYPE)&&0==(128&e.substep_shaping)){M.calc_noise(t,a,n,new T,null);for(var r=0;r<576;r++){var i=0;0!=t.l3_enc[r]&&(i=Math.abs(t.xr[r])),s[r]=i}r=0;var _=8;t.block_type==Pe.SHORT_TYPE&&(_=6);do{var o,l,f,c,h=t.width[_];if(r+=h,!(1<=n[_]||(Te.sort(s,r-h,h),j.EQ(s[r-1],0)))){o=(1-n[_])*a[_],c=l=0;do{var u;for(f=1;c+f<h&&!j.NEQ(s[c+r-h],s[c+r+f-h]);f++);if(o<(u=s[c+r-h]*s[c+r-h]*f)){0!=c&&(l=s[c+r-h-1]);break}o-=u,c+=f}while(c<h);if(!j.EQ(l,0))for(;Math.abs(t.xr[r-h])<=l&&(t.l3_enc[r-h]=0),0<--h;);}}while(++_<t.psymax);t.part2_3_length=w.noquant_count_bits(e,t,null)}},this.outer_loop=function(e,t,a,s,n,r){var i=e.internal_flags,_=new x,o=Ae(576),l=Ae(z.SFBMAX),f=new T,c=new function(){this.global_gain=0,this.sfb_count1=0,this.step=Be(39),this.noise=Ae(39),this.noise_log=Ae(39)},h=9999999,u=!1,b=!1,m=0;if(function(e,t,a,s,n){var r,i=e.CurrentStep[s],_=!1,o=e.OldValue[s],l=R.BINSEARCH_NONE;for(t.global_gain=o,a-=t.part2_length;;){var f;if(r=w.count_bits(e,n,t,null),1==i||r==a)break;a<r?(l==R.BINSEARCH_DOWN&&(_=!0),_&&(i/=2),l=R.BINSEARCH_UP,f=i):(l==R.BINSEARCH_UP&&(_=!0),_&&(i/=2),l=R.BINSEARCH_DOWN,f=-i),t.global_gain+=f,t.global_gain<0&&(_=!(t.global_gain=0)),255<t.global_gain&&(t.global_gain=255,_=!0)}for(;a<r&&t.global_gain<255;)t.global_gain++,r=w.count_bits(e,n,t,null);e.CurrentStep[s]=4<=o-t.global_gain?4:2,e.OldValue[s]=t.global_gain,t.part2_3_length=r}(i,t,r,n,s),0==i.noise_shaping)return 100;M.calc_noise(t,a,l,f,c),f.bits=t.part2_3_length,_.assign(t);var p=0;for($.arraycopy(s,0,o,0,576);!u;){do{var v,d=new T,g=255;if(v=0!=(2&i.substep_shaping)?20:3,i.sfb21_extra){if(1<l[_.sfbmax])break;if(_.block_type==Pe.SHORT_TYPE&&(1<l[_.sfbmax+1]||1<l[_.sfbmax+2]))break}if(!A(e,_,l,s,b))break;0!=_.scalefac_scale&&(g=254);var S=r-_.part2_length;if(S<=0)break;for(;(_.part2_3_length=w.count_bits(i,s,_,c))>S&&_.global_gain<=g;)_.global_gain++;if(_.global_gain>g)break;if(0==f.over_count){for(;(_.part2_3_length=w.count_bits(i,s,_,c))>h&&_.global_gain<=g;)_.global_gain++;if(_.global_gain>g)break}if(M.calc_noise(_,a,l,d,c),d.bits=_.part2_3_length,0!=(B(t.block_type!=Pe.SHORT_TYPE?e.quant_comp:e.quant_comp_short,f,d,_,l)?1:0))h=t.part2_3_length,f=d,t.assign(_),p=0,$.arraycopy(s,0,o,0,576);else if(0==i.full_outer_loop){if(++p>v&&0==f.over_count)break;if(3==i.noise_shaping_amp&&b&&30<p)break;if(3==i.noise_shaping_amp&&b&&15<_.global_gain-m)break}}while(_.global_gain+_.scalefac_scale<255);3==i.noise_shaping_amp?b?u=!0:(_.assign(t),$.arraycopy(o,0,s,0,576),p=0,m=_.global_gain,b=!0):u=!0}return e.VBR==ye.vbr_rh||e.VBR==ye.vbr_mtrh?$.arraycopy(o,0,s,0,576):0!=(1&i.substep_shaping)&&trancate_smallspectrums(i,t,a,s),f.over_count},this.iteration_finish_one=function(e,t,a){var s=e.l3_side,n=s.tt[t][a];w.best_scalefac_store(e,t,a,s),1==e.use_best_huffman&&w.best_huffman_divide(e,n),g.ResvAdjust(e,n)},this.VBR_encode_granule=function(e,t,a,s,n,r,i){var _,o=e.internal_flags,l=new x,f=Ae(576),c=i,h=i+1,u=(i+r)/2,b=0,m=o.sfb21_extra;for(Te.fill(l.l3_enc,0);o.sfb21_extra=!(c-42<u)&&m,outer_loop(e,t,a,s,n,u)<=0?(b=1,h=t.part2_3_length,l.assign(t),$.arraycopy(s,0,f,0,576),_=(i=h-32)-r,u=(i+r)/2):(_=i-(r=u+32),u=(i+r)/2,0!=b&&(b=2,t.assign(l),$.arraycopy(f,0,s,0,576))),12<_;);o.sfb21_extra=m,2==b&&$.arraycopy(l.l3_enc,0,t.l3_enc,0,576)},this.get_framebits=function(e,t){var a=e.internal_flags;a.bitrate_index=a.VBR_min_bitrate;var s=v.getframebits(e);a.bitrate_index=1,s=v.getframebits(e);for(var n=1;n<=a.VBR_max_bitrate;n++){a.bitrate_index=n;var r=new F(s);t[n]=g.ResvFrameBegin(e,r),s=r.bits}},this.VBR_old_prepare=function(e,t,a,s,n,r,i,_,o){var l,f=e.internal_flags,c=0,h=1,u=0;f.bitrate_index=f.VBR_max_bitrate;var b=g.ResvFrameBegin(e,new 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i=e.l3_side.tt[n][r],_=t[n][r],o=0,l=0;l<i.psy_lmax;l++)_[o++]*=1+.029*l*l/Pe.SBMAX_l/Pe.SBMAX_l;if(i.block_type==Pe.SHORT_TYPE)for(l=i.sfb_smin;l<Pe.SBMAX_s;l++)_[o++]*=1+.029*l*l/Pe.SBMAX_s/Pe.SBMAX_s,_[o++]*=1+.029*l*l/Pe.SBMAX_s/Pe.SBMAX_s,_[o++]*=1+.029*l*l/Pe.SBMAX_s/Pe.SBMAX_s;s[n][r]=0|Math.max(a[n][r],.9*s[n][r])}},this.VBR_new_prepare=function(e,t,a,s,n,r){var i,_=e.internal_flags,o=1,l=0,f=0;if(e.free_format){_.bitrate_index=0;c=new F(l);i=g.ResvFrameBegin(e,c),l=c.bits,n[0]=i}else{_.bitrate_index=_.VBR_max_bitrate;var c=new F(l);g.ResvFrameBegin(e,c),l=c.bits,get_framebits(e,n),i=n[_.VBR_max_bitrate]}for(var h=0;h<_.mode_gr;h++){M.on_pe(e,t,r[h],l,h,0),_.mode_ext==Pe.MPG_MD_MS_LR&&ms_convert(_.l3_side,h);for(var u=0;u<_.channels_out;++u){var b=_.l3_side.tt[h][u];_.masking_lower=Math.pow(10,.1*_.PSY.mask_adjust),init_outer_loop(_,b),0!=M.calc_xmin(e,a[h][u],b,s[h][u])&&(o=0),f+=r[h][u]}}for(h=0;h<_.mode_gr;h++)for(u=0;u<_.channels_out;u++)i<f&&(r[h][u]*=i,r[h][u]/=f);return 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m=int((t[_][o]-700)/1.4),p=c.tt[_][o];s[_][o]=int(i*h),p.block_type==Pe.SHORT_TYPE&&m<h/2&&(m=h/2),3*h/2<m?m=3*h/2:m<0&&(m=0),s[_][o]+=m}s[_][o]>Z.MAX_BITS_PER_CHANNEL&&(s[_][o]=Z.MAX_BITS_PER_CHANNEL),b+=s[_][o]}if(Z.MAX_BITS_PER_GRANULE<b)for(o=0;o<f.channels_out;++o)s[_][o]*=Z.MAX_BITS_PER_GRANULE,s[_][o]/=b}if(f.mode_ext==Pe.MPG_MD_MS_LR)for(_=0;_<f.mode_gr;_++)M.reduce_side(s[_],a[_],h*f.channels_out,Z.MAX_BITS_PER_GRANULE);for(_=l=0;_<f.mode_gr;_++)for(o=0;o<f.channels_out;o++)s[_][o]>Z.MAX_BITS_PER_CHANNEL&&(s[_][o]=Z.MAX_BITS_PER_CHANNEL),l+=s[_][o];if(l>r[0])for(_=0;_<f.mode_gr;_++)for(o=0;o<f.channels_out;o++)s[_][o]*=r[0],s[_][o]/=l}}function Y(){this.thm=new i,this.en=new i}function Pe(){var E=Pe.FFTOFFSET,P=Pe.MPG_MD_MS_LR,H=null,L=this.psy=null,I=null,V=null;this.setModules=function(e,t,a,s){H=e,this.psy=t,L=t,I=s,V=a};var N=new function(){var 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W(e,t,a){for(var s,n,r,i=10,_=t+238-14-286,o=-15;o<0;o++){var l,f,c;l=h[i+-10],f=e[_+-224]*l,c=e[t+224]*l,l=h[i+-9],f+=e[_+-160]*l,c+=e[t+160]*l,l=h[i+-8],f+=e[_+-96]*l,c+=e[t+96]*l,l=h[i+-7],f+=e[_+-32]*l,c+=e[t+32]*l,l=h[i+-6],f+=e[_+32]*l,c+=e[t+-32]*l,l=h[i+-5],f+=e[_+96]*l,c+=e[t+-96]*l,l=h[i+-4],f+=e[_+160]*l,c+=e[t+-160]*l,l=h[i+-3],f+=e[_+224]*l,c+=e[t+-224]*l,l=h[i+-2],f+=e[t+-256]*l,c-=e[_+256]*l,l=h[i+-1],f+=e[t+-192]*l,c-=e[_+192]*l,l=h[i+0],f+=e[t+-128]*l,c-=e[_+128]*l,l=h[i+1],f+=e[t+-64]*l,c-=e[_+64]*l,l=h[i+2],f+=e[t+0]*l,c-=e[_+0]*l,l=h[i+3],f+=e[t+64]*l,c-=e[_+-64]*l,l=h[i+4],f+=e[t+128]*l,c-=e[_+-128]*l,l=h[i+5],f+=e[t+192]*l,l=(c-=e[_+-192]*l)-(f*=h[i+6]),a[30+2*o]=c+f,a[31+2*o]=h[i+7]*l,i+=18,t--,_++}c=e[t+-16]*h[i+-10],f=e[t+-32]*h[i+-2],c+=(e[t+-48]-e[t+16])*h[i+-9],f+=e[t+-96]*h[i+-1],c+=(e[t+-80]+e[t+48])*h[i+-8],f+=e[t+-160]*h[i+0],c+=(e[t+-112]-e[t+80])*h[i+-7],f+=e[t+-224]*h[i+1],c+=(e[t+-144]+e[t+112])*h[i+-6],f-=e[t+32]*h[i+2],c+=(e[t+-176]-e[t+144])*h[i+-5],f-=e[t+96]*h[i+3],c+=(e[t+-208]+e[t+176])*h[i+-4],f-=e[t+160]*h[i+4],c+=(e[t+-240]-e[t+208])*h[i+-3],s=(f-=e[t+224])-c,n=f+c,c=a[14],f=a[15]-c,a[31]=n+c,a[30]=s+f,a[15]=s-f,a[14]=n-c,r=a[28]-a[0],a[0]+=a[28],a[28]=r*h[i+-36+7],r=a[29]-a[1],a[1]+=a[29],a[29]=r*h[i+-36+7],r=a[26]-a[2],a[2]+=a[26],a[26]=r*h[i+-72+7],r=a[27]-a[3],a[3]+=a[27],a[27]=r*h[i+-72+7],r=a[24]-a[4],a[4]+=a[24],a[24]=r*h[i+-108+7],r=a[25]-a[5],a[5]+=a[25],a[25]=r*h[i+-108+7],r=a[22]-a[6],a[6]+=a[22],a[22]=r*ee.SQRT2,r=a[23]-a[7],a[7]+=a[23],a[23]=r*ee.SQRT2-a[7],a[7]-=a[6],a[22]-=a[7],a[23]-=a[22],r=a[6],a[6]=a[31]-r,a[31]=a[31]+r,r=a[7],a[7]=a[30]-r,a[30]=a[30]+r,r=a[22],a[22]=a[15]-r,a[15]=a[15]+r,r=a[23],a[23]=a[14]-r,a[14]=a[14]+r,r=a[20]-a[8],a[8]+=a[20],a[20]=r*h[i+-180+7],r=a[21]-a[9],a[9]+=a[21],a[21]=r*h[i+-180+7],r=a[18]-a[10],a[10]+=a[18],a[18]=r*h[i+-216+7],r=a[19]-a[11],a[11]+=a[19],a[19]=r*h[i+-216+7],r=a[16]-a[12],a[12]+=a[16],a[16]=r*h[i+-252+7],r=a[17]-a[13],a[13]+=a[17],a[17]=r*h[i+-252+7],r=-a[20]+a[24],a[20]+=a[24],a[24]=r*h[i+-216+7],r=-a[21]+a[25],a[21]+=a[25],a[25]=r*h[i+-216+7],r=a[4]-a[8],a[4]+=a[8],a[8]=r*h[i+-216+7],r=a[5]-a[9],a[5]+=a[9],a[9]=r*h[i+-216+7],r=a[0]-a[12],a[0]+=a[12],a[12]=r*h[i+-72+7],r=a[1]-a[13],a[1]+=a[13],a[13]=r*h[i+-72+7],r=a[16]-a[28],a[16]+=a[28],a[28]=r*h[i+-72+7],r=-a[17]+a[29],a[17]+=a[29],a[29]=r*h[i+-72+7],r=ee.SQRT2*(a[2]-a[10]),a[2]+=a[10],a[10]=r,r=ee.SQRT2*(a[3]-a[11]),a[3]+=a[11],a[11]=r,r=ee.SQRT2*(-a[18]+a[26]),a[18]+=a[26],a[26]=r-a[18],r=ee.SQRT2*(-a[19]+a[27]),a[19]+=a[27],a[27]=r-a[19],r=a[2],a[19]-=a[3],a[3]-=r,a[2]=a[31]-r,a[31]+=r,r=a[3],a[11]-=a[19],a[18]-=r,a[3]=a[30]-r,a[30]+=r,r=a[18],a[27]-=a[11],a[19]-=r,a[18]=a[15]-r,a[15]+=r,r=a[19],a[10]-=r,a[19]=a[14]-r,a[14]+=r,r=a[10],a[11]-=r,a[10]=a[23]-r,a[23]+=r,r=a[11],a[26]-=r,a[11]=a[22]-r,a[22]+=r,r=a[26],a[27]-=r,a[26]=a[7]-r,a[7]+=r,r=a[27],a[27]=a[6]-r,a[6]+=r,r=ee.SQRT2*(a[0]-a[4]),a[0]+=a[4],a[4]=r,r=ee.SQRT2*(a[1]-a[5]),a[1]+=a[5],a[5]=r,r=ee.SQRT2*(a[16]-a[20]),a[16]+=a[20],a[20]=r,r=ee.SQRT2*(a[17]-a[21]),a[17]+=a[21],a[21]=r,r=-ee.SQRT2*(a[8]-a[12]),a[8]+=a[12],a[12]=r-a[8],r=-ee.SQRT2*(a[9]-a[13]),a[9]+=a[13],a[13]=r-a[9],r=-ee.SQRT2*(a[25]-a[29]),a[25]+=a[29],a[29]=r-a[25],r=-ee.SQRT2*(a[24]+a[28]),a[24]-=a[28],a[28]=r-a[24],r=a[24]-a[16],a[24]=r,r=a[20]-r,a[20]=r,r=a[28]-r,a[28]=r,r=a[25]-a[17],a[25]=r,r=a[21]-r,a[21]=r,r=a[29]-r,a[29]=r,r=a[17]-a[1],a[17]=r,r=a[9]-r,a[9]=r,r=a[25]-r,a[25]=r,r=a[5]-r,a[5]=r,r=a[21]-r,a[21]=r,r=a[13]-r,a[13]=r,r=a[29]-r,a[29]=r,r=a[1]-a[0],a[1]=r,r=a[16]-r,a[16]=r,r=a[17]-r,a[17]=r,r=a[8]-r,a[8]=r,r=a[9]-r,a[9]=r,r=a[24]-r,a[24]=r,r=a[25]-r,a[25]=r,r=a[4]-r,a[4]=r,r=a[5]-r,a[5]=r,r=a[20]-r,a[20]=r,r=a[21]-r,a[21]=r,r=a[12]-r,a[12]=r,r=a[13]-r,a[13]=r,r=a[28]-r,a[28]=r,r=a[29]-r,a[29]=r,r=a[0],a[0]+=a[31],a[31]-=r,r=a[1],a[1]+=a[30],a[30]-=r,r=a[16],a[16]+=a[15],a[15]-=r,r=a[17],a[17]+=a[14],a[14]-=r,r=a[8],a[8]+=a[23],a[23]-=r,r=a[9],a[9]+=a[22],a[22]-=r,r=a[24],a[24]+=a[7],a[7]-=r,r=a[25],a[25]+=a[6],a[6]-=r,r=a[4],a[4]+=a[27],a[27]-=r,r=a[5],a[5]+=a[26],a[26]-=r,r=a[20],a[20]+=a[11],a[11]-=r,r=a[21],a[21]+=a[10],a[10]-=r,r=a[12],a[12]+=a[19],a[19]-=r,r=a[13],a[13]+=a[18],a[18]-=r,r=a[28],a[28]+=a[3],a[3]-=r,r=a[29],a[29]+=a[2],a[2]-=r}function J(e,t){for(var a=0;a<3;a++){var s,n,r,i,_,o;n=(i=e[t+6]*z[Pe.SHORT_TYPE][0]-e[t+15])+(s=e[t+0]*z[Pe.SHORT_TYPE][2]-e[t+9]),r=i-s,_=(i=e[t+15]*z[Pe.SHORT_TYPE][0]+e[t+6])+(s=e[t+9]*z[Pe.SHORT_TYPE][2]+e[t+0]),o=-i+s,s=2.069978111953089e-11*(e[t+3]*z[Pe.SHORT_TYPE][1]-e[t+12]),i=2.069978111953089e-11*(e[t+12]*z[Pe.SHORT_TYPE][1]+e[t+3]),e[t+0]=1.90752519173728e-11*n+s,e[t+15]=1.90752519173728e-11*-_+i,r=.8660254037844387*r*1.907525191737281e-11,_=.5*_*1.907525191737281e-11+i,e[t+3]=r-_,e[t+6]=r+_,n=.5*n*1.907525191737281e-11-s,o=.8660254037844387*o*1.907525191737281e-11,e[t+9]=n+o,e[t+12]=n-o,t++}}this.mdct_sub48=function(e,t,a){for(var s,n,r,i,_,o,l,f,c,h,u,b,m,p,v,d,g,S,M,w,R,B=t,A=286,k=0;k<e.channels_out;k++){for(var T=0;T<e.mode_gr;T++){for(var x,y=e.l3_side.tt[T][k],E=y.xr,P=0,H=e.sb_sample[k][1-T],L=0,I=0;I<9;I++)for(W(B,A,H[L]),W(B,A+32,H[L+1]),L+=2,A+=64,x=1;x<32;x+=2)H[L-1][x]*=-1;for(x=0;x<32;x++,P+=18){var V=y.block_type,N=e.sb_sample[k][T],O=e.sb_sample[k][1-T];if(0!=y.mixed_block_flag&&x<2&&(V=0),e.amp_filter[x]<1e-12)Te.fill(E,P+0,P+18,0);else{if(e.amp_filter[x]<1)for(I=0;I<18;I++)O[I][U[x]]*=e.amp_filter[x];if(V==Pe.SHORT_TYPE){for(I=-3;I<0;I++){var Y=z[Pe.SHORT_TYPE][I+3];E[P+3*I+9]=N[9+I][U[x]]*Y-N[8-I][U[x]],E[P+3*I+18]=N[14-I][U[x]]*Y+N[15+I][U[x]],E[P+3*I+10]=N[15+I][U[x]]*Y-N[14-I][U[x]],E[P+3*I+19]=O[2-I][U[x]]*Y+O[3+I][U[x]],E[P+3*I+11]=O[3+I][U[x]]*Y-O[2-I][U[x]],E[P+3*I+20]=O[8-I][U[x]]*Y+O[9+I][U[x]]}J(E,P)}else{var D=Ae(18);for(I=-9;I<0;I++){var X,q;X=z[V][I+27]*O[I+9][U[x]]+z[V][I+36]*O[8-I][U[x]],q=z[V][I+9]*N[I+9][U[x]]-z[V][I+18]*N[8-I][U[x]],D[I+9]=X-q*Z[3+I+9],D[I+18]=X*Z[3+I+9]+q}s=E,n=P,R=w=M=S=g=d=v=p=m=b=u=h=c=f=l=o=_=i=void 0,o=(r=D)[17]-r[9],f=r[15]-r[11],c=r[14]-r[12],h=r[0]+r[8],u=r[1]+r[7],b=r[2]+r[6],m=r[3]+r[5],s[n+17]=h+b-m-(u-r[4]),_=(h+b-m)*K[19]+(u-r[4]),i=(o-f-c)*K[18],s[n+5]=i+_,s[n+6]=i-_,l=(r[16]-r[10])*K[18],u=u*K[19]+r[4],i=o*K[12]+l+f*K[13]+c*K[14],_=-h*K[16]+u-b*K[17]+m*K[15],s[n+1]=i+_,s[n+2]=i-_,i=o*K[13]-l-f*K[14]+c*K[12],_=-h*K[17]+u-b*K[15]+m*K[16],s[n+9]=i+_,s[n+10]=i-_,i=o*K[14]-l+f*K[12]-c*K[13],_=h*K[15]-u+b*K[16]-m*K[17],s[n+13]=i+_,s[n+14]=i-_,p=r[8]-r[0],d=r[6]-r[2],g=r[5]-r[3],S=r[17]+r[9],M=r[16]+r[10],w=r[15]+r[11],R=r[14]+r[12],s[n+0]=S+w+R+(M+r[13]),i=(S+w+R)*K[19]-(M+r[13]),_=(p-d+g)*K[18],s[n+11]=i+_,s[n+12]=i-_,v=(r[7]-r[1])*K[18],M=r[13]-M*K[19],i=S*K[15]-M+w*K[16]+R*K[17],_=p*K[14]+v+d*K[12]+g*K[13],s[n+3]=i+_,s[n+4]=i-_,i=-S*K[17]+M-w*K[15]-R*K[16],_=p*K[13]+v-d*K[14]-g*K[12],s[n+7]=i+_,s[n+8]=i-_,i=-S*K[16]+M-w*K[17]-R*K[15],_=p*K[12]-v+d*K[13]-g*K[14],s[n+15]=i+_,s[n+16]=i-_}}if(V!=Pe.SHORT_TYPE&&0!=x)for(I=7;0<=I;--I){var j,C;j=E[P+I]*G[20+I]+E[P+-1-I]*Q[28+I],C=E[P+I]*Q[28+I]-E[P+-1-I]*G[20+I],E[P+-1-I]=j,E[P+I]=C}}}if(B=a,A=286,1==e.mode_gr)for(var F=0;F<18;F++)$.arraycopy(e.sb_sample[k][1][F],0,e.sb_sample[k][0][F],0,32)}}};this.lame_encode_mp3_frame=function(e,t,a,s,n,r){var i,_=O([2,2]);_[0][0]=new Y,_[0][1]=new Y,_[1][0]=new Y,_[1][1]=new Y;var o,l=O([2,2]);l[0][0]=new Y,l[0][1]=new Y,l[1][0]=new Y,l[1][1]=new Y;var f,c,h,u=[null,null],b=e.internal_flags,m=ke([2,4]),p=[.5,.5],v=[[0,0],[0,0]],d=[[0,0],[0,0]];if(u[0]=t,u[1]=a,0==b.lame_encode_frame_init&&function(e,t){var a,s,n=e.internal_flags;if(0==n.lame_encode_frame_init){var r,i,_=Ae(2014),o=Ae(2014);for(n.lame_encode_frame_init=1,i=r=0;r<286+576*(1+n.mode_gr);++r)r<576*n.mode_gr?(_[r]=0,2==n.channels_out&&(o[r]=0)):(_[r]=t[0][i],2==n.channels_out&&(o[r]=t[1][i]),++i);for(s=0;s<n.mode_gr;s++)for(a=0;a<n.channels_out;a++)n.l3_side.tt[s][a].block_type=Pe.SHORT_TYPE;N.mdct_sub48(n,_,o)}}(e,u),b.padding=0,(b.slot_lag-=b.frac_SpF)<0&&(b.slot_lag+=e.out_samplerate,b.padding=1),0!=b.psymodel){var g=[null,null],S=0,M=Be(2);for(h=0;h<b.mode_gr;h++){for(c=0;c<b.channels_out;c++)g[c]=u[c],S=576+576*h-Pe.FFTOFFSET;if(0!=(e.VBR==ye.vbr_mtrh||e.VBR==ye.vbr_mt?L.L3psycho_anal_vbr(e,g,S,h,_,l,v[h],d[h],m[h],M):L.L3psycho_anal_ns(e,g,S,h,_,l,v[h],d[h],m[h],M)))return-4;for(e.mode==Ee.JOINT_STEREO&&(p[h]=m[h][2]+m[h][3],0<p[h]&&(p[h]=m[h][3]/p[h])),c=0;c<b.channels_out;c++){var w=b.l3_side.tt[h][c];w.block_type=M[c],w.mixed_block_flag=0}}}else for(h=0;h<b.mode_gr;h++)for(c=0;c<b.channels_out;c++)b.l3_side.tt[h][c].block_type=Pe.NORM_TYPE,b.l3_side.tt[h][c].mixed_block_flag=0,d[h][c]=v[h][c]=700;if(function(e){var t,a;if(0!=e.ATH.useAdjust)if(a=e.loudness_sq[0][0],t=e.loudness_sq[1][0],2==e.channels_out?(a+=e.loudness_sq[0][1],t+=e.loudness_sq[1][1]):(a+=a,t+=t),2==e.mode_gr&&(a=Math.max(a,t)),a*=.5,.03125<(a*=e.ATH.aaSensitivityP))1<=e.ATH.adjust?e.ATH.adjust=1:e.ATH.adjust<e.ATH.adjustLimit&&(e.ATH.adjust=e.ATH.adjustLimit),e.ATH.adjustLimit=1;else{var s=31.98*a+625e-6;e.ATH.adjust>=s?(e.ATH.adjust*=.075*s+.925,e.ATH.adjust<s&&(e.ATH.adjust=s)):e.ATH.adjustLimit>=s?e.ATH.adjust=s:e.ATH.adjust<e.ATH.adjustLimit&&(e.ATH.adjust=e.ATH.adjustLimit),e.ATH.adjustLimit=s}else e.ATH.adjust=1}(b),N.mdct_sub48(b,u[0],u[1]),b.mode_ext=Pe.MPG_MD_LR_LR,e.force_ms)b.mode_ext=Pe.MPG_MD_MS_LR;else if(e.mode==Ee.JOINT_STEREO){var R=0,B=0;for(h=0;h<b.mode_gr;h++)for(c=0;c<b.channels_out;c++)R+=d[h][c],B+=v[h][c];if(R<=1*B){var A=b.l3_side.tt[0],k=b.l3_side.tt[b.mode_gr-1];A[0].block_type==A[1].block_type&&k[0].block_type==k[1].block_type&&(b.mode_ext=Pe.MPG_MD_MS_LR)}}if(b.mode_ext==P?(o=l,f=d):(o=_,f=v),e.analysis&&null!=b.pinfo)for(h=0;h<b.mode_gr;h++)for(c=0;c<b.channels_out;c++)b.pinfo.ms_ratio[h]=b.ms_ratio[h],b.pinfo.ms_ener_ratio[h]=p[h],b.pinfo.blocktype[h][c]=b.l3_side.tt[h][c].block_type,b.pinfo.pe[h][c]=f[h][c],$.arraycopy(b.l3_side.tt[h][c].xr,0,b.pinfo.xr[h][c],0,576),b.mode_ext==P&&(b.pinfo.ers[h][c]=b.pinfo.ers[h][c+2],$.arraycopy(b.pinfo.energy[h][c+2],0,b.pinfo.energy[h][c],0,b.pinfo.energy[h][c].length));if(e.VBR==ye.vbr_off||e.VBR==ye.vbr_abr){var T,x;for(T=0;T<18;T++)b.nsPsy.pefirbuf[T]=b.nsPsy.pefirbuf[T+1];for(h=x=0;h<b.mode_gr;h++)for(c=0;c<b.channels_out;c++)x+=f[h][c];for(b.nsPsy.pefirbuf[18]=x,x=b.nsPsy.pefirbuf[9],T=0;T<9;T++)x+=(b.nsPsy.pefirbuf[T]+b.nsPsy.pefirbuf[18-T])*Pe.fircoef[T];for(x=3350*b.mode_gr*b.channels_out/x,h=0;h<b.mode_gr;h++)for(c=0;c<b.channels_out;c++)f[h][c]*=x}if(b.iteration_loop.iteration_loop(e,f,p,o),H.format_bitstream(e),i=H.copy_buffer(b,s,n,r,1),e.bWriteVbrTag&&I.addVbrFrame(e),e.analysis&&null!=b.pinfo){for(c=0;c<b.channels_out;c++){var y;for(y=0;y<E;y++)b.pinfo.pcmdata[c][y]=b.pinfo.pcmdata[c][y+e.framesize];for(y=E;y<1600;y++)b.pinfo.pcmdata[c][y]=u[c][y-E]}V.set_frame_pinfo(e,o)}return function(e){var t,a;for(e.bitrate_stereoMode_Hist[e.bitrate_index][4]++,e.bitrate_stereoMode_Hist[15][4]++,2==e.channels_out&&(e.bitrate_stereoMode_Hist[e.bitrate_index][e.mode_ext]++,e.bitrate_stereoMode_Hist[15][e.mode_ext]++),t=0;t<e.mode_gr;++t)for(a=0;a<e.channels_out;++a){var s=0|e.l3_side.tt[t][a].block_type;0!=e.l3_side.tt[t][a].mixed_block_flag&&(s=4),e.bitrate_blockType_Hist[e.bitrate_index][s]++,e.bitrate_blockType_Hist[e.bitrate_index][5]++,e.bitrate_blockType_Hist[15][s]++,e.bitrate_blockType_Hist[15][5]++}}(b),i}}function i(){this.l=Ae(Pe.SBMAX_l),this.s=ke([Pe.SBMAX_s,3]);var s=this;this.assign=function(e){$.arraycopy(e.l,0,s.l,0,Pe.SBMAX_l);for(var t=0;t<Pe.SBMAX_s;t++)for(var a=0;a<3;a++)s.s[t][a]=e.s[t][a]}}function Z(){var e=40;function t(){this.write_timing=0,this.ptr=0,this.buf=B(e)}this.Class_ID=0,this.lame_encode_frame_init=0,this.iteration_init_init=0,this.fill_buffer_resample_init=0,this.mfbuf=ke([2,Z.MFSIZE]),this.mode_gr=0,this.channels_in=0,this.channels_out=0,this.resample_ratio=0,this.mf_samples_to_encode=0,this.mf_size=0,this.VBR_min_bitrate=0,this.VBR_max_bitrate=0,this.bitrate_index=0,this.samplerate_index=0,this.mode_ext=0,this.lowpass1=0,this.lowpass2=0,this.highpass1=0,this.highpass2=0,this.noise_shaping=0,this.noise_shaping_amp=0,this.substep_shaping=0,this.psymodel=0,this.noise_shaping_stop=0,this.subblock_gain=0,this.use_best_huffman=0,this.full_outer_loop=0,this.l3_side=new function(){this.tt=[[null,null],[null,null]],this.main_data_begin=0,this.private_bits=0,this.resvDrain_pre=0,this.resvDrain_post=0,this.scfsi=[Be(4),Be(4)];for(var e=0;e<2;e++)for(var t=0;t<2;t++)this.tt[e][t]=new x},this.ms_ratio=Ae(2),this.padding=0,this.frac_SpF=0,this.slot_lag=0,this.tag_spec=null,this.nMusicCRC=0,this.OldValue=Be(2),this.CurrentStep=Be(2),this.masking_lower=0,this.bv_scf=Be(576),this.pseudohalf=Be(z.SFBMAX),this.sfb21_extra=!1,this.inbuf_old=new Array(2),this.blackfilt=new Array(2*Z.BPC+1),this.itime=s(2),this.sideinfo_len=0,this.sb_sample=ke([2,2,18,Pe.SBLIMIT]),this.amp_filter=Ae(32),this.header=new Array(Z.MAX_HEADER_BUF),this.h_ptr=0,this.w_ptr=0,this.ancillary_flag=0,this.ResvSize=0,this.ResvMax=0,this.scalefac_band=new r,this.minval_l=Ae(Pe.CBANDS),this.minval_s=Ae(Pe.CBANDS),this.nb_1=ke([4,Pe.CBANDS]),this.nb_2=ke([4,Pe.CBANDS]),this.nb_s1=ke([4,Pe.CBANDS]),this.nb_s2=ke([4,Pe.CBANDS]),this.s3_ss=null,this.s3_ll=null,this.decay=0,this.thm=new Array(4),this.en=new Array(4),this.tot_ener=Ae(4),this.loudness_sq=ke([2,2]),this.loudness_sq_save=Ae(2),this.mld_l=Ae(Pe.SBMAX_l),this.mld_s=Ae(Pe.SBMAX_s),this.bm_l=Be(Pe.SBMAX_l),this.bo_l=Be(Pe.SBMAX_l),this.bm_s=Be(Pe.SBMAX_s),this.bo_s=Be(Pe.SBMAX_s),this.npart_l=0,this.npart_s=0,this.s3ind=X([Pe.CBANDS,2]),this.s3ind_s=X([Pe.CBANDS,2]),this.numlines_s=Be(Pe.CBANDS),this.numlines_l=Be(Pe.CBANDS),this.rnumlines_l=Ae(Pe.CBANDS),this.mld_cb_l=Ae(Pe.CBANDS),this.mld_cb_s=Ae(Pe.CBANDS),this.numlines_s_num1=0,this.numlines_l_num1=0,this.pe=Ae(4),this.ms_ratio_s_old=0,this.ms_ratio_l_old=0,this.ms_ener_ratio_old=0,this.blocktype_old=Be(2),this.nsPsy=new function(){this.last_en_subshort=ke([4,9]),this.lastAttacks=Be(4),this.pefirbuf=Ae(19),this.longfact=Ae(Pe.SBMAX_l),this.shortfact=Ae(Pe.SBMAX_s),this.attackthre=0,this.attackthre_s=0},this.VBR_seek_table=new function(){this.sum=0,this.seen=0,this.want=0,this.pos=0,this.size=0,this.bag=null,this.nVbrNumFrames=0,this.nBytesWritten=0,this.TotalFrameSize=0},this.ATH=null,this.PSY=null,this.nogap_total=0,this.nogap_current=0,this.decode_on_the_fly=!0,this.findReplayGain=!0,this.findPeakSample=!0,this.PeakSample=0,this.RadioGain=0,this.AudiophileGain=0,this.rgdata=null,this.noclipGainChange=0,this.noclipScale=0,this.bitrate_stereoMode_Hist=X([16,5]),this.bitrate_blockType_Hist=X([16,6]),this.pinfo=null,this.hip=null,this.in_buffer_nsamples=0,this.in_buffer_0=null,this.in_buffer_1=null,this.iteration_loop=null;for(var a=0;a<this.en.length;a++)this.en[a]=new i;for(a=0;a<this.thm.length;a++)this.thm[a]=new i;for(a=0;a<this.header.length;a++)this.header[a]=new t}function G(){var A=new function(){var u=Ae(Pe.BLKSIZE),m=Ae(Pe.BLKSIZE_s/2),T=[.9238795325112867,.3826834323650898,.9951847266721969,.0980171403295606,.9996988186962042,.02454122852291229,.9999811752826011,.006135884649154475];function p(e,t,a){var s,n,r,i=0,_=t+(a<<=1);s=4;do{var o,l,f,c,h,u,b;for(b=s>>1,u=(h=(c=s)<<1)+c,s=h<<1,r=(n=t)+b;M=e[n+0]-e[n+c],S=e[n+0]+e[n+c],A=e[n+h]-e[n+u],R=e[n+h]+e[n+u],e[n+h]=S-R,e[n+0]=S+R,e[n+u]=M-A,e[n+c]=M+A,M=e[r+0]-e[r+c],S=e[r+0]+e[r+c],A=ee.SQRT2*e[r+u],R=ee.SQRT2*e[r+h],e[r+h]=S-R,e[r+0]=S+R,e[r+u]=M-A,e[r+c]=M+A,r+=s,(n+=s)<_;);for(l=T[i+0],o=T[i+1],f=1;f<b;f++){var m,p;m=1-2*o*o,p=2*o*l,n=t+f,r=t+c-f;do{var v,d,g,S,M,w,R,B,A,k;d=p*e[n+c]-m*e[r+c],v=m*e[n+c]+p*e[r+c],M=e[n+0]-v,S=e[n+0]+v,w=e[r+0]-d,g=e[r+0]+d,d=p*e[n+u]-m*e[r+u],v=m*e[n+u]+p*e[r+u],A=e[n+h]-v,R=e[n+h]+v,k=e[r+h]-d,B=e[r+h]+d,d=o*R-l*k,v=l*R+o*k,e[n+h]=S-v,e[n+0]=S+v,e[r+u]=w-d,e[r+c]=w+d,d=l*B-o*A,v=o*B+l*A,e[r+h]=g-v,e[r+0]=g+v,e[n+u]=M-d,e[n+c]=M+d,r+=s,n+=s}while(n<_);l=(m=l)*T[i+0]-o*T[i+1],o=m*T[i+1]+o*T[i+0]}i+=2}while(s<a)}var v=[0,128,64,192,32,160,96,224,16,144,80,208,48,176,112,240,8,136,72,200,40,168,104,232,24,152,88,216,56,184,120,248,4,132,68,196,36,164,100,228,20,148,84,212,52,180,116,244,12,140,76,204,44,172,108,236,28,156,92,220,60,188,124,252,2,130,66,194,34,162,98,226,18,146,82,210,50,178,114,242,10,138,74,202,42,170,106,234,26,154,90,218,58,186,122,250,6,134,70,198,38,166,102,230,22,150,86,214,54,182,118,246,14,142,78,206,46,174,110,238,30,158,94,222,62,190,126,254];this.fft_short=function(e,t,a,s,n){for(var r=0;r<3;r++){var i=Pe.BLKSIZE_s/2,_=65535&192*(r+1),o=Pe.BLKSIZE_s/8-1;do{var l,f,c,h,u,b=255&v[o<<2];f=(l=m[b]*s[a][n+b+_])-(u=m[127-b]*s[a][n+b+_+128]),l+=u,h=(c=m[b+64]*s[a][n+b+_+64])-(u=m[63-b]*s[a][n+b+_+192]),c+=u,i-=4,t[r][i+0]=l+c,t[r][i+2]=l-c,t[r][i+1]=f+h,t[r][i+3]=f-h,f=(l=m[b+1]*s[a][n+b+_+1])-(u=m[126-b]*s[a][n+b+_+129]),l+=u,h=(c=m[b+65]*s[a][n+b+_+65])-(u=m[62-b]*s[a][n+b+_+193]),c+=u,t[r][i+Pe.BLKSIZE_s/2+0]=l+c,t[r][i+Pe.BLKSIZE_s/2+2]=l-c,t[r][i+Pe.BLKSIZE_s/2+1]=f+h,t[r][i+Pe.BLKSIZE_s/2+3]=f-h}while(0<=--o);p(t[r],i,Pe.BLKSIZE_s/2)}},this.fft_long=function(e,t,a,s,n){var r=Pe.BLKSIZE/8-1,i=Pe.BLKSIZE/2;do{var _,o,l,f,c,h=255&v[r];o=(_=u[h]*s[a][n+h])-(c=u[h+512]*s[a][n+h+512]),_+=c,f=(l=u[h+256]*s[a][n+h+256])-(c=u[h+768]*s[a][n+h+768]),l+=c,t[0+(i-=4)]=_+l,t[i+2]=_-l,t[i+1]=o+f,t[i+3]=o-f,o=(_=u[h+1]*s[a][n+h+1])-(c=u[h+513]*s[a][n+h+513]),_+=c,f=(l=u[h+257]*s[a][n+h+257])-(c=u[h+769]*s[a][n+h+769]),l+=c,t[i+Pe.BLKSIZE/2+0]=_+l,t[i+Pe.BLKSIZE/2+2]=_-l,t[i+Pe.BLKSIZE/2+1]=o+f,t[i+Pe.BLKSIZE/2+3]=o-f}while(0<=--r);p(t,i,Pe.BLKSIZE/2)},this.init_fft=function(e){for(var t=0;t<Pe.BLKSIZE;t++)u[t]=.42-.5*Math.cos(2*Math.PI*(t+.5)/Pe.BLKSIZE)+.08*Math.cos(4*Math.PI*(t+.5)/Pe.BLKSIZE);for(t=0;t<Pe.BLKSIZE_s/2;t++)m[t]=.5*(1-Math.cos(2*Math.PI*(t+.5)/Pe.BLKSIZE_s))}},k=2.302585092994046,oe=2,le=16,d=2,g=16,E=.34,n=1/217621504/(Pe.BLKSIZE/2),fe=.3,ce=21,S=.2302585093;function M(e){return e}function Y(e,t){for(var a=0,s=0;s<Pe.BLKSIZE/2;++s)a+=e[s]*t.ATH.eql_w[s];return a*=n}function he(e,t,a,s,n,r,i,_,o,l,f){var c=e.internal_flags;if(o<2)A.fft_long(c,s[n],o,l,f),A.fft_short(c,r[i],o,l,f);else if(2==o){for(var h=Pe.BLKSIZE-1;0<=h;--h){var u=s[n+0][h],b=s[n+1][h];s[n+0][h]=(u+b)*ee.SQRT2*.5,s[n+1][h]=(u-b)*ee.SQRT2*.5}for(var m=2;0<=m;--m)for(h=Pe.BLKSIZE_s-1;0<=h;--h){u=r[i+0][m][h],b=r[i+1][m][h];r[i+0][m][h]=(u+b)*ee.SQRT2*.5,r[i+1][m][h]=(u-b)*ee.SQRT2*.5}}t[0]=M(s[n+0][0]),t[0]*=t[0];for(h=Pe.BLKSIZE/2-1;0<=h;--h){var p=s[n+0][Pe.BLKSIZE/2-h],v=s[n+0][Pe.BLKSIZE/2+h];t[Pe.BLKSIZE/2-h]=M(.5*(p*p+v*v))}for(m=2;0<=m;--m){a[m][0]=r[i+0][m][0],a[m][0]*=a[m][0];for(h=Pe.BLKSIZE_s/2-1;0<=h;--h){p=r[i+0][m][Pe.BLKSIZE_s/2-h],v=r[i+0][m][Pe.BLKSIZE_s/2+h];a[m][Pe.BLKSIZE_s/2-h]=M(.5*(p*p+v*v))}}var d=0;for(h=11;h<Pe.HBLKSIZE;h++)d+=t[h];if(c.tot_ener[o]=d,e.analysis){for(h=0;h<Pe.HBLKSIZE;h++)c.pinfo.energy[_][o][h]=c.pinfo.energy_save[o][h],c.pinfo.energy_save[o][h]=t[h];c.pinfo.pe[_][o]=c.pe[o]}2==e.athaa_loudapprox&&o<2&&(c.loudness_sq[_][o]=c.loudness_sq_save[o],c.loudness_sq_save[o]=Y(t,c))}var T,x,y,P=8,H=23,L=15,ue=[1,.79433,.63096,.63096,.63096,.63096,.63096,.25119,.11749];var f=[3.3246*3.3246,3.23837*3.23837,9.9500500969,9.0247369744,8.1854926609,7.0440875649,2.46209*2.46209,2.284*2.284,4.4892710641,1.96552*1.96552,1.82335*1.82335,1.69146*1.69146,2.4621061921,2.1508568964,1.37074*1.37074,1.31036*1.31036,1.5691069696,1.4555939904,1.16203*1.16203,1.2715945225,1.09428*1.09428,1.0659*1.0659,1.0779838276,1.0382591025,1],c=[1.7782755904,1.35879*1.35879,1.38454*1.38454,1.39497*1.39497,1.40548*1.40548,1.3537*1.3537,1.6999465924,1.22321*1.22321,1.3169398564,1],h=[5.5396212496,2.29259*2.29259,4.9868695969,2.12675*2.12675,2.02545*2.02545,1.87894*1.87894,1.74303*1.74303,1.61695*1.61695,2.2499700001,1.39148*1.39148,1.29083*1.29083,1.19746*1.19746,1.2339655056,1.0779838276];function be(e,t,a,s,n,r){var i;if(e<t){if(!(t<e*x))return e+t;i=t/e}else{if(t*x<=e)return e+t;i=e/t}if(e+=t,s+3<=6){if(T<=i)return e;var _=0|ee.FAST_LOG10_X(i,16);return e*c[_]}var o,l;_=0|ee.FAST_LOG10_X(i,16);return t=0!=r?n.ATH.cb_s[a]*n.ATH.adjust:n.ATH.cb_l[a]*n.ATH.adjust,e<y*t?t<e?(o=1,_<=13&&(o=h[_]),l=ee.FAST_LOG10_X(e/t,10/15),e*((f[_]-o)*l+o)):13<_?e:e*h[_]:e*f[_]}var r=[1.7782755904,1.35879*1.35879,1.38454*1.38454,1.39497*1.39497,1.40548*1.40548,1.3537*1.3537,1.6999465924,1.22321*1.22321,1.3169398564,1];function B(e,t,a){var s;if(e<0&&(e=0),t<0&&(t=0),e<=0)return t;if(t<=0)return e;if(s=e<t?t/e:e/t,-2<=a&&a<=2){if(T<=s)return e+t;var n=0|ee.FAST_LOG10_X(s,16);return(e+t)*r[n]}return s<x?e+t:(e<t&&(e=t),e)}function me(e,t,a,s,n){var r,i,_=0,o=0;for(r=i=0;r<Pe.SBMAX_s;++i,++r){for(var l=e.bo_s[r],f=e.npart_s,c=l<f?l:f;i<c;)_+=t[i],o+=a[i],i++;if(e.en[s].s[r][n]=_,e.thm[s].s[r][n]=o,f<=i){++r;break}var h=e.PSY.bo_s_weight[r],u=1-h;_=h*t[i],o=h*a[i],e.en[s].s[r][n]+=_,e.thm[s].s[r][n]+=o,_=u*t[i],o=u*a[i]}for(;r<Pe.SBMAX_s;++r)e.en[s].s[r][n]=0,e.thm[s].s[r][n]=0}function pe(e,t,a,s){var n,r,i=0,_=0;for(n=r=0;n<Pe.SBMAX_l;++r,++n){for(var o=e.bo_l[n],l=e.npart_l,f=o<l?o:l;r<f;)i+=t[r],_+=a[r],r++;if(e.en[s].l[n]=i,e.thm[s].l[n]=_,l<=r){++n;break}var c=e.PSY.bo_l_weight[n],h=1-c;i=c*t[r],_=c*a[r],e.en[s].l[n]+=i,e.thm[s].l[n]+=_,i=h*t[r],_=h*a[r]}for(;n<Pe.SBMAX_l;++n)e.en[s].l[n]=0,e.thm[s].l[n]=0}function ve(e,t,a,s,n,r){var i,_,o=e.internal_flags;for(_=i=0;_<o.npart_s;++_){for(var l=0,f=0,c=o.numlines_s[_],h=0;h<c;++h,++i){var u=t[r][i];l+=u,f<u&&(f=u)}a[_]=l}for(i=_=0;_<o.npart_s;_++){var b=o.s3ind_s[_][0],m=o.s3_ss[i++]*a[b];for(++b;b<=o.s3ind_s[_][1];)m+=o.s3_ss[i]*a[b],++i,++b;var p=d*o.nb_s1[n][_];if(s[_]=Math.min(m,p),o.blocktype_old[1&n]==Pe.SHORT_TYPE){p=g*o.nb_s2[n][_];var v=s[_];s[_]=Math.min(p,v)}o.nb_s2[n][_]=o.nb_s1[n][_],o.nb_s1[n][_]=m}for(;_<=Pe.CBANDS;++_)a[_]=0,s[_]=0}function de(e,t,a){return 1<=a?e:a<=0?t:0<t?Math.pow(e/t,a)*t:0}var o=[11.8,13.6,17.2,32,46.5,51.3,57.5,67.1,71.5,84.6,97.6,130];function ge(e,t){for(var a=309.07,s=0;s<Pe.SBMAX_s-1;s++)for(var n=0;n<3;n++){var r=e.thm.s[s][n];if(0<r){var i=r*t,_=e.en.s[s][n];i<_&&(a+=1e10*i<_?o[s]*(10*k):o[s]*ee.FAST_LOG10(_/i))}}return a}var _=[6.8,5.8,5.8,6.4,6.5,9.9,12.1,14.4,15,18.9,21.6,26.9,34.2,40.2,46.8,56.5,60.7,73.9,85.7,93.4,126.1];function Se(e,t){for(var a=281.0575,s=0;s<Pe.SBMAX_l-1;s++){var n=e.thm.l[s];if(0<n){var r=n*t,i=e.en.l[s];r<i&&(a+=1e10*r<i?_[s]*(10*k):_[s]*ee.FAST_LOG10(i/r))}}return a}function Me(e,t,a,s,n){var r,i;for(r=i=0;r<e.npart_l;++r){var _,o=0,l=0;for(_=0;_<e.numlines_l[r];++_,++i){var f=t[i];o+=f,l<f&&(l=f)}a[r]=o,s[r]=l,n[r]=o*e.rnumlines_l[r]}}function we(e,t,a,s){var n=ue.length-1,r=0,i=a[r]+a[r+1];0<i?((_=t[r])<t[r+1]&&(_=t[r+1]),n<(o=0|(i=20*(2*_-i)/(i*(e.numlines_l[r]+e.numlines_l[r+1]-1))))&&(o=n),s[r]=o):s[r]=0;for(r=1;r<e.npart_l-1;r++){var _,o;if(0<(i=a[r-1]+a[r]+a[r+1]))(_=t[r-1])<t[r]&&(_=t[r]),_<t[r+1]&&(_=t[r+1]),n<(o=0|(i=20*(3*_-i)/(i*(e.numlines_l[r-1]+e.numlines_l[r]+e.numlines_l[r+1]-1))))&&(o=n),s[r]=o;else s[r]=0}0<(i=a[r-1]+a[r])?((_=t[r-1])<t[r]&&(_=t[r]),n<(o=0|(i=20*(2*_-i)/(i*(e.numlines_l[r-1]+e.numlines_l[r]-1))))&&(o=n),s[r]=o):s[r]=0}var Re=[-1.730326e-17,-.01703172,-1.349528e-17,.0418072,-6.73278e-17,-.0876324,-3.0835e-17,.1863476,-1.104424e-16,-.627638];function D(e,t,a,s,n,r,i,_){var o=e.internal_flags;if(s<2)A.fft_long(o,i[_],s,t,a);else if(2==s)for(var l=Pe.BLKSIZE-1;0<=l;--l){var f=i[_+0][l],c=i[_+1][l];i[_+0][l]=(f+c)*ee.SQRT2*.5,i[_+1][l]=(f-c)*ee.SQRT2*.5}r[0]=M(i[_+0][0]),r[0]*=r[0];for(l=Pe.BLKSIZE/2-1;0<=l;--l){var h=i[_+0][Pe.BLKSIZE/2-l],u=i[_+0][Pe.BLKSIZE/2+l];r[Pe.BLKSIZE/2-l]=M(.5*(h*h+u*u))}var b=0;for(l=11;l<Pe.HBLKSIZE;l++)b+=r[l];if(o.tot_ener[s]=b,e.analysis){for(l=0;l<Pe.HBLKSIZE;l++)o.pinfo.energy[n][s][l]=o.pinfo.energy_save[s][l],o.pinfo.energy_save[s][l]=r[l];o.pinfo.pe[n][s]=o.pe[s]}}function X(e,t,a,s,n,r,i,_){var o=e.internal_flags;if(0==n&&s<2&&A.fft_short(o,i[_],s,t,a),2==s)for(var l=Pe.BLKSIZE_s-1;0<=l;--l){var f=i[_+0][n][l],c=i[_+1][n][l];i[_+0][n][l]=(f+c)*ee.SQRT2*.5,i[_+1][n][l]=(f-c)*ee.SQRT2*.5}r[n][0]=i[_+0][n][0],r[n][0]*=r[n][0];for(l=Pe.BLKSIZE_s/2-1;0<=l;--l){var h=i[_+0][n][Pe.BLKSIZE_s/2-l],u=i[_+0][n][Pe.BLKSIZE_s/2+l];r[n][Pe.BLKSIZE_s/2-l]=M(.5*(h*h+u*u))}}this.L3psycho_anal_ns=function(e,t,a,s,n,r,i,_,o,l){var f,c,h,u,b,m,p,v,d,g,S=e.internal_flags,M=ke([2,Pe.BLKSIZE]),w=ke([2,3,Pe.BLKSIZE_s]),R=Ae(Pe.CBANDS+1),B=Ae(Pe.CBANDS+1),A=Ae(Pe.CBANDS+2),k=Be(2),T=Be(2),x=ke([2,576]),y=Be(Pe.CBANDS+2),E=Be(Pe.CBANDS+2);for(Te.fill(E,0),f=S.channels_out,e.mode==Ee.JOINT_STEREO&&(f=4),d=e.VBR==ye.vbr_off?0==S.ResvMax?0:S.ResvSize/S.ResvMax*.5:e.VBR==ye.vbr_rh||e.VBR==ye.vbr_mtrh||e.VBR==ye.vbr_mt?.6:1,c=0;c<S.channels_out;c++){var P=t[c],H=a+576-350-ce+192;for(u=0;u<576;u++){var L,I;for(L=P[H+u+10],b=I=0;b<(ce-1)/2-1;b+=2)L+=Re[b]*(P[H+u+b]+P[H+u+ce-b]),I+=Re[b+1]*(P[H+u+b+1]+P[H+u+ce-b-1]);x[c][u]=L+I}n[s][c].en.assign(S.en[c]),n[s][c].thm.assign(S.thm[c]),2<f&&(r[s][c].en.assign(S.en[c+2]),r[s][c].thm.assign(S.thm[c+2]))}for(c=0;c<f;c++){var V,N=Ae(12),O=[0,0,0,0],Y=Ae(12),D=1,X=Ae(Pe.CBANDS),q=Ae(Pe.CBANDS),j=[0,0,0,0],C=Ae(Pe.HBLKSIZE),F=ke([3,Pe.HBLKSIZE_s]);for(u=0;u<3;u++)N[u]=S.nsPsy.last_en_subshort[c][u+6],Y[u]=N[u]/S.nsPsy.last_en_subshort[c][u+4],O[0]+=N[u];if(2==c)for(u=0;u<576;u++){var z,Z;z=x[0][u],Z=x[1][u],x[0][u]=z+Z,x[1][u]=z-Z}var K=x[1&c],G=0;for(u=0;u<9;u++){for(var Q=G+64,U=1;G<Q;G++)U<Math.abs(K[G])&&(U=Math.abs(K[G]));S.nsPsy.last_en_subshort[c][u]=N[u+3]=U,O[1+u/3]+=U,U>N[u+3-2]?U/=N[u+3-2]:U=N[u+3-2]>10*U?N[u+3-2]/(10*U):0,Y[u+3]=U}if(e.analysis){var W=Y[0];for(u=1;u<12;u++)W<Y[u]&&(W=Y[u]);S.pinfo.ers[s][c]=S.pinfo.ers_save[c],S.pinfo.ers_save[c]=W}for(V=3==c?S.nsPsy.attackthre_s:S.nsPsy.attackthre,u=0;u<12;u++)0==j[u/3]&&Y[u]>V&&(j[u/3]=u%3+1);for(u=1;u<4;u++){(O[u-1]>O[u]?O[u-1]/O[u]:O[u]/O[u-1])<1.7&&(j[u]=0,1==u&&(j[0]=0))}for(0!=j[0]&&0!=S.nsPsy.lastAttacks[c]&&(j[0]=0),3!=S.nsPsy.lastAttacks[c]&&j[0]+j[1]+j[2]+j[3]==0||((D=0)!=j[1]&&0!=j[0]&&(j[1]=0),0!=j[2]&&0!=j[1]&&(j[2]=0),0!=j[3]&&0!=j[2]&&(j[3]=0)),c<2?T[c]=D:0==D&&(T[0]=T[1]=0),o[c]=S.tot_ener[c],he(e,C,F,M,1&c,w,1&c,s,c,t,a),Me(S,C,R,X,q),we(S,X,q,y),v=0;v<3;v++){var J,$;for(ve(e,F,B,A,c,v),me(S,B,A,c,v),p=0;p<Pe.SBMAX_s;p++){if($=S.thm[c].s[p][v],$*=.8,2<=j[v]||1==j[v+1]){var ee=0!=v?v-1:2;U=de(S.thm[c].s[p][ee],$,.6*d);$=Math.min($,U)}if(1==j[v]){ee=0!=v?v-1:2,U=de(S.thm[c].s[p][ee],$,fe*d);$=Math.min($,U)}else if(0!=v&&3==j[v-1]||0==v&&3==S.nsPsy.lastAttacks[c]){ee=2!=v?v+1:0,U=de(S.thm[c].s[p][ee],$,fe*d);$=Math.min($,U)}J=N[3*v+3]+N[3*v+4]+N[3*v+5],6*N[3*v+5]<J&&($*=.5,6*N[3*v+4]<J&&($*=.5)),S.thm[c].s[p][v]=$}}for(S.nsPsy.lastAttacks[c]=j[2],h=m=0;h<S.npart_l;h++){for(var te=S.s3ind[h][0],ae=R[te]*ue[y[te]],se=S.s3_ll[m++]*ae;++te<=S.s3ind[h][1];)ae=R[te]*ue[y[te]],se=be(se,S.s3_ll[m++]*ae,te,te-h,S,0);se*=.158489319246111,S.blocktype_old[1&c]==Pe.SHORT_TYPE?A[h]=se:A[h]=de(Math.min(se,Math.min(oe*S.nb_1[c][h],le*S.nb_2[c][h])),se,d),S.nb_2[c][h]=S.nb_1[c][h],S.nb_1[c][h]=se}for(;h<=Pe.CBANDS;++h)R[h]=0,A[h]=0;pe(S,R,A,c)}(e.mode!=Ee.STEREO&&e.mode!=Ee.JOINT_STEREO||0<e.interChRatio&&function(e,t){var a=e.internal_flags;if(1<a.channels_out){for(var s=0;s<Pe.SBMAX_l;s++){var n=a.thm[0].l[s],r=a.thm[1].l[s];a.thm[0].l[s]+=r*t,a.thm[1].l[s]+=n*t}for(s=0;s<Pe.SBMAX_s;s++)for(var i=0;i<3;i++)n=a.thm[0].s[s][i],r=a.thm[1].s[s][i],a.thm[0].s[s][i]+=r*t,a.thm[1].s[s][i]+=n*t}}(e,e.interChRatio),e.mode==Ee.JOINT_STEREO)&&(!function(e){for(var t=0;t<Pe.SBMAX_l;t++)if(!(e.thm[0].l[t]>1.58*e.thm[1].l[t]||e.thm[1].l[t]>1.58*e.thm[0].l[t])){var a=e.mld_l[t]*e.en[3].l[t],s=Math.max(e.thm[2].l[t],Math.min(e.thm[3].l[t],a));a=e.mld_l[t]*e.en[2].l[t];var n=Math.max(e.thm[3].l[t],Math.min(e.thm[2].l[t],a));e.thm[2].l[t]=s,e.thm[3].l[t]=n}for(t=0;t<Pe.SBMAX_s;t++)for(var r=0;r<3;r++)e.thm[0].s[t][r]>1.58*e.thm[1].s[t][r]||e.thm[1].s[t][r]>1.58*e.thm[0].s[t][r]||(a=e.mld_s[t]*e.en[3].s[t][r],s=Math.max(e.thm[2].s[t][r],Math.min(e.thm[3].s[t][r],a)),a=e.mld_s[t]*e.en[2].s[t][r],n=Math.max(e.thm[3].s[t][r],Math.min(e.thm[2].s[t][r],a)),e.thm[2].s[t][r]=s,e.thm[3].s[t][r]=n)}(S),g=e.msfix,0<Math.abs(g)&&function(e,t,a){var s=t,n=Math.pow(10,a);t*=2,s*=2;for(var r=0;r<Pe.SBMAX_l;r++)f=e.ATH.cb_l[e.bm_l[r]]*n,(_=Math.min(Math.max(e.thm[0].l[r],f),Math.max(e.thm[1].l[r],f)))*t<(o=Math.max(e.thm[2].l[r],f))+(l=Math.max(e.thm[3].l[r],f))&&(o*=c=_*s/(o+l),l*=c),e.thm[2].l[r]=Math.min(o,e.thm[2].l[r]),e.thm[3].l[r]=Math.min(l,e.thm[3].l[r]);for(n*=Pe.BLKSIZE_s/Pe.BLKSIZE,r=0;r<Pe.SBMAX_s;r++)for(var i=0;i<3;i++){var _,o,l,f,c;f=e.ATH.cb_s[e.bm_s[r]]*n,(_=Math.min(Math.max(e.thm[0].s[r][i],f),Math.max(e.thm[1].s[r][i],f)))*t<(o=Math.max(e.thm[2].s[r][i],f))+(l=Math.max(e.thm[3].s[r][i],f))&&(o*=c=_*t/(o+l),l*=c),e.thm[2].s[r][i]=Math.min(e.thm[2].s[r][i],o),e.thm[3].s[r][i]=Math.min(e.thm[3].s[r][i],l)}}(S,g,e.ATHlower*S.ATH.adjust));for(function(e,t,a,s){var n=e.internal_flags;e.short_blocks!=xe.short_block_coupled||0!=t[0]&&0!=t[1]||(t[0]=t[1]=0);for(var r=0;r<n.channels_out;r++)s[r]=Pe.NORM_TYPE,e.short_blocks==xe.short_block_dispensed&&(t[r]=1),e.short_blocks==xe.short_block_forced&&(t[r]=0),0!=t[r]?n.blocktype_old[r]==Pe.SHORT_TYPE&&(s[r]=Pe.STOP_TYPE):(s[r]=Pe.SHORT_TYPE,n.blocktype_old[r]==Pe.NORM_TYPE&&(n.blocktype_old[r]=Pe.START_TYPE),n.blocktype_old[r]==Pe.STOP_TYPE&&(n.blocktype_old[r]=Pe.SHORT_TYPE)),a[r]=n.blocktype_old[r],n.blocktype_old[r]=s[r]}(e,T,l,k),c=0;c<f;c++){var ne,re,ie,_e=0;1<c?(ne=_,_e=-2,re=Pe.NORM_TYPE,l[0]!=Pe.SHORT_TYPE&&l[1]!=Pe.SHORT_TYPE||(re=Pe.SHORT_TYPE),ie=r[s][c-2]):(ne=i,_e=0,re=l[c],ie=n[s][c]),ne[_e+c]=re==Pe.SHORT_TYPE?ge(ie,S.masking_lower):Se(ie,S.masking_lower),e.analysis&&(S.pinfo.pe[s][c]=ne[_e+c])}return 0};var q=[-1.730326e-17,-.01703172,-1.349528e-17,.0418072,-6.73278e-17,-.0876324,-3.0835e-17,.1863476,-1.104424e-16,-.627638];function j(e,t,a){if(0==a)for(var s=0;s<e.npart_s;s++)e.nb_s2[t][s]=e.nb_s1[t][s],e.nb_s1[t][s]=0}function C(e,t){for(var a=0;a<e.npart_l;a++)e.nb_2[t][a]=e.nb_1[t][a],e.nb_1[t][a]=0}function F(e,t,a,s,n,r){var i,_,o,l=e.internal_flags,f=new float[Pe.CBANDS],c=Ae(Pe.CBANDS),h=new int[Pe.CBANDS];for(o=_=0;o<l.npart_s;++o){var u=0,b=0,m=l.numlines_s[o];for(i=0;i<m;++i,++_){var p=t[r][_];u+=p,b<p&&(b=p)}a[o]=u,f[o]=b,c[o]=u/m}for(;o<Pe.CBANDS;++o)f[o]=0,c[o]=0;for(function(e,t,a,s){var n=ue.length-1,r=0,i=a[r]+a[r+1];for(0<i?((_=t[r])<t[r+1]&&(_=t[r+1]),n<(o=0|(i=20*(2*_-i)/(i*(e.numlines_s[r]+e.numlines_s[r+1]-1))))&&(o=n),s[r]=o):s[r]=0,r=1;r<e.npart_s-1;r++){var _,o;0<(i=a[r-1]+a[r]+a[r+1])?((_=t[r-1])<t[r]&&(_=t[r]),_<t[r+1]&&(_=t[r+1]),n<(o=0|(i=20*(3*_-i)/(i*(e.numlines_s[r-1]+e.numlines_s[r]+e.numlines_s[r+1]-1))))&&(o=n),s[r]=o):s[r]=0}0<(i=a[r-1]+a[r])?((_=t[r-1])<t[r]&&(_=t[r]),n<(o=0|(i=20*(2*_-i)/(i*(e.numlines_s[r-1]+e.numlines_s[r]-1))))&&(o=n),s[r]=o):s[r]=0}(l,f,c,h),_=o=0;o<l.npart_s;o++){var v,d,g,S,M,w=l.s3ind_s[o][0],R=l.s3ind_s[o][1];for(v=h[w],d=1,S=l.s3_ss[_]*a[w]*ue[h[w]],++_,++w;w<=R;)v+=h[w],d+=1,S=B(S,g=l.s3_ss[_]*a[w]*ue[h[w]],w-o),++_,++w;S*=M=.5*ue[v=(1+2*v)/(2*d)],s[o]=S,l.nb_s2[n][o]=l.nb_s1[n][o],l.nb_s1[n][o]=S,g=f[o],g*=l.minval_s[o],g*=M,s[o]>g&&(s[o]=g),1<l.masking_lower&&(s[o]*=l.masking_lower),s[o]>a[o]&&(s[o]=a[o]),l.masking_lower<1&&(s[o]*=l.masking_lower)}for(;o<Pe.CBANDS;++o)a[o]=0,s[o]=0}function z(e,t,a,s,n){var r,i=Ae(Pe.CBANDS),_=Ae(Pe.CBANDS),o=Be(Pe.CBANDS+2);Me(e,t,a,i,_),we(e,i,_,o);var l=0;for(r=0;r<e.npart_l;r++){var f,c,h,u=e.s3ind[r][0],b=e.s3ind[r][1],m=0,p=0;for(m=o[u],p+=1,c=e.s3_ll[l]*a[u]*ue[o[u]],++l,++u;u<=b;)m+=o[u],p+=1,c=B(c,f=e.s3_ll[l]*a[u]*ue[o[u]],u-r),++l,++u;if(c*=h=.5*ue[m=(1+2*m)/(2*p)],e.blocktype_old[1&n]==Pe.SHORT_TYPE){var v=oe*e.nb_1[n][r];s[r]=0<v?Math.min(c,v):Math.min(c,a[r]*fe)}else{var d=le*e.nb_2[n][r],g=oe*e.nb_1[n][r];d<=0&&(d=c),g<=0&&(g=c),v=e.blocktype_old[1&n]==Pe.NORM_TYPE?Math.min(g,d):g,s[r]=Math.min(c,v)}e.nb_2[n][r]=e.nb_1[n][r],e.nb_1[n][r]=c,f=i[r],f*=e.minval_l[r],f*=h,s[r]>f&&(s[r]=f),1<e.masking_lower&&(s[r]*=e.masking_lower),s[r]>a[r]&&(s[r]=a[r]),e.masking_lower<1&&(s[r]*=e.masking_lower)}for(;r<Pe.CBANDS;++r)a[r]=0,s[r]=0}function Z(e,t,a,s,n,r,i){for(var _,o,l=2*r,f=0<r?Math.pow(10,n):1,c=0;c<i;++c){var h=e[2][c],u=e[3][c],b=t[0][c],m=t[1][c],p=t[2][c],v=t[3][c];if(b<=1.58*m&&m<=1.58*b){var d=a[c]*u,g=a[c]*h;o=Math.max(p,Math.min(v,d)),_=Math.max(v,Math.min(p,g))}else o=p,_=v;if(0<r){var S,M,w=s[c]*f;if(S=Math.min(Math.max(b,w),Math.max(m,w)),0<(M=(p=Math.max(o,w))+(v=Math.max(_,w)))&&S*l<M){var R=S*l/M;p*=R,v*=R}o=Math.min(p,o),_=Math.min(v,_)}h<o&&(o=h),u<_&&(_=u),t[2][c]=o,t[3][c]=_}}function w(e,t){var a;return(a=0<=e?27*-e:e*t)<=-72?0:Math.exp(a*S)}function R(e){var t,a,s=0;for(s=0;1e-20<w(s,e);s-=1);for(n=s,r=0;1e-12<Math.abs(r-n);)0<w(s=(r+n)/2,e)?r=s:n=s;t=n;var n,r;s=0;for(s=0;1e-20<w(s,e);s+=1);for(n=0,r=s;1e-12<Math.abs(r-n);)0<w(s=(r+n)/2,e)?n=s:r=s;a=r;var i,_=0;for(i=0;i<=1e3;++i){_+=w(s=t+i*(a-t)/1e3,e)}return 1001/(_*(a-t))}function I(e){return e<0&&(e=0),e*=.001,13*Math.atan(.76*e)+3.5*Math.atan(e*e/56.25)}function V(e,t,a,s,n,r,i,_,o,l,f,c){var h,u=Ae(Pe.CBANDS+1),b=_/(15<c?1152:384),m=Be(Pe.HBLKSIZE);_/=o;var p=0,v=0;for(h=0;h<Pe.CBANDS;h++){var d;for(T=I(_*p),u[h]=_*p,d=p;I(_*d)-T<E&&d<=o/2;d++);for(e[h]=d-p,v=h+1;p<d;)m[p++]=h;if(o/2<p){p=o/2,++h;break}}u[h]=_*p;for(var g=0;g<c;g++){var S,M,w,R,B;w=l[g],R=l[g+1],(S=0|Math.floor(.5+f*(w-.5)))<0&&(S=0),o/2<(M=0|Math.floor(.5+f*(R-.5)))&&(M=o/2),a[g]=(m[S]+m[M])/2,t[g]=m[M];var A=b*R;i[g]=(A-u[t[g]])/(u[t[g]+1]-u[t[g]]),i[g]<0?i[g]=0:1<i[g]&&(i[g]=1),B=I(_*l[g]*f),B=Math.min(B,15.5)/15.5,r[g]=Math.pow(10,1.25*(1-Math.cos(Math.PI*B))-2.5)}for(var k=p=0;k<v;k++){var T,x,y=e[k];T=I(_*p),x=I(_*(p+y-1)),s[k]=.5*(T+x),T=I(_*(p-.5)),x=I(_*(p+y-.5)),n[k]=x-T,p+=y}return v}function N(e,t,a,s,n,r){var i,_,o,l,f,c,h=ke([Pe.CBANDS,Pe.CBANDS]),u=0;if(r)for(var b=0;b<t;b++)for(i=0;i<t;i++){var m=(_=a[b]-a[i],c=f=l=o=void 0,o=_,l=.5<=(o*=0<=o?3:1.5)&&o<=2.5?8*((c=o-.5)*c-2*c):0,((f=15.811389+7.5*(o+=.474)-17.5*Math.sqrt(1+o*o))<=-60?0:(o=Math.exp((l+f)*S),o/=.6609193))*s[i]);h[b][i]=m*n[b]}else for(i=0;i<t;i++){var p=15+Math.min(21/a[i],12),v=R(p);for(b=0;b<t;b++){m=v*w(a[b]-a[i],p)*s[i];h[b][i]=m*n[b]}}for(b=0;b<t;b++){for(i=0;i<t&&!(0<h[b][i]);i++);for(e[b][0]=i,i=t-1;0<i&&!(0<h[b][i]);i--);e[b][1]=i,u+=e[b][1]-e[b][0]+1}var d=Ae(u),g=0;for(b=0;b<t;b++)for(i=e[b][0];i<=e[b][1];i++)d[g++]=h[b][i];return d}function O(e){var t=I(e);return t=Math.min(t,15.5)/15.5,Math.pow(10,1.25*(1-Math.cos(Math.PI*t))-2.5)}function s(e,t){return e<-.3&&(e=3410),e/=1e3,e=Math.max(.1,e),3.64*Math.pow(e,-.8)-6.8*Math.exp(-.6*Math.pow(e-3.4,2))+6*Math.exp(-.15*Math.pow(e-8.7,2))+.001*(.6+.04*t)*Math.pow(e,4)}this.L3psycho_anal_vbr=function(e,t,a,s,n,r,i,_,o,l){var f,c,h,u,b,m=e.internal_flags,p=Ae(Pe.HBLKSIZE),v=ke([3,Pe.HBLKSIZE_s]),d=ke([2,Pe.BLKSIZE]),g=ke([2,3,Pe.BLKSIZE_s]),S=ke([4,Pe.CBANDS]),M=ke([4,Pe.CBANDS]),w=ke([4,3]),R=[[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]],B=Be(2),A=e.mode==Ee.JOINT_STEREO?4:m.channels_out;!function(e,t,a,s,n,r,i,_,o,l){for(var f=ke([2,576]),c=e.internal_flags,h=c.channels_out,u=e.mode==Ee.JOINT_STEREO?4:h,b=0;b<h;b++){firbuf=t[b];for(var m=a+576-350-ce+192,p=0;p<576;p++){var v,d;v=firbuf[m+p+10];for(var g=d=0;g<(ce-1)/2-1;g+=2)v+=q[g]*(firbuf[m+p+g]+firbuf[m+p+ce-g]),d+=q[g+1]*(firbuf[m+p+g+1]+firbuf[m+p+ce-g-1]);f[b][p]=v+d}n[s][b].en.assign(c.en[b]),n[s][b].thm.assign(c.thm[b]),2<u&&(r[s][b].en.assign(c.en[b+2]),r[s][b].thm.assign(c.thm[b+2]))}for(b=0;b<u;b++){var S=Ae(12),M=Ae(12),w=[0,0,0,0],R=f[1&b],B=0,A=3==b?c.nsPsy.attackthre_s:c.nsPsy.attackthre,k=1;if(2==b)for(p=0,g=576;0<g;++p,--g){var T=f[0][p],x=f[1][p];f[0][p]=T+x,f[1][p]=T-x}for(p=0;p<3;p++)M[p]=c.nsPsy.last_en_subshort[b][p+6],S[p]=M[p]/c.nsPsy.last_en_subshort[b][p+4],w[0]+=M[p];for(p=0;p<9;p++){for(var y=B+64,E=1;B<y;B++)E<Math.abs(R[B])&&(E=Math.abs(R[B]));c.nsPsy.last_en_subshort[b][p]=M[p+3]=E,w[1+p/3]+=E,E>M[p+3-2]?E/=M[p+3-2]:E=M[p+3-2]>10*E?M[p+3-2]/(10*E):0,S[p+3]=E}for(p=0;p<3;++p){var P=M[3*p+3]+M[3*p+4]+M[3*p+5],H=1;6*M[3*p+5]<P&&(H*=.5,6*M[3*p+4]<P&&(H*=.5)),_[b][p]=H}if(e.analysis){var L=S[0];for(p=1;p<12;p++)L<S[p]&&(L=S[p]);c.pinfo.ers[s][b]=c.pinfo.ers_save[b],c.pinfo.ers_save[b]=L}for(p=0;p<12;p++)0==o[b][p/3]&&S[p]>A&&(o[b][p/3]=p%3+1);for(p=1;p<4;p++){var I=w[p-1],V=w[p];Math.max(I,V)<4e4&&I<1.7*V&&V<1.7*I&&(1==p&&o[b][0]<=o[b][p]&&(o[b][0]=0),o[b][p]=0)}o[b][0]<=c.nsPsy.lastAttacks[b]&&(o[b][0]=0),3!=c.nsPsy.lastAttacks[b]&&o[b][0]+o[b][1]+o[b][2]+o[b][3]==0||((k=0)!=o[b][1]&&0!=o[b][0]&&(o[b][1]=0),0!=o[b][2]&&0!=o[b][1]&&(o[b][2]=0),0!=o[b][3]&&0!=o[b][2]&&(o[b][3]=0)),b<2?l[b]=k:0==k&&(l[0]=l[1]=0),i[b]=c.tot_ener[b]}}(e,t,a,s,n,r,o,w,R,B),function(e,t){var a=e.internal_flags;e.short_blocks!=xe.short_block_coupled||0!=t[0]&&0!=t[1]||(t[0]=t[1]=0);for(var s=0;s<a.channels_out;s++)e.short_blocks==xe.short_block_dispensed&&(t[s]=1),e.short_blocks==xe.short_block_forced&&(t[s]=0)}(e,B);for(var k=0;k<A;k++){D(e,t,a,k,s,p,d,x=1&k),c=s,h=k,u=p,b=void 0,b=(f=e).internal_flags,2==f.athaa_loudapprox&&h<2&&(b.loudness_sq[c][h]=b.loudness_sq_save[h],b.loudness_sq_save[h]=Y(u,b)),0!=B[x]?z(m,p,S[k],M[k],k):C(m,k)}B[0]+B[1]==2&&e.mode==Ee.JOINT_STEREO&&Z(S,M,m.mld_cb_l,m.ATH.cb_l,e.ATHlower*m.ATH.adjust,e.msfix,m.npart_l);for(k=0;k<A;k++){0!=B[x=1&k]&&pe(m,S[k],M[k],k)}for(var T=0;T<3;T++){for(k=0;k<A;++k){0!=B[x=1&k]?j(m,k,T):(X(e,t,a,k,T,v,g,x),F(e,v,S[k],M[k],k,T))}B[0]+B[1]==0&&e.mode==Ee.JOINT_STEREO&&Z(S,M,m.mld_cb_s,m.ATH.cb_s,e.ATHlower*m.ATH.adjust,e.msfix,m.npart_s);for(k=0;k<A;++k){0==B[x=1&k]&&me(m,S[k],M[k],k,T)}}for(k=0;k<A;k++){var x;if(0==B[x=1&k])for(var y=0;y<Pe.SBMAX_s;y++){var E=Ae(3);for(T=0;T<3;T++){var P=m.thm[k].s[y][T];if(P*=.8,2<=R[k][T]||1==R[k][T+1]){var H=0!=T?T-1:2,L=de(m.thm[k].s[y][H],P,.36);P=Math.min(P,L)}else if(1==R[k][T]){H=0!=T?T-1:2,L=de(m.thm[k].s[y][H],P,.6*fe);P=Math.min(P,L)}else if(0!=T&&3==R[k][T-1]||0==T&&3==m.nsPsy.lastAttacks[k]){H=2!=T?T+1:0,L=de(m.thm[k].s[y][H],P,.6*fe);P=Math.min(P,L)}P*=w[k][T],E[T]=P}for(T=0;T<3;T++)m.thm[k].s[y][T]=E[T]}}for(k=0;k<A;k++)m.nsPsy.lastAttacks[k]=R[k][2];!function(e,t,a){for(var s=e.internal_flags,n=0;n<s.channels_out;n++){var r=Pe.NORM_TYPE;0!=t[n]?s.blocktype_old[n]==Pe.SHORT_TYPE&&(r=Pe.STOP_TYPE):(r=Pe.SHORT_TYPE,s.blocktype_old[n]==Pe.NORM_TYPE&&(s.blocktype_old[n]=Pe.START_TYPE),s.blocktype_old[n]==Pe.STOP_TYPE&&(s.blocktype_old[n]=Pe.SHORT_TYPE)),a[n]=s.blocktype_old[n],s.blocktype_old[n]=r}}(e,B,l);for(k=0;k<A;k++){var I,V,N,O;1<k?(I=_,V=-2,N=Pe.NORM_TYPE,l[0]!=Pe.SHORT_TYPE&&l[1]!=Pe.SHORT_TYPE||(N=Pe.SHORT_TYPE),O=r[s][k-2]):(I=i,V=0,N=l[k],O=n[s][k]),I[V+k]=N==Pe.SHORT_TYPE?ge(O,m.masking_lower):Se(O,m.masking_lower),e.analysis&&(m.pinfo.pe[s][k]=I[V+k])}return 0},this.psymodel_init=function(e){var t,a=e.internal_flags,s=!0,n=13,r=0,i=0,_=-8.25,o=-4.5,l=Ae(Pe.CBANDS),f=Ae(Pe.CBANDS),c=Ae(Pe.CBANDS),h=e.out_samplerate;switch(e.experimentalZ){default:case 0:s=!0;break;case 1:s=e.VBR!=ye.vbr_mtrh&&e.VBR!=ye.vbr_mt;break;case 2:s=!1;break;case 3:n=8,r=-1.75,i=-.0125,_=-8.25,o=-2.25}for(a.ms_ener_ratio_old=.25,a.blocktype_old[0]=a.blocktype_old[1]=Pe.NORM_TYPE,t=0;t<4;++t){for(var u=0;u<Pe.CBANDS;++u)a.nb_1[t][u]=1e20,a.nb_2[t][u]=1e20,a.nb_s1[t][u]=a.nb_s2[t][u]=1;for(var b=0;b<Pe.SBMAX_l;b++)a.en[t].l[b]=1e20,a.thm[t].l[b]=1e20;for(u=0;u<3;++u){for(b=0;b<Pe.SBMAX_s;b++)a.en[t].s[b][u]=1e20,a.thm[t].s[b][u]=1e20;a.nsPsy.lastAttacks[t]=0}for(u=0;u<9;u++)a.nsPsy.last_en_subshort[t][u]=10}for(a.loudness_sq_save[0]=a.loudness_sq_save[1]=0,a.npart_l=V(a.numlines_l,a.bo_l,a.bm_l,l,f,a.mld_l,a.PSY.bo_l_weight,h,Pe.BLKSIZE,a.scalefac_band.l,Pe.BLKSIZE/1152,Pe.SBMAX_l),t=0;t<a.npart_l;t++){var m=r;l[t]>=n&&(m=i*(l[t]-n)/(24-n)+r*(24-l[t])/(24-n)),c[t]=Math.pow(10,m/10),0<a.numlines_l[t]?a.rnumlines_l[t]=1/a.numlines_l[t]:a.rnumlines_l[t]=0}a.s3_ll=N(a.s3ind,a.npart_l,l,f,c,s);var p;u=0;for(t=0;t<a.npart_l;t++){g=K.MAX_VALUE;for(var v=0;v<a.numlines_l[t];v++,u++){var d=h*u/(1e3*Pe.BLKSIZE);S=this.ATHformula(1e3*d,e)-20,S=Math.pow(10,.1*S),(S*=a.numlines_l[t])<g&&(g=S)}a.ATH.cb_l[t]=g,6<(g=20*l[t]/10-20)&&(g=100),g<-15&&(g=-15),g-=8,a.minval_l[t]=Math.pow(10,g/10)*a.numlines_l[t]}for(a.npart_s=V(a.numlines_s,a.bo_s,a.bm_s,l,f,a.mld_s,a.PSY.bo_s_weight,h,Pe.BLKSIZE_s,a.scalefac_band.s,Pe.BLKSIZE_s/384,Pe.SBMAX_s),t=u=0;t<a.npart_s;t++){var g;m=_;l[t]>=n&&(m=o*(l[t]-n)/(24-n)+_*(24-l[t])/(24-n)),c[t]=Math.pow(10,m/10),g=K.MAX_VALUE;for(v=0;v<a.numlines_s[t];v++,u++){var S;d=h*u/(1e3*Pe.BLKSIZE_s);S=this.ATHformula(1e3*d,e)-20,S=Math.pow(10,.1*S),(S*=a.numlines_s[t])<g&&(g=S)}a.ATH.cb_s[t]=g,g=7*l[t]/12-7,12<l[t]&&(g*=1+3.1*Math.log(1+g)),l[t]<12&&(g*=1+2.3*Math.log(1-g)),g<-15&&(g=-15),g-=8,a.minval_s[t]=Math.pow(10,g/10)*a.numlines_s[t]}a.s3_ss=N(a.s3ind_s,a.npart_s,l,f,c,s),T=Math.pow(10,(P+1)/16),x=Math.pow(10,(H+1)/16),y=Math.pow(10,L/10),A.init_fft(a),a.decay=Math.exp(-1*k/(.01*h/192)),p=3.5,0!=(2&e.exp_nspsytune)&&(p=1),0<Math.abs(e.msfix)&&(p=e.msfix),e.msfix=p;for(var M=0;M<a.npart_l;M++)a.s3ind[M][1]>a.npart_l-1&&(a.s3ind[M][1]=a.npart_l-1);var w=576*a.mode_gr/h;if(a.ATH.decay=Math.pow(10,-1.2*w),a.ATH.adjust=.01,-(a.ATH.adjustLimit=1)!=e.ATHtype){var R=e.out_samplerate/Pe.BLKSIZE,B=0;for(t=d=0;t<Pe.BLKSIZE/2;++t)d+=R,a.ATH.eql_w[t]=1/Math.pow(10,this.ATHformula(d,e)/10),B+=a.ATH.eql_w[t];for(B=1/B,t=Pe.BLKSIZE/2;0<=--t;)a.ATH.eql_w[t]*=B}for(M=u=0;M<a.npart_s;++M)for(t=0;t<a.numlines_s[M];++t)++u;for(M=u=0;M<a.npart_l;++M)for(t=0;t<a.numlines_l[M];++t)++u;for(t=u=0;t<a.npart_l;t++){d=h*(u+a.numlines_l[t]/2)/(1*Pe.BLKSIZE);a.mld_cb_l[t]=O(d),u+=a.numlines_l[t]}for(;t<Pe.CBANDS;++t)a.mld_cb_l[t]=1;for(t=u=0;t<a.npart_s;t++){d=h*(u+a.numlines_s[t]/2)/(1*Pe.BLKSIZE_s);a.mld_cb_s[t]=O(d),u+=a.numlines_s[t]}for(;t<Pe.CBANDS;++t)a.mld_cb_s[t]=1;return 0},this.ATHformula=function(e,t){var a;switch(t.ATHtype){case 0:a=s(e,9);break;case 1:a=s(e,-1);break;case 2:a=s(e,0);break;case 3:a=s(e,1)+6;break;case 4:a=s(e,t.ATHcurve);break;default:a=s(e,0)}return a}}function Q(){var _=this;Q.V9=410,Q.V8=420,Q.V7=430,Q.V6=440,Q.V5=450,Q.V4=460,Q.V3=470,Q.V2=480,Q.V1=490,Q.V0=500,Q.R3MIX=1e3,Q.STANDARD=1001,Q.EXTREME=1002,Q.INSANE=1003,Q.STANDARD_FAST=1004,Q.EXTREME_FAST=1005,Q.MEDIUM=1006,Q.MEDIUM_FAST=1007;var w,R,g,S,M;Q.LAME_MAXMP3BUFFER=147456;var B,A,k,T=new G;function x(){this.lowerlimit=0}function n(e,t){this.lowpass=t}this.enc=new Pe,this.setModules=function(e,t,a,s,n,r,i,_,o){w=e,R=t,g=a,S=s,M=n,B=r,i,A=_,k=o,this.enc.setModules(R,T,S,B)};var y=4294479419;function E(e){return 1<e?0:e<=0?1:Math.cos(Math.PI/2*e)}function P(e,t){switch(e){case 44100:return t.version=1,0;case 48e3:return t.version=1;case 32e3:return t.version=1,2;case 22050:return t.version=0;case 24e3:return t.version=0,1;case 16e3:return t.version=0,2;case 11025:return t.version=0;case 12e3:return t.version=0,1;case 8e3:return t.version=0,2;default:return t.version=0,-1}}function H(e,t,a){a<16e3&&(t=2);for(var s=C.bitrate_table[t][1],n=2;n<=14;n++)0<C.bitrate_table[t][n]&&Math.abs(C.bitrate_table[t][n]-e)<Math.abs(s-e)&&(s=C.bitrate_table[t][n]);return s}function L(e,t,a){a<16e3&&(t=2);for(var s=0;s<=14;s++)if(0<C.bitrate_table[t][s]&&C.bitrate_table[t][s]==e)return s;return-1}function I(e,t){var a=[new n(8,2e3),new n(16,3700),new n(24,3900),new n(32,5500),new n(40,7e3),new n(48,7500),new n(56,1e4),new n(64,11e3),new n(80,13500),new n(96,15100),new n(112,15600),new n(128,17e3),new n(160,17500),new n(192,18600),new n(224,19400),new n(256,19700),new n(320,20500)],s=_.nearestBitrateFullIndex(t);e.lowerlimit=a[s].lowpass}function V(e){var t=Pe.BLKSIZE+e.framesize-Pe.FFTOFFSET;return t=Math.max(t,512+e.framesize-32)}function N(e,t,a,s,n,r){var i=_.enc.lame_encode_mp3_frame(e,t,a,s,n,r);return e.frameNum++,i}function O(){this.n_in=0,this.n_out=0}function f(){this.num_used=0}function Y(e,t,a){var s=Math.PI*t;(e/=a)<0&&(e=0),1<e&&(e=1);var n=e-.5,r=.42-.5*Math.cos(2*e*Math.PI)+.08*Math.cos(4*e*Math.PI);return Math.abs(n)<1e-9?s/Math.PI:r*Math.sin(a*s*n)/(Math.PI*a*n)}function c(e,t,a,s,n,r,i,_,o){var l,f,c=e.internal_flags,h=0,u=e.out_samplerate/function e(t,a){return 0!=a?e(a,t%a):t}(e.out_samplerate,e.in_samplerate);Z.BPC<u&&(u=Z.BPC);var b=Math.abs(c.resample_ratio-Math.floor(.5+c.resample_ratio))<1e-4?1:0,m=1/c.resample_ratio;1<m&&(m=1);var p=31;0==p%2&&--p;var v=(p+=b)+1;if(0==c.fill_buffer_resample_init){for(c.inbuf_old[0]=Ae(v),c.inbuf_old[1]=Ae(v),l=0;l<=2*u;++l)c.blackfilt[l]=Ae(v);for(c.itime[0]=0,h=c.itime[1]=0;h<=2*u;h++){var d=0,g=(h-u)/(2*u);for(l=0;l<=p;l++)d+=c.blackfilt[h][l]=Y(l-g,m,p);for(l=0;l<=p;l++)c.blackfilt[h][l]/=d}c.fill_buffer_resample_init=1}var S=c.inbuf_old[o];for(f=0;f<s;f++){var M,w;if(M=f*c.resample_ratio,i<=p+(h=0|Math.floor(M-c.itime[o]))-p/2)break;g=M-c.itime[o]-(h+p%2*.5);w=0|Math.floor(2*g*u+u+.5);var R=0;for(l=0;l<=p;++l){var B=l+h-p/2;R+=(B<0?S[v+B]:n[r+B])*c.blackfilt[w][l]}t[a+f]=R}if(_.num_used=Math.min(i,p+h-p/2),c.itime[o]+=_.num_used-f*c.resample_ratio,_.num_used>=v)for(l=0;l<v;l++)S[l]=n[r+_.num_used+l-v];else{var A=v-_.num_used;for(l=0;l<A;++l)S[l]=S[l+_.num_used];for(h=0;l<v;++l,++h)S[l]=n[r+h]}return f}function D(e,t,a,s,n,r){var i=e.internal_flags;if(i.resample_ratio<.9999||1.0001<i.resample_ratio)for(var _=0;_<i.channels_out;_++){var o=new f;r.n_out=c(e,t[_],i.mf_size,e.framesize,a[_],s,n,o,_),r.n_in=o.num_used}else{r.n_out=Math.min(e.framesize,n),r.n_in=r.n_out;for(var l=0;l<r.n_out;++l)t[0][i.mf_size+l]=a[0][s+l],2==i.channels_out&&(t[1][i.mf_size+l]=a[1][s+l])}}this.lame_init=function(){var e,t,a=new function(){this.class_id=0,this.num_samples=0,this.num_channels=0,this.in_samplerate=0,this.out_samplerate=0,this.scale=0,this.scale_left=0,this.scale_right=0,this.analysis=!1,this.bWriteVbrTag=!1,this.decode_only=!1,this.quality=0,this.mode=Ee.STEREO,this.force_ms=!1,this.free_format=!1,this.findReplayGain=!1,this.decode_on_the_fly=!1,this.write_id3tag_automatic=!1,this.brate=0,this.compression_ratio=0,this.copyright=0,this.original=0,this.extension=0,this.emphasis=0,this.error_protection=0,this.strict_ISO=!1,this.disable_reservoir=!1,this.quant_comp=0,this.quant_comp_short=0,this.experimentalY=!1,this.experimentalZ=0,this.exp_nspsytune=0,this.preset=0,this.VBR=null,this.VBR_q_frac=0,this.VBR_q=0,this.VBR_mean_bitrate_kbps=0,this.VBR_min_bitrate_kbps=0,this.VBR_max_bitrate_kbps=0,this.VBR_hard_min=0,this.lowpassfreq=0,this.highpassfreq=0,this.lowpasswidth=0,this.highpasswidth=0,this.maskingadjust=0,this.maskingadjust_short=0,this.ATHonly=!1,this.ATHshort=!1,this.noATH=!1,this.ATHtype=0,this.ATHcurve=0,this.ATHlower=0,this.athaa_type=0,this.athaa_loudapprox=0,this.athaa_sensitivity=0,this.short_blocks=null,this.useTemporal=!1,this.interChRatio=0,this.msfix=0,this.tune=!1,this.tune_value_a=0,this.version=0,this.encoder_delay=0,this.encoder_padding=0,this.framesize=0,this.frameNum=0,this.lame_allocated_gfp=0,this.internal_flags=null};return 0!=((e=a).class_id=y,t=e.internal_flags=new Z,e.mode=Ee.NOT_SET,e.original=1,e.in_samplerate=44100,e.num_channels=2,e.num_samples=-1,e.bWriteVbrTag=!0,e.quality=-1,e.short_blocks=null,t.subblock_gain=-1,e.lowpassfreq=0,e.highpassfreq=0,e.lowpasswidth=-1,e.highpasswidth=-1,e.VBR=ye.vbr_off,e.VBR_q=4,e.ATHcurve=-1,e.VBR_mean_bitrate_kbps=128,e.VBR_min_bitrate_kbps=0,e.VBR_max_bitrate_kbps=0,e.VBR_hard_min=0,t.VBR_min_bitrate=1,t.VBR_max_bitrate=13,e.quant_comp=-1,e.quant_comp_short=-1,e.msfix=-1,t.resample_ratio=1,t.OldValue[0]=180,t.OldValue[1]=180,t.CurrentStep[0]=4,t.CurrentStep[1]=4,t.masking_lower=1,t.nsPsy.attackthre=-1,t.nsPsy.attackthre_s=-1,e.scale=-1,e.athaa_type=-1,e.ATHtype=-1,e.athaa_loudapprox=-1,e.athaa_sensitivity=0,e.useTemporal=null,e.interChRatio=-1,t.mf_samples_to_encode=Pe.ENCDELAY+Pe.POSTDELAY,e.encoder_padding=0,t.mf_size=Pe.ENCDELAY-Pe.MDCTDELAY,e.findReplayGain=!1,e.decode_on_the_fly=!1,t.decode_on_the_fly=!1,t.findReplayGain=!1,t.findPeakSample=!1,t.RadioGain=0,t.AudiophileGain=0,t.noclipGainChange=0,t.noclipScale=-1,e.preset=0,e.write_id3tag_automatic=!0,0)?null:(a.lame_allocated_gfp=1,a)},this.nearestBitrateFullIndex=function(e){var t=[8,16,24,32,40,48,56,64,80,96,112,128,160,192,224,256,320],a=0,s=0,n=0,r=0;r=t[16],s=t[n=16],a=16;for(var i=0;i<16;i++)if(Math.max(e,t[i+1])!=e){r=t[i+1],n=i+1,s=t[i],a=i;break}return e-s<r-e?a:n},this.lame_init_params=function(e){var t,a,s,n=e.internal_flags;if(n.Class_ID=0,null==n.ATH&&(n.ATH=new function(){this.useAdjust=0,this.aaSensitivityP=0,this.adjust=0,this.adjustLimit=0,this.decay=0,this.floor=0,this.l=Ae(Pe.SBMAX_l),this.s=Ae(Pe.SBMAX_s),this.psfb21=Ae(Pe.PSFB21),this.psfb12=Ae(Pe.PSFB12),this.cb_l=Ae(Pe.CBANDS),this.cb_s=Ae(Pe.CBANDS),this.eql_w=Ae(Pe.BLKSIZE/2)}),null==n.PSY&&(n.PSY=new function(){this.mask_adjust=0,this.mask_adjust_short=0,this.bo_l_weight=Ae(Pe.SBMAX_l),this.bo_s_weight=Ae(Pe.SBMAX_s)}),null==n.rgdata&&(n.rgdata=new function(){}),n.channels_in=e.num_channels,1==n.channels_in&&(e.mode=Ee.MONO),n.channels_out=e.mode==Ee.MONO?1:2,n.mode_ext=Pe.MPG_MD_MS_LR,e.mode==Ee.MONO&&(e.force_ms=!1),e.VBR==ye.vbr_off&&128!=e.VBR_mean_bitrate_kbps&&0==e.brate&&(e.brate=e.VBR_mean_bitrate_kbps),e.VBR==ye.vbr_off||e.VBR==ye.vbr_mtrh||e.VBR==ye.vbr_mt||(e.free_format=!1),e.VBR==ye.vbr_off&&0==e.brate&&j.EQ(e.compression_ratio,0)&&(e.compression_ratio=11.025),e.VBR==ye.vbr_off&&0<e.compression_ratio&&(0==e.out_samplerate&&(e.out_samplerate=map2MP3Frequency(int(.97*e.in_samplerate))),e.brate=0|16*e.out_samplerate*n.channels_out/(1e3*e.compression_ratio),n.samplerate_index=P(e.out_samplerate,e),e.free_format||(e.brate=H(e.brate,e.version,e.out_samplerate))),0!=e.out_samplerate&&(e.out_samplerate<16e3?(e.VBR_mean_bitrate_kbps=Math.max(e.VBR_mean_bitrate_kbps,8),e.VBR_mean_bitrate_kbps=Math.min(e.VBR_mean_bitrate_kbps,64)):e.out_samplerate<32e3?(e.VBR_mean_bitrate_kbps=Math.max(e.VBR_mean_bitrate_kbps,8),e.VBR_mean_bitrate_kbps=Math.min(e.VBR_mean_bitrate_kbps,160)):(e.VBR_mean_bitrate_kbps=Math.max(e.VBR_mean_bitrate_kbps,32),e.VBR_mean_bitrate_kbps=Math.min(e.VBR_mean_bitrate_kbps,320))),0==e.lowpassfreq){var r=16e3;switch(e.VBR){case ye.vbr_off:I(i=new x,e.brate),r=i.lowerlimit;break;case ye.vbr_abr:var i;I(i=new x,e.VBR_mean_bitrate_kbps),r=i.lowerlimit;break;case ye.vbr_rh:var _=[19500,19e3,18600,18e3,17500,16e3,15600,14900,12500,1e4,3950];if(0<=e.VBR_q&&e.VBR_q<=9){var o=_[e.VBR_q],l=_[e.VBR_q+1],f=e.VBR_q_frac;r=linear_int(o,l,f)}else r=19500;break;default:_=[19500,19e3,18500,18e3,17500,16500,15500,14500,12500,9500,3950];if(0<=e.VBR_q&&e.VBR_q<=9){o=_[e.VBR_q],l=_[e.VBR_q+1],f=e.VBR_q_frac;r=linear_int(o,l,f)}else r=19500}e.mode!=Ee.MONO||e.VBR!=ye.vbr_off&&e.VBR!=ye.vbr_abr||(r*=1.5),e.lowpassfreq=0|r}if(0==e.out_samplerate&&(2*e.lowpassfreq>e.in_samplerate&&(e.lowpassfreq=e.in_samplerate/2),e.out_samplerate=(t=0|e.lowpassfreq,a=e.in_samplerate,s=44100,48e3<=a?s=48e3:44100<=a?s=44100:32e3<=a?s=32e3:24e3<=a?s=24e3:22050<=a?s=22050:16e3<=a?s=16e3:12e3<=a?s=12e3:11025<=a?s=11025:8e3<=a&&(s=8e3),-1==t?s:(t<=15960&&(s=44100),t<=15250&&(s=32e3),t<=11220&&(s=24e3),t<=9970&&(s=22050),t<=7230&&(s=16e3),t<=5420&&(s=12e3),t<=4510&&(s=11025),t<=3970&&(s=8e3),a<s?44100<a?48e3:32e3<a?44100:24e3<a?32e3:22050<a?24e3:16e3<a?22050:12e3<a?16e3:11025<a?12e3:8e3<a?11025:8e3:s))),e.lowpassfreq=Math.min(20500,e.lowpassfreq),e.lowpassfreq=Math.min(e.out_samplerate/2,e.lowpassfreq),e.VBR==ye.vbr_off&&(e.compression_ratio=16*e.out_samplerate*n.channels_out/(1e3*e.brate)),e.VBR==ye.vbr_abr&&(e.compression_ratio=16*e.out_samplerate*n.channels_out/(1e3*e.VBR_mean_bitrate_kbps)),e.bWriteVbrTag||(e.findReplayGain=!1,e.decode_on_the_fly=!1,n.findPeakSample=!1),n.findReplayGain=e.findReplayGain,n.decode_on_the_fly=e.decode_on_the_fly,n.decode_on_the_fly&&(n.findPeakSample=!0),n.findReplayGain&&w.InitGainAnalysis(n.rgdata,e.out_samplerate)==q.INIT_GAIN_ANALYSIS_ERROR)return e.internal_flags=null,-6;switch(n.decode_on_the_fly&&!e.decode_only&&(null!=n.hip&&k.hip_decode_exit(n.hip),n.hip=k.hip_decode_init()),n.mode_gr=e.out_samplerate<=24e3?1:2,e.framesize=576*n.mode_gr,e.encoder_delay=Pe.ENCDELAY,n.resample_ratio=e.in_samplerate/e.out_samplerate,e.VBR){case ye.vbr_mt:case ye.vbr_rh:case ye.vbr_mtrh:e.compression_ratio=[5.7,6.5,7.3,8.2,10,11.9,13,14,15,16.5][e.VBR_q];break;case ye.vbr_abr:e.compression_ratio=16*e.out_samplerate*n.channels_out/(1e3*e.VBR_mean_bitrate_kbps);break;default:e.compression_ratio=16*e.out_samplerate*n.channels_out/(1e3*e.brate)}if(e.mode==Ee.NOT_SET&&(e.mode=Ee.JOINT_STEREO),0<e.highpassfreq?(n.highpass1=2*e.highpassfreq,0<=e.highpasswidth?n.highpass2=2*(e.highpassfreq+e.highpasswidth):n.highpass2=2*e.highpassfreq,n.highpass1/=e.out_samplerate,n.highpass2/=e.out_samplerate):(n.highpass1=0,n.highpass2=0),0<e.lowpassfreq?(n.lowpass2=2*e.lowpassfreq,0<=e.lowpasswidth?(n.lowpass1=2*(e.lowpassfreq-e.lowpasswidth),n.lowpass1<0&&(n.lowpass1=0)):n.lowpass1=2*e.lowpassfreq,n.lowpass1/=e.out_samplerate,n.lowpass2/=e.out_samplerate):(n.lowpass1=0,n.lowpass2=0),function(e){var t=e.internal_flags,a=32,s=-1;if(0<t.lowpass1){for(var n=999,r=0;r<=31;r++)(l=r/31)>=t.lowpass2&&(a=Math.min(a,r)),t.lowpass1<l&&l<t.lowpass2&&(n=Math.min(n,r));t.lowpass1=999==n?(a-.75)/31:(n-.75)/31,t.lowpass2=a/31}if(0<t.highpass2&&t.highpass2<.75/31*.9&&(t.highpass1=0,t.highpass2=0,$.err.println("Warning: highpass filter disabled.  highpass frequency too small\n")),0<t.highpass2){var i=-1;for(r=0;r<=31;r++)(l=r/31)<=t.highpass1&&(s=Math.max(s,r)),t.highpass1<l&&l<t.highpass2&&(i=Math.max(i,r));t.highpass1=s/31,t.highpass2=-1==i?(s+.75)/31:(i+.75)/31}for(r=0;r<32;r++){var _,o,l=r/31;_=t.highpass2>t.highpass1?E((t.highpass2-l)/(t.highpass2-t.highpass1+1e-20)):1,o=t.lowpass2>t.lowpass1?E((l-t.lowpass1)/(t.lowpass2-t.lowpass1+1e-20)):1,t.amp_filter[r]=_*o}}(e),n.samplerate_index=P(e.out_samplerate,e),n.samplerate_index<0)return e.internal_flags=null,-1;if(e.VBR==ye.vbr_off){if(e.free_format)n.bitrate_index=0;else if(e.brate=H(e.brate,e.version,e.out_samplerate),n.bitrate_index=L(e.brate,e.version,e.out_samplerate),n.bitrate_index<=0)return e.internal_flags=null,-1}else n.bitrate_index=1;e.analysis&&(e.bWriteVbrTag=!1),null!=n.pinfo&&(e.bWriteVbrTag=!1),R.init_bit_stream_w(n);for(var c,h,u,b=n.samplerate_index+3*e.version+6*(e.out_samplerate<16e3?1:0),m=0;m<Pe.SBMAX_l+1;m++)n.scalefac_band.l[m]=S.sfBandIndex[b].l[m];for(m=0;m<Pe.PSFB21+1;m++){var p=(n.scalefac_band.l[22]-n.scalefac_band.l[21])/Pe.PSFB21,v=n.scalefac_band.l[21]+m*p;n.scalefac_band.psfb21[m]=v}n.scalefac_band.psfb21[Pe.PSFB21]=576;for(m=0;m<Pe.SBMAX_s+1;m++)n.scalefac_band.s[m]=S.sfBandIndex[b].s[m];for(m=0;m<Pe.PSFB12+1;m++){p=(n.scalefac_band.s[13]-n.scalefac_band.s[12])/Pe.PSFB12,v=n.scalefac_band.s[12]+m*p;n.scalefac_band.psfb12[m]=v}for(n.scalefac_band.psfb12[Pe.PSFB12]=192,1==e.version?n.sideinfo_len=1==n.channels_out?21:36:n.sideinfo_len=1==n.channels_out?13:21,e.error_protection&&(n.sideinfo_len+=2),h=(c=e).internal_flags,c.frameNum=0,c.write_id3tag_automatic&&A.id3tag_write_v2(c),h.bitrate_stereoMode_Hist=X([16,5]),h.bitrate_blockType_Hist=X([16,6]),h.PeakSample=0,c.bWriteVbrTag&&B.InitVbrTag(c),n.Class_ID=y,u=0;u<19;u++)n.nsPsy.pefirbuf[u]=700*n.mode_gr*n.channels_out;switch(-1==e.ATHtype&&(e.ATHtype=4),e.VBR){case ye.vbr_mt:e.VBR=ye.vbr_mtrh;case ye.vbr_mtrh:null==e.useTemporal&&(e.useTemporal=!1),g.apply_preset(e,500-10*e.VBR_q,0),e.quality<0&&(e.quality=LAME_DEFAULT_QUALITY),e.quality<5&&(e.quality=0),5<e.quality&&(e.quality=5),n.PSY.mask_adjust=e.maskingadjust,n.PSY.mask_adjust_short=e.maskingadjust_short,e.experimentalY?n.sfb21_extra=!1:n.sfb21_extra=44e3<e.out_samplerate,n.iteration_loop=new VBRNewIterationLoop(M);break;case ye.vbr_rh:g.apply_preset(e,500-10*e.VBR_q,0),n.PSY.mask_adjust=e.maskingadjust,n.PSY.mask_adjust_short=e.maskingadjust_short,e.experimentalY?n.sfb21_extra=!1:n.sfb21_extra=44e3<e.out_samplerate,6<e.quality&&(e.quality=6),e.quality<0&&(e.quality=LAME_DEFAULT_QUALITY),n.iteration_loop=new VBROldIterationLoop(M);break;default:var d;n.sfb21_extra=!1,e.quality<0&&(e.quality=LAME_DEFAULT_QUALITY),(d=e.VBR)==ye.vbr_off&&(e.VBR_mean_bitrate_kbps=e.brate),g.apply_preset(e,e.VBR_mean_bitrate_kbps,0),e.VBR=d,n.PSY.mask_adjust=e.maskingadjust,n.PSY.mask_adjust_short=e.maskingadjust_short,n.iteration_loop=d==ye.vbr_off?new function(e){var t=e;this.quantize=t,this.iteration_loop=function(e,t,a,s){var n,r=e.internal_flags,i=Ae(z.SFBMAX),_=Ae(576),o=Be(2),l=0,f=r.l3_side,c=new F(l);this.quantize.rv.ResvFrameBegin(e,c),l=c.bits;for(var h=0;h<r.mode_gr;h++){n=this.quantize.qupvt.on_pe(e,t,o,l,h,h),r.mode_ext==Pe.MPG_MD_MS_LR&&(this.quantize.ms_convert(r.l3_side,h),this.quantize.qupvt.reduce_side(o,a[h],l,n));for(var u=0;u<r.channels_out;u++){var b,m,p=f.tt[h][u];p.block_type!=Pe.SHORT_TYPE?(b=0,m=r.PSY.mask_adjust-b):(b=0,m=r.PSY.mask_adjust_short-b),r.masking_lower=Math.pow(10,.1*m),this.quantize.init_outer_loop(r,p),this.quantize.init_xrpow(r,p,_)&&(this.quantize.qupvt.calc_xmin(e,s[h][u],p,i),this.quantize.outer_loop(e,p,i,_,u,o[u])),this.quantize.iteration_finish_one(r,h,u)}}this.quantize.rv.ResvFrameEnd(r,l)}}(M):new ABRIterationLoop(M)}if(e.VBR!=ye.vbr_off){if(n.VBR_min_bitrate=1,n.VBR_max_bitrate=14,e.out_samplerate<16e3&&(n.VBR_max_bitrate=8),0!=e.VBR_min_bitrate_kbps&&(e.VBR_min_bitrate_kbps=H(e.VBR_min_bitrate_kbps,e.version,e.out_samplerate),n.VBR_min_bitrate=L(e.VBR_min_bitrate_kbps,e.version,e.out_samplerate),n.VBR_min_bitrate<0))return-1;if(0!=e.VBR_max_bitrate_kbps&&(e.VBR_max_bitrate_kbps=H(e.VBR_max_bitrate_kbps,e.version,e.out_samplerate),n.VBR_max_bitrate=L(e.VBR_max_bitrate_kbps,e.version,e.out_samplerate),n.VBR_max_bitrate<0))return-1;e.VBR_min_bitrate_kbps=C.bitrate_table[e.version][n.VBR_min_bitrate],e.VBR_max_bitrate_kbps=C.bitrate_table[e.version][n.VBR_max_bitrate],e.VBR_mean_bitrate_kbps=Math.min(C.bitrate_table[e.version][n.VBR_max_bitrate],e.VBR_mean_bitrate_kbps),e.VBR_mean_bitrate_kbps=Math.max(C.bitrate_table[e.version][n.VBR_min_bitrate],e.VBR_mean_bitrate_kbps)}return e.tune&&(n.PSY.mask_adjust+=e.tune_value_a,n.PSY.mask_adjust_short+=e.tune_value_a),function(e){var t=e.internal_flags;switch(e.quality){default:case 9:t.psymodel=0,t.noise_shaping=0,t.noise_shaping_amp=0,t.noise_shaping_stop=0,t.use_best_huffman=0,t.full_outer_loop=0;break;case 8:e.quality=7;case 7:t.psymodel=1,t.noise_shaping=0,t.noise_shaping_amp=0,t.noise_shaping_stop=0,t.use_best_huffman=0,t.full_outer_loop=0;break;case 6:case 5:t.psymodel=1,0==t.noise_shaping&&(t.noise_shaping=1),t.noise_shaping_amp=0,t.noise_shaping_stop=0,-1==t.subblock_gain&&(t.subblock_gain=1),t.use_best_huffman=0,t.full_outer_loop=0;break;case 4:t.psymodel=1,0==t.noise_shaping&&(t.noise_shaping=1),t.noise_shaping_amp=0,t.noise_shaping_stop=0,-1==t.subblock_gain&&(t.subblock_gain=1),t.use_best_huffman=1,t.full_outer_loop=0;break;case 3:t.psymodel=1,0==t.noise_shaping&&(t.noise_shaping=1),t.noise_shaping_amp=1,-(t.noise_shaping_stop=1)==t.subblock_gain&&(t.subblock_gain=1),t.use_best_huffman=1,t.full_outer_loop=0;break;case 2:t.psymodel=1,0==t.noise_shaping&&(t.noise_shaping=1),0==t.substep_shaping&&(t.substep_shaping=2),t.noise_shaping_amp=1,-(t.noise_shaping_stop=1)==t.subblock_gain&&(t.subblock_gain=1),t.use_best_huffman=1,t.full_outer_loop=0;break;case 1:case 0:t.psymodel=1,0==t.noise_shaping&&(t.noise_shaping=1),0==t.substep_shaping&&(t.substep_shaping=2),t.noise_shaping_amp=2,-(t.noise_shaping_stop=1)==t.subblock_gain&&(t.subblock_gain=1),t.use_best_huffman=1,t.full_outer_loop=0}}(e),e.athaa_type<0?n.ATH.useAdjust=3:n.ATH.useAdjust=e.athaa_type,n.ATH.aaSensitivityP=Math.pow(10,e.athaa_sensitivity/-10),null==e.short_blocks&&(e.short_blocks=xe.short_block_allowed),e.short_blocks!=xe.short_block_allowed||e.mode!=Ee.JOINT_STEREO&&e.mode!=Ee.STEREO||(e.short_blocks=xe.short_block_coupled),e.quant_comp<0&&(e.quant_comp=1),e.quant_comp_short<0&&(e.quant_comp_short=0),e.msfix<0&&(e.msfix=0),e.exp_nspsytune=1|e.exp_nspsytune,e.internal_flags.nsPsy.attackthre<0&&(e.internal_flags.nsPsy.attackthre=G.NSATTACKTHRE),e.internal_flags.nsPsy.attackthre_s<0&&(e.internal_flags.nsPsy.attackthre_s=G.NSATTACKTHRE_S),e.scale<0&&(e.scale=1),e.ATHtype<0&&(e.ATHtype=4),e.ATHcurve<0&&(e.ATHcurve=4),e.athaa_loudapprox<0&&(e.athaa_loudapprox=2),e.interChRatio<0&&(e.interChRatio=0),null==e.useTemporal&&(e.useTemporal=!0),n.slot_lag=n.frac_SpF=0,e.VBR==ye.vbr_off&&(n.slot_lag=n.frac_SpF=72e3*(e.version+1)*e.brate%e.out_samplerate|0),S.iteration_init(e),T.psymodel_init(e),0},this.lame_encode_flush=function(e,t,a,s){var n,r,i,_,o=e.internal_flags,l=m([2,1152]),f=0,c=o.mf_samples_to_encode-Pe.POSTDELAY,h=V(e);if(o.mf_samples_to_encode<1)return 0;for(n=0,e.in_samplerate!=e.out_samplerate&&(c+=16*e.out_samplerate/e.in_samplerate),(i=e.framesize-c%e.framesize)<576&&(i+=e.framesize),_=(c+(e.encoder_padding=i))/e.framesize;0<_&&0<=f;){var u=h-o.mf_size,b=e.frameNum;u*=e.in_samplerate,1152<(u/=e.out_samplerate)&&(u=1152),u<1&&(u=1),r=s-n,0==s&&(r=0),a+=f=this.lame_encode_buffer(e,l[0],l[1],u,t,a,r),n+=f,_-=b!=e.frameNum?1:0}if(f<(o.mf_samples_to_encode=0))return f;if(r=s-n,0==s&&(r=0),R.flush_bitstream(e),(f=R.copy_buffer(o,t,a,r,1))<0)return f;if(a+=f,r=s-(n+=f),0==s&&(r=0),e.write_id3tag_automatic){if(A.id3tag_write_v1(e),(f=R.copy_buffer(o,t,a,r,0))<0)return f;n+=f}return n},this.lame_encode_buffer=function(e,t,a,s,n,r,i){var _,o,l=e.internal_flags,f=[null,null];if(l.Class_ID!=y)return-3;if(0==s)return 0;o=s,(null==(_=l).in_buffer_0||_.in_buffer_nsamples<o)&&(_.in_buffer_0=Ae(o),_.in_buffer_1=Ae(o),_.in_buffer_nsamples=o),f[0]=l.in_buffer_0,f[1]=l.in_buffer_1;for(var c=0;c<s;c++)f[0][c]=t[c],1<l.channels_in&&(f[1][c]=a[c]);return function(e,t,a,s,n,r,i){var _,o,l,f,c,h=e.internal_flags,u=0,b=[null,null],m=[null,null];if(h.Class_ID!=y)return-3;if(0==s)return 0;if((c=R.copy_buffer(h,n,r,i,0))<0)return c;if(r+=c,u+=c,m[0]=t,m[1]=a,j.NEQ(e.scale,0)&&j.NEQ(e.scale,1))for(o=0;o<s;++o)m[0][o]*=e.scale,2==h.channels_out&&(m[1][o]*=e.scale);if(j.NEQ(e.scale_left,0)&&j.NEQ(e.scale_left,1))for(o=0;o<s;++o)m[0][o]*=e.scale_left;if(j.NEQ(e.scale_right,0)&&j.NEQ(e.scale_right,1))for(o=0;o<s;++o)m[1][o]*=e.scale_right;if(2==e.num_channels&&1==h.channels_out)for(o=0;o<s;++o)m[0][o]=.5*(m[0][o]+m[1][o]),m[1][o]=0;f=V(e),b[0]=h.mfbuf[0],b[1]=h.mfbuf[1];var p=0;for(;0<s;){var v=[null,null],d=0,g=0;v[0]=m[0],v[1]=m[1];var S=new O;if(D(e,b,v,p,s,S),d=S.n_in,g=S.n_out,h.findReplayGain&&!h.decode_on_the_fly&&w.AnalyzeSamples(h.rgdata,b[0],h.mf_size,b[1],h.mf_size,g,h.channels_out)==q.GAIN_ANALYSIS_ERROR)return-6;if(s-=d,p+=d,h.channels_out,h.mf_size+=g,h.mf_samples_to_encode<1&&(h.mf_samples_to_encode=Pe.ENCDELAY+Pe.POSTDELAY),h.mf_samples_to_encode+=g,h.mf_size>=f){var M=i-u;if(0==i&&(M=0),(_=N(e,b[0],b[1],n,r,M))<0)return _;for(r+=_,u+=_,h.mf_size-=e.framesize,h.mf_samples_to_encode-=e.framesize,l=0;l<h.channels_out;l++)for(o=0;o<h.mf_size;o++)b[l][o]=b[l][o+e.framesize]}}return u}(e,f[0],f[1],s,n,r,i)}}z.SFBMAX=3*Pe.SBMAX_s,Pe.ENCDELAY=576,Pe.POSTDELAY=1152,Pe.FFTOFFSET=224+(Pe.MDCTDELAY=48),Pe.DECDELAY=528,Pe.SBLIMIT=32,Pe.CBANDS=64,Pe.SBPSY_l=21,Pe.SBPSY_s=12,Pe.SBMAX_l=22,Pe.SBMAX_s=13,Pe.PSFB21=6,Pe.PSFB12=6,Pe.HBLKSIZE=(Pe.BLKSIZE=1024)/2+1,Pe.HBLKSIZE_s=(Pe.BLKSIZE_s=256)/2+1,Pe.NORM_TYPE=0,Pe.START_TYPE=1,Pe.SHORT_TYPE=2,Pe.STOP_TYPE=3,Pe.MPG_MD_LR_LR=0,Pe.MPG_MD_LR_I=1,Pe.MPG_MD_MS_LR=2,Pe.MPG_MD_MS_I=3,Pe.fircoef=[-.1039435,-.1892065,5*-.0432472,-.155915,3.898045e-17,.0467745*5,.50455,.756825,.187098*5],Z.MFSIZE=3456+Pe.ENCDELAY-Pe.MDCTDELAY,Z.MAX_HEADER_BUF=256,Z.MAX_BITS_PER_CHANNEL=4095,Z.MAX_BITS_PER_GRANULE=7680,Z.BPC=320,z.SFBMAX=3*Pe.SBMAX_s,t.Mp3Encoder=function(s,e,t){3!=arguments.length&&(console.error("WARN: Mp3Encoder(channels, samplerate, kbps) not specified"),s=1,e=44100,t=128);var n=new Q,a=new function(){this.setModules=function(e,t){}},r=new q,i=new j,_=new function(){function e(e,t,a,s,n,r,i,_,o,l,f,c,h,u,b){this.vbr_q=e,this.quant_comp=t,this.quant_comp_s=a,this.expY=s,this.st_lrm=n,this.st_s=r,this.masking_adj=i,this.masking_adj_short=_,this.ath_lower=o,this.ath_curve=l,this.ath_sensitivity=f,this.interch=c,this.safejoint=h,this.sfb21mod=u,this.msfix=b}function t(e,t,a,s,n,r,i,_,o,l,f,c,h,u){this.quant_comp=t,this.quant_comp_s=a,this.safejoint=s,this.nsmsfix=n,this.st_lrm=r,this.st_s=i,this.nsbass=_,this.scale=o,this.masking_adj=l,this.ath_lower=f,this.ath_curve=c,this.interch=h,this.sfscale=u}var i;this.setModules=function(e){i=e};var f=[new e(0,9,9,0,5.2,125,-4.2,-6.3,4.8,1,0,0,2,21,.97),new e(1,9,9,0,5.3,125,-3.6,-5.6,4.5,1.5,0,0,2,21,1.35),new e(2,9,9,0,5.6,125,-2.2,-3.5,2.8,2,0,0,2,21,1.49),new e(3,9,9,1,5.8,130,-1.8,-2.8,2.6,3,-4,0,2,20,1.64),new e(4,9,9,1,6,135,-.7,-1.1,1.1,3.5,-8,0,2,0,1.79),new e(5,9,9,1,6.4,140,.5,.4,-7.5,4,-12,2e-4,0,0,1.95),new e(6,9,9,1,6.6,145,.67,.65,-14.7,6.5,-19,4e-4,0,0,2.3),new e(7,9,9,1,6.6,145,.8,.75,-19.7,8,-22,6e-4,0,0,2.7),new e(8,9,9,1,6.6,145,1.2,1.15,-27.5,10,-23,7e-4,0,0,0),new e(9,9,9,1,6.6,145,1.6,1.6,-36,11,-25,8e-4,0,0,0),new e(10,9,9,1,6.6,145,2,2,-36,12,-25,8e-4,0,0,0)],c=[new e(0,9,9,0,4.2,25,-7,-4,7.5,1,0,0,2,26,.97),new e(1,9,9,0,4.2,25,-5.6,-3.6,4.5,1.5,0,0,2,21,1.35),new e(2,9,9,0,4.2,25,-4.4,-1.8,2,2,0,0,2,18,1.49),new e(3,9,9,1,4.2,25,-3.4,-1.25,1.1,3,-4,0,2,15,1.64),new e(4,9,9,1,4.2,25,-2.2,.1,0,3.5,-8,0,2,0,1.79),new e(5,9,9,1,4.2,25,-1,1.65,-7.7,4,-12,2e-4,0,0,1.95),new e(6,9,9,1,4.2,25,-0,2.47,-7.7,6.5,-19,4e-4,0,0,2),new e(7,9,9,1,4.2,25,.5,2,-14.5,8,-22,6e-4,0,0,2),new e(8,9,9,1,4.2,25,1,2.4,-22,10,-23,7e-4,0,0,2),new e(9,9,9,1,4.2,25,1.5,2.95,-30,11,-25,8e-4,0,0,2),new e(10,9,9,1,4.2,25,2,2.95,-36,12,-30,8e-4,0,0,2)];function s(e,t,a){var s,n,r=e.VBR==ye.vbr_rh?f:c,i=e.VBR_q_frac,_=r[t],o=r[t+1],l=_;_.st_lrm=_.st_lrm+i*(o.st_lrm-_.st_lrm),_.st_s=_.st_s+i*(o.st_s-_.st_s),_.masking_adj=_.masking_adj+i*(o.masking_adj-_.masking_adj),_.masking_adj_short=_.masking_adj_short+i*(o.masking_adj_short-_.masking_adj_short),_.ath_lower=_.ath_lower+i*(o.ath_lower-_.ath_lower),_.ath_curve=_.ath_curve+i*(o.ath_curve-_.ath_curve),_.ath_sensitivity=_.ath_sensitivity+i*(o.ath_sensitivity-_.ath_sensitivity),_.interch=_.interch+i*(o.interch-_.interch),_.msfix=_.msfix+i*(o.msfix-_.msfix),s=e,(n=l.vbr_q)<0&&(n=0),9<n&&(n=9),s.VBR_q=n,(s.VBR_q_frac=0)!=a?e.quant_comp=l.quant_comp:0<Math.abs(e.quant_comp- -1)||(e.quant_comp=l.quant_comp),0!=a?e.quant_comp_short=l.quant_comp_s:0<Math.abs(e.quant_comp_short- -1)||(e.quant_comp_short=l.quant_comp_s),0!=l.expY&&(e.experimentalY=0!=l.expY),0!=a?e.internal_flags.nsPsy.attackthre=l.st_lrm:0<Math.abs(e.internal_flags.nsPsy.attackthre- -1)||(e.internal_flags.nsPsy.attackthre=l.st_lrm),0!=a?e.internal_flags.nsPsy.attackthre_s=l.st_s:0<Math.abs(e.internal_flags.nsPsy.attackthre_s- -1)||(e.internal_flags.nsPsy.attackthre_s=l.st_s),0!=a?e.maskingadjust=l.masking_adj:0<Math.abs(e.maskingadjust-0)||(e.maskingadjust=l.masking_adj),0!=a?e.maskingadjust_short=l.masking_adj_short:0<Math.abs(e.maskingadjust_short-0)||(e.maskingadjust_short=l.masking_adj_short),0!=a?e.ATHlower=-l.ath_lower/10:0<Math.abs(10*-e.ATHlower-0)||(e.ATHlower=-l.ath_lower/10),0!=a?e.ATHcurve=l.ath_curve:0<Math.abs(e.ATHcurve- -1)||(e.ATHcurve=l.ath_curve),0!=a?e.athaa_sensitivity=l.ath_sensitivity:0<Math.abs(e.athaa_sensitivity- -1)||(e.athaa_sensitivity=l.ath_sensitivity),0<l.interch&&(0!=a?e.interChRatio=l.interch:0<Math.abs(e.interChRatio- -1)||(e.interChRatio=l.interch)),0<l.safejoint&&(e.exp_nspsytune=e.exp_nspsytune|l.safejoint),0<l.sfb21mod&&(e.exp_nspsytune=e.exp_nspsytune|l.sfb21mod<<20),0!=a?e.msfix=l.msfix:0<Math.abs(e.msfix- -1)||(e.msfix=l.msfix),0==a&&(e.VBR_q=t,e.VBR_q_frac=i)}var _=[new t(8,9,9,0,0,6.6,145,0,.95,0,-30,11,.0012,1),new t(16,9,9,0,0,6.6,145,0,.95,0,-25,11,.001,1),new t(24,9,9,0,0,6.6,145,0,.95,0,-20,11,.001,1),new t(32,9,9,0,0,6.6,145,0,.95,0,-15,11,.001,1),new t(40,9,9,0,0,6.6,145,0,.95,0,-10,11,9e-4,1),new t(48,9,9,0,0,6.6,145,0,.95,0,-10,11,9e-4,1),new t(56,9,9,0,0,6.6,145,0,.95,0,-6,11,8e-4,1),new t(64,9,9,0,0,6.6,145,0,.95,0,-2,11,8e-4,1),new t(80,9,9,0,0,6.6,145,0,.95,0,0,8,7e-4,1),new t(96,9,9,0,2.5,6.6,145,0,.95,0,1,5.5,6e-4,1),new t(112,9,9,0,2.25,6.6,145,0,.95,0,2,4.5,5e-4,1),new t(128,9,9,0,1.95,6.4,140,0,.95,0,3,4,2e-4,1),new t(160,9,9,1,1.79,6,135,0,.95,-2,5,3.5,0,1),new t(192,9,9,1,1.49,5.6,125,0,.97,-4,7,3,0,0),new t(224,9,9,1,1.25,5.2,125,0,.98,-6,9,2,0,0),new t(256,9,9,1,.97,5.2,125,0,1,-8,10,1,0,0),new t(320,9,9,1,.9,5.2,125,0,1,-10,12,0,0,0)];function n(e,t,a){var s=t,n=i.nearestBitrateFullIndex(t);if(e.VBR=ye.vbr_abr,e.VBR_mean_bitrate_kbps=s,e.VBR_mean_bitrate_kbps=Math.min(e.VBR_mean_bitrate_kbps,320),e.VBR_mean_bitrate_kbps=Math.max(e.VBR_mean_bitrate_kbps,8),e.brate=e.VBR_mean_bitrate_kbps,320<e.VBR_mean_bitrate_kbps&&(e.disable_reservoir=!0),0<_[n].safejoint&&(e.exp_nspsytune=2|e.exp_nspsytune),0<_[n].sfscale&&(e.internal_flags.noise_shaping=2),0<Math.abs(_[n].nsbass)){var r=int(4*_[n].nsbass);r<0&&(r+=64),e.exp_nspsytune=e.exp_nspsytune|r<<2}return 0!=a?e.quant_comp=_[n].quant_comp:0<Math.abs(e.quant_comp- -1)||(e.quant_comp=_[n].quant_comp),0!=a?e.quant_comp_short=_[n].quant_comp_s:0<Math.abs(e.quant_comp_short- -1)||(e.quant_comp_short=_[n].quant_comp_s),0!=a?e.msfix=_[n].nsmsfix:0<Math.abs(e.msfix- -1)||(e.msfix=_[n].nsmsfix),0!=a?e.internal_flags.nsPsy.attackthre=_[n].st_lrm:0<Math.abs(e.internal_flags.nsPsy.attackthre- -1)||(e.internal_flags.nsPsy.attackthre=_[n].st_lrm),0!=a?e.internal_flags.nsPsy.attackthre_s=_[n].st_s:0<Math.abs(e.internal_flags.nsPsy.attackthre_s- -1)||(e.internal_flags.nsPsy.attackthre_s=_[n].st_s),0!=a?e.scale=_[n].scale:0<Math.abs(e.scale- -1)||(e.scale=_[n].scale),0!=a?e.maskingadjust=_[n].masking_adj:0<Math.abs(e.maskingadjust-0)||(e.maskingadjust=_[n].masking_adj),0<_[n].masking_adj?0!=a?e.maskingadjust_short=.9*_[n].masking_adj:0<Math.abs(e.maskingadjust_short-0)||(e.maskingadjust_short=.9*_[n].masking_adj):0!=a?e.maskingadjust_short=1.1*_[n].masking_adj:0<Math.abs(e.maskingadjust_short-0)||(e.maskingadjust_short=1.1*_[n].masking_adj),0!=a?e.ATHlower=-_[n].ath_lower/10:0<Math.abs(10*-e.ATHlower-0)||(e.ATHlower=-_[n].ath_lower/10),0!=a?e.ATHcurve=_[n].ath_curve:0<Math.abs(e.ATHcurve- -1)||(e.ATHcurve=_[n].ath_curve),0!=a?e.interChRatio=_[n].interch:0<Math.abs(e.interChRatio- -1)||(e.interChRatio=_[n].interch),t}this.apply_preset=function(e,t,a){switch(t){case Q.R3MIX:t=Q.V3,e.VBR=ye.vbr_mtrh;break;case Q.MEDIUM:t=Q.V4,e.VBR=ye.vbr_rh;break;case Q.MEDIUM_FAST:t=Q.V4,e.VBR=ye.vbr_mtrh;break;case Q.STANDARD:t=Q.V2,e.VBR=ye.vbr_rh;break;case Q.STANDARD_FAST:t=Q.V2,e.VBR=ye.vbr_mtrh;break;case Q.EXTREME:t=Q.V0,e.VBR=ye.vbr_rh;break;case Q.EXTREME_FAST:t=Q.V0,e.VBR=ye.vbr_mtrh;break;case Q.INSANE:return t=320,e.preset=t,n(e,t,a),e.VBR=ye.vbr_off,t}switch(e.preset=t){case Q.V9:return s(e,9,a),t;case Q.V8:return s(e,8,a),t;case Q.V7:return s(e,7,a),t;case Q.V6:return s(e,6,a),t;case Q.V5:return s(e,5,a),t;case Q.V4:return s(e,4,a),t;case Q.V3:return s(e,3,a),t;case Q.V2:return s(e,2,a),t;case Q.V1:return s(e,1,a),t;case Q.V0:return s(e,0,a),t}return 8<=t&&t<=320?n(e,t,a):(e.preset=0,t)}},o=new y,l=new w,f=new M,c=new function(){this.getLameVersion=function(){return"3.98.4"},this.getLameShortVersion=function(){return"3.98.4"},this.getLameVeryShortVersion=function(){return"LAME3.98r"},this.getPsyVersion=function(){return"0.93"},this.getLameUrl=function(){return"http://www.mp3dev.org/"},this.getLameOsBitness=function(){return"32bits"}},h=new function(){this.setModules=function(e,t){}},u=new function(){var o;this.setModules=function(e){o=e},this.ResvFrameBegin=function(e,t){var a,s=e.internal_flags,n=s.l3_side,r=o.getframebits(e);t.bits=(r-8*s.sideinfo_len)/s.mode_gr;var i=2048*s.mode_gr-8;320<e.brate?a=8*int(1e3*e.brate/(e.out_samplerate/1152)/8+.5):(a=11520,e.strict_ISO&&(a=8*int(32e4/(e.out_samplerate/1152)/8+.5))),s.ResvMax=a-r,s.ResvMax>i&&(s.ResvMax=i),(s.ResvMax<0||e.disable_reservoir)&&(s.ResvMax=0);var _=t.bits*s.mode_gr+Math.min(s.ResvSize,s.ResvMax);return a<_&&(_=a),n.resvDrain_pre=0,null!=s.pinfo&&(s.pinfo.mean_bits=t.bits/2,s.pinfo.resvsize=s.ResvSize),_},this.ResvMaxBits=function(e,t,a,s){var n,r=e.internal_flags,i=r.ResvSize,_=r.ResvMax;0!=s&&(i+=t),0!=(1&r.substep_shaping)&&(_*=.9),a.bits=t,9*_<10*i?(n=i-9*_/10,a.bits+=n,r.substep_shaping|=128):(n=0,r.substep_shaping&=127,e.disable_reservoir||0!=(1&r.substep_shaping)||(a.bits-=.1*t));var o=i<6*r.ResvMax/10?i:6*r.ResvMax/10;return(o-=n)<0&&(o=0),o},this.ResvAdjust=function(e,t){e.ResvSize-=t.part2_3_length+t.part2_length},this.ResvFrameEnd=function(e,t){var a,s=e.l3_side;e.ResvSize+=t*e.mode_gr;var n=0;s.resvDrain_post=0,(s.resvDrain_pre=0)!=(a=e.ResvSize%8)&&(n+=a),0<(a=e.ResvSize-n-e.ResvMax)&&(n+=a);var r=Math.min(8*s.main_data_begin,n)/8;s.resvDrain_pre+=8*r,n-=8*r,e.ResvSize-=8*r,s.main_data_begin-=r,s.resvDrain_post+=n,e.ResvSize-=n}},b=new k,m=new function(){this.setModules=function(e,t,a){}},p=new function(){};n.setModules(r,i,_,o,l,f,c,h,p),i.setModules(r,p,c,f),h.setModules(i,c),_.setModules(n),l.setModules(i,u,o,b),o.setModules(b,u,n.enc.psy),u.setModules(i),b.setModules(o),f.setModules(n,i,c),a.setModules(m,p),m.setModules(c,h,_);var v=n.lame_init();v.num_channels=s,v.in_samplerate=e,v.out_samplerate=e,v.brate=t,v.mode=Ee.STEREO,v.quality=3,v.bWriteVbrTag=!1,v.disable_reservoir=!0,v.write_id3tag_automatic=!1,n.lame_init_params(v);var d=1152,g=0|1.25*d+7200,S=B(g);this.encodeBuffer=function(e,t){1==s&&(t=e),e.length>d&&(d=e.length,S=B(g=0|1.25*d+7200));var a=n.lame_encode_buffer(v,e,t,e.length,S,0,g);return new Int8Array(S.subarray(0,a))},this.flush=function(){var e=n.lame_encode_flush(v,S,0,g);return new Int8Array(S.subarray(0,e))}}}t(),Recorder.lamejs=t}();
\ No newline at end of file

demos/streaming_asr_server/web/static/js/recorder/engine/pcm.js

-/*
-录音
-https://github.com/xiangyuecn/Recorder
-src: engine/pcm.js
-*/
-!function(){"use strict";Recorder.prototype.enc_pcm={stable:!0,testmsg:"pcm为未封装的原始音频数据，pcm数据文件无法直接播放；支持位数8位、16位（填在比特率里面），采样率取值无限制"},Recorder.prototype.pcm=function(e,t,r){var a=this.set,n=e.length,o=8==a.bitRate?8:16,c=new ArrayBuffer(n*(o/8)),s=new DataView(c),l=0;if(8==o)for(var p=0;p<n;p++,l++){var i=128+(e[p]>>8);s.setInt8(l,i,!0)}else for(p=0;p<n;p++,l+=2)s.setInt16(l,e[p],!0);t(new Blob([s.buffer],{type:"audio/pcm"}))},Recorder.pcm2wav=function(e,a,n){e.slice&&null!=e.type&&(e={blob:e});var o=e.sampleRate||16e3,c=e.bitRate||16;if(e.sampleRate&&e.bitRate||console.warn("pcm2wav必须提供sampleRate和bitRate"),Recorder.prototype.wav){var s=new FileReader;s.onloadend=function(){var e;if(8==c){var t=new Uint8Array(s.result);e=new Int16Array(t.length);for(var r=0;r<t.length;r++)e[r]=t[r]-128<<8}else e=new Int16Array(s.result);Recorder({type:"wav",sampleRate:o,bitRate:c}).mock(e,o).stop(function(e,t){a(e,t)},n)},s.readAsArrayBuffer(e.blob)}else n("pcm2wav必须先加载wav编码器wav.js")}}();
\ No newline at end of file

demos/streaming_asr_server/web/static/js/recorder/engine/wav.js

-/*
-录音
-https://github.com/xiangyuecn/Recorder
-src: engine/wav.js
-*/
-!function(){"use strict";Recorder.prototype.enc_wav={stable:!0,testmsg:"支持位数8位、16位（填在比特率里面），采样率取值无限制"},Recorder.prototype.wav=function(t,e,n){var r=this.set,a=t.length,o=r.sampleRate,f=8==r.bitRate?8:16,i=a*(f/8),s=new ArrayBuffer(44+i),c=new DataView(s),u=0,v=function(t){for(var e=0;e<t.length;e++,u++)c.setUint8(u,t.charCodeAt(e))},w=function(t){c.setUint16(u,t,!0),u+=2},l=function(t){c.setUint32(u,t,!0),u+=4};if(v("RIFF"),l(36+i),v("WAVE"),v("fmt "),l(16),w(1),w(1),l(o),l(o*(f/8)),w(f/8),w(f),v("data"),l(i),8==f)for(var p=0;p<a;p++,u++){var d=128+(t[p]>>8);c.setInt8(u,d,!0)}else for(p=0;p<a;p++,u+=2)c.setInt16(u,t[p],!0);e(new Blob([c.buffer],{type:"audio/wav"}))}}();
\ No newline at end of file

demos/streaming_asr_server/web/static/js/recorder/extensions/frequency.histogram.view.js

-/*
-录音
-https://github.com/xiangyuecn/Recorder
-src: extensions/frequency.histogram.view.js
-*/
-!function(){"use strict";var t=function(t){return new e(t)},e=function(t){var e=this,r={scale:2,fps:20,lineCount:30,widthRatio:.6,spaceWidth:0,minHeight:0,position:-1,mirrorEnable:!1,stripeEnable:!0,stripeHeight:3,stripeMargin:6,fallDuration:1e3,stripeFallDuration:3500,linear:[0,"rgba(0,187,17,1)",.5,"rgba(255,215,0,1)",1,"rgba(255,102,0,1)"],stripeLinear:null,shadowBlur:0,shadowColor:"#bbb",stripeShadowBlur:-1,stripeShadowColor:"",onDraw:function(t,e){}};for(var a in t)r[a]=t[a];e.set=t=r;var i=t.elem;i&&("string"==typeof i?i=document.querySelector(i):i.length&&(i=i[0])),i&&(t.width=i.offsetWidth,t.height=i.offsetHeight);var o=t.scale,l=t.width*o,n=t.height*o,h=e.elem=document.createElement("div"),s=["","transform-origin:0 0;","transform:scale("+1/o+");"];h.innerHTML='<div style="width:'+t.width+"px;height:"+t.height+'px;overflow:hidden"><div style="width:'+l+"px;height:"+n+"px;"+s.join("-webkit-")+s.join("-ms-")+s.join("-moz-")+s.join("")+'"><canvas/></div></div>';var f=e.canvas=h.querySelector("canvas");e.ctx=f.getContext("2d");if(f.width=l,f.height=n,i&&(i.innerHTML="",i.appendChild(h)),!Recorder.LibFFT)throw new Error("需要lib.fft.js支持");e.fft=Recorder.LibFFT(1024),e.lastH=[],e.stripesH=[]};e.prototype=t.prototype={genLinear:function(t,e,r,a){for(var i=t.createLinearGradient(0,r,0,a),o=0;o<e.length;)i.addColorStop(e[o++],e[o++]);return i},input:function(t,e,r){var a=this;a.sampleRate=r,a.pcmData=t,a.pcmPos=0,a.inputTime=Date.now(),a.schedule()},schedule:function(){var t=this,e=t.set,r=Math.floor(1e3/e.fps);t.timer||(t.timer=setInterval(function(){t.schedule()},r));var a=Date.now(),i=t.drawTime||0;if(a-t.inputTime>1.3*e.stripeFallDuration)return clearInterval(t.timer),void(t.timer=0);if(!(a-i<r)){t.drawTime=a;for(var o=t.fft.bufferSize,l=t.pcmData,n=t.pcmPos,h=new Int16Array(o),s=0;s<o&&n<l.length;s++,n++)h[s]=l[n];t.pcmPos=n;var f=t.fft.transform(h);t.draw(f,t.sampleRate)}},draw:function(t,e){var r=this,a=r.set,i=r.ctx,o=a.scale,l=a.width*o,n=a.height*o,h=a.lineCount,s=r.fft.bufferSize,f=a.position,d=Math.abs(a.position),c=1==f?0:n,p=n;d<1&&(c=p/=2,p=Math.floor(p*(1+d)),c=Math.floor(0<f?c*(1-d):c*(1+d)));for(var u=r.lastH,v=r.stripesH,w=Math.ceil(p/(a.fallDuration/(1e3/a.fps))),g=Math.ceil(p/(a.stripeFallDuration/(1e3/a.fps))),m=a.stripeMargin*o,M=1<<(Math.round(Math.log(s)/Math.log(2)+3)<<1),b=Math.log(M)/Math.log(10),L=20*Math.log(32767)/Math.log(10),y=s/2,S=Math.min(y,Math.floor(5e3*y/(e/2))),C=S==y,H=C?h:Math.round(.8*h),R=S/H,D=C?0:(y-S)/(h-H),x=0,F=0;F<h;F++){var T=Math.ceil(x);x+=F<H?R:D;for(var B=Math.min(Math.ceil(x),y),E=0,j=T;j<B;j++)E=Math.max(E,Math.abs(t[j]));var I=M<E?Math.floor(17*(Math.log(E)/Math.log(10)-b)):0,q=p*Math.min(I/L,1);u[F]=(u[F]||0)-w,q<u[F]&&(q=u[F]),q<0&&(q=0),u[F]=q;var z=v[F]||0;if(q&&z<q+m)v[F]=q+m;else{var P=z-g;P<0&&(P=0),v[F]=P}}i.clearRect(0,0,l,n);var W=r.genLinear(i,a.linear,c,c-p),k=a.stripeLinear&&r.genLinear(i,a.stripeLinear,c,c-p)||W,A=r.genLinear(i,a.linear,c,c+p),G=a.stripeLinear&&r.genLinear(i,a.stripeLinear,c,c+p)||A;i.shadowBlur=a.shadowBlur*o,i.shadowColor=a.shadowColor;var V=a.mirrorEnable,J=V?2*h-1:h,K=a.widthRatio,N=a.spaceWidth*o;0!=N&&(K=(l-N*(J+1))/l);for(var O=Math.max(1*o,Math.floor(l*K/J)),Q=(l-J*O)/(J+1),U=a.minHeight*o,X=V?l/2-(Q+O/2):0,Y=(F=0,X);F<h;F++)Y+=Q,$=Math.floor(Y),q=Math.max(u[F],U),0!=c&&(_=c-q,i.fillStyle=W,i.fillRect($,_,O,q)),c!=n&&(i.fillStyle=A,i.fillRect($,c,O,q)),Y+=O;if(a.stripeEnable){var Z=a.stripeShadowBlur;i.shadowBlur=(-1==Z?a.shadowBlur:Z)*o,i.shadowColor=a.stripeShadowColor||a.shadowColor;var $,_,tt=a.stripeHeight*o;for(F=0,Y=X;F<h;F++)Y+=Q,$=Math.floor(Y),q=v[F],0!=c&&((_=c-q-tt)<0&&(_=0),i.fillStyle=k,i.fillRect($,_,O,tt)),c!=n&&(n<(_=c+q)+tt&&(_=n-tt),i.fillStyle=G,i.fillRect($,_,O,tt)),Y+=O}if(V){var et=Math.floor(l/2);i.save(),i.scale(-1,1),i.drawImage(r.canvas,Math.ceil(l/2),0,et,n,-et,0,et,n),i.restore()}a.onDraw(t,e)}},Recorder.FrequencyHistogramView=t}();
\ No newline at end of file

demos/streaming_asr_server/web/static/js/recorder/extensions/lib.fft.js

-/*
-录音
-https://github.com/xiangyuecn/Recorder
-src: extensions/lib.fft.js
-*/
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\ No newline at end of file

demos/streaming_asr_server/web/static/js/recorder/recorder-core.js

-/*
-录音
-https://github.com/xiangyuecn/Recorder
-src: recorder-core.js
-*/
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r=t||e.inputBuffer.getChannelData(0),a=r.length,o=new Int16Array(a),s=0,i=0;i<a;i++){var c=Math.max(-1,Math.min(1,r[i]));c=c<0?32768*c:32767*c,o[i]=c,s+=Math.abs(c)}for(var f in u)u[f](o,s);return}else M(l+"多余回调",3)},s="ScriptProcessor",l="audioWorklet",i="Recorder",c=i+" "+l,f="RecProc",p=r.createScriptProcessor||r.createJavaScriptNode,v="。由于"+l+"内部1秒375次回调，在移动端可能会有性能问题导致回调丢失录音变短，PC端无影响，暂不建议开启"+l+"。",m=function(){h=n.isWorklet=!1,I(n),M("Connect采用老的"+s+"，"+(A[k]?"但已":"可")+"设置"+i+"."+k+"=true尝试启用"+l+v,3);var e=n._p=p.call(r,t,1,1);a.connect(e),e.connect(r.destination),e.onaudioprocess=function(e){o(e)}},h=n.isWorklet=!p||A[k],d=y.AudioWorkletNode;if(h&&r[l]&&d){var g,S=function(){return h&&n._na},_=n._na=function(){""!==g&&(clearTimeout(g),g=setTimeout(function(){g=0,M(l+"未返回任何音频，恢复使用"+s,3),S()&&p&&m()},500))},C=function(){if(S()){var e=n._n=new 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l=t/n;1<l?f=Math.floor(f/l):(l=1,n=t),f+=i.length;for(var p=new Int16Array(f),v=0,u=0;u<i.length;u++)p[v]=i[u],v++;for(var m=e.length;o<m;o++){for(var h=e[o],u=s,d=h.length;u<d;){var g=Math.floor(u),S=Math.ceil(u),_=u-g,C=h[g],y=S<d?h[S]:(e[o+1]||[C])[0]||0;p[v]=C+(y-C)*_,v++,u+=l}s=u-d}i=null;var k=p.length%c;if(0<k){var I=2*(p.length-k);i=new Int16Array(p.buffer.slice(I)),p=new Int16Array(p.buffer.slice(0,I))}return{index:o,offset:s,frameNext:i,sampleRate:n,data:p}},A.PowerLevel=function(e,t){var n=e/t||0;return n<1251?Math.round(n/1250*10):Math.round(Math.min(100,Math.max(0,100*(1+Math.log(n/1e4)/Math.log(10)))))};var M=function(e,t){var n=new Date,r=("0"+n.getMinutes()).substr(-2)+":"+("0"+n.getSeconds()).substr(-2)+"."+("00"+n.getMilliseconds()).substr(-3),a=this&&this.envIn&&this.envCheck&&this.id,o=["["+r+" Recorder"+(a?":"+a:"")+"]"+e],s=arguments,i=y.console||{},c=2,f=i.log;for("number"==typeof 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n._stop(),n.isMock=1,n.mockEnvInfo=null,n.buffers=[e],n.recSize=e.length,n.srcSampleRate=t,n},envCheck:function(e){var t,n=this.set;return t||(this[n.type+"_envCheck"]?t=this[n.type+"_envCheck"](e,n):n.takeoffEncodeChunk&&(t=n.type+"类型不支持设置takeoffEncodeChunk")),t||""},envStart:function(e,t){var n=this,r=n.set;if(n.isMock=e?1:0,n.mockEnvInfo=e,n.buffers=[],n.recSize=0,n.envInLast=0,n.envInFirst=0,n.envInFix=0,n.envInFixTs=[],r.sampleRate=Math.min(t,r.sampleRate),n.srcSampleRate=t,n.engineCtx=0,n[r.type+"_start"]){var a=n.engineCtx=n[r.type+"_start"](r);a&&(a.pcmDatas=[],a.pcmSize=0)}},envResume:function(){this.envInFixTs=[]},envIn:function(e,t){var a=this,o=a.set,s=a.engineCtx,n=a.srcSampleRate,r=e.length,i=A.PowerLevel(t,r),c=a.buffers,f=c.length;c.push(e);var u=c,l=f,p=Date.now(),v=Math.round(r/n*1e3);a.envInLast=p,1==a.buffers.length&&(a.envInFirst=p-v);var m=a.envInFixTs;m.splice(0,0,{t:p,d:v});for(var h=p,d=0,g=0;g<m.length;g++){var S=m[g];if(3e3<p-S.t){m.length=g;break}h=S.t,d+=S.d}var _=m[1],C=p-h;if(C/3<C-d&&(_&&1e3<C||6<=m.length)){var y=p-_.t-v;if(v/5<y){var k=!o.disableEnvInFix;if(a.CLog("["+p+"]"+(k?"":"未")+"补偿"+y+"ms",3),a.envInFix+=y,k){var I=new Int16Array(y*n/1e3);r+=I.length,c.push(I)}}}var M=a.recSize,x=r,b=M+x;if(a.recSize=b,s){var R=A.SampleData(c,n,o.sampleRate,s.chunkInfo);s.chunkInfo=R,b=(M=s.pcmSize)+(x=R.data.length),s.pcmSize=b,c=s.pcmDatas,f=c.length,c.push(R.data),n=R.sampleRate}var L=Math.round(b/n*1e3),w=c.length,T=u.length,z=function(){for(var e=O?0:-x,t=null==c[0],n=f;n<w;n++){var r=c[n];null==r?t=1:(e+=r.length,s&&r.length&&a[o.type+"_encode"](s,r))}if(t&&s)for(n=l,u[0]&&(n=0);n<T;n++)u[n]=null;t&&(e=O?x:0,c[0]=null),s?s.pcmSize+=e:a.recSize+=e},O=o.onProcess(c,i,L,n,f,z);if(!0===O){var D=0;for(g=f;g<w;g++)null==c[g]?D=1:c[g]=new Int16Array(0);D?a.CLog("未进入异步前不能清除buffers",3):s?s.pcmSize-=x:a.recSize-=x}else z()},start:function(){var e=this,t=A.Ctx,n=1;if(e.set.sourceStream?e.Stream||(n=0):A.IsOpen()||(n=0),n)if(e.CLog("开始录音"),e._stop(),e.state=0,e.envStart(null,t.sampleRate),e._SO&&e._SO+1!=e._S)e.CLog("start被中断",3);else{e._SO=0;var r=function(){e.state=1,e.resume()};"suspended"==t.state?(e.CLog("wait ctx resume..."),e.state=3,t.resume().then(function(){e.CLog("ctx resume"),3==e.state&&r()})):r()}else e.CLog("未open",1)},pause:function(){var e=this;e.state&&(e.state=2,e.CLog("pause"),delete e._streamStore().Stream._call[e.id])},resume:function(){var e,n=this;if(n.state){n.state=1,n.CLog("resume"),n.envResume();var t=n._streamStore();t.Stream._call[n.id]=function(e,t){1==n.state&&n.envIn(e,t)},(e=(t||A).Stream)._na&&e._na()}},_stop:function(e){var t=this,n=t.set;t.isMock||t._S++,t.state&&(t.pause(),t.state=0),!e&&t[n.type+"_stop"]&&(t[n.type+"_stop"](t.engineCtx),t.engineCtx=0)},stop:function(n,t,e){var r,a=this,o=a.set;a.CLog("stop "+(a.envInLast?a.envInLast-a.envInFirst+"ms 补"+a.envInFix+"ms":"-"));var s=function(){a._stop(),e&&a.close()},i=function(e){a.CLog("结束录音失败："+e,1),t&&t(e),s()},c=function(e,t){if(a.CLog("结束录音 编码"+(Date.now()-r)+"ms 音频"+t+"ms/"+e.size+"b"),o.takeoffEncodeChunk)a.CLog("启用takeoffEncodeChunk后stop返回的blob长度为0不提供音频数据",3);else if(e.size<Math.max(100,t/2))return void i("生成的"+o.type+"无效");n&&n(e,t),s()};if(!a.isMock){var f=3==a.state;if(!a.state||f)return void i("未开始录音"+(f?"，开始录音前无用户交互导致AudioContext未运行":""));a._stop(!0)}var u=a.recSize;if(u)if(a.buffers[0])if(a[o.type]){if(a.isMock){var l=a.envCheck(a.mockEnvInfo||{envName:"mock",canProcess:!1});if(l)return void i("录音错误："+l)}var p=a.engineCtx;if(a[o.type+"_complete"]&&p){var v=Math.round(p.pcmSize/o.sampleRate*1e3);return r=Date.now(),void a[o.type+"_complete"](p,function(e){c(e,v)},i)}r=Date.now();var m=A.SampleData(a.buffers,a.srcSampleRate,o.sampleRate);o.sampleRate=m.sampleRate;var h=m.data;v=Math.round(h.length/o.sampleRate*1e3),a.CLog("采样"+u+"->"+h.length+" 花:"+(Date.now()-r)+"ms"),setTimeout(function(){r=Date.now(),a[o.type](h,function(e){c(e,v)},function(e){i(e)})})}else i("未加载"+o.type+"编码器");else i("音频buffers被释放");else i("未采集到录音")}},y.Recorder&&y.Recorder.Destroy(),(y.Recorder=A).LM="2022-03-05 11:53:19",A.TrafficImgUrl="//ia.51.la/go1?id=20469973&pvFlag=1",A.Traffic=function(){var e=A.TrafficImgUrl;if(e){var t=A.Traffic,n=location.href.replace(/#.*/,"");if(0==e.indexOf("//")&&(e=/^https:/i.test(n)?"https:"+e:"http:"+e),!t[n]){t[n]=1;var r=new Image;r.src=e,M("Traffic Analysis Image: Recorder.TrafficImgUrl="+A.TrafficImgUrl)}}}}(window),"function"==typeof define&&define.amd&&define(function(){return Recorder}),"object"==typeof module&&module.exports&&(module.exports=Recorder);
\ No newline at end of file

demos/streaming_asr_server/web/templates/index.html

-<!DOCTYPE html>
-<html>
-
-<head>
-  <meta charset="UTF-8">
-  <title>PaddleSpeech Serving-语音实时转写</title>
-  <link rel="shortcut icon" href="./static/paddle.ico">
-  <script src="../static/js/jquery-3.2.1.min.js"></script>
-  <script src="../static/js/recorder/recorder-core.js"></script>
-  <script src="../static/js/recorder/extensions/lib.fft.js"></script>
-  <script src="../static/js/recorder/extensions/frequency.histogram.view.js"></script>
-  <script src="../static/js/recorder/engine/pcm.js"></script>
-  <script src="../static/js/SoundRecognizer.js"></script>
-  <link rel="stylesheet" href="../static/css/style.css">
-  <link rel="stylesheet" href="../static/css/font-awesome.min.css">
-</head>
-
-<body>
-  <div class="asr-content">
-    <div class="audio-banner">
-      <div class="weaper">
-        <div class="text-content">
-          <p><span class="title">PaddleSpeech Serving简介</span></p>
-          <p class="con-container">
-            <span class="con">PaddleSpeech 是基于飞桨 PaddlePaddle 的语音方向的开源模型库，用于语音和音频中的各种关键任务的开发。PaddleSpeech Serving是基于python + fastapi 的语音算法模型的C/S类型后端服务，旨在统一paddle speech下的各语音算子来对外提供后端服务。</span>
-          </p>
-        </div>
-        <div class="img-con">
-          <img src="../static/image/PaddleSpeech_logo.png" alt="" />
-        </div>
-      </div>
-    </div>
-    <div class="audio-experience">
-      <div class="asr-box">
-        <h2>产品体验</h2>
-        <div id="client-word-recorder" style="position: relative;">
-          <div class="pd">
-            <div style="text-align:center;height:20px;width:100%;
-                        border:0px solid #bcbcbc;color:#000;box-sizing: border-box;display:inline-block"
-              class="recwave">
-            </div>
-          </div>
-        </div>
-        <div class="voice-container">
-          <div class="voice-input">
-            <span>WebSocket URL：</span>
-            <input type="text" id="socketUrl" class="websocket-url" value="ws://127.0.0.1:8091/ws/asr"
-              placeholder="请输入服务器地址，如：ws://127.0.0.1:8091/ws/asr">
-            <div class="start-voice">
-              <button type="primary" id="beginBtn" class="voice-btn">
-                <span class="fa fa-microphone"> 开始识别</span>
-              </button>
-              <button type="primary" id="endBtn" class="voice-btn end">
-                <span class="fa fa-microphone-slash"> 结束识别</span>
-              </button>
-              <div id="timeBox" class="time-box flex-display-1">
-                <span class="total-time">识别中，<i id="timeCount"></i> 秒后自动停止识别</span>
-              </div>
-            </div>
-          </div>
-          <div class="voice">
-            <div class="result-text" id="resultPanel">此处显示识别结果</div>
-          </div>
-        </div>
-      </div>
-    </div>
-  </div>
-  <script>
-    var wenetWs = null
-    var timeLoop = null
-    var result = ""
-    $(document).ready(function () {
-      $('#beginBtn').on('click', startRecording)
-      $('#endBtn').on('click', stopRecording)
-    })
-
-    function openWebSocket(url) {
-      if ("WebSocket" in window) {
-        wenetWs = new WebSocket(url)
-        wenetWs.onopen = function () {
-          console.log("Websocket 连接成功，开始识别")
-          wenetWs.send(JSON.stringify({
-            "signal": "start"
-          }))
-        }
-        wenetWs.onmessage = function (_msg) { parseResult(_msg.data) }
-        wenetWs.onclose = function () {
-          console.log("WebSocket 连接断开")
-        }
-        wenetWs.onerror = function () { console.log("WebSocket 连接失败") }
-      }
-    }
-
-    function parseResult(data) {
-      var data = JSON.parse(data)
-      console.log('result json:', data)
-      var result = data.result
-      console.log(result)
-      $("#resultPanel").html(result)
-    }
-
-    function TransferUpload(number, blobOrNull, duration, blobRec, isClose) {
-      if (blobOrNull) {
-        var blob = blobOrNull
-        var encTime = blob.encTime
-        var reader = new FileReader()
-        reader.onloadend = function () { wenetWs.send(reader.result) }
-        reader.readAsArrayBuffer(blob)
-      }
-    }
-
-    function startRecording() {
-      // Check socket url
-      var socketUrl = $('#socketUrl').val()
-      if (!socketUrl.trim()) {
-        alert('请输入 WebSocket 服务器地址，如：ws://127.0.0.1:8091/ws/asr')
-        $('#socketUrl').focus()
-        return
-      }
-      // init recorder
-      SoundRecognizer.init({
-        soundType: 'pcm',
-        sampleRate: 16000,
-        recwaveElm: '.recwave',
-        translerCallBack: TransferUpload
-      })
-      openWebSocket(socketUrl)
-
-      // Change button state
-      $('#beginBtn').hide()
-      $('#endBtn, #timeBox').addClass('show')
-      // Start countdown
-      var seconds = 180
-      $('#timeCount').text(seconds)
-      timeLoop = setInterval(function () {
-        seconds--
-        $('#timeCount').text(seconds)
-        if (seconds === 0) {
-          stopRecording()
-        }
-      }, 1000)
-    }
-
-    function stopRecording() {
-      wenetWs.send(JSON.stringify({ "signal": "end" }))
-      SoundRecognizer.recordClose()
-
-      $('#endBtn').add($('#timeBox')).removeClass('show')
-      $('#beginBtn').show()
-      $('#timeCount').text('')
-      clearInterval(timeLoop)
-    }
-  </script>
-</body>
-
-</html>

docs/requirements.txt

@@ -22,6 +22,7 @@ onnxruntime
 pandas
 paddlenlp
 paddlespeech_feat
+Pillow>=9.0.0
 praatio==5.0.0
 pypinyin
 pypinyin-dict

docs/source/released_model.md

@@ -10,7 +10,7 @@ Acoustic Model | Training Data | Token-based | Size | Descriptions | CER | WER |
 [Ds2 Offline Aishell ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr0_deepspeech2_offline_aishell_ckpt_1.0.1.model.tar.gz)| Aishell Dataset | Char-based | 1.4 GB | 2 Conv + 5 bidirectional LSTM layers| 0.0554 |-| 151 h | [Ds2 Offline Aishell ASR0](../../examples/aishell/asr0) | inference/python |
 [Conformer Online Wenetspeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/asr1/asr1_chunk_conformer_wenetspeech_ckpt_1.0.0a.model.tar.gz) | WenetSpeech Dataset | Char-based | 457 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.11 (test\_net) 0.1879 (test\_meeting) |-| 10000 h |- | python |
 [Conformer Online Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_chunk_conformer_aishell_ckpt_0.2.0.model.tar.gz) | Aishell Dataset | Char-based | 189 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring| 0.0544 |-| 151 h | [Conformer Online Aishell ASR1](../../examples/aishell/asr1) | python |
-[Conformer Offline Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_0.1.2.model.tar.gz) | Aishell Dataset | Char-based | 189 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0464 |-| 151 h | [Conformer Offline Aishell ASR1](../../examples/aishell/asr1) | python |
+[Conformer Offline Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_conformer_aishell_ckpt_1.0.1.model.tar.gz) | Aishell Dataset | Char-based | 189 MB  | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0460 |-| 151 h | [Conformer Offline Aishell ASR1](../../examples/aishell/asr1) | python |
 [Transformer Aishell ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/aishell/asr1/asr1_transformer_aishell_ckpt_0.1.1.model.tar.gz) | Aishell Dataset | Char-based | 128 MB | Encoder:Transformer, Decoder:Transformer, Decoding method: Attention rescoring | 0.0523 || 151 h | [Transformer  Aishell ASR1](../../examples/aishell/asr1) | python |
 [Ds2 Offline Librispeech ASR0 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr0/asr0_deepspeech2_offline_librispeech_ckpt_1.0.1.model.tar.gz)| Librispeech Dataset | Char-based | 1.3 GB | 2 Conv + 5 bidirectional LSTM layers| - |0.0467| 960 h | [Ds2 Offline Librispeech ASR0](../../examples/librispeech/asr0) | inference/python |
 [Conformer Librispeech ASR1 Model](https://paddlespeech.bj.bcebos.com/s2t/librispeech/asr1/asr1_conformer_librispeech_ckpt_0.1.1.model.tar.gz) | Librispeech Dataset | subword-based | 191 MB | Encoder:Conformer, Decoder:Transformer, Decoding method: Attention rescoring |-| 0.0338 | 960 h | [Conformer Librispeech ASR1](../../examples/librispeech/asr1) | python |

examples/aishell/asr1/RESULTS.md

@@ -2,13 +2,13 @@
 
 ## Conformer
 paddle version: 2.2.2  
-paddlespeech version: 0.2.0
+paddlespeech version: 1.0.1
 | Model | Params | Config | Augmentation| Test set | Decode method | Loss | CER |
 | --- | --- | --- | --- | --- | --- | --- | --- | 
-| conformer | 47.07M  | conf/conformer.yaml | spec_aug | test | attention | - | 0.0530 |
-| conformer | 47.07M  | conf/conformer.yaml | spec_aug | test | ctc_greedy_search | - | 0.0495 |
-| conformer | 47.07M  | conf/conformer.yaml | spec_aug| test | ctc_prefix_beam_search | - | 0.0494 | 
-| conformer | 47.07M  | conf/conformer.yaml | spec_aug | test | attention_rescoring | - | 0.0464 | 
+| conformer | 47.07M  | conf/conformer.yaml | spec_aug | test | attention | - | 0.0522 |
+| conformer | 47.07M  | conf/conformer.yaml | spec_aug | test | ctc_greedy_search | - | 0.0481 |
+| conformer | 47.07M  | conf/conformer.yaml | spec_aug| test | ctc_prefix_beam_search | - | 0.0480 | 
+| conformer | 47.07M  | conf/conformer.yaml | spec_aug | test | attention_rescoring | - | 0.0460 | 
 
 
 ## Conformer Streaming

examples/aishell/asr1/conf/conformer.yaml

@@ -57,7 +57,7 @@ feat_dim: 80
 stride_ms: 10.0
 window_ms: 25.0
 sortagrad: 0 # Feed samples from shortest to longest ; -1: enabled for all epochs, 0: disabled, other: enabled for 'other' epochs 
-batch_size: 64
+batch_size: 32
 maxlen_in: 512  # if input length  > maxlen-in, batchsize is automatically reduced
 maxlen_out: 150  # if output length > maxlen-out, batchsize is automatically reduced
 minibatches: 0 # for debug
@@ -73,10 +73,10 @@ num_encs: 1
 ###########################################
 #                Training                 #
 ###########################################
-n_epoch: 240 
-accum_grad: 2
+n_epoch: 150 
+accum_grad: 8
 global_grad_clip: 5.0
-dist_sampler: True
+dist_sampler: False
 optim: adam
 optim_conf:
   lr: 0.002

examples/csmsc/vits/README.md

@@ -144,3 +144,34 @@ optional arguments:
 6. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 
 ## Pretrained Model
+
+The pretrained model can be downloaded here:
+
+- [vits_csmsc_ckpt_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/vits/vits_csmsc_ckpt_1.1.0.zip) (add_blank=true)
+
+VITS checkpoint contains files listed below.
+```text
+vits_csmsc_ckpt_1.1.0
+├── default.yaml              # default config used to train vitx
+├── phone_id_map.txt          # phone vocabulary file when training vits
+└── snapshot_iter_350000.pdz  # model parameters and optimizer states
+```
+
+ps: This ckpt is not good enough, a better result is training
+
+You can use the following scripts to synthesize for `${BIN_DIR}/../sentences.txt` using pretrained VITS.
+
+```bash
+source path.sh
+add_blank=true
+
+FLAGS_allocator_strategy=naive_best_fit \
+FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+python3 ${BIN_DIR}/synthesize_e2e.py \
+    --config=vits_csmsc_ckpt_1.1.0/default.yaml \
+    --ckpt=vits_csmsc_ckpt_1.1.0/snapshot_iter_350000.pdz \
+    --phones_dict=vits_csmsc_ckpt_1.1.0/phone_id_map.txt \
+    --output_dir=exp/default/test_e2e \
+    --text=${BIN_DIR}/../sentences.txt \
+    --add-blank=${add_blank} 
+```

examples/csmsc/vits/local/synthesize.sh

@@ -15,4 +15,4 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
         --phones_dict=dump/phone_id_map.txt \
         --test_metadata=dump/test/norm/metadata.jsonl \
         --output_dir=${train_output_path}/test
-fi
\ No newline at end of file
+fi

examples/csmsc/vits/local/train.sh

@@ -3,6 +3,11 @@
 config_path=$1
 train_output_path=$2
 
+# install monotonic_align
+cd ${MAIN_ROOT}/paddlespeech/t2s/models/vits/monotonic_align
+python3 setup.py build_ext --inplace
+cd -
+
 python3 ${BIN_DIR}/train.py \
     --train-metadata=dump/train/norm/metadata.jsonl \
     --dev-metadata=dump/dev/norm/metadata.jsonl \

examples/voxceleb/sv0/local/data.sh

@@ -74,7 +74,7 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    # convert the m4a to wav
    # and we will not delete the original m4a file
    echo "start to convert the m4a to wav"
-   bash local/convert.sh ${TARGET_DIR}/voxceleb/vox2/test/ || exit 1;
+   bash local/convert.sh ${TARGET_DIR}/voxceleb/vox2/ || exit 1;
    
    if [ $? -ne 0 ]; then
       echo "Convert voxceleb2 dataset from m4a to wav failed. Terminated."

paddlespeech/audio/transform/spec_augment.py

@@ -14,10 +14,8 @@
 # Modified from espnet(https://github.com/espnet/espnet)
 """Spec Augment module for preprocessing i.e., data augmentation"""
 import random
-
 import numpy
 from PIL import Image
-from PIL.Image import BICUBIC
 
 from .functional import FuncTrans
 
@@ -46,9 +44,10 @@ def time_warp(x, max_time_warp=80, inplace=False, mode="PIL"):
         warped = random.randrange(center - window, center +
                                   window) + 1  # 1 ... t - 1
 
-        left = Image.fromarray(x[:center]).resize((x.shape[1], warped), BICUBIC)
+        left = Image.fromarray(x[:center]).resize((x.shape[1], warped),
+                                                  Image.BICUBIC)
         right = Image.fromarray(x[center:]).resize((x.shape[1], t - warped),
-                                                   BICUBIC)
+                                                   Image.BICUBIC)
         if inplace:
             x[:warped] = left
             x[warped:] = right

paddlespeech/cli/asr/infer.py

@@ -133,11 +133,11 @@ class ASRExecutor(BaseExecutor):
         """
         Init model and other resources from a specific path.
         """
-        logger.info("start to init the model")
+        logger.debug("start to init the model")
         # default max_len: unit:second
         self.max_len = 50
         if hasattr(self, 'model'):
-            logger.info('Model had been initialized.')
+            logger.debug('Model had been initialized.')
             return
 
         if cfg_path is None or ckpt_path is None:
@@ -151,15 +151,15 @@ class ASRExecutor(BaseExecutor):
             self.ckpt_path = os.path.join(
                 self.res_path,
                 self.task_resource.res_dict['ckpt_path'] + ".pdparams")
-            logger.info(self.res_path)
+            logger.debug(self.res_path)
 
         else:
             self.cfg_path = os.path.abspath(cfg_path)
             self.ckpt_path = os.path.abspath(ckpt_path + ".pdparams")
             self.res_path = os.path.dirname(
                 os.path.dirname(os.path.abspath(self.cfg_path)))
-        logger.info(self.cfg_path)
-        logger.info(self.ckpt_path)
+        logger.debug(self.cfg_path)
+        logger.debug(self.ckpt_path)
 
         #Init body.
         self.config = CfgNode(new_allowed=True)
@@ -216,7 +216,7 @@ class ASRExecutor(BaseExecutor):
                 max_len = self.config.encoder_conf.max_len
 
             self.max_len = frame_shift_ms * max_len * subsample_rate
-            logger.info(
+            logger.debug(
                 f"The asr server limit max duration len: {self.max_len}")
 
     def preprocess(self, model_type: str, input: Union[str, os.PathLike]):
@@ -227,15 +227,15 @@ class ASRExecutor(BaseExecutor):
 
         audio_file = input
         if isinstance(audio_file, (str, os.PathLike)):
-            logger.info("Preprocess audio_file:" + audio_file)
+            logger.debug("Preprocess audio_file:" + audio_file)
 
         # Get the object for feature extraction
         if "deepspeech2" in model_type or "conformer" in model_type or "transformer" in model_type:
-            logger.info("get the preprocess conf")
+            logger.debug("get the preprocess conf")
             preprocess_conf = self.config.preprocess_config
             preprocess_args = {"train": False}
             preprocessing = Transformation(preprocess_conf)
-            logger.info("read the audio file")
+            logger.debug("read the audio file")
             audio, audio_sample_rate = soundfile.read(
                 audio_file, dtype="int16", always_2d=True)
             if self.change_format:
@@ -255,7 +255,7 @@ class ASRExecutor(BaseExecutor):
             else:
                 audio = audio[:, 0]
 
-            logger.info(f"audio shape: {audio.shape}")
+            logger.debug(f"audio shape: {audio.shape}")
             # fbank
             audio = preprocessing(audio, **preprocess_args)
 
@@ -264,19 +264,19 @@ class ASRExecutor(BaseExecutor):
 
             self._inputs["audio"] = audio
             self._inputs["audio_len"] = audio_len
-            logger.info(f"audio feat shape: {audio.shape}")
+            logger.debug(f"audio feat shape: {audio.shape}")
 
         else:
             raise Exception("wrong type")
 
-        logger.info("audio feat process success")
+        logger.debug("audio feat process success")
 
     @paddle.no_grad()
     def infer(self, model_type: str):
         """
         Model inference and result stored in self.output.
         """
-        logger.info("start to infer the model to get the output")
+        logger.debug("start to infer the model to get the output")
         cfg = self.config.decode
         audio = self._inputs["audio"]
         audio_len = self._inputs["audio_len"]
@@ -293,7 +293,7 @@ class ASRExecutor(BaseExecutor):
             self._outputs["result"] = result_transcripts[0]
 
         elif "conformer" in model_type or "transformer" in model_type:
-            logger.info(
+            logger.debug(
                 f"we will use the transformer like model : {model_type}")
             try:
                 result_transcripts = self.model.decode(
@@ -352,7 +352,7 @@ class ASRExecutor(BaseExecutor):
                 logger.error("Please input the right audio file path")
                 return False
 
-        logger.info("checking the audio file format......")
+        logger.debug("checking the audio file format......")
         try:
             audio, audio_sample_rate = soundfile.read(
                 audio_file, dtype="int16", always_2d=True)
@@ -374,7 +374,7 @@ class ASRExecutor(BaseExecutor):
                  sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
                  ")
             return False
-        logger.info("The sample rate is %d" % audio_sample_rate)
+        logger.debug("The sample rate is %d" % audio_sample_rate)
         if audio_sample_rate != self.sample_rate:
             logger.warning("The sample rate of the input file is not {}.\n \
                             The program will resample the wav file to {}.\n \
@@ -383,28 +383,28 @@ class ASRExecutor(BaseExecutor):
                         ".format(self.sample_rate, self.sample_rate))
             if force_yes is False:
                 while (True):
-                    logger.info(
+                    logger.debug(
                         "Whether to change the sample rate and the channel. Y: change the sample. N: exit the prgream."
                     )
                     content = input("Input(Y/N):")
                     if content.strip() == "Y" or content.strip(
                     ) == "y" or content.strip() == "yes" or content.strip(
                     ) == "Yes":
-                        logger.info(
+                        logger.debug(
                             "change the sampele rate, channel to 16k and 1 channel"
                         )
                         break
                     elif content.strip() == "N" or content.strip(
                     ) == "n" or content.strip() == "no" or content.strip(
                     ) == "No":
-                        logger.info("Exit the program")
+                        logger.debug("Exit the program")
                         return False
                     else:
                         logger.warning("Not regular input, please input again")
 
             self.change_format = True
         else:
-            logger.info("The audio file format is right")
+            logger.debug("The audio file format is right")
             self.change_format = False
 
         return True

paddlespeech/cli/cls/infer.py

@@ -92,7 +92,7 @@ class CLSExecutor(BaseExecutor):
             Init model and other resources from a specific path.
         """
         if hasattr(self, 'model'):
-            logger.info('Model had been initialized.')
+            logger.debug('Model had been initialized.')
             return
 
         if label_file is None or ckpt_path is None:
@@ -135,14 +135,14 @@ class CLSExecutor(BaseExecutor):
             Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
         """
         feat_conf = self._conf['feature']
-        logger.info(feat_conf)
+        logger.debug(feat_conf)
         waveform, _ = load(
             file=audio_file,
             sr=feat_conf['sample_rate'],
             mono=True,
             dtype='float32')
         if isinstance(audio_file, (str, os.PathLike)):
-            logger.info("Preprocessing audio_file:" + audio_file)
+            logger.debug("Preprocessing audio_file:" + audio_file)
 
         # Feature extraction
         feature_extractor = LogMelSpectrogram(

paddlespeech/cli/download.py

@@ -61,7 +61,7 @@ def _get_unique_endpoints(trainer_endpoints):
             continue
         ips.add(ip)
         unique_endpoints.add(endpoint)
-    logger.info("unique_endpoints {}".format(unique_endpoints))
+    logger.debug("unique_endpoints {}".format(unique_endpoints))
     return unique_endpoints
 
 
@@ -96,7 +96,7 @@ def get_path_from_url(url,
     # data, and the same ip will only download data once.
     unique_endpoints = _get_unique_endpoints(ParallelEnv().trainer_endpoints[:])
     if osp.exists(fullpath) and check_exist and _md5check(fullpath, md5sum):
-        logger.info("Found {}".format(fullpath))
+        logger.debug("Found {}".format(fullpath))
     else:
         if ParallelEnv().current_endpoint in unique_endpoints:
             fullpath = _download(url, root_dir, md5sum, method=method)
@@ -118,7 +118,7 @@ def _get_download(url, fullname):
     try:
         req = requests.get(url, stream=True)
     except Exception as e:  # requests.exceptions.ConnectionError
-        logger.info("Downloading {} from {} failed with exception {}".format(
+        logger.debug("Downloading {} from {} failed with exception {}".format(
             fname, url, str(e)))
         return False
 
@@ -190,7 +190,7 @@ def _download(url, path, md5sum=None, method='get'):
     fullname = osp.join(path, fname)
     retry_cnt = 0
 
-    logger.info("Downloading {} from {}".format(fname, url))
+    logger.debug("Downloading {} from {}".format(fname, url))
     while not (osp.exists(fullname) and _md5check(fullname, md5sum)):
         if retry_cnt < DOWNLOAD_RETRY_LIMIT:
             retry_cnt += 1
@@ -209,7 +209,7 @@ def _md5check(fullname, md5sum=None):
     if md5sum is None:
         return True
 
-    logger.info("File {} md5 checking...".format(fullname))
+    logger.debug("File {} md5 checking...".format(fullname))
     md5 = hashlib.md5()
     with open(fullname, 'rb') as f:
         for chunk in iter(lambda: f.read(4096), b""):
@@ -217,8 +217,8 @@ def _md5check(fullname, md5sum=None):
     calc_md5sum = md5.hexdigest()
 
     if calc_md5sum != md5sum:
-        logger.info("File {} md5 check failed, {}(calc) != "
-                    "{}(base)".format(fullname, calc_md5sum, md5sum))
+        logger.debug("File {} md5 check failed, {}(calc) != "
+                     "{}(base)".format(fullname, calc_md5sum, md5sum))
         return False
     return True
 
@@ -227,7 +227,7 @@ def _decompress(fname):
     """
     Decompress for zip and tar file
     """
-    logger.info("Decompressing {}...".format(fname))
+    logger.debug("Decompressing {}...".format(fname))
 
     # For protecting decompressing interupted,
     # decompress to fpath_tmp directory firstly, if decompress

paddlespeech/cli/executor.py

@@ -217,7 +217,7 @@ class BaseExecutor(ABC):
             logging.getLogger(name) for name in logging.root.manager.loggerDict
         ]
         for l in loggers:
-            l.disabled = True
+            l.setLevel(logging.ERROR)
 
     def show_rtf(self, info: Dict[str, List[float]]):
         """

paddlespeech/cli/kws/infer.py

@@ -88,7 +88,7 @@ class KWSExecutor(BaseExecutor):
             Init model and other resources from a specific path.
         """
         if hasattr(self, 'model'):
-            logger.info('Model had been initialized.')
+            logger.debug('Model had been initialized.')
             return
 
         if ckpt_path is None:
@@ -141,7 +141,7 @@ class KWSExecutor(BaseExecutor):
         assert os.path.isfile(audio_file)
         waveform, _ = load(audio_file)
         if isinstance(audio_file, (str, os.PathLike)):
-            logger.info("Preprocessing audio_file:" + audio_file)
+            logger.debug("Preprocessing audio_file:" + audio_file)
 
         # Feature extraction
         waveform = paddle.to_tensor(waveform).unsqueeze(0)

paddlespeech/cli/log.py

@@ -49,7 +49,7 @@ class Logger(object):
         self.handler.setFormatter(self.format)
 
         self.logger.addHandler(self.handler)
-        self.logger.setLevel(logging.DEBUG)
+        self.logger.setLevel(logging.INFO)
         self.logger.propagate = False
 
     def __call__(self, log_level: str, msg: str):

paddlespeech/cli/st/infer.py

@@ -110,7 +110,7 @@ class STExecutor(BaseExecutor):
         """
         decompressed_path = download_and_decompress(self.kaldi_bins, MODEL_HOME)
         decompressed_path = os.path.abspath(decompressed_path)
-        logger.info("Kaldi_bins stored in: {}".format(decompressed_path))
+        logger.debug("Kaldi_bins stored in: {}".format(decompressed_path))
         if "LD_LIBRARY_PATH" in os.environ:
             os.environ["LD_LIBRARY_PATH"] += f":{decompressed_path}"
         else:
@@ -128,7 +128,7 @@ class STExecutor(BaseExecutor):
             Init model and other resources from a specific path.
         """
         if hasattr(self, 'model'):
-            logger.info('Model had been initialized.')
+            logger.debug('Model had been initialized.')
             return
 
         if cfg_path is None or ckpt_path is None:
@@ -140,8 +140,8 @@ class STExecutor(BaseExecutor):
             self.ckpt_path = os.path.join(
                 self.task_resource.res_dir,
                 self.task_resource.res_dict['ckpt_path'])
-            logger.info(self.cfg_path)
-            logger.info(self.ckpt_path)
+            logger.debug(self.cfg_path)
+            logger.debug(self.ckpt_path)
             res_path = self.task_resource.res_dir
         else:
             self.cfg_path = os.path.abspath(cfg_path)
@@ -192,7 +192,7 @@ class STExecutor(BaseExecutor):
             Input content can be a file(wav).
         """
         audio_file = os.path.abspath(wav_file)
-        logger.info("Preprocess audio_file:" + audio_file)
+        logger.debug("Preprocess audio_file:" + audio_file)
 
         if "fat_st" in model_type:
             cmvn = self.config.cmvn_path

paddlespeech/cli/text/infer.py

@@ -98,7 +98,7 @@ class TextExecutor(BaseExecutor):
             Init model and other resources from a specific path.
         """
         if hasattr(self, 'model'):
-            logger.info('Model had been initialized.')
+            logger.debug('Model had been initialized.')
             return
 
         self.task = task

paddlespeech/cli/tts/infer.py

@@ -173,16 +173,23 @@ class TTSExecutor(BaseExecutor):
         Init model and other resources from a specific path.
         """
         if hasattr(self, 'am_inference') and hasattr(self, 'voc_inference'):
-            logger.info('Models had been initialized.')
+            logger.debug('Models had been initialized.')
             return
+
         # am
+        if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
+            use_pretrained_am = True
+        else:
+            use_pretrained_am = False
+
         am_tag = am + '-' + lang
         self.task_resource.set_task_model(
             model_tag=am_tag,
             model_type=0,  # am
+            skip_download=not use_pretrained_am,
             version=None,  # default version
         )
-        if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
+        if use_pretrained_am:
             self.am_res_path = self.task_resource.res_dir
             self.am_config = os.path.join(self.am_res_path,
                                           self.task_resource.res_dict['config'])
@@ -193,9 +200,9 @@ class TTSExecutor(BaseExecutor):
             # must have phones_dict in acoustic
             self.phones_dict = os.path.join(
                 self.am_res_path, self.task_resource.res_dict['phones_dict'])
-            logger.info(self.am_res_path)
-            logger.info(self.am_config)
-            logger.info(self.am_ckpt)
+            logger.debug(self.am_res_path)
+            logger.debug(self.am_config)
+            logger.debug(self.am_ckpt)
         else:
             self.am_config = os.path.abspath(am_config)
             self.am_ckpt = os.path.abspath(am_ckpt)
@@ -220,13 +227,19 @@ class TTSExecutor(BaseExecutor):
                 self.speaker_dict = speaker_dict
 
         # voc
+        if voc_ckpt is None or voc_config is None or voc_stat is None:
+            use_pretrained_voc = True
+        else:
+            use_pretrained_voc = False
+
         voc_tag = voc + '-' + lang
         self.task_resource.set_task_model(
             model_tag=voc_tag,
             model_type=1,  # vocoder
+            skip_download=not use_pretrained_voc,
             version=None,  # default version
         )
-        if voc_ckpt is None or voc_config is None or voc_stat is None:
+        if use_pretrained_voc:
             self.voc_res_path = self.task_resource.voc_res_dir
             self.voc_config = os.path.join(
                 self.voc_res_path, self.task_resource.voc_res_dict['config'])
@@ -235,9 +248,9 @@ class TTSExecutor(BaseExecutor):
             self.voc_stat = os.path.join(
                 self.voc_res_path,
                 self.task_resource.voc_res_dict['speech_stats'])
-            logger.info(self.voc_res_path)
-            logger.info(self.voc_config)
-            logger.info(self.voc_ckpt)
+            logger.debug(self.voc_res_path)
+            logger.debug(self.voc_config)
+            logger.debug(self.voc_ckpt)
         else:
             self.voc_config = os.path.abspath(voc_config)
             self.voc_ckpt = os.path.abspath(voc_ckpt)
@@ -254,21 +267,18 @@ class TTSExecutor(BaseExecutor):
         with open(self.phones_dict, "r") as f:
             phn_id = [line.strip().split() for line in f.readlines()]
         vocab_size = len(phn_id)
-        print("vocab_size:", vocab_size)
 
         tone_size = None
         if self.tones_dict:
             with open(self.tones_dict, "r") as f:
                 tone_id = [line.strip().split() for line in f.readlines()]
             tone_size = len(tone_id)
-            print("tone_size:", tone_size)
 
         spk_num = None
         if self.speaker_dict:
             with open(self.speaker_dict, 'rt') as f:
                 spk_id = [line.strip().split() for line in f.readlines()]
             spk_num = len(spk_id)
-            print("spk_num:", spk_num)
 
         # frontend
         if lang == 'zh':
@@ -278,7 +288,6 @@ class TTSExecutor(BaseExecutor):
 
         elif lang == 'en':
             self.frontend = English(phone_vocab_path=self.phones_dict)
-        print("frontend done!")
 
         # acoustic model
         odim = self.am_config.n_mels
@@ -311,7 +320,6 @@ class TTSExecutor(BaseExecutor):
         am_normalizer = ZScore(am_mu, am_std)
         self.am_inference = am_inference_class(am_normalizer, am)
         self.am_inference.eval()
-        print("acoustic model done!")
 
         # vocoder
         # model: {model_name}_{dataset}
@@ -334,7 +342,6 @@ class TTSExecutor(BaseExecutor):
         voc_normalizer = ZScore(voc_mu, voc_std)
         self.voc_inference = voc_inference_class(voc_normalizer, voc)
         self.voc_inference.eval()
-        print("voc done!")
 
     def preprocess(self, input: Any, *args, **kwargs):
         """
@@ -375,7 +382,7 @@ class TTSExecutor(BaseExecutor):
                 text, merge_sentences=merge_sentences)
             phone_ids = input_ids["phone_ids"]
         else:
-            print("lang should in {'zh', 'en'}!")
+            logger.error("lang should in {'zh', 'en'}!")
         self.frontend_time = time.time() - frontend_st
 
         self.am_time = 0

paddlespeech/cli/vector/infer.py

@@ -117,7 +117,7 @@ class VectorExecutor(BaseExecutor):
 
         # stage 2: read the input data and store them as a list
         task_source = self.get_input_source(parser_args.input)
-        logger.info(f"task source: {task_source}")
+        logger.debug(f"task source: {task_source}")
 
         # stage 3: process the audio one by one
         # we do action according the task type
@@ -127,13 +127,13 @@ class VectorExecutor(BaseExecutor):
             try:
                 # extract the speaker audio embedding
                 if parser_args.task == "spk":
-                    logger.info("do vector spk task")
+                    logger.debug("do vector spk task")
                     res = self(input_, model, sample_rate, config, ckpt_path,
                                device)
                     task_result[id_] = res
                 elif parser_args.task == "score":
-                    logger.info("do vector score task")
-                    logger.info(f"input content {input_}")
+                    logger.debug("do vector score task")
+                    logger.debug(f"input content {input_}")
                     if len(input_.split()) != 2:
                         logger.error(
                             f"vector score task input {input_} wav num is not two,"
@@ -142,7 +142,7 @@ class VectorExecutor(BaseExecutor):
 
                     # get the enroll and test embedding
                     enroll_audio, test_audio = input_.split()
-                    logger.info(
+                    logger.debug(
                         f"score task, enroll audio: {enroll_audio}, test audio: {test_audio}"
                     )
                     enroll_embedding = self(enroll_audio, model, sample_rate,
@@ -158,8 +158,8 @@ class VectorExecutor(BaseExecutor):
                 has_exceptions = True
                 task_result[id_] = f'{e.__class__.__name__}: {e}'
 
-        logger.info("task result as follows: ")
-        logger.info(f"{task_result}")
+        logger.debug("task result as follows: ")
+        logger.debug(f"{task_result}")
 
         # stage 4: process the all the task results
         self.process_task_results(parser_args.input, task_result,
@@ -207,7 +207,7 @@ class VectorExecutor(BaseExecutor):
         """
         if not hasattr(self, "score_func"):
             self.score_func = paddle.nn.CosineSimilarity(axis=0)
-            logger.info("create the cosine score function ")
+            logger.debug("create the cosine score function ")
 
         score = self.score_func(
             paddle.to_tensor(enroll_embedding),
@@ -244,7 +244,7 @@ class VectorExecutor(BaseExecutor):
             sys.exit(-1)
 
         # stage 1: set the paddle runtime host device
-        logger.info(f"device type: {device}")
+        logger.debug(f"device type: {device}")
         paddle.device.set_device(device)
 
         # stage 2: read the specific pretrained model
@@ -283,7 +283,7 @@ class VectorExecutor(BaseExecutor):
         # stage 0: avoid to init the mode again
         self.task = task
         if hasattr(self, "model"):
-            logger.info("Model has been initialized")
+            logger.debug("Model has been initialized")
             return
 
         # stage 1: get the model and config path
@@ -294,7 +294,7 @@ class VectorExecutor(BaseExecutor):
             sample_rate_str = "16k" if sample_rate == 16000 else "8k"
             tag = model_type + "-" + sample_rate_str
             self.task_resource.set_task_model(tag, version=None)
-            logger.info(f"load the pretrained model: {tag}")
+            logger.debug(f"load the pretrained model: {tag}")
             # get the model from the pretrained list
             # we download the pretrained model and store it in the res_path
             self.res_path = self.task_resource.res_dir
@@ -312,19 +312,19 @@ class VectorExecutor(BaseExecutor):
             self.res_path = os.path.dirname(
                 os.path.dirname(os.path.abspath(self.cfg_path)))
 
-        logger.info(f"start to read the ckpt from {self.ckpt_path}")
-        logger.info(f"read the config from {self.cfg_path}")
-        logger.info(f"get the res path {self.res_path}")
+        logger.debug(f"start to read the ckpt from {self.ckpt_path}")
+        logger.debug(f"read the config from {self.cfg_path}")
+        logger.debug(f"get the res path {self.res_path}")
 
         # stage 2: read and config and init the model body
         self.config = CfgNode(new_allowed=True)
         self.config.merge_from_file(self.cfg_path)
 
         # stage 3: get the model name to instance the model network with dynamic_import
-        logger.info("start to dynamic import the model class")
+        logger.debug("start to dynamic import the model class")
         model_name = model_type[:model_type.rindex('_')]
         model_class = self.task_resource.get_model_class(model_name)
-        logger.info(f"model name {model_name}")
+        logger.debug(f"model name {model_name}")
         model_conf = self.config.model
         backbone = model_class(**model_conf)
         model = SpeakerIdetification(
@@ -333,11 +333,11 @@ class VectorExecutor(BaseExecutor):
         self.model.eval()
 
         # stage 4: load the model parameters
-        logger.info("start to set the model parameters to model")
+        logger.debug("start to set the model parameters to model")
         model_dict = paddle.load(self.ckpt_path)
         self.model.set_state_dict(model_dict)
 
-        logger.info("create the model instance success")
+        logger.debug("create the model instance success")
 
     @paddle.no_grad()
     def infer(self, model_type: str):
@@ -349,14 +349,14 @@ class VectorExecutor(BaseExecutor):
         # stage 0: get the feat and length from _inputs
         feats = self._inputs["feats"]
         lengths = self._inputs["lengths"]
-        logger.info("start to do backbone network model forward")
-        logger.info(
+        logger.debug("start to do backbone network model forward")
+        logger.debug(
             f"feats shape:{feats.shape}, lengths shape: {lengths.shape}")
 
         # stage 1: get the audio embedding
         # embedding from (1, emb_size, 1) -> (emb_size)
         embedding = self.model.backbone(feats, lengths).squeeze().numpy()
-        logger.info(f"embedding size: {embedding.shape}")
+        logger.debug(f"embedding size: {embedding.shape}")
 
         # stage 2: put the embedding and dim info to _outputs property
         #          the embedding type is numpy.array
@@ -380,12 +380,13 @@ class VectorExecutor(BaseExecutor):
         """
         audio_file = input_file
         if isinstance(audio_file, (str, os.PathLike)):
-            logger.info(f"Preprocess audio file: {audio_file}")
+            logger.debug(f"Preprocess audio file: {audio_file}")
 
         # stage 1: load the audio sample points
         #    Note: this process must match the training process
         waveform, sr = load_audio(audio_file)
-        logger.info(f"load the audio sample points, shape is: {waveform.shape}")
+        logger.debug(
+            f"load the audio sample points, shape is: {waveform.shape}")
 
         # stage 2: get the audio feat
         # Note: Now we only support fbank feature
@@ -396,9 +397,9 @@ class VectorExecutor(BaseExecutor):
                 n_mels=self.config.n_mels,
                 window_size=self.config.window_size,
                 hop_length=self.config.hop_size)
-            logger.info(f"extract the audio feat, shape is: {feat.shape}")
+            logger.debug(f"extract the audio feat, shape is: {feat.shape}")
         except Exception as e:
-            logger.info(f"feat occurs exception {e}")
+            logger.debug(f"feat occurs exception {e}")
             sys.exit(-1)
 
         feat = paddle.to_tensor(feat).unsqueeze(0)
@@ -411,11 +412,11 @@ class VectorExecutor(BaseExecutor):
 
         # stage 4: store the feat and length in the _inputs,
         #          which will be used in other function
-        logger.info(f"feats shape: {feat.shape}")
+        logger.debug(f"feats shape: {feat.shape}")
         self._inputs["feats"] = feat
         self._inputs["lengths"] = lengths
 
-        logger.info("audio extract the feat success")
+        logger.debug("audio extract the feat success")
 
     def _check(self, audio_file: str, sample_rate: int):
         """Check if the model sample match the audio sample rate 
@@ -441,7 +442,7 @@ class VectorExecutor(BaseExecutor):
                 logger.error("Please input the right audio file path")
                 return False
 
-        logger.info("checking the aduio file format......")
+        logger.debug("checking the aduio file format......")
         try:
             audio, audio_sample_rate = soundfile.read(
                 audio_file, dtype="float32", always_2d=True)
@@ -458,7 +459,7 @@ class VectorExecutor(BaseExecutor):
                  ")
             return False
 
-        logger.info(f"The sample rate is {audio_sample_rate}")
+        logger.debug(f"The sample rate is {audio_sample_rate}")
 
         if audio_sample_rate != self.sample_rate:
             logger.error("The sample rate of the input file is not {}.\n \
@@ -468,6 +469,6 @@ class VectorExecutor(BaseExecutor):
                         ".format(self.sample_rate, self.sample_rate))
             sys.exit(-1)
         else:
-            logger.info("The audio file format is right")
+            logger.debug("The audio file format is right")
 
         return True

paddlespeech/resource/resource.py

@@ -60,6 +60,7 @@ class CommonTaskResource:
     def set_task_model(self,
                        model_tag: str,
                        model_type: int=0,
+                       skip_download: bool=False,
                        version: Optional[str]=None):
         """Set model tag and version of current task.
 
@@ -83,16 +84,18 @@ class CommonTaskResource:
             self.version = version
             self.res_dict = self.pretrained_models[model_tag][version]
             self._format_path(self.res_dict)
-            self.res_dir = self._fetch(self.res_dict,
-                                       self._get_model_dir(model_type))
+            if not skip_download:
+                self.res_dir = self._fetch(self.res_dict,
+                                           self._get_model_dir(model_type))
         else:
             assert self.task == 'tts', 'Vocoder will only be used in tts task.'
             self.voc_model_tag = model_tag
             self.voc_version = version
             self.voc_res_dict = self.pretrained_models[model_tag][version]
             self._format_path(self.voc_res_dict)
-            self.voc_res_dir = self._fetch(self.voc_res_dict,
-                                           self._get_model_dir(model_type))
+            if not skip_download:
+                self.voc_res_dir = self._fetch(self.voc_res_dict,
+                                               self._get_model_dir(model_type))
 
     @staticmethod
     def get_model_class(model_name) -> List[object]:

paddlespeech/s2t/exps/deepspeech2/bin/export.py

@@ -35,12 +35,6 @@ if __name__ == "__main__":
     # save jit model to
     parser.add_argument(
         "--export_path", type=str, help="path of the jit model to save")
-    parser.add_argument(
-        '--nxpu',
-        type=int,
-        default=0,
-        choices=[0, 1],
-        help="if nxpu == 0 and ngpu == 0, use cpu.")
     args = parser.parse_args()
     print_arguments(args)
 

paddlespeech/s2t/exps/deepspeech2/bin/test.py

@@ -35,12 +35,6 @@ if __name__ == "__main__":
     # save asr result to
     parser.add_argument(
         "--result_file", type=str, help="path of save the asr result")
-    parser.add_argument(
-        '--nxpu',
-        type=int,
-        default=0,
-        choices=[0, 1],
-        help="if nxpu == 0 and ngpu == 0, use cpu.")
     args = parser.parse_args()
     print_arguments(args, globals())
 

paddlespeech/s2t/exps/deepspeech2/bin/test_export.py

@@ -38,12 +38,6 @@ if __name__ == "__main__":
     #load jit model from
     parser.add_argument(
         "--export_path", type=str, help="path of the jit model to save")
-    parser.add_argument(
-        '--nxpu',
-        type=int,
-        default=0,
-        choices=[0, 1],
-        help="if nxpu == 0 and ngpu == 0, use cpu.")
     parser.add_argument(
         "--enable-auto-log", action="store_true", help="use auto log")
     args = parser.parse_args()

paddlespeech/s2t/exps/deepspeech2/bin/train.py

@@ -31,12 +31,6 @@ def main(config, args):
 
 if __name__ == "__main__":
     parser = default_argument_parser()
-    parser.add_argument(
-        '--nxpu',
-        type=int,
-        default=0,
-        choices=[0, 1],
-        help="if nxpu == 0 and ngpu == 0, use cpu.")
     args = parser.parse_args()
     print_arguments(args, globals())
 

paddlespeech/s2t/frontend/augmentor/spec_augment.py

@@ -16,7 +16,6 @@ import random
 
 import numpy as np
 from PIL import Image
-from PIL.Image import BICUBIC
 
 from paddlespeech.s2t.frontend.augmentor.base import AugmentorBase
 from paddlespeech.s2t.utils.log import Log
@@ -164,9 +163,9 @@ class SpecAugmentor(AugmentorBase):
                                       window) + 1  # 1 ... t - 1
 
             left = Image.fromarray(x[:center]).resize((x.shape[1], warped),
-                                                      BICUBIC)
+                                                      Image.BICUBIC)
             right = Image.fromarray(x[center:]).resize((x.shape[1], t - warped),
-                                                       BICUBIC)
+                                                       Image.BICUBIC)
             if self.inplace:
                 x[:warped] = left
                 x[warped:] = right

paddlespeech/s2t/frontend/featurizer/text_featurizer.py

@@ -226,10 +226,10 @@ class TextFeaturizer():
         sos_id = vocab_list.index(SOS) if SOS in vocab_list else -1
         space_id = vocab_list.index(SPACE) if SPACE in vocab_list else -1
 
-        logger.info(f"BLANK id: {blank_id}")
-        logger.info(f"UNK id: {unk_id}")
-        logger.info(f"EOS id: {eos_id}")
-        logger.info(f"SOS id: {sos_id}")
-        logger.info(f"SPACE id: {space_id}")
-        logger.info(f"MASKCTC id: {maskctc_id}")
+        logger.debug(f"BLANK id: {blank_id}")
+        logger.debug(f"UNK id: {unk_id}")
+        logger.debug(f"EOS id: {eos_id}")
+        logger.debug(f"SOS id: {sos_id}")
+        logger.debug(f"SPACE id: {space_id}")
+        logger.debug(f"MASKCTC id: {maskctc_id}")
         return token2id, id2token, vocab_list, unk_id, eos_id, blank_id

paddlespeech/s2t/models/u2/u2.py

@@ -827,7 +827,7 @@ class U2Model(U2DecodeModel):
 
         # encoder
         encoder_type = configs.get('encoder', 'transformer')
-        logger.info(f"U2 Encoder type: {encoder_type}")
+        logger.debug(f"U2 Encoder type: {encoder_type}")
         if encoder_type == 'transformer':
             encoder = TransformerEncoder(
                 input_dim, global_cmvn=global_cmvn, **configs['encoder_conf'])
@@ -894,7 +894,7 @@ class U2Model(U2DecodeModel):
         if checkpoint_path:
             infos = checkpoint.Checkpoint().load_parameters(
                 model, checkpoint_path=checkpoint_path)
-            logger.info(f"checkpoint info: {infos}")
+            logger.debug(f"checkpoint info: {infos}")
         layer_tools.summary(model)
         return model
 

paddlespeech/s2t/modules/loss.py

@@ -37,9 +37,9 @@ class CTCLoss(nn.Layer):
         self.loss = nn.CTCLoss(blank=blank, reduction=reduction)
         self.batch_average = batch_average
 
-        logger.info(
+        logger.debug(
             f"CTCLoss Loss reduction: {reduction}, div-bs: {batch_average}")
-        logger.info(f"CTCLoss Grad Norm Type: {grad_norm_type}")
+        logger.debug(f"CTCLoss Grad Norm Type: {grad_norm_type}")
 
         assert grad_norm_type in ('instance', 'batch', 'frame', None)
         self.norm_by_times = False
@@ -70,7 +70,8 @@ class CTCLoss(nn.Layer):
             param = {}
         self._kwargs = {k: v for k, v in kwargs.items() if k in param}
         _notin = {k: v for k, v in kwargs.items() if k not in param}
-        logger.info(f"{self.loss} kwargs:{self._kwargs}, not support: {_notin}")
+        logger.debug(
+            f"{self.loss} kwargs:{self._kwargs}, not support: {_notin}")
 
     def forward(self, logits, ys_pad, hlens, ys_lens):
         """Compute CTC loss.

paddlespeech/s2t/training/cli.py

@@ -82,6 +82,12 @@ def default_argument_parser(parser=None):
         type=int,
         default=1,
         help="number of parallel processes. 0 for cpu.")
+    train_group.add_argument(
+        '--nxpu',
+        type=int,
+        default=0,
+        choices=[0, 1],
+        help="if nxpu == 0 and ngpu == 0, use cpu.")
     train_group.add_argument(
         "--config", metavar="CONFIG_FILE", help="config file.")
     train_group.add_argument(

paddlespeech/s2t/utils/tensor_utils.py

@@ -94,7 +94,7 @@ def pad_sequence(sequences: List[paddle.Tensor],
     for i, tensor in enumerate(sequences):
         length = tensor.shape[0]
         # use index notation to prevent duplicate references to the tensor
-        logger.info(
+        logger.debug(
             f"length {length}, out_tensor {out_tensor.shape}, tensor {tensor.shape}"
         )
         if batch_first:

paddlespeech/server/bin/paddlespeech_client.py

@@ -123,7 +123,6 @@ class TTSClientExecutor(BaseExecutor):
             time_end = time.time()
             time_consume = time_end - time_start
             response_dict = res.json()
-            logger.info(response_dict["message"])
             logger.info("Save synthesized audio successfully on %s." % (output))
             logger.info("Audio duration: %f s." %
                         (response_dict['result']['duration']))
@@ -702,7 +701,6 @@ class VectorClientExecutor(BaseExecutor):
                 test_audio=args.test,
                 task=task)
             time_end = time.time()
-            logger.info(f"The vector: {res}")
             logger.info("Response time %f s." % (time_end - time_start))
             return True
         except Exception as e:

paddlespeech/server/engine/acs/python/acs_engine.py

@@ -30,7 +30,7 @@ class ACSEngine(BaseEngine):
         """The ACSEngine Engine
         """
         super(ACSEngine, self).__init__()
-        logger.info("Create the ACSEngine Instance")
+        logger.debug("Create the ACSEngine Instance")
         self.word_list = []
 
     def init(self, config: dict):
@@ -42,7 +42,7 @@ class ACSEngine(BaseEngine):
         Returns:
             bool: The engine instance flag
         """
-        logger.info("Init the acs engine")
+        logger.debug("Init the acs engine")
         try:
             self.config = config
             self.device = self.config.get("device", paddle.get_device())
@@ -50,7 +50,7 @@ class ACSEngine(BaseEngine):
             # websocket default ping timeout is 20 seconds
             self.ping_timeout = self.config.get("ping_timeout", 20)
             paddle.set_device(self.device)
-            logger.info(f"ACS Engine set the device: {self.device}")
+            logger.debug(f"ACS Engine set the device: {self.device}")
 
         except BaseException as e:
             logger.error(
@@ -66,7 +66,9 @@ class ACSEngine(BaseEngine):
         self.url = "ws://" + self.config.asr_server_ip + ":" + str(
             self.config.asr_server_port) + "/paddlespeech/asr/streaming"
 
-        logger.info("Init the acs engine successfully")
+        logger.info("Initialize acs server engine successfully on device: %s." %
+                    (self.device))
+
         return True
 
     def read_search_words(self):
@@ -95,12 +97,12 @@ class ACSEngine(BaseEngine):
         Returns:
             _type_: _description_
         """
-        logger.info("send a message to the server")
+        logger.debug("send a message to the server")
         if self.url is None:
             logger.error("No asr server, please input valid ip and port")
             return ""
         ws = websocket.WebSocket()
-        logger.info(f"set the ping timeout: {self.ping_timeout} seconds")
+        logger.debug(f"set the ping timeout: {self.ping_timeout} seconds")
         ws.connect(self.url, ping_timeout=self.ping_timeout)
         audio_info = json.dumps(
             {
@@ -123,7 +125,7 @@ class ACSEngine(BaseEngine):
             logger.info(f"audio result: {msg}")
 
         # 3. send chunk audio data to engine
-        logger.info("send the end signal")
+        logger.debug("send the end signal")
         audio_info = json.dumps(
             {
                 "name": "test.wav",
@@ -197,7 +199,7 @@ class ACSEngine(BaseEngine):
                 start = max(time_stamp[m.start(0)]['bg'] - offset, 0)
 
                 end = min(time_stamp[m.end(0) - 1]['ed'] + offset, max_ed)
-                logger.info(f'start: {start}, end: {end}')
+                logger.debug(f'start: {start}, end: {end}')
                 acs_result.append({'w': w, 'bg': start, 'ed': end})
 
         return acs_result, asr_result
@@ -212,7 +214,7 @@ class ACSEngine(BaseEngine):
         Returns:
             acs_result, asr_result: the acs result and the asr result
         """
-        logger.info("start to process the audio content search")
+        logger.debug("start to process the audio content search")
         msg = self.get_asr_content(io.BytesIO(audio_data))
 
         acs_result, asr_result = self.get_macthed_word(msg)

paddlespeech/server/engine/asr/online/onnx/asr_engine.py

@@ -44,7 +44,7 @@ class PaddleASRConnectionHanddler:
             asr_engine (ASREngine): the global asr engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "create an paddle asr connection handler to process the websocket connection"
         )
         self.config = asr_engine.config  # server config
@@ -152,12 +152,12 @@ class PaddleASRConnectionHanddler:
         self.output_reset()
 
     def extract_feat(self, samples: ByteString):
-        logger.info("Online ASR extract the feat")
+        logger.debug("Online ASR extract the feat")
         samples = np.frombuffer(samples, dtype=np.int16)
         assert samples.ndim == 1
 
         self.num_samples += samples.shape[0]
-        logger.info(
+        logger.debug(
             f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
         )
 
@@ -168,7 +168,7 @@ class PaddleASRConnectionHanddler:
         else:
             assert self.remained_wav.ndim == 1  # (T,)
             self.remained_wav = np.concatenate([self.remained_wav, samples])
-        logger.info(
+        logger.debug(
             f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
         )
 
@@ -202,14 +202,14 @@ class PaddleASRConnectionHanddler:
         # update remained wav
         self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
 
-        logger.info(
+        logger.debug(
             f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
         )
-        logger.info(
+        logger.debug(
             f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
         )
-        logger.info(f"global samples: {self.num_samples}")
-        logger.info(f"global frames: {self.num_frames}")
+        logger.debug(f"global samples: {self.num_samples}")
+        logger.debug(f"global frames: {self.num_frames}")
 
     def decode(self, is_finished=False):
         """advance decoding
@@ -237,7 +237,7 @@ class PaddleASRConnectionHanddler:
                 return
 
             num_frames = self.cached_feat.shape[1]
-            logger.info(
+            logger.debug(
                 f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
             )
 
@@ -355,7 +355,7 @@ class ASRServerExecutor(ASRExecutor):
 
             lm_url = self.task_resource.res_dict['lm_url']
             lm_md5 = self.task_resource.res_dict['lm_md5']
-            logger.info(f"Start to load language model {lm_url}")
+            logger.debug(f"Start to load language model {lm_url}")
             self.download_lm(
                 lm_url,
                 os.path.dirname(self.config.decode.lang_model_path), lm_md5)
@@ -367,7 +367,7 @@ class ASRServerExecutor(ASRExecutor):
 
         if "deepspeech2" in self.model_type:
             # AM predictor
-            logger.info("ASR engine start to init the am predictor")
+            logger.debug("ASR engine start to init the am predictor")
             self.am_predictor = onnx_infer.get_sess(
                 model_path=self.am_model, sess_conf=self.am_predictor_conf)
         else:
@@ -400,7 +400,7 @@ class ASRServerExecutor(ASRExecutor):
         self.num_decoding_left_chunks = num_decoding_left_chunks
         # conf for paddleinference predictor or onnx
         self.am_predictor_conf = am_predictor_conf
-        logger.info(f"model_type: {self.model_type}")
+        logger.debug(f"model_type: {self.model_type}")
 
         sample_rate_str = '16k' if sample_rate == 16000 else '8k'
         tag = model_type + '-' + lang + '-' + sample_rate_str
@@ -422,12 +422,11 @@ class ASRServerExecutor(ASRExecutor):
         #     self.res_path, self.task_resource.res_dict[
         #         'params']) if am_params is None else os.path.abspath(am_params)
 
-        logger.info("Load the pretrained model:")
-        logger.info(f"  tag = {tag}")
-        logger.info(f"  res_path: {self.res_path}")
-        logger.info(f"  cfg path: {self.cfg_path}")
-        logger.info(f"  am_model path: {self.am_model}")
-        # logger.info(f"  am_params path: {self.am_params}")
+        logger.debug("Load the pretrained model:")
+        logger.debug(f"  tag = {tag}")
+        logger.debug(f"  res_path: {self.res_path}")
+        logger.debug(f"  cfg path: {self.cfg_path}")
+        logger.debug(f"  am_model path: {self.am_model}")
 
         #Init body.
         self.config = CfgNode(new_allowed=True)
@@ -436,7 +435,7 @@ class ASRServerExecutor(ASRExecutor):
         if self.config.spm_model_prefix:
             self.config.spm_model_prefix = os.path.join(
                 self.res_path, self.config.spm_model_prefix)
-            logger.info(f"spm model path: {self.config.spm_model_prefix}")
+            logger.debug(f"spm model path: {self.config.spm_model_prefix}")
 
         self.vocab = self.config.vocab_filepath
 
@@ -450,7 +449,7 @@ class ASRServerExecutor(ASRExecutor):
         # AM predictor
         self.init_model()
 
-        logger.info(f"create the {model_type} model success")
+        logger.debug(f"create the {model_type} model success")
         return True
 
 
@@ -501,7 +500,7 @@ class ASREngine(BaseEngine):
                 "If all GPU or XPU is used, you can set the server to 'cpu'")
             sys.exit(-1)
 
-        logger.info(f"paddlespeech_server set the device: {self.device}")
+        logger.debug(f"paddlespeech_server set the device: {self.device}")
 
         if not self.init_model():
             logger.error(
@@ -509,7 +508,8 @@ class ASREngine(BaseEngine):
             )
             return False
 
-        logger.info("Initialize ASR server engine successfully.")
+        logger.info("Initialize ASR server engine successfully on device: %s." %
+                    (self.device))
         return True
 
     def new_handler(self):

paddlespeech/server/engine/asr/online/paddleinference/asr_engine.py

@@ -44,7 +44,7 @@ class PaddleASRConnectionHanddler:
             asr_engine (ASREngine): the global asr engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "create an paddle asr connection handler to process the websocket connection"
         )
         self.config = asr_engine.config  # server config
@@ -157,7 +157,7 @@ class PaddleASRConnectionHanddler:
         assert samples.ndim == 1
 
         self.num_samples += samples.shape[0]
-        logger.info(
+        logger.debug(
             f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
         )
 
@@ -168,7 +168,7 @@ class PaddleASRConnectionHanddler:
         else:
             assert self.remained_wav.ndim == 1  # (T,)
             self.remained_wav = np.concatenate([self.remained_wav, samples])
-        logger.info(
+        logger.debug(
             f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
         )
 
@@ -202,14 +202,14 @@ class PaddleASRConnectionHanddler:
         # update remained wav
         self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
 
-        logger.info(
+        logger.debug(
             f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
         )
-        logger.info(
+        logger.debug(
             f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
         )
-        logger.info(f"global samples: {self.num_samples}")
-        logger.info(f"global frames: {self.num_frames}")
+        logger.debug(f"global samples: {self.num_samples}")
+        logger.debug(f"global frames: {self.num_frames}")
 
     def decode(self, is_finished=False):
         """advance decoding
@@ -237,13 +237,13 @@ class PaddleASRConnectionHanddler:
                 return
 
             num_frames = self.cached_feat.shape[1]
-            logger.info(
+            logger.debug(
                 f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
             )
 
             # the cached feat must be larger decoding_window
             if num_frames < decoding_window and not is_finished:
-                logger.info(
+                logger.debug(
                     f"frame feat num is less than {decoding_window}, please input more pcm data"
                 )
                 return None, None
@@ -294,7 +294,7 @@ class PaddleASRConnectionHanddler:
         Returns:
             logprob: poster probability.
         """
-        logger.info("start to decoce one chunk for deepspeech2")
+        logger.debug("start to decoce one chunk for deepspeech2")
         input_names = self.am_predictor.get_input_names()
         audio_handle = self.am_predictor.get_input_handle(input_names[0])
         audio_len_handle = self.am_predictor.get_input_handle(input_names[1])
@@ -369,7 +369,7 @@ class ASRServerExecutor(ASRExecutor):
 
             lm_url = self.task_resource.res_dict['lm_url']
             lm_md5 = self.task_resource.res_dict['lm_md5']
-            logger.info(f"Start to load language model {lm_url}")
+            logger.debug(f"Start to load language model {lm_url}")
             self.download_lm(
                 lm_url,
                 os.path.dirname(self.config.decode.lang_model_path), lm_md5)
@@ -381,7 +381,7 @@ class ASRServerExecutor(ASRExecutor):
 
         if "deepspeech2" in self.model_type:
             # AM predictor
-            logger.info("ASR engine start to init the am predictor")
+            logger.debug("ASR engine start to init the am predictor")
             self.am_predictor = init_predictor(
                 model_file=self.am_model,
                 params_file=self.am_params,
@@ -415,7 +415,7 @@ class ASRServerExecutor(ASRExecutor):
         self.num_decoding_left_chunks = num_decoding_left_chunks
         # conf for paddleinference predictor or onnx
         self.am_predictor_conf = am_predictor_conf
-        logger.info(f"model_type: {self.model_type}")
+        logger.debug(f"model_type: {self.model_type}")
 
         sample_rate_str = '16k' if sample_rate == 16000 else '8k'
         tag = model_type + '-' + lang + '-' + sample_rate_str
@@ -437,12 +437,12 @@ class ASRServerExecutor(ASRExecutor):
             self.res_path = os.path.dirname(
                 os.path.dirname(os.path.abspath(self.cfg_path)))
 
-        logger.info("Load the pretrained model:")
-        logger.info(f"  tag = {tag}")
-        logger.info(f"  res_path: {self.res_path}")
-        logger.info(f"  cfg path: {self.cfg_path}")
-        logger.info(f"  am_model path: {self.am_model}")
-        logger.info(f"  am_params path: {self.am_params}")
+        logger.debug("Load the pretrained model:")
+        logger.debug(f"  tag = {tag}")
+        logger.debug(f"  res_path: {self.res_path}")
+        logger.debug(f"  cfg path: {self.cfg_path}")
+        logger.debug(f"  am_model path: {self.am_model}")
+        logger.debug(f"  am_params path: {self.am_params}")
 
         #Init body.
         self.config = CfgNode(new_allowed=True)
@@ -451,7 +451,7 @@ class ASRServerExecutor(ASRExecutor):
         if self.config.spm_model_prefix:
             self.config.spm_model_prefix = os.path.join(
                 self.res_path, self.config.spm_model_prefix)
-            logger.info(f"spm model path: {self.config.spm_model_prefix}")
+            logger.debug(f"spm model path: {self.config.spm_model_prefix}")
 
         self.vocab = self.config.vocab_filepath
 
@@ -465,7 +465,7 @@ class ASRServerExecutor(ASRExecutor):
         # AM predictor
         self.init_model()
 
-        logger.info(f"create the {model_type} model success")
+        logger.debug(f"create the {model_type} model success")
         return True
 
 
@@ -516,7 +516,7 @@ class ASREngine(BaseEngine):
                 "If all GPU or XPU is used, you can set the server to 'cpu'")
             sys.exit(-1)
 
-        logger.info(f"paddlespeech_server set the device: {self.device}")
+        logger.debug(f"paddlespeech_server set the device: {self.device}")
 
         if not self.init_model():
             logger.error(
@@ -524,7 +524,9 @@ class ASREngine(BaseEngine):
             )
             return False
 
-        logger.info("Initialize ASR server engine successfully.")
+        logger.info("Initialize ASR server engine successfully on device: %s." %
+                    (self.device))
+
         return True
 
     def new_handler(self):

paddlespeech/server/engine/asr/online/python/asr_engine.py

@@ -49,7 +49,7 @@ class PaddleASRConnectionHanddler:
             asr_engine (ASREngine): the global asr engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "create an paddle asr connection handler to process the websocket connection"
         )
         self.config = asr_engine.config  # server config
@@ -107,7 +107,7 @@ class PaddleASRConnectionHanddler:
             # acoustic model
             self.model = self.asr_engine.executor.model
             self.continuous_decoding = self.config.continuous_decoding
-            logger.info(f"continue decoding: {self.continuous_decoding}")
+            logger.debug(f"continue decoding: {self.continuous_decoding}")
 
             # ctc decoding config
             self.ctc_decode_config = self.asr_engine.executor.config.decode
@@ -207,7 +207,7 @@ class PaddleASRConnectionHanddler:
         assert samples.ndim == 1
 
         self.num_samples += samples.shape[0]
-        logger.info(
+        logger.debug(
             f"This package receive {samples.shape[0]} pcm data. Global samples:{self.num_samples}"
         )
 
@@ -218,7 +218,7 @@ class PaddleASRConnectionHanddler:
         else:
             assert self.remained_wav.ndim == 1  # (T,)
             self.remained_wav = np.concatenate([self.remained_wav, samples])
-        logger.info(
+        logger.debug(
             f"The concatenation of remain and now audio samples length is: {self.remained_wav.shape}"
         )
 
@@ -252,14 +252,14 @@ class PaddleASRConnectionHanddler:
         # update remained wav
         self.remained_wav = self.remained_wav[self.n_shift * num_frames:]
 
-        logger.info(
+        logger.debug(
             f"process the audio feature success, the cached feat shape: {self.cached_feat.shape}"
         )
-        logger.info(
+        logger.debug(
             f"After extract feat, the cached remain the audio samples: {self.remained_wav.shape}"
         )
-        logger.info(f"global samples: {self.num_samples}")
-        logger.info(f"global frames: {self.num_frames}")
+        logger.debug(f"global samples: {self.num_samples}")
+        logger.debug(f"global frames: {self.num_frames}")
 
     def decode(self, is_finished=False):
         """advance decoding
@@ -283,24 +283,24 @@ class PaddleASRConnectionHanddler:
             stride = subsampling * decoding_chunk_size
 
             if self.cached_feat is None:
-                logger.info("no audio feat, please input more pcm data")
+                logger.debug("no audio feat, please input more pcm data")
                 return
 
             num_frames = self.cached_feat.shape[1]
-            logger.info(
+            logger.debug(
                 f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
             )
 
             # the cached feat must be larger decoding_window
             if num_frames < decoding_window and not is_finished:
-                logger.info(
+                logger.debug(
                     f"frame feat num is less than {decoding_window}, please input more pcm data"
                 )
                 return None, None
 
             # if is_finished=True, we need at least context frames
             if num_frames < context:
-                logger.info(
+                logger.debug(
                     "flast {num_frames} is less than context {context} frames, and we cannot do model forward"
                 )
                 return None, None
@@ -354,7 +354,7 @@ class PaddleASRConnectionHanddler:
         Returns:
             logprob: poster probability.
         """
-        logger.info("start to decoce one chunk for deepspeech2")
+        logger.debug("start to decoce one chunk for deepspeech2")
         input_names = self.am_predictor.get_input_names()
         audio_handle = self.am_predictor.get_input_handle(input_names[0])
         audio_len_handle = self.am_predictor.get_input_handle(input_names[1])
@@ -391,7 +391,7 @@ class PaddleASRConnectionHanddler:
 
         self.decoder.next(output_chunk_probs, output_chunk_lens)
         trans_best, trans_beam = self.decoder.decode()
-        logger.info(f"decode one best result for deepspeech2: {trans_best[0]}")
+        logger.debug(f"decode one best result for deepspeech2: {trans_best[0]}")
         return trans_best[0]
 
     @paddle.no_grad()
@@ -402,7 +402,7 @@ class PaddleASRConnectionHanddler:
         # reset endpiont state
         self.endpoint_state = False
 
-        logger.info(
+        logger.debug(
             "Conformer/Transformer: start to decode with advanced_decoding method"
         )
         cfg = self.ctc_decode_config
@@ -427,25 +427,25 @@ class PaddleASRConnectionHanddler:
         stride = subsampling * decoding_chunk_size
 
         if self.cached_feat is None:
-            logger.info("no audio feat, please input more pcm data")
+            logger.debug("no audio feat, please input more pcm data")
             return
 
         # (B=1,T,D)
         num_frames = self.cached_feat.shape[1]
-        logger.info(
+        logger.debug(
             f"Required decoding window {decoding_window} frames, and the connection has {num_frames} frames"
         )
 
         # the cached feat must be larger decoding_window
         if num_frames < decoding_window and not is_finished:
-            logger.info(
+            logger.debug(
                 f"frame feat num is less than {decoding_window}, please input more pcm data"
             )
             return None, None
 
         # if is_finished=True, we need at least context frames
         if num_frames < context:
-            logger.info(
+            logger.debug(
                 "flast {num_frames} is less than context {context} frames, and we cannot do model forward"
             )
             return None, None
@@ -489,7 +489,7 @@ class PaddleASRConnectionHanddler:
             self.encoder_out = ys
         else:
             self.encoder_out = paddle.concat([self.encoder_out, ys], axis=1)
-        logger.info(
+        logger.debug(
             f"This connection handler encoder out shape: {self.encoder_out.shape}"
         )
 
@@ -513,7 +513,8 @@ class PaddleASRConnectionHanddler:
             if self.endpointer.endpoint_detected(ctc_probs.numpy(),
                                                  decoding_something):
                 self.endpoint_state = True
-                logger.info(f"Endpoint is detected at {self.num_frames} frame.")
+                logger.debug(
+                    f"Endpoint is detected at {self.num_frames} frame.")
 
         # advance cache of feat
         assert self.cached_feat.shape[0] == 1  #(B=1,T,D)
@@ -526,7 +527,7 @@ class PaddleASRConnectionHanddler:
     def update_result(self):
         """Conformer/Transformer hyps to result.
         """
-        logger.info("update the final result")
+        logger.debug("update the final result")
         hyps = self.hyps
 
         # output results and tokenids
@@ -560,16 +561,16 @@ class PaddleASRConnectionHanddler:
         only for conformer and transformer model.
         """
         if "deepspeech2" in self.model_type:
-            logger.info("deepspeech2 not support rescoring decoding.")
+            logger.debug("deepspeech2 not support rescoring decoding.")
             return
 
         if "attention_rescoring" != self.ctc_decode_config.decoding_method:
-            logger.info(
+            logger.debug(
                 f"decoding method not match: {self.ctc_decode_config.decoding_method}, need attention_rescoring"
             )
             return
 
-        logger.info("rescoring the final result")
+        logger.debug("rescoring the final result")
 
         # last decoding for last audio
         self.searcher.finalize_search()
@@ -685,7 +686,6 @@ class PaddleASRConnectionHanddler:
                 "bg": global_offset_in_sec + start,
                 "ed": global_offset_in_sec + end
             })
-            # logger.info(f"{word_time_stamp[-1]}")
 
         self.word_time_stamp = word_time_stamp
         logger.info(f"word time stamp: {self.word_time_stamp}")
@@ -707,13 +707,13 @@ class ASRServerExecutor(ASRExecutor):
 
             lm_url = self.task_resource.res_dict['lm_url']
             lm_md5 = self.task_resource.res_dict['lm_md5']
-            logger.info(f"Start to load language model {lm_url}")
+            logger.debug(f"Start to load language model {lm_url}")
             self.download_lm(
                 lm_url,
                 os.path.dirname(self.config.decode.lang_model_path), lm_md5)
         elif "conformer" in self.model_type or "transformer" in self.model_type:
             with UpdateConfig(self.config):
-                logger.info("start to create the stream conformer asr engine")
+                logger.debug("start to create the stream conformer asr engine")
                 # update the decoding method
                 if self.decode_method:
                     self.config.decode.decoding_method = self.decode_method
@@ -726,7 +726,7 @@ class ASRServerExecutor(ASRExecutor):
                 if self.config.decode.decoding_method not in [
                         "ctc_prefix_beam_search", "attention_rescoring"
                 ]:
-                    logger.info(
+                    logger.debug(
                         "we set the decoding_method to attention_rescoring")
                     self.config.decode.decoding_method = "attention_rescoring"
 
@@ -739,7 +739,7 @@ class ASRServerExecutor(ASRExecutor):
     def init_model(self) -> None:
         if "deepspeech2" in self.model_type:
             # AM predictor
-            logger.info("ASR engine start to init the am predictor")
+            logger.debug("ASR engine start to init the am predictor")
             self.am_predictor = init_predictor(
                 model_file=self.am_model,
                 params_file=self.am_params,
@@ -748,7 +748,7 @@ class ASRServerExecutor(ASRExecutor):
             # load model
             # model_type: {model_name}_{dataset}
             model_name = self.model_type[:self.model_type.rindex('_')]
-            logger.info(f"model name: {model_name}")
+            logger.debug(f"model name: {model_name}")
             model_class = self.task_resource.get_model_class(model_name)
             model = model_class.from_config(self.config)
             self.model = model
@@ -782,7 +782,7 @@ class ASRServerExecutor(ASRExecutor):
         self.num_decoding_left_chunks = num_decoding_left_chunks
         # conf for paddleinference predictor or onnx
         self.am_predictor_conf = am_predictor_conf
-        logger.info(f"model_type: {self.model_type}")
+        logger.debug(f"model_type: {self.model_type}")
 
         sample_rate_str = '16k' if sample_rate == 16000 else '8k'
         tag = model_type + '-' + lang + '-' + sample_rate_str
@@ -804,12 +804,12 @@ class ASRServerExecutor(ASRExecutor):
             self.res_path = os.path.dirname(
                 os.path.dirname(os.path.abspath(self.cfg_path)))
 
-        logger.info("Load the pretrained model:")
-        logger.info(f"  tag = {tag}")
-        logger.info(f"  res_path: {self.res_path}")
-        logger.info(f"  cfg path: {self.cfg_path}")
-        logger.info(f"  am_model path: {self.am_model}")
-        logger.info(f"  am_params path: {self.am_params}")
+        logger.debug("Load the pretrained model:")
+        logger.debug(f"  tag = {tag}")
+        logger.debug(f"  res_path: {self.res_path}")
+        logger.debug(f"  cfg path: {self.cfg_path}")
+        logger.debug(f"  am_model path: {self.am_model}")
+        logger.debug(f"  am_params path: {self.am_params}")
 
         #Init body.
         self.config = CfgNode(new_allowed=True)
@@ -818,7 +818,7 @@ class ASRServerExecutor(ASRExecutor):
         if self.config.spm_model_prefix:
             self.config.spm_model_prefix = os.path.join(
                 self.res_path, self.config.spm_model_prefix)
-            logger.info(f"spm model path: {self.config.spm_model_prefix}")
+            logger.debug(f"spm model path: {self.config.spm_model_prefix}")
 
         self.vocab = self.config.vocab_filepath
 
@@ -832,7 +832,7 @@ class ASRServerExecutor(ASRExecutor):
         # AM predictor
         self.init_model()
 
-        logger.info(f"create the {model_type} model success")
+        logger.debug(f"create the {model_type} model success")
         return True
 
 
@@ -883,7 +883,7 @@ class ASREngine(BaseEngine):
                 "If all GPU or XPU is used, you can set the server to 'cpu'")
             sys.exit(-1)
 
-        logger.info(f"paddlespeech_server set the device: {self.device}")
+        logger.debug(f"paddlespeech_server set the device: {self.device}")
 
         if not self.init_model():
             logger.error(
@@ -891,7 +891,9 @@ class ASREngine(BaseEngine):
             )
             return False
 
-        logger.info("Initialize ASR server engine successfully.")
+        logger.info("Initialize ASR server engine successfully on device: %s." %
+                    (self.device))
+
         return True
 
     def new_handler(self):

paddlespeech/server/engine/asr/paddleinference/asr_engine.py

@@ -65,10 +65,10 @@ class ASRServerExecutor(ASRExecutor):
                                          self.task_resource.res_dict['model'])
             self.am_params = os.path.join(self.res_path,
                                           self.task_resource.res_dict['params'])
-            logger.info(self.res_path)
-            logger.info(self.cfg_path)
-            logger.info(self.am_model)
-            logger.info(self.am_params)
+            logger.debug(self.res_path)
+            logger.debug(self.cfg_path)
+            logger.debug(self.am_model)
+            logger.debug(self.am_params)
         else:
             self.cfg_path = os.path.abspath(cfg_path)
             self.am_model = os.path.abspath(am_model)
@@ -236,16 +236,16 @@ class PaddleASRConnectionHandler(ASRServerExecutor):
         if self._check(
                 io.BytesIO(audio_data), self.asr_engine.config.sample_rate,
                 self.asr_engine.config.force_yes):
-            logger.info("start running asr engine")
+            logger.debug("start running asr engine")
             self.preprocess(self.asr_engine.config.model_type,
                             io.BytesIO(audio_data))
             st = time.time()
             self.infer(self.asr_engine.config.model_type)
             infer_time = time.time() - st
             self.output = self.postprocess()  # Retrieve result of asr.
-            logger.info("end inferring asr engine")
+            logger.debug("end inferring asr engine")
         else:
-            logger.info("file check failed!")
+            logger.error("file check failed!")
             self.output = None
 
         logger.info("inference time: {}".format(infer_time))

paddlespeech/server/engine/asr/python/asr_engine.py

@@ -104,7 +104,7 @@ class PaddleASRConnectionHandler(ASRServerExecutor):
             if self._check(
                     io.BytesIO(audio_data), self.asr_engine.config.sample_rate,
                     self.asr_engine.config.force_yes):
-                logger.info("start run asr engine")
+                logger.debug("start run asr engine")
                 self.preprocess(self.asr_engine.config.model,
                                 io.BytesIO(audio_data))
                 st = time.time()
@@ -112,7 +112,7 @@ class PaddleASRConnectionHandler(ASRServerExecutor):
                 infer_time = time.time() - st
                 self.output = self.postprocess()  # Retrieve result of asr.
             else:
-                logger.info("file check failed!")
+                logger.error("file check failed!")
                 self.output = None
 
             logger.info("inference time: {}".format(infer_time))

paddlespeech/server/engine/cls/paddleinference/cls_engine.py

@@ -67,22 +67,22 @@ class CLSServerExecutor(CLSExecutor):
             self.params_path = os.path.abspath(params_path)
             self.label_file = os.path.abspath(label_file)
 
-        logger.info(self.cfg_path)
-        logger.info(self.model_path)
-        logger.info(self.params_path)
-        logger.info(self.label_file)
+        logger.debug(self.cfg_path)
+        logger.debug(self.model_path)
+        logger.debug(self.params_path)
+        logger.debug(self.label_file)
 
         # config
         with open(self.cfg_path, 'r') as f:
             self._conf = yaml.safe_load(f)
-        logger.info("Read cfg file successfully.")
+        logger.debug("Read cfg file successfully.")
 
         # labels
         self._label_list = []
         with open(self.label_file, 'r') as f:
             for line in f:
                 self._label_list.append(line.strip())
-        logger.info("Read label file successfully.")
+        logger.debug("Read label file successfully.")
 
         # Create predictor
         self.predictor_conf = predictor_conf
@@ -90,7 +90,7 @@ class CLSServerExecutor(CLSExecutor):
             model_file=self.model_path,
             params_file=self.params_path,
             predictor_conf=self.predictor_conf)
-        logger.info("Create predictor successfully.")
+        logger.debug("Create predictor successfully.")
 
     @paddle.no_grad()
     def infer(self):
@@ -148,7 +148,8 @@ class CLSEngine(BaseEngine):
             logger.error(e)
             return False
 
-        logger.info("Initialize CLS server engine successfully.")
+        logger.info("Initialize CLS server engine successfully on device: %s." %
+                    (self.device))
         return True
 
 
@@ -160,7 +161,7 @@ class PaddleCLSConnectionHandler(CLSServerExecutor):
             cls_engine (CLSEngine): The CLS engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "Create PaddleCLSConnectionHandler to process the cls request")
 
         self._inputs = OrderedDict()
@@ -183,7 +184,7 @@ class PaddleCLSConnectionHandler(CLSServerExecutor):
         self.infer()
         infer_time = time.time() - st
 
-        logger.info("inference time: {}".format(infer_time))
+        logger.debug("inference time: {}".format(infer_time))
         logger.info("cls engine type: inference")
 
     def postprocess(self, topk: int):

paddlespeech/server/engine/cls/python/cls_engine.py

@@ -88,7 +88,7 @@ class PaddleCLSConnectionHandler(CLSServerExecutor):
             cls_engine (CLSEngine): The CLS engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "Create PaddleCLSConnectionHandler to process the cls request")
 
         self._inputs = OrderedDict()
@@ -110,7 +110,7 @@ class PaddleCLSConnectionHandler(CLSServerExecutor):
         self.infer()
         infer_time = time.time() - st
 
-        logger.info("inference time: {}".format(infer_time))
+        logger.debug("inference time: {}".format(infer_time))
         logger.info("cls engine type: python")
 
     def postprocess(self, topk: int):

paddlespeech/server/engine/engine_factory.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 from typing import Text
 
-from ..utils.log import logger
+from paddlespeech.cli.log import logger
 
 __all__ = ['EngineFactory']
 

paddlespeech/server/engine/engine_warmup.py

@@ -45,7 +45,7 @@ def warm_up(engine_and_type: str, warm_up_time: int=3) -> bool:
             logger.error("Please check tte engine type.")
 
         try:
-            logger.info("Start to warm up tts engine.")
+            logger.debug("Start to warm up tts engine.")
             for i in range(warm_up_time):
                 connection_handler = PaddleTTSConnectionHandler(tts_engine)
                 if flag_online:
@@ -53,7 +53,7 @@ def warm_up(engine_and_type: str, warm_up_time: int=3) -> bool:
                             text=sentence,
                             lang=tts_engine.lang,
                             am=tts_engine.config.am):
-                        logger.info(
+                        logger.debug(
                             f"The first response time of the {i} warm up: {connection_handler.first_response_time} s"
                         )
                         break
@@ -62,7 +62,7 @@ def warm_up(engine_and_type: str, warm_up_time: int=3) -> bool:
                     st = time.time()
                     connection_handler.infer(text=sentence)
                     et = time.time()
-                    logger.info(
+                    logger.debug(
                         f"The response time of the {i} warm up: {et - st} s")
         except Exception as e:
             logger.error("Failed to warm up on tts engine.")

paddlespeech/server/engine/text/python/text_engine.py

@@ -28,7 +28,7 @@ class PaddleTextConnectionHandler:
             text_engine (TextEngine): The Text engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "Create PaddleTextConnectionHandler to process the text request")
         self.text_engine = text_engine
         self.task = self.text_engine.executor.task
@@ -130,7 +130,7 @@ class TextEngine(BaseEngine):
         """The Text Engine
         """
         super(TextEngine, self).__init__()
-        logger.info("Create the TextEngine Instance")
+        logger.debug("Create the TextEngine Instance")
 
     def init(self, config: dict):
         """Init the Text Engine
@@ -141,7 +141,7 @@ class TextEngine(BaseEngine):
         Returns:
             bool: The engine instance flag
         """
-        logger.info("Init the text engine")
+        logger.debug("Init the text engine")
         try:
             self.config = config
             if self.config.device:
@@ -150,7 +150,7 @@ class TextEngine(BaseEngine):
                 self.device = paddle.get_device()
 
             paddle.set_device(self.device)
-            logger.info(f"Text Engine set the device: {self.device}")
+            logger.debug(f"Text Engine set the device: {self.device}")
         except BaseException as e:
             logger.error(
                 "Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
@@ -168,5 +168,6 @@ class TextEngine(BaseEngine):
             ckpt_path=config.ckpt_path,
             vocab_file=config.vocab_file)
 
-        logger.info("Init the text engine successfully")
+        logger.info("Initialize Text server engine successfully on device: %s."
+                    % (self.device))
         return True

paddlespeech/server/engine/tts/online/onnx/tts_engine.py

@@ -62,7 +62,7 @@ class TTSServerExecutor(TTSExecutor):
             (hasattr(self, 'am_encoder_infer_sess') and
              hasattr(self, 'am_decoder_sess') and hasattr(
                  self, 'am_postnet_sess'))) and hasattr(self, 'voc_inference'):
-            logger.info('Models had been initialized.')
+            logger.debug('Models had been initialized.')
             return
         # am
         am_tag = am + '-' + lang
@@ -85,8 +85,7 @@ class TTSServerExecutor(TTSExecutor):
             else:
                 self.am_ckpt = os.path.abspath(am_ckpt[0])
                 self.phones_dict = os.path.abspath(phones_dict)
-                self.am_res_path = os.path.dirname(
-                    os.path.abspath(am_ckpt))
+                self.am_res_path = os.path.dirname(os.path.abspath(am_ckpt))
 
             # create am sess
             self.am_sess = get_sess(self.am_ckpt, am_sess_conf)
@@ -119,8 +118,7 @@ class TTSServerExecutor(TTSExecutor):
                 self.am_postnet = os.path.abspath(am_ckpt[2])
                 self.phones_dict = os.path.abspath(phones_dict)
                 self.am_stat = os.path.abspath(am_stat)
-                self.am_res_path = os.path.dirname(
-                    os.path.abspath(am_ckpt[0]))
+                self.am_res_path = os.path.dirname(os.path.abspath(am_ckpt[0]))
 
             # create am sess
             self.am_encoder_infer_sess = get_sess(self.am_encoder_infer,
@@ -130,13 +128,13 @@ class TTSServerExecutor(TTSExecutor):
 
             self.am_mu, self.am_std = np.load(self.am_stat)
 
-        logger.info(f"self.phones_dict: {self.phones_dict}")
-        logger.info(f"am model dir: {self.am_res_path}")
-        logger.info("Create am sess successfully.")
+        logger.debug(f"self.phones_dict: {self.phones_dict}")
+        logger.debug(f"am model dir: {self.am_res_path}")
+        logger.debug("Create am sess successfully.")
 
         # voc model info
         voc_tag = voc + '-' + lang
-        
+
         if voc_ckpt is None:
             self.task_resource.set_task_model(
                 model_tag=voc_tag,
@@ -149,16 +147,16 @@ class TTSServerExecutor(TTSExecutor):
         else:
             self.voc_ckpt = os.path.abspath(voc_ckpt)
             self.voc_res_path = os.path.dirname(os.path.abspath(self.voc_ckpt))
-        logger.info(self.voc_res_path)
+        logger.debug(self.voc_res_path)
 
         # create voc sess
         self.voc_sess = get_sess(self.voc_ckpt, voc_sess_conf)
-        logger.info("Create voc sess successfully.")
+        logger.debug("Create voc sess successfully.")
 
         with open(self.phones_dict, "r") as f:
             phn_id = [line.strip().split() for line in f.readlines()]
         self.vocab_size = len(phn_id)
-        logger.info(f"vocab_size: {self.vocab_size}")
+        logger.debug(f"vocab_size: {self.vocab_size}")
 
         # frontend
         self.tones_dict = None
@@ -169,7 +167,7 @@ class TTSServerExecutor(TTSExecutor):
 
         elif lang == 'en':
             self.frontend = English(phone_vocab_path=self.phones_dict)
-        logger.info("frontend done!")
+        logger.debug("frontend done!")
 
 
 class TTSEngine(BaseEngine):
@@ -267,7 +265,7 @@ class PaddleTTSConnectionHandler:
             tts_engine (TTSEngine): The TTS engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "Create PaddleTTSConnectionHandler to process the tts request")
 
         self.tts_engine = tts_engine

paddlespeech/server/engine/tts/online/python/tts_engine.py

@@ -102,16 +102,22 @@ class TTSServerExecutor(TTSExecutor):
         Init model and other resources from a specific path.
         """
         if hasattr(self, 'am_inference') and hasattr(self, 'voc_inference'):
-            logger.info('Models had been initialized.')
+            logger.debug('Models had been initialized.')
             return
         # am model info
+        if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
+            use_pretrained_am = True
+        else:
+            use_pretrained_am = False
+
         am_tag = am + '-' + lang
         self.task_resource.set_task_model(
             model_tag=am_tag,
             model_type=0,  # am
+            skip_download=not use_pretrained_am,
             version=None,  # default version
         )
-        if am_ckpt is None or am_config is None or am_stat is None or phones_dict is None:
+        if use_pretrained_am:
             self.am_res_path = self.task_resource.res_dir
             self.am_config = os.path.join(self.am_res_path,
                                           self.task_resource.res_dict['config'])
@@ -122,29 +128,33 @@ class TTSServerExecutor(TTSExecutor):
             # must have phones_dict in acoustic
             self.phones_dict = os.path.join(
                 self.am_res_path, self.task_resource.res_dict['phones_dict'])
-            print("self.phones_dict:", self.phones_dict)
-            logger.info(self.am_res_path)
-            logger.info(self.am_config)
-            logger.info(self.am_ckpt)
+            logger.debug(self.am_res_path)
+            logger.debug(self.am_config)
+            logger.debug(self.am_ckpt)
         else:
             self.am_config = os.path.abspath(am_config)
             self.am_ckpt = os.path.abspath(am_ckpt)
             self.am_stat = os.path.abspath(am_stat)
             self.phones_dict = os.path.abspath(phones_dict)
             self.am_res_path = os.path.dirname(os.path.abspath(self.am_config))
-        print("self.phones_dict:", self.phones_dict)
 
         self.tones_dict = None
         self.speaker_dict = None
 
         # voc model info
+        if voc_ckpt is None or voc_config is None or voc_stat is None:
+            use_pretrained_voc = True
+        else:
+            use_pretrained_voc = False
+
         voc_tag = voc + '-' + lang
         self.task_resource.set_task_model(
             model_tag=voc_tag,
             model_type=1,  # vocoder
+            skip_download=not use_pretrained_voc,
             version=None,  # default version
         )
-        if voc_ckpt is None or voc_config is None or voc_stat is None:
+        if use_pretrained_voc:
             self.voc_res_path = self.task_resource.voc_res_dir
             self.voc_config = os.path.join(
                 self.voc_res_path, self.task_resource.voc_res_dict['config'])
@@ -153,9 +163,9 @@ class TTSServerExecutor(TTSExecutor):
             self.voc_stat = os.path.join(
                 self.voc_res_path,
                 self.task_resource.voc_res_dict['speech_stats'])
-            logger.info(self.voc_res_path)
-            logger.info(self.voc_config)
-            logger.info(self.voc_ckpt)
+            logger.debug(self.voc_res_path)
+            logger.debug(self.voc_config)
+            logger.debug(self.voc_ckpt)
         else:
             self.voc_config = os.path.abspath(voc_config)
             self.voc_ckpt = os.path.abspath(voc_ckpt)
@@ -172,7 +182,6 @@ class TTSServerExecutor(TTSExecutor):
         with open(self.phones_dict, "r") as f:
             phn_id = [line.strip().split() for line in f.readlines()]
         self.vocab_size = len(phn_id)
-        print("vocab_size:", self.vocab_size)
 
         # frontend
         if lang == 'zh':
@@ -182,7 +191,6 @@ class TTSServerExecutor(TTSExecutor):
 
         elif lang == 'en':
             self.frontend = English(phone_vocab_path=self.phones_dict)
-        print("frontend done!")
 
         # am infer info
         self.am_name = am[:am.rindex('_')]
@@ -197,7 +205,6 @@ class TTSServerExecutor(TTSExecutor):
                 self.am_name + '_inference')
             self.am_inference = am_inference_class(am_normalizer, am)
             self.am_inference.eval()
-        print("acoustic model done!")
 
         # voc infer info
         self.voc_name = voc[:voc.rindex('_')]
@@ -208,7 +215,6 @@ class TTSServerExecutor(TTSExecutor):
                                                                  '_inference')
         self.voc_inference = voc_inference_class(voc_normalizer, voc)
         self.voc_inference.eval()
-        print("voc done!")
 
 
 class TTSEngine(BaseEngine):
@@ -297,7 +303,7 @@ class PaddleTTSConnectionHandler:
             tts_engine (TTSEngine): The TTS engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "Create PaddleTTSConnectionHandler to process the tts request")
 
         self.tts_engine = tts_engine
@@ -357,7 +363,7 @@ class PaddleTTSConnectionHandler:
                 text, merge_sentences=merge_sentences)
             phone_ids = input_ids["phone_ids"]
         else:
-            print("lang should in {'zh', 'en'}!")
+            logger.error("lang should in {'zh', 'en'}!")
         frontend_et = time.time()
         self.frontend_time = frontend_et - frontend_st
 

paddlespeech/server/engine/tts/paddleinference/tts_engine.py

@@ -65,16 +65,22 @@ class TTSServerExecutor(TTSExecutor):
         Init model and other resources from a specific path.
         """
         if hasattr(self, 'am_predictor') and hasattr(self, 'voc_predictor'):
-            logger.info('Models had been initialized.')
+            logger.debug('Models had been initialized.')
             return
         # am
+        if am_model is None or am_params is None or phones_dict is None:
+            use_pretrained_am = True
+        else:
+            use_pretrained_am = False
+
         am_tag = am + '-' + lang
         self.task_resource.set_task_model(
             model_tag=am_tag,
             model_type=0,  # am
+            skip_download=not use_pretrained_am,
             version=None,  # default version
         )
-        if am_model is None or am_params is None or phones_dict is None:
+        if use_pretrained_am:
             self.am_res_path = self.task_resource.res_dir
             self.am_model = os.path.join(self.am_res_path,
                                          self.task_resource.res_dict['model'])
@@ -85,16 +91,16 @@ class TTSServerExecutor(TTSExecutor):
                 self.am_res_path, self.task_resource.res_dict['phones_dict'])
             self.am_sample_rate = self.task_resource.res_dict['sample_rate']
 
-            logger.info(self.am_res_path)
-            logger.info(self.am_model)
-            logger.info(self.am_params)
+            logger.debug(self.am_res_path)
+            logger.debug(self.am_model)
+            logger.debug(self.am_params)
         else:
             self.am_model = os.path.abspath(am_model)
             self.am_params = os.path.abspath(am_params)
             self.phones_dict = os.path.abspath(phones_dict)
             self.am_sample_rate = am_sample_rate
             self.am_res_path = os.path.dirname(os.path.abspath(self.am_model))
-        logger.info("self.phones_dict: {}".format(self.phones_dict))
+        logger.debug("self.phones_dict: {}".format(self.phones_dict))
 
         # for speedyspeech
         self.tones_dict = None
@@ -113,13 +119,19 @@ class TTSServerExecutor(TTSExecutor):
                 self.speaker_dict = speaker_dict
 
         # voc
+        if voc_model is None or voc_params is None:
+            use_pretrained_voc = True
+        else:
+            use_pretrained_voc = False
+
         voc_tag = voc + '-' + lang
         self.task_resource.set_task_model(
             model_tag=voc_tag,
             model_type=1,  # vocoder
+            skip_download=not use_pretrained_voc,
             version=None,  # default version
         )
-        if voc_model is None or voc_params is None:
+        if use_pretrained_voc:
             self.voc_res_path = self.task_resource.voc_res_dir
             self.voc_model = os.path.join(
                 self.voc_res_path, self.task_resource.voc_res_dict['model'])
@@ -127,9 +139,9 @@ class TTSServerExecutor(TTSExecutor):
                 self.voc_res_path, self.task_resource.voc_res_dict['params'])
             self.voc_sample_rate = self.task_resource.voc_res_dict[
                 'sample_rate']
-            logger.info(self.voc_res_path)
-            logger.info(self.voc_model)
-            logger.info(self.voc_params)
+            logger.debug(self.voc_res_path)
+            logger.debug(self.voc_model)
+            logger.debug(self.voc_params)
         else:
             self.voc_model = os.path.abspath(voc_model)
             self.voc_params = os.path.abspath(voc_params)
@@ -144,21 +156,21 @@ class TTSServerExecutor(TTSExecutor):
         with open(self.phones_dict, "r") as f:
             phn_id = [line.strip().split() for line in f.readlines()]
         vocab_size = len(phn_id)
-        logger.info("vocab_size: {}".format(vocab_size))
+        logger.debug("vocab_size: {}".format(vocab_size))
 
         tone_size = None
         if self.tones_dict:
             with open(self.tones_dict, "r") as f:
                 tone_id = [line.strip().split() for line in f.readlines()]
             tone_size = len(tone_id)
-            logger.info("tone_size: {}".format(tone_size))
+            logger.debug("tone_size: {}".format(tone_size))
 
         spk_num = None
         if self.speaker_dict:
             with open(self.speaker_dict, 'rt') as f:
                 spk_id = [line.strip().split() for line in f.readlines()]
             spk_num = len(spk_id)
-            logger.info("spk_num: {}".format(spk_num))
+            logger.debug("spk_num: {}".format(spk_num))
 
         # frontend
         if lang == 'zh':
@@ -168,7 +180,7 @@ class TTSServerExecutor(TTSExecutor):
 
         elif lang == 'en':
             self.frontend = English(phone_vocab_path=self.phones_dict)
-        logger.info("frontend done!")
+        logger.debug("frontend done!")
 
         # Create am predictor
         self.am_predictor_conf = am_predictor_conf
@@ -176,7 +188,7 @@ class TTSServerExecutor(TTSExecutor):
             model_file=self.am_model,
             params_file=self.am_params,
             predictor_conf=self.am_predictor_conf)
-        logger.info("Create AM predictor successfully.")
+        logger.debug("Create AM predictor successfully.")
 
         # Create voc predictor
         self.voc_predictor_conf = voc_predictor_conf
@@ -184,7 +196,7 @@ class TTSServerExecutor(TTSExecutor):
             model_file=self.voc_model,
             params_file=self.voc_params,
             predictor_conf=self.voc_predictor_conf)
-        logger.info("Create Vocoder predictor successfully.")
+        logger.debug("Create Vocoder predictor successfully.")
 
     @paddle.no_grad()
     def infer(self,
@@ -316,7 +328,8 @@ class TTSEngine(BaseEngine):
             logger.error(e)
             return False
 
-        logger.info("Initialize TTS server engine successfully.")
+        logger.info("Initialize TTS server engine successfully on device: %s." %
+                    (self.device))
         return True
 
 
@@ -328,7 +341,7 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
             tts_engine (TTSEngine): The TTS engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "Create PaddleTTSConnectionHandler to process the tts request")
 
         self.tts_engine = tts_engine
@@ -366,23 +379,23 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
         if target_fs == 0 or target_fs > original_fs:
             target_fs = original_fs
             wav_tar_fs = wav
-            logger.info(
+            logger.debug(
                 "The sample rate of synthesized audio is the same as model, which is {}Hz".
                 format(original_fs))
         else:
             wav_tar_fs = librosa.resample(
                 np.squeeze(wav), original_fs, target_fs)
-            logger.info(
+            logger.debug(
                 "The sample rate of model is {}Hz and the target sample rate is {}Hz. Converting the sample rate of the synthesized audio successfully.".
                 format(original_fs, target_fs))
         # transform volume
         wav_vol = wav_tar_fs * volume
-        logger.info("Transform the volume of the audio successfully.")
+        logger.debug("Transform the volume of the audio successfully.")
 
         # transform speed
         try:  # windows not support soxbindings
             wav_speed = change_speed(wav_vol, speed, target_fs)
-            logger.info("Transform the speed of the audio successfully.")
+            logger.debug("Transform the speed of the audio successfully.")
         except ServerBaseException:
             raise ServerBaseException(
                 ErrorCode.SERVER_INTERNAL_ERR,
@@ -399,7 +412,7 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
         wavfile.write(buf, target_fs, wav_speed)
         base64_bytes = base64.b64encode(buf.read())
         wav_base64 = base64_bytes.decode('utf-8')
-        logger.info("Audio to string successfully.")
+        logger.debug("Audio to string successfully.")
 
         # save audio
         if audio_path is not None:
@@ -487,15 +500,15 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
             logger.error(e)
             sys.exit(-1)
 
-        logger.info("AM model: {}".format(self.config.am))
-        logger.info("Vocoder model: {}".format(self.config.voc))
-        logger.info("Language: {}".format(lang))
+        logger.debug("AM model: {}".format(self.config.am))
+        logger.debug("Vocoder model: {}".format(self.config.voc))
+        logger.debug("Language: {}".format(lang))
         logger.info("tts engine type: python")
 
         logger.info("audio duration: {}".format(duration))
-        logger.info("frontend inference time: {}".format(self.frontend_time))
-        logger.info("AM inference time: {}".format(self.am_time))
-        logger.info("Vocoder inference time: {}".format(self.voc_time))
+        logger.debug("frontend inference time: {}".format(self.frontend_time))
+        logger.debug("AM inference time: {}".format(self.am_time))
+        logger.debug("Vocoder inference time: {}".format(self.voc_time))
         logger.info("total inference time: {}".format(infer_time))
         logger.info(
             "postprocess (change speed, volume, target sample rate) time: {}".
@@ -503,6 +516,6 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
         logger.info("total generate audio time: {}".format(infer_time +
                                                            postprocess_time))
         logger.info("RTF: {}".format(rtf))
-        logger.info("device: {}".format(self.tts_engine.device))
+        logger.debug("device: {}".format(self.tts_engine.device))
 
         return lang, target_sample_rate, duration, wav_base64

paddlespeech/server/engine/tts/python/tts_engine.py

@@ -105,7 +105,7 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
             tts_engine (TTSEngine): The TTS engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "Create PaddleTTSConnectionHandler to process the tts request")
 
         self.tts_engine = tts_engine
@@ -143,23 +143,23 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
         if target_fs == 0 or target_fs > original_fs:
             target_fs = original_fs
             wav_tar_fs = wav
-            logger.info(
+            logger.debug(
                 "The sample rate of synthesized audio is the same as model, which is {}Hz".
                 format(original_fs))
         else:
             wav_tar_fs = librosa.resample(
                 np.squeeze(wav), original_fs, target_fs)
-            logger.info(
+            logger.debug(
                 "The sample rate of model is {}Hz and the target sample rate is {}Hz. Converting the sample rate of the synthesized audio successfully.".
                 format(original_fs, target_fs))
         # transform volume
         wav_vol = wav_tar_fs * volume
-        logger.info("Transform the volume of the audio successfully.")
+        logger.debug("Transform the volume of the audio successfully.")
 
         # transform speed
         try:  # windows not support soxbindings
             wav_speed = change_speed(wav_vol, speed, target_fs)
-            logger.info("Transform the speed of the audio successfully.")
+            logger.debug("Transform the speed of the audio successfully.")
         except ServerBaseException:
             raise ServerBaseException(
                 ErrorCode.SERVER_INTERNAL_ERR,
@@ -176,7 +176,7 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
         wavfile.write(buf, target_fs, wav_speed)
         base64_bytes = base64.b64encode(buf.read())
         wav_base64 = base64_bytes.decode('utf-8')
-        logger.info("Audio to string successfully.")
+        logger.debug("Audio to string successfully.")
 
         # save audio
         if audio_path is not None:
@@ -264,15 +264,15 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
             logger.error(e)
             sys.exit(-1)
 
-        logger.info("AM model: {}".format(self.config.am))
-        logger.info("Vocoder model: {}".format(self.config.voc))
-        logger.info("Language: {}".format(lang))
+        logger.debug("AM model: {}".format(self.config.am))
+        logger.debug("Vocoder model: {}".format(self.config.voc))
+        logger.debug("Language: {}".format(lang))
         logger.info("tts engine type: python")
 
         logger.info("audio duration: {}".format(duration))
-        logger.info("frontend inference time: {}".format(self.frontend_time))
-        logger.info("AM inference time: {}".format(self.am_time))
-        logger.info("Vocoder inference time: {}".format(self.voc_time))
+        logger.debug("frontend inference time: {}".format(self.frontend_time))
+        logger.debug("AM inference time: {}".format(self.am_time))
+        logger.debug("Vocoder inference time: {}".format(self.voc_time))
         logger.info("total inference time: {}".format(infer_time))
         logger.info(
             "postprocess (change speed, volume, target sample rate) time: {}".
@@ -280,6 +280,6 @@ class PaddleTTSConnectionHandler(TTSServerExecutor):
         logger.info("total generate audio time: {}".format(infer_time +
                                                            postprocess_time))
         logger.info("RTF: {}".format(rtf))
-        logger.info("device: {}".format(self.tts_engine.device))
+        logger.debug("device: {}".format(self.tts_engine.device))
 
         return lang, target_sample_rate, duration, wav_base64

paddlespeech/server/engine/vector/python/vector_engine.py

@@ -33,7 +33,7 @@ class PaddleVectorConnectionHandler:
             vector_engine (VectorEngine): The Vector engine
         """
         super().__init__()
-        logger.info(
+        logger.debug(
             "Create PaddleVectorConnectionHandler to process the vector request")
         self.vector_engine = vector_engine
         self.executor = self.vector_engine.executor
@@ -54,7 +54,7 @@ class PaddleVectorConnectionHandler:
         Returns:
             str: the punctuation text
         """
-        logger.info(
+        logger.debug(
             f"start to extract the do vector {self.task} from the http request")
         if self.task == "spk" and task == "spk":
             embedding = self.extract_audio_embedding(audio_data)
@@ -81,17 +81,17 @@ class PaddleVectorConnectionHandler:
         Returns:
             float: the score between enroll and test audio
         """
-        logger.info("start to extract the enroll audio embedding")
+        logger.debug("start to extract the enroll audio embedding")
         enroll_emb = self.extract_audio_embedding(enroll_audio)
 
-        logger.info("start to extract the test audio embedding")
+        logger.debug("start to extract the test audio embedding")
         test_emb = self.extract_audio_embedding(test_audio)
 
-        logger.info(
+        logger.debug(
             "start to get the score between the enroll and test embedding")
         score = self.executor.get_embeddings_score(enroll_emb, test_emb)
 
-        logger.info(f"get the enroll vs test score: {score}")
+        logger.debug(f"get the enroll vs test score: {score}")
         return score
 
     @paddle.no_grad()
@@ -106,11 +106,12 @@ class PaddleVectorConnectionHandler:
         # because the soundfile will change the io.BytesIO(audio) to the end
         # thus we should convert the base64 string to io.BytesIO when we need the audio data
         if not self.executor._check(io.BytesIO(audio), sample_rate):
-            logger.info("check the audio sample rate occurs error")
+            logger.debug("check the audio sample rate occurs error")
             return np.array([0.0])
 
         waveform, sr = load_audio(io.BytesIO(audio))
-        logger.info(f"load the audio sample points, shape is: {waveform.shape}")
+        logger.debug(
+            f"load the audio sample points, shape is: {waveform.shape}")
 
         # stage 2: get the audio feat
         # Note: Now we only support fbank feature
@@ -121,9 +122,9 @@ class PaddleVectorConnectionHandler:
                 n_mels=self.config.n_mels,
                 window_size=self.config.window_size,
                 hop_length=self.config.hop_size)
-            logger.info(f"extract the audio feats, shape is: {feats.shape}")
+            logger.debug(f"extract the audio feats, shape is: {feats.shape}")
         except Exception as e:
-            logger.info(f"feats occurs exception {e}")
+            logger.error(f"feats occurs exception {e}")
             sys.exit(-1)
 
         feats = paddle.to_tensor(feats).unsqueeze(0)
@@ -159,7 +160,7 @@ class VectorEngine(BaseEngine):
         """The Vector Engine
         """
         super(VectorEngine, self).__init__()
-        logger.info("Create the VectorEngine Instance")
+        logger.debug("Create the VectorEngine Instance")
 
     def init(self, config: dict):
         """Init the Vector Engine
@@ -170,7 +171,7 @@ class VectorEngine(BaseEngine):
         Returns:
             bool: The engine instance flag
         """
-        logger.info("Init the vector engine")
+        logger.debug("Init the vector engine")
         try:
             self.config = config
             if self.config.device:
@@ -179,7 +180,7 @@ class VectorEngine(BaseEngine):
                 self.device = paddle.get_device()
 
             paddle.set_device(self.device)
-            logger.info(f"Vector Engine set the device: {self.device}")
+            logger.debug(f"Vector Engine set the device: {self.device}")
         except BaseException as e:
             logger.error(
                 "Set device failed, please check if device is already used and the parameter 'device' in the yaml file"
@@ -196,5 +197,7 @@ class VectorEngine(BaseEngine):
             ckpt_path=config.ckpt_path,
             task=config.task)
 
-        logger.info("Init the Vector engine successfully")
+        logger.info(
+            "Initialize Vector server engine successfully on device: %s." %
+            (self.device))
         return True

paddlespeech/server/utils/audio_handler.py

@@ -138,7 +138,7 @@ class ASRWsAudioHandler:
         Returns:
             str: the final asr result
         """
-        logging.info("send a message to the server")
+        logging.debug("send a message to the server")
 
         if self.url is None:
             logger.error("No asr server, please input valid ip and port")
@@ -160,7 +160,7 @@ class ASRWsAudioHandler:
                 separators=(',', ': '))
             await ws.send(audio_info)
             msg = await ws.recv()
-            logger.info("client receive msg={}".format(msg))
+            logger.debug("client receive msg={}".format(msg))
 
             # 3. send chunk audio data to engine
             for chunk_data in self.read_wave(wavfile_path):
@@ -170,7 +170,7 @@ class ASRWsAudioHandler:
 
                 if self.punc_server and len(msg["result"]) > 0:
                     msg["result"] = self.punc_server.run(msg["result"])
-                logger.info("client receive msg={}".format(msg))
+                logger.debug("client receive msg={}".format(msg))
 
             # 4. we must send finished signal to the server
             audio_info = json.dumps(
@@ -310,7 +310,7 @@ class TTSWsHandler:
             start_request = json.dumps({"task": "tts", "signal": "start"})
             await ws.send(start_request)
             msg = await ws.recv()
-            logger.info(f"client receive msg={msg}")
+            logger.debug(f"client receive msg={msg}")
             msg = json.loads(msg)
             session = msg["session"]
 
@@ -319,7 +319,7 @@ class TTSWsHandler:
             request = json.dumps({"text": text_base64})
             st = time.time()
             await ws.send(request)
-            logging.info("send a message to the server")
+            logging.debug("send a message to the server")
 
             # 4. Process the received response
             message = await ws.recv()
@@ -543,7 +543,6 @@ class VectorHttpHandler:
             "sample_rate": sample_rate,
         }
 
-        logger.info(self.url)
         res = requests.post(url=self.url, data=json.dumps(data))
 
         return res.json()

paddlespeech/server/utils/audio_process.py

@@ -169,7 +169,7 @@ def save_audio(bytes_data, audio_path, sample_rate: int=24000) -> bool:
             sample_rate=sample_rate)
         os.remove("./tmp.pcm")
     else:
-        print("Only supports saved audio format is pcm or wav")
+        logger.error("Only supports saved audio format is pcm or wav")
         return False
 
     return True

paddlespeech/server/utils/log.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import functools
-import logging
-
-__all__ = [
-    'logger',
-]
-
-
-class Logger(object):
-    def __init__(self, name: str=None):
-        name = 'PaddleSpeech' if not name else name
-        self.logger = logging.getLogger(name)
-
-        log_config = {
-            'DEBUG': 10,
-            'INFO': 20,
-            'TRAIN': 21,
-            'EVAL': 22,
-            'WARNING': 30,
-            'ERROR': 40,
-            'CRITICAL': 50,
-            'EXCEPTION': 100,
-        }
-        for key, level in log_config.items():
-            logging.addLevelName(level, key)
-            if key == 'EXCEPTION':
-                self.__dict__[key.lower()] = self.logger.exception
-            else:
-                self.__dict__[key.lower()] = functools.partial(self.__call__,
-                                                               level)
-
-        self.format = logging.Formatter(
-            fmt='[%(asctime)-15s] [%(levelname)8s] - %(message)s')
-
-        self.handler = logging.StreamHandler()
-        self.handler.setFormatter(self.format)
-
-        self.logger.addHandler(self.handler)
-        self.logger.setLevel(logging.DEBUG)
-        self.logger.propagate = False
-
-    def __call__(self, log_level: str, msg: str):
-        self.logger.log(log_level, msg)
-
-
-logger = Logger()

paddlespeech/server/utils/onnx_infer.py

@@ -16,11 +16,11 @@ from typing import Optional
 
 import onnxruntime as ort
 
-from .log import logger
+from paddlespeech.cli.log import logger
 
 
 def get_sess(model_path: Optional[os.PathLike]=None, sess_conf: dict=None):
-    logger.info(f"ort sessconf: {sess_conf}")
+    logger.debug(f"ort sessconf: {sess_conf}")
     sess_options = ort.SessionOptions()
     sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
     if sess_conf.get('graph_optimization_level', 99) == 0:
@@ -34,7 +34,7 @@ def get_sess(model_path: Optional[os.PathLike]=None, sess_conf: dict=None):
         # fastspeech2/mb_melgan can't use trt now!
         if sess_conf.get("use_trt", 0):
             providers = ['TensorrtExecutionProvider']
-    logger.info(f"ort providers: {providers}")
+    logger.debug(f"ort providers: {providers}")
 
     if 'cpu_threads' in sess_conf:
         sess_options.intra_op_num_threads = sess_conf.get("cpu_threads", 0)

paddlespeech/server/utils/util.py

@@ -13,6 +13,8 @@
 import base64
 import math
 
+from paddlespeech.cli.log import logger
+
 
 def wav2base64(wav_file: str):
     """
@@ -61,7 +63,7 @@ def get_chunks(data, block_size, pad_size, step):
     elif step == "voc":
         data_len = data.shape[0]
     else:
-        print("Please set correct type to get chunks, am or voc")
+        logger.error("Please set correct type to get chunks, am or voc")
 
     chunks = []
     n = math.ceil(data_len / block_size)
@@ -73,7 +75,7 @@ def get_chunks(data, block_size, pad_size, step):
         elif step == "voc":
             chunks.append(data[start:end, :])
         else:
-            print("Please set correct type to get chunks, am or voc")
+            logger.error("Please set correct type to get chunks, am or voc")
     return chunks
 
 

paddlespeech/t2s/models/fastspeech2/fastspeech2.py

@@ -141,71 +141,133 @@ class FastSpeech2(nn.Layer):
             init_dec_alpha: float=1.0, ):
         """Initialize FastSpeech2 module.
         Args:
-            idim (int): Dimension of the inputs.
-            odim (int): Dimension of the outputs.
-            adim (int): Attention dimension.
-            aheads (int): Number of attention heads.
-            elayers (int): Number of encoder layers.
-            eunits (int): Number of encoder hidden units.
-            dlayers (int): Number of decoder layers.
-            dunits (int): Number of decoder hidden units.
-            postnet_layers (int): Number of postnet layers.
-            postnet_chans (int): Number of postnet channels.
-            postnet_filts (int): Kernel size of postnet.
-            postnet_dropout_rate (float): Dropout rate in postnet.
-            use_scaled_pos_enc (bool): Whether to use trainable scaled pos encoding.
-            use_batch_norm (bool): Whether to use batch normalization in encoder prenet.
-            encoder_normalize_before (bool): Whether to apply layernorm layer before encoder block.
-            decoder_normalize_before (bool): Whether to apply layernorm layer before decoder block.
-            encoder_concat_after (bool): Whether to concatenate attention layer's input and output in encoder.
-            decoder_concat_after (bool): Whether to concatenate attention layer's input  and output in decoder.
-            reduction_factor (int): Reduction factor.
-            encoder_type (str): Encoder type ("transformer" or "conformer").
-            decoder_type (str): Decoder type ("transformer" or "conformer").
-            transformer_enc_dropout_rate (float): Dropout rate in encoder except attention and positional encoding.
-            transformer_enc_positional_dropout_rate (float): Dropout rate after encoder positional encoding.
-            transformer_enc_attn_dropout_rate (float): Dropout rate in encoder self-attention module.
-            transformer_dec_dropout_rate (float): Dropout rate in decoder except attention & positional encoding.
-            transformer_dec_positional_dropout_rate (float): Dropout rate after decoder positional encoding.
-            transformer_dec_attn_dropout_rate (float): Dropout rate in decoder self-attention module.
-            conformer_pos_enc_layer_type (str): Pos encoding layer type in conformer.
-            conformer_self_attn_layer_type (str): Self-attention layer type in conformer
-            conformer_activation_type (str): Activation function type in conformer.
-            use_macaron_style_in_conformer (bool): Whether to use macaron style FFN.
-            use_cnn_in_conformer (bool): Whether to use CNN in conformer.
-            zero_triu (bool): Whether to use zero triu in relative self-attention module.
-            conformer_enc_kernel_size (int): Kernel size of encoder conformer.
-            conformer_dec_kernel_size (int): Kernel size of decoder conformer.
-            duration_predictor_layers (int): Number of duration predictor layers.
-            duration_predictor_chans (int): Number of duration predictor channels.
-            duration_predictor_kernel_size (int): Kernel size of duration predictor.
-            duration_predictor_dropout_rate (float): Dropout rate in duration predictor.
-            pitch_predictor_layers (int): Number of pitch predictor layers.
-            pitch_predictor_chans (int): Number of pitch predictor channels.
-            pitch_predictor_kernel_size (int): Kernel size of pitch predictor.
-            pitch_predictor_dropout_rate (float): Dropout rate in pitch predictor.
-            pitch_embed_kernel_size (float): Kernel size of pitch embedding.
-            pitch_embed_dropout_rate (float): Dropout rate for pitch embedding.
-            stop_gradient_from_pitch_predictor (bool): Whether to stop gradient from pitch predictor to encoder.
-            energy_predictor_layers (int): Number of energy predictor layers.
-            energy_predictor_chans (int): Number of energy predictor channels.
-            energy_predictor_kernel_size (int): Kernel size of energy predictor.
-            energy_predictor_dropout_rate (float): Dropout rate in energy predictor.
-            energy_embed_kernel_size (float): Kernel size of energy embedding.
-            energy_embed_dropout_rate (float): Dropout rate for energy embedding.
-            stop_gradient_from_energy_predictor（bool): Whether to stop gradient from energy predictor to encoder.
-            spk_num (Optional[int]): Number of speakers. If not None, assume that the spk_embed_dim is not None,
+            idim (int): 
+                Dimension of the inputs.
+            odim (int): 
+                Dimension of the outputs.
+            adim (int): 
+                Attention dimension.
+            aheads (int): 
+                Number of attention heads.
+            elayers (int): 
+                Number of encoder layers.
+            eunits (int): 
+                Number of encoder hidden units.
+            dlayers (int): 
+                Number of decoder layers.
+            dunits (int): 
+                Number of decoder hidden units.
+            postnet_layers (int): 
+                Number of postnet layers.
+            postnet_chans (int): 
+                Number of postnet channels.
+            postnet_filts (int): 
+                Kernel size of postnet.
+            postnet_dropout_rate (float): 
+                Dropout rate in postnet.
+            use_scaled_pos_enc (bool): 
+                Whether to use trainable scaled pos encoding.
+            use_batch_norm (bool): 
+                Whether to use batch normalization in encoder prenet.
+            encoder_normalize_before (bool): 
+                Whether to apply layernorm layer before encoder block.
+            decoder_normalize_before (bool): 
+                Whether to apply layernorm layer before decoder block.
+            encoder_concat_after (bool): 
+                Whether to concatenate attention layer's input and output in encoder.
+            decoder_concat_after (bool): 
+                Whether to concatenate attention layer's input  and output in decoder.
+            reduction_factor (int): 
+                Reduction factor.
+            encoder_type (str): 
+                Encoder type ("transformer" or "conformer").
+            decoder_type (str): 
+                Decoder type ("transformer" or "conformer").
+            transformer_enc_dropout_rate (float): 
+                Dropout rate in encoder except attention and positional encoding.
+            transformer_enc_positional_dropout_rate (float): 
+                Dropout rate after encoder positional encoding.
+            transformer_enc_attn_dropout_rate (float): 
+                Dropout rate in encoder self-attention module.
+            transformer_dec_dropout_rate (float): 
+                Dropout rate in decoder except attention & positional encoding.
+            transformer_dec_positional_dropout_rate (float):
+                Dropout rate after decoder positional encoding.
+            transformer_dec_attn_dropout_rate (float): 
+                Dropout rate in decoder self-attention module.
+            conformer_pos_enc_layer_type (str): 
+                Pos encoding layer type in conformer.
+            conformer_self_attn_layer_type (str): 
+                Self-attention layer type in conformer
+            conformer_activation_type (str): 
+                Activation function type in conformer.
+            use_macaron_style_in_conformer (bool): 
+                Whether to use macaron style FFN.
+            use_cnn_in_conformer (bool): 
+                Whether to use CNN in conformer.
+            zero_triu (bool): 
+                Whether to use zero triu in relative self-attention module.
+            conformer_enc_kernel_size (int): 
+                Kernel size of encoder conformer.
+            conformer_dec_kernel_size (int): 
+                Kernel size of decoder conformer.
+            duration_predictor_layers (int): 
+                Number of duration predictor layers.
+            duration_predictor_chans (int): 
+                Number of duration predictor channels.
+            duration_predictor_kernel_size (int): 
+                Kernel size of duration predictor.
+            duration_predictor_dropout_rate (float): 
+                Dropout rate in duration predictor.
+            pitch_predictor_layers (int): 
+                Number of pitch predictor layers.
+            pitch_predictor_chans (int):
+                Number of pitch predictor channels.
+            pitch_predictor_kernel_size (int): 
+                Kernel size of pitch predictor.
+            pitch_predictor_dropout_rate (float): 
+                Dropout rate in pitch predictor.
+            pitch_embed_kernel_size (float): 
+                Kernel size of pitch embedding.
+            pitch_embed_dropout_rate (float): 
+                Dropout rate for pitch embedding.
+            stop_gradient_from_pitch_predictor (bool): 
+                Whether to stop gradient from pitch predictor to encoder.
+            energy_predictor_layers (int): 
+                Number of energy predictor layers.
+            energy_predictor_chans (int): 
+                Number of energy predictor channels.
+            energy_predictor_kernel_size (int): 
+                Kernel size of energy predictor.
+            energy_predictor_dropout_rate (float): 
+                Dropout rate in energy predictor.
+            energy_embed_kernel_size (float): 
+                Kernel size of energy embedding.
+            energy_embed_dropout_rate (float): 
+                Dropout rate for energy embedding.
+            stop_gradient_from_energy_predictor（bool): 
+                Whether to stop gradient from energy predictor to encoder.
+            spk_num (Optional[int]): 
+                Number of speakers. If not None, assume that the spk_embed_dim is not None,
                 spk_ids will be provided as the input and use spk_embedding_table.
-            spk_embed_dim (Optional[int]): Speaker embedding dimension. If not None, 
+            spk_embed_dim (Optional[int]): 
+                Speaker embedding dimension. If not None, 
                 assume that spk_emb will be provided as the input or spk_num is not None.
-            spk_embed_integration_type (str): How to integrate speaker embedding.
-            tone_num (Optional[int]): Number of tones. If not None, assume that the
+            spk_embed_integration_type (str): 
+                How to integrate speaker embedding.
+            tone_num (Optional[int]): 
+                Number of tones. If not None, assume that the
                 tone_ids will be provided as the input and use tone_embedding_table.
-            tone_embed_dim (Optional[int]): Tone embedding dimension. If not None, assume that tone_num is not None.
-            tone_embed_integration_type (str): How to integrate tone embedding.
-            init_type (str): How to initialize transformer parameters.
-            init_enc_alpha （float): Initial value of alpha in scaled pos encoding of the encoder.
-            init_dec_alpha (float): Initial value of alpha in scaled pos encoding of the decoder.
+            tone_embed_dim (Optional[int]):
+                Tone embedding dimension. If not None, assume that tone_num is not None.
+            tone_embed_integration_type (str): 
+                How to integrate tone embedding.
+            init_type (str): 
+                How to initialize transformer parameters.
+            init_enc_alpha （float): 
+                Initial value of alpha in scaled pos encoding of the encoder.
+            init_dec_alpha (float): 
+                Initial value of alpha in scaled pos encoding of the decoder.
     
         """
         assert check_argument_types()
@@ -258,7 +320,6 @@ class FastSpeech2(nn.Layer):
             padding_idx=self.padding_idx)
 
         if encoder_type == "transformer":
-            print("encoder_type is transformer")
             self.encoder = TransformerEncoder(
                 idim=idim,
                 attention_dim=adim,
@@ -275,7 +336,6 @@ class FastSpeech2(nn.Layer):
                 positionwise_layer_type=positionwise_layer_type,
                 positionwise_conv_kernel_size=positionwise_conv_kernel_size, )
         elif encoder_type == "conformer":
-            print("encoder_type is conformer")
             self.encoder = ConformerEncoder(
                 idim=idim,
                 attention_dim=adim,
@@ -362,7 +422,6 @@ class FastSpeech2(nn.Layer):
         # NOTE: we use encoder as decoder
         # because fastspeech's decoder is the same as encoder
         if decoder_type == "transformer":
-            print("decoder_type is transformer")
             self.decoder = TransformerEncoder(
                 idim=0,
                 attention_dim=adim,
@@ -380,7 +439,6 @@ class FastSpeech2(nn.Layer):
                 positionwise_layer_type=positionwise_layer_type,
                 positionwise_conv_kernel_size=positionwise_conv_kernel_size, )
         elif decoder_type == "conformer":
-            print("decoder_type is conformer")
             self.decoder = ConformerEncoder(
                 idim=0,
                 attention_dim=adim,
@@ -453,20 +511,29 @@ class FastSpeech2(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            text(Tensor(int64)): Batch of padded token ids (B, Tmax).
-            text_lengths(Tensor(int64)): Batch of lengths of each input (B,).
-            speech(Tensor): Batch of padded target features (B, Lmax, odim).
-            speech_lengths(Tensor(int64)): Batch of the lengths of each target (B,).
-            durations(Tensor(int64)): Batch of padded durations (B, Tmax).
-            pitch(Tensor): Batch of padded token-averaged pitch (B, Tmax, 1).
-            energy(Tensor): Batch of padded token-averaged energy (B, Tmax, 1).
-            tone_id(Tensor, optional(int64)): Batch of padded tone ids  (B, Tmax).
-            spk_emb(Tensor, optional): Batch of speaker embeddings (B, spk_embed_dim).
-            spk_id(Tnesor, optional(int64)): Batch of speaker ids (B,)
+            text(Tensor(int64)): 
+                Batch of padded token ids (B, Tmax).
+            text_lengths(Tensor(int64)): 
+                Batch of lengths of each input (B,).
+            speech(Tensor): 
+                Batch of padded target features (B, Lmax, odim).
+            speech_lengths(Tensor(int64)): 
+                Batch of the lengths of each target (B,).
+            durations(Tensor(int64)): 
+                Batch of padded durations (B, Tmax).
+            pitch(Tensor): 
+                Batch of padded token-averaged pitch (B, Tmax, 1).
+            energy(Tensor): 
+                Batch of padded token-averaged energy (B, Tmax, 1).
+            tone_id(Tensor, optional(int64)): 
+                Batch of padded tone ids  (B, Tmax).
+            spk_emb(Tensor, optional): 
+                Batch of speaker embeddings (B, spk_embed_dim).
+            spk_id(Tnesor, optional(int64)): 
+                Batch of speaker ids (B,)
 
         Returns:
 
-        
         """
 
         # input of embedding must be int64
@@ -662,20 +729,28 @@ class FastSpeech2(nn.Layer):
         """Generate the sequence of features given the sequences of characters.
 
         Args:
-            text(Tensor(int64)): Input sequence of characters (T,).
-            durations(Tensor, optional (int64)): Groundtruth of duration (T,).
-            pitch(Tensor, optional): Groundtruth of token-averaged pitch (T, 1).
-            energy(Tensor, optional): Groundtruth of token-averaged energy (T, 1).
-            alpha(float, optional): Alpha to control the speed.
-            use_teacher_forcing(bool, optional): Whether to use teacher forcing.
+            text(Tensor(int64)): 
+                Input sequence of characters (T,).
+            durations(Tensor, optional (int64)): 
+                Groundtruth of duration (T,).
+            pitch(Tensor, optional): 
+                Groundtruth of token-averaged pitch (T, 1).
+            energy(Tensor, optional): 
+                Groundtruth of token-averaged energy (T, 1).
+            alpha(float, optional): 
+                Alpha to control the speed.
+            use_teacher_forcing(bool, optional): 
+                Whether to use teacher forcing.
                 If true, groundtruth of duration, pitch and energy will be used.
-            spk_emb(Tensor, optional, optional): peaker embedding vector (spk_embed_dim,). (Default value = None)
-            spk_id(Tensor, optional(int64), optional): spk ids (1,). (Default value = None)
-            tone_id(Tensor, optional(int64), optional): tone ids (T,). (Default value = None)
+            spk_emb(Tensor, optional, optional): 
+                peaker embedding vector (spk_embed_dim,). (Default value = None)
+            spk_id(Tensor, optional(int64), optional): 
+                spk ids (1,). (Default value = None)
+            tone_id(Tensor, optional(int64), optional): 
+                tone ids (T,). (Default value = None)
 
         Returns:
 
-        
         """
         # input of embedding must be int64
         x = paddle.cast(text, 'int64')
@@ -724,8 +799,10 @@ class FastSpeech2(nn.Layer):
         """Integrate speaker embedding with hidden states.
 
         Args:
-            hs(Tensor): Batch of hidden state sequences (B, Tmax, adim).
-            spk_emb(Tensor): Batch of speaker embeddings (B, spk_embed_dim).
+            hs(Tensor): 
+                Batch of hidden state sequences (B, Tmax, adim).
+            spk_emb(Tensor): 
+                Batch of speaker embeddings (B, spk_embed_dim).
 
         Returns:
 
@@ -749,8 +826,10 @@ class FastSpeech2(nn.Layer):
         """Integrate speaker embedding with hidden states.
 
         Args:
-            hs(Tensor): Batch of hidden state sequences (B, Tmax, adim).
-            tone_embs(Tensor): Batch of speaker embeddings (B, Tmax, tone_embed_dim).
+            hs(Tensor): 
+                Batch of hidden state sequences (B, Tmax, adim).
+            tone_embs(Tensor): 
+                Batch of speaker embeddings (B, Tmax, tone_embed_dim).
 
         Returns:
 
@@ -773,10 +852,12 @@ class FastSpeech2(nn.Layer):
         """Make masks for self-attention.
 
         Args:
-            ilens(Tensor): Batch of lengths (B,).
+            ilens(Tensor): 
+                Batch of lengths (B,).
 
         Returns:
-            Tensor: Mask tensor for self-attention. dtype=paddle.bool
+            Tensor: 
+                Mask tensor for self-attention. dtype=paddle.bool
 
         Examples:
             >>> ilens = [5, 3]
@@ -858,19 +939,32 @@ class StyleFastSpeech2Inference(FastSpeech2Inference):
         """
 
         Args:
-            text(Tensor(int64)): Input sequence of characters (T,).
-            durations(paddle.Tensor/np.ndarray, optional (int64)): Groundtruth of duration (T,), this will overwrite the set of durations_scale and durations_bias
+            text(Tensor(int64)): 
+                Input sequence of characters (T,).
+            durations(paddle.Tensor/np.ndarray, optional (int64)): 
+                Groundtruth of duration (T,), this will overwrite the set of durations_scale and durations_bias
             durations_scale(int/float, optional): 
+
             durations_bias(int/float, optional): 
-            pitch(paddle.Tensor/np.ndarray, optional): Groundtruth of token-averaged pitch (T, 1), this will overwrite the set of pitch_scale and pitch_bias
-            pitch_scale(int/float, optional): In denormed HZ domain.
-            pitch_bias(int/float, optional): In denormed HZ domain.
-            energy(paddle.Tensor/np.ndarray, optional): Groundtruth of token-averaged energy (T, 1), this will overwrite the set of energy_scale and energy_bias
-            energy_scale(int/float, optional): In denormed domain.
-            energy_bias(int/float, optional): In denormed domain.
-            robot: bool:  (Default value = False)
-            spk_emb: (Default value = None)
-            spk_id: (Default value = None)
+
+            pitch(paddle.Tensor/np.ndarray, optional): 
+                Groundtruth of token-averaged pitch (T, 1), this will overwrite the set of pitch_scale and pitch_bias
+            pitch_scale(int/float, optional): 
+                In denormed HZ domain.
+            pitch_bias(int/float, optional): 
+                In denormed HZ domain.
+            energy(paddle.Tensor/np.ndarray, optional): 
+                Groundtruth of token-averaged energy (T, 1), this will overwrite the set of energy_scale and energy_bias
+            energy_scale(int/float, optional): 
+                In denormed domain.
+            energy_bias(int/float, optional): 
+                In denormed domain.
+            robot(bool) (Default value = False):
+
+            spk_emb(Default value = None):
+
+            spk_id(Default value = None):
+
 
         Returns:
             Tensor: logmel
@@ -949,8 +1043,10 @@ class FastSpeech2Loss(nn.Layer):
                  use_weighted_masking: bool=False):
         """Initialize feed-forward Transformer loss module.
         Args:
-            use_masking (bool): Whether to apply masking for padded part in loss calculation.
-            use_weighted_masking (bool): Whether to weighted masking in loss calculation.
+            use_masking (bool): 
+                Whether to apply masking for padded part in loss calculation.
+            use_weighted_masking (bool): 
+                Whether to weighted masking in loss calculation.
         """
         assert check_argument_types()
         super().__init__()
@@ -982,17 +1078,28 @@ class FastSpeech2Loss(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            after_outs(Tensor): Batch of outputs after postnets (B, Lmax, odim).
-            before_outs(Tensor): Batch of outputs before postnets (B, Lmax, odim).
-            d_outs(Tensor): Batch of outputs of duration predictor (B, Tmax).
-            p_outs(Tensor): Batch of outputs of pitch predictor (B, Tmax, 1).
-            e_outs(Tensor): Batch of outputs of energy predictor (B, Tmax, 1).
-            ys(Tensor): Batch of target features (B, Lmax, odim).
-            ds(Tensor): Batch of durations (B, Tmax).
-            ps(Tensor): Batch of target token-averaged pitch (B, Tmax, 1).
-            es(Tensor): Batch of target token-averaged energy (B, Tmax, 1).
-            ilens(Tensor): Batch of the lengths of each input (B,).
-            olens(Tensor): Batch of the lengths of each target (B,).
+            after_outs(Tensor):  
+                Batch of outputs after postnets (B, Lmax, odim).
+            before_outs(Tensor): 
+                Batch of outputs before postnets (B, Lmax, odim).
+            d_outs(Tensor): 
+                Batch of outputs of duration predictor (B, Tmax).
+            p_outs(Tensor): 
+                Batch of outputs of pitch predictor (B, Tmax, 1).
+            e_outs(Tensor): 
+                Batch of outputs of energy predictor (B, Tmax, 1).
+            ys(Tensor): 
+                Batch of target features (B, Lmax, odim).
+            ds(Tensor): 
+                Batch of durations (B, Tmax).
+            ps(Tensor): 
+                Batch of target token-averaged pitch (B, Tmax, 1).
+            es(Tensor): 
+                Batch of target token-averaged energy (B, Tmax, 1).
+            ilens(Tensor): 
+                Batch of the lengths of each input (B,).
+            olens(Tensor): 
+                Batch of the lengths of each target (B,).
 
         Returns:
 

paddlespeech/t2s/models/hifigan/hifigan.py

@@ -50,20 +50,34 @@ class HiFiGANGenerator(nn.Layer):
             init_type: str="xavier_uniform", ):
         """Initialize HiFiGANGenerator module.
         Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            channels (int): Number of hidden representation channels.
-            global_channels (int): Number of global conditioning channels.
-            kernel_size (int): Kernel size of initial and final conv layer.
-            upsample_scales (list): List of upsampling scales.
-            upsample_kernel_sizes (list): List of kernel sizes for upsampling layers.
-            resblock_kernel_sizes (list): List of kernel sizes for residual blocks.
-            resblock_dilations (list): List of dilation list for residual blocks.
-            use_additional_convs (bool): Whether to use additional conv layers in residual blocks.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
-            use_weight_norm (bool): Whether to use weight norm.
+            in_channels (int): 
+                Number of input channels.
+            out_channels (int): 
+                Number of output channels.
+            channels (int): 
+                Number of hidden representation channels.
+            global_channels (int): 
+                Number of global conditioning channels.
+            kernel_size (int): 
+                Kernel size of initial and final conv layer.
+            upsample_scales (list): 
+                List of upsampling scales.
+            upsample_kernel_sizes (list): 
+                List of kernel sizes for upsampling layers.
+            resblock_kernel_sizes (list): 
+                List of kernel sizes for residual blocks.
+            resblock_dilations (list): 
+                List of dilation list for residual blocks.
+            use_additional_convs (bool): 
+                Whether to use additional conv layers in residual blocks.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): 
+                Activation function module name.
+            nonlinear_activation_params (dict): 
+                Hyperparameters for activation function.
+            use_weight_norm (bool): 
+                Whether to use weight norm.
                 If set to true, it will be applied to all of the conv layers.
         """
         super().__init__()
@@ -199,9 +213,10 @@ class HiFiGANGenerator(nn.Layer):
     def inference(self, c, g: Optional[paddle.Tensor]=None):
         """Perform inference.
         Args:
-            c (Tensor): Input tensor (T, in_channels).
-                normalize_before (bool): Whether to perform normalization.
-            g (Optional[Tensor]): Global conditioning tensor (global_channels, 1).
+            c (Tensor): 
+                Input tensor (T, in_channels).
+            g (Optional[Tensor]): 
+                Global conditioning tensor (global_channels, 1).
         Returns:
             Tensor:
                 Output tensor (T ** prod(upsample_scales), out_channels).
@@ -233,20 +248,33 @@ class HiFiGANPeriodDiscriminator(nn.Layer):
         """Initialize HiFiGANPeriodDiscriminator module.
 
         Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            period (int): Period.
-            kernel_sizes (list): Kernel sizes of initial conv layers and the final conv layer.
-            channels (int): Number of initial channels.
-            downsample_scales (list): List of downsampling scales.
-            max_downsample_channels (int): Number of maximum downsampling channels.
-            use_additional_convs (bool): Whether to use additional conv layers in residual blocks.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
-            use_weight_norm (bool): Whether to use weight norm.
+            in_channels (int): 
+                Number of input channels.
+            out_channels (int): 
+                Number of output channels.
+            period (int): 
+                Period.
+            kernel_sizes (list): 
+                Kernel sizes of initial conv layers and the final conv layer.
+            channels (int): 
+                Number of initial channels.
+            downsample_scales (list): 
+                List of downsampling scales.
+            max_downsample_channels (int): 
+                Number of maximum downsampling channels.
+            use_additional_convs (bool): 
+                Whether to use additional conv layers in residual blocks.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): 
+                Activation function module name.
+            nonlinear_activation_params (dict): 
+                Hyperparameters for activation function.
+            use_weight_norm (bool): 
+                Whether to use weight norm.
                 If set to true, it will be applied to all of the conv layers.
-            use_spectral_norm (bool): Whether to use spectral norm.
+            use_spectral_norm (bool): 
+                Whether to use spectral norm.
                 If set to true, it will be applied to all of the conv layers.
         """
         super().__init__()
@@ -298,7 +326,8 @@ class HiFiGANPeriodDiscriminator(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            c (Tensor): Input tensor (B, in_channels, T).
+            c (Tensor): 
+                Input tensor (B, in_channels, T).
         Returns:
             list: List of each layer's tensors.
         """
@@ -367,8 +396,10 @@ class HiFiGANMultiPeriodDiscriminator(nn.Layer):
         """Initialize HiFiGANMultiPeriodDiscriminator module.
 
         Args:
-            periods (list): List of periods.
-            discriminator_params (dict): Parameters for hifi-gan period discriminator module.
+            periods (list): 
+                List of periods.
+            discriminator_params (dict): 
+                Parameters for hifi-gan period discriminator module.
                 The period parameter will be overwritten.
         """
         super().__init__()
@@ -385,7 +416,8 @@ class HiFiGANMultiPeriodDiscriminator(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            x (Tensor): Input noise signal (B, 1, T).
+            x (Tensor): 
+                Input noise signal (B, 1, T).
         Returns:
             List: List of list of each discriminator outputs, which consists of each layer output tensors.
         """
@@ -417,16 +449,25 @@ class HiFiGANScaleDiscriminator(nn.Layer):
         """Initilize HiFiGAN scale discriminator module.
 
         Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            kernel_sizes (list): List of four kernel sizes. The first will be used for the first conv layer,
+            in_channels (int): 
+                Number of input channels.
+            out_channels (int): 
+                Number of output channels.
+            kernel_sizes (list): 
+                List of four kernel sizes. The first will be used for the first conv layer,
                 and the second is for downsampling part, and the remaining two are for output layers.
-            channels (int): Initial number of channels for conv layer.
-            max_downsample_channels (int): Maximum number of channels for downsampling layers.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            downsample_scales (list): List of downsampling scales.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            channels (int): 
+                Initial number of channels for conv layer.
+            max_downsample_channels (int): 
+                Maximum number of channels for downsampling layers.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            downsample_scales (list): 
+                List of downsampling scales.
+            nonlinear_activation (str): 
+                Activation function module name.
+            nonlinear_activation_params (dict): 
+                Hyperparameters for activation function.
             use_weight_norm (bool): Whether to use weight norm.
                 If set to true, it will be applied to all of the conv layers.
             use_spectral_norm (bool): Whether to use spectral norm.
@@ -614,7 +655,8 @@ class HiFiGANMultiScaleDiscriminator(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            x (Tensor): Input noise signal (B, 1, T).
+            x (Tensor): 
+                Input noise signal (B, 1, T).
         Returns:
             List: List of list of each discriminator outputs, which consists of each layer output tensors.
         """
@@ -675,14 +717,21 @@ class HiFiGANMultiScaleMultiPeriodDiscriminator(nn.Layer):
         """Initilize HiFiGAN multi-scale + multi-period discriminator module.
 
         Args:
-            scales (int): Number of multi-scales.
-            scale_downsample_pooling (str): Pooling module name for downsampling of the inputs.
-            scale_downsample_pooling_params (dict): Parameters for the above pooling module.
-            scale_discriminator_params (dict): Parameters for hifi-gan scale discriminator module.
-            follow_official_norm （bool): Whether to follow the norm setting of the official implementaion. 
+            scales (int): 
+                Number of multi-scales.
+            scale_downsample_pooling (str): 
+                Pooling module name for downsampling of the inputs.
+            scale_downsample_pooling_params (dict): 
+                Parameters for the above pooling module.
+            scale_discriminator_params (dict): 
+                Parameters for hifi-gan scale discriminator module.
+            follow_official_norm （bool): 
+                Whether to follow the norm setting of the official implementaion. 
                 The first discriminator uses spectral norm and the other discriminators use weight norm.
-            periods (list): List of periods.
-            period_discriminator_params (dict): Parameters for hifi-gan period discriminator module.
+            periods (list): 
+                List of periods.
+            period_discriminator_params (dict): 
+                Parameters for hifi-gan period discriminator module.
                 The period parameter will be overwritten.
         """
         super().__init__()
@@ -704,7 +753,8 @@ class HiFiGANMultiScaleMultiPeriodDiscriminator(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            x (Tensor): Input noise signal (B, 1, T).
+            x (Tensor): 
+                Input noise signal (B, 1, T).
         Returns:
             List:
                 List of list of each discriminator outputs,

paddlespeech/t2s/models/melgan/melgan.py

@@ -53,24 +53,38 @@ class MelGANGenerator(nn.Layer):
         """Initialize MelGANGenerator module.
 
         Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels,
+            in_channels (int): 
+                Number of input channels.
+            out_channels (int): 
+                Number of output channels,
                 the number of sub-band is out_channels in multi-band melgan.
-            kernel_size (int): Kernel size of initial and final conv layer.
-            channels (int): Initial number of channels for conv layer.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            upsample_scales (List[int]): List of upsampling scales.
-            stack_kernel_size (int): Kernel size of dilated conv layers in residual stack.
-            stacks (int): Number of stacks in a single residual stack.
-            nonlinear_activation (Optional[str], optional): Non linear activation in upsample network, by default None
-            nonlinear_activation_params (Dict[str, Any], optional): Parameters passed to the linear activation in the upsample network, 
-                by default {}
-            pad (str): Padding function module name before dilated convolution layer.
-            pad_params (dict): Hyperparameters for padding function.
-            use_final_nonlinear_activation (nn.Layer): Activation function for the final layer.
-            use_weight_norm (bool): Whether to use weight norm.
+            kernel_size (int): 
+                Kernel size of initial and final conv layer.
+            channels (int): 
+                Initial number of channels for conv layer.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            upsample_scales (List[int]): 
+                List of upsampling scales.
+            stack_kernel_size (int): 
+                Kernel size of dilated conv layers in residual stack.
+            stacks (int): 
+                Number of stacks in a single residual stack.
+            nonlinear_activation (Optional[str], optional): 
+                Non linear activation in upsample network, by default None
+            nonlinear_activation_params (Dict[str, Any], optional): 
+                Parameters passed to the linear activation in the upsample network, by default {}
+            pad (str): 
+                Padding function module name before dilated convolution layer.
+            pad_params (dict): 
+                Hyperparameters for padding function.
+            use_final_nonlinear_activation (nn.Layer): 
+                Activation function for the final layer.
+            use_weight_norm (bool): 
+                Whether to use weight norm.
                 If set to true, it will be applied to all of the conv layers.
-            use_causal_conv (bool): Whether to use causal convolution.
+            use_causal_conv (bool):
+                Whether to use causal convolution.
         """
         super().__init__()
 
@@ -194,7 +208,8 @@ class MelGANGenerator(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            c (Tensor): Input tensor (B, in_channels, T).
+            c (Tensor): 
+                Input tensor (B, in_channels, T).
         Returns:
             Tensor: Output tensor (B, out_channels, T ** prod(upsample_scales)).
         """
@@ -244,7 +259,8 @@ class MelGANGenerator(nn.Layer):
         """Perform inference.
 
         Args:
-            c (Union[Tensor, ndarray]): Input tensor (T, in_channels).
+            c (Union[Tensor, ndarray]): 
+                Input tensor (T, in_channels).
         Returns:
             Tensor: Output tensor (out_channels*T ** prod(upsample_scales), 1).
         """
@@ -279,20 +295,30 @@ class MelGANDiscriminator(nn.Layer):
         """Initilize MelGAN discriminator module.
 
         Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
+            in_channels (int): 
+                Number of input channels.
+            out_channels (int): 
+                Number of output channels.
             kernel_sizes (List[int]): List of two kernel sizes. The prod will be used for the first conv layer,
                 and the first and the second kernel sizes will be used for the last two layers.
                 For example if kernel_sizes = [5, 3], the first layer kernel size will be 5 * 3 = 15,
                 the last two layers' kernel size will be 5 and 3, respectively.
-            channels (int): Initial number of channels for conv layer.
-            max_downsample_channels (int): Maximum number of channels for downsampling layers.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            downsample_scales (List[int]): List of downsampling scales.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
-            pad (str): Padding function module name before dilated convolution layer.
-            pad_params (dict): Hyperparameters for padding function.
+            channels (int): 
+                Initial number of channels for conv layer.
+            max_downsample_channels (int): 
+                Maximum number of channels for downsampling layers.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            downsample_scales (List[int]): 
+                List of downsampling scales.
+            nonlinear_activation (str): 
+                Activation function module name.
+            nonlinear_activation_params (dict): 
+                Hyperparameters for activation function.
+            pad (str): 
+                Padding function module name before dilated convolution layer.
+            pad_params (dict): 
+                Hyperparameters for padding function.
         """
         super().__init__()
 
@@ -364,7 +390,8 @@ class MelGANDiscriminator(nn.Layer):
     def forward(self, x):
         """Calculate forward propagation.
         Args:
-            x (Tensor): Input noise signal (B, 1, T).
+            x (Tensor): 
+                Input noise signal (B, 1, T).
         Returns:
             List: List of output tensors of each layer (for feat_match_loss).
         """
@@ -406,22 +433,37 @@ class MelGANMultiScaleDiscriminator(nn.Layer):
         """Initilize MelGAN multi-scale discriminator module.
 
         Args:
-            in_channels (int): Number of input channels.
-            out_channels (int): Number of output channels.
-            scales (int): Number of multi-scales.
-            downsample_pooling (str): Pooling module name for downsampling of the inputs.
-            downsample_pooling_params (dict): Parameters for the above pooling module.
-            kernel_sizes (List[int]): List of two kernel sizes. The sum will be used for the first conv layer,
+            in_channels (int): 
+                Number of input channels.
+            out_channels (int): 
+                Number of output channels.
+            scales (int): 
+                Number of multi-scales.
+            downsample_pooling (str): 
+                Pooling module name for downsampling of the inputs.
+            downsample_pooling_params (dict): 
+                Parameters for the above pooling module.
+            kernel_sizes (List[int]): 
+                List of two kernel sizes. The sum will be used for the first conv layer,
                 and the first and the second kernel sizes will be used for the last two layers.
-            channels (int): Initial number of channels for conv layer.
-            max_downsample_channels (int): Maximum number of channels for downsampling layers.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            downsample_scales (List[int]): List of downsampling scales.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
-            pad (str): Padding function module name before dilated convolution layer.
-            pad_params (dict): Hyperparameters for padding function.
-            use_causal_conv (bool): Whether to use causal convolution.
+            channels (int): 
+                Initial number of channels for conv layer.
+            max_downsample_channels (int): 
+                Maximum number of channels for downsampling layers.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            downsample_scales (List[int]): 
+                List of downsampling scales.
+            nonlinear_activation (str): 
+                Activation function module name.
+            nonlinear_activation_params (dict): 
+                Hyperparameters for activation function.
+            pad (str): 
+                Padding function module name before dilated convolution layer.
+            pad_params (dict): 
+                Hyperparameters for padding function.
+            use_causal_conv (bool): 
+                Whether to use causal convolution.
         """
         super().__init__()
 
@@ -464,7 +506,8 @@ class MelGANMultiScaleDiscriminator(nn.Layer):
     def forward(self, x):
         """Calculate forward propagation.
         Args:
-            x (Tensor): Input noise signal (B, 1, T).
+            x (Tensor):
+                Input noise signal (B, 1, T).
         Returns:
             List: List of list of each discriminator outputs, which consists of each layer output tensors.
         """

paddlespeech/t2s/models/melgan/style_melgan.py

@@ -54,20 +54,34 @@ class StyleMelGANGenerator(nn.Layer):
         """Initilize Style MelGAN generator.
 
         Args:
-            in_channels (int): Number of input noise channels.
-            aux_channels (int): Number of auxiliary input channels.
-            channels (int): Number of channels for conv layer.
-            out_channels (int): Number of output channels.
-            kernel_size (int): Kernel size of conv layers.
-            dilation (int): Dilation factor for conv layers.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            noise_upsample_scales (list): List of noise upsampling scales.
-            noise_upsample_activation (str): Activation function module name for noise upsampling.
-            noise_upsample_activation_params (dict): Hyperparameters for the above activation function.
-            upsample_scales (list): List of upsampling scales.
-            upsample_mode (str): Upsampling mode in TADE layer.
-            gated_function (str): Gated function in TADEResBlock ("softmax" or "sigmoid").
-            use_weight_norm (bool): Whether to use weight norm.
+            in_channels (int): 
+                Number of input noise channels.
+            aux_channels (int): 
+                Number of auxiliary input channels.
+            channels (int): 
+                Number of channels for conv layer.
+            out_channels (int): 
+                Number of output channels.
+            kernel_size (int): 
+                Kernel size of conv layers.
+            dilation (int): 
+                Dilation factor for conv layers.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            noise_upsample_scales (list): 
+                List of noise upsampling scales.
+            noise_upsample_activation (str): 
+                Activation function module name for noise upsampling.
+            noise_upsample_activation_params (dict): 
+                Hyperparameters for the above activation function.
+            upsample_scales (list): 
+                List of upsampling scales.
+            upsample_mode (str): 
+                Upsampling mode in TADE layer.
+            gated_function (str): 
+                Gated function in TADEResBlock ("softmax" or "sigmoid").
+            use_weight_norm (bool): 
+                Whether to use weight norm.
                 If set to true, it will be applied to all of the conv layers.
         """
         super().__init__()
@@ -194,7 +208,8 @@ class StyleMelGANGenerator(nn.Layer):
     def inference(self, c):
         """Perform inference.
         Args:
-            c (Tensor): Input tensor (T, in_channels).
+            c (Tensor): 
+                Input tensor (T, in_channels).
         Returns:
             Tensor: Output tensor (T ** prod(upsample_scales), out_channels).
         """
@@ -258,11 +273,16 @@ class StyleMelGANDiscriminator(nn.Layer):
         """Initilize Style MelGAN discriminator.
 
         Args:
-            repeats (int): Number of repititons to apply RWD.
-            window_sizes (list): List of random window sizes.
-            pqmf_params (list): List of list of Parameters for PQMF modules
-            discriminator_params (dict): Parameters for base discriminator module.
-            use_weight_nom (bool): Whether to apply weight normalization.
+            repeats (int): 
+                Number of repititons to apply RWD.
+            window_sizes (list): 
+                List of random window sizes.
+            pqmf_params (list): 
+                List of list of Parameters for PQMF modules
+            discriminator_params (dict): 
+                Parameters for base discriminator module.
+            use_weight_nom (bool): 
+                Whether to apply weight normalization.
         """
         super().__init__()
 
@@ -299,7 +319,8 @@ class StyleMelGANDiscriminator(nn.Layer):
     def forward(self, x):
         """Calculate forward propagation.
         Args:
-            x (Tensor): Input tensor (B, 1, T).
+            x (Tensor): 
+                Input tensor (B, 1, T).
         Returns:
             List: List of discriminator outputs, #items in the list will be
                 equal to repeats * #discriminators.

paddlespeech/t2s/models/parallel_wavegan/parallel_wavegan.py

@@ -32,29 +32,45 @@ class PWGGenerator(nn.Layer):
     """Wave Generator for Parallel WaveGAN
 
     Args:
-        in_channels (int, optional): Number of channels of the input waveform, by default 1
-        out_channels (int, optional): Number of channels of the output waveform, by default 1
-        kernel_size (int, optional): Kernel size of the residual blocks inside, by default 3
-        layers (int, optional): Number of residual blocks inside, by default 30
-        stacks (int, optional): The number of groups to split the residual blocks into, by default 3
+        in_channels (int, optional): 
+            Number of channels of the input waveform, by default 1
+        out_channels (int, optional): 
+            Number of channels of the output waveform, by default 1
+        kernel_size (int, optional): 
+            Kernel size of the residual blocks inside, by default 3
+        layers (int, optional): 
+            Number of residual blocks inside, by default 30
+        stacks (int, optional):
+            The number of groups to split the residual blocks into, by default 3
             Within each group, the dilation of the residual block grows exponentially.
-        residual_channels (int, optional): Residual channel of the residual blocks, by default 64
-        gate_channels (int, optional): Gate channel of the residual blocks, by default 128
-        skip_channels (int, optional): Skip channel of the residual blocks, by default 64
-        aux_channels (int, optional): Auxiliary channel of the residual blocks, by default 80
-        aux_context_window (int, optional): The context window size of the first convolution applied to the 
-            auxiliary input, by default 2
-        dropout (float, optional): Dropout of the residual blocks, by default 0.
-        bias (bool, optional): Whether to use bias in residual blocks, by default True
-        use_weight_norm (bool, optional): Whether to use weight norm in all convolutions, by default True
-        use_causal_conv (bool, optional): Whether to use causal padding in the upsample network and residual 
-            blocks, by default False
-        upsample_scales (List[int], optional): Upsample scales of the upsample network, by default [4, 4, 4, 4]
-        nonlinear_activation (Optional[str], optional): Non linear activation in upsample network, by default None
-        nonlinear_activation_params (Dict[str, Any], optional): Parameters passed to the linear activation in the upsample network, 
-            by default {}
-        interpolate_mode (str, optional): Interpolation mode of the upsample network, by default "nearest"
-        freq_axis_kernel_size (int, optional): Kernel size along the frequency axis of the upsample network, by default 1
+        residual_channels (int, optional): 
+            Residual channel of the residual blocks, by default 64
+        gate_channels (int, optional): 
+            Gate channel of the residual blocks, by default 128
+        skip_channels (int, optional): 
+            Skip channel of the residual blocks, by default 64
+        aux_channels (int, optional): 
+            Auxiliary channel of the residual blocks, by default 80
+        aux_context_window (int, optional): 
+            The context window size of the first convolution applied to the auxiliary input, by default 2
+        dropout (float, optional): 
+            Dropout of the residual blocks, by default 0.
+        bias (bool, optional): 
+            Whether to use bias in residual blocks, by default True
+        use_weight_norm (bool, optional): 
+            Whether to use weight norm in all convolutions, by default True
+        use_causal_conv (bool, optional): 
+            Whether to use causal padding in the upsample network and residual blocks, by default False
+        upsample_scales (List[int], optional): 
+            Upsample scales of the upsample network, by default [4, 4, 4, 4]
+        nonlinear_activation (Optional[str], optional): 
+            Non linear activation in upsample network, by default None
+        nonlinear_activation_params (Dict[str, Any], optional): 
+            Parameters passed to the linear activation in the upsample network, by default {}
+        interpolate_mode (str, optional): 
+            Interpolation mode of the upsample network, by default "nearest"
+        freq_axis_kernel_size (int, optional): 
+            Kernel size along the frequency axis of the upsample network, by default 1
     """
 
     def __init__(
@@ -147,9 +163,11 @@ class PWGGenerator(nn.Layer):
         """Generate waveform.
 
         Args:
-            x(Tensor): Shape (N, C_in, T), The input waveform.
-            c(Tensor): Shape (N, C_aux, T'). The auxiliary input (e.g. spectrogram). It
-            is upsampled to match the time resolution of the input.
+            x(Tensor): 
+                Shape (N, C_in, T), The input waveform.
+            c(Tensor): 
+                Shape (N, C_aux, T'). The auxiliary input (e.g. spectrogram). 
+                It is upsampled to match the time resolution of the input.
 
         Returns:
             Tensor: Shape (N, C_out, T), the generated waveform.
@@ -195,8 +213,10 @@ class PWGGenerator(nn.Layer):
         """Waveform generation. This function is used for single instance inference.
 
         Args:
-            c(Tensor, optional, optional): Shape (T', C_aux), the auxiliary input, by default None
-            x(Tensor, optional): Shape (T, C_in), the noise waveform, by default None
+            c(Tensor, optional, optional): 
+                Shape (T', C_aux), the auxiliary input, by default None
+            x(Tensor, optional): 
+                Shape (T, C_in), the noise waveform, by default None
 
         Returns:
             Tensor: Shape (T, C_out), the generated waveform
@@ -214,20 +234,28 @@ class PWGDiscriminator(nn.Layer):
     """A convolutional discriminator for audio.
 
     Args:
-        in_channels (int, optional): Number of channels of the input audio, by default 1
-        out_channels (int, optional): Output feature size, by default 1
-        kernel_size (int, optional): Kernel size of convolutional sublayers, by default 3
-        layers (int, optional): Number of layers, by default 10
-        conv_channels (int, optional): Feature size of the convolutional sublayers, by default 64
-        dilation_factor (int, optional): The factor with which dilation of each convolutional sublayers grows 
+        in_channels (int, optional): 
+            Number of channels of the input audio, by default 1
+        out_channels (int, optional): 
+            Output feature size, by default 1
+        kernel_size (int, optional): 
+            Kernel size of convolutional sublayers, by default 3
+        layers (int, optional): 
+            Number of layers, by default 10
+        conv_channels (int, optional): 
+            Feature size of the convolutional sublayers, by default 64
+        dilation_factor (int, optional): 
+            The factor with which dilation of each convolutional sublayers grows 
             exponentially if it is greater than 1, else the dilation of each convolutional sublayers grows linearly, 
             by default 1
-        nonlinear_activation (str, optional): The activation after each convolutional sublayer, by default "leakyrelu"
-        nonlinear_activation_params (Dict[str, Any], optional): The parameters passed to the activation's initializer, by default 
-            {"negative_slope": 0.2}
-        bias (bool, optional): Whether to use bias in convolutional sublayers, by default True
-        use_weight_norm (bool, optional): Whether to use weight normalization at all convolutional sublayers, 
-            by default True
+        nonlinear_activation (str, optional): 
+            The activation after each convolutional sublayer, by default "leakyrelu"
+        nonlinear_activation_params (Dict[str, Any], optional): 
+            The parameters passed to the activation's initializer, by default {"negative_slope": 0.2}
+        bias (bool, optional): 
+            Whether to use bias in convolutional sublayers, by default True
+        use_weight_norm (bool, optional): 
+            Whether to use weight normalization at all convolutional sublayers, by default True
     """
 
     def __init__(
@@ -290,7 +318,8 @@ class PWGDiscriminator(nn.Layer):
         """
 
         Args:
-            x (Tensor): Shape (N, in_channels, num_samples), the input audio.
+            x (Tensor): 
+                Shape (N, in_channels, num_samples), the input audio.
 
         Returns:
             Tensor: Shape (N, out_channels, num_samples), the predicted logits.
@@ -318,24 +347,35 @@ class ResidualPWGDiscriminator(nn.Layer):
     """A wavenet-style discriminator for audio.
 
     Args:
-        in_channels (int, optional): Number of channels of the input audio, by default 1
-        out_channels (int, optional): Output feature size, by default 1
-        kernel_size (int, optional): Kernel size of residual blocks, by default 3
-        layers (int, optional): Number of residual blocks, by default 30
-        stacks (int, optional): Number of groups of residual blocks, within which the dilation 
+        in_channels (int, optional): 
+            Number of channels of the input audio, by default 1
+        out_channels (int, optional): 
+            Output feature size, by default 1
+        kernel_size (int, optional): 
+            Kernel size of residual blocks, by default 3
+        layers (int, optional): 
+            Number of residual blocks, by default 30
+        stacks (int, optional): 
+            Number of groups of residual blocks, within which the dilation 
             of each residual blocks grows exponentially, by default 3
-        residual_channels (int, optional): Residual channels of residual blocks, by default 64
-        gate_channels (int, optional): Gate channels of residual blocks, by default 128
-        skip_channels (int, optional): Skip channels of residual blocks, by default 64
-        dropout (float, optional): Dropout probability of residual blocks, by default 0.
-        bias (bool, optional): Whether to use bias in residual blocks, by default True
-        use_weight_norm (bool, optional): Whether to use weight normalization in all convolutional layers, 
-            by default True
-        use_causal_conv (bool, optional): Whether to use causal convolution in residual blocks, by default False
-        nonlinear_activation (str, optional): Activation after convolutions other than those in residual blocks, 
-            by default "leakyrelu"
-        nonlinear_activation_params (Dict[str, Any], optional): Parameters to pass to the activation, 
-            by default {"negative_slope": 0.2}
+        residual_channels (int, optional): 
+            Residual channels of residual blocks, by default 64
+        gate_channels (int, optional): 
+            Gate channels of residual blocks, by default 128
+        skip_channels (int, optional): 
+            Skip channels of residual blocks, by default 64
+        dropout (float, optional): 
+            Dropout probability of residual blocks, by default 0.
+        bias (bool, optional): 
+            Whether to use bias in residual blocks, by default True
+        use_weight_norm (bool, optional): 
+            Whether to use weight normalization in all convolutional layers, by default True
+        use_causal_conv (bool, optional): 
+            Whether to use causal convolution in residual blocks, by default False
+        nonlinear_activation (str, optional): 
+            Activation after convolutions other than those in residual blocks, by default "leakyrelu"
+        nonlinear_activation_params (Dict[str, Any], optional): 
+            Parameters to pass to the activation, by default {"negative_slope": 0.2}
     """
 
     def __init__(
@@ -405,7 +445,8 @@ class ResidualPWGDiscriminator(nn.Layer):
     def forward(self, x):
         """
         Args:
-            x(Tensor): Shape (N, in_channels, num_samples), the input audio.↩
+            x(Tensor): 
+                Shape (N, in_channels, num_samples), the input audio.↩
 
         Returns:
             Tensor: Shape (N, out_channels, num_samples), the predicted logits.

paddlespeech/t2s/models/speedyspeech/speedyspeech.py

@@ -29,10 +29,14 @@ class ResidualBlock(nn.Layer):
                  n: int=2):
         """SpeedySpeech encoder module.
         Args:
-            channels (int, optional): Feature size of the residual output(and also the input).
-            kernel_size (int, optional): Kernel size of the 1D convolution.
-            dilation (int, optional): Dilation of the 1D convolution.
-            n (int): Number of blocks.
+            channels (int, optional): 
+                Feature size of the residual output(and also the input).
+            kernel_size (int, optional): 
+                Kernel size of the 1D convolution.
+            dilation (int, optional): 
+                Dilation of the 1D convolution.
+            n (int): 
+                Number of blocks.
         """
 
         super().__init__()
@@ -57,7 +61,8 @@ class ResidualBlock(nn.Layer):
     def forward(self, x: paddle.Tensor):
         """Calculate forward propagation.
         Args:
-            x(Tensor): Batch of input sequences (B, hidden_size, Tmax).
+            x(Tensor): 
+                Batch of input sequences (B, hidden_size, Tmax).
         Returns:
             Tensor: The residual output (B, hidden_size, Tmax).
         """
@@ -89,8 +94,10 @@ class TextEmbedding(nn.Layer):
     def forward(self, text: paddle.Tensor, tone: paddle.Tensor=None):
         """Calculate forward propagation.
         Args:
-            text(Tensor(int64)): Batch of padded token ids (B, Tmax).
-            tones(Tensor, optional(int64)): Batch of padded tone ids (B, Tmax).
+            text(Tensor(int64)): 
+                Batch of padded token ids (B, Tmax).
+            tones(Tensor, optional(int64)): 
+                Batch of padded tone ids (B, Tmax).
         Returns:
             Tensor: The residual output (B, Tmax, embedding_size).
         """
@@ -109,12 +116,18 @@ class TextEmbedding(nn.Layer):
 class SpeedySpeechEncoder(nn.Layer):
     """SpeedySpeech encoder module.
     Args:
-        vocab_size (int): Dimension of the inputs.
-        tone_size (Optional[int]): Number of tones.
-        hidden_size (int): Number of encoder hidden units.
-        kernel_size (int): Kernel size of encoder.
-        dilations (List[int]): Dilations of encoder.
-        spk_num (Optional[int]): Number of speakers. 
+        vocab_size (int): 
+            Dimension of the inputs.
+        tone_size (Optional[int]): 
+            Number of tones.
+        hidden_size (int): 
+            Number of encoder hidden units.
+        kernel_size (int): 
+            Kernel size of encoder.
+        dilations (List[int]): 
+            Dilations of encoder.
+        spk_num (Optional[int]): 
+            Number of speakers. 
     """
 
     def __init__(self,
@@ -161,9 +174,12 @@ class SpeedySpeechEncoder(nn.Layer):
                 spk_id: paddle.Tensor=None):
         """Encoder input sequence.
         Args:
-            text(Tensor(int64)): Batch of padded token ids (B, Tmax).
-            tones(Tensor, optional(int64)): Batch of padded tone ids (B, Tmax).
-            spk_id(Tnesor, optional(int64)): Batch of speaker ids (B,)
+            text(Tensor(int64)): 
+                Batch of padded token ids (B, Tmax).
+            tones(Tensor, optional(int64)): 
+                Batch of padded tone ids (B, Tmax).
+            spk_id(Tnesor, optional(int64)): 
+                Batch of speaker ids (B,)
 
         Returns:
             Tensor: Output tensor (B, Tmax, hidden_size).
@@ -192,7 +208,8 @@ class DurationPredictor(nn.Layer):
     def forward(self, x: paddle.Tensor):
         """Calculate forward propagation.
         Args:
-            x(Tensor): Batch of input sequences (B, Tmax, hidden_size).
+            x(Tensor): 
+                Batch of input sequences (B, Tmax, hidden_size).
 
         Returns:
             Tensor: Batch of predicted durations in log domain (B, Tmax).
@@ -212,10 +229,14 @@ class SpeedySpeechDecoder(nn.Layer):
                  ]):
         """SpeedySpeech decoder module.
         Args:
-            hidden_size (int): Number of decoder hidden units.
-            kernel_size (int): Kernel size of decoder.
-            output_size (int): Dimension of the outputs.
-            dilations (List[int]): Dilations of decoder.
+            hidden_size (int): 
+                Number of decoder hidden units.
+            kernel_size (int): 
+                Kernel size of decoder.
+            output_size (int): 
+                Dimension of the outputs.
+            dilations (List[int]): 
+                Dilations of decoder.
         """
         super().__init__()
         res_blocks = [
@@ -230,7 +251,8 @@ class SpeedySpeechDecoder(nn.Layer):
     def forward(self, x):
         """Decoder input sequence.
         Args:
-            x(Tensor): Input tensor (B, time, hidden_size).
+            x(Tensor): 
+                Input tensor (B, time, hidden_size).
 
         Returns:
             Tensor: Output tensor (B, time, output_size).
@@ -261,18 +283,30 @@ class SpeedySpeech(nn.Layer):
             positional_dropout_rate: int=0.1):
         """Initialize SpeedySpeech module.
         Args:
-            vocab_size (int): Dimension of the inputs.
-            encoder_hidden_size (int): Number of encoder hidden units.
-            encoder_kernel_size (int): Kernel size of encoder.
-            encoder_dilations (List[int]): Dilations of encoder.
-            duration_predictor_hidden_size (int): Number of duration predictor hidden units.
-            decoder_hidden_size (int): Number of decoder hidden units.
-            decoder_kernel_size (int): Kernel size of decoder.
-            decoder_dilations (List[int]): Dilations of decoder.
-            decoder_output_size (int): Dimension of the outputs.
-            tone_size (Optional[int]): Number of tones.
-            spk_num (Optional[int]): Number of speakers. 
-            init_type (str): How to initialize transformer parameters.
+            vocab_size (int): 
+                Dimension of the inputs.
+            encoder_hidden_size (int): 
+                Number of encoder hidden units.
+            encoder_kernel_size (int): 
+                Kernel size of encoder.
+            encoder_dilations (List[int]): 
+                Dilations of encoder.
+            duration_predictor_hidden_size (int):
+                Number of duration predictor hidden units.
+            decoder_hidden_size (int): 
+                Number of decoder hidden units.
+            decoder_kernel_size (int): 
+                Kernel size of decoder.
+            decoder_dilations (List[int]): 
+                Dilations of decoder.
+            decoder_output_size (int): 
+                Dimension of the outputs.
+            tone_size (Optional[int]): 
+                Number of tones.
+            spk_num (Optional[int]): 
+                Number of speakers. 
+            init_type (str): 
+                How to initialize transformer parameters.
     
         """
         super().__init__()
@@ -304,14 +338,20 @@ class SpeedySpeech(nn.Layer):
                 spk_id: paddle.Tensor=None):
         """Calculate forward propagation.
         Args:
-            text(Tensor(int64)): Batch of padded token ids (B, Tmax).
-            durations(Tensor(int64)): Batch of padded durations (B, Tmax).
-            tones(Tensor, optional(int64)): Batch of padded tone ids  (B, Tmax).
-            spk_id(Tnesor, optional(int64)): Batch of speaker ids (B,)
+            text(Tensor(int64)): 
+                Batch of padded token ids (B, Tmax).
+            durations(Tensor(int64)): 
+                Batch of padded durations (B, Tmax).
+            tones(Tensor, optional(int64)): 
+                Batch of padded tone ids  (B, Tmax).
+            spk_id(Tnesor, optional(int64)): 
+                Batch of speaker ids (B,)
 
         Returns:
-            Tensor: Output tensor (B, T_frames, decoder_output_size).
-            Tensor: Predicted durations (B, Tmax).
+            Tensor: 
+                Output tensor (B, T_frames, decoder_output_size).
+            Tensor: 
+                Predicted durations (B, Tmax).
         """
         # input of embedding must be int64
         text = paddle.cast(text, 'int64')
@@ -336,10 +376,14 @@ class SpeedySpeech(nn.Layer):
                   spk_id: paddle.Tensor=None):
         """Generate the sequence of features given the sequences of characters.
         Args:
-            text(Tensor(int64)): Input sequence of characters (T,).
-            tones(Tensor, optional(int64)): Batch of padded tone ids (T, ).
-            durations(Tensor, optional (int64)): Groundtruth of duration (T,).
-            spk_id(Tensor, optional(int64), optional): spk ids (1,). (Default value = None)
+            text(Tensor(int64)): 
+                Input sequence of characters (T,).
+            tones(Tensor, optional(int64)): 
+                Batch of padded tone ids (T, ).
+            durations(Tensor, optional (int64)): 
+                Groundtruth of duration (T,).
+            spk_id(Tensor, optional(int64), optional): 
+                spk ids (1,). (Default value = None)
 
         Returns:
             Tensor: logmel (T, decoder_output_size).

paddlespeech/t2s/models/tacotron2/tacotron2.py

@@ -83,38 +83,67 @@ class Tacotron2(nn.Layer):
             init_type: str="xavier_uniform", ):
         """Initialize Tacotron2 module.
         Args:
-            idim (int): Dimension of the inputs.
-            odim (int): Dimension of the outputs.
-            embed_dim (int): Dimension of the token embedding.
-            elayers (int): Number of encoder blstm layers.
-            eunits (int): Number of encoder blstm units.
-            econv_layers (int): Number of encoder conv layers.
-            econv_filts (int): Number of encoder conv filter size.
-            econv_chans (int): Number of encoder conv filter channels.
-            dlayers (int): Number of decoder lstm layers.
-            dunits (int): Number of decoder lstm units.
-            prenet_layers (int): Number of prenet layers.
-            prenet_units (int): Number of prenet units.
-            postnet_layers (int): Number of postnet layers.
-            postnet_filts (int): Number of postnet filter size.
-            postnet_chans (int): Number of postnet filter channels.
-            output_activation (str): Name of activation function for outputs.
-            adim (int): Number of dimension of mlp in attention.
-            aconv_chans (int): Number of attention conv filter channels.
-            aconv_filts (int): Number of attention conv filter size.
-            cumulate_att_w (bool): Whether to cumulate previous attention weight.
-            use_batch_norm (bool): Whether to use batch normalization.
-            use_concate (bool): Whether to concat enc outputs w/ dec lstm outputs.
-            reduction_factor (int): Reduction factor.
-            spk_num (Optional[int]): Number of speakers. If set to > 1, assume that the
+            idim (int): 
+                Dimension of the inputs.
+            odim (int): 
+                Dimension of the outputs.
+            embed_dim (int): 
+                Dimension of the token embedding.
+            elayers (int): 
+                Number of encoder blstm layers.
+            eunits (int): 
+                Number of encoder blstm units.
+            econv_layers (int): 
+                Number of encoder conv layers.
+            econv_filts (int): 
+                Number of encoder conv filter size.
+            econv_chans (int): 
+                Number of encoder conv filter channels.
+            dlayers (int): 
+                Number of decoder lstm layers.
+            dunits (int): 
+                Number of decoder lstm units.
+            prenet_layers (int): 
+                Number of prenet layers.
+            prenet_units (int): 
+                Number of prenet units.
+            postnet_layers (int): 
+                Number of postnet layers.
+            postnet_filts (int): 
+                Number of postnet filter size.
+            postnet_chans (int): 
+                Number of postnet filter channels.
+            output_activation (str): 
+                Name of activation function for outputs.
+            adim (int): 
+                Number of dimension of mlp in attention.
+            aconv_chans (int): 
+                Number of attention conv filter channels.
+            aconv_filts (int): 
+                Number of attention conv filter size.
+            cumulate_att_w (bool): 
+                Whether to cumulate previous attention weight.
+            use_batch_norm (bool): 
+                Whether to use batch normalization.
+            use_concate (bool): 
+                Whether to concat enc outputs w/ dec lstm outputs.
+            reduction_factor (int): 
+                Reduction factor.
+            spk_num (Optional[int]): 
+                Number of speakers. If set to > 1, assume that the
                 sids will be provided as the input and use sid embedding layer.
-            lang_num (Optional[int]): Number of languages. If set to > 1, assume that the
+            lang_num (Optional[int]): 
+                Number of languages. If set to > 1, assume that the
                 lids will be provided as the input and use sid embedding layer.
-            spk_embed_dim (Optional[int]): Speaker embedding dimension. If set to > 0,
+            spk_embed_dim (Optional[int]): 
+                Speaker embedding dimension. If set to > 0,
                 assume that spk_emb will be provided as the input.
-            spk_embed_integration_type (str): How to integrate speaker embedding.
-            dropout_rate (float): Dropout rate.
-            zoneout_rate (float): Zoneout rate.
+            spk_embed_integration_type (str): 
+                How to integrate speaker embedding.
+            dropout_rate (float): 
+                Dropout rate.
+            zoneout_rate (float): 
+                Zoneout rate.
         """
         assert check_argument_types()
         super().__init__()
@@ -230,18 +259,28 @@ class Tacotron2(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            text (Tensor(int64)): Batch of padded character ids (B, T_text).
-            text_lengths (Tensor(int64)): Batch of lengths of each input batch (B,).
-            speech (Tensor): Batch of padded target features (B, T_feats, odim).
-            speech_lengths (Tensor(int64)): Batch of the lengths of each target (B,).
-            spk_emb (Optional[Tensor]): Batch of speaker embeddings (B, spk_embed_dim).
-            spk_id (Optional[Tensor]): Batch of speaker IDs (B, 1).
-            lang_id (Optional[Tensor]): Batch of language IDs (B, 1).
+            text (Tensor(int64)):   
+                Batch of padded character ids (B, T_text).
+            text_lengths (Tensor(int64)): 
+                Batch of lengths of each input batch (B,).
+            speech (Tensor):
+                 Batch of padded target features (B, T_feats, odim).
+            speech_lengths (Tensor(int64)): 
+                Batch of the lengths of each target (B,).
+            spk_emb (Optional[Tensor]): 
+                Batch of speaker embeddings (B, spk_embed_dim).
+            spk_id (Optional[Tensor]): 
+                Batch of speaker IDs (B, 1).
+            lang_id (Optional[Tensor]): 
+                Batch of language IDs (B, 1).
 
         Returns:
-            Tensor: Loss scalar value.
-            Dict: Statistics to be monitored.
-            Tensor: Weight value if not joint training else model outputs.
+            Tensor: 
+                Loss scalar value.
+            Dict: 
+                Statistics to be monitored.
+            Tensor: 
+                Weight value if not joint training else model outputs.
 
         """
         text = text[:, :text_lengths.max()]
@@ -329,18 +368,30 @@ class Tacotron2(nn.Layer):
         """Generate the sequence of features given the sequences of characters.
 
         Args:
-            text (Tensor(int64)): Input sequence of characters (T_text,).
-            speech (Optional[Tensor]): Feature sequence to extract style (N, idim).
-            spk_emb (ptional[Tensor]): Speaker embedding (spk_embed_dim,).
-            spk_id (Optional[Tensor]): Speaker ID (1,).
-            lang_id (Optional[Tensor]): Language ID (1,).
-            threshold (float): Threshold in inference.
-            minlenratio (float): Minimum length ratio in inference.
-            maxlenratio (float): Maximum length ratio in inference.
-            use_att_constraint (bool): Whether to apply attention constraint.
-            backward_window (int): Backward window in attention constraint.
-            forward_window (int): Forward window in attention constraint.
-            use_teacher_forcing (bool): Whether to use teacher forcing.
+            text (Tensor(int64)): 
+                Input sequence of characters (T_text,).
+            speech (Optional[Tensor]): 
+                Feature sequence to extract style (N, idim).
+            spk_emb (ptional[Tensor]): 
+                Speaker embedding (spk_embed_dim,).
+            spk_id (Optional[Tensor]): 
+                Speaker ID (1,).
+            lang_id (Optional[Tensor]): 
+                Language ID (1,).
+            threshold (float): 
+                Threshold in inference.
+            minlenratio (float): 
+                Minimum length ratio in inference.
+            maxlenratio (float): 
+                Maximum length ratio in inference.
+            use_att_constraint (bool): 
+                Whether to apply attention constraint.
+            backward_window (int): 
+                Backward window in attention constraint.
+            forward_window (int): 
+                    Forward window in attention constraint.
+            use_teacher_forcing (bool): 
+                Whether to use teacher forcing.
 
         Returns:
             Dict[str, Tensor]

paddlespeech/t2s/models/transformer_tts/transformer_tts.py

@@ -49,66 +49,124 @@ class TransformerTTS(nn.Layer):
         https://arxiv.org/pdf/1809.08895.pdf
 
     Args:
-        idim (int): Dimension of the inputs.
-        odim (int): Dimension of the outputs.
-        embed_dim (int, optional): Dimension of character embedding.
-        eprenet_conv_layers (int, optional): Number of encoder prenet convolution layers.
-        eprenet_conv_chans (int, optional): Number of encoder prenet convolution channels.
-        eprenet_conv_filts (int, optional): Filter size of encoder prenet convolution.
-        dprenet_layers (int, optional): Number of decoder prenet layers.
-        dprenet_units (int, optional): Number of decoder prenet hidden units.
-        elayers (int, optional): Number of encoder layers.
-        eunits (int, optional): Number of encoder hidden units.
-        adim (int, optional): Number of attention transformation dimensions.
-        aheads (int, optional): Number of heads for multi head attention.
-        dlayers (int, optional): Number of decoder layers.
-        dunits (int, optional): Number of decoder hidden units.
-        postnet_layers (int, optional): Number of postnet layers.
-        postnet_chans (int, optional): Number of postnet channels.
-        postnet_filts (int, optional): Filter size of postnet.
-        use_scaled_pos_enc (pool, optional): Whether to use trainable scaled positional encoding.
-        use_batch_norm (bool, optional): Whether to use batch normalization in encoder prenet.
-        encoder_normalize_before (bool, optional): Whether to perform layer normalization before encoder block.
-        decoder_normalize_before (bool, optional): Whether to perform layer normalization before decoder block.
-        encoder_concat_after (bool, optional): Whether to concatenate attention layer's input and output in encoder.
-        decoder_concat_after (bool, optional): Whether to concatenate attention layer's input and output in decoder.
-        positionwise_layer_type (str, optional): Position-wise operation type.
-        positionwise_conv_kernel_size (int, optional): Kernel size in position wise conv 1d.
-        reduction_factor (int, optional): Reduction factor.
-        spk_embed_dim (int, optional): Number of speaker embedding dimenstions.
-        spk_embed_integration_type (str, optional): How to integrate speaker embedding.
-        use_gst (str, optional): Whether to use global style token.
-        gst_tokens (int, optional): The number of GST embeddings.
-        gst_heads (int, optional): The number of heads in GST multihead attention.
-        gst_conv_layers (int, optional): The number of conv layers in GST.
-        gst_conv_chans_list (Sequence[int], optional): List of the number of channels of conv layers in GST.
-        gst_conv_kernel_size (int, optional): Kernal size of conv layers in GST.
-        gst_conv_stride (int, optional): Stride size of conv layers in GST.
-        gst_gru_layers (int, optional): The number of GRU layers in GST.
-        gst_gru_units (int, optional): The number of GRU units in GST.
-        transformer_lr (float, optional): Initial value of learning rate.
-        transformer_warmup_steps (int, optional): Optimizer warmup steps.
-        transformer_enc_dropout_rate (float, optional): Dropout rate in encoder except attention and positional encoding.
-        transformer_enc_positional_dropout_rate (float, optional): Dropout rate after encoder positional encoding.
-        transformer_enc_attn_dropout_rate （float, optional): Dropout rate in encoder self-attention module.
-        transformer_dec_dropout_rate (float, optional): Dropout rate in decoder except attention & positional encoding.
-        transformer_dec_positional_dropout_rate (float, optional): Dropout rate after decoder positional encoding.
-        transformer_dec_attn_dropout_rate （float, optional): Dropout rate in deocoder self-attention module.
-        transformer_enc_dec_attn_dropout_rate (float, optional): Dropout rate in encoder-deocoder attention module.
-        init_type (str, optional): How to initialize transformer parameters.
-        init_enc_alpha （float, optional）: Initial value of alpha in scaled pos encoding of the encoder.
-        init_dec_alpha (float, optional): Initial value of alpha in scaled pos encoding of the decoder.
-        eprenet_dropout_rate (float, optional): Dropout rate in encoder prenet.
-        dprenet_dropout_rate (float, optional): Dropout rate in decoder prenet.
-        postnet_dropout_rate (float, optional): Dropout rate in postnet.
-        use_masking (bool, optional): Whether to apply masking for padded part in loss calculation.
-        use_weighted_masking (bool, optional): Whether to apply weighted masking in loss calculation.
-        bce_pos_weight (float, optional): Positive sample weight in bce calculation (only for use_masking=true).
-        loss_type (str, optional): How to calculate loss.
-        use_guided_attn_loss (bool, optional): Whether to use guided attention loss.
-        num_heads_applied_guided_attn (int, optional): Number of heads in each layer to apply guided attention loss.
-        num_layers_applied_guided_attn (int, optional): Number of layers to apply guided attention loss.
-            List of module names to apply guided attention loss.
+        idim (int): 
+            Dimension of the inputs.
+        odim (int): 
+            Dimension of the outputs.
+        embed_dim (int, optional): 
+            Dimension of character embedding.
+        eprenet_conv_layers (int, optional): 
+            Number of encoder prenet convolution layers.
+        eprenet_conv_chans (int, optional): 
+            Number of encoder prenet convolution channels.
+        eprenet_conv_filts (int, optional): 
+            Filter size of encoder prenet convolution.
+        dprenet_layers (int, optional): 
+            Number of decoder prenet layers.
+        dprenet_units (int, optional): 
+            Number of decoder prenet hidden units.
+        elayers (int, optional): 
+            Number of encoder layers.
+        eunits (int, optional): 
+            Number of encoder hidden units.
+        adim (int, optional): 
+            Number of attention transformation dimensions.
+        aheads (int, optional): 
+            Number of heads for multi head attention.
+        dlayers (int, optional): 
+            Number of decoder layers.
+        dunits (int, optional): 
+            Number of decoder hidden units.
+        postnet_layers (int, optional): 
+            Number of postnet layers.
+        postnet_chans (int, optional): 
+            Number of postnet channels.
+        postnet_filts (int, optional): 
+            Filter size of postnet.
+        use_scaled_pos_enc (pool, optional): 
+            Whether to use trainable scaled positional encoding.
+        use_batch_norm (bool, optional): 
+            Whether to use batch normalization in encoder prenet.
+        encoder_normalize_before (bool, optional): 
+            Whether to perform layer normalization before encoder block.
+        decoder_normalize_before (bool, optional): 
+            Whether to perform layer normalization before decoder block.
+        encoder_concat_after (bool, optional): 
+            Whether to concatenate attention layer's input and output in encoder.
+        decoder_concat_after (bool, optional): 
+            Whether to concatenate attention layer's input and output in decoder.
+        positionwise_layer_type (str, optional): 
+            Position-wise operation type.
+        positionwise_conv_kernel_size (int, optional): 
+            Kernel size in position wise conv 1d.
+        reduction_factor (int, optional): 
+            Reduction factor.
+        spk_embed_dim (int, optional): 
+            Number of speaker embedding dimenstions.
+        spk_embed_integration_type (str, optional): 
+            How to integrate speaker embedding.
+        use_gst (str, optional): 
+            Whether to use global style token.
+        gst_tokens (int, optional): 
+            The number of GST embeddings.
+        gst_heads (int, optional): 
+            The number of heads in GST multihead attention.
+        gst_conv_layers (int, optional): 
+            The number of conv layers in GST.
+        gst_conv_chans_list (Sequence[int], optional): 
+            List of the number of channels of conv layers in GST.
+        gst_conv_kernel_size (int, optional): 
+            Kernal size of conv layers in GST.
+        gst_conv_stride (int, optional): 
+            Stride size of conv layers in GST.
+        gst_gru_layers (int, optional): 
+            The number of GRU layers in GST.
+        gst_gru_units (int, optional): 
+            The number of GRU units in GST.
+        transformer_lr (float, optional): 
+            Initial value of learning rate.
+        transformer_warmup_steps (int, optional): 
+            Optimizer warmup steps.
+        transformer_enc_dropout_rate (float, optional): 
+            Dropout rate in encoder except attention and positional encoding.
+        transformer_enc_positional_dropout_rate (float, optional): 
+            Dropout rate after encoder positional encoding.
+        transformer_enc_attn_dropout_rate （float, optional): 
+            Dropout rate in encoder self-attention module.
+        transformer_dec_dropout_rate (float, optional): 
+            Dropout rate in decoder except attention & positional encoding.
+        transformer_dec_positional_dropout_rate (float, optional): 
+            Dropout rate after decoder positional encoding.
+        transformer_dec_attn_dropout_rate （float, optional): 
+            Dropout rate in deocoder self-attention module.
+        transformer_enc_dec_attn_dropout_rate (float, optional): 
+            Dropout rate in encoder-deocoder attention module.
+        init_type (str, optional): 
+            How to initialize transformer parameters.
+        init_enc_alpha （float, optional）: 
+            Initial value of alpha in scaled pos encoding of the encoder.
+        init_dec_alpha (float, optional): 
+            Initial value of alpha in scaled pos encoding of the decoder.
+        eprenet_dropout_rate (float, optional): 
+            Dropout rate in encoder prenet.
+        dprenet_dropout_rate (float, optional): 
+            Dropout rate in decoder prenet.
+        postnet_dropout_rate (float, optional): 
+            Dropout rate in postnet.
+        use_masking (bool, optional): 
+            Whether to apply masking for padded part in loss calculation.
+        use_weighted_masking (bool, optional): 
+            Whether to apply weighted masking in loss calculation.
+        bce_pos_weight (float, optional): 
+            Positive sample weight in bce calculation (only for use_masking=true).
+        loss_type (str, optional): 
+            How to calculate loss.
+        use_guided_attn_loss (bool, optional): 
+            Whether to use guided attention loss.
+        num_heads_applied_guided_attn (int, optional):
+            Number of heads in each layer to apply guided attention loss.
+        num_layers_applied_guided_attn (int, optional): 
+            Number of layers to apply guided attention loss.
     """
 
     def __init__(

paddlespeech/t2s/models/waveflow.py

@@ -33,8 +33,10 @@ def fold(x, n_group):
     """Fold audio or spectrogram's temporal dimension in to groups.
 
     Args:
-        x(Tensor): The input tensor. shape=(*, time_steps)
-        n_group(int): The size of a group.
+        x(Tensor): 
+            The input tensor. shape=(*, time_steps)
+        n_group(int): 
+            The size of a group.
 
     Returns:
         Tensor: Folded tensor. shape=(*, time_steps // n_group, group)
@@ -53,7 +55,8 @@ class UpsampleNet(nn.LayerList):
     on mel and time dimension.
 
     Args:
-        upscale_factors(List[int], optional): Time upsampling factors for each Conv2DTranspose Layer.
+        upscale_factors(List[int], optional): 
+            Time upsampling factors for each Conv2DTranspose Layer.
             The ``UpsampleNet`` contains ``len(upscale_factor)`` Conv2DTranspose
             Layers. Each upscale_factor is used as the ``stride`` for the
             corresponding Conv2DTranspose. Defaults to [16, 16], this the default
@@ -94,8 +97,10 @@ class UpsampleNet(nn.LayerList):
         """Forward pass of the ``UpsampleNet``
 
         Args:
-            x(Tensor): The input spectrogram. shape=(batch_size, input_channels, time_steps)
-            trim_conv_artifact(bool, optional, optional): Trim deconvolution artifact at each layer. Defaults to False.
+            x(Tensor): 
+                The input spectrogram. shape=(batch_size, input_channels, time_steps)
+            trim_conv_artifact(bool, optional, optional): 
+                Trim deconvolution artifact at each layer. Defaults to False.
 
         Returns:
            Tensor: The upsampled spectrogram. shape=(batch_size, input_channels, time_steps * upsample_factor)
@@ -123,10 +128,14 @@ class ResidualBlock(nn.Layer):
     and output.
 
     Args:
-        channels (int): Feature size of the input.
-        cond_channels (int): Featuer size of the condition.
-        kernel_size (Tuple[int]): Kernel size of the Convolution2d applied to the input.
-        dilations (int): Dilations of the Convolution2d applied to the input.
+        channels (int): 
+            Feature size of the input.
+        cond_channels (int): 
+            Featuer size of the condition.
+        kernel_size (Tuple[int]): 
+            Kernel size of the Convolution2d applied to the input.
+        dilations (int): 
+            Dilations of the Convolution2d applied to the input.
     """
 
     def __init__(self, channels, cond_channels, kernel_size, dilations):
@@ -173,12 +182,16 @@ class ResidualBlock(nn.Layer):
         """Compute output for a whole folded sequence.
 
         Args:
-            x (Tensor): The input. [shape=(batch_size, channel, height, width)]
-            condition (Tensor [shape=(batch_size, condition_channel, height, width)]): The local condition.
+            x (Tensor): 
+                The input. [shape=(batch_size, channel, height, width)]
+            condition (Tensor [shape=(batch_size, condition_channel, height, width)]): 
+                The local condition.
 
         Returns: 
-            res (Tensor): The residual output. [shape=(batch_size, channel, height, width)]
-            skip (Tensor): The skip output. [shape=(batch_size, channel, height, width)]
+            res (Tensor): 
+                The residual output. [shape=(batch_size, channel, height, width)]
+            skip (Tensor): 
+                The skip output. [shape=(batch_size, channel, height, width)]
         """
         x_in = x
         x = self.conv(x)
@@ -216,12 +229,16 @@ class ResidualBlock(nn.Layer):
         """Compute the output for a row and update the buffer.
 
         Args:
-            x_row (Tensor): A row of the input. shape=(batch_size, channel, 1, width)
-            condition_row (Tensor): A row of the condition. shape=(batch_size, condition_channel, 1, width)
+            x_row (Tensor): 
+                A row of the input. shape=(batch_size, channel, 1, width)
+            condition_row (Tensor): 
+                A row of the condition. shape=(batch_size, condition_channel, 1, width)
 
         Returns:
-            res (Tensor): A row of the the residual output. shape=(batch_size, channel, 1, width)
-            skip (Tensor): A row of the skip output. shape=(batch_size, channel, 1, width)
+            res (Tensor): 
+                A row of the the residual output. shape=(batch_size, channel, 1, width)
+            skip (Tensor): 
+                A row of the skip output. shape=(batch_size, channel, 1, width)
 
         """
         x_row_in = x_row
@@ -258,11 +275,16 @@ class ResidualNet(nn.LayerList):
     """A stack of several ResidualBlocks. It merges condition at each layer.
 
     Args:
-        n_layer (int): Number of ResidualBlocks in the ResidualNet.
-        residual_channels (int): Feature size of each ResidualBlocks.
-        condition_channels (int): Feature size of the condition.
-        kernel_size (Tuple[int]): Kernel size of each ResidualBlock.
-        dilations_h (List[int]): Dilation in height dimension of every ResidualBlock.
+        n_layer (int): 
+            Number of ResidualBlocks in the ResidualNet.
+        residual_channels (int): 
+            Feature size of each ResidualBlocks.
+        condition_channels (int): 
+            Feature size of the condition.
+        kernel_size (Tuple[int]): 
+            Kernel size of each ResidualBlock.
+        dilations_h (List[int]): 
+            Dilation in height dimension of every ResidualBlock.
 
     Raises:
         ValueError: If the length of dilations_h does not equals n_layers.
@@ -288,11 +310,13 @@ class ResidualNet(nn.LayerList):
         """Comput the output of given the input and the condition.
 
         Args:
-            x (Tensor): The input. shape=(batch_size, channel, height, width)
-            condition (Tensor): The local condition. shape=(batch_size, condition_channel, height, width)
+            x (Tensor): 
+                The input. shape=(batch_size, channel, height, width)
+            condition (Tensor): 
+                The local condition. shape=(batch_size, condition_channel, height, width)
             
         Returns: 
-            Tensor : The output, which is an aggregation of all the skip outputs. shape=(batch_size, channel, height, width)
+            Tensor: The output, which is an aggregation of all the skip outputs. shape=(batch_size, channel, height, width)
             
         """
         skip_connections = []
@@ -312,12 +336,16 @@ class ResidualNet(nn.LayerList):
         """Compute the output for a row and update the buffers.
 
         Args:
-            x_row (Tensor): A row of the input. shape=(batch_size, channel, 1, width)
-            condition_row (Tensor):  A row of the condition. shape=(batch_size, condition_channel, 1, width)
+            x_row (Tensor): 
+                A row of the input. shape=(batch_size, channel, 1, width)
+            condition_row (Tensor):  
+                A row of the condition. shape=(batch_size, condition_channel, 1, width)
             
         Returns:
-            res (Tensor): A row of the the residual output. shape=(batch_size, channel, 1, width) 
-            skip (Tensor): A row of the skip output. shape=(batch_size, channel, 1, width)
+            res (Tensor): 
+                A row of the the residual output. shape=(batch_size, channel, 1, width) 
+            skip (Tensor): 
+                A row of the skip output. shape=(batch_size, channel, 1, width)
                 
         """
         skip_connections = []
@@ -337,11 +365,16 @@ class Flow(nn.Layer):
     sampling.
 
     Args:
-        n_layers (int): Number of ResidualBlocks in the Flow.
-        channels (int): Feature size of the ResidualBlocks.
-        mel_bands (int): Feature size of the mel spectrogram (mel bands).
-        kernel_size (Tuple[int]): Kernel size of each ResisualBlocks in the Flow.
-        n_group (int): Number of timesteps to the folded into a group.
+        n_layers (int): 
+            Number of ResidualBlocks in the Flow.
+        channels (int): 
+            Feature size of the ResidualBlocks.
+        mel_bands (int): 
+            Feature size of the mel spectrogram (mel bands).
+        kernel_size (Tuple[int]): 
+            Kernel size of each ResisualBlocks in the Flow.
+        n_group (int): 
+            Number of timesteps to the folded into a group.
     """
     dilations_dict = {
         8: [1, 1, 1, 1, 1, 1, 1, 1],
@@ -393,11 +426,14 @@ class Flow(nn.Layer):
         a sample from p(X) into a sample from p(Z).
 
         Args:
-            x (Tensor): A input sample of the distribution p(X). shape=(batch, 1, height, width)
-            condition (Tensor): The local condition. shape=(batch, condition_channel, height, width)
+            x (Tensor): 
+                A input sample of the distribution p(X). shape=(batch, 1, height, width)
+            condition (Tensor): 
+                The local condition. shape=(batch, condition_channel, height, width)
             
         Returns:
-            z (Tensor): shape(batch, 1, height, width), the transformed sample.
+            z (Tensor): 
+                shape(batch, 1, height, width), the transformed sample.
             Tuple[Tensor, Tensor]:
                 The parameter of the transformation.
                 logs (Tensor): shape(batch, 1, height - 1, width), the log scale of the transformation from x to z.
@@ -433,8 +469,10 @@ class Flow(nn.Layer):
         p(Z) and transform the sample. It is a auto regressive transformation.
 
         Args:
-            z(Tensor): A sample of the distribution p(Z). shape=(batch, 1, time_steps
-            condition(Tensor): The local condition. shape=(batch, condition_channel, time_steps)
+            z(Tensor): 
+                A sample of the distribution p(Z). shape=(batch, 1, time_steps
+            condition(Tensor): 
+                The local condition. shape=(batch, condition_channel, time_steps)
         Returns:
             Tensor:
                 The transformed sample. shape=(batch, 1, height, width)
@@ -462,12 +500,18 @@ class WaveFlow(nn.LayerList):
     flows.
 
     Args:
-        n_flows (int): Number of flows in the WaveFlow model.
-        n_layers (int): Number of ResidualBlocks in each Flow.
-        n_group (int): Number of timesteps to fold as a group.
-        channels (int): Feature size of each ResidualBlock.
-        mel_bands (int): Feature size of mel spectrogram (mel bands).
-        kernel_size (Union[int, List[int]]): Kernel size of the convolution layer in each ResidualBlock.
+        n_flows (int): 
+            Number of flows in the WaveFlow model.
+        n_layers (int): 
+            Number of ResidualBlocks in each Flow.
+        n_group (int): 
+            Number of timesteps to fold as a group.
+        channels (int): 
+            Feature size of each ResidualBlock.
+        mel_bands (int): 
+            Feature size of mel spectrogram (mel bands).
+        kernel_size (Union[int, List[int]]): 
+            Kernel size of the convolution layer in each ResidualBlock.
     """
 
     def __init__(self, n_flows, n_layers, n_group, channels, mel_bands,
@@ -518,12 +562,16 @@ class WaveFlow(nn.LayerList):
         condition.
 
         Args:
-            x (Tensor): The audio. shape=(batch_size, time_steps)
-            condition (Tensor): The local condition (mel spectrogram here). shape=(batch_size, condition channel, time_steps)
+            x (Tensor): 
+                The audio. shape=(batch_size, time_steps)
+            condition (Tensor): 
+                The local condition (mel spectrogram here). shape=(batch_size, condition channel, time_steps)
                 
         Returns:
-            Tensor: The transformed random variable. shape=(batch_size, time_steps)
-            Tensor: The log determinant of the jacobian of the transformation from x to z. shape=(1,)
+            Tensor: 
+                The transformed random variable. shape=(batch_size, time_steps)
+            Tensor: 
+                The log determinant of the jacobian of the transformation from x to z. shape=(1,)
         """
         # x: (B, T)
         # condition: (B, C, T) upsampled condition
@@ -559,12 +607,13 @@ class WaveFlow(nn.LayerList):
         autoregressive manner.
 
         Args:
-            z (Tensor): A sample of the distribution p(Z). shape=(batch, 1, time_steps
-            condition (Tensor): The local condition. shape=(batch, condition_channel, time_steps)    
+            z (Tensor): 
+                A sample of the distribution p(Z). shape=(batch, 1, time_steps
+            condition (Tensor): 
+                The local condition. shape=(batch, condition_channel, time_steps)    
 
         Returns: 
             Tensor: The transformed sample (audio here). shape=(batch_size, time_steps)
-            
         """
 
         z, condition = self._trim(z, condition)
@@ -590,13 +639,20 @@ class ConditionalWaveFlow(nn.LayerList):
     """ConditionalWaveFlow, a UpsampleNet with a WaveFlow model.
 
     Args:
-        upsample_factors (List[int]): Upsample factors for the upsample net.
-        n_flows (int): Number of flows in the WaveFlow model.
-        n_layers (int): Number of ResidualBlocks in each Flow.
-        n_group (int): Number of timesteps to fold as a group.
-        channels (int): Feature size of each ResidualBlock.
-        n_mels (int): Feature size of mel spectrogram (mel bands).
-        kernel_size (Union[int, List[int]]): Kernel size of the convolution layer in each ResidualBlock.
+        upsample_factors (List[int]): 
+            Upsample factors for the upsample net.
+        n_flows (int): 
+            Number of flows in the WaveFlow model.
+        n_layers (int): 
+            Number of ResidualBlocks in each Flow.
+        n_group (int): 
+            Number of timesteps to fold as a group.
+        channels (int): 
+            Feature size of each ResidualBlock.
+        n_mels (int): 
+            Feature size of mel spectrogram (mel bands).
+        kernel_size (Union[int, List[int]]): 
+            Kernel size of the convolution layer in each ResidualBlock.
         """
 
     def __init__(self,
@@ -622,12 +678,16 @@ class ConditionalWaveFlow(nn.LayerList):
         the determinant of the jacobian of the transformation from x to z.
 
         Args:
-            audio(Tensor): The audio. shape=(B, T)
-            mel(Tensor): The mel spectrogram. shape=(B, C_mel, T_mel)
+            audio(Tensor): 
+                The audio. shape=(B, T)
+            mel(Tensor): 
+                The mel spectrogram. shape=(B, C_mel, T_mel)
 
         Returns:
-            Tensor: The inversely transformed random variable z (x to z). shape=(B, T)
-            Tensor: the log of the determinant of the jacobian of the transformation from x to z. shape=(1,)
+            Tensor: 
+                The inversely transformed random variable z (x to z). shape=(B, T)
+            Tensor: 
+                the log of the determinant of the jacobian of the transformation from x to z. shape=(1,)
         """
         condition = self.encoder(mel)
         z, log_det_jacobian = self.decoder(audio, condition)
@@ -638,10 +698,12 @@ class ConditionalWaveFlow(nn.LayerList):
         """Generate raw audio given mel spectrogram.
 
         Args:
-            mel(np.ndarray): Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel)
+            mel(np.ndarray): 
+                Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel)
 
         Returns:
-            Tensor: The synthesized audio, where``T <= T_mel * upsample_factors``. shape=(B, T)
+            Tensor: 
+                The synthesized audio, where``T <= T_mel * upsample_factors``. shape=(B, T)
         """
         start = time.time()
         condition = self.encoder(mel, trim_conv_artifact=True)  # (B, C, T)
@@ -657,7 +719,8 @@ class ConditionalWaveFlow(nn.LayerList):
         """Generate raw audio given mel spectrogram.
 
         Args:
-            mel(np.ndarray): Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel)
+            mel(np.ndarray): 
+                Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel)
 
         Returns:
             np.ndarray: The synthesized audio. shape=(T,)
@@ -673,8 +736,10 @@ class ConditionalWaveFlow(nn.LayerList):
         """Build a ConditionalWaveFlow model from a pretrained model.
 
         Args:
-            config(yacs.config.CfgNode): model configs
-            checkpoint_path(Path or str): the path of pretrained model checkpoint, without extension name
+            config(yacs.config.CfgNode): 
+                model configs
+            checkpoint_path(Path or str): 
+                the path of pretrained model checkpoint, without extension name
 
         Returns:
             ConditionalWaveFlow The model built from pretrained result.
@@ -694,8 +759,8 @@ class WaveFlowLoss(nn.Layer):
     """Criterion of a WaveFlow model.
 
     Args:
-        sigma (float): The standard deviation of the gaussian noise used in WaveFlow, 
-            by default 1.0.
+        sigma (float): 
+            The standard deviation of the gaussian noise used in WaveFlow, by default 1.0.
     """
 
     def __init__(self, sigma=1.0):
@@ -708,8 +773,10 @@ class WaveFlowLoss(nn.Layer):
         log_det_jacobian of transformation from x to z.
 
         Args:
-            z(Tensor): The transformed random variable (x to z). shape=(B, T)
-            log_det_jacobian(Tensor): The log of the determinant of the jacobian matrix of the
+            z(Tensor): 
+                The transformed random variable (x to z). shape=(B, T)
+            log_det_jacobian(Tensor): 
+                The log of the determinant of the jacobian matrix of the
                 transformation from x to z.  shape=(1,)
 
         Returns:
@@ -726,7 +793,8 @@ class ConditionalWaveFlow2Infer(ConditionalWaveFlow):
         """Generate raw audio given mel spectrogram.
 
         Args:
-            mel (np.ndarray): Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel)
+            mel (np.ndarray): 
+                Mel spectrogram of an utterance(in log-magnitude). shape=(C_mel, T_mel)
             
         Returns:
             np.ndarray: The synthesized audio. shape=(T,)

paddlespeech/t2s/models/wavernn/wavernn.py

@@ -165,19 +165,29 @@ class WaveRNN(nn.Layer):
             init_type: str="xavier_uniform", ):
         '''
         Args:
-            rnn_dims (int, optional): Hidden dims of RNN Layers.
-            fc_dims (int, optional): Dims of FC Layers.
-            bits (int, optional): bit depth of signal.
-            aux_context_window (int, optional): The context window size of the first convolution applied to the 
-                auxiliary input, by default 2
-            upsample_scales (List[int], optional): Upsample scales of the upsample network.
-            aux_channels (int, optional): Auxiliary channel of the residual blocks.
-            compute_dims (int, optional): Dims of Conv1D in MelResNet.
-            res_out_dims (int, optional): Dims of output in MelResNet.
-            res_blocks (int, optional): Number of residual blocks.
-            mode (str, optional): Output mode of the WaveRNN vocoder. 
+            rnn_dims (int, optional): 
+                Hidden dims of RNN Layers.
+            fc_dims (int, optional): 
+                Dims of FC Layers.
+            bits (int, optional): 
+                bit depth of signal.
+            aux_context_window (int, optional): 
+                The context window size of the first convolution applied to the auxiliary input, by default 2
+            upsample_scales (List[int], optional): 
+                Upsample scales of the upsample network.
+            aux_channels (int, optional): 
+                Auxiliary channel of the residual blocks.
+            compute_dims (int, optional): 
+                Dims of Conv1D in MelResNet.
+            res_out_dims (int, optional): 
+                Dims of output in MelResNet.
+            res_blocks (int, optional): 
+                Number of residual blocks.
+            mode (str, optional): 
+                Output mode of the WaveRNN vocoder. 
                 `MOL` for Mixture of Logistic Distribution, and `RAW` for quantized bits as the model's output.
-            init_type (str): How to initialize parameters.
+            init_type (str): 
+                How to initialize parameters.
         '''
         super().__init__()
         self.mode = mode
@@ -226,8 +236,10 @@ class WaveRNN(nn.Layer):
     def forward(self, x, c):
         '''
         Args:
-            x (Tensor): wav sequence, [B, T]
-            c (Tensor): mel spectrogram [B, C_aux, T']
+            x (Tensor): 
+                wav sequence, [B, T]
+            c (Tensor): 
+                mel spectrogram [B, C_aux, T']
 
             T = (T' - 2 * aux_context_window ) * hop_length
         Returns:
@@ -280,10 +292,14 @@ class WaveRNN(nn.Layer):
                  gen_display: bool=False):
         """
         Args:
-            c(Tensor): input mels, (T', C_aux)
-            batched(bool): generate in batch or not
-            target(int): target number of samples to be generated in each batch entry
-            overlap(int): number of samples for crossfading between batches
+            c(Tensor): 
+                input mels, (T', C_aux)
+            batched(bool): 
+                generate in batch or not
+            target(int): 
+                target number of samples to be generated in each batch entry
+            overlap(int): 
+                number of samples for crossfading between batches
             mu_law(bool)
         Returns: 
             wav sequence: Output (T' * prod(upsample_scales), out_channels, C_out).
@@ -404,7 +420,8 @@ class WaveRNN(nn.Layer):
     def pad_tensor(self, x, pad, side='both'):
         '''
         Args:
-            x(Tensor): mel, [1, n_frames, 80]
+            x(Tensor): 
+                mel, [1, n_frames, 80]
             pad(int): 
             side(str, optional):  (Default value = 'both')
 
@@ -428,12 +445,15 @@ class WaveRNN(nn.Layer):
         Overlap will be used for crossfading in xfade_and_unfold()
 
         Args:
-            x(Tensor): Upsampled conditioning features. mels or aux
+            x(Tensor): 
+                Upsampled conditioning features. mels or aux
                 shape=(1, T, features)
                 mels: [1, T, 80]
                 aux: [1, T, 128]
-            target(int): Target timesteps for each index of batch
-            overlap(int): Timesteps for both xfade and rnn warmup
+            target(int): 
+                Target timesteps for each index of batch
+            overlap(int): 
+                Timesteps for both xfade and rnn warmup
 
         Returns:
             Tensor: 

paddlespeech/t2s/modules/causal_conv.py

@@ -42,7 +42,8 @@ class CausalConv1D(nn.Layer):
     def forward(self, x):
         """Calculate forward propagation.
         Args:
-            x (Tensor): Input tensor (B, in_channels, T).
+            x (Tensor): 
+                Input tensor (B, in_channels, T).
         Returns: 
             Tensor: Output tensor (B, out_channels, T).
         """
@@ -67,7 +68,8 @@ class CausalConv1DTranspose(nn.Layer):
     def forward(self, x):
         """Calculate forward propagation.
         Args:
-            x (Tensor): Input tensor (B, in_channels, T_in).
+            x (Tensor): 
+                Input tensor (B, in_channels, T_in).
         Returns:
             Tensor: Output tensor (B, out_channels, T_out).
         """

paddlespeech/t2s/modules/conformer/convolution.py

@@ -20,8 +20,10 @@ class ConvolutionModule(nn.Layer):
     """ConvolutionModule in Conformer model.
 
     Args:
-        channels (int): The number of channels of conv layers.
-        kernel_size (int): Kernerl size of conv layers.
+        channels (int): 
+            The number of channels of conv layers.
+        kernel_size (int): 
+            Kernerl size of conv layers.
     """
 
     def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
@@ -59,7 +61,8 @@ class ConvolutionModule(nn.Layer):
         """Compute convolution module.
 
         Args:
-            x (Tensor): Input tensor (#batch, time, channels).
+            x (Tensor): 
+                Input tensor (#batch, time, channels).
         Returns:
             Tensor: Output tensor (#batch, time, channels).
         """

paddlespeech/t2s/modules/conformer/encoder_layer.py

@@ -23,25 +23,34 @@ class EncoderLayer(nn.Layer):
     """Encoder layer module.
     
     Args:
-        size (int): Input dimension.
-        self_attn (nn.Layer): Self-attention module instance.
+        size (int): 
+            Input dimension.
+        self_attn (nn.Layer): 
+            Self-attention module instance.
             `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
             can be used as the argument.
-        feed_forward (nn.Layer): Feed-forward module instance.
+        feed_forward (nn.Layer): 
+            Feed-forward module instance.
             `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
             can be used as the argument.
-        feed_forward_macaron (nn.Layer): Additional feed-forward module instance.
+        feed_forward_macaron (nn.Layer): 
+            Additional feed-forward module instance.
             `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
             can be used as the argument.
-        conv_module (nn.Layer): Convolution module instance.
+        conv_module (nn.Layer): 
+            Convolution module instance.
             `ConvlutionModule` instance can be used as the argument.
-        dropout_rate (float): Dropout rate.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
+        dropout_rate (float): 
+            Dropout rate.
+        normalize_before (bool): 
+            Whether to use layer_norm before the first block.
+        concat_after (bool): 
+            Whether to concat attention layer's input and output.
             if True, additional linear will be applied.
             i.e. x -> x + linear(concat(x, att(x)))
             if False, no additional linear will be applied. i.e. x -> x + att(x)
-        stochastic_depth_rate (float): Proability to skip this layer.
+        stochastic_depth_rate (float): 
+            Proability to skip this layer.
             During training, the layer may skip residual computation and return input
             as-is with given probability.
     """
@@ -86,15 +95,19 @@ class EncoderLayer(nn.Layer):
         """Compute encoded features.
 
         Args:
-            x_input(Union[Tuple, Tensor]): Input tensor w/ or w/o pos emb.
+            x_input(Union[Tuple, Tensor]): 
+                Input tensor w/ or w/o pos emb.
                 - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
                 - w/o pos emb: Tensor (#batch, time, size).
-            mask(Tensor): Mask tensor for the input (#batch, time).
+            mask(Tensor): 
+                Mask tensor for the input (#batch, time).
             cache (Tensor): 
 
         Returns:
-            Tensor: Output tensor (#batch, time, size).
-            Tensor: Mask tensor (#batch, time).
+            Tensor: 
+                Output tensor (#batch, time, size).
+            Tensor: 
+                Mask tensor (#batch, time).
         """
         if isinstance(x_input, tuple):
             x, pos_emb = x_input[0], x_input[1]

paddlespeech/t2s/modules/conv.py

@@ -42,13 +42,19 @@ class Conv1dCell(nn.Conv1D):
     class.
 
     Args:
-        in_channels (int): The feature size of the input.
-        out_channels (int): The feature size of the output.
-        kernel_size (int or Tuple[int]): The size of the kernel.
-        dilation (int or Tuple[int]): The dilation of the convolution, by default 1
-        weight_attr (ParamAttr, Initializer, str or bool, optional) : The parameter attribute of the convolution kernel, 
+        in_channels (int): 
+            The feature size of the input.
+        out_channels (int): 
+            The feature size of the output.
+        kernel_size (int or Tuple[int]): 
+            The size of the kernel.
+        dilation (int or Tuple[int]): 
+            The dilation of the convolution, by default 1
+        weight_attr (ParamAttr, Initializer, str or bool, optional): 
+            The parameter attribute of the convolution kernel, 
             by default None.
-        bias_attr (ParamAttr, Initializer, str or bool, optional):The parameter attribute of the bias. 
+        bias_attr (ParamAttr, Initializer, str or bool, optional):
+            The parameter attribute of the bias. 
             If ``False``, this layer does not have a bias, by default None.
             
     Examples: 
@@ -122,7 +128,8 @@ class Conv1dCell(nn.Conv1D):
         """Initialize the buffer for the step input.
 
         Args:
-            x_t (Tensor): The step input. shape=(batch_size, in_channels)
+            x_t (Tensor): 
+                The step input. shape=(batch_size, in_channels)
             
         """
         batch_size, _ = x_t.shape
@@ -134,7 +141,8 @@ class Conv1dCell(nn.Conv1D):
         """Shift the buffer by one step.
 
         Args:
-            x_t (Tensor): The step input. shape=(batch_size, in_channels)
+            x_t (Tensor): T
+                he step input. shape=(batch_size, in_channels)
             
         """
         self._buffer = paddle.concat(
@@ -144,10 +152,12 @@ class Conv1dCell(nn.Conv1D):
         """Add step input and compute step output.
 
         Args:
-            x_t (Tensor): The step input. shape=(batch_size, in_channels)
+            x_t (Tensor): 
+                The step input. shape=(batch_size, in_channels)
           
         Returns: 
-            y_t (Tensor): The step output. shape=(batch_size, out_channels)
+            y_t (Tensor): 
+                The step output. shape=(batch_size, out_channels)
 
         """
         batch_size = x_t.shape[0]
@@ -173,10 +183,14 @@ class Conv1dBatchNorm(nn.Layer):
     """A Conv1D Layer followed by a BatchNorm1D.
 
     Args:
-        in_channels (int): The feature size of the input.
-        out_channels (int): The feature size of the output.
-        kernel_size (int): The size of the convolution kernel.
-        stride (int, optional): The stride of the convolution, by default 1.
+        in_channels (int): 
+            The feature size of the input.
+        out_channels (int): 
+            The feature size of the output.
+        kernel_size (int): 
+            The size of the convolution kernel.
+        stride (int, optional): 
+            The stride of the convolution, by default 1.
         padding (int, str or Tuple[int], optional):
             The padding of the convolution.
             If int, a symmetrical padding is applied before convolution;
@@ -189,9 +203,12 @@ class Conv1dBatchNorm(nn.Layer):
         bias_attr (ParamAttr, Initializer, str or bool, optional):
             The parameter attribute of the bias of the convolution,
             by defaultNone.
-        data_format (str ["NCL" or "NLC"], optional): The data layout of the input, by default "NCL"
-        momentum (float, optional): The momentum of the BatchNorm1D layer, by default 0.9
-        epsilon (float, optional): The epsilon of the BatchNorm1D layer, by default 1e-05
+        data_format (str ["NCL" or "NLC"], optional): 
+            The data layout of the input, by default "NCL"
+        momentum (float, optional): 
+            The momentum of the BatchNorm1D layer, by default 0.9
+        epsilon (float, optional): 
+            The epsilon of the BatchNorm1D layer, by default 1e-05
     """
 
     def __init__(self,
@@ -225,12 +242,13 @@ class Conv1dBatchNorm(nn.Layer):
         """Forward pass of the Conv1dBatchNorm layer.
         
         Args:
-            x (Tensor): The input tensor. Its data layout depends on ``data_format``. 
-            shape=(B, C_in, T_in) or (B, T_in, C_in)
+            x (Tensor): 
+                The input tensor. Its data layout depends on ``data_format``. 
+                shape=(B, C_in, T_in) or (B, T_in, C_in)
     
         Returns:
-            Tensor: The output tensor. 
-                shape=(B, C_out, T_out) or (B, T_out, C_out)
+            Tensor: 
+                The output tensor. shape=(B, C_out, T_out) or (B, T_out, C_out)
                 
         """
         x = self.conv(x)

paddlespeech/t2s/modules/geometry.py

@@ -19,8 +19,10 @@ def shuffle_dim(x, axis, perm=None):
     """Permute input tensor along aixs given the permutation or randomly.
     
     Args:
-        x (Tensor): The input tensor.
-        axis (int): The axis to shuffle.
+        x (Tensor): 
+            The input tensor.
+        axis (int): 
+            The axis to shuffle.
         perm (List[int], ndarray, optional): 
             The order to reorder the tensor along the ``axis``-th dimension.
             It is a permutation of ``[0, d)``, where d is the size of the

paddlespeech/t2s/modules/layer_norm.py

@@ -19,8 +19,10 @@ from paddle import nn
 class LayerNorm(nn.LayerNorm):
     """Layer normalization module.
     Args:
-        nout (int): Output dim size.
-        dim (int): Dimension to be normalized.
+        nout (int): 
+            Output dim size.
+        dim (int): 
+            Dimension to be normalized.
     """
 
     def __init__(self, nout, dim=-1):
@@ -32,7 +34,8 @@ class LayerNorm(nn.LayerNorm):
         """Apply layer normalization.
 
         Args:
-            x (Tensor):Input tensor.
+            x (Tensor):
+                Input tensor.
 
         Returns: 
             Tensor: Normalized tensor.

paddlespeech/t2s/modules/losses.py

@@ -269,8 +269,10 @@ class GuidedAttentionLoss(nn.Layer):
         """Make masks indicating non-padded part.
 
         Args:
-            ilens(Tensor(int64) or List): Batch of lengths (B,).
-            olens(Tensor(int64) or List): Batch of lengths (B,).
+            ilens(Tensor(int64) or List): 
+                Batch of lengths (B,).
+            olens(Tensor(int64) or List): 
+                Batch of lengths (B,).
 
         Returns:
             Tensor: Mask tensor indicating non-padded part.
@@ -322,9 +324,12 @@ class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss):
         """Calculate forward propagation.
 
         Args:
-            att_ws(Tensor): Batch of multi head attention weights (B, H, T_max_out, T_max_in).
-            ilens(Tensor): Batch of input lenghts (B,).
-            olens(Tensor): Batch of output lenghts (B,).
+            att_ws(Tensor): 
+                Batch of multi head attention weights (B, H, T_max_out, T_max_in).
+            ilens(Tensor): 
+                Batch of input lenghts (B,).
+            olens(Tensor): 
+                Batch of output lenghts (B,).
 
         Returns:
             Tensor: Guided attention loss value.
@@ -354,9 +359,12 @@ class Tacotron2Loss(nn.Layer):
         """Initialize Tactoron2 loss module.
 
         Args:
-            use_masking (bool): Whether to apply masking for padded part in loss calculation.
-            use_weighted_masking (bool): Whether to apply weighted masking in loss calculation.
-            bce_pos_weight (float): Weight of positive sample of stop token.
+            use_masking (bool): 
+                Whether to apply masking for padded part in loss calculation.
+            use_weighted_masking (bool): 
+                Whether to apply weighted masking in loss calculation.
+            bce_pos_weight (float): 
+                Weight of positive sample of stop token.
         """
         super().__init__()
         assert (use_masking != use_weighted_masking) or not use_masking
@@ -374,17 +382,25 @@ class Tacotron2Loss(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            after_outs(Tensor): Batch of outputs after postnets (B, Lmax, odim).
-            before_outs(Tensor): Batch of outputs before postnets (B, Lmax, odim).
-            logits(Tensor): Batch of stop logits (B, Lmax).
-            ys(Tensor): Batch of padded target features (B, Lmax, odim).
-            stop_labels(Tensor(int64)): Batch of the sequences of stop token labels (B, Lmax).
+            after_outs(Tensor): 
+                Batch of outputs after postnets (B, Lmax, odim).
+            before_outs(Tensor): 
+                Batch of outputs before postnets (B, Lmax, odim).
+            logits(Tensor): 
+                Batch of stop logits (B, Lmax).
+            ys(Tensor): 
+                Batch of padded target features (B, Lmax, odim).
+            stop_labels(Tensor(int64)): 
+                Batch of the sequences of stop token labels (B, Lmax).
             olens(Tensor(int64)): 
 
         Returns:
-            Tensor: L1 loss value.
-            Tensor: Mean square error loss value.
-            Tensor: Binary cross entropy loss value.
+            Tensor: 
+                L1 loss value.
+            Tensor: 
+                Mean square error loss value.
+            Tensor: 
+                Binary cross entropy loss value.
         """
         # make mask and apply it
         if self.use_masking:
@@ -437,16 +453,24 @@ def stft(x,
          pad_mode='reflect'):
     """Perform STFT and convert to magnitude spectrogram.
     Args:
-        x(Tensor): Input signal tensor (B, T).
-        fft_size(int): FFT size.
-        hop_size(int): Hop size.
-        win_length(int, optional): window : str, optional (Default value = None)
-        window(str, optional): Name of window function, see `scipy.signal.get_window` for more
-            details. Defaults to "hann".
-        center(bool, optional, optional): center (bool, optional): Whether to pad `x` to make that the
+        x(Tensor): 
+            Input signal tensor (B, T).
+        fft_size(int): 
+            FFT size.
+        hop_size(int): 
+            Hop size.
+        win_length(int, optional): 
+        window (str, optional):
+            (Default value = None)
+        window(str, optional): 
+            Name of window function, see `scipy.signal.get_window` for more details. Defaults to "hann".
+        center(bool, optional, optional): center (bool, optional): 
+            Whether to pad `x` to make that the
             :math:`t \times hop\\_length` at the center of :math:`t`-th frame. Default: `True`.
-        pad_mode(str, optional, optional):  (Default value = 'reflect')
-        hop_length:  (Default value = None)
+        pad_mode(str, optional, optional):  
+            (Default value = 'reflect')
+        hop_length:  
+            (Default value = None)
 
     Returns:
         Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
@@ -480,8 +504,10 @@ class SpectralConvergenceLoss(nn.Layer):
     def forward(self, x_mag, y_mag):
         """Calculate forward propagation.
         Args: 
-            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
-            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
+            x_mag (Tensor):
+                 Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
+            y_mag (Tensor): 
+                Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
         Returns:
             Tensor: Spectral convergence loss value.
         """
@@ -501,8 +527,10 @@ class LogSTFTMagnitudeLoss(nn.Layer):
     def forward(self, x_mag, y_mag):
         """Calculate forward propagation.
         Args:
-            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
-            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
+            x_mag (Tensor): 
+                Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
+            y_mag (Tensor):
+                Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
         Returns:
             Tensor: Log STFT magnitude loss value.
         """
@@ -531,11 +559,15 @@ class STFTLoss(nn.Layer):
     def forward(self, x, y):
         """Calculate forward propagation.
         Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
+            x (Tensor): 
+                Predicted signal (B, T).
+            y (Tensor): 
+                Groundtruth signal (B, T).
         Returns:
-            Tensor: Spectral convergence loss value.
-            Tensor: Log STFT magnitude loss value.
+            Tensor: 
+                Spectral convergence loss value.
+            Tensor: 
+                Log STFT magnitude loss value.
         """
         x_mag = stft(x, self.fft_size, self.shift_size, self.win_length,
                      self.window)
@@ -558,10 +590,14 @@ class MultiResolutionSTFTLoss(nn.Layer):
             window="hann", ):
         """Initialize Multi resolution STFT loss module.
         Args:
-            fft_sizes (list): List of FFT sizes.
-            hop_sizes (list): List of hop sizes.
-            win_lengths (list): List of window lengths.
-            window (str): Window function type.
+            fft_sizes (list): 
+                List of FFT sizes.
+            hop_sizes (list): 
+                List of hop sizes.
+            win_lengths (list): 
+                List of window lengths.
+            window (str): 
+                Window function type.
         """
         super().__init__()
         assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
@@ -573,11 +609,15 @@ class MultiResolutionSTFTLoss(nn.Layer):
         """Calculate forward propagation.
         
         Args:
-            x (Tensor): Predicted signal (B, T) or (B, #subband, T).
-            y (Tensor): Groundtruth signal (B, T) or (B, #subband, T).
+            x (Tensor): 
+                Predicted signal (B, T) or (B, #subband, T).
+            y (Tensor): 
+                Groundtruth signal (B, T) or (B, #subband, T).
         Returns:
-            Tensor: Multi resolution spectral convergence loss value.
-            Tensor: Multi resolution log STFT magnitude loss value.
+            Tensor: 
+                Multi resolution spectral convergence loss value.
+            Tensor: 
+                Multi resolution log STFT magnitude loss value.
         """
         if len(x.shape) == 3:
             # (B, C, T) -> (B x C, T)
@@ -615,9 +655,11 @@ class GeneratorAdversarialLoss(nn.Layer):
     def forward(self, outputs):
         """Calcualate generator adversarial loss.
         Args:
-            outputs (Tensor or List): Discriminator outputs or list of discriminator outputs.
+            outputs (Tensor or List): 
+                Discriminator outputs or list of discriminator outputs.
         Returns:
-            Tensor: Generator adversarial loss value.
+            Tensor: 
+                Generator adversarial loss value.
         """
         if isinstance(outputs, (tuple, list)):
             adv_loss = 0.0
@@ -659,13 +701,15 @@ class DiscriminatorAdversarialLoss(nn.Layer):
         """Calcualate discriminator adversarial loss.
 
         Args:
-            outputs_hat (Tensor or list): Discriminator outputs or list of
-                discriminator outputs calculated from generator outputs.
-            outputs (Tensor or list): Discriminator outputs or list of
-                discriminator outputs calculated from groundtruth.
+            outputs_hat (Tensor or list): 
+                Discriminator outputs or list of discriminator outputs calculated from generator outputs.
+            outputs (Tensor or list): 
+                Discriminator outputs or list of discriminator outputs calculated from groundtruth.
         Returns:
-            Tensor: Discriminator real loss value.
-            Tensor: Discriminator fake loss value.
+            Tensor: 
+                Discriminator real loss value.
+            Tensor: 
+                Discriminator fake loss value.
         """
         if isinstance(outputs, (tuple, list)):
             real_loss = 0.0
@@ -766,9 +810,12 @@ def masked_l1_loss(prediction, target, mask):
     """Compute maksed L1 loss.
 
     Args:
-        prediction(Tensor): The prediction.
-        target(Tensor): The target. The shape should be broadcastable to ``prediction``.
-        mask(Tensor): The mask. The shape should be broadcatable to the broadcasted shape of
+        prediction(Tensor): 
+            The prediction.
+        target(Tensor): 
+            The target. The shape should be broadcastable to ``prediction``.
+        mask(Tensor): 
+            The mask. The shape should be broadcatable to the broadcasted shape of
             ``prediction`` and ``target``.
 
     Returns:
@@ -916,8 +963,10 @@ class MelSpectrogramLoss(nn.Layer):
     def forward(self, y_hat, y):
         """Calculate Mel-spectrogram loss.
         Args:
-            y_hat(Tensor): Generated single tensor (B, 1, T).
-            y(Tensor): Groundtruth single tensor (B, 1, T).
+            y_hat(Tensor): 
+                Generated single tensor (B, 1, T).
+            y(Tensor): 
+                Groundtruth single tensor (B, 1, T).
 
         Returns:
             Tensor: Mel-spectrogram loss value.
@@ -947,9 +996,11 @@ class FeatureMatchLoss(nn.Layer):
         """Calcualate feature matching loss.
 
         Args:
-            feats_hat(list): List of list of discriminator outputs
+            feats_hat(list): 
+                List of list of discriminator outputs
                 calcuated from generater outputs.
-            feats(list): List of list of discriminator outputs
+            feats(list): 
+                List of list of discriminator outputs
 
         Returns:
             Tensor: Feature matching loss value.
@@ -986,11 +1037,16 @@ class KLDivergenceLoss(nn.Layer):
         """Calculate KL divergence loss.
 
         Args:
-            z_p (Tensor): Flow hidden representation (B, H, T_feats).
-            logs_q (Tensor): Posterior encoder projected scale (B, H, T_feats).
-            m_p (Tensor): Expanded text encoder projected mean (B, H, T_feats).
-            logs_p (Tensor): Expanded text encoder projected scale (B, H, T_feats).
-            z_mask (Tensor): Mask tensor (B, 1, T_feats).
+            z_p (Tensor): 
+                Flow hidden representation (B, H, T_feats).
+            logs_q (Tensor): 
+                Posterior encoder projected scale (B, H, T_feats).
+            m_p (Tensor): 
+                Expanded text encoder projected mean (B, H, T_feats).
+            logs_p (Tensor): 
+                Expanded text encoder projected scale (B, H, T_feats).
+            z_mask (Tensor): 
+                Mask tensor (B, 1, T_feats).
 
         Returns:
             Tensor: KL divergence loss.

paddlespeech/t2s/modules/nets_utils.py

@@ -25,8 +25,10 @@ def pad_list(xs, pad_value):
     """Perform padding for the list of tensors.
 
     Args:
-        xs (List[Tensor]): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
-        pad_value (float): Value for padding.
+        xs (List[Tensor]): 
+            List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
+        pad_value (float): 
+            Value for padding.
 
     Returns:
         Tensor: Padded tensor (B, Tmax, `*`).
@@ -55,10 +57,13 @@ def make_pad_mask(lengths, xs=None, length_dim=-1):
     """Make mask tensor containing indices of padded part.
 
     Args:
-        lengths (Tensor(int64)): Batch of lengths (B,).
-        xs (Tensor, optional): The reference tensor.
+        lengths (Tensor(int64)): 
+            Batch of lengths (B,).
+        xs (Tensor, optional): 
+            The reference tensor.
             If set, masks will be the same shape as this tensor.
-        length_dim (int, optional): Dimension indicator of the above tensor.
+        length_dim (int, optional): 
+            Dimension indicator of the above tensor.
             See the example.
 
     Returns:
@@ -147,7 +152,7 @@ def make_pad_mask(lengths, xs=None, length_dim=-1):
     seq_range = paddle.arange(0, maxlen, dtype=paddle.int64)
     seq_range_expand = seq_range.unsqueeze(0).expand([bs, maxlen])
     seq_length_expand = lengths.unsqueeze(-1)
-    mask = seq_range_expand >= seq_length_expand
+    mask = seq_range_expand >= seq_length_expand.cast(seq_range_expand.dtype)
 
     if xs is not None:
         assert paddle.shape(xs)[0] == bs, (paddle.shape(xs)[0], bs)
@@ -166,14 +171,18 @@ def make_non_pad_mask(lengths, xs=None, length_dim=-1):
     """Make mask tensor containing indices of non-padded part.
 
     Args:
-        lengths (Tensor(int64) or List): Batch of lengths (B,).
-        xs (Tensor, optional): The reference tensor.
+        lengths (Tensor(int64) or List): 
+            Batch of lengths (B,).
+        xs (Tensor, optional): 
+            The reference tensor.
             If set, masks will be the same shape as this tensor.
-        length_dim (int, optional): Dimension indicator of the above tensor.
+        length_dim (int, optional): 
+            Dimension indicator of the above tensor.
             See the example.
 
     Returns:
-        Tensor(bool): mask tensor containing indices of padded part bool.
+        Tensor(bool): 
+            mask tensor containing indices of padded part bool.
 
     Examples:
         With only lengths.
@@ -257,8 +266,10 @@ def initialize(model: nn.Layer, init: str):
     Custom initialization routines can be implemented into submodules
 
     Args:
-        model (nn.Layer): Target.
-        init (str): Method of initialization.
+        model (nn.Layer): 
+            Target.
+        init (str):
+            Method of initialization.
     """
     assert check_argument_types()
 
@@ -285,12 +296,17 @@ def get_random_segments(
         segment_size: int, ) -> Tuple[paddle.Tensor, paddle.Tensor]:
     """Get random segments.
     Args:
-        x (Tensor): Input tensor (B, C, T).
-        x_lengths (Tensor): Length tensor (B,).
-        segment_size (int): Segment size.
+        x (Tensor): 
+            Input tensor (B, C, T).
+        x_lengths (Tensor): 
+            Length tensor (B,).
+        segment_size (int): 
+            Segment size.
     Returns:
-        Tensor: Segmented tensor (B, C, segment_size).
-        Tensor: Start index tensor (B,).
+        Tensor: 
+            Segmented tensor (B, C, segment_size).
+        Tensor: 
+            Start index tensor (B,).
     """
     b, c, t = paddle.shape(x)
     max_start_idx = x_lengths - segment_size
@@ -306,9 +322,12 @@ def get_segments(
         segment_size: int, ) -> paddle.Tensor:
     """Get segments.
     Args:
-        x (Tensor): Input tensor (B, C, T).
-        start_idxs (Tensor): Start index tensor (B,).
-        segment_size (int): Segment size.
+        x (Tensor): 
+            Input tensor (B, C, T).
+        start_idxs (Tensor): 
+            Start index tensor (B,).
+        segment_size (int): 
+            Segment size.
     Returns:
         Tensor: Segmented tensor (B, C, segment_size).
     """
@@ -353,14 +372,20 @@ def phones_masking(xs_pad: paddle.Tensor,
                    span_bdy: paddle.Tensor=None):
     '''
     Args:
-        xs_pad (paddle.Tensor): input speech (B, Tmax, D).
-        src_mask (paddle.Tensor): mask of speech (B, 1, Tmax).
-        align_start (paddle.Tensor): frame level phone alignment start (B, Tmax2).
-        align_end (paddle.Tensor): frame level phone alignment end (B, Tmax2).
-        align_start_lens (paddle.Tensor): length of align_start (B, ).
+        xs_pad (paddle.Tensor): 
+            input speech (B, Tmax, D).
+        src_mask (paddle.Tensor): 
+            mask of speech (B, 1, Tmax).
+        align_start (paddle.Tensor): 
+            frame level phone alignment start (B, Tmax2).
+        align_end (paddle.Tensor): 
+            frame level phone alignment end (B, Tmax2).
+        align_start_lens (paddle.Tensor): 
+            length of align_start (B, ).
         mlm_prob (float):
         mean_phn_span (int):
-        span_bdy (paddle.Tensor): masked mel boundary of input speech (B, 2).
+        span_bdy (paddle.Tensor): 
+            masked mel boundary of input speech (B, 2).
     Returns:
         paddle.Tensor[bool]: masked position of input speech (B, Tmax).
     '''
@@ -416,19 +441,29 @@ def phones_text_masking(xs_pad: paddle.Tensor,
                         span_bdy: paddle.Tensor=None):
     '''
     Args:
-        xs_pad (paddle.Tensor): input speech (B, Tmax, D).
-        src_mask (paddle.Tensor): mask of speech (B, 1, Tmax).
-        text_pad (paddle.Tensor): input text (B, Tmax2).
-        text_mask (paddle.Tensor): mask of text (B, 1, Tmax2).
-        align_start (paddle.Tensor): frame level phone alignment start (B, Tmax2).
-        align_end (paddle.Tensor): frame level phone alignment end (B, Tmax2).
-        align_start_lens (paddle.Tensor): length of align_start (B, ).
+        xs_pad (paddle.Tensor): 
+            input speech (B, Tmax, D).
+        src_mask (paddle.Tensor): 
+            mask of speech (B, 1, Tmax).
+        text_pad (paddle.Tensor): 
+            input text (B, Tmax2).
+        text_mask (paddle.Tensor):
+            mask of text (B, 1, Tmax2).
+        align_start (paddle.Tensor): 
+            frame level phone alignment start (B, Tmax2).
+        align_end (paddle.Tensor): 
+            frame level phone alignment end (B, Tmax2).
+        align_start_lens (paddle.Tensor): 
+            length of align_start (B, ).
         mlm_prob (float):
         mean_phn_span (int):
-        span_bdy (paddle.Tensor): masked mel boundary of input speech (B, 2).
+        span_bdy (paddle.Tensor): 
+            masked mel boundary of input speech (B, 2).
     Returns:
-        paddle.Tensor[bool]: masked position of input speech (B, Tmax).
-        paddle.Tensor[bool]: masked position of input text (B, Tmax2).
+        paddle.Tensor[bool]: 
+            masked position of input speech (B, Tmax).
+        paddle.Tensor[bool]: 
+            masked position of input text (B, Tmax2).
     '''
     bz, sent_len, _ = paddle.shape(xs_pad)
     masked_pos = paddle.zeros((bz, sent_len))
@@ -488,12 +523,18 @@ def get_seg_pos(speech_pad: paddle.Tensor,
                 seg_emb: bool=False):
     '''
     Args:
-        speech_pad (paddle.Tensor): input speech (B, Tmax, D).
-        text_pad (paddle.Tensor): input text (B, Tmax2).
-        align_start (paddle.Tensor): frame level phone alignment start (B, Tmax2).
-        align_end (paddle.Tensor): frame level phone alignment end (B, Tmax2).
-        align_start_lens (paddle.Tensor): length of align_start (B, ).
-        seg_emb (bool): whether to use segment embedding.
+        speech_pad (paddle.Tensor): 
+            input speech (B, Tmax, D).
+        text_pad (paddle.Tensor): 
+            input text (B, Tmax2).
+        align_start (paddle.Tensor): 
+            frame level phone alignment start (B, Tmax2).
+        align_end (paddle.Tensor): 
+            frame level phone alignment end (B, Tmax2).
+        align_start_lens (paddle.Tensor): 
+            length of align_start (B, ).
+        seg_emb (bool): 
+            whether to use segment embedding.
     Returns:
         paddle.Tensor[int]: n-th phone of each mel, 0<=n<=Tmax2 (B, Tmax).
             eg: 
@@ -579,8 +620,10 @@ def random_spans_noise_mask(length: int,
     def _random_seg(num_items, num_segs):
         """Partition a sequence of items randomly into non-empty segments.
         Args:
-            num_items: an integer scalar > 0
-            num_segs: an integer scalar in [1, num_items]
+            num_items: 
+                an integer scalar > 0
+            num_segs: 
+                an integer scalar in [1, num_items]
         Returns:
             a Tensor with shape [num_segs] containing positive integers that add
             up to num_items

paddlespeech/t2s/modules/pqmf.py

@@ -26,9 +26,12 @@ def design_prototype_filter(taps=62, cutoff_ratio=0.142, beta=9.0):
     filters of cosine modulated filterbanks`_.
 
     Args:
-        taps (int): The number of filter taps.
-        cutoff_ratio (float): Cut-off frequency ratio.
-        beta (float): Beta coefficient for kaiser window.
+        taps (int): 
+            The number of filter taps.
+        cutoff_ratio (float): 
+            Cut-off frequency ratio.
+        beta (float): 
+            Beta coefficient for kaiser window.
     Returns:
         ndarray:
             Impluse response of prototype filter (taps + 1,).
@@ -66,10 +69,14 @@ class PQMF(nn.Layer):
         See dicussion in https://github.com/kan-bayashi/ParallelWaveGAN/issues/195.
 
         Args:
-            subbands (int): The number of subbands.
-            taps (int): The number of filter taps.
-            cutoff_ratio (float): Cut-off frequency ratio.
-            beta (float): Beta coefficient for kaiser window.
+            subbands (int): 
+                The number of subbands.
+            taps (int): 
+                The number of filter taps.
+            cutoff_ratio (float): 
+                Cut-off frequency ratio.
+            beta (float): 
+                Beta coefficient for kaiser window.
         """
         super().__init__()
 
@@ -103,7 +110,8 @@ class PQMF(nn.Layer):
     def analysis(self, x):
         """Analysis with PQMF.
         Args:
-            x (Tensor): Input tensor (B, 1, T).
+            x (Tensor): 
+                Input tensor (B, 1, T).
         Returns:
             Tensor: Output tensor (B, subbands, T // subbands).
         """
@@ -113,7 +121,8 @@ class PQMF(nn.Layer):
     def synthesis(self, x):
         """Synthesis with PQMF.
         Args:
-            x (Tensor): Input tensor (B, subbands, T // subbands).
+            x (Tensor): 
+                Input tensor (B, subbands, T // subbands).
         Returns:
             Tensor: Output tensor (B, 1, T).
         """

paddlespeech/t2s/modules/predictor/duration_predictor.py

@@ -50,12 +50,18 @@ class DurationPredictor(nn.Layer):
         """Initilize duration predictor module.
 
         Args:
-            idim (int):Input dimension.
-            n_layers (int, optional): Number of convolutional layers.
-            n_chans (int, optional): Number of channels of convolutional layers.
-            kernel_size (int, optional): Kernel size of convolutional layers.
-            dropout_rate (float, optional): Dropout rate.
-            offset (float, optional): Offset value to avoid nan in log domain.
+            idim (int):
+                Input dimension.
+            n_layers (int, optional): 
+                Number of convolutional layers.
+            n_chans (int, optional): 
+                Number of channels of convolutional layers.
+            kernel_size (int, optional): 
+                Kernel size of convolutional layers.
+            dropout_rate (float, optional): 
+                Dropout rate.
+            offset (float, optional): 
+                Offset value to avoid nan in log domain.
 
         """
         super().__init__()
@@ -99,8 +105,10 @@ class DurationPredictor(nn.Layer):
     def forward(self, xs, x_masks=None):
         """Calculate forward propagation.
         Args:
-            xs(Tensor): Batch of input sequences (B, Tmax, idim).
-            x_masks(ByteTensor, optional, optional): Batch of masks indicating padded part (B, Tmax). (Default value = None)
+            xs(Tensor): 
+                Batch of input sequences (B, Tmax, idim).
+            x_masks(ByteTensor, optional, optional): 
+                Batch of masks indicating padded part (B, Tmax). (Default value = None)
 
         Returns:
             Tensor: Batch of predicted durations in log domain (B, Tmax).
@@ -110,8 +118,10 @@ class DurationPredictor(nn.Layer):
     def inference(self, xs, x_masks=None):
         """Inference duration.
         Args:
-            xs(Tensor): Batch of input sequences (B, Tmax, idim).
-            x_masks(Tensor(bool), optional, optional): Batch of masks indicating padded part (B, Tmax). (Default value = None)
+            xs(Tensor): 
+                Batch of input sequences (B, Tmax, idim).
+            x_masks(Tensor(bool), optional, optional): 
+                Batch of masks indicating padded part (B, Tmax). (Default value = None)
 
         Returns:
             Tensor: Batch of predicted durations in linear domain int64 (B, Tmax).
@@ -140,8 +150,10 @@ class DurationPredictorLoss(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            outputs(Tensor): Batch of prediction durations in log domain (B, T)
-            targets(Tensor): Batch of groundtruth durations in linear domain (B, T)
+            outputs(Tensor): 
+                Batch of prediction durations in log domain (B, T)
+            targets(Tensor): 
+                Batch of groundtruth durations in linear domain (B, T)
 
         Returns: 
             Tensor: Mean squared error loss value.

paddlespeech/t2s/modules/predictor/length_regulator.py

@@ -36,7 +36,8 @@ class LengthRegulator(nn.Layer):
         """Initilize length regulator module.
 
         Args:
-            pad_value (float, optional): Value used for padding.
+            pad_value (float, optional): 
+                Value used for padding.
 
         """
         super().__init__()
@@ -97,9 +98,12 @@ class LengthRegulator(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            xs (Tensor): Batch of sequences of char or phoneme embeddings (B, Tmax, D).
-            ds (Tensor(int64)): Batch of durations of each frame (B, T).
-            alpha (float, optional): Alpha value to control speed of speech.
+            xs (Tensor): 
+                Batch of sequences of char or phoneme embeddings (B, Tmax, D).
+            ds (Tensor(int64)): 
+                Batch of durations of each frame (B, T).
+            alpha (float, optional): 
+                Alpha value to control speed of speech.
 
         Returns:
             Tensor: replicated input tensor based on durations (B, T*, D).

paddlespeech/t2s/modules/predictor/variance_predictor.py

@@ -43,11 +43,16 @@ class VariancePredictor(nn.Layer):
         """Initilize duration predictor module.
 
         Args:
-            idim (int): Input dimension.
-            n_layers (int, optional): Number of convolutional layers.
-            n_chans (int, optional): Number of channels of convolutional layers.
-            kernel_size (int, optional): Kernel size of convolutional layers.
-            dropout_rate (float, optional): Dropout rate.
+            idim (int): 
+                Input dimension.
+            n_layers (int, optional): 
+                Number of convolutional layers.
+            n_chans (int, optional): 
+                Number of channels of convolutional layers.
+            kernel_size (int, optional): 
+                Kernel size of convolutional layers.
+            dropout_rate (float, optional): 
+                Dropout rate.
         """
         assert check_argument_types()
         super().__init__()
@@ -74,11 +79,14 @@ class VariancePredictor(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            xs (Tensor): Batch of input sequences (B, Tmax, idim).
-            x_masks (Tensor(bool), optional): Batch of masks indicating padded part (B, Tmax, 1).
+            xs (Tensor): 
+                Batch of input sequences (B, Tmax, idim).
+            x_masks (Tensor(bool), optional): 
+                Batch of masks indicating padded part (B, Tmax, 1).
 
         Returns:
-            Tensor: Batch of predicted sequences (B, Tmax, 1).
+            Tensor: 
+                Batch of predicted sequences (B, Tmax, 1).
         """
         # (B, idim, Tmax)
         xs = xs.transpose([0, 2, 1])

paddlespeech/t2s/modules/residual_block.py

@@ -29,15 +29,24 @@ class WaveNetResidualBlock(nn.Layer):
     refer to `WaveNet: A Generative Model for Raw Audio <https://arxiv.org/abs/1609.03499>`_.
 
     Args:
-        kernel_size (int, optional): Kernel size of the 1D convolution, by default 3
-        residual_channels (int, optional): Feature size of the residual output(and also the input), by default 64
-        gate_channels (int, optional): Output feature size of the 1D convolution, by default 128
-        skip_channels (int, optional): Feature size of the skip output, by default 64
-        aux_channels (int, optional): Feature size of the auxiliary input (e.g. spectrogram), by default 80
-        dropout (float, optional): Probability of the dropout before the 1D convolution, by default 0.
-        dilation (int, optional): Dilation of the 1D convolution, by default 1
-        bias (bool, optional): Whether to use bias in the 1D convolution, by default True
-        use_causal_conv (bool, optional): Whether to use causal padding for the 1D convolution, by default False
+        kernel_size (int, optional): 
+            Kernel size of the 1D convolution, by default 3
+        residual_channels (int, optional): 
+            Feature size of the residual output(and also the input), by default 64
+        gate_channels (int, optional): 
+            Output feature size of the 1D convolution, by default 128
+        skip_channels (int, optional): 
+            Feature size of the skip output, by default 64
+        aux_channels (int, optional): 
+            Feature size of the auxiliary input (e.g. spectrogram), by default 80
+        dropout (float, optional): 
+            Probability of the dropout before the 1D convolution, by default 0.
+        dilation (int, optional): 
+            Dilation of the 1D convolution, by default 1
+        bias (bool, optional): 
+            Whether to use bias in the 1D convolution, by default True
+        use_causal_conv (bool, optional): 
+            Whether to use causal padding for the 1D convolution, by default False
     """
 
     def __init__(self,
@@ -81,13 +90,17 @@ class WaveNetResidualBlock(nn.Layer):
     def forward(self, x, c):
         """
         Args:
-            x (Tensor): the input features. Shape (N, C_res, T)
-            c (Tensor): the auxiliary input. Shape (N, C_aux, T)
+            x (Tensor): 
+                the input features. Shape (N, C_res, T)
+            c (Tensor):
+                the auxiliary input. Shape (N, C_aux, T)
 
         Returns:
-            res (Tensor): Shape (N, C_res, T), the residual output, which is used as the 
+            res (Tensor): 
+                Shape (N, C_res, T), the residual output, which is used as the 
                 input of the next ResidualBlock in a stack of ResidualBlocks.
-            skip (Tensor): Shape (N, C_skip, T), the skip output, which is collected among
+            skip (Tensor): 
+                Shape (N, C_skip, T), the skip output, which is collected among
                 each layer in a stack of ResidualBlocks.
         """
         x_input = x
@@ -121,13 +134,20 @@ class HiFiGANResidualBlock(nn.Layer):
     ):
         """Initialize HiFiGANResidualBlock module.
         Args:
-            kernel_size (int): Kernel size of dilation convolution layer.
-            channels (int): Number of channels for convolution layer.
-            dilations (List[int]): List of dilation factors.
-            use_additional_convs (bool): Whether to use additional convolution layers.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (dict): Hyperparameters for activation function.
+            kernel_size (int): 
+                Kernel size of dilation convolution layer.
+            channels (int): 
+                Number of channels for convolution layer.
+            dilations (List[int]): 
+                List of dilation factors.
+            use_additional_convs (bool): 
+                Whether to use additional convolution layers.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): 
+                Activation function module name.
+            nonlinear_activation_params (dict): 
+                Hyperparameters for activation function.
         """
         super().__init__()
 
@@ -167,7 +187,8 @@ class HiFiGANResidualBlock(nn.Layer):
     def forward(self, x):
         """Calculate forward propagation.
         Args:
-            x (Tensor): Input tensor (B, channels, T).
+            x (Tensor): 
+                Input tensor (B, channels, T).
         Returns:
             Tensor: Output tensor (B, channels, T).
         """

paddlespeech/t2s/modules/residual_stack.py

@@ -39,15 +39,24 @@ class ResidualStack(nn.Layer):
         """Initialize ResidualStack module.
 
         Args:
-            kernel_size (int): Kernel size of dilation convolution layer.
-            channels (int): Number of channels of convolution layers.
-            dilation (int): Dilation factor.
-            bias (bool): Whether to add bias parameter in convolution layers.
-            nonlinear_activation (str): Activation function module name.
-            nonlinear_activation_params (Dict[str,Any]): Hyperparameters for activation function.
-            pad (str): Padding function module name before dilated convolution layer.
-            pad_params (Dict[str, Any]): Hyperparameters for padding function.
-            use_causal_conv (bool): Whether to use causal convolution.
+            kernel_size (int): 
+                Kernel size of dilation convolution layer.
+            channels (int): 
+                Number of channels of convolution layers.
+            dilation (int): 
+                Dilation factor.
+            bias (bool): 
+                Whether to add bias parameter in convolution layers.
+            nonlinear_activation (str): 
+                Activation function module name.
+            nonlinear_activation_params (Dict[str,Any]): 
+                Hyperparameters for activation function.
+            pad (str): 
+                Padding function module name before dilated convolution layer.
+            pad_params (Dict[str, Any]): 
+                Hyperparameters for padding function.
+            use_causal_conv (bool): 
+                Whether to use causal convolution.
         """
         super().__init__()
         # for compatibility
@@ -95,7 +104,8 @@ class ResidualStack(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            c (Tensor): Input tensor (B, channels, T).
+            c (Tensor): 
+                Input tensor (B, channels, T).
         Returns:     
             Tensor: Output tensor (B, chennels, T).
         """

paddlespeech/t2s/modules/style_encoder.py

@@ -32,16 +32,26 @@ class StyleEncoder(nn.Layer):
         Speech Synthesis`: https://arxiv.org/abs/1803.09017
     
     Args:
-        idim (int, optional): Dimension of the input mel-spectrogram.
-        gst_tokens (int, optional): The number of GST embeddings.
-        gst_token_dim (int, optional): Dimension of each GST embedding.
-        gst_heads (int, optional): The number of heads in GST multihead attention.
-        conv_layers (int, optional): The number of conv layers in the reference encoder.
-        conv_chans_list (Sequence[int], optional): List of the number of channels of conv layers in the referece encoder.
-        conv_kernel_size (int, optional): Kernal size of conv layers in the reference encoder.
-        conv_stride (int, optional): Stride size of conv layers in the reference encoder.
-        gru_layers (int, optional): The number of GRU layers in the reference encoder.
-        gru_units (int, optional):The number of GRU units in the reference encoder.
+        idim (int, optional): 
+            Dimension of the input mel-spectrogram.
+        gst_tokens (int, optional): 
+            The number of GST embeddings.
+        gst_token_dim (int, optional): 
+            Dimension of each GST embedding.
+        gst_heads (int, optional): 
+            The number of heads in GST multihead attention.
+        conv_layers (int, optional): 
+            The number of conv layers in the reference encoder.
+        conv_chans_list (Sequence[int], optional): 
+            List of the number of channels of conv layers in the referece encoder.
+        conv_kernel_size (int, optional): 
+            Kernal size of conv layers in the reference encoder.
+        conv_stride (int, optional): 
+            Stride size of conv layers in the reference encoder.
+        gru_layers (int, optional): 
+            The number of GRU layers in the reference encoder.
+        gru_units (int, optional):
+            The number of GRU units in the reference encoder.
 
     Todo:
         * Support manual weight specification in inference.
@@ -82,7 +92,8 @@ class StyleEncoder(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            speech (Tensor): Batch of padded target features (B, Lmax, odim).
+            speech (Tensor): 
+                Batch of padded target features (B, Lmax, odim).
 
         Returns: 
             Tensor: Style token embeddings (B, token_dim).
@@ -104,13 +115,20 @@ class ReferenceEncoder(nn.Layer):
         Speech Synthesis`: https://arxiv.org/abs/1803.09017
     
     Args:
-        idim (int, optional): Dimension of the input mel-spectrogram.
-        conv_layers (int, optional): The number of conv layers in the reference encoder.
-        conv_chans_list: (Sequence[int], optional): List of the number of channels of conv layers in the referece encoder.
-        conv_kernel_size (int, optional): Kernal size of conv layers in the reference encoder.
-        conv_stride (int, optional): Stride size of conv layers in the reference encoder.
-        gru_layers (int, optional): The number of GRU layers in the reference encoder.
-        gru_units (int, optional): The number of GRU units in the reference encoder.
+        idim (int, optional): 
+            Dimension of the input mel-spectrogram.
+        conv_layers (int, optional): 
+            The number of conv layers in the reference encoder.
+        conv_chans_list: (Sequence[int], optional): 
+            List of the number of channels of conv layers in the referece encoder.
+        conv_kernel_size (int, optional): 
+            Kernal size of conv layers in the reference encoder.
+        conv_stride (int, optional): 
+            Stride size of conv layers in the reference encoder.
+        gru_layers (int, optional): 
+            The number of GRU layers in the reference encoder.
+        gru_units (int, optional): 
+            The number of GRU units in the reference encoder.
 
     """
 
@@ -168,7 +186,8 @@ class ReferenceEncoder(nn.Layer):
     def forward(self, speech: paddle.Tensor) -> paddle.Tensor:
         """Calculate forward propagation.
         Args:
-            speech (Tensor): Batch of padded target features (B, Lmax, idim).
+            speech (Tensor): 
+                Batch of padded target features (B, Lmax, idim).
 
         Returns:
             Tensor: Reference embedding (B, gru_units)
@@ -200,11 +219,16 @@ class StyleTokenLayer(nn.Layer):
     .. _`Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End
         Speech Synthesis`: https://arxiv.org/abs/1803.09017
     Args:
-        ref_embed_dim (int, optional): Dimension of the input reference embedding.
-        gst_tokens (int, optional): The number of GST embeddings.
-        gst_token_dim (int, optional): Dimension of each GST embedding.
-        gst_heads (int, optional): The number of heads in GST multihead attention.
-        dropout_rate (float, optional): Dropout rate in multi-head attention.
+        ref_embed_dim (int, optional): 
+            Dimension of the input reference embedding.
+        gst_tokens (int, optional): 
+            The number of GST embeddings.
+        gst_token_dim (int, optional): 
+            Dimension of each GST embedding.
+        gst_heads (int, optional): 
+            The number of heads in GST multihead attention.
+        dropout_rate (float, optional): 
+            Dropout rate in multi-head attention.
 
     """
 
@@ -236,7 +260,8 @@ class StyleTokenLayer(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            ref_embs (Tensor): Reference embeddings (B, ref_embed_dim).
+            ref_embs (Tensor):
+                Reference embeddings (B, ref_embed_dim).
 
         Returns: 
             Tensor: Style token embeddings (B, gst_token_dim).

paddlespeech/t2s/modules/tacotron2/attentions.py

@@ -31,10 +31,14 @@ def _apply_attention_constraint(e,
     Text-to-Speech with Convolutional Sequence Learning`_.
 
     Args:
-        e(Tensor): Attention energy before applying softmax (1, T).
-       last_attended_idx(int): The index of the inputs of the last attended [0, T].
-       backward_window(int, optional, optional): Backward window size in attention constraint. (Default value = 1)
-       forward_window(int, optional, optional): Forward window size in attetion constraint. (Default value = 3)
+        e(Tensor): 
+            Attention energy before applying softmax (1, T).
+        last_attended_idx(int): 
+            The index of the inputs of the last attended [0, T].
+        backward_window(int, optional, optional): 
+            Backward window size in attention constraint. (Default value = 1)
+        forward_window(int, optional, optional): 
+            Forward window size in attetion constraint. (Default value = 3)
 
     Returns:
         Tensor: Monotonic constrained attention energy (1, T).
@@ -62,12 +66,18 @@ class AttLoc(nn.Layer):
         (https://arxiv.org/pdf/1506.07503.pdf)
 
     Args:
-        eprojs (int): projection-units of encoder
-        dunits (int): units of decoder
-        att_dim (int): attention dimension
-        aconv_chans (int): channels of attention convolution
-        aconv_filts (int): filter size of attention convolution
-        han_mode (bool): flag to swith on mode of hierarchical attention and not store pre_compute_enc_h
+        eprojs (int): 
+            projection-units of encoder
+        dunits (int): 
+            units of decoder
+        att_dim (int): 
+            attention dimension
+        aconv_chans (int): 
+            channels of attention convolution
+        aconv_filts (int): 
+            filter size of attention convolution
+        han_mode (bool): 
+            flag to swith on mode of hierarchical attention and not store pre_compute_enc_h
     """
 
     def __init__(self,
@@ -117,18 +127,29 @@ class AttLoc(nn.Layer):
             forward_window=3, ):
         """Calculate AttLoc forward propagation.
         Args:
-            enc_hs_pad(Tensor): padded encoder hidden state (B, T_max, D_enc)
-            enc_hs_len(Tensor): padded encoder hidden state length (B)
-            dec_z(Tensor dec_z): decoder hidden state (B, D_dec)
-            att_prev(Tensor): previous attention weight (B, T_max)
-            scaling(float, optional): scaling parameter before applying softmax (Default value = 2.0)
-            forward_window(Tensor, optional): forward window size when constraining attention (Default value = 3)
-            last_attended_idx(int, optional): index of the inputs of the last attended (Default value = None)
-            backward_window(int, optional): backward window size in attention constraint (Default value = 1)
-            forward_window(int, optional): forward window size in attetion constraint (Default value = 3)
+            enc_hs_pad(Tensor): 
+                padded encoder hidden state (B, T_max, D_enc)
+            enc_hs_len(Tensor): 
+                padded encoder hidden state length (B)
+            dec_z(Tensor dec_z): 
+                decoder hidden state (B, D_dec)
+            att_prev(Tensor): 
+                previous attention weight (B, T_max)
+            scaling(float, optional): 
+                scaling parameter before applying softmax (Default value = 2.0)
+            forward_window(Tensor, optional): 
+                    forward window size when constraining attention (Default value = 3)
+            last_attended_idx(int, optional): 
+                index of the inputs of the last attended (Default value = None)
+            backward_window(int, optional): 
+                backward window size in attention constraint (Default value = 1)
+            forward_window(int, optional): 
+                    forward window size in attetion constraint (Default value = 3)
         Returns:
-            Tensor: attention weighted encoder state (B, D_enc)
-            Tensor: previous attention weights (B, T_max)
+            Tensor: 
+                attention weighted encoder state (B, D_enc)
+            Tensor: 
+                previous attention weights (B, T_max)
         """
         batch = paddle.shape(enc_hs_pad)[0]
         # pre-compute all h outside the decoder loop
@@ -192,11 +213,16 @@ class AttForward(nn.Layer):
         (https://arxiv.org/pdf/1807.06736.pdf)
 
     Args:
-        eprojs (int): projection-units of encoder
-        dunits (int): units of decoder
-        att_dim (int): attention dimension
-        aconv_chans (int): channels of attention convolution
-        aconv_filts (int): filter size of attention convolution
+        eprojs (int): 
+            projection-units of encoder
+        dunits (int): 
+            units of decoder
+        att_dim (int): 
+            attention dimension
+        aconv_chans (int): 
+            channels of attention convolution
+        aconv_filts (int): 
+            filter size of attention convolution
     """
 
     def __init__(self, eprojs, dunits, att_dim, aconv_chans, aconv_filts):
@@ -239,18 +265,28 @@ class AttForward(nn.Layer):
         """Calculate AttForward forward propagation.
 
         Args:
-            enc_hs_pad(Tensor): padded encoder hidden state (B, T_max, D_enc)
-            enc_hs_len(list): padded encoder hidden state length (B,)
-            dec_z(Tensor): decoder hidden state (B, D_dec)
-            att_prev(Tensor): attention weights of previous step (B, T_max)
-            scaling(float, optional): scaling parameter before applying softmax (Default value = 1.0)
-            last_attended_idx(int, optional): index of the inputs of the last attended (Default value = None)
-            backward_window(int, optional): backward window size in attention constraint (Default value = 1)
-            forward_window(int, optional):  (Default value = 3)
+            enc_hs_pad(Tensor): 
+                padded encoder hidden state (B, T_max, D_enc)
+            enc_hs_len(list): 
+                padded encoder hidden state length (B,)
+            dec_z(Tensor): 
+                decoder hidden state (B, D_dec)
+            att_prev(Tensor): 
+                attention weights of previous step (B, T_max)
+            scaling(float, optional): 
+                scaling parameter before applying softmax (Default value = 1.0)
+            last_attended_idx(int, optional): 
+                index of the inputs of the last attended (Default value = None)
+            backward_window(int, optional): 
+                backward window size in attention constraint (Default value = 1)
+            forward_window(int, optional):  
+                (Default value = 3)
 
         Returns:
-            Tensor: attention weighted encoder state (B, D_enc)
-            Tensor: previous attention weights (B, T_max)
+            Tensor: 
+                attention weighted encoder state (B, D_enc)
+            Tensor: 
+                previous attention weights (B, T_max)
         """
         batch = len(enc_hs_pad)
         # pre-compute all h outside the decoder loop
@@ -321,12 +357,18 @@ class AttForwardTA(nn.Layer):
             (https://arxiv.org/pdf/1807.06736.pdf)
 
     Args:
-        eunits (int): units of encoder
-        dunits (int): units of decoder
-        att_dim (int): attention dimension
-        aconv_chans (int): channels of attention convolution
-        aconv_filts (int): filter size of attention convolution
-        odim (int): output dimension
+        eunits (int): 
+            units of encoder
+        dunits (int): 
+            units of decoder
+        att_dim (int): 
+            attention dimension
+        aconv_chans (int):  
+            channels of attention convolution
+        aconv_filts (int): 
+            filter size of attention convolution
+        odim (int): 
+            output dimension
     """
 
     def __init__(self, eunits, dunits, att_dim, aconv_chans, aconv_filts, odim):
@@ -372,19 +414,30 @@ class AttForwardTA(nn.Layer):
         """Calculate AttForwardTA forward propagation.
 
         Args:
-            enc_hs_pad(Tensor): padded encoder hidden state (B, Tmax, eunits)
-            enc_hs_len(list Tensor): padded encoder hidden state length (B,)
-            dec_z(Tensor): decoder hidden state (B, dunits)
-            att_prev(Tensor): attention weights of previous step (B, T_max)
-            out_prev(Tensor): decoder outputs of previous step (B, odim)
-            scaling(float, optional): scaling parameter before applying softmax (Default value = 1.0)
-            last_attended_idx(int, optional): index of the inputs of the last attended (Default value = None)
-            backward_window(int, optional): backward window size in attention constraint (Default value = 1)
-            forward_window(int, optional):  (Default value = 3)
+            enc_hs_pad(Tensor): 
+                padded encoder hidden state (B, Tmax, eunits)
+            enc_hs_len(list Tensor): 
+                padded encoder hidden state length (B,)
+            dec_z(Tensor): 
+                decoder hidden state (B, dunits)
+            att_prev(Tensor): 
+                attention weights of previous step (B, T_max)
+            out_prev(Tensor): 
+                decoder outputs of previous step (B, odim)
+            scaling(float, optional): 
+                scaling parameter before applying softmax (Default value = 1.0)
+            last_attended_idx(int, optional): 
+                index of the inputs of the last attended (Default value = None)
+            backward_window(int, optional): 
+                backward window size in attention constraint (Default value = 1)
+            forward_window(int, optional):  
+                (Default value = 3)
 
         Returns:
-            Tensor: attention weighted encoder state (B, dunits)
-            Tensor: previous attention weights (B, Tmax)
+            Tensor: 
+                attention weighted encoder state (B, dunits)
+            Tensor: 
+                previous attention weights (B, Tmax)
         """
         batch = len(enc_hs_pad)
         # pre-compute all h outside the decoder loop

paddlespeech/t2s/modules/tacotron2/decoder.py

@@ -45,10 +45,14 @@ class Prenet(nn.Layer):
         """Initialize prenet module.
 
         Args:
-            idim (int): Dimension of the inputs.
-            odim (int): Dimension of the outputs.
-            n_layers (int, optional): The number of prenet layers.
-            n_units (int, optional): The number of prenet units.
+            idim (int): 
+                Dimension of the inputs.
+            odim (int): 
+                Dimension of the outputs.
+            n_layers (int, optional): 
+                The number of prenet layers.
+            n_units (int, optional): 
+                The number of prenet units.
         """
         super().__init__()
         self.dropout_rate = dropout_rate
@@ -62,7 +66,8 @@ class Prenet(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            x (Tensor): Batch of input tensors (B, ..., idim).
+            x (Tensor): 
+                Batch of input tensors (B, ..., idim).
 
         Returns: 
             Tensor: Batch of output tensors (B, ..., odim).
@@ -212,7 +217,8 @@ class ZoneOutCell(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            inputs (Tensor): Batch of input tensor (B, input_size).
+            inputs (Tensor): 
+                Batch of input tensor (B, input_size).
             hidden (tuple):
                 - Tensor: Batch of initial hidden states (B, hidden_size).
                 - Tensor: Batch of initial cell states (B, hidden_size).
@@ -277,26 +283,39 @@ class Decoder(nn.Layer):
         """Initialize Tacotron2 decoder module.
 
         Args:
-            idim (int): Dimension of the inputs.
-            odim (int): Dimension of the outputs.
-            att (nn.Layer): Instance of attention class.
-            dlayers (int, optional): The number of decoder lstm layers.
-            dunits (int, optional): The number of decoder lstm units.
-            prenet_layers (int, optional): The number of prenet layers.
-            prenet_units (int, optional): The number of prenet units.
-            postnet_layers (int, optional): The number of postnet layers.
-            postnet_filts (int, optional): The number of postnet filter size.
-            postnet_chans (int, optional): The number of postnet filter channels.
-            output_activation_fn (nn.Layer, optional): Activation function for outputs.
-            cumulate_att_w (bool, optional): Whether to cumulate previous attention weight.
-            use_batch_norm (bool, optional): Whether to use batch normalization.
-            use_concate : bool, optional
+            idim (int): 
+                Dimension of the inputs.
+            odim (int): 
+                Dimension of the outputs.
+            att (nn.Layer): 
+                Instance of attention class.
+            dlayers (int, optional): 
+                The number of decoder lstm layers.
+            dunits (int, optional): 
+                The number of decoder lstm units.
+            prenet_layers (int, optional): 
+                The number of prenet layers.
+            prenet_units (int, optional): 
+                The number of prenet units.
+            postnet_layers (int, optional): 
+                The number of postnet layers.
+            postnet_filts (int, optional): 
+                The number of postnet filter size.
+            postnet_chans (int, optional): 
+                The number of postnet filter channels.
+            output_activation_fn (nn.Layer, optional): 
+                Activation function for outputs.
+            cumulate_att_w (bool, optional): 
+                Whether to cumulate previous attention weight.
+            use_batch_norm (bool, optional): 
+                Whether to use batch normalization.
+            use_concate (bool, optional):
                 Whether to concatenate encoder embedding with decoder lstm outputs.
-            dropout_rate : float, optional
+            dropout_rate (float, optional):
                 Dropout rate.
-            zoneout_rate : float, optional
+            zoneout_rate (float, optional):
                 Zoneout rate.
-            reduction_factor : int, optional
+            reduction_factor (int, optional):
                 Reduction factor.
         """
         super().__init__()
@@ -363,15 +382,22 @@ class Decoder(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            hs (Tensor): Batch of the sequences of padded hidden states (B, Tmax, idim).
-            hlens (Tensor(int64) padded): Batch of lengths of each input batch (B,).
-            ys (Tensor): Batch of the sequences of padded target features (B, Lmax, odim).
+            hs (Tensor): 
+                Batch of the sequences of padded hidden states (B, Tmax, idim).
+            hlens (Tensor(int64) padded): 
+                Batch of lengths of each input batch (B,).
+            ys (Tensor): 
+                Batch of the sequences of padded target features (B, Lmax, odim).
 
         Returns:
-            Tensor: Batch of output tensors after postnet (B, Lmax, odim).
-            Tensor: Batch of output tensors before postnet (B, Lmax, odim).
-            Tensor: Batch of logits of stop prediction (B, Lmax).
-            Tensor: Batch of attention weights (B, Lmax, Tmax).
+            Tensor: 
+                Batch of output tensors after postnet (B, Lmax, odim).
+            Tensor: 
+                Batch of output tensors before postnet (B, Lmax, odim).
+            Tensor: 
+                Batch of logits of stop prediction (B, Lmax).
+            Tensor: 
+                Batch of attention weights (B, Lmax, Tmax).
             
         Note: 
             This computation is performed in teacher-forcing manner.
@@ -471,20 +497,30 @@ class Decoder(nn.Layer):
             forward_window=None, ):
         """Generate the sequence of features given the sequences of characters.
         Args:
-            h(Tensor): Input sequence of encoder hidden states (T, C).
-            threshold(float, optional, optional): Threshold to stop generation. (Default value = 0.5)
-            minlenratio(float, optional, optional): Minimum length ratio. If set to 1.0 and the length of input is 10,
+            h(Tensor): 
+                Input sequence of encoder hidden states (T, C).
+            threshold(float, optional, optional): 
+                Threshold to stop generation. (Default value = 0.5)
+            minlenratio(float, optional, optional): 
+                Minimum length ratio. If set to 1.0 and the length of input is 10,
                 the minimum length of outputs will be 10 * 1 = 10. (Default value = 0.0)
-            maxlenratio(float, optional, optional): Minimum length ratio. If set to 10 and the length of input is 10,
+            maxlenratio(float, optional, optional):
+                 Minimum length ratio. If set to 10 and the length of input is 10,
                 the maximum length of outputs will be 10 * 10 = 100. (Default value = 0.0)
-            use_att_constraint(bool, optional): Whether to apply attention constraint introduced in `Deep Voice 3`_. (Default value = False)
-            backward_window(int, optional): Backward window size in attention constraint. (Default value = None)
-            forward_window(int, optional):  (Default value = None)
+            use_att_constraint(bool, optional): 
+                Whether to apply attention constraint introduced in `Deep Voice 3`_. (Default value = False)
+            backward_window(int, optional): 
+                Backward window size in attention constraint. (Default value = None)
+            forward_window(int, optional):  
+                    (Default value = None)
 
         Returns:
-            Tensor: Output sequence of features (L, odim).
-            Tensor: Output sequence of stop probabilities (L,).
-            Tensor: Attention weights (L, T).
+            Tensor: 
+                Output sequence of features (L, odim).
+            Tensor: 
+                Output sequence of stop probabilities (L,).
+            Tensor: 
+                Attention weights (L, T).
 
         Note: 
             This computation is performed in auto-regressive manner.
@@ -625,9 +661,12 @@ class Decoder(nn.Layer):
         """Calculate all of the attention weights.
 
         Args:
-            hs (Tensor): Batch of the sequences of padded hidden states (B, Tmax, idim).
-            hlens (Tensor(int64)): Batch of lengths of each input batch (B,).
-            ys (Tensor): Batch of the sequences of padded target features (B, Lmax, odim).
+            hs (Tensor): 
+                Batch of the sequences of padded hidden states (B, Tmax, idim).
+            hlens (Tensor(int64)): 
+                Batch of lengths of each input batch (B,).
+            ys (Tensor): 
+                Batch of the sequences of padded target features (B, Lmax, odim).
 
         Returns:
             numpy.ndarray:

paddlespeech/t2s/modules/tacotron2/encoder.py

@@ -46,17 +46,28 @@ class Encoder(nn.Layer):
             padding_idx=0, ):
         """Initialize Tacotron2 encoder module.
         Args:
-            idim (int): Dimension of the inputs.
-            input_layer (str): Input layer type.
-            embed_dim (int, optional): Dimension of character embedding.
-            elayers (int, optional): The number of encoder blstm layers.
-            eunits (int, optional): The number of encoder blstm units.
-            econv_layers (int, optional): The number of encoder conv layers.
-            econv_filts (int, optional): The number of encoder conv filter size.
-            econv_chans (int, optional): The number of encoder conv filter channels.
-            use_batch_norm (bool, optional): Whether to use batch normalization.
-            use_residual (bool, optional): Whether to use residual connection.
-            dropout_rate (float, optional): Dropout rate.
+            idim (int): 
+                Dimension of the inputs.
+            input_layer (str): 
+                Input layer type.
+            embed_dim (int, optional): 
+                Dimension of character embedding.
+            elayers (int, optional): 
+                The number of encoder blstm layers.
+            eunits (int, optional): 
+                The number of encoder blstm units.
+            econv_layers (int, optional): 
+                The number of encoder conv layers.
+            econv_filts (int, optional): 
+                The number of encoder conv filter size.
+            econv_chans (int, optional): 
+                The number of encoder conv filter channels.
+            use_batch_norm (bool, optional): 
+                Whether to use batch normalization.
+            use_residual (bool, optional): 
+                Whether to use residual connection.
+            dropout_rate (float, optional): 
+                Dropout rate.
 
         """
         super().__init__()
@@ -127,14 +138,18 @@ class Encoder(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            xs (Tensor): Batch of the padded sequence. Either character ids (B, Tmax)
+            xs (Tensor): 
+                Batch of the padded sequence. Either character ids (B, Tmax)
                 or acoustic feature (B, Tmax, idim * encoder_reduction_factor). 
                 Padded value should be 0.
-            ilens (Tensor(int64)): Batch of lengths of each input batch (B,).
+            ilens (Tensor(int64)): 
+                Batch of lengths of each input batch (B,).
 
         Returns:
-            Tensor: Batch of the sequences of encoder states(B, Tmax, eunits).
-            Tensor(int64): Batch of lengths of each sequence (B,)
+            Tensor: 
+                Batch of the sequences of encoder states(B, Tmax, eunits).
+            Tensor(int64): 
+                Batch of lengths of each sequence (B,)
         """
         xs = self.embed(xs).transpose([0, 2, 1])
         if self.convs is not None:
@@ -161,8 +176,8 @@ class Encoder(nn.Layer):
         """Inference.
 
         Args:
-            x (Tensor): The sequeunce of character ids (T,) 
-                or acoustic feature (T, idim * encoder_reduction_factor).
+            x (Tensor): 
+                The sequeunce of character ids (T,) or acoustic feature (T, idim * encoder_reduction_factor).
 
         Returns:
             Tensor: The sequences of encoder states(T, eunits).

paddlespeech/t2s/modules/tade_res_block.py

@@ -60,11 +60,15 @@ class TADELayer(nn.Layer):
     def forward(self, x, c):
         """Calculate forward propagation.
         Args:
-            x (Tensor): Input tensor (B, in_channels, T).
-            c (Tensor): Auxiliary input tensor (B, aux_channels, T).
+            x (Tensor): 
+                Input tensor (B, in_channels, T).
+            c (Tensor): 
+                Auxiliary input tensor (B, aux_channels, T).
         Returns:
-            Tensor: Output tensor (B, in_channels, T * upsample_factor).
-            Tensor: Upsampled aux tensor (B, in_channels, T * upsample_factor).
+            Tensor: 
+                Output tensor (B, in_channels, T * upsample_factor).
+            Tensor:
+                Upsampled aux tensor (B, in_channels, T * upsample_factor).
         """
 
         x = self.norm(x)
@@ -138,11 +142,15 @@ class TADEResBlock(nn.Layer):
         """Calculate forward propagation.
         Args:
 
-            x (Tensor): Input tensor (B, in_channels, T).
-            c (Tensor): Auxiliary input tensor (B, aux_channels, T).
+            x (Tensor): 
+                Input tensor (B, in_channels, T).
+            c (Tensor): 
+                Auxiliary input tensor (B, aux_channels, T).
         Returns:
-            Tensor: Output tensor (B, in_channels, T * upsample_factor).
-            Tensor: Upsampled auxirialy tensor (B, in_channels, T * upsample_factor).
+            Tensor: 
+                Output tensor (B, in_channels, T * upsample_factor).
+            Tensor: 
+                Upsampled auxirialy tensor (B, in_channels, T * upsample_factor).
         """
         residual = x
         x, c = self.tade1(x, c)

paddlespeech/t2s/modules/transformer/attention.py

@@ -25,9 +25,12 @@ from paddlespeech.t2s.modules.masked_fill import masked_fill
 class MultiHeadedAttention(nn.Layer):
     """Multi-Head Attention layer.
     Args:
-        n_head (int): The number of heads.
-        n_feat (int): The number of features.
-        dropout_rate (float): Dropout rate.
+        n_head (int): 
+            The number of heads.
+        n_feat (int): 
+            The number of features.
+        dropout_rate (float): 
+            Dropout rate.
     """
 
     def __init__(self, n_head, n_feat, dropout_rate):
@@ -48,14 +51,20 @@ class MultiHeadedAttention(nn.Layer):
         """Transform query, key and value.
 
         Args:
-            query(Tensor): query tensor (#batch, time1, size).
-            key(Tensor): Key tensor (#batch, time2, size).
-            value(Tensor): Value tensor (#batch, time2, size).
+            query(Tensor): 
+                query tensor (#batch, time1, size).
+            key(Tensor): 
+                Key tensor (#batch, time2, size).
+            value(Tensor): 
+                Value tensor (#batch, time2, size).
 
         Returns:
-            Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
-            Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
-            Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
+            Tensor: 
+                Transformed query tensor (#batch, n_head, time1, d_k).
+            Tensor: 
+                Transformed key tensor (#batch, n_head, time2, d_k).
+            Tensor: 
+                Transformed value tensor (#batch, n_head, time2, d_k).
         """
         n_batch = paddle.shape(query)[0]
 
@@ -77,9 +86,12 @@ class MultiHeadedAttention(nn.Layer):
         """Compute attention context vector.
 
         Args:
-            value(Tensor): Transformed value (#batch, n_head, time2, d_k).
-            scores(Tensor): Attention score (#batch, n_head, time1, time2).
-            mask(Tensor, optional): Mask (#batch, 1, time2) or (#batch, time1, time2). (Default value = None)
+            value(Tensor): 
+                Transformed value (#batch, n_head, time2, d_k).
+            scores(Tensor): 
+                Attention score (#batch, n_head, time1, time2).
+            mask(Tensor, optional): 
+                Mask (#batch, 1, time2) or (#batch, time1, time2). (Default value = None)
 
         Returns:
             Tensor: Transformed value (#batch, time1, d_model) weighted by the attention score (#batch, time1, time2).
@@ -113,10 +125,14 @@ class MultiHeadedAttention(nn.Layer):
         """Compute scaled dot product attention.
 
         Args:
-            query(Tensor): Query tensor (#batch, time1, size).
-            key(Tensor): Key tensor (#batch, time2, size).
-            value(Tensor): Value tensor (#batch, time2, size).
-            mask(Tensor, optional): Mask tensor (#batch, 1, time2) or (#batch, time1, time2). (Default value = None)
+            query(Tensor): 
+                Query tensor (#batch, time1, size).
+            key(Tensor): 
+                Key tensor (#batch, time2, size).
+            value(Tensor): 
+                Value tensor (#batch, time2, size).
+            mask(Tensor, optional): 
+                Mask tensor (#batch, 1, time2) or (#batch, time1, time2). (Default value = None)
 
         Returns:
             Tensor: Output tensor (#batch, time1, d_model).
@@ -134,10 +150,14 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
     Paper: https://arxiv.org/abs/1901.02860
 
     Args:
-        n_head (int): The number of heads.
-        n_feat (int): The number of features.
-        dropout_rate (float): Dropout rate.
-        zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
+        n_head (int): 
+            The number of heads.
+        n_feat (int): 
+            The number of features.
+        dropout_rate (float): 
+            Dropout rate.
+        zero_triu (bool): 
+            Whether to zero the upper triangular part of attention matrix.
     """
 
     def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
@@ -161,10 +181,11 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
     def rel_shift(self, x):
         """Compute relative positional encoding.
         Args:
-            x(Tensor): Input tensor (batch, head, time1, 2*time1-1).
+            x(Tensor): 
+                Input tensor (batch, head, time1, 2*time1-1).
 
         Returns:
-            Tensor:Output tensor.
+            Tensor: Output tensor.
         """
         b, h, t1, t2 = paddle.shape(x)
         zero_pad = paddle.zeros((b, h, t1, 1))
@@ -183,11 +204,16 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
         """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
 
         Args:
-            query(Tensor): Query tensor (#batch, time1, size).
-            key(Tensor): Key tensor (#batch, time2, size).
-            value(Tensor): Value tensor (#batch, time2, size).
-            pos_emb(Tensor): Positional embedding tensor (#batch, 2*time1-1, size).
-            mask(Tensor): Mask tensor (#batch, 1, time2) or (#batch, time1, time2).
+            query(Tensor): 
+                Query tensor (#batch, time1, size).
+            key(Tensor): 
+                Key tensor (#batch, time2, size).
+            value(Tensor): 
+                Value tensor (#batch, time2, size).
+            pos_emb(Tensor): 
+                Positional embedding tensor (#batch, 2*time1-1, size).
+            mask(Tensor): 
+                Mask tensor (#batch, 1, time2) or (#batch, time1, time2).
 
         Returns:
             Tensor: Output tensor (#batch, time1, d_model).
@@ -228,10 +254,14 @@ class LegacyRelPositionMultiHeadedAttention(MultiHeadedAttention):
     Paper: https://arxiv.org/abs/1901.02860
 
     Args:
-        n_head (int): The number of heads.
-        n_feat (int): The number of features.
-        dropout_rate (float): Dropout rate.
-        zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
+        n_head (int): 
+            The number of heads.
+        n_feat (int): 
+            The number of features.
+        dropout_rate (float): 
+            Dropout rate.
+        zero_triu (bool): 
+            Whether to zero the upper triangular part of attention matrix.
     """
 
     def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False):
@@ -255,8 +285,8 @@ class LegacyRelPositionMultiHeadedAttention(MultiHeadedAttention):
     def rel_shift(self, x):
         """Compute relative positional encoding.
         Args:
-            x(Tensor): Input tensor (batch, head, time1, time2).
-
+            x(Tensor): 
+                Input tensor (batch, head, time1, time2).
         Returns:
             Tensor:Output tensor.
         """

paddlespeech/t2s/modules/transformer/decoder.py

@@ -37,28 +37,46 @@ class Decoder(nn.Layer):
     """Transfomer decoder module.
 
     Args:
-        odim (int): Output diminsion.
-        self_attention_layer_type (str): Self-attention layer type.
-        attention_dim (int): Dimention of attention.
-        attention_heads (int): The number of heads of multi head attention.
-        conv_wshare (int): The number of kernel of convolution. Only used in
+        odim (int): 
+            Output diminsion.
+        self_attention_layer_type (str): 
+            Self-attention layer type.
+        attention_dim (int): 
+            Dimention of attention.
+        attention_heads (int): 
+            The number of heads of multi head attention.
+        conv_wshare (int):
+            The number of kernel of convolution. Only used in
             self_attention_layer_type == "lightconv*" or "dynamiconv*".
-        conv_kernel_length (Union[int, str]):Kernel size str of convolution
+        conv_kernel_length (Union[int, str]):
+            Kernel size str of convolution
             (e.g. 71_71_71_71_71_71). Only used in self_attention_layer_type == "lightconv*" or "dynamiconv*".
-        conv_usebias (bool): Whether to use bias in convolution. Only used in
+        conv_usebias (bool): 
+            Whether to use bias in convolution. Only used in
             self_attention_layer_type == "lightconv*" or "dynamiconv*".
-        linear_units(int): The number of units of position-wise feed forward.
-        num_blocks (int): The number of decoder blocks.
-        dropout_rate (float): Dropout rate.
-        positional_dropout_rate (float): Dropout rate after adding positional encoding.
-        self_attention_dropout_rate (float): Dropout rate in self-attention.
-        src_attention_dropout_rate (float): Dropout rate in source-attention.
-        input_layer (Union[str, nn.Layer]): Input layer type.
-        use_output_layer (bool): Whether to use output layer.
-        pos_enc_class (nn.Layer): Positional encoding module class.
+        linear_units(int): 
+            The number of units of position-wise feed forward.
+        num_blocks (int): 
+            The number of decoder blocks.
+        dropout_rate (float): 
+            Dropout rate.
+        positional_dropout_rate (float): 
+            Dropout rate after adding positional encoding.
+        self_attention_dropout_rate (float): 
+            Dropout rate in self-attention.
+        src_attention_dropout_rate (float): 
+            Dropout rate in source-attention.
+        input_layer (Union[str, nn.Layer]): 
+            Input layer type.
+        use_output_layer (bool): 
+            Whether to use output layer.
+        pos_enc_class (nn.Layer): 
+            Positional encoding module class.
             `PositionalEncoding `or `ScaledPositionalEncoding`
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
+        normalize_before (bool): 
+            Whether to use layer_norm before the first block.
+        concat_after (bool): 
+            Whether to concat attention layer's input and output.
             if True, additional linear will be applied.
             i.e. x -> x + linear(concat(x, att(x)))
             if False, no additional linear will be applied. i.e. x -> x + att(x)
@@ -143,17 +161,22 @@ class Decoder(nn.Layer):
     def forward(self, tgt, tgt_mask, memory, memory_mask):
         """Forward decoder.
         Args:
-            tgt(Tensor): Input token ids, int64 (#batch, maxlen_out) if input_layer == "embed".
+            tgt(Tensor): 
+                Input token ids, int64 (#batch, maxlen_out) if input_layer == "embed".
                 In the other case, input tensor (#batch, maxlen_out, odim).
-            tgt_mask(Tensor): Input token mask (#batch, maxlen_out).
-            memory(Tensor): Encoded memory, float32 (#batch, maxlen_in, feat).
-            memory_mask(Tensor): Encoded memory mask (#batch, maxlen_in).
+            tgt_mask(Tensor): 
+                Input token mask (#batch, maxlen_out).
+            memory(Tensor): 
+                Encoded memory, float32 (#batch, maxlen_in, feat).
+            memory_mask(Tensor): 
+                Encoded memory mask (#batch, maxlen_in).
 
         Returns:
             Tensor:
                 Decoded token score before softmax (#batch, maxlen_out, odim) if use_output_layer is True. 
                 In the other case,final block outputs (#batch, maxlen_out, attention_dim).
-            Tensor: Score mask before softmax (#batch, maxlen_out).
+            Tensor: 
+                Score mask before softmax (#batch, maxlen_out).
 
         """
         x = self.embed(tgt)
@@ -169,14 +192,20 @@ class Decoder(nn.Layer):
         """Forward one step.
 
         Args:
-            tgt(Tensor): Input token ids, int64 (#batch, maxlen_out).
-            tgt_mask(Tensor): Input token mask (#batch, maxlen_out).
-            memory(Tensor): Encoded memory, float32 (#batch, maxlen_in, feat).
-            cache((List[Tensor]), optional): List of cached tensors. (Default value = None)
+            tgt(Tensor): 
+                Input token ids, int64 (#batch, maxlen_out).
+            tgt_mask(Tensor): 
+                Input token mask (#batch, maxlen_out).
+            memory(Tensor): 
+                Encoded memory, float32 (#batch, maxlen_in, feat).
+            cache((List[Tensor]), optional): 
+                List of cached tensors. (Default value = None)
 
         Returns:
-            Tensor: Output tensor (batch, maxlen_out, odim).
-            List[Tensor]: List of cache tensors of each decoder layer.
+            Tensor: 
+                Output tensor (batch, maxlen_out, odim).
+            List[Tensor]: 
+                List of cache tensors of each decoder layer.
 
         """
         x = self.embed(tgt)
@@ -219,9 +248,12 @@ class Decoder(nn.Layer):
         """Score new token batch (required).
 
         Args:
-            ys(Tensor): paddle.int64 prefix tokens (n_batch, ylen).
-            states(List[Any]): Scorer states for prefix tokens.
-            xs(Tensor): The encoder feature that generates ys (n_batch, xlen, n_feat).
+            ys(Tensor): 
+                paddle.int64 prefix tokens (n_batch, ylen).
+            states(List[Any]): 
+                Scorer states for prefix tokens.
+            xs(Tensor): 
+                The encoder feature that generates ys (n_batch, xlen, n_feat).
 
         Returns:
             tuple[Tensor, List[Any]]:

paddlespeech/t2s/modules/transformer/decoder_layer.py

@@ -24,16 +24,23 @@ class DecoderLayer(nn.Layer):
 
  
     Args:
-        size (int): Input dimension.
-        self_attn (nn.Layer): Self-attention module instance.
+        size (int): 
+            Input dimension.
+        self_attn (nn.Layer): 
+            Self-attention module instance.
             `MultiHeadedAttention` instance can be used as the argument.
-        src_attn (nn.Layer): Self-attention module instance.
+        src_attn (nn.Layer): 
+            Self-attention module instance.
             `MultiHeadedAttention` instance can be used as the argument.
-        feed_forward (nn.Layer): Feed-forward module instance.
+        feed_forward (nn.Layer): 
+            Feed-forward module instance.
             `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance can be used as the argument.
-        dropout_rate (float): Dropout rate.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
+        dropout_rate (float):
+            Dropout rate.
+        normalize_before (bool):
+             Whether to use layer_norm before the first block.
+        concat_after (bool): 
+            Whether to concat attention layer's input and output.
             if True, additional linear will be applied.
             i.e. x -> x + linear(concat(x, att(x)))
             if False, no additional linear will be applied. i.e. x -> x + att(x)
@@ -69,11 +76,16 @@ class DecoderLayer(nn.Layer):
         """Compute decoded features.
 
         Args:
-            tgt(Tensor): Input tensor (#batch, maxlen_out, size).
-            tgt_mask(Tensor): Mask for input tensor (#batch, maxlen_out).
-            memory(Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
-            memory_mask(Tensor): Encoded memory mask (#batch, maxlen_in).
-            cache(List[Tensor], optional): List of cached tensors.
+            tgt(Tensor): 
+                Input tensor (#batch, maxlen_out, size).
+            tgt_mask(Tensor): 
+                Mask for input tensor (#batch, maxlen_out).
+            memory(Tensor): 
+                Encoded memory, float32 (#batch, maxlen_in, size).
+            memory_mask(Tensor): 
+                Encoded memory mask (#batch, maxlen_in).
+            cache(List[Tensor], optional): 
+                List of cached tensors.
                 Each tensor shape should be (#batch, maxlen_out - 1, size). (Default value = None)
         Returns:
             Tensor

paddlespeech/t2s/modules/transformer/embedding.py

@@ -23,11 +23,16 @@ class PositionalEncoding(nn.Layer):
     """Positional encoding.
 
     Args:
-        d_model (int):  Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_len (int): Maximum input length.
-        reverse (bool): Whether to reverse the input position.
-        type (str): dtype of param
+        d_model (int):
+            Embedding dimension.
+        dropout_rate (float): 
+            Dropout rate.
+        max_len (int): 
+            Maximum input length.
+        reverse (bool): 
+            Whether to reverse the input position.
+        type (str): 
+            dtype of param
     """
 
     def __init__(self,
@@ -68,7 +73,8 @@ class PositionalEncoding(nn.Layer):
         """Add positional encoding.
 
         Args:
-            x (Tensor): Input tensor (batch, time, `*`).
+            x (Tensor): 
+                Input tensor (batch, time, `*`).
 
         Returns:
             Tensor: Encoded tensor (batch, time, `*`).
@@ -84,10 +90,14 @@ class ScaledPositionalEncoding(PositionalEncoding):
     See Sec. 3.2  https://arxiv.org/abs/1809.08895
 
     Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_len (int): Maximum input length.
-        dtype (str): dtype of param
+        d_model (int): 
+            Embedding dimension.
+        dropout_rate (float): 
+            Dropout rate.
+        max_len (int): 
+            Maximum input length.
+        dtype (str): 
+            dtype of param
     """
 
     def __init__(self, d_model, dropout_rate, max_len=5000, dtype="float32"):
@@ -111,7 +121,8 @@ class ScaledPositionalEncoding(PositionalEncoding):
         """Add positional encoding.
 
         Args:
-            x (Tensor): Input tensor (batch, time, `*`).
+            x (Tensor): 
+                Input tensor (batch, time, `*`).
         Returns:
             Tensor: Encoded tensor (batch, time, `*`).
         """
@@ -127,9 +138,12 @@ class RelPositionalEncoding(nn.Layer):
     See : Appendix B in https://arxiv.org/abs/1901.02860
 
     Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_len (int): Maximum input length.
+        d_model (int): 
+            Embedding dimension.
+        dropout_rate (float): 
+            Dropout rate.
+        max_len (int): 
+            Maximum input length.
     """
 
     def __init__(self, d_model, dropout_rate, max_len=5000, dtype="float32"):
@@ -175,7 +189,8 @@ class RelPositionalEncoding(nn.Layer):
     def forward(self, x: paddle.Tensor):
         """Add positional encoding.
         Args:
-            x (Tensor):Input tensor (batch, time, `*`).
+            x (Tensor):
+                Input tensor (batch, time, `*`).
         Returns:
             Tensor: Encoded tensor (batch, time, `*`).
         """
@@ -195,18 +210,24 @@ class LegacyRelPositionalEncoding(PositionalEncoding):
     See : Appendix B in https://arxiv.org/abs/1901.02860
 
     Args:
-        d_model (int): Embedding dimension.
-        dropout_rate (float): Dropout rate.
-        max_len (int): Maximum input length.
+        d_model (int): 
+            Embedding dimension.
+        dropout_rate (float): 
+            Dropout rate.
+        max_len (int): 
+            Maximum input length.
 
     """
 
     def __init__(self, d_model: int, dropout_rate: float, max_len: int=5000):
         """
         Args:
-            d_model (int): Embedding dimension.
-            dropout_rate (float): Dropout rate.
-            max_len (int, optional): [Maximum input length.]. Defaults to 5000.
+            d_model (int): 
+                Embedding dimension.
+            dropout_rate (float): 
+                Dropout rate.
+            max_len (int, optional): 
+                [Maximum input length.]. Defaults to 5000.
         """
         super().__init__(d_model, dropout_rate, max_len, reverse=True)
 
@@ -234,10 +255,13 @@ class LegacyRelPositionalEncoding(PositionalEncoding):
     def forward(self, x: paddle.Tensor):
         """Compute positional encoding.
         Args:
-            x (paddle.Tensor): Input tensor (batch, time, `*`).
+            x (Tensor): 
+                Input tensor (batch, time, `*`).
         Returns:
-            paddle.Tensor: Encoded tensor (batch, time, `*`).
-            paddle.Tensor: Positional embedding tensor (1, time, `*`).
+            Tensor: 
+                Encoded tensor (batch, time, `*`).
+            Tensor: 
+                Positional embedding tensor (1, time, `*`).
         """
         self.extend_pe(x)
         x = x * self.xscale

paddlespeech/t2s/modules/transformer/encoder.py

@@ -38,32 +38,55 @@ class BaseEncoder(nn.Layer):
     """Base Encoder module.
 
     Args:
-        idim (int): Input dimension.
-        attention_dim (int): Dimention of attention.
-        attention_heads (int): The number of heads of multi head attention.
-        linear_units (int): The number of units of position-wise feed forward.
-        num_blocks (int): The number of decoder blocks.
-        dropout_rate (float): Dropout rate.
-        positional_dropout_rate (float): Dropout rate after adding positional encoding.
-        attention_dropout_rate (float): Dropout rate in attention.
-        input_layer (Union[str, nn.Layer]): Input layer type.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
+        idim (int): 
+            Input dimension.
+        attention_dim (int): 
+            Dimention of attention.
+        attention_heads (int): 
+            The number of heads of multi head attention.
+        linear_units (int): 
+            The number of units of position-wise feed forward.
+        num_blocks (int): 
+            The number of decoder blocks.
+        dropout_rate (float): 
+            Dropout rate.
+        positional_dropout_rate (float): 
+            Dropout rate after adding positional encoding.
+        attention_dropout_rate (float): 
+            Dropout rate in attention.
+        input_layer (Union[str, nn.Layer]): 
+            Input layer type.
+        normalize_before (bool): 
+            Whether to use layer_norm before the first block.
+        concat_after (bool): 
+            Whether to concat attention layer's input and output.
             if True, additional linear will be applied.
             i.e. x -> x + linear(concat(x, att(x)))
             if False, no additional linear will be applied. i.e. x -> x + att(x)
-        positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
-        positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
-        macaron_style (bool): Whether to use macaron style for positionwise layer.
-        pos_enc_layer_type (str): Encoder positional encoding layer type.
-        selfattention_layer_type (str): Encoder attention layer type.
-        activation_type (str): Encoder activation function type.
-        use_cnn_module (bool): Whether to use convolution module.
-        zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
-        cnn_module_kernel (int): Kernerl size of convolution module.
-        padding_idx (int): Padding idx for input_layer=embed.
-        stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
-        intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
+        positionwise_layer_type (str): 
+            "linear", "conv1d", or "conv1d-linear".
+        positionwise_conv_kernel_size (int): 
+            Kernel size of positionwise conv1d layer.
+        macaron_style (bool): 
+            Whether to use macaron style for positionwise layer.
+        pos_enc_layer_type (str): 
+            Encoder positional encoding layer type.
+        selfattention_layer_type (str): 
+            Encoder attention layer type.
+        activation_type (str): 
+            Encoder activation function type.
+        use_cnn_module (bool): 
+            Whether to use convolution module.
+        zero_triu (bool): 
+            Whether to zero the upper triangular part of attention matrix.
+        cnn_module_kernel (int): 
+            Kernerl size of convolution module.
+        padding_idx (int): 
+            Padding idx for input_layer=embed.
+        stochastic_depth_rate (float): 
+            Maximum probability to skip the encoder layer.
+        intermediate_layers (Union[List[int], None]): 
+            indices of intermediate CTC layer.
             indices start from 1.
             if not None, intermediate outputs are returned (which changes return type
             signature.)
@@ -266,12 +289,16 @@ class BaseEncoder(nn.Layer):
         """Encode input sequence.
 
         Args:
-            xs (Tensor): Input tensor (#batch, time, idim).
-            masks (Tensor): Mask tensor (#batch, 1, time).
+            xs (Tensor): 
+                Input tensor (#batch, time, idim).
+            masks (Tensor): 
+                Mask tensor (#batch, 1, time).
 
         Returns: 
-            Tensor: Output tensor (#batch, time, attention_dim).
-            Tensor: Mask tensor (#batch, 1, time).
+            Tensor: 
+                Output tensor (#batch, time, attention_dim).
+            Tensor: 
+                Mask tensor (#batch, 1, time).
         """
         xs = self.embed(xs)
         xs, masks = self.encoders(xs, masks)
@@ -284,26 +311,43 @@ class TransformerEncoder(BaseEncoder):
     """Transformer encoder module.
 
     Args:
-        idim (int): Input dimension.
-        attention_dim (int): Dimention of attention.
-        attention_heads (int): The number of heads of multi head attention.
-        linear_units (int): The number of units of position-wise feed forward.
-        num_blocks (int): The number of decoder blocks.
-        dropout_rate (float): Dropout rate.
-        positional_dropout_rate (float): Dropout rate after adding positional encoding.
-        attention_dropout_rate (float): Dropout rate in attention.
-        input_layer (Union[str, paddle.nn.Layer]): Input layer type.
-        pos_enc_layer_type (str): Encoder positional encoding layer type.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
+        idim (int): 
+            Input dimension.
+        attention_dim (int): 
+            Dimention of attention.
+        attention_heads (int): 
+            The number of heads of multi head attention.
+        linear_units (int): 
+            The number of units of position-wise feed forward.
+        num_blocks (int): 
+            The number of decoder blocks.
+        dropout_rate (float): 
+            Dropout rate.
+        positional_dropout_rate (float): 
+            Dropout rate after adding positional encoding.
+        attention_dropout_rate (float): 
+            Dropout rate in attention.
+        input_layer (Union[str, paddle.nn.Layer]): 
+            Input layer type.
+        pos_enc_layer_type (str): 
+            Encoder positional encoding layer type.
+        normalize_before (bool): 
+            Whether to use layer_norm before the first block.
+        concat_after (bool): 
+            Whether to concat attention layer's input and output.
             if True, additional linear will be applied.
             i.e. x -> x + linear(concat(x, att(x)))
             if False, no additional linear will be applied. i.e. x -> x + att(x)
-        positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
-        positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
-        selfattention_layer_type (str): Encoder attention layer type.
-        activation_type (str): Encoder activation function type.
-        padding_idx (int): Padding idx for input_layer=embed.
+        positionwise_layer_type (str): 
+            "linear", "conv1d", or "conv1d-linear".
+        positionwise_conv_kernel_size (int): 
+            Kernel size of positionwise conv1d layer.
+        selfattention_layer_type (str): 
+            Encoder attention layer type.
+        activation_type (str): 
+            Encoder activation function type.
+        padding_idx (int): 
+            Padding idx for input_layer=embed.
     """
 
     def __init__(
@@ -350,12 +394,16 @@ class TransformerEncoder(BaseEncoder):
         """Encoder input sequence.
 
         Args:
-            xs(Tensor): Input tensor (#batch, time, idim).
-            masks(Tensor): Mask tensor (#batch, 1, time).
+            xs(Tensor): 
+                Input tensor (#batch, time, idim).
+            masks(Tensor): 
+                Mask tensor (#batch, 1, time).
 
         Returns:
-            Tensor: Output tensor (#batch, time, attention_dim).
-            Tensor: Mask tensor (#batch, 1, time).
+            Tensor: 
+                Output tensor (#batch, time, attention_dim).
+            Tensor: 
+                Mask tensor (#batch, 1, time).
         """
         xs = self.embed(xs)
         xs, masks = self.encoders(xs, masks)
@@ -367,14 +415,20 @@ class TransformerEncoder(BaseEncoder):
         """Encode input frame.
 
         Args:
-            xs (Tensor): Input tensor.
-            masks (Tensor): Mask tensor.
-            cache (List[Tensor]): List of cache tensors.
+            xs (Tensor): 
+                Input tensor.
+            masks (Tensor): 
+                Mask tensor.
+            cache (List[Tensor]): 
+                List of cache tensors.
 
         Returns:
-            Tensor: Output tensor.
-            Tensor: Mask tensor.
-            List[Tensor]: List of new cache tensors.
+            Tensor:
+                 Output tensor.
+            Tensor:
+                 Mask tensor.
+            List[Tensor]: 
+                List of new cache tensors.
         """
 
         xs = self.embed(xs)
@@ -393,32 +447,55 @@ class ConformerEncoder(BaseEncoder):
     """Conformer encoder module.
 
     Args:
-        idim (int): Input dimension.
-        attention_dim (int): Dimention of attention.
-        attention_heads (int): The number of heads of multi head attention.
-        linear_units (int): The number of units of position-wise feed forward.
-        num_blocks (int): The number of decoder blocks.
-        dropout_rate (float): Dropout rate.
-        positional_dropout_rate (float): Dropout rate after adding positional encoding.
-        attention_dropout_rate (float): Dropout rate in attention.
-        input_layer (Union[str, nn.Layer]): Input layer type.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool):Whether to concat attention layer's input and output.
+        idim (int): 
+            Input dimension.
+        attention_dim (int): 
+            Dimention of attention.
+        attention_heads (int): 
+            The number of heads of multi head attention.
+        linear_units (int): 
+            The number of units of position-wise feed forward.
+        num_blocks (int): 
+            The number of decoder blocks.
+        dropout_rate (float): 
+            Dropout rate.
+        positional_dropout_rate (float): 
+            Dropout rate after adding positional encoding.
+        attention_dropout_rate (float): 
+            Dropout rate in attention.
+        input_layer (Union[str, nn.Layer]): 
+            Input layer type.
+        normalize_before (bool): 
+            Whether to use layer_norm before the first block.
+        concat_after (bool):
+            Whether to concat attention layer's input and output.
             if True, additional linear will be applied.
             i.e. x -> x + linear(concat(x, att(x)))
             if False, no additional linear will be applied. i.e. x -> x + att(x)
-        positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
-        positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
-        macaron_style (bool): Whether to use macaron style for positionwise layer.
-        pos_enc_layer_type (str): Encoder positional encoding layer type.
-        selfattention_layer_type (str): Encoder attention layer type.
-        activation_type (str): Encoder activation function type.
-        use_cnn_module (bool): Whether to use convolution module.
-        zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
-        cnn_module_kernel (int): Kernerl size of convolution module.
-        padding_idx (int): Padding idx for input_layer=embed.
-        stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
-        intermediate_layers (Union[List[int], None]):indices of intermediate CTC layer. indices start from 1.
+        positionwise_layer_type (str): 
+            "linear", "conv1d", or "conv1d-linear".
+        positionwise_conv_kernel_size (int): 
+            Kernel size of positionwise conv1d layer.
+        macaron_style (bool): 
+            Whether to use macaron style for positionwise layer.
+        pos_enc_layer_type (str): 
+            Encoder positional encoding layer type.
+        selfattention_layer_type (str): 
+            Encoder attention layer type.
+        activation_type (str): 
+            Encoder activation function type.
+        use_cnn_module (bool): 
+            Whether to use convolution module.
+        zero_triu (bool): 
+            Whether to zero the upper triangular part of attention matrix.
+        cnn_module_kernel (int): 
+            Kernerl size of convolution module.
+        padding_idx (int): 
+            Padding idx for input_layer=embed.
+        stochastic_depth_rate (float): 
+            Maximum probability to skip the encoder layer.
+        intermediate_layers (Union[List[int], None]):
+            indices of intermediate CTC layer. indices start from 1.
             if not None, intermediate outputs are returned (which changes return type signature.)
     """
 
@@ -478,11 +555,15 @@ class ConformerEncoder(BaseEncoder):
         """Encode input sequence.
 
         Args:
-            xs (Tensor): Input tensor (#batch, time, idim).
-            masks (Tensor): Mask tensor (#batch, 1, time).
+            xs (Tensor): 
+                Input tensor (#batch, time, idim).
+            masks (Tensor): 
+                Mask tensor (#batch, 1, time).
         Returns:
-            Tensor: Output tensor (#batch, time, attention_dim).
-            Tensor: Mask tensor (#batch, 1, time).
+            Tensor: 
+                Output tensor (#batch, time, attention_dim).
+            Tensor: 
+                Mask tensor (#batch, 1, time).
         """
         if isinstance(self.embed, (Conv2dSubsampling)):
             xs, masks = self.embed(xs, masks)
@@ -539,7 +620,8 @@ class Conv1dResidualBlock(nn.Layer):
     def forward(self, xs):
         """Encode input sequence.
         Args:
-            xs (Tensor): Input tensor (#batch, idim, T).
+            xs (Tensor): 
+                Input tensor (#batch, idim, T).
         Returns:
             Tensor: Output tensor (#batch, odim, T).
         """
@@ -582,8 +664,10 @@ class CNNDecoder(nn.Layer):
     def forward(self, xs, masks=None):
         """Encode input sequence.
         Args:
-            xs (Tensor): Input tensor (#batch, time, idim).
-            masks (Tensor): Mask tensor (#batch, 1, time).
+            xs (Tensor): 
+                Input tensor (#batch, time, idim).
+            masks (Tensor): 
+                Mask tensor (#batch, 1, time).
         Returns:
             Tensor: Output tensor (#batch, time, odim).
         """
@@ -629,8 +713,10 @@ class CNNPostnet(nn.Layer):
     def forward(self, xs, masks=None):
         """Encode input sequence.
         Args:
-            xs (Tensor): Input tensor (#batch, odim, time).
-            masks (Tensor): Mask tensor (#batch, 1, time).
+            xs (Tensor): 
+                Input tensor (#batch, odim, time).
+            masks (Tensor): 
+                Mask tensor (#batch, 1, time).
         Returns:
             Tensor: Output tensor (#batch, odim, time).
         """

paddlespeech/t2s/modules/transformer/encoder_layer.py

@@ -21,14 +21,20 @@ class EncoderLayer(nn.Layer):
     """Encoder layer module.
 
     Args:
-        size (int): Input dimension.
-        self_attn (nn.Layer): Self-attention module instance.
+        size (int): 
+            Input dimension.
+        self_attn (nn.Layer): 
+            Self-attention module instance.
             `MultiHeadedAttention`  instance can be used as the argument.
-        feed_forward (nn.Layer): Feed-forward module instance.
+        feed_forward (nn.Layer): 
+            Feed-forward module instance.
             `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance can be used as the argument.
-        dropout_rate (float): Dropout rate.
-        normalize_before (bool): Whether to use layer_norm before the first block.
-        concat_after (bool): Whether to concat attention layer's input and output.
+        dropout_rate (float): 
+            Dropout rate.
+        normalize_before (bool): 
+            Whether to use layer_norm before the first block.
+        concat_after (bool): 
+            Whether to concat attention layer's input and output.
             if True, additional linear will be applied.
             i.e. x -> x + linear(concat(x, att(x)))
             if False, no additional linear will be applied. i.e. x -> x + att(x)
@@ -59,13 +65,18 @@ class EncoderLayer(nn.Layer):
         """Compute encoded features.
 
         Args:
-            x(Tensor): Input tensor (#batch, time, size).
-            mask(Tensor): Mask tensor for the input (#batch, time).
-            cache(Tensor, optional): Cache tensor of the input (#batch, time - 1, size). 
+            x(Tensor): 
+                Input tensor (#batch, time, size).
+            mask(Tensor): 
+                Mask tensor for the input (#batch, time).
+            cache(Tensor, optional): 
+                Cache tensor of the input (#batch, time - 1, size). 
 
         Returns:
-            Tensor: Output tensor (#batch, time, size).
-            Tensor: Mask tensor (#batch, time).
+            Tensor: 
+                Output tensor (#batch, time, size).
+            Tensor: 
+                Mask tensor (#batch, time).
         """
         residual = x
         if self.normalize_before:

paddlespeech/t2s/modules/transformer/lightconv.py

@@ -31,12 +31,18 @@ class LightweightConvolution(nn.Layer):
     https://github.com/pytorch/fairseq/tree/master/fairseq
 
     Args:
-        wshare (int): the number of kernel of convolution
-        n_feat (int): the number of features
-        dropout_rate (float): dropout_rate
-        kernel_size (int): kernel size (length)
-        use_kernel_mask (bool): Use causal mask or not for convolution kernel
-        use_bias (bool): Use bias term or not.
+        wshare (int): 
+            the number of kernel of convolution
+        n_feat (int): 
+            the number of features
+        dropout_rate (float): 
+            dropout_rate
+        kernel_size (int): 
+            kernel size (length)
+        use_kernel_mask (bool): 
+            Use causal mask or not for convolution kernel
+        use_bias (bool): 
+            Use bias term or not.
 
     """
 
@@ -94,10 +100,14 @@ class LightweightConvolution(nn.Layer):
         This is just for compatibility with self-attention layer (attention.py)
 
         Args:
-            query (Tensor): input tensor. (batch, time1, d_model)
-            key (Tensor): NOT USED. (batch, time2, d_model)  
-            value (Tensor): NOT USED. (batch, time2, d_model) 
-            mask : (Tensor): (batch, time1, time2) mask
+            query (Tensor): 
+                input tensor. (batch, time1, d_model)
+            key (Tensor): 
+                NOT USED. (batch, time2, d_model)  
+            value (Tensor): 
+                NOT USED. (batch, time2, d_model) 
+            mask : (Tensor):
+                (batch, time1, time2) mask
 
         Return:
             Tensor: ouput. (batch, time1, d_model) 

paddlespeech/t2s/modules/transformer/mask.py

@@ -19,8 +19,10 @@ def subsequent_mask(size, dtype=paddle.bool):
     """Create mask for subsequent steps (size, size).
 
     Args:
-        size (int): size of mask
-        dtype (paddle.dtype): result dtype
+        size (int): 
+            size of mask
+        dtype (paddle.dtype): 
+            result dtype
     Return:
         Tensor:
             >>> subsequent_mask(3)
@@ -36,9 +38,12 @@ def target_mask(ys_in_pad, ignore_id, dtype=paddle.bool):
     """Create mask for decoder self-attention.
 
     Args:
-        ys_pad (Tensor): batch of padded target sequences (B, Lmax)
-        ignore_id (int): index of padding
-        dtype (paddle.dtype): result dtype
+        ys_pad (Tensor): 
+            batch of padded target sequences (B, Lmax)
+        ignore_id (int): 
+            index of padding
+        dtype (paddle.dtype): 
+            result dtype
     Return: 
         Tensor: (B, Lmax, Lmax)
     """

paddlespeech/t2s/modules/transformer/multi_layer_conv.py

@@ -32,10 +32,14 @@ class MultiLayeredConv1d(nn.Layer):
         """Initialize MultiLayeredConv1d module.
 
         Args: 
-            in_chans (int): Number of input channels.
-            hidden_chans (int): Number of hidden channels.
-            kernel_size (int): Kernel size of conv1d.
-            dropout_rate (float): Dropout rate.
+            in_chans (int): 
+                Number of input channels.
+            hidden_chans (int): 
+                Number of hidden channels.
+            kernel_size (int): 
+                Kernel size of conv1d.
+            dropout_rate (float): 
+                Dropout rate.
 
         """
         super().__init__()
@@ -58,7 +62,8 @@ class MultiLayeredConv1d(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            x (Tensor): Batch of input tensors (B, T, in_chans).
+            x (Tensor): 
+                Batch of input tensors (B, T, in_chans).
 
         Returns: 
             Tensor: Batch of output tensors (B, T, in_chans).
@@ -79,10 +84,14 @@ class Conv1dLinear(nn.Layer):
         """Initialize Conv1dLinear module.
 
         Args:
-            in_chans (int): Number of input channels.
-            hidden_chans (int): Number of hidden channels.
-            kernel_size (int): Kernel size of conv1d.
-            dropout_rate (float): Dropout rate.
+            in_chans (int): 
+                Number of input channels.
+            hidden_chans (int): 
+                Number of hidden channels.
+            kernel_size (int): 
+                Kernel size of conv1d.
+            dropout_rate (float):
+                Dropout rate.
         """
         super().__init__()
         self.w_1 = nn.Conv1D(
@@ -99,7 +108,8 @@ class Conv1dLinear(nn.Layer):
         """Calculate forward propagation.
 
         Args:
-            x (Tensor): Batch of input tensors (B, T, in_chans).
+            x (Tensor): 
+                Batch of input tensors (B, T, in_chans).
 
         Returns:
             Tensor: Batch of output tensors (B, T, in_chans).

paddlespeech/t2s/modules/transformer/positionwise_feed_forward.py

@@ -21,9 +21,12 @@ class PositionwiseFeedForward(nn.Layer):
     """Positionwise feed forward layer.
 
     Args:
-        idim (int): Input dimenstion.
-        hidden_units (int): The number of hidden units.
-        dropout_rate (float): Dropout rate.
+        idim (int): 
+            Input dimenstion.
+        hidden_units (int): 
+            The number of hidden units.
+        dropout_rate (float): 
+            Dropout rate.
     """
 
     def __init__(self,

paddlespeech/t2s/modules/transformer/repeat.py

@@ -30,8 +30,10 @@ def repeat(N, fn):
     """Repeat module N times.
 
     Args:
-        N (int): Number of repeat time.
-        fn (Callable): Function to generate module.
+        N (int): 
+            Number of repeat time.
+        fn (Callable): 
+            Function to generate module.
 
     Returns:
         MultiSequential: Repeated model instance.

paddlespeech/t2s/modules/transformer/subsampling.py

@@ -23,10 +23,14 @@ class Conv2dSubsampling(nn.Layer):
     """Convolutional 2D subsampling (to 1/4 length).
 
     Args:
-        idim (int): Input dimension.
-        odim (int): Output dimension.
-        dropout_rate (float): Dropout rate.
-        pos_enc (nn.Layer): Custom position encoding layer.
+        idim (int): 
+            Input dimension.
+        odim (int): 
+            Output dimension.
+        dropout_rate (float): 
+            Dropout rate.
+        pos_enc (nn.Layer): 
+            Custom position encoding layer.
     """
 
     def __init__(self, idim, odim, dropout_rate, pos_enc=None):
@@ -45,11 +49,15 @@ class Conv2dSubsampling(nn.Layer):
     def forward(self, x, x_mask):
         """Subsample x.
         Args:
-            x (Tensor): Input tensor (#batch, time, idim).
-            x_mask (Tensor): Input mask (#batch, 1, time).
+            x (Tensor): 
+                Input tensor (#batch, time, idim).
+            x_mask (Tensor): 
+                Input mask (#batch, 1, time).
         Returns:
-            Tensor: Subsampled tensor (#batch, time', odim), where time' = time // 4.
-            Tensor: Subsampled mask (#batch, 1, time'), where time' = time // 4.
+            Tensor: 
+                Subsampled tensor (#batch, time', odim), where time' = time // 4.
+            Tensor: 
+                Subsampled mask (#batch, 1, time'), where time' = time // 4.
         """
         # (b, c, t, f)
         x = x.unsqueeze(1)

paddlespeech/t2s/modules/upsample.py

@@ -28,9 +28,12 @@ class Stretch2D(nn.Layer):
         """Strech an image (or image-like object) with some interpolation.
 
         Args:
-            w_scale (int): Scalar of width.
-            h_scale (int): Scalar of the height.
-            mode (str, optional): Interpolation mode, modes suppored are "nearest", "bilinear", 
+            w_scale (int): 
+                Scalar of width.
+            h_scale (int): 
+                Scalar of the height.
+            mode (str, optional): 
+                Interpolation mode, modes suppored are "nearest", "bilinear", 
                 "trilinear", "bicubic", "linear" and "area",by default "nearest"
         For more details about interpolation, see 
             `paddle.nn.functional.interpolate <https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/nn/functional/interpolate_en.html>`_.
@@ -44,11 +47,12 @@ class Stretch2D(nn.Layer):
         """
 
         Args: 
-            x (Tensor): Shape (N, C, H, W)
+            x (Tensor): 
+                Shape (N, C, H, W)
 
         Returns:
-            Tensor: The stretched image.
-                Shape (N, C, H', W'), where ``H'=h_scale * H``, ``W'=w_scale * W``.
+            Tensor: 
+                The stretched image. Shape (N, C, H', W'), where ``H'=h_scale * H``, ``W'=w_scale * W``.
             
         """
         out = F.interpolate(
@@ -61,12 +65,18 @@ class UpsampleNet(nn.Layer):
     convolutions.
 
     Args:
-        upsample_scales (List[int]): Upsampling factors for each strech.
-        nonlinear_activation (Optional[str], optional): Activation after each convolution, by default None
-        nonlinear_activation_params (Dict[str, Any], optional): Parameters passed to construct the activation, by default {}
-        interpolate_mode (str, optional): Interpolation mode of the strech, by default "nearest"
-        freq_axis_kernel_size (int, optional): Convolution kernel size along the frequency axis, by default 1
-        use_causal_conv (bool, optional): Whether to use causal padding before convolution, by default False
+        upsample_scales (List[int]): 
+            Upsampling factors for each strech.
+        nonlinear_activation (Optional[str], optional): 
+            Activation after each convolution, by default None
+        nonlinear_activation_params (Dict[str, Any], optional): 
+            Parameters passed to construct the activation, by default {}
+        interpolate_mode (str, optional): 
+            Interpolation mode of the strech, by default "nearest"
+        freq_axis_kernel_size (int, optional): 
+            Convolution kernel size along the frequency axis, by default 1
+        use_causal_conv (bool, optional): 
+            Whether to use causal padding before convolution, by default False
             If True, Causal padding is used along the time axis, 
             i.e. padding amount is ``receptive field - 1`` and 0 for before and after, respectively.
             If False, "same" padding is used along the time axis.
@@ -106,7 +116,8 @@ class UpsampleNet(nn.Layer):
     def forward(self, c):
         """
         Args:
-            c (Tensor): spectrogram. Shape (N, F, T)
+            c (Tensor): 
+                spectrogram. Shape (N, F, T)
 
         Returns: 
             Tensor: upsampled spectrogram.
@@ -126,17 +137,25 @@ class ConvInUpsampleNet(nn.Layer):
     UpsampleNet.
     
     Args:
-        upsample_scales (List[int]): Upsampling factors for each strech.
-        nonlinear_activation (Optional[str], optional): Activation after each convolution, by default None
-        nonlinear_activation_params (Dict[str, Any], optional): Parameters passed to construct the activation, by default {}
-        interpolate_mode (str, optional): Interpolation mode of the strech, by default "nearest"
-        freq_axis_kernel_size (int, optional): Convolution kernel size along the frequency axis, by default 1
-        aux_channels (int, optional): Feature size of the input, by default 80
-        aux_context_window (int, optional): Context window of the first 1D convolution applied to the input. It 
+        upsample_scales (List[int]): 
+            Upsampling factors for each strech.
+        nonlinear_activation (Optional[str], optional): 
+            Activation after each convolution, by default None
+        nonlinear_activation_params (Dict[str, Any], optional): 
+            Parameters passed to construct the activation, by default {}
+        interpolate_mode (str, optional): 
+            Interpolation mode of the strech, by default "nearest"
+        freq_axis_kernel_size (int, optional): 
+            Convolution kernel size along the frequency axis, by default 1
+        aux_channels (int, optional): 
+            Feature size of the input, by default 80
+        aux_context_window (int, optional): 
+            Context window of the first 1D convolution applied to the input. It 
             related to the kernel size of the convolution, by default 0
             If use causal convolution, the kernel size is ``window + 1``, 
             else the kernel size is ``2 * window + 1``.
-        use_causal_conv (bool, optional): Whether to use causal padding before convolution, by default False
+        use_causal_conv (bool, optional):
+            Whether to use causal padding before convolution, by default False
             If True, Causal padding is used along the time axis, i.e. padding 
             amount is ``receptive field - 1`` and 0 for before and after, respectively.
             If False, "same" padding is used along the time axis.
@@ -171,7 +190,8 @@ class ConvInUpsampleNet(nn.Layer):
     def forward(self, c):
         """
         Args:
-            c (Tensor): spectrogram. Shape (N, F, T)
+            c (Tensor): 
+                spectrogram. Shape (N, F, T)
 
         Returns:
             Tensors: upsampled spectrogram. Shape (N, F, T'), where ``T' = upsample_factor * T``, 

paddlespeech/t2s/training/experiment.py

@@ -58,8 +58,10 @@ class ExperimentBase(object):
     need.
 
     Args:
-        config (yacs.config.CfgNode): The configuration used for the experiment.
-        args (argparse.Namespace): The parsed command line arguments.
+        config (yacs.config.CfgNode): 
+            The configuration used for the experiment.
+        args (argparse.Namespace): 
+            The parsed command line arguments.
 
     Examples:
         >>> def main_sp(config, args):

paddlespeech/t2s/utils/checkpoint.py

@@ -25,7 +25,8 @@ def _load_latest_checkpoint(checkpoint_dir: str) -> int:
     """Get the iteration number corresponding to the latest saved checkpoint.
 
     Args:
-        checkpoint_dir (str): the directory where checkpoint is saved.
+        checkpoint_dir (str):
+            the directory where checkpoint is saved.
 
     Returns:
         int: the latest iteration number.
@@ -46,8 +47,10 @@ def _save_checkpoint(checkpoint_dir: str, iteration: int):
     """Save the iteration number of the latest model to be checkpointed.
 
     Args:
-        checkpoint_dir (str): the directory where checkpoint is saved.
-        iteration (int): the latest iteration number.
+        checkpoint_dir (str): 
+            the directory where checkpoint is saved.
+        iteration (int): 
+            the latest iteration number.
 
     Returns:
         None
@@ -65,11 +68,14 @@ def load_parameters(model,
     """Load a specific model checkpoint from disk.
 
     Args:
-        model (Layer): model to load parameters.
-        optimizer (Optimizer, optional): optimizer to load states if needed.
-            Defaults to None.
-        checkpoint_dir (str, optional): the directory where checkpoint is saved.
-        checkpoint_path (str, optional): if specified, load the checkpoint
+        model (Layer): 
+            model to load parameters.
+        optimizer (Optimizer, optional): 
+            optimizer to load states if needed. Defaults to None.
+        checkpoint_dir (str, optional): 
+            the directory where checkpoint is saved.
+        checkpoint_path (str, optional): 
+            if specified, load the checkpoint
             stored in the checkpoint_path and the argument 'checkpoint_dir' will
             be ignored. Defaults to None.
 
@@ -113,11 +119,14 @@ def save_parameters(checkpoint_dir, iteration, model, optimizer=None):
     """Checkpoint the latest trained model parameters.
 
     Args:
-        checkpoint_dir (str): the directory where checkpoint is saved.
-        iteration (int): the latest iteration number.
-        model (Layer): model to be checkpointed.
-        optimizer (Optimizer, optional): optimizer to be checkpointed.
-            Defaults to None.
+        checkpoint_dir (str): 
+            the directory where checkpoint is saved.
+        iteration (int): 
+            the latest iteration number.
+        model (Layer): 
+            model to be checkpointed.
+        optimizer (Optimizer, optional): 
+            optimizer to be checkpointed. Defaults to None.
 
     Returns:
         None

paddlespeech/t2s/utils/error_rate.py

@@ -71,10 +71,14 @@ def word_errors(reference, hypothesis, ignore_case=False, delimiter=' '):
     hypothesis sequence in word-level.
 
     Args:
-        reference (str): The reference sentence.
-        hypothesis (str): The hypothesis sentence.
-        ignore_case (bool): Whether case-sensitive or not.
-        delimiter (char(str)): Delimiter of input sentences.
+        reference (str): 
+            The reference sentence.
+        hypothesis (str): 
+            The hypothesis sentence.
+        ignore_case (bool): 
+            Whether case-sensitive or not.
+        delimiter (char(str)): 
+            Delimiter of input sentences.
 
     Returns:
         list: Levenshtein distance and word number of reference sentence.

paddlespeech/t2s/utils/h5_utils.py

@@ -24,8 +24,10 @@ import numpy as np
 def read_hdf5(filename: Union[Path, str], dataset_name: str) -> Any:
     """Read a dataset from a HDF5 file.
     Args:
-        filename (Union[Path, str]): Path of the HDF5 file.
-        dataset_name (str): Name of the dataset to read.
+        filename (Union[Path, str]): 
+            Path of the HDF5 file.
+        dataset_name (str): 
+            Name of the dataset to read.
 
     Returns:
         Any: The retrieved dataset.

paddlespeech/t2s/utils/internals.py

@@ -22,7 +22,8 @@ def convert_dtype_to_np_dtype_(dtype):
     Convert paddle's data type to corrsponding numpy data type.
 
     Args:
-        dtype(np.dtype): the data type in paddle.
+        dtype(np.dtype): 
+            the data type in paddle.
 
     Returns:
         type: the data type in numpy.

setup.py

@@ -48,6 +48,7 @@ base = [
     "pandas",
     "paddlenlp",
     "paddlespeech_feat",
+    "Pillow>=9.0.0"
     "praatio==5.0.0",
     "pypinyin",
     "pypinyin-dict",
@@ -77,7 +78,7 @@ server = [
     "fastapi",
     "uvicorn",
     "pattern_singleton",
-    "websockets",
+    "websockets" 
 ]
 
 requirements = {
@@ -89,7 +90,6 @@ requirements = {
         "gpustat",
         "paddlespeech_ctcdecoders",
         "phkit",
-        "Pillow",
         "pybind11",
         "pypi-kenlm",
         "snakeviz",