Merge branch 'develop' of https://github.com/PaddlePaddle/PaddleSpeech into add_vc2

README.md

@@ -613,7 +613,7 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
       </td>
     </tr>
     <tr>
-      <td rowspan="3">Voice Cloning</td>
+      <td rowspan="4">Voice Cloning</td>
       <td>GE2E</td>
       <td >Librispeech, etc.</td>
       <td>
@@ -633,13 +633,20 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
       <td>
       <a href = "./examples/aishell3/vc1">ge2e-fastspeech2-aishell3</a>
       </td>
+    </tr>
+    <tr>
+      <td>GE2E + VITS</td>
+      <td>AISHELL-3</td>
+      <td>
+      <a href = "./examples/aishell3/vits-vc">ge2e-vits-aishell3</a>
+      </td>
     </tr>
      <tr>
       <td rowspan="3">End-to-End</td>
       <td>VITS</td>
-      <td >CSMSC</td>
+      <td>CSMSC / AISHELL-3</td>
       <td>
-      <a href = "./examples/csmsc/vits">VITS-csmsc</a>
+      <a href = "./examples/csmsc/vits">VITS-csmsc</a> / <a href = "./examples/aishell3/vits">VITS-aishell3</a>
       </td>
     </tr>
   </tbody>

README_cn.md

@@ -164,6 +164,22 @@
   - 🧩 级联模型应用: 作为传统语音任务的扩展，我们结合了自然语言处理、计算机视觉等任务，实现更接近实际需求的产业级应用。
 
 
+### 近期活动
+
+ ❗️重磅❗️飞桨智慧金融行业系列直播课
+✅ 覆盖智能风控、智能运维、智能营销、智能客服四大金融主流场景
+
+📆 9月6日-9月29日每周二、四19:00
++ 智慧金融行业深入洞察
++ 8节理论+实践精品直播课
++ 10+真实产业场景范例教学及实践
++ 更有免费算力+结业证书等礼品等你来拿
+扫码报名码住直播链接，与行业精英深度交流
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/30135920/188431897-a02f028f-dd13-41e8-8ff6-749468cdc850.jpg"  width = "200"  />
+</div>
+
 ### 近期更新
 
 - 👑 2022.05.13: PaddleSpeech 发布 [PP-ASR](./docs/source/asr/PPASR_cn.md) 流式语音识别系统、[PP-TTS](./docs/source/tts/PPTTS_cn.md) 流式语音合成系统、[PP-VPR](docs/source/vpr/PPVPR_cn.md) 全链路声纹识别系统
@@ -608,7 +624,7 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
       </td>
     </tr>
     <tr>
-      <td rowspan="3">声音克隆</td>
+      <td rowspan="4">声音克隆</td>
       <td>GE2E</td>
       <td >Librispeech, etc.</td>
       <td>
@@ -629,13 +645,20 @@ 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>
+      <td>GE2E + VITS</td>
+      <td>AISHELL-3</td>
+      <td>
+      <a href = "./examples/aishell3/vits-vc">ge2e-vits-aishell3</a>
+      </td>
+    </tr>
     </tr>
      <tr>
       <td rowspan="3">端到端</td>
       <td>VITS</td>
-      <td >CSMSC</td>
+      <td>CSMSC / AISHELL-3</td>
       <td>
-      <a href = "./examples/csmsc/vits">VITS-csmsc</a>
+      <a href = "./examples/csmsc/vits">VITS-csmsc</a> / <a href = "./examples/aishell3/vits">VITS-aishell3</a>
       </td>
     </tr>
   </tbody>

demos/audio_searching/README.md

@@ -226,6 +226,12 @@ recall and elapsed time statistics are shown in the following figure：
 
 The retrieval framework based on Milvus takes about 2.9 milliseconds to retrieve on the premise of 90% recall rate, and it takes about 500 milliseconds for feature extraction (testing audio takes about 5 seconds), that is, a single audio test takes about 503 milliseconds in total, which can meet most application scenarios.
 
+* compute embeding takes 500 ms
+* retrieval with cosine takes 2.9 ms
+* total takes 503 ms
+
+> test audio is 5 sec
+
 ### 6.Pretrained Models
 
 Here is a list of pretrained models released by PaddleSpeech :

demos/audio_searching/src/operations/load.py

@@ -26,8 +26,9 @@ def get_audios(path):
     """
     supported_formats = [".wav", ".mp3", ".ogg", ".flac", ".m4a"]
     return [
-        item for sublist in [[os.path.join(dir, file) for file in files]
-                             for dir, _, files in list(os.walk(path))]
+        item
+        for sublist in [[os.path.join(dir, file) for file in files]
+                        for dir, _, files in list(os.walk(path))]
         for item in sublist if os.path.splitext(item)[1] in supported_formats
     ]
 

demos/metaverse/README.md

+([简体中文](./README_cn.md)|English)
+
 # Metaverse
 ## Introduction
 Metaverse is a new Internet application and social form integrating virtual reality produced by integrating a variety of new technologies. 

demos/metaverse/README_cn.md

+(简体中文|[English](./README.md))
+
+# Metaverse
+
+## 简介
+
+Metaverse 是一种新的互联网应用和社交形式，融合了多种新技术，产生了虚拟现实。
+
+这个演示是一个让图片中的名人“说话”的实现。通过 `PaddleSpeech` 的 `TTS` 模块和 `PaddleGAN` 的组合，我们集成了安装和特定模块到一个 shell 脚本中。
+
+## 使用
+
+您可以使用 `PaddleSpeech` 的 `TTS` 模块和 `PaddleGAN` 让您最喜欢的人说出指定的内容，并构建您的虚拟人。
+
+运行 `run.sh` 完成所有基本程序，包括安装。
+
+```bash
+./run.sh
+```
+
+在 `run.sh`, 先会执行 `source path.sh` 来设置好环境变量。
+
+如果您想尝试您的句子，请替换 `sentences.txt` 中的句子。
+
+如果您想尝试图像，请将图像替换 shell 脚本中的 `download/Lamarr.png` 。
+
+结果已显示在我们的 [notebook](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/tutorial/tts/tts_tutorial.ipynb)。

demos/speaker_verification/README.md

@@ -19,6 +19,7 @@ The input of this cli demo should be a WAV file(`.wav`), and the sample rate mus
 Here are sample files for this demo that can be downloaded:
 ```bash
 wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
+wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.wav
 ```
 
 ### 3. Usage

demos/speaker_verification/README_cn.md

@@ -19,6 +19,7 @@
 ```bash
 # 该音频的内容是数字串 85236145389
 wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
+wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.wav
 ```
 ### 3. 使用方法
 - 命令行 (推荐使用)

demos/speech_web/API.md

@@ -401,4 +401,4 @@ curl -X 'GET' \
   "code": 0,
   "result":"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA",
   "message": "ok"
-```
+```
\ No newline at end of file

demos/speech_web/speech_server/main.py

@@ -3,48 +3,48 @@
 # 2. 接收录音音频，返回识别结果
 # 3. 接收ASR识别结果，返回NLP对话结果
 # 4. 接收NLP对话结果，返回TTS音频
-
+import argparse
 import base64
-import yaml
-import os
-import json
 import datetime
+import json
+import os
+from typing import List
+
+import aiofiles
 import librosa
 import soundfile as sf
-import numpy as np
-import argparse
 import uvicorn
-import aiofiles
-from typing import Optional, List 
-from pydantic import BaseModel
-from fastapi import FastAPI, Header, File, UploadFile, Form, Cookie, WebSocket, WebSocketDisconnect
+from fastapi import FastAPI
+from fastapi import File
+from fastapi import Form
+from fastapi import UploadFile
+from fastapi import WebSocket
+from fastapi import WebSocketDisconnect
 from fastapi.responses import StreamingResponse
-from starlette.responses import FileResponse
-from starlette.middleware.cors import CORSMiddleware
-from starlette.requests import Request
-from starlette.websockets import WebSocketState as WebSocketState
-
+from pydantic import BaseModel
 from src.AudioManeger import AudioMannger
-from src.util import *
 from src.robot import Robot
-from src.WebsocketManeger import ConnectionManager
 from src.SpeechBase.vpr import VPR
+from src.util import *
+from src.WebsocketManeger import ConnectionManager
+from starlette.middleware.cors import CORSMiddleware
+from starlette.requests import Request
+from starlette.responses import FileResponse
+from starlette.websockets import WebSocketState as WebSocketState
 
 from paddlespeech.server.engine.asr.online.python.asr_engine import PaddleASRConnectionHanddler
 from paddlespeech.server.utils.audio_process import float2pcm
 
-
 # 解析配置
-parser = argparse.ArgumentParser(
-        prog='PaddleSpeechDemo', add_help=True)
+parser = argparse.ArgumentParser(prog='PaddleSpeechDemo', add_help=True)
 
 parser.add_argument(
-        "--port",
-        action="store",
-        type=int,
-        help="port of the app",
-        default=8010,
-        required=False)
+    "--port",
+    action="store",
+    type=int,
+    help="port of the app",
+    default=8010,
+    required=False)
 
 args = parser.parse_args()
 port = args.port
@@ -60,39 +60,41 @@ ie_model_path = "source/model"
 UPLOAD_PATH = "source/vpr"
 WAV_PATH = "source/wav"
 
-
-base_sources = [
-    UPLOAD_PATH, WAV_PATH
-]
+base_sources = [UPLOAD_PATH, WAV_PATH]
 for path in base_sources:
     os.makedirs(path, exist_ok=True)
 
-
 # 初始化
 app = FastAPI()
-chatbot = Robot(asr_config, tts_config, asr_init_path, ie_model_path=ie_model_path)
+chatbot = Robot(
+    asr_config, tts_config, asr_init_path, ie_model_path=ie_model_path)
 manager = ConnectionManager()
 aumanager = AudioMannger(chatbot)
 aumanager.init()
-vpr = VPR(db_path, dim = 192, top_k = 5)
+vpr = VPR(db_path, dim=192, top_k=5)
+
 
 # 服务配置
 class NlpBase(BaseModel):
     chat: str
 
+
 class TtsBase(BaseModel):
-    text: str 
+    text: str
+
 
 class Audios:
     def __init__(self) -> None:
         self.audios = b""
 
+
 audios = Audios()
 
 ######################################################################
 ########################### ASR 服务 #################################
 #####################################################################
 
+
 # 接收文件，返回ASR结果
 # 上传文件
 @app.post("/asr/offline")
@@ -101,7 +103,8 @@ async def speech2textOffline(files: List[UploadFile]):
     asr_res = ""
     for file in files[:1]:
         # 生成时间戳
-        now_name = "asr_offline_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
+        now_name = "asr_offline_" + datetime.datetime.strftime(
+            datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
         out_file_path = os.path.join(WAV_PATH, now_name)
         async with aiofiles.open(out_file_path, 'wb') as out_file:
             content = await file.read()  # async read
@@ -110,10 +113,9 @@ async def speech2textOffline(files: List[UploadFile]):
         # 返回ASR识别结果
         asr_res = chatbot.speech2text(out_file_path)
         return SuccessRequest(result=asr_res)
-        # else:
-            # return ErrorRequest(message="文件不是.wav格式")
     return ErrorRequest(message="上传文件为空")
 
+
 # 接收文件，同时将wav强制转成16k, int16类型
 @app.post("/asr/offlinefile")
 async def speech2textOfflineFile(files: List[UploadFile]):
@@ -121,7 +123,8 @@ async def speech2textOfflineFile(files: List[UploadFile]):
     asr_res = ""
     for file in files[:1]:
         # 生成时间戳
-        now_name = "asr_offline_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
+        now_name = "asr_offline_" + datetime.datetime.strftime(
+            datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
         out_file_path = os.path.join(WAV_PATH, now_name)
         async with aiofiles.open(out_file_path, 'wb') as out_file:
             content = await file.read()  # async read
@@ -132,22 +135,18 @@ async def speech2textOfflineFile(files: List[UploadFile]):
         wav = float2pcm(wav)  # float32 to int16
         wav_bytes = wav.tobytes()  # to bytes
         wav_base64 = base64.b64encode(wav_bytes).decode('utf8')
-        
+
         # 将文件重新写入
         now_name = now_name[:-4] + "_16k" + ".wav"
         out_file_path = os.path.join(WAV_PATH, now_name)
-        sf.write(out_file_path,wav,16000)
+        sf.write(out_file_path, wav, 16000)
 
         # 返回ASR识别结果
         asr_res = chatbot.speech2text(out_file_path)
-        response_res = {
-            "asr_result": asr_res,
-            "wav_base64": wav_base64
-        }
+        response_res = {"asr_result": asr_res, "wav_base64": wav_base64}
         return SuccessRequest(result=response_res)
-        
-    return ErrorRequest(message="上传文件为空")
 
+    return ErrorRequest(message="上传文件为空")
 
 
 # 流式接收测试
@@ -161,15 +160,17 @@ async def speech2textOnlineRecive(files: List[UploadFile]):
     print(f"audios长度变化: {len(audios.audios)}")
     return SuccessRequest(message="接收成功")
 
+
 # 采集环境噪音大小
 @app.post("/asr/collectEnv")
 async def collectEnv(files: List[UploadFile]):
-     for file in files[:1]:
+    for file in files[:1]:
         content = await file.read()  # async read
         # 初始化, wav 前44字节是头部信息
         aumanager.compute_env_volume(content[44:])
         vad_ = aumanager.vad_threshold
-        return SuccessRequest(result=vad_,message="采集环境噪音成功")
+        return SuccessRequest(result=vad_, message="采集环境噪音成功")
+
 
 # 停止录音
 @app.get("/asr/stopRecord")
@@ -179,6 +180,7 @@ async def stopRecord():
     print("Online录音暂停")
     return SuccessRequest(message="停止成功")
 
+
 # 恢复录音
 @app.get("/asr/resumeRecord")
 async def resumeRecord():
@@ -187,7 +189,7 @@ async def resumeRecord():
     return SuccessRequest(message="Online录音恢复")
 
 
-# 聊天用的ASR
+# 聊天用的 ASR
 @app.websocket("/ws/asr/offlineStream")
 async def websocket_endpoint(websocket: WebSocket):
     await manager.connect(websocket)
@@ -210,9 +212,9 @@ async def websocket_endpoint(websocket: WebSocket):
         # print(f"用户-{user}-离开")
 
 
-# Online识别的ASR
+    # 流式识别的 ASR
 @app.websocket('/ws/asr/onlineStream')
-async def websocket_endpoint(websocket: WebSocket):
+async def websocket_endpoint_online(websocket: WebSocket):
     """PaddleSpeech Online ASR Server api
 
     Args:
@@ -298,12 +300,14 @@ async def websocket_endpoint(websocket: WebSocket):
     except WebSocketDisconnect:
         pass
 
+
 ######################################################################
 ########################### NLP 服务 #################################
 #####################################################################
 
+
 @app.post("/nlp/chat")
-async def chatOffline(nlp_base:NlpBase):
+async def chatOffline(nlp_base: NlpBase):
     chat = nlp_base.chat
     if not chat:
         return ErrorRequest(message="传入文本为空")
@@ -311,8 +315,9 @@ async def chatOffline(nlp_base:NlpBase):
         res = chatbot.chat(chat)
         return SuccessRequest(result=res)
 
+
 @app.post("/nlp/ie")
-async def ieOffline(nlp_base:NlpBase):
+async def ieOffline(nlp_base: NlpBase):
     nlp_text = nlp_base.chat
     if not nlp_text:
         return ErrorRequest(message="传入文本为空")
@@ -320,17 +325,20 @@ async def ieOffline(nlp_base:NlpBase):
         res = chatbot.ie(nlp_text)
         return SuccessRequest(result=res)
 
+
 ######################################################################
 ########################### TTS 服务 #################################
 #####################################################################
 
+
 @app.post("/tts/offline")
-async def text2speechOffline(tts_base:TtsBase):
+async def text2speechOffline(tts_base: TtsBase):
     text = tts_base.text
     if not text:
         return ErrorRequest(message="文本为空")
     else:
-        now_name = "tts_"+ datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
+        now_name = "tts_" + datetime.datetime.strftime(
+            datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
         out_file_path = os.path.join(WAV_PATH, now_name)
         # 保存为文件，再转成base64传输
         chatbot.text2speech(text, outpath=out_file_path)
@@ -339,12 +347,14 @@ async def text2speechOffline(tts_base:TtsBase):
         base_str = base64.b64encode(data_bin)
         return SuccessRequest(result=base_str)
 
+
 # http流式TTS
 @app.post("/tts/online")
 async def stream_tts(request_body: TtsBase):
     text = request_body.text
     return StreamingResponse(chatbot.text2speechStreamBytes(text=text))
 
+
 # ws流式TTS
 @app.websocket("/ws/tts/online")
 async def stream_ttsWS(websocket: WebSocket):
@@ -356,17 +366,11 @@ async def stream_ttsWS(websocket: WebSocket):
             if text:
                 for sub_wav in chatbot.text2speechStream(text=text):
                     # print("发送sub wav: ", len(sub_wav))
-                    res = {
-                        "wav": sub_wav,
-                        "done": False
-                    }
+                    res = {"wav": sub_wav, "done": False}
                     await websocket.send_json(res)
-                
+
                 # 输送结束
-                res = {
-                        "wav": sub_wav,
-                        "done": True
-                    }
+                res = {"wav": sub_wav, "done": True}
                 await websocket.send_json(res)
             # manager.disconnect(websocket)
 
@@ -396,8 +400,9 @@ async def vpr_enroll(table_name: str=None,
             return {'status': False, 'msg': "spk_id can not be None"}
         # Save the upload data to server.
         content = await audio.read()
-        now_name = "vpr_enroll_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
-        audio_path =  os.path.join(UPLOAD_PATH, now_name)
+        now_name = "vpr_enroll_" + datetime.datetime.strftime(
+            datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
+        audio_path = os.path.join(UPLOAD_PATH, now_name)
 
         with open(audio_path, "wb+") as f:
             f.write(content)
@@ -413,20 +418,19 @@ async def vpr_recog(request: Request,
                     audio: UploadFile=File(...)):
     # Voice print recognition online
     # try:
-        # Save the upload data to server.
+    # Save the upload data to server.
     content = await audio.read()
-    now_name = "vpr_query_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
-    query_audio_path =  os.path.join(UPLOAD_PATH, now_name)
+    now_name = "vpr_query_" + datetime.datetime.strftime(
+        datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav"
+    query_audio_path = os.path.join(UPLOAD_PATH, now_name)
     with open(query_audio_path, "wb+") as f:
-        f.write(content)        
+        f.write(content)
     spk_ids, paths, scores = vpr.do_search_vpr(query_audio_path)
 
     res = dict(zip(spk_ids, zip(paths, scores)))
     # Sort results by distance metric, closest distances first
     res = sorted(res.items(), key=lambda item: item[1][1], reverse=True)
     return res
-    # except Exception as e:
-        # return {'status': False, 'msg': e}, 400
 
 
 @app.post('/vpr/del')
@@ -460,17 +464,18 @@ async def vpr_database64(vprId: int):
             return {'status': False, 'msg': "vpr_id can not be None"}
         audio_path = vpr.do_get_wav(vprId)
         # 返回base64
-        
+
         # 将文件转成16k, 16bit类型的wav文件
         wav, sr = librosa.load(audio_path, sr=16000)
         wav = float2pcm(wav)  # float32 to int16
         wav_bytes = wav.tobytes()  # to bytes
         wav_base64 = base64.b64encode(wav_bytes).decode('utf8')
-        
+
         return SuccessRequest(result=wav_base64)
     except Exception as e:
         return {'status': False, 'msg': e}, 400
 
+
 @app.get('/vpr/data')
 async def vpr_data(vprId: int):
     # Get the audio file from path by spk_id in MySQL
@@ -482,11 +487,6 @@ async def vpr_data(vprId: int):
     except Exception as e:
         return {'status': False, 'msg': e}, 400
 
+
 if __name__ == '__main__':
     uvicorn.run(app=app, host='0.0.0.0', port=port)
-    
-
-
-
-
-

demos/speech_web/speech_server/requirements.txt

 aiofiles
+faiss-cpu
 fastapi
 librosa
 numpy
+paddlenlp
+paddlepaddle
+paddlespeech
 pydantic
-scikit_learn
+python-multipartscikit_learn
 SoundFile
 starlette
 uvicorn
-paddlepaddle
-paddlespeech
-paddlenlp
-faiss-cpu
-python-multipart
\ No newline at end of file

demos/speech_web/speech_server/src/AudioManeger.py

-import imp
-from queue import Queue
-import numpy as np
+import datetime
 import os
 import wave
-import random
-import datetime
+
+import numpy as np
+
 from .util import randName
 
 
 class AudioMannger:
-    def __init__(self, robot, frame_length=160, frame=10, data_width=2, vad_default = 300):
+    def __init__(self,
+                 robot,
+                 frame_length=160,
+                 frame=10,
+                 data_width=2,
+                 vad_default=300):
         # 二进制 pcm 流 
         self.audios = b''
         self.asr_result = ""
@@ -20,8 +24,9 @@ class AudioMannger:
         os.makedirs(self.file_dir, exist_ok=True)
         self.vad_deafult = vad_default
         self.vad_threshold = vad_default
-        self.vad_threshold_path = os.path.join(self.file_dir, "vad_threshold.npy")
-        
+        self.vad_threshold_path = os.path.join(self.file_dir,
+                                               "vad_threshold.npy")
+
         # 10ms 一帧
         self.frame_length = frame_length
         # 10帧，检测一次 vad
@@ -30,67 +35,64 @@ class AudioMannger:
         self.data_width = data_width
         # window
         self.window_length = frame_length * frame * data_width
-        
+
         # 是否开始录音
         self.on_asr = False
-        self.silence_cnt  = 0
+        self.silence_cnt = 0
         self.max_silence_cnt = 4
         self.is_pause = False  # 录音暂停与恢复
-        
-        
-    
+
     def init(self):
         if os.path.exists(self.vad_threshold_path):
             # 平均响度文件存在
             self.vad_threshold = np.load(self.vad_threshold_path)
-        
-    
+
     def clear_audio(self):
         # 清空 pcm 累积片段与 asr 识别结果
         self.audios = b''
-    
+
     def clear_asr(self):
         self.asr_result = ""
-    
-    
+
     def compute_chunk_volume(self, start_index, pcm_bins):
         # 根据帧长计算能量平均值
-        pcm_bin = pcm_bins[start_index: start_index + self.window_length]
+        pcm_bin = pcm_bins[start_index:start_index + self.window_length]
         # 转成 numpy
         pcm_np = np.frombuffer(pcm_bin, np.int16)
         # 归一化 + 计算响度
         x = pcm_np.astype(np.float32)
         x = np.abs(x)
-        return np.mean(x) 
-        
-    
+        return np.mean(x)
+
     def is_speech(self, start_index, pcm_bins):
         # 检查是否没
         if start_index > len(pcm_bins):
             return False
         # 检查从这个 start 开始是否为静音帧
-        energy = self.compute_chunk_volume(start_index=start_index, pcm_bins=pcm_bins)
+        energy = self.compute_chunk_volume(
+            start_index=start_index, pcm_bins=pcm_bins)
         # print(energy)
         if energy > self.vad_threshold:
             return True
         else:
             return False
-    
+
     def compute_env_volume(self, pcm_bins):
         max_energy = 0
         start = 0
         while start < len(pcm_bins):
-            energy = self.compute_chunk_volume(start_index=start, pcm_bins=pcm_bins)
+            energy = self.compute_chunk_volume(
+                start_index=start, pcm_bins=pcm_bins)
             if energy > max_energy:
                 max_energy = energy
             start += self.window_length
         self.vad_threshold = max_energy + 100 if max_energy > self.vad_deafult else self.vad_deafult
-        
+
         # 保存成文件
         np.save(self.vad_threshold_path, self.vad_threshold)
         print(f"vad 阈值大小: {self.vad_threshold}")
         print(f"环境采样保存: {os.path.realpath(self.vad_threshold_path)}")
-    
+
     def stream_asr(self, pcm_bin):
         # 先把 pcm_bin 送进去做端点检测
         start = 0
@@ -99,7 +101,7 @@ class AudioMannger:
                 self.on_asr = True
                 self.silence_cnt = 0
                 print("录音中")
-                self.audios += pcm_bin[ start : start + self.window_length]
+                self.audios += pcm_bin[start:start + self.window_length]
             else:
                 if self.on_asr:
                     self.silence_cnt += 1
@@ -110,41 +112,42 @@ class AudioMannger:
                         print("录音停止")
                         # audios 保存为 wav, 送入 ASR
                         if len(self.audios) > 2 * 16000:
-                            file_path = os.path.join(self.file_dir, "asr_" + datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d%H%M%S') + randName() + ".wav")
+                            file_path = os.path.join(
+                                self.file_dir,
+                                "asr_" + datetime.datetime.strftime(
+                                    datetime.datetime.now(),
+                                    '%Y%m%d%H%M%S') + randName() + ".wav")
                             self.save_audio(file_path=file_path)
                             self.asr_result = self.robot.speech2text(file_path)
                         self.clear_audio()
-                        return self.asr_result   
+                        return self.asr_result
                     else:
                         # 正常接收
                         print("录音中 静音")
-                        self.audios += pcm_bin[ start : start + self.window_length]
+                        self.audios += pcm_bin[start:start + self.window_length]
             start += self.window_length
         return ""
-    
+
     def save_audio(self, file_path):
         print("保存音频")
-        wf = wave.open(file_path, 'wb')          # 创建一个音频文件，名字为“01.wav"
-        wf.setnchannels(1)                      # 设置声道数为2
-        wf.setsampwidth(2)                      # 设置采样深度为
-        wf.setframerate(16000)                  # 设置采样率为16000
+        wf = wave.open(file_path, 'wb')  # 创建一个音频文件，名字为“01.wav"
+        wf.setnchannels(1)  # 设置声道数为2
+        wf.setsampwidth(2)  # 设置采样深度为
+        wf.setframerate(16000)  # 设置采样率为16000
         # 将数据写入创建的音频文件
         wf.writeframes(self.audios)
         # 写完后将文件关闭
         wf.close()
-    
+
     def end(self):
         # audios 保存为 wav, 送入 ASR
         file_path = os.path.join(self.file_dir, "asr.wav")
         self.save_audio(file_path=file_path)
         return self.robot.speech2text(file_path)
-    
+
     def stop(self):
         self.is_pause = True
         self.audios = b''
-    
+
     def resume(self):
         self.is_pause = False
-    
-    
-    
\ No newline at end of file

demos/speech_web/speech_server/src/SpeechBase/asr.py

-from re import sub
 import numpy as np
-import paddle
-import librosa
-import soundfile
 
 from paddlespeech.server.engine.asr.online.python.asr_engine import ASREngine
 from paddlespeech.server.engine.asr.online.python.asr_engine import PaddleASRConnectionHanddler
 from paddlespeech.server.utils.config import get_config
 
+
 def readWave(samples):
     x_len = len(samples)
 
@@ -31,20 +28,23 @@ def readWave(samples):
 
 
 class ASR:
-    def __init__(self, config_path, ) -> None:
+    def __init__(
+            self,
+            config_path, ) -> None:
         self.config = get_config(config_path)['asr_online']
         self.engine = ASREngine()
         self.engine.init(self.config)
         self.connection_handler = PaddleASRConnectionHanddler(self.engine)
-    
+
     def offlineASR(self, samples, sample_rate=16000):
-        x_chunk, x_chunk_lens = self.engine.preprocess(samples=samples, sample_rate=sample_rate)
+        x_chunk, x_chunk_lens = self.engine.preprocess(
+            samples=samples, sample_rate=sample_rate)
         self.engine.run(x_chunk, x_chunk_lens)
         result = self.engine.postprocess()
         self.engine.reset()
         return result
 
-    def onlineASR(self, samples:bytes=None, is_finished=False):
+    def onlineASR(self, samples: bytes=None, is_finished=False):
         if not is_finished:
             # 流式开始
             self.connection_handler.extract_feat(samples)
@@ -58,5 +58,3 @@ class ASR:
             asr_results = self.connection_handler.get_result()
             self.connection_handler.reset()
             return asr_results
-
-        
\ No newline at end of file

demos/speech_web/speech_server/src/SpeechBase/nlp.py

 from paddlenlp import Taskflow
 
+
 class NLP:
     def __init__(self, ie_model_path=None):
         schema = ["时间", "出发地", "目的地", "费用"]
         if ie_model_path:
-            self.ie_model = Taskflow("information_extraction",
-                                    schema=schema, task_path=ie_model_path)
+            self.ie_model = Taskflow(
+                "information_extraction",
+                schema=schema,
+                task_path=ie_model_path)
         else:
-            self.ie_model = Taskflow("information_extraction",
-                                    schema=schema)
-            
+            self.ie_model = Taskflow("information_extraction", schema=schema)
+
         self.dialogue_model = Taskflow("dialogue")
-    
+
     def chat(self, text):
         result = self.dialogue_model([text])
         return result[0]
-    
+
     def ie(self, text):
         result = self.ie_model(text)
         return result
-
-    
\ No newline at end of file

demos/speech_web/speech_server/src/SpeechBase/sql_helper.py

 import base64
-import sqlite3
 import os
+import sqlite3
+
 import numpy as np
-from pkg_resources import resource_stream
 
 
-def dict_factory(cursor, row):  
-    d = {}  
-    for idx, col in enumerate(cursor.description):  
-        d[col[0]] = row[idx]  
-    return d 
+def dict_factory(cursor, row):
+    d = {}
+    for idx, col in enumerate(cursor.description):
+        d[col[0]] = row[idx]
+    return d
+
 
 class DataBase(object):
-    def __init__(self, db_path:str):
+    def __init__(self, db_path: str):
         db_path = os.path.realpath(db_path)
 
         if os.path.exists(db_path):
@@ -21,12 +22,12 @@ class DataBase(object):
             db_path_dir = os.path.dirname(db_path)
             os.makedirs(db_path_dir, exist_ok=True)
             self.db_path = db_path
-        
+
         self.conn = sqlite3.connect(self.db_path)
         self.conn.row_factory = dict_factory
         self.cursor = self.conn.cursor()
         self.init_database()
-    
+
     def init_database(self):
         """
         初始化数据库， 若表不存在则创建
@@ -41,20 +42,21 @@ class DataBase(object):
         """
         self.cursor.execute(sql)
         self.conn.commit()
-    
+
     def execute_base(self, sql, data_dict):
         self.cursor.execute(sql, data_dict)
         self.conn.commit()
-    
-    def insert_one(self, username, vector_base64:str, wav_path):
+
+    def insert_one(self, username, vector_base64: str, wav_path):
         if not os.path.exists(wav_path):
             return None, "wav not exists"
         else:
-            sql = f"""
+            sql = """
             insert into 
             vprtable (username, vector, wavpath)
             values (?, ?, ?)
             """
+
             try:
                 self.cursor.execute(sql, (username, vector_base64, wav_path))
                 self.conn.commit()
@@ -63,25 +65,27 @@ class DataBase(object):
             except Exception as e:
                 print(e)
                 return None, e
-            
+
     def select_all(self):
         sql = """
         SELECT * from vprtable
         """
         result = self.cursor.execute(sql).fetchall()
         return result
-    
+
     def select_by_id(self, vpr_id):
         sql = f"""
         SELECT * from vprtable WHERE `id` = {vpr_id}
         """
+
         result = self.cursor.execute(sql).fetchall()
         return result
-    
+
     def select_by_username(self, username):
         sql = f"""
         SELECT * from vprtable WHERE `username` = '{username}'
         """
+
         result = self.cursor.execute(sql).fetchall()
         return result
 
@@ -89,28 +93,30 @@ class DataBase(object):
         sql = f"""
         DELETE from vprtable WHERE `username`='{username}'
         """
+
         self.cursor.execute(sql)
         self.conn.commit()
-    
+
     def drop_all(self):
-        sql = f"""
+        sql = """
         DELETE from vprtable
         """
+
         self.cursor.execute(sql)
         self.conn.commit()
-    
+
     def drop_table(self):
-        sql = f"""
+        sql = """
             DROP TABLE vprtable
         """
+
         self.cursor.execute(sql)
         self.conn.commit()
-    
-    def encode_vector(self, vector:np.ndarray):
+
+    def encode_vector(self, vector: np.ndarray):
         return base64.b64encode(vector).decode('utf8')
-    
+
     def decode_vector(self, vector_base64, dtype=np.float32):
         b = base64.b64decode(vector_base64)
         vc = np.frombuffer(b, dtype=dtype)
         return vc
-    
\ No newline at end of file

demos/speech_web/speech_server/src/SpeechBase/tts.py

@@ -5,18 +5,19 @@
 # 2. 加载模型
 # 3. 端到端推理
 # 4. 流式推理
-
 import base64
-import math
 import logging
+import math
+
 import numpy as np
-from paddlespeech.server.utils.onnx_infer import get_sess
-from paddlespeech.t2s.frontend.zh_frontend import Frontend
-from paddlespeech.server.utils.util import denorm, get_chunks
+
+from paddlespeech.server.engine.tts.online.onnx.tts_engine import TTSEngine
 from paddlespeech.server.utils.audio_process import float2pcm
 from paddlespeech.server.utils.config import get_config
+from paddlespeech.server.utils.util import denorm
+from paddlespeech.server.utils.util import get_chunks
+from paddlespeech.t2s.frontend.zh_frontend import Frontend
 
-from paddlespeech.server.engine.tts.online.onnx.tts_engine import TTSEngine
 
 class TTS:
     def __init__(self, config_path):
@@ -26,12 +27,12 @@ class TTS:
         self.engine.init(self.config)
         self.executor = self.engine.executor
         #self.engine.warm_up()
-        
+
         # 前端初始化
         self.frontend = Frontend(
-                phone_vocab_path=self.engine.executor.phones_dict,
-                tone_vocab_path=None)
-    
+            phone_vocab_path=self.engine.executor.phones_dict,
+            tone_vocab_path=None)
+
     def depadding(self, data, chunk_num, chunk_id, block, pad, upsample):
         """ 
         Streaming inference removes the result of pad inference
@@ -48,39 +49,37 @@ class TTS:
             data = data[front_pad * upsample:(front_pad + block) * upsample]
 
         return data
-         
+
     def offlineTTS(self, text):
         get_tone_ids = False
         merge_sentences = False
-        
+
         input_ids = self.frontend.get_input_ids(
-                text,
-                merge_sentences=merge_sentences,
-                get_tone_ids=get_tone_ids)
+            text, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids)
         phone_ids = input_ids["phone_ids"]
         wav_list = []
         for i in range(len(phone_ids)):
             orig_hs = self.engine.executor.am_encoder_infer_sess.run(
-                            None, input_feed={'text': phone_ids[i].numpy()}
-                            )
+                None, input_feed={'text': phone_ids[i].numpy()})
             hs = orig_hs[0]
             am_decoder_output = self.engine.executor.am_decoder_sess.run(
-                        None, input_feed={'xs': hs})
+                None, input_feed={'xs': hs})
             am_postnet_output = self.engine.executor.am_postnet_sess.run(
-                        None,
-                        input_feed={
-                            'xs': np.transpose(am_decoder_output[0], (0, 2, 1))
-                        })
+                None,
+                input_feed={
+                    'xs': np.transpose(am_decoder_output[0], (0, 2, 1))
+                })
             am_output_data = am_decoder_output + np.transpose(
                 am_postnet_output[0], (0, 2, 1))
             normalized_mel = am_output_data[0][0]
-            mel = denorm(normalized_mel, self.engine.executor.am_mu, self.engine.executor.am_std)
+            mel = denorm(normalized_mel, self.engine.executor.am_mu,
+                         self.engine.executor.am_std)
             wav = self.engine.executor.voc_sess.run(
-                            output_names=None, input_feed={'logmel': mel})[0]
+                output_names=None, input_feed={'logmel': mel})[0]
             wav_list.append(wav)
         wavs = np.concatenate(wav_list)
         return wavs
-    
+
     def streamTTS(self, text):
 
         get_tone_ids = False
@@ -88,9 +87,7 @@ class TTS:
 
         # front 
         input_ids = self.frontend.get_input_ids(
-                text,
-                merge_sentences=merge_sentences,
-                get_tone_ids=get_tone_ids)
+            text, merge_sentences=merge_sentences, get_tone_ids=get_tone_ids)
         phone_ids = input_ids["phone_ids"]
 
         for i in range(len(phone_ids)):
@@ -105,14 +102,15 @@ class TTS:
                 mel = mel[0]
 
                 # voc streaming
-                mel_chunks = get_chunks(mel, self.config.voc_block, self.config.voc_pad, "voc")
+                mel_chunks = get_chunks(mel, self.config.voc_block,
+                                        self.config.voc_pad, "voc")
                 voc_chunk_num = len(mel_chunks)
                 for i, mel_chunk in enumerate(mel_chunks):
                     sub_wav = self.executor.voc_sess.run(
                         output_names=None, input_feed={'logmel': mel_chunk})
-                    sub_wav = self.depadding(sub_wav[0], voc_chunk_num, i,
-                                             self.config.voc_block, self.config.voc_pad,
-                                             self.config.voc_upsample)
+                    sub_wav = self.depadding(
+                        sub_wav[0], voc_chunk_num, i, self.config.voc_block,
+                        self.config.voc_pad, self.config.voc_upsample)
 
                     yield self.after_process(sub_wav)
 
@@ -130,7 +128,8 @@ class TTS:
                 end = min(self.config.voc_block + self.config.voc_pad, mel_len)
 
                 # streaming am
-                hss = get_chunks(orig_hs, self.config.am_block, self.config.am_pad, "am")
+                hss = get_chunks(orig_hs, self.config.am_block,
+                                 self.config.am_pad, "am")
                 am_chunk_num = len(hss)
                 for i, hs in enumerate(hss):
                     am_decoder_output = self.executor.am_decoder_sess.run(
@@ -147,7 +146,8 @@ class TTS:
                     sub_mel = denorm(normalized_mel, self.executor.am_mu,
                                      self.executor.am_std)
                     sub_mel = self.depadding(sub_mel, am_chunk_num, i,
-                                             self.config.am_block, self.config.am_pad, 1)
+                                             self.config.am_block,
+                                             self.config.am_pad, 1)
 
                     if i == 0:
                         mel_streaming = sub_mel
@@ -165,23 +165,22 @@ class TTS:
                             output_names=None, input_feed={'logmel': voc_chunk})
                         sub_wav = self.depadding(
                             sub_wav[0], voc_chunk_num, voc_chunk_id,
-                            self.config.voc_block, self.config.voc_pad, self.config.voc_upsample)
+                            self.config.voc_block, self.config.voc_pad,
+                            self.config.voc_upsample)
 
                         yield self.after_process(sub_wav)
 
                         voc_chunk_id += 1
-                        start = max(
-                            0, voc_chunk_id * self.config.voc_block - self.config.voc_pad)
-                        end = min(
-                            (voc_chunk_id + 1) * self.config.voc_block + self.config.voc_pad,
-                            mel_len)
+                        start = max(0, voc_chunk_id * self.config.voc_block -
+                                    self.config.voc_pad)
+                        end = min((voc_chunk_id + 1) * self.config.voc_block +
+                                  self.config.voc_pad, mel_len)
 
             else:
                 logging.error(
                     "Only support fastspeech2_csmsc or fastspeech2_cnndecoder_csmsc on streaming tts."
-                ) 
+                )
 
-    
     def streamTTSBytes(self, text):
         for wav in self.engine.executor.infer(
                 text=text,
@@ -191,19 +190,14 @@ class TTS:
             wav = float2pcm(wav)  # float32 to int16
             wav_bytes = wav.tobytes()  # to bytes
             yield wav_bytes
-        
-    
+
     def after_process(self, wav):
         # for tvm
         wav = float2pcm(wav)  # float32 to int16
         wav_bytes = wav.tobytes()  # to bytes
         wav_base64 = base64.b64encode(wav_bytes).decode('utf8')  # to base64
         return wav_base64
-    
+
     def streamTTS_TVM(self, text):
         # 用 TVM 优化
         pass
-
-    
-    
-        
\ No newline at end of file

demos/speech_web/speech_server/src/SpeechBase/vpr.py

 # vpr Demo 没有使用 mysql 与 muilvs, 仅用于docker演示
 import logging
+
 import faiss
-from matplotlib import use
 import numpy as np
+
 from .sql_helper import DataBase
 from .vpr_encode import get_audio_embedding
 
+
 class VPR:
     def __init__(self, db_path, dim, top_k) -> None:
         # 初始化
@@ -14,15 +16,15 @@ class VPR:
         self.top_k = top_k
         self.dtype = np.float32
         self.vpr_idx = 0
-        
+
         # db 初始化
         self.db = DataBase(db_path)
-        
+
         # faiss 初始化
         index_ip = faiss.IndexFlatIP(dim)
         self.index_ip = faiss.IndexIDMap(index_ip)
         self.init()
-    
+
     def init(self):
         # demo 初始化，把 mysql中的向量注册到 faiss 中
         sql_dbs = self.db.select_all()
@@ -34,12 +36,13 @@ class VPR:
                 if len(vc.shape) == 1:
                     vc = np.expand_dims(vc, axis=0)
                 # 构建数据库
-                self.index_ip.add_with_ids(vc, np.array((idx,)).astype('int64'))
+                self.index_ip.add_with_ids(vc, np.array(
+                    (idx, )).astype('int64'))
             logging.info("faiss 构建完毕")
-    
+
     def faiss_enroll(self, idx, vc):
-        self.index_ip.add_with_ids(vc, np.array((idx,)).astype('int64'))
-    
+        self.index_ip.add_with_ids(vc, np.array((idx, )).astype('int64'))
+
     def vpr_enroll(self, username, wav_path):
         # 注册声纹
         emb = get_audio_embedding(wav_path)
@@ -53,21 +56,22 @@ class VPR:
         else:
             last_idx, mess = None
         return last_idx
-    
+
     def vpr_recog(self, wav_path):
         # 识别声纹
         emb_search = get_audio_embedding(wav_path)
-        
+
         if emb_search is not None:
             emb_search = np.expand_dims(emb_search, axis=0)
             D, I = self.index_ip.search(emb_search, self.top_k)
             D = D.tolist()[0]
-            I = I.tolist()[0]            
-            return [(round(D[i] * 100, 2 ), I[i]) for i in range(len(D)) if I[i] != -1]
+            I = I.tolist()[0]
+            return [(round(D[i] * 100, 2), I[i]) for i in range(len(D))
+                    if I[i] != -1]
         else:
             logging.error("识别失败")
             return None
-    
+
     def do_search_vpr(self, wav_path):
         spk_ids, paths, scores = [], [], []
         recog_result = self.vpr_recog(wav_path)
@@ -78,41 +82,39 @@ class VPR:
                 scores.append(score)
                 paths.append("")
         return spk_ids, paths, scores
-    
+
     def vpr_del(self, username):
         # 根据用户username, 删除声纹
         # 查用户ID，删除对应向量
         res = self.db.select_by_username(username)
         for r in res:
             idx = r['id']
-            self.index_ip.remove_ids(np.array((idx,)).astype('int64'))
-        
+            self.index_ip.remove_ids(np.array((idx, )).astype('int64'))
+
         self.db.drop_by_username(username)
-    
+
     def vpr_list(self):
         # 获取数据列表
         return self.db.select_all()
-    
+
     def do_list(self):
         spk_ids, vpr_ids = [], []
         for res in self.db.select_all():
             spk_ids.append(res['username'])
             vpr_ids.append(res['id'])
-        return spk_ids, vpr_ids 
-    
+        return spk_ids, vpr_ids
+
     def do_get_wav(self, vpr_idx):
-         res = self.db.select_by_id(vpr_idx)
-         return res[0]['wavpath']
-         
-    
+        res = self.db.select_by_id(vpr_idx)
+        return res[0]['wavpath']
+
     def vpr_data(self, idx):
         # 获取对应ID的数据
         res = self.db.select_by_id(idx)
         return res
-    
+
     def vpr_droptable(self):
         # 删除表
         self.db.drop_table()
         # 清空 faiss
         self.index_ip.reset()
-        

demos/speech_web/speech_server/src/SpeechBase/vpr_encode.py

-from paddlespeech.cli.vector import VectorExecutor
-import numpy as np
 import logging
 
+import numpy as np
+
+from paddlespeech.cli.vector import VectorExecutor
+
 vector_executor = VectorExecutor()
 
+
 def get_audio_embedding(path):
     """
     Use vpr_inference to generate embedding of audio
@@ -16,5 +19,3 @@ def get_audio_embedding(path):
     except Exception as e:
         logging.error(f"Error with embedding:{e}")
         return None
-
-    
\ No newline at end of file

demos/speech_web/speech_server/src/WebsocketManeger.py

@@ -2,6 +2,7 @@ from typing import List
 
 from fastapi import WebSocket
 
+
 class ConnectionManager:
     def __init__(self):
         # 存放激活的ws连接对象
@@ -28,4 +29,4 @@ class ConnectionManager:
             await connection.send_text(message)
 
 
-manager = ConnectionManager()
+manager = ConnectionManager()
\ No newline at end of file

demos/speech_web/speech_server/src/robot.py

-from paddlespeech.cli.asr.infer import ASRExecutor
-import soundfile as sf
 import os
-import librosa
 
+import soundfile as sf
 from src.SpeechBase.asr import ASR
-from src.SpeechBase.tts import TTS
 from src.SpeechBase.nlp import NLP
+from src.SpeechBase.tts import TTS
+
+from paddlespeech.cli.asr.infer import ASRExecutor
 
 
 class Robot:
-    def __init__(self, asr_config, tts_config,asr_init_path,
+    def __init__(self,
+                 asr_config,
+                 tts_config,
+                 asr_init_path,
                  ie_model_path=None) -> None:
         self.nlp = NLP(ie_model_path=ie_model_path)
         self.asr = ASR(config_path=asr_config)
         self.tts = TTS(config_path=tts_config)
         self.tts_sample_rate = 24000
         self.asr_sample_rate = 16000
-        
+
         # 流式识别效果不如端到端的模型，这里流式模型与端到端模型分开
         self.asr_model = ASRExecutor()
         self.asr_name = "conformer_wenetspeech"
         self.warm_up_asrmodel(asr_init_path)
-        
 
-    def warm_up_asrmodel(self, asr_init_path):        
+    def warm_up_asrmodel(self, asr_init_path):
         if not os.path.exists(asr_init_path):
             path_dir = os.path.dirname(asr_init_path)
             if not os.path.exists(path_dir):
                 os.makedirs(path_dir, exist_ok=True)
-            
+
             # TTS生成，采样率24000
             text = "生成初始音频"
             self.text2speech(text, asr_init_path)
-            
+
         # asr model初始化
-        self.asr_model(asr_init_path, model=self.asr_name,lang='zh',
-                 sample_rate=16000, force_yes=True)
-        
-    
+        self.asr_model(
+            asr_init_path,
+            model=self.asr_name,
+            lang='zh',
+            sample_rate=16000,
+            force_yes=True)
+
     def speech2text(self, audio_file):
         self.asr_model.preprocess(self.asr_name, audio_file)
         self.asr_model.infer(self.asr_name)
         res = self.asr_model.postprocess()
         return res
-    
+
     def text2speech(self, text, outpath):
         wav = self.tts.offlineTTS(text)
-        sf.write(
-            outpath, wav, samplerate=self.tts_sample_rate)
+        sf.write(outpath, wav, samplerate=self.tts_sample_rate)
         res = wav
         return res
-    
+
     def text2speechStream(self, text):
         for sub_wav_base64 in self.tts.streamTTS(text=text):
             yield sub_wav_base64
-    
+
     def text2speechStreamBytes(self, text):
         for wav_bytes in self.tts.streamTTSBytes(text=text):
             yield wav_bytes
@@ -66,5 +70,3 @@ class Robot:
     def ie(self, text):
         result = self.nlp.ie(text)
         return result
-    
-    
\ No newline at end of file

demos/speech_web/speech_server/src/util.py

 import random
 
+
 def randName(n=5):
-    return "".join(random.sample('zyxwvutsrqponmlkjihgfedcba',n))
+    return "".join(random.sample('zyxwvutsrqponmlkjihgfedcba', n))
+
 
 def SuccessRequest(result=None, message="ok"):
-    return {
-        "code": 0,
-        "result":result,
-        "message": message
-    }
+    return {"code": 0, "result": result, "message": message}
+
 
 def ErrorRequest(result=None, message="error"):
-    return {
-        "code": -1,
-        "result":result,
-        "message": message
-    }
+    return {"code": -1, "result": result, "message": message}
\ No newline at end of file

demos/story_talker/README.md

+([简体中文](./README_cn.md)|English)
+
 # Story Talker
 ## Introduction
 Storybooks are very important children's enlightenment books, but parents usually don't have enough time to read storybooks for their children. For very young children, they may not understand the Chinese characters in storybooks. Or sometimes, children just want to "listen" but don't want to "read".

demos/story_talker/README_cn.md

+
+(简体中文|[English](./README.md))
+
+# Story Talker
+
+## 简介
+
+故事书是非常重要的儿童启蒙书，但家长通常没有足够的时间为孩子读故事书。对于非常小的孩子，他们可能不理解故事书中的汉字。或有时，孩子们只是想“听”，而不想“读”。
+
+您可以使用 `PaddleOCR` 获取故事书的文本，并通过 `PaddleSpeech` 的 `TTS` 模块进行阅读。
+
+## 使用
+
+运行以下命令行开始：
+
+```
+./run.sh
+```
+
+结果已显示在 [notebook](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/tutorial/tts/tts_tutorial.ipynb)。

demos/streaming_asr_server/conf/application.yaml

@@ -28,6 +28,7 @@ asr_online:
     sample_rate: 16000
     cfg_path: 
     decode_method: 
+    num_decoding_left_chunks: -1
     force_yes: True
     device: 'cpu' # cpu or gpu:id
     decode_method: "attention_rescoring"

demos/streaming_asr_server/local/rtf_from_log.py

@@ -34,7 +34,7 @@ if __name__ == '__main__':
     n = 0
     for m in rtfs:
         # not accurate, may have duplicate log
-        n += 1  
+        n += 1
         T += m['T']
         P += m['P']
 

demos/style_fs2/README.md

+([简体中文](./README_cn.md)|English)
+
 # Style FastSpeech2
 ## Introduction
 [FastSpeech2](https://arxiv.org/abs/2006.04558)  is a classical acoustic model for Text-to-Speech synthesis, which introduces controllable speech input, including `phoneme duration`、 `energy` and `pitch`. 

demos/style_fs2/README_cn.md

+(简体中文|[English](./README.md))
+
+# Style FastSpeech2
+
+## 简介
+
+[FastSpeech2](https://arxiv.org/abs/2006.04558)  是用于语音合成的经典声学模型，它引入了可控语音输入，包括 `phoneme duration` 、 `energy` 和 `pitch` 。
+
+在预测阶段，您可以更改这些变量以获得一些有趣的结果。
+
+例如:
+
+1.  `FastSpeech2` 中的 `duration` 可以控制音频的速度 ，并保持 `pitch` 。（在某些语音工具中，增加速度将增加音调，反之亦然。）
+2. 当我们将一个句子的 `pitch` 设置为平均值并将音素的 `tones` 设置为 `1` 时，我们将获得 `robot-style` 的音色。
+3. 当我们提高成年女性的 `pitch` （比例固定）时，我们会得到 `child-style` 的音色。
+
+句子中不同音素的 `duration` 和 `pitch` 可以具有不同的比例。您可以设置不同的音阶比例来强调或削弱某些音素的发音。
+
+## 运行
+
+运行以下命令行开始：
+
+```
+./run.sh
+```
+
+在 `run.sh`, 会首先执行 `source path.sh` 去设置好环境变量。
+
+如果您想尝试您的句子，请替换 `sentences.txt`中的句子。
+
+更多的细节，请查看 `style_syn.py`。
+
+语音样例可以在 [style-control-in-fastspeech2](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html#style-control-in-fastspeech2) 查看。

docs/requirements.txt

-myst-parser
-numpydoc
-recommonmark>=0.5.0
-sphinx
-sphinx-autobuild
-sphinx-markdown-tables
-sphinx_rtd_theme
-paddlepaddle>=2.2.2
+braceexpand
+colorlog
 editdistance
+fastapi
 g2p_en
 g2pM
 h5py
@@ -14,40 +9,45 @@ inflect
 jieba
 jsonlines
 kaldiio
+keyboard
 librosa==0.8.1
 loguru
 matplotlib
+myst-parser
 nara_wpe
+numpydoc
 onnxruntime==1.10.0
 opencc
-pandas
 paddlenlp
+paddlepaddle>=2.2.2
 paddlespeech_feat
+pandas
+pathos == 0.2.8
+pattern_singleton
 Pillow>=9.0.0
 praatio==5.0.0
+prettytable
 pypinyin
 pypinyin-dict
 python-dateutil
 pyworld==0.2.12
+recommonmark>=0.5.0
 resampy==0.2.2
 sacrebleu
 scipy
 sentencepiece~=0.1.96
 soundfile~=0.10
+sphinx
+sphinx-autobuild
+sphinx-markdown-tables
+sphinx_rtd_theme
 textgrid
 timer
 tqdm
 typeguard
+uvicorn
 visualdl
 webrtcvad
+websockets
 yacs~=0.1.8
-prettytable
 zhon
-colorlog
-pathos == 0.2.8
-fastapi
-websockets
-keyboard
-uvicorn
-pattern_singleton
-braceexpand
\ No newline at end of file

docs/source/conf.py

@@ -20,10 +20,11 @@
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
+import os
+import sys
+
 import recommonmark.parser
 import sphinx_rtd_theme
-import sys
-import os
 sys.path.insert(0, os.path.abspath('../..'))
 
 autodoc_mock_imports = ["soundfile", "librosa"]

examples/aishell3/ernie_sat/run.sh

@@ -3,7 +3,7 @@
 set -e
 source path.sh
 
-gpus=0,1
+gpus=0,1,2,3,4,5,6,7
 stage=0
 stop_stage=100
 

examples/aishell3/tts3/run.sh

@@ -44,8 +44,8 @@ fi
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # install paddle2onnx
     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
-    if [[ -z "$version" || ${version} != '0.9.8' ]]; then
-        pip install paddle2onnx==0.9.8
+    if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+        pip install paddle2onnx==1.0.0
     fi
     ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_aishell3
     # considering the balance between speed and quality, we recommend that you use hifigan as vocoder

examples/aishell3/vits-vc/README.md

+# VITS with AISHELL-3
+This example contains code used to train a [VITS](https://arxiv.org/abs/2106.06103) model with [AISHELL-3](http://www.aishelltech.com/aishell_3). The trained model can be used in Voice Cloning Task, We refer to the model structure of  [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558.pdf). The general steps are as follows:
+1. Speaker Encoder: We use Speaker Verification to train a speaker encoder. Datasets used in this task are different from those used in `VITS` because the transcriptions are not needed, we use more datasets, refer to  [ge2e](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/ge2e).
+2. Synthesizer and Vocoder: We use the trained speaker encoder to generate speaker embedding for each sentence in AISHELL-3. This embedding is an extra input of `VITS` which will be concated with encoder outputs. The vocoder is part of `VITS` due to its special structure.
+
+## Dataset
+### Download and Extract
+Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
+
+### Get MFA Result and Extract
+We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get phonemes for VITS, the durations of MFA are not needed here.
+You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
+
+## Pretrained GE2E Model
+We use pretrained GE2E model to generate speaker embedding for each sentence.
+
+Download pretrained GE2E model from here [ge2e_ckpt_0.3.zip](https://bj.bcebos.com/paddlespeech/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip), and `unzip` it.
+
+## Get Started
+Assume the path to the dataset is `~/datasets/data_aishell3`.
+Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
+Assume the path to the pretrained ge2e model is `./ge2e_ckpt_0.3`.
+
+Run the command below to
+1. **source path**.
+2. preprocess the dataset.
+3. train the model.
+4. synthesize waveform from `metadata.jsonl`.
+5. start a voice cloning inference.
+
+```bash
+./run.sh
+```
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+
+### Data Preprocessing
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} ${ge2e_ckpt_path}
+```
+When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
+
+```text
+dump
+├── dev
+│   ├── norm
+│   └── raw
+├── embed
+│   ├── SSB0005
+│   ├── SSB0009
+│   ├── ...
+│   └── ...
+├── phone_id_map.txt
+├── speaker_id_map.txt
+├── test
+│   ├── norm
+│   └── raw
+└── train
+    ├── feats_stats.npy
+    ├── norm
+    └── raw
+```
+The `embed` contains the generated speaker embedding for each sentence in AISHELL-3, which has the same file structure with wav files and the format is  `.npy`.
+
+The computing time of utterance embedding can be x hours.
+
+The dataset is split into 3 parts, namely `train`, `dev`, and` test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains wave and linear spectrogram of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in `dump/train/feats_stats.npy`.
+
+Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains phones, text_lengths, feats, feats_lengths, the path of linear spectrogram features, the path of raw waves, speaker, and the id of each utterance.
+
+The preprocessing step is very similar to that one of [vits](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vits), but there is one more `ge2e/inference` step here.
+
+### Model Training
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
+```
+The training step is very similar to that one of [vits](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vits), but we should set `--voice-cloning=True` when calling `${BIN_DIR}/train.py`.
+
+### Synthesizing
+
+`./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
+
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
+```
+```text
+usage: synthesize.py [-h] [--config CONFIG] [--ckpt CKPT]
+                     [--phones_dict PHONES_DICT] [--speaker_dict SPEAKER_DICT]
+                     [--voice-cloning VOICE_CLONING] [--ngpu NGPU]
+                     [--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR]
+
+Synthesize with VITS
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --config CONFIG       Config of VITS.
+  --ckpt CKPT           Checkpoint file of VITS.
+  --phones_dict PHONES_DICT
+                        phone vocabulary file.
+  --speaker_dict SPEAKER_DICT
+                        speaker id map file.
+  --voice-cloning VOICE_CLONING
+                        whether training voice cloning model.
+  --ngpu NGPU           if ngpu == 0, use cpu.
+  --test_metadata TEST_METADATA
+                        test metadata.
+  --output_dir OUTPUT_DIR
+                        output dir.
+```
+The synthesizing step is very similar to that one of [vits](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/aishell3/vits), but we should set `--voice-cloning=True` when calling `${BIN_DIR}/../synthesize.py`.
+
+### Voice Cloning
+Assume there are some  reference audios in `./ref_audio`
+```text
+ref_audio
+├── 001238.wav
+├── LJ015-0254.wav
+└── audio_self_test.mp3
+```
+`./local/voice_cloning.sh` calls `${BIN_DIR}/voice_cloning.py`
+
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${add_blank} ${ref_audio_dir}
+```
+
+If you want to convert a speaker audio file to refered speaker, run:
+
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} ${ge2e_params_path} ${add_blank} ${ref_audio_dir} ${src_audio_path}
+```
+
+<!-- TODO display these after we trained the model -->
+<!-- 
+## Pretrained Model
+
+The pretrained model can be downloaded here:
+
+- [vits_vc_aishell3_ckpt_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/vits/vits_vc_aishell3_ckpt_1.1.0.zip) (add_blank=true)
+
+VITS checkpoint contains files listed below.
+(There is no need for `speaker_id_map.txt` here )
+
+```text
+vits_vc_aishell3_ckpt_1.1.0
+├── default.yaml              # default config used to train vitx
+├── phone_id_map.txt          # phone vocabulary file when training vits
+└── snapshot_iter_333000.pdz  # model parameters and optimizer states
+```
+
+ps: This ckpt is not good enough, a better result is training
+
+-->

examples/aishell3/vits-vc/conf/default.yaml

+# This configuration tested on 4 GPUs (V100) with 32GB GPU
+# memory. It takes around 2 weeks to finish the training
+# but 100k iters model should generate reasonable results.
+###########################################################
+#                FEATURE EXTRACTION SETTING               #
+###########################################################
+
+fs: 22050         # sr
+n_fft: 1024        # FFT size (samples).
+n_shift: 256       # Hop size (samples). 12.5ms
+win_length: null   # Window length (samples). 50ms
+                   # If set to null, it will be the same as fft_size.
+window: "hann"     # Window function.
+
+
+##########################################################
+#                  TTS MODEL SETTING                     #
+##########################################################
+model:
+    # generator related
+    generator_type: vits_generator
+    generator_params:
+        hidden_channels: 192
+        spk_embed_dim: 256
+        global_channels: 256
+        segment_size: 32
+        text_encoder_attention_heads: 2
+        text_encoder_ffn_expand: 4
+        text_encoder_blocks: 6
+        text_encoder_positionwise_layer_type: "conv1d"
+        text_encoder_positionwise_conv_kernel_size: 3
+        text_encoder_positional_encoding_layer_type: "rel_pos"
+        text_encoder_self_attention_layer_type: "rel_selfattn"
+        text_encoder_activation_type: "swish"
+        text_encoder_normalize_before: True
+        text_encoder_dropout_rate: 0.1
+        text_encoder_positional_dropout_rate: 0.0
+        text_encoder_attention_dropout_rate: 0.1
+        use_macaron_style_in_text_encoder: True
+        use_conformer_conv_in_text_encoder: False
+        text_encoder_conformer_kernel_size: -1
+        decoder_kernel_size: 7
+        decoder_channels: 512
+        decoder_upsample_scales: [8, 8, 2, 2]
+        decoder_upsample_kernel_sizes: [16, 16, 4, 4]
+        decoder_resblock_kernel_sizes: [3, 7, 11]
+        decoder_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
+        use_weight_norm_in_decoder: True
+        posterior_encoder_kernel_size: 5
+        posterior_encoder_layers: 16
+        posterior_encoder_stacks: 1
+        posterior_encoder_base_dilation: 1
+        posterior_encoder_dropout_rate: 0.0
+        use_weight_norm_in_posterior_encoder: True
+        flow_flows: 4
+        flow_kernel_size: 5
+        flow_base_dilation: 1
+        flow_layers: 4
+        flow_dropout_rate: 0.0
+        use_weight_norm_in_flow: True
+        use_only_mean_in_flow: True
+        stochastic_duration_predictor_kernel_size: 3
+        stochastic_duration_predictor_dropout_rate: 0.5
+        stochastic_duration_predictor_flows: 4
+        stochastic_duration_predictor_dds_conv_layers: 3
+    # discriminator related
+    discriminator_type: hifigan_multi_scale_multi_period_discriminator
+    discriminator_params:
+        scales: 1
+        scale_downsample_pooling: "AvgPool1D"
+        scale_downsample_pooling_params:
+            kernel_size: 4
+            stride: 2
+            padding: 2
+        scale_discriminator_params:
+            in_channels: 1
+            out_channels: 1
+            kernel_sizes: [15, 41, 5, 3]
+            channels: 128
+            max_downsample_channels: 1024
+            max_groups: 16
+            bias: True
+            downsample_scales: [2, 2, 4, 4, 1]
+            nonlinear_activation: "leakyrelu"
+            nonlinear_activation_params:
+                negative_slope: 0.1
+            use_weight_norm: True
+            use_spectral_norm: False
+        follow_official_norm: False
+        periods: [2, 3, 5, 7, 11]
+        period_discriminator_params:
+            in_channels: 1
+            out_channels: 1
+            kernel_sizes: [5, 3]
+            channels: 32
+            downsample_scales: [3, 3, 3, 3, 1]
+            max_downsample_channels: 1024
+            bias: True
+            nonlinear_activation: "leakyrelu"
+            nonlinear_activation_params:
+                negative_slope: 0.1
+            use_weight_norm: True
+            use_spectral_norm: False
+    # others
+    sampling_rate: 22050          # needed in the inference for saving wav
+    cache_generator_outputs: True # whether to cache generator outputs in the training
+          
+###########################################################
+#                        LOSS SETTING                     #
+###########################################################
+# loss function related
+generator_adv_loss_params:
+    average_by_discriminators: False # whether to average loss value by #discriminators
+    loss_type: mse                   # loss type, "mse" or "hinge"
+discriminator_adv_loss_params:
+    average_by_discriminators: False # whether to average loss value by #discriminators
+    loss_type: mse                   # loss type, "mse" or "hinge"
+feat_match_loss_params:
+    average_by_discriminators: False # whether to average loss value by #discriminators
+    average_by_layers: False         # whether to average loss value by #layers of each discriminator
+    include_final_outputs: True      # whether to include final outputs for loss calculation
+mel_loss_params:
+    fs: 22050          # must be the same as the training data
+    fft_size: 1024        # fft points
+    hop_size: 256    # hop size
+    win_length: null   # window length
+    window: hann       # window type
+    num_mels: 80         # number of Mel basis
+    fmin: 0            # minimum frequency for Mel basis
+    fmax: null         # maximum frequency for Mel basis
+    log_base: null     # null represent natural log
+
+###########################################################
+#               ADVERSARIAL LOSS SETTING                  #
+###########################################################
+lambda_adv: 1.0        # loss scaling coefficient for adversarial loss
+lambda_mel: 45.0       # loss scaling coefficient for Mel loss
+lambda_feat_match: 2.0 # loss scaling coefficient for feat match loss
+lambda_dur: 1.0        # loss scaling coefficient for duration loss
+lambda_kl: 1.0         # loss scaling coefficient for KL divergence loss
+# others
+sampling_rate: 22050          # needed in the inference for saving wav
+cache_generator_outputs: True # whether to cache generator outputs in the training
+
+
+###########################################################
+#                  DATA LOADER SETTING                    #
+###########################################################
+batch_size: 50              # Batch size.
+num_workers: 4              # Number of workers in DataLoader.
+
+##########################################################
+#            OPTIMIZER & SCHEDULER SETTING               #
+##########################################################
+# optimizer setting for generator
+generator_optimizer_params:
+    beta1: 0.8
+    beta2: 0.99
+    epsilon: 1.0e-9
+    weight_decay: 0.0
+generator_scheduler: exponential_decay
+generator_scheduler_params:
+    learning_rate: 2.0e-4
+    gamma: 0.999875                   
+
+# optimizer setting for discriminator
+discriminator_optimizer_params:
+    beta1: 0.8
+    beta2: 0.99
+    epsilon: 1.0e-9
+    weight_decay: 0.0
+discriminator_scheduler: exponential_decay
+discriminator_scheduler_params:
+    learning_rate: 2.0e-4          
+    gamma: 0.999875
+generator_first: False # whether to start updating generator first
+
+##########################################################
+#                OTHER TRAINING SETTING                  #
+##########################################################
+num_snapshots: 10            # max number of snapshots to keep while training
+train_max_steps: 350000      # Number of training steps. == total_iters / ngpus, total_iters = 1000000
+save_interval_steps: 1000    # Interval steps to save checkpoint.
+eval_interval_steps: 250     # Interval steps to evaluate the network.
+seed: 777                    # random seed number

examples/aishell3/vits-vc/local/preprocess.sh

+#!/bin/bash
+
+stage=0
+stop_stage=100
+
+config_path=$1
+add_blank=$2
+ge2e_ckpt_path=$3
+
+# gen speaker embedding
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    python3 ${MAIN_ROOT}/paddlespeech/vector/exps/ge2e/inference.py \
+        --input=~/datasets/data_aishell3/train/wav/ \
+        --output=dump/embed \
+        --checkpoint_path=${ge2e_ckpt_path}
+fi
+
+# copy from tts3/preprocess
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # get durations from MFA's result
+    echo "Generate durations.txt from MFA results ..."
+    python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
+        --inputdir=./aishell3_alignment_tone \
+        --output durations.txt \
+        --config=${config_path}
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # extract features
+    echo "Extract features ..."
+    python3 ${BIN_DIR}/preprocess.py \
+        --dataset=aishell3 \
+        --rootdir=~/datasets/data_aishell3/ \
+        --dumpdir=dump \
+        --dur-file=durations.txt \
+        --config=${config_path} \
+        --num-cpu=20 \
+        --cut-sil=True \
+        --spk_emb_dir=dump/embed
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    # get features' stats(mean and std)
+    echo "Get features' stats ..."
+    python3 ${MAIN_ROOT}/utils/compute_statistics.py \
+        --metadata=dump/train/raw/metadata.jsonl \
+        --field-name="feats"
+fi
+
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    # normalize and covert phone/speaker to id, dev and test should use train's stats
+    echo "Normalize ..."
+    python3 ${BIN_DIR}/normalize.py \
+        --metadata=dump/train/raw/metadata.jsonl \
+        --dumpdir=dump/train/norm \
+        --feats-stats=dump/train/feats_stats.npy \
+        --phones-dict=dump/phone_id_map.txt \
+        --speaker-dict=dump/speaker_id_map.txt \
+        --add-blank=${add_blank} \
+        --skip-wav-copy
+
+    python3 ${BIN_DIR}/normalize.py \
+        --metadata=dump/dev/raw/metadata.jsonl \
+        --dumpdir=dump/dev/norm \
+        --feats-stats=dump/train/feats_stats.npy \
+        --phones-dict=dump/phone_id_map.txt \
+        --speaker-dict=dump/speaker_id_map.txt \
+        --add-blank=${add_blank} \
+        --skip-wav-copy
+
+    python3 ${BIN_DIR}/normalize.py \
+        --metadata=dump/test/raw/metadata.jsonl \
+        --dumpdir=dump/test/norm \
+        --feats-stats=dump/train/feats_stats.npy \
+        --phones-dict=dump/phone_id_map.txt \
+        --speaker-dict=dump/speaker_id_map.txt \
+        --add-blank=${add_blank} \
+        --skip-wav-copy
+fi

examples/aishell3/vits-vc/local/synthesize.sh

+#!/bin/bash
+
+config_path=$1
+train_output_path=$2
+ckpt_name=$3
+stage=0
+stop_stage=0
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/synthesize.py \
+        --config=${config_path} \
+        --ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --phones_dict=dump/phone_id_map.txt \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test \
+        --voice-cloning=True
+fi

examples/aishell3/vits-vc/local/train.sh

+#!/bin/bash
+
+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 ${BIN_DIR}/train.py \
+    --train-metadata=dump/train/norm/metadata.jsonl \
+    --dev-metadata=dump/dev/norm/metadata.jsonl \
+    --config=${config_path} \
+    --output-dir=${train_output_path} \
+    --ngpu=4 \
+    --phones-dict=dump/phone_id_map.txt \
+    --voice-cloning=True

examples/aishell3/vits-vc/local/voice_cloning.sh

+#!/bin/bash
+
+config_path=$1
+train_output_path=$2
+ckpt_name=$3
+ge2e_params_path=$4
+add_blank=$5
+ref_audio_dir=$6
+src_audio_path=$7
+
+FLAGS_allocator_strategy=naive_best_fit \
+FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+python3 ${BIN_DIR}/voice_cloning.py \
+    --config=${config_path} \
+    --ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+    --ge2e_params_path=${ge2e_params_path} \
+    --phones_dict=dump/phone_id_map.txt \
+    --text="凯莫瑞安联合体的经济崩溃迫在眉睫。" \
+    --audio-path=${src_audio_path} \
+    --input-dir=${ref_audio_dir} \
+    --output-dir=${train_output_path}/vc_syn \
+    --add-blank=${add_blank}

examples/aishell3/vits-vc/path.sh

+#!/bin/bash
+export MAIN_ROOT=`realpath ${PWD}/../../../`
+
+export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
+export LC_ALL=C
+
+export PYTHONDONTWRITEBYTECODE=1
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
+
+MODEL=vits
+export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
\ No newline at end of file

examples/aishell3/vits-vc/run.sh

+#!/bin/bash
+
+set -e
+source path.sh
+
+gpus=0,1,2,3
+stage=0
+stop_stage=100
+
+conf_path=conf/default.yaml
+train_output_path=exp/default
+ckpt_name=snapshot_iter_153.pdz
+add_blank=true
+ref_audio_dir=ref_audio
+src_audio_path=''
+
+# not include ".pdparams" here
+ge2e_ckpt_path=./ge2e_ckpt_0.3/step-3000000
+
+# include ".pdparams" here
+ge2e_params_path=${ge2e_ckpt_path}.pdparams
+
+# with the following command, you can choose the stage range you want to run
+# such as `./run.sh --stage 0 --stop-stage 0`
+# this can not be mixed use with `$1`, `$2` ...
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # prepare data
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/preprocess.sh ${conf_path} ${add_blank}  ${ge2e_ckpt_path} || exit -1
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # train model, all `ckpt` under `train_output_path/checkpoints/` dir
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/voice_cloning.sh ${conf_path} ${train_output_path} ${ckpt_name} \
+        ${ge2e_params_path} ${add_blank} ${ref_audio_dir} ${src_audio_path} || exit -1
+fi

examples/aishell3/vits/README.md

+# VITS with AISHELL-3
+This example contains code used to train a [VITS](https://arxiv.org/abs/2106.06103) model with [AISHELL-3](http://www.aishelltech.com/aishell_3).
+
+AISHELL-3 is a large-scale and high-fidelity multi-speaker Mandarin speech corpus that could be used to train multi-speaker Text-to-Speech (TTS) systems.
+
+We use AISHELL-3 to train a multi-speaker VITS model here.
+## Dataset
+### Download and Extract
+Download AISHELL-3 from it's [Official Website](http://www.aishelltech.com/aishell_3) and extract it to `~/datasets`. Then the dataset is in the directory `~/datasets/data_aishell3`.
+
+### Get MFA Result and Extract
+We use [MFA2.x](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) to get phonemes for VITS, the durations of MFA are not needed here.
+You can download from here [aishell3_alignment_tone.tar.gz](https://paddlespeech.bj.bcebos.com/MFA/AISHELL-3/with_tone/aishell3_alignment_tone.tar.gz), or train your MFA model reference to [mfa example](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/other/mfa) (use MFA1.x now) of our repo.
+
+## Get Started
+Assume the path to the dataset is `~/datasets/data_aishell3`.
+Assume the path to the MFA result of AISHELL-3 is `./aishell3_alignment_tone`.
+Run the command below to
+1. **source path**.
+2. preprocess the dataset.
+3. train the model.
+4. synthesize wavs.
+    - synthesize waveform from `metadata.jsonl`.
+    - synthesize waveform from a text file.
+
+```bash
+./run.sh
+```
+You can choose a range of stages you want to run, or set `stage` equal to `stop-stage` to use only one stage, for example, running the following command will only preprocess the dataset.
+```bash
+./run.sh --stage 0 --stop-stage 0
+```
+
+### Data Preprocessing
+```bash
+./local/preprocess.sh ${conf_path}
+```
+When it is done. A `dump` folder is created in the current directory. The structure of the dump folder is listed below.
+
+```text
+dump
+├── dev
+│   ├── norm
+│   └── raw
+├── phone_id_map.txt
+├── speaker_id_map.txt
+├── test
+│   ├── norm
+│   └── raw
+└── train
+    ├── feats_stats.npy
+    ├── norm
+    └── raw
+```
+The dataset is split into 3 parts, namely `train`, `dev`, and` test`, each of which contains a `norm` and `raw` subfolder. The raw folder contains wave and linear spectrogram of each utterance, while the norm folder contains normalized ones. The statistics used to normalize features are computed from the training set, which is located in `dump/train/feats_stats.npy`.
+
+Also, there is a `metadata.jsonl` in each subfolder. It is a table-like file that contains phones, text_lengths, feats, feats_lengths, the path of linear spectrogram features, the path of raw waves, speaker, and the id of each utterance.
+
+### Model Training
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path}
+```
+`./local/train.sh` calls `${BIN_DIR}/train.py`.
+Here's the complete help message.
+```text
+usage: train.py [-h] [--config CONFIG] [--train-metadata TRAIN_METADATA]
+                [--dev-metadata DEV_METADATA] [--output-dir OUTPUT_DIR]
+                [--ngpu NGPU] [--phones-dict PHONES_DICT]
+                [--speaker-dict SPEAKER_DICT] [--voice-cloning VOICE_CLONING]
+
+Train a VITS model.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --config CONFIG       config file to overwrite default config.
+  --train-metadata TRAIN_METADATA
+                        training data.
+  --dev-metadata DEV_METADATA
+                        dev data.
+  --output-dir OUTPUT_DIR
+                        output dir.
+  --ngpu NGPU           if ngpu == 0, use cpu.
+  --phones-dict PHONES_DICT
+                        phone vocabulary file.
+  --speaker-dict SPEAKER_DICT
+                        speaker id map file for multiple speaker model.
+  --voice-cloning VOICE_CLONING
+                        whether training voice cloning model.
+```
+1. `--config` is a config file in yaml format to overwrite the default config, which can be found at `conf/default.yaml`.
+2. `--train-metadata` and `--dev-metadata` should be the metadata file in the normalized subfolder of `train` and `dev` in the `dump` folder.
+3. `--output-dir` is the directory to save the results of the experiment. Checkpoints are saved in `checkpoints/` inside this directory.
+4. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
+5. `--phones-dict` is the path of the phone vocabulary file.
+6. `--speaker-dict` is the path of the speaker id map file when training a multi-speaker VITS.
+
+### Synthesizing
+
+`./local/synthesize.sh` calls `${BIN_DIR}/synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
+
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name}
+```
+```text
+usage: synthesize.py [-h] [--config CONFIG] [--ckpt CKPT]
+                     [--phones_dict PHONES_DICT] [--speaker_dict SPEAKER_DICT]
+                     [--voice-cloning VOICE_CLONING] [--ngpu NGPU]
+                     [--test_metadata TEST_METADATA] [--output_dir OUTPUT_DIR]
+
+Synthesize with VITS
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --config CONFIG       Config of VITS.
+  --ckpt CKPT           Checkpoint file of VITS.
+  --phones_dict PHONES_DICT
+                        phone vocabulary file.
+  --speaker_dict SPEAKER_DICT
+                        speaker id map file.
+  --voice-cloning VOICE_CLONING
+                        whether training voice cloning model.
+  --ngpu NGPU           if ngpu == 0, use cpu.
+  --test_metadata TEST_METADATA
+                        test metadata.
+  --output_dir OUTPUT_DIR
+                        output dir.
+```
+`./local/synthesize_e2e.sh` calls `${BIN_DIR}/synthesize_e2e.py`, which can synthesize waveform from text file.
+```bash
+CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name}
+```
+```text
+usage: synthesize_e2e.py [-h] [--config CONFIG] [--ckpt CKPT]
+                         [--phones_dict PHONES_DICT]
+                         [--speaker_dict SPEAKER_DICT] [--spk_id SPK_ID]
+                         [--lang LANG]
+                         [--inference_dir INFERENCE_DIR] [--ngpu NGPU]
+                         [--text TEXT] [--output_dir OUTPUT_DIR]
+
+Synthesize with VITS
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --config CONFIG       Config of VITS.
+  --ckpt CKPT           Checkpoint file of VITS.
+  --phones_dict PHONES_DICT
+                        phone vocabulary file.
+  --speaker_dict SPEAKER_DICT
+                        speaker id map file.
+  --spk_id SPK_ID       spk id for multi speaker acoustic model
+  --lang LANG           Choose model language. zh or en
+  --inference_dir INFERENCE_DIR
+                        dir to save inference models
+  --ngpu NGPU           if ngpu == 0, use cpu.
+  --text TEXT           text to synthesize, a 'utt_id sentence' pair per line.
+  --output_dir OUTPUT_DIR
+                        output dir.
+```
+1. `--config`, `--ckpt`, `--phones_dict` and `--speaker_dict` are arguments for acoustic model, which correspond to the 3 files in the VITS pretrained model.
+2. `--lang` is the model language, which can be `zh` or `en`.
+3. `--test_metadata` should be the metadata file in the normalized subfolder of `test`  in the `dump` folder.
+4. `--text` is the text file, which contains sentences to synthesize.
+5. `--output_dir` is the directory to save synthesized audio files.
+6. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
+
+<!-- TODO display these after we trained the model -->
+<!-- 
+## Pretrained Model
+
+The pretrained model can be downloaded here:
+
+- [vits_aishell3_ckpt_1.1.0.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/vits/vits_aishell3_ckpt_1.1.0.zip) (add_blank=true)
+
+VITS checkpoint contains files listed below.
+```text
+vits_aishell3_ckpt_1.1.0
+├── default.yaml              # default config used to train vitx
+├── phone_id_map.txt          # phone vocabulary file when training vits
+├── speaker_id_map.txt        # speaker id map file when training a multi-speaker vits
+└── snapshot_iter_333000.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 ${BIN_DIR}/synthesize_e2e.py \
+    --config=vits_aishell3_ckpt_1.1.0/default.yaml \
+    --ckpt=vits_aishell3_ckpt_1.1.0/snapshot_iter_333000.pdz \
+    --phones_dict=vits_aishell3_ckpt_1.1.0/phone_id_map.txt \
+    --speaker_dict=vits_aishell3_ckpt_1.1.0/speaker_id_map.txt \
+    --output_dir=exp/default/test_e2e \
+    --text=${BIN_DIR}/../sentences.txt \
+    --add-blank=${add_blank} 
+```
+-->

examples/aishell3/vits/conf/default.yaml

+# This configuration tested on 4 GPUs (V100) with 32GB GPU
+# memory. It takes around 2 weeks to finish the training
+# but 100k iters model should generate reasonable results.
+###########################################################
+#                FEATURE EXTRACTION SETTING               #
+###########################################################
+
+fs: 22050         # sr
+n_fft: 1024        # FFT size (samples).
+n_shift: 256       # Hop size (samples). 12.5ms
+win_length: null   # Window length (samples). 50ms
+                   # If set to null, it will be the same as fft_size.
+window: "hann"     # Window function.
+
+
+##########################################################
+#                  TTS MODEL SETTING                     #
+##########################################################
+model:
+    # generator related
+    generator_type: vits_generator
+    generator_params:
+        hidden_channels: 192
+        global_channels: 256
+        segment_size: 32
+        text_encoder_attention_heads: 2
+        text_encoder_ffn_expand: 4
+        text_encoder_blocks: 6
+        text_encoder_positionwise_layer_type: "conv1d"
+        text_encoder_positionwise_conv_kernel_size: 3
+        text_encoder_positional_encoding_layer_type: "rel_pos"
+        text_encoder_self_attention_layer_type: "rel_selfattn"
+        text_encoder_activation_type: "swish"
+        text_encoder_normalize_before: True
+        text_encoder_dropout_rate: 0.1
+        text_encoder_positional_dropout_rate: 0.0
+        text_encoder_attention_dropout_rate: 0.1
+        use_macaron_style_in_text_encoder: True
+        use_conformer_conv_in_text_encoder: False
+        text_encoder_conformer_kernel_size: -1
+        decoder_kernel_size: 7
+        decoder_channels: 512
+        decoder_upsample_scales: [8, 8, 2, 2]
+        decoder_upsample_kernel_sizes: [16, 16, 4, 4]
+        decoder_resblock_kernel_sizes: [3, 7, 11]
+        decoder_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
+        use_weight_norm_in_decoder: True
+        posterior_encoder_kernel_size: 5
+        posterior_encoder_layers: 16
+        posterior_encoder_stacks: 1
+        posterior_encoder_base_dilation: 1
+        posterior_encoder_dropout_rate: 0.0
+        use_weight_norm_in_posterior_encoder: True
+        flow_flows: 4
+        flow_kernel_size: 5
+        flow_base_dilation: 1
+        flow_layers: 4
+        flow_dropout_rate: 0.0
+        use_weight_norm_in_flow: True
+        use_only_mean_in_flow: True
+        stochastic_duration_predictor_kernel_size: 3
+        stochastic_duration_predictor_dropout_rate: 0.5
+        stochastic_duration_predictor_flows: 4
+        stochastic_duration_predictor_dds_conv_layers: 3
+    # discriminator related
+    discriminator_type: hifigan_multi_scale_multi_period_discriminator
+    discriminator_params:
+        scales: 1
+        scale_downsample_pooling: "AvgPool1D"
+        scale_downsample_pooling_params:
+            kernel_size: 4
+            stride: 2
+            padding: 2
+        scale_discriminator_params:
+            in_channels: 1
+            out_channels: 1
+            kernel_sizes: [15, 41, 5, 3]
+            channels: 128
+            max_downsample_channels: 1024
+            max_groups: 16
+            bias: True
+            downsample_scales: [2, 2, 4, 4, 1]
+            nonlinear_activation: "leakyrelu"
+            nonlinear_activation_params:
+                negative_slope: 0.1
+            use_weight_norm: True
+            use_spectral_norm: False
+        follow_official_norm: False
+        periods: [2, 3, 5, 7, 11]
+        period_discriminator_params:
+            in_channels: 1
+            out_channels: 1
+            kernel_sizes: [5, 3]
+            channels: 32
+            downsample_scales: [3, 3, 3, 3, 1]
+            max_downsample_channels: 1024
+            bias: True
+            nonlinear_activation: "leakyrelu"
+            nonlinear_activation_params:
+                negative_slope: 0.1
+            use_weight_norm: True
+            use_spectral_norm: False
+    # others
+    sampling_rate: 22050          # needed in the inference for saving wav
+    cache_generator_outputs: True # whether to cache generator outputs in the training
+          
+###########################################################
+#                        LOSS SETTING                     #
+###########################################################
+# loss function related
+generator_adv_loss_params:
+    average_by_discriminators: False # whether to average loss value by #discriminators
+    loss_type: mse                   # loss type, "mse" or "hinge"
+discriminator_adv_loss_params:
+    average_by_discriminators: False # whether to average loss value by #discriminators
+    loss_type: mse                   # loss type, "mse" or "hinge"
+feat_match_loss_params:
+    average_by_discriminators: False # whether to average loss value by #discriminators
+    average_by_layers: False         # whether to average loss value by #layers of each discriminator
+    include_final_outputs: True      # whether to include final outputs for loss calculation
+mel_loss_params:
+    fs: 22050          # must be the same as the training data
+    fft_size: 1024        # fft points
+    hop_size: 256    # hop size
+    win_length: null   # window length
+    window: hann       # window type
+    num_mels: 80         # number of Mel basis
+    fmin: 0            # minimum frequency for Mel basis
+    fmax: null         # maximum frequency for Mel basis
+    log_base: null     # null represent natural log
+
+###########################################################
+#               ADVERSARIAL LOSS SETTING                  #
+###########################################################
+lambda_adv: 1.0        # loss scaling coefficient for adversarial loss
+lambda_mel: 45.0       # loss scaling coefficient for Mel loss
+lambda_feat_match: 2.0 # loss scaling coefficient for feat match loss
+lambda_dur: 1.0        # loss scaling coefficient for duration loss
+lambda_kl: 1.0         # loss scaling coefficient for KL divergence loss
+# others
+sampling_rate: 22050          # needed in the inference for saving wav
+cache_generator_outputs: True # whether to cache generator outputs in the training
+
+
+###########################################################
+#                  DATA LOADER SETTING                    #
+###########################################################
+batch_size: 50              # Batch size.
+num_workers: 4              # Number of workers in DataLoader.
+
+##########################################################
+#            OPTIMIZER & SCHEDULER SETTING               #
+##########################################################
+# optimizer setting for generator
+generator_optimizer_params:
+    beta1: 0.8
+    beta2: 0.99
+    epsilon: 1.0e-9
+    weight_decay: 0.0
+generator_scheduler: exponential_decay
+generator_scheduler_params:
+    learning_rate: 2.0e-4
+    gamma: 0.999875                   
+
+# optimizer setting for discriminator
+discriminator_optimizer_params:
+    beta1: 0.8
+    beta2: 0.99
+    epsilon: 1.0e-9
+    weight_decay: 0.0
+discriminator_scheduler: exponential_decay
+discriminator_scheduler_params:
+    learning_rate: 2.0e-4          
+    gamma: 0.999875
+generator_first: False # whether to start updating generator first
+
+##########################################################
+#                OTHER TRAINING SETTING                  #
+##########################################################
+num_snapshots: 10            # max number of snapshots to keep while training
+train_max_steps: 350000      # Number of training steps. == total_iters / ngpus, total_iters = 1000000
+save_interval_steps: 1000    # Interval steps to save checkpoint.
+eval_interval_steps: 250     # Interval steps to evaluate the network.
+seed: 777                    # random seed number

examples/aishell3/vits/local/preprocess.sh

+#!/bin/bash
+
+stage=0
+stop_stage=100
+
+config_path=$1
+add_blank=$2
+
+# copy from tts3/preprocess
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # get durations from MFA's result
+    echo "Generate durations.txt from MFA results ..."
+    python3 ${MAIN_ROOT}/utils/gen_duration_from_textgrid.py \
+        --inputdir=./aishell3_alignment_tone \
+        --output durations.txt \
+        --config=${config_path}
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # extract features
+    echo "Extract features ..."
+    python3 ${BIN_DIR}/preprocess.py \
+        --dataset=aishell3 \
+        --rootdir=~/datasets/data_aishell3/ \
+        --dumpdir=dump \
+        --dur-file=durations.txt \
+        --config=${config_path} \
+        --num-cpu=20 \
+        --cut-sil=True
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    # get features' stats(mean and std)
+    echo "Get features' stats ..."
+    python3 ${MAIN_ROOT}/utils/compute_statistics.py \
+        --metadata=dump/train/raw/metadata.jsonl \
+        --field-name="feats"
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    # normalize and covert phone/speaker to id, dev and test should use train's stats
+    echo "Normalize ..."
+    python3 ${BIN_DIR}/normalize.py \
+        --metadata=dump/train/raw/metadata.jsonl \
+        --dumpdir=dump/train/norm \
+        --feats-stats=dump/train/feats_stats.npy \
+        --phones-dict=dump/phone_id_map.txt \
+        --speaker-dict=dump/speaker_id_map.txt \
+        --add-blank=${add_blank} \
+        --skip-wav-copy
+
+    python3 ${BIN_DIR}/normalize.py \
+        --metadata=dump/dev/raw/metadata.jsonl \
+        --dumpdir=dump/dev/norm \
+        --feats-stats=dump/train/feats_stats.npy \
+        --phones-dict=dump/phone_id_map.txt \
+        --speaker-dict=dump/speaker_id_map.txt \
+        --add-blank=${add_blank} \
+        --skip-wav-copy
+
+    python3 ${BIN_DIR}/normalize.py \
+        --metadata=dump/test/raw/metadata.jsonl \
+        --dumpdir=dump/test/norm \
+        --feats-stats=dump/train/feats_stats.npy \
+        --phones-dict=dump/phone_id_map.txt \
+        --speaker-dict=dump/speaker_id_map.txt \
+        --add-blank=${add_blank} \
+        --skip-wav-copy
+fi

examples/aishell3/vits/local/synthesize.sh

+#!/bin/bash
+
+config_path=$1
+train_output_path=$2
+ckpt_name=$3
+stage=0
+stop_stage=0
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/synthesize.py \
+        --config=${config_path} \
+        --ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --phones_dict=dump/phone_id_map.txt \
+        --speaker_dict=dump/speaker_id_map.txt \
+        --test_metadata=dump/test/norm/metadata.jsonl \
+        --output_dir=${train_output_path}/test
+fi

examples/aishell3/vits/local/synthesize_e2e.sh

+#!/bin/bash
+
+config_path=$1
+train_output_path=$2
+ckpt_name=$3
+add_blank=$4
+
+stage=0
+stop_stage=0
+
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    FLAGS_allocator_strategy=naive_best_fit \
+    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
+    python3 ${BIN_DIR}/synthesize_e2e.py \
+        --config=${config_path} \
+        --ckpt=${train_output_path}/checkpoints/${ckpt_name} \
+        --phones_dict=dump/phone_id_map.txt \
+        --speaker_dict=dump/speaker_id_map.txt \
+        --spk_id=0 \
+        --output_dir=${train_output_path}/test_e2e \
+        --text=${BIN_DIR}/../sentences.txt \
+        --add-blank=${add_blank}
+fi

examples/aishell3/vits/local/train.sh

+#!/bin/bash
+
+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 ${BIN_DIR}/train.py \
+    --train-metadata=dump/train/norm/metadata.jsonl \
+    --dev-metadata=dump/dev/norm/metadata.jsonl \
+    --config=${config_path} \
+    --output-dir=${train_output_path} \
+    --ngpu=4 \
+    --phones-dict=dump/phone_id_map.txt \
+    --speaker-dict=dump/speaker_id_map.txt

examples/aishell3/vits/path.sh

+#!/bin/bash
+export MAIN_ROOT=`realpath ${PWD}/../../../`
+
+export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
+export LC_ALL=C
+
+export PYTHONDONTWRITEBYTECODE=1
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
+
+MODEL=vits
+export BIN_DIR=${MAIN_ROOT}/paddlespeech/t2s/exps/${MODEL}
\ No newline at end of file

examples/aishell3/vits/run.sh

+#!/bin/bash
+
+set -e
+source path.sh
+
+gpus=0,1,2,3
+stage=0
+stop_stage=100
+
+conf_path=conf/default.yaml
+train_output_path=exp/default
+ckpt_name=snapshot_iter_153.pdz
+add_blank=true
+
+# with the following command, you can choose the stage range you want to run
+# such as `./run.sh --stage 0 --stop-stage 0`
+# this can not be mixed use with `$1`, `$2` ...
+source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    # prepare data
+    ./local/preprocess.sh ${conf_path} ${add_blank}|| exit -1
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    # train model, all `ckpt` under `train_output_path/checkpoints/` dir
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
+fi
+
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} ${add_blank}|| exit -1
+fi

examples/aishell3_vctk/ernie_sat/run.sh

@@ -3,7 +3,7 @@
 set -e
 source path.sh
 
-gpus=0,1
+gpus=0,1,2,3,4,5,6,7
 stage=0
 stop_stage=100
 

examples/csmsc/tts2/run.sh

@@ -46,8 +46,8 @@ fi
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # install paddle2onnx
     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
-    if [[ -z "$version" || ${version} != '0.9.8' ]]; then
-        pip install paddle2onnx==0.9.8
+    if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+        pip install paddle2onnx==1.0.0
     fi
     ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx speedyspeech_csmsc
     # considering the balance between speed and quality, we recommend that you use hifigan as vocoder

examples/csmsc/tts3/run.sh

@@ -46,8 +46,8 @@ fi
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # install paddle2onnx
     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
-    if [[ -z "$version" || ${version} != '0.9.8' ]]; then
-        pip install paddle2onnx==0.9.8
+    if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+        pip install paddle2onnx==1.0.0
     fi
     ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc
     # considering the balance between speed and quality, we recommend that you use hifigan as vocoder

examples/csmsc/tts3/run_cnndecoder.sh

@@ -59,8 +59,8 @@ fi
 if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
     # install paddle2onnx
     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
-    if [[ -z "$version" || ${version} != '0.9.8' ]]; then
-        pip install paddle2onnx==0.9.8
+    if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+        pip install paddle2onnx==1.0.0
     fi
     ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc
     # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
@@ -79,8 +79,8 @@ fi
 if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
     # install paddle2onnx
     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
-    if [[ -z "$version" || ${version} != '0.9.8' ]]; then
-        pip install paddle2onnx==0.9.8
+    if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+        pip install paddle2onnx==1.0.0
     fi
     # streaming acoustic model
     ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_encoder_infer

examples/csmsc/vits/run.sh

@@ -3,7 +3,7 @@
 set -e
 source path.sh
 
-gpus=0,1
+gpus=0,1,2,3
 stage=0
 stop_stage=100
 

examples/hey_snips/kws0/conf/mdtc.yaml

@@ -3,7 +3,7 @@
 #                   Data                  #
 ###########################################
 dataset: 'paddlespeech.audio.datasets:HeySnips'
-data_dir: '/PATH/TO/DATA/hey_snips_research_6k_en_train_eval_clean_ter'
+data_dir: '../tests/hey_snips_research_6k_en_train_eval_clean_ter'
 
 ############################################
 #           Network Architecture           #
@@ -46,4 +46,4 @@ num_workers: 16
 checkpoint: './checkpoint/epoch_100/model.pdparams'
 score_file: './scores.txt'
 stats_file: './stats.0.txt'
-img_file: './det.png'
+img_file: './det.png'
\ No newline at end of file

examples/iwslt2012/punc0/README.md

@@ -18,12 +18,62 @@
 ./run.sh --stage 3 --stop-stage 3
 ```
 ## Pretrained Model
-The pretrained model can be downloaded here [ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip).
+The pretrained model can be downloaded here:
+
+[ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip)
+
+[ernie-3.0-base.tar.gz](https://paddlespeech.bj.bcebos.com/punc_restore/ernie-3.0-base.tar.gz)
+
+[ernie-3.0-medium.tar.gz](https://paddlespeech.bj.bcebos.com/punc_restore/ernie-3.0-medium.tar.gz)
+
+[ernie-3.0-micro.tar.gz](https://paddlespeech.bj.bcebos.com/punc_restore/ernie-3.0-micro.tar.gz)
+
+[ernie-mini.tar.gz](https://paddlespeech.bj.bcebos.com/punc_restore/ernie-mini.tar.gz)
+
+[ernie-nano.tar.gz](https://paddlespeech.bj.bcebos.com/punc_restore/ernie-nano.tar.gz)
+
+[ernie-tiny.tar.gz](https://paddlespeech.bj.bcebos.com/punc_restore/ernie-tiny.tar.gz)
 
 ### Test Result
-- Ernie
+- Ernie 1.0
     |       |COMMA  |  PERIOD | QUESTION | OVERALL|
     |:-----:|:-----:|:-----:|:-----:|:-----:|  
     |Precision  |0.510955  |0.526462  |0.820755  |0.619391|
     |Recall     |0.517433  |0.564179  |0.861386  |0.647666|
     |F1         |0.514173  |0.544669  |0.840580  |0.633141|
+- Ernie-tiny
+    |       |COMMA  |  PERIOD | QUESTION | OVERALL|
+    |:-----:|:-----:|:-----:|:-----:|:-----:|  
+    |Precision  |0.733177  |0.721448  |0.754717  |0.736447|
+    |Recall     |0.380740  |0.524646  |0.733945  |0.546443|
+    |F1         |0.501204  |0.607506  |0.744186  |0.617632|
+- Ernie-3.0-base-zh
+    |       |COMMA  |  PERIOD | QUESTION | OVERALL|
+    |:-----:|:-----:|:-----:|:-----:|:-----:|  
+    |Precision  |0.805947  |0.764160  |0.858491  |0.809532|
+    |Recall     |0.399070  |0.567978  |0.850467  |0.605838|
+    |F1         |0.533817  |0.651623  |0.854460  |0.679967|
+- Ernie-3.0-medium-zh
+    |       |COMMA  |  PERIOD | QUESTION | OVERALL|
+    |:-----:|:-----:|:-----:|:-----:|:-----:|  
+    |Precision  |0.730829  |0.699164  |0.707547  |0.712514|
+    |Recall     |0.388196  |0.533286  |0.797872  |0.573118|
+    |F1         |0.507058  |0.605062  |0.750000  |0.620707|
+- Ernie-3.0-mini-zh
+    |       |COMMA  |  PERIOD | QUESTION | OVERALL|
+    |:-----:|:-----:|:-----:|:-----:|:-----:|  
+    |Precision  |0.757433  |0.708449  |0.707547  |0.724477|
+    |Recall     |0.355752  |0.506977  |0.735294  |0.532674|
+    |F1         |0.484121  |0.591015  |0.721154  |0.598763|
+- Ernie-3.0-micro-zh
+    |       |COMMA  |  PERIOD | QUESTION | OVERALL|
+    |:-----:|:-----:|:-----:|:-----:|:-----:|  
+    |Precision  |0.733959  |0.679666  |0.726415  |0.713347|
+    |Recall     |0.332742  |0.483487  |0.712963  |0.509731|
+    |F1         |0.457896  |0.565033  |0.719626  |0.580852|
+- Ernie-3.0-nano-zh
+    |       |COMMA  |  PERIOD | QUESTION | OVERALL|
+    |:-----:|:-----:|:-----:|:-----:|:-----:|  
+    |Precision  |0.693271  |0.682451  |0.754717  |0.710146|
+    |Recall     |0.327784  |0.491968  |0.666667  |0.495473|
+    |F1         |0.445114  |0.571762  |0.707965  |0.574947|

examples/iwslt2012/punc0/local/preprocess.py

 import argparse
-import os
+
 
 def process_sentence(line):
-   if line == '': return ''
-   res = line[0]
-   for i in range(1, len(line)):
-       res += (' ' + line[i])
-   return res
+    if line == '':
+        return ''
+    res = line[0]
+    for i in range(1, len(line)):
+        res += (' ' + line[i])
+    return res
+
 
 if __name__ == "__main__":
-   paser = argparse.ArgumentParser(description = "Input filename")
-   paser.add_argument('-input_file')
-   paser.add_argument('-output_file')
-   sentence_cnt = 0
-   args = paser.parse_args()
-   with open(args.input_file, 'r') as f:
-       with open(args.output_file, 'w') as write_f:
-           while True:
-               line = f.readline()
-               if line:
-                   sentence_cnt += 1 
-                   write_f.write(process_sentence(line))
-               else:
-                   break
-   print('preprocess over')
-   print('total sentences number:', sentence_cnt)
+    paser = argparse.ArgumentParser(description="Input filename")
+    paser.add_argument('-input_file')
+    paser.add_argument('-output_file')
+    sentence_cnt = 0
+    args = paser.parse_args()
+    with open(args.input_file, 'r') as f:
+        with open(args.output_file, 'w') as write_f:
+            while True:
+                line = f.readline()
+                if line:
+                    sentence_cnt += 1
+                    write_f.write(process_sentence(line))
+                else:
+                    break
+    print('preprocess over')
+    print('total sentences number:', sentence_cnt)

examples/ljspeech/tts3/run.sh

@@ -46,8 +46,8 @@ fi
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # install paddle2onnx
     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
-    if [[ -z "$version" || ${version} != '0.9.8' ]]; then
-        pip install paddle2onnx==0.9.8
+    if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+        pip install paddle2onnx==1.0.0
     fi
     ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_ljspeech
     # considering the balance between speed and quality, we recommend that you use hifigan as vocoder

examples/other/g2p/README.md

@@ -12,13 +12,13 @@ Run the command below to get the results of the test.
 ./run.sh
 ```
 
-The `avg WER` of g2p is: 0.024169315564825305
+The `avg WER` of g2p is: 0.024075726733983775
 
 ```text
      ,--------------------------------------------------------------------.
      |                         ./exp/g2p/text.g2p                         |
      |--------------------------------------------------------------------|
      | SPKR   | # Snt    # Wrd  | Corr    Sub    Del    Ins    Err  S.Err |
-     | Sum/Avg|  9996   299181  | 97.6    2.4    0.0    0.0    2.4   49.2 |
+     | Sum/Avg|  9996   299181  | 97.6    2.4    0.0    0.0    2.4   49.0 |
      `--------------------------------------------------------------------'
 ```

examples/other/tts_finetune/tts3/finetune.py

@@ -17,15 +17,14 @@ from pathlib import Path
 from typing import Union
 
 import yaml
-from paddle import distributed as dist
-from yacs.config import CfgNode
-
-from paddlespeech.t2s.exps.fastspeech2.train import train_sp
-
 from local.check_oov import get_check_result
 from local.extract import extract_feature
 from local.label_process import get_single_label
 from local.prepare_env import generate_finetune_env
+from paddle import distributed as dist
+from yacs.config import CfgNode
+
+from paddlespeech.t2s.exps.fastspeech2.train import train_sp
 from utils.gen_duration_from_textgrid import gen_duration_from_textgrid
 
 DICT_EN = 'tools/aligner/cmudict-0.7b'

examples/vctk/ernie_sat/run.sh

@@ -3,7 +3,7 @@
 set -e
 source path.sh
 
-gpus=0,1
+gpus=0,1,2,3,4,5,6,7
 stage=0
 stop_stage=100
 

examples/vctk/tts3/run.sh

@@ -44,8 +44,8 @@ fi
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # install paddle2onnx
     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
-    if [[ -z "$version" || ${version} != '0.9.8' ]]; then
-        pip install paddle2onnx==0.9.8
+    if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+        pip install paddle2onnx==1.0.0
     fi
     ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_vctk
     # considering the balance between speed and quality, we recommend that you use hifigan as vocoder

examples/voxceleb/sv0/RESULT.md

@@ -5,3 +5,7 @@
 | Model | Number of Params | Release | Config | dim | Test set |  Cosine | Cosine + S-Norm | 
 | --- | --- | --- | --- | --- | --- | --- | ---- |
 | ECAPA-TDNN | 85M | 0.2.1 | conf/ecapa_tdnn.yaml | 192 | test | 0.8188 | 0.7815|
+
+> [SpeechBrain result](https://github.com/speechbrain/speechbrain/tree/develop/recipes/VoxCeleb/SpeakerRec#speaker-verification-using-ecapa-tdnn-embeddings):  
+> EER = 0.90% (voxceleb1 + voxceleb2) without s-norm  
+> EER = 0.80% (voxceleb1 + voxceleb2) with s-norm.  

examples/zh_en_tts/tts3/run.sh

@@ -47,8 +47,8 @@ fi
 if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # install paddle2onnx
     version=$(echo `pip list |grep "paddle2onnx"` |awk -F" " '{print $2}')
-    if [[ -z "$version" || ${version} != '0.9.8' ]]; then
-        pip install paddle2onnx==0.9.8
+    if [[ -z "$version" || ${version} != '1.0.0' ]]; then
+        pip install paddle2onnx==1.0.0
     fi
     ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_mix
     # considering the balance between speed and quality, we recommend that you use hifigan as vocoder

paddlespeech/__init__.py

@@ -14,5 +14,3 @@
 import _locale
 
 _locale._getdefaultlocale = (lambda *args: ['en_US', 'utf8'])
-
-

paddlespeech/audio/__init__.py

@@ -14,12 +14,12 @@
 from . import compliance
 from . import datasets
 from . import features
-from . import text
-from . import transform
-from . import streamdata
 from . import functional
 from . import io
 from . import metric
 from . import sox_effects
+from . import streamdata
+from . import text
+from . import transform
 from .backends import load
 from .backends import save

paddlespeech/audio/streamdata/__init__.py

@@ -4,67 +4,66 @@
 # Modified from https://github.com/webdataset/webdataset
 #
 # flake8: noqa
-
-from .cache import (
-    cached_tarfile_samples,
-    cached_tarfile_to_samples,
-    lru_cleanup,
-    pipe_cleaner,
-)
-from .compat import WebDataset, WebLoader, FluidWrapper
-from .extradatasets import MockDataset, with_epoch, with_length
-from .filters import (
-    associate,
-    batched,
-    decode,
-    detshuffle,
-    extract_keys,
-    getfirst,
-    info,
-    map,
-    map_dict,
-    map_tuple,
-    pipelinefilter,
-    rename,
-    rename_keys,
-    audio_resample,
-    select,
-    shuffle,
-    slice,
-    to_tuple,
-    transform_with,
-    unbatched,
-    xdecode,
-    audio_data_filter,
-    audio_tokenize,
-    audio_resample,
-    audio_compute_fbank,
-    audio_spec_aug,
-    sort,
-    audio_padding,
-    audio_cmvn,
-    placeholder,
-)
-from .handlers import (
-    ignore_and_continue,
-    ignore_and_stop,
-    reraise_exception,
-    warn_and_continue,
-    warn_and_stop,
-)
+from .cache import cached_tarfile_samples
+from .cache import cached_tarfile_to_samples
+from .cache import lru_cleanup
+from .cache import pipe_cleaner
+from .compat import FluidWrapper
+from .compat import WebDataset
+from .compat import WebLoader
+from .extradatasets import MockDataset
+from .extradatasets import with_epoch
+from .extradatasets import with_length
+from .filters import associate
+from .filters import audio_cmvn
+from .filters import audio_compute_fbank
+from .filters import audio_data_filter
+from .filters import audio_padding
+from .filters import audio_resample
+from .filters import audio_spec_aug
+from .filters import audio_tokenize
+from .filters import batched
+from .filters import decode
+from .filters import detshuffle
+from .filters import extract_keys
+from .filters import getfirst
+from .filters import info
+from .filters import map
+from .filters import map_dict
+from .filters import map_tuple
+from .filters import pipelinefilter
+from .filters import placeholder
+from .filters import rename
+from .filters import rename_keys
+from .filters import select
+from .filters import shuffle
+from .filters import slice
+from .filters import sort
+from .filters import to_tuple
+from .filters import transform_with
+from .filters import unbatched
+from .filters import xdecode
+from .handlers import ignore_and_continue
+from .handlers import ignore_and_stop
+from .handlers import reraise_exception
+from .handlers import warn_and_continue
+from .handlers import warn_and_stop
+from .mix import RandomMix
+from .mix import RoundRobin
 from .pipeline import DataPipeline
-from .shardlists import (
-    MultiShardSample,
-    ResampledShards,
-    SimpleShardList,
-    non_empty,
-    resampled,
-    shardspec,
-    single_node_only,
-    split_by_node,
-    split_by_worker,
-)
-from .tariterators import tarfile_samples, tarfile_to_samples
-from .utils import PipelineStage, repeatedly
-from .writer import ShardWriter, TarWriter, numpy_dumps
-from .mix import RandomMix, RoundRobin
+from .shardlists import MultiShardSample
+from .shardlists import non_empty
+from .shardlists import resampled
+from .shardlists import ResampledShards
+from .shardlists import shardspec
+from .shardlists import SimpleShardList
+from .shardlists import single_node_only
+from .shardlists import split_by_node
+from .shardlists import split_by_worker
+from .tariterators import tarfile_samples
+from .tariterators import tarfile_to_samples
+from .utils import PipelineStage
+from .utils import repeatedly
+from .writer import numpy_dumps
+from .writer import ShardWriter
+from .writer import TarWriter

paddlespeech/audio/streamdata/autodecode.py

@@ -5,18 +5,19 @@
 # See the LICENSE file for licensing terms (BSD-style).
 # Modified from https://github.com/webdataset/webdataset
 #
-
 """Automatically decode webdataset samples."""
-
-import io, json, os, pickle, re, tempfile
+import io
+import json
+import os
+import pickle
+import re
+import tempfile
 from functools import partial
 
 import numpy as np
-
 """Extensions passed on to the image decoder."""
 image_extensions = "jpg jpeg png ppm pgm pbm pnm".split()
 
-
 ################################################################
 # handle basic datatypes
 ################################################################
@@ -128,7 +129,7 @@ def call_extension_handler(key, data, f, extensions):
         target = target.split(".")
         if len(target) > len(extension):
             continue
-        if extension[-len(target) :] == target:
+        if extension[-len(target):] == target:
             return f(data)
     return None
 
@@ -268,7 +269,6 @@ def imagehandler(imagespec, extensions=image_extensions):
 ################################################################
 # torch video
 ################################################################
-
 '''
 def torch_video(key, data):
     """Decode video using the torchvideo library.
@@ -289,7 +289,6 @@ def torch_video(key, data):
         return torchvision.io.read_video(fname, pts_unit="sec")
 '''
 
-
 ################################################################
 # paddlespeech.audio
 ################################################################
@@ -359,7 +358,6 @@ def gzfilter(key, data):
 # decode entire training amples
 ################################################################
 
-
 default_pre_handlers = [gzfilter]
 default_post_handlers = [basichandlers]
 
@@ -387,7 +385,8 @@ class Decoder:
             pre = default_pre_handlers
         if post is None:
             post = default_post_handlers
-        assert all(callable(h) for h in handlers), f"one of {handlers} not callable"
+        assert all(callable(h)
+                   for h in handlers), f"one of {handlers} not callable"
         assert all(callable(h) for h in pre), f"one of {pre} not callable"
         assert all(callable(h) for h in post), f"one of {post} not callable"
         self.handlers = pre + handlers + post

paddlespeech/audio/streamdata/cache.py

@@ -2,7 +2,10 @@
 # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 # See the LICENSE file for licensing terms (BSD-style).
 # Modified from https://github.com/webdataset/webdataset
-import itertools, os, random, re, sys
+import os
+import random
+import re
+import sys
 from urllib.parse import urlparse
 
 from . import filters
@@ -40,7 +43,7 @@ def lru_cleanup(cache_dir, cache_size, keyfn=os.path.getctime, verbose=False):
         os.remove(fname)
 
 
-def download(url, dest, chunk_size=1024 ** 2, verbose=False):
+def download(url, dest, chunk_size=1024**2, verbose=False):
     """Download a file from `url` to `dest`."""
     temp = dest + f".temp{os.getpid()}"
     with gopen.gopen(url) as stream:
@@ -65,12 +68,11 @@ def pipe_cleaner(spec):
 
 
 def get_file_cached(
-    spec,
-    cache_size=-1,
-    cache_dir=None,
-    url_to_name=pipe_cleaner,
-    verbose=False,
-):
+        spec,
+        cache_size=-1,
+        cache_dir=None,
+        url_to_name=pipe_cleaner,
+        verbose=False, ):
     if cache_size == -1:
         cache_size = default_cache_size
     if cache_dir is None:
@@ -107,15 +109,14 @@ verbose_cache = int(os.environ.get("WDS_VERBOSE_CACHE", "0"))
 
 
 def cached_url_opener(
-    data,
-    handler=reraise_exception,
-    cache_size=-1,
-    cache_dir=None,
-    url_to_name=pipe_cleaner,
-    validator=check_tar_format,
-    verbose=False,
-    always=False,
-):
+        data,
+        handler=reraise_exception,
+        cache_size=-1,
+        cache_dir=None,
+        url_to_name=pipe_cleaner,
+        validator=check_tar_format,
+        verbose=False,
+        always=False, ):
     """Given a stream of url names (packaged in `dict(url=url)`), yield opened streams."""
     verbose = verbose or verbose_cache
     for sample in data:
@@ -132,8 +133,7 @@ def cached_url_opener(
                     cache_size=cache_size,
                     cache_dir=cache_dir,
                     url_to_name=url_to_name,
-                    verbose=verbose,
-                )
+                    verbose=verbose, )
             if verbose:
                 print("# opening %s" % dest, file=sys.stderr)
             assert os.path.exists(dest)
@@ -143,9 +143,8 @@ def cached_url_opener(
                     data = f.read(200)
                 os.remove(dest)
                 raise ValueError(
-                    "%s (%s) is not a tar archive, but a %s, contains %s"
-                    % (dest, url, ftype, repr(data))
-                )
+                    "%s (%s) is not a tar archive, but a %s, contains %s" %
+                    (dest, url, ftype, repr(data)))
             try:
                 stream = open(dest, "rb")
                 sample.update(stream=stream)
@@ -158,7 +157,7 @@ def cached_url_opener(
                     continue
                 raise exn
         except Exception as exn:
-            exn.args = exn.args + (url,)
+            exn.args = exn.args + (url, )
             if handler(exn):
                 continue
             else:
@@ -166,14 +165,13 @@ def cached_url_opener(
 
 
 def cached_tarfile_samples(
-    src,
-    handler=reraise_exception,
-    cache_size=-1,
-    cache_dir=None,
-    verbose=False,
-    url_to_name=pipe_cleaner,
-    always=False,
-):
+        src,
+        handler=reraise_exception,
+        cache_size=-1,
+        cache_dir=None,
+        verbose=False,
+        url_to_name=pipe_cleaner,
+        always=False, ):
     streams = cached_url_opener(
         src,
         handler=handler,
@@ -181,8 +179,7 @@ def cached_tarfile_samples(
         cache_dir=cache_dir,
         verbose=verbose,
         url_to_name=url_to_name,
-        always=always,
-    )
+        always=always, )
     samples = tar_file_and_group_expander(streams, handler=handler)
     return samples
 

paddlespeech/audio/streamdata/compat.py

@@ -2,17 +2,17 @@
 # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 # See the LICENSE file for licensing terms (BSD-style).
 # Modified from https://github.com/webdataset/webdataset
-from dataclasses import dataclass
-from itertools import islice
-from typing import List
-
-import braceexpand, yaml
+import yaml
 
 from . import autodecode
-from . import cache, filters, shardlists, tariterators
+from . import cache
+from . import filters
+from . import shardlists
+from . import tariterators
 from .filters import reraise_exception
+from .paddle_utils import DataLoader
+from .paddle_utils import IterableDataset
 from .pipeline import DataPipeline
-from .paddle_utils import DataLoader, IterableDataset
 
 
 class FluidInterface:
@@ -26,7 +26,8 @@ class FluidInterface:
         return self.compose(filters.unbatched())
 
     def listed(self, batchsize, partial=True):
-        return self.compose(filters.batched(), batchsize=batchsize, collation_fn=None)
+        return self.compose(
+            filters.batched(), batchsize=batchsize, collation_fn=None)
 
     def unlisted(self):
         return self.compose(filters.unlisted())
@@ -43,9 +44,19 @@ class FluidInterface:
     def map(self, f, handler=reraise_exception):
         return self.compose(filters.map(f, handler=handler))
 
-    def decode(self, *args, pre=None, post=None, only=None, partial=False, handler=reraise_exception):
-        handlers = [autodecode.ImageHandler(x) if isinstance(x, str) else x for x in args]
-        decoder = autodecode.Decoder(handlers, pre=pre, post=post, only=only, partial=partial)
+    def decode(self,
+               *args,
+               pre=None,
+               post=None,
+               only=None,
+               partial=False,
+               handler=reraise_exception):
+        handlers = [
+            autodecode.ImageHandler(x) if isinstance(x, str) else x
+            for x in args
+        ]
+        decoder = autodecode.Decoder(
+            handlers, pre=pre, post=post, only=only, partial=partial)
         return self.map(decoder, handler=handler)
 
     def map_dict(self, handler=reraise_exception, **kw):
@@ -80,12 +91,12 @@ class FluidInterface:
 
     def audio_data_filter(self, *args, **kw):
         return self.compose(filters.audio_data_filter(*args, **kw))
-    
+
     def audio_tokenize(self, *args, **kw):
         return self.compose(filters.audio_tokenize(*args, **kw))
 
     def resample(self, *args, **kw):
-        return self.compose(filters.resample(*args, **kw)) 
+        return self.compose(filters.resample(*args, **kw))
 
     def audio_compute_fbank(self, *args, **kw):
         return self.compose(filters.audio_compute_fbank(*args, **kw))
@@ -102,27 +113,28 @@ class FluidInterface:
     def audio_cmvn(self, cmvn_file):
         return self.compose(filters.audio_cmvn(cmvn_file))
 
+
 class WebDataset(DataPipeline, FluidInterface):
     """Small fluid-interface wrapper for DataPipeline."""
 
     def __init__(
-        self,
-        urls,
-        handler=reraise_exception,
-        resampled=False,
-        repeat=False,
-        shardshuffle=None,
-        cache_size=0,
-        cache_dir=None,
-        detshuffle=False,
-        nodesplitter=shardlists.single_node_only,
-        verbose=False,
-    ):
+            self,
+            urls,
+            handler=reraise_exception,
+            resampled=False,
+            repeat=False,
+            shardshuffle=None,
+            cache_size=0,
+            cache_dir=None,
+            detshuffle=False,
+            nodesplitter=shardlists.single_node_only,
+            verbose=False, ):
         super().__init__()
         if isinstance(urls, IterableDataset):
             assert not resampled
             self.append(urls)
-        elif isinstance(urls, str) and (urls.endswith(".yaml") or urls.endswith(".yml")):
+        elif isinstance(urls, str) and (urls.endswith(".yaml") or
+                                        urls.endswith(".yml")):
             with (open(urls)) as stream:
                 spec = yaml.safe_load(stream)
             assert "datasets" in spec
@@ -152,9 +164,7 @@ class WebDataset(DataPipeline, FluidInterface):
                     handler=handler,
                     verbose=verbose,
                     cache_size=cache_size,
-                    cache_dir=cache_dir,
-                )
-            )
+                    cache_dir=cache_dir, ))
 
 
 class FluidWrapper(DataPipeline, FluidInterface):

paddlespeech/audio/streamdata/extradatasets.py

@@ -5,20 +5,10 @@
 # See the LICENSE file for licensing terms (BSD-style).
 # Modified from https://github.com/webdataset/webdataset
 #
-
-
 """Train PyTorch models directly from POSIX tar archive.
 
 Code works locally or over HTTP connections.
 """
-
-import itertools as itt
-import os
-import random
-import sys
-
-import braceexpand
-
 from . import utils
 from .paddle_utils import IterableDataset
 from .utils import PipelineStage
@@ -63,8 +53,7 @@ class repeatedly(IterableDataset, PipelineStage):
         return utils.repeatedly(
             source,
             nepochs=self.nepochs,
-            nbatches=self.nbatches,
-        )
+            nbatches=self.nbatches, )
 
 
 class with_epoch(IterableDataset):

paddlespeech/audio/streamdata/filters.py

@@ -3,7 +3,6 @@
 # This file is part of the WebDataset library.
 # See the LICENSE file for licensing terms (BSD-style).
 #
-
 # Modified from https://github.com/webdataset/webdataset
 # Modified from wenet(https://github.com/wenet-e2e/wenet)
 """A collection of iterators for data transformations.
@@ -12,28 +11,29 @@ These functions are plain iterator functions. You can find curried versions
 in webdataset.filters, and you can find IterableDataset wrappers in
 webdataset.processing.
 """
-
 import io
-from fnmatch import fnmatch
+import itertools
+import os
+import random
 import re
-import itertools, os, random, sys, time
-from functools import reduce, wraps
+import sys
+import time
+from fnmatch import fnmatch
+from functools import reduce
 
-import numpy as np
+import paddle
 
 from . import autodecode
-from . import  utils
-from .paddle_utils import PaddleTensor
-from .utils import PipelineStage
-
+from . import utils
 from .. import backends
 from ..compliance import kaldi
-import paddle
 from ..transform.cmvn import GlobalCMVN
-from ..utils.tensor_utils import pad_sequence
-from ..transform.spec_augment import time_warp
-from ..transform.spec_augment import time_mask
 from ..transform.spec_augment import freq_mask
+from ..transform.spec_augment import time_mask
+from ..transform.spec_augment import time_warp
+from ..utils.tensor_utils import pad_sequence
+from .utils import PipelineStage
+
 
 class FilterFunction(object):
     """Helper class for currying pipeline stages.
@@ -159,10 +159,12 @@ def transform_with(sample, transformers):
             result[i] = f(sample[i])
     return result
 
+
 ###
 # Iterators
 ###
 
+
 def _info(data, fmt=None, n=3, every=-1, width=50, stream=sys.stderr, name=""):
     """Print information about the samples that are passing through.
 
@@ -278,10 +280,16 @@ def _log_keys(data, logfile=None):
 log_keys = pipelinefilter(_log_keys)
 
 
+def _minedecode(x):
+    if isinstance(x, str):
+        return autodecode.imagehandler(x)
+    else:
+        return x
+
+
 def _decode(data, *args, handler=reraise_exception, **kw):
     """Decode data based on the decoding functions given as arguments."""
-
-    decoder = lambda x: autodecode.imagehandler(x) if isinstance(x, str) else x
+    decoder = _minedecode
     handlers = [decoder(x) for x in args]
     f = autodecode.Decoder(handlers, **kw)
 
@@ -325,15 +333,24 @@ def _rename(data, handler=reraise_exception, keep=True, **kw):
     for sample in data:
         try:
             if not keep:
-                yield {k: getfirst(sample, v, missing_is_error=True) for k, v in kw.items()}
+                yield {
+                    k: getfirst(sample, v, missing_is_error=True)
+                    for k, v in kw.items()
+                }
             else:
 
                 def listify(v):
                     return v.split(";") if isinstance(v, str) else v
 
                 to_be_replaced = {x for v in kw.values() for x in listify(v)}
-                result = {k: v for k, v in sample.items() if k not in to_be_replaced}
-                result.update({k: getfirst(sample, v, missing_is_error=True) for k, v in kw.items()})
+                result = {
+                    k: v
+                    for k, v in sample.items() if k not in to_be_replaced
+                }
+                result.update({
+                    k: getfirst(sample, v, missing_is_error=True)
+                    for k, v in kw.items()
+                })
                 yield result
         except Exception as exn:
             if handler(exn):
@@ -381,7 +398,11 @@ def _map_dict(data, handler=reraise_exception, **kw):
 map_dict = pipelinefilter(_map_dict)
 
 
-def _to_tuple(data, *args, handler=reraise_exception, missing_is_error=True, none_is_error=None):
+def _to_tuple(data,
+              *args,
+              handler=reraise_exception,
+              missing_is_error=True,
+              none_is_error=None):
     """Convert dict samples to tuples."""
     if none_is_error is None:
         none_is_error = missing_is_error
@@ -390,7 +411,10 @@ def _to_tuple(data, *args, handler=reraise_exception, missing_is_error=True, non
 
     for sample in data:
         try:
-            result = tuple([getfirst(sample, f, missing_is_error=missing_is_error) for f in args])
+            result = tuple([
+                getfirst(sample, f, missing_is_error=missing_is_error)
+                for f in args
+            ])
             if none_is_error and any(x is None for x in result):
                 raise ValueError(f"to_tuple {args} got {sample.keys()}")
             yield result
@@ -463,19 +487,28 @@ rsample = pipelinefilter(_rsample)
 slice = pipelinefilter(itertools.islice)
 
 
-def _extract_keys(source, *patterns, duplicate_is_error=True, ignore_missing=False):
+def _extract_keys(source,
+                  *patterns,
+                  duplicate_is_error=True,
+                  ignore_missing=False):
     for sample in source:
         result = []
         for pattern in patterns:
-            pattern = pattern.split(";") if isinstance(pattern, str) else pattern
-            matches = [x for x in sample.keys() if any(fnmatch("." + x, p) for p in pattern)]
+            pattern = pattern.split(";") if isinstance(pattern,
+                                                       str) else pattern
+            matches = [
+                x for x in sample.keys()
+                if any(fnmatch("." + x, p) for p in pattern)
+            ]
             if len(matches) == 0:
                 if ignore_missing:
                     continue
                 else:
-                    raise ValueError(f"Cannot find {pattern} in sample keys {sample.keys()}.")
+                    raise ValueError(
+                        f"Cannot find {pattern} in sample keys {sample.keys()}.")
             if len(matches) > 1 and duplicate_is_error:
-                raise ValueError(f"Multiple sample keys {sample.keys()} match {pattern}.")
+                raise ValueError(
+                    f"Multiple sample keys {sample.keys()} match {pattern}.")
             value = sample[matches[0]]
             result.append(value)
         yield tuple(result)
@@ -484,7 +517,12 @@ def _extract_keys(source, *patterns, duplicate_is_error=True, ignore_missing=Fal
 extract_keys = pipelinefilter(_extract_keys)
 
 
-def _rename_keys(source, *args, keep_unselected=False, must_match=True, duplicate_is_error=True, **kw):
+def _rename_keys(source,
+                 *args,
+                 keep_unselected=False,
+                 must_match=True,
+                 duplicate_is_error=True,
+                 **kw):
     renamings = [(pattern, output) for output, pattern in args]
     renamings += [(pattern, output) for output, pattern in kw.items()]
     for sample in source:
@@ -504,11 +542,15 @@ def _rename_keys(source, *args, keep_unselected=False, must_match=True, duplicat
                 continue
             if new_name in new_sample:
                 if duplicate_is_error:
-                    raise ValueError(f"Duplicate value in sample {sample.keys()} after rename.")
+                    raise ValueError(
+                        f"Duplicate value in sample {sample.keys()} after rename."
+                    )
                 continue
             new_sample[new_name] = value
         if must_match and not all(matched.values()):
-            raise ValueError(f"Not all patterns ({matched}) matched sample keys ({sample.keys()}).")
+            raise ValueError(
+                f"Not all patterns ({matched}) matched sample keys ({sample.keys()})."
+            )
 
         yield new_sample
 
@@ -541,18 +583,18 @@ def find_decoder(decoders, path):
     if fname.startswith("__"):
         return lambda x: x
     for pattern, fun in decoders[::-1]:
-        if fnmatch(fname.lower(), pattern) or fnmatch("." + fname.lower(), pattern):
+        if fnmatch(fname.lower(), pattern) or fnmatch("." + fname.lower(),
+                                                      pattern):
             return fun
     return None
 
 
 def _xdecode(
-    source,
-    *args,
-    must_decode=True,
-    defaults=default_decoders,
-    **kw,
-):
+        source,
+        *args,
+        must_decode=True,
+        defaults=default_decoders,
+        **kw, ):
     decoders = list(defaults) + list(args)
     decoders += [("*." + k, v) for k, v in kw.items()]
     for sample in source:
@@ -575,18 +617,18 @@ def _xdecode(
             new_sample[path] = value
         yield new_sample
 
-xdecode = pipelinefilter(_xdecode)
 
+xdecode = pipelinefilter(_xdecode)
 
 
 def _audio_data_filter(source,
-           frame_shift=10,
-           max_length=10240,
-           min_length=10,
-           token_max_length=200,
-           token_min_length=1,
-           min_output_input_ratio=0.0005,
-           max_output_input_ratio=1):
+                       frame_shift=10,
+                       max_length=10240,
+                       min_length=10,
+                       token_max_length=200,
+                       token_min_length=1,
+                       min_output_input_ratio=0.0005,
+                       max_output_input_ratio=1):
     """ Filter sample according to feature and label length
         Inplace operation.
 
@@ -613,7 +655,8 @@ def _audio_data_filter(source,
         assert 'wav' in sample
         assert 'label' in sample
         # sample['wav'] is paddle.Tensor, we have 100 frames every second (default)
-        num_frames = sample['wav'].shape[1] / sample['sample_rate'] * (1000 / frame_shift)
+        num_frames = sample['wav'].shape[1] / sample['sample_rate'] * (
+            1000 / frame_shift)
         if num_frames < min_length:
             continue
         if num_frames > max_length:
@@ -629,13 +672,15 @@ def _audio_data_filter(source,
                 continue
         yield sample
 
+
 audio_data_filter = pipelinefilter(_audio_data_filter)
 
+
 def _audio_tokenize(source,
-             symbol_table,
-             bpe_model=None,
-             non_lang_syms=None,
-             split_with_space=False):
+                    symbol_table,
+                    bpe_model=None,
+                    non_lang_syms=None,
+                    split_with_space=False):
     """ Decode text to chars or BPE
         Inplace operation
 
@@ -693,8 +738,10 @@ def _audio_tokenize(source,
         sample['label'] = label
         yield sample
 
+
 audio_tokenize = pipelinefilter(_audio_tokenize)
 
+
 def _audio_resample(source, resample_rate=16000):
     """ Resample data.
         Inplace operation.
@@ -713,18 +760,22 @@ def _audio_resample(source, resample_rate=16000):
         waveform = sample['wav']
         if sample_rate != resample_rate:
             sample['sample_rate'] = resample_rate
-            sample['wav'] = paddle.to_tensor(backends.soundfile_backend.resample(
-                waveform.numpy(), src_sr = sample_rate, target_sr = resample_rate
-            ))
+            sample['wav'] = paddle.to_tensor(
+                backends.soundfile_backend.resample(
+                    waveform.numpy(),
+                    src_sr=sample_rate,
+                    target_sr=resample_rate))
         yield sample
 
+
 audio_resample = pipelinefilter(_audio_resample)
 
+
 def _audio_compute_fbank(source,
-                  num_mel_bins=80,
-                  frame_length=25,
-                  frame_shift=10,
-                  dither=0.0):
+                         num_mel_bins=80,
+                         frame_length=25,
+                         frame_shift=10,
+                         dither=0.0):
     """ Extract fbank
 
         Args:
@@ -746,30 +797,33 @@ def _audio_compute_fbank(source,
         waveform = sample['wav']
         waveform = waveform * (1 << 15)
         # Only keep fname, feat, label
-        mat = kaldi.fbank(waveform,
-                          n_mels=num_mel_bins,
-                          frame_length=frame_length,
-                          frame_shift=frame_shift,
-                          dither=dither,
-                          energy_floor=0.0,
-                          sr=sample_rate)
+        mat = kaldi.fbank(
+            waveform,
+            n_mels=num_mel_bins,
+            frame_length=frame_length,
+            frame_shift=frame_shift,
+            dither=dither,
+            energy_floor=0.0,
+            sr=sample_rate)
         yield dict(fname=sample['fname'], label=sample['label'], feat=mat)
 
 
 audio_compute_fbank = pipelinefilter(_audio_compute_fbank)
 
-def _audio_spec_aug(source,
-            max_w=5, 
-            w_inplace=True, 
-            w_mode="PIL",
-            max_f=30,
-            num_f_mask=2, 
-            f_inplace=True, 
-            f_replace_with_zero=False,
-            max_t=40, 
-            num_t_mask=2, 
-            t_inplace=True, 
-            t_replace_with_zero=False,):
+
+def _audio_spec_aug(
+        source,
+        max_w=5,
+        w_inplace=True,
+        w_mode="PIL",
+        max_f=30,
+        num_f_mask=2,
+        f_inplace=True,
+        f_replace_with_zero=False,
+        max_t=40,
+        num_t_mask=2,
+        t_inplace=True,
+        t_replace_with_zero=False, ):
     """ Do spec augmentation
         Inplace operation
 
@@ -793,12 +847,23 @@ def _audio_spec_aug(source,
     for sample in source:
         x = sample['feat']
         x = x.numpy()
-        x = time_warp(x, max_time_warp=max_w, inplace = w_inplace, mode= w_mode)
-        x = freq_mask(x, F = max_f, n_mask = num_f_mask, inplace = f_inplace, replace_with_zero = f_replace_with_zero)
-        x = time_mask(x, T = max_t, n_mask = num_t_mask, inplace = t_inplace, replace_with_zero = t_replace_with_zero)
+        x = time_warp(x, max_time_warp=max_w, inplace=w_inplace, mode=w_mode)
+        x = freq_mask(
+            x,
+            F=max_f,
+            n_mask=num_f_mask,
+            inplace=f_inplace,
+            replace_with_zero=f_replace_with_zero)
+        x = time_mask(
+            x,
+            T=max_t,
+            n_mask=num_t_mask,
+            inplace=t_inplace,
+            replace_with_zero=t_replace_with_zero)
         sample['feat'] = paddle.to_tensor(x, dtype=paddle.float32)
         yield sample
 
+
 audio_spec_aug = pipelinefilter(_audio_spec_aug)
 
 
@@ -829,8 +894,10 @@ def _sort(source, sort_size=500):
     for x in buf:
         yield x
 
+
 sort = pipelinefilter(_sort)
 
+
 def _batched(source, batch_size=16):
     """ Static batch the data by `batch_size`
 
@@ -850,8 +917,10 @@ def _batched(source, batch_size=16):
     if len(buf) > 0:
         yield buf
 
+
 batched = pipelinefilter(_batched)
 
+
 def dynamic_batched(source, max_frames_in_batch=12000):
     """ Dynamic batch the data until the total frames in batch
         reach `max_frames_in_batch`
@@ -892,8 +961,8 @@ def _audio_padding(source):
     """
     for sample in source:
         assert isinstance(sample, list)
-        feats_length = paddle.to_tensor([x['feat'].shape[0] for x in sample],
-                                    dtype="int64")
+        feats_length = paddle.to_tensor(
+            [x['feat'].shape[0] for x in sample], dtype="int64")
         order = paddle.argsort(feats_length, descending=True)
         feats_lengths = paddle.to_tensor(
             [sample[i]['feat'].shape[0] for i in order], dtype="int64")
@@ -902,20 +971,20 @@ def _audio_padding(source):
         sorted_labels = [
             paddle.to_tensor(sample[i]['label'], dtype="int32") for i in order
         ]
-        label_lengths = paddle.to_tensor([x.shape[0] for x in sorted_labels],
-                                     dtype="int64")
-        padded_feats = pad_sequence(sorted_feats,
-                                    batch_first=True,
-                                    padding_value=0)
-        padding_labels = pad_sequence(sorted_labels,
-                                      batch_first=True,
-                                      padding_value=-1)
-
-        yield (sorted_keys, padded_feats, feats_lengths, padding_labels, 
+        label_lengths = paddle.to_tensor(
+            [x.shape[0] for x in sorted_labels], dtype="int64")
+        padded_feats = pad_sequence(
+            sorted_feats, batch_first=True, padding_value=0)
+        padding_labels = pad_sequence(
+            sorted_labels, batch_first=True, padding_value=-1)
+
+        yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
                label_lengths)
 
+
 audio_padding = pipelinefilter(_audio_padding)
 
+
 def _audio_cmvn(source, cmvn_file):
     global_cmvn = GlobalCMVN(cmvn_file)
     for batch in source:
@@ -923,13 +992,16 @@ def _audio_cmvn(source, cmvn_file):
         padded_feats = padded_feats.numpy()
         padded_feats = global_cmvn(padded_feats)
         padded_feats = paddle.to_tensor(padded_feats, dtype=paddle.float32)
-        yield (sorted_keys, padded_feats, feats_lengths, padding_labels, 
-           label_lengths)
+        yield (sorted_keys, padded_feats, feats_lengths, padding_labels,
+               label_lengths)
+
 
 audio_cmvn = pipelinefilter(_audio_cmvn)
 
+
 def _placeholder(source):
     for data in source:
         yield data
 
+
 placeholder = pipelinefilter(_placeholder)

paddlespeech/audio/streamdata/gopen.py

@@ -3,12 +3,12 @@
 # This file is part of the WebDataset library.
 # See the LICENSE file for licensing terms (BSD-style).
 #
-
-
 """Open URLs by calling subcommands."""
-
-import os, sys, re
-from subprocess import PIPE, Popen
+import os
+import re
+import sys
+from subprocess import PIPE
+from subprocess import Popen
 from urllib.parse import urlparse
 
 # global used for printing additional node information during verbose output
@@ -31,14 +31,13 @@ class Pipe:
     """
 
     def __init__(
-        self,
-        *args,
-        mode=None,
-        timeout=7200.0,
-        ignore_errors=False,
-        ignore_status=[],
-        **kw,
-    ):
+            self,
+            *args,
+            mode=None,
+            timeout=7200.0,
+            ignore_errors=False,
+            ignore_status=[],
+            **kw, ):
         """Create an IO Pipe."""
         self.ignore_errors = ignore_errors
         self.ignore_status = [0] + ignore_status
@@ -75,8 +74,7 @@ class Pipe:
         if verbose:
             print(
                 f"pipe exit [{self.status} {os.getpid()}:{self.proc.pid}] {self.args} {info}",
-                file=sys.stderr,
-            )
+                file=sys.stderr, )
         if self.status not in self.ignore_status and not self.ignore_errors:
             raise Exception(f"{self.args}: exit {self.status} (read) {info}")
 
@@ -114,9 +112,11 @@ class Pipe:
         self.close()
 
 
-def set_options(
-    obj, timeout=None, ignore_errors=None, ignore_status=None, handler=None
-):
+def set_options(obj,
+                timeout=None,
+                ignore_errors=None,
+                ignore_status=None,
+                handler=None):
     """Set options for Pipes.
 
     This function can be called on any stream. It will set pipe options only
@@ -168,16 +168,14 @@ def gopen_pipe(url, mode="rb", bufsize=8192):
             mode=mode,
             shell=True,
             bufsize=bufsize,
-            ignore_status=[141],
-        )  # skipcq: BAN-B604
+            ignore_status=[141], )  # skipcq: BAN-B604
     elif mode[0] == "w":
         return Pipe(
             cmd,
             mode=mode,
             shell=True,
             bufsize=bufsize,
-            ignore_status=[141],
-        )  # skipcq: BAN-B604
+            ignore_status=[141], )  # skipcq: BAN-B604
     else:
         raise ValueError(f"{mode}: unknown mode")
 
@@ -196,8 +194,7 @@ def gopen_curl(url, mode="rb", bufsize=8192):
             mode=mode,
             shell=True,
             bufsize=bufsize,
-            ignore_status=[141, 23],
-        )  # skipcq: BAN-B604
+            ignore_status=[141, 23], )  # skipcq: BAN-B604
     elif mode[0] == "w":
         cmd = f"curl -s -L -T - '{url}'"
         return Pipe(
@@ -205,8 +202,7 @@ def gopen_curl(url, mode="rb", bufsize=8192):
             mode=mode,
             shell=True,
             bufsize=bufsize,
-            ignore_status=[141, 26],
-        )  # skipcq: BAN-B604
+            ignore_status=[141, 26], )  # skipcq: BAN-B604
     else:
         raise ValueError(f"{mode}: unknown mode")
 
@@ -226,15 +222,13 @@ def gopen_htgs(url, mode="rb", bufsize=8192):
             mode=mode,
             shell=True,
             bufsize=bufsize,
-            ignore_status=[141, 23],
-        )  # skipcq: BAN-B604
+            ignore_status=[141, 23], )  # skipcq: BAN-B604
     elif mode[0] == "w":
         raise ValueError(f"{mode}: cannot write")
     else:
         raise ValueError(f"{mode}: unknown mode")
 
 
-
 def gopen_gsutil(url, mode="rb", bufsize=8192):
     """Open a URL with `curl`.
 
@@ -249,8 +243,7 @@ def gopen_gsutil(url, mode="rb", bufsize=8192):
             mode=mode,
             shell=True,
             bufsize=bufsize,
-            ignore_status=[141, 23],
-        )  # skipcq: BAN-B604
+            ignore_status=[141, 23], )  # skipcq: BAN-B604
     elif mode[0] == "w":
         cmd = f"gsutil cp - '{url}'"
         return Pipe(
@@ -258,13 +251,11 @@ def gopen_gsutil(url, mode="rb", bufsize=8192):
             mode=mode,
             shell=True,
             bufsize=bufsize,
-            ignore_status=[141, 26],
-        )  # skipcq: BAN-B604
+            ignore_status=[141, 26], )  # skipcq: BAN-B604
     else:
         raise ValueError(f"{mode}: unknown mode")
 
 
-
 def gopen_error(url, *args, **kw):
     """Raise a value error.
 
@@ -285,8 +276,7 @@ gopen_schemes = dict(
     ftps=gopen_curl,
     scp=gopen_curl,
     gs=gopen_gsutil,
-    htgs=gopen_htgs,
-)
+    htgs=gopen_htgs, )
 
 
 def gopen(url, mode="rb", bufsize=8192, **kw):

paddlespeech/audio/streamdata/handlers.py

@@ -3,7 +3,6 @@
 # This file is part of the WebDataset library.
 # See the LICENSE file for licensing terms (BSD-style).
 #
-
 """Pluggable exception handlers.
 
 These are functions that take an exception as an argument and then return...
@@ -14,8 +13,8 @@ These are functions that take an exception as an argument and then return...
 
 They are used as handler= arguments in much of the library.
 """
-
-import time, warnings
+import time
+import warnings
 
 
 def reraise_exception(exn):

paddlespeech/audio/streamdata/mix.py

@@ -5,17 +5,12 @@
 # See the LICENSE file for licensing terms (BSD-style).
 # Modified from https://github.com/webdataset/webdataset
 #
-
 """Classes for mixing samples from multiple sources."""
-
-import itertools, os, random, time, sys
-from functools import reduce, wraps
+import random
 
 import numpy as np
 
-from . import autodecode, utils
-from .paddle_utils import PaddleTensor, IterableDataset
-from .utils import PipelineStage
+from .paddle_utils import IterableDataset
 
 
 def round_robin_shortest(*sources):

paddlespeech/audio/streamdata/paddle_utils.py

@@ -5,12 +5,11 @@
 # See the LICENSE file for licensing terms (BSD-style).
 # Modified from https://github.com/webdataset/webdataset
 #
-
 """Mock implementations of paddle interfaces when paddle is not available."""
 
-
 try:
-    from paddle.io import DataLoader, IterableDataset
+    from paddle.io import DataLoader
+    from paddle.io import IterableDataset
 except ModuleNotFoundError:
 
     class IterableDataset:
@@ -22,12 +21,3 @@ except ModuleNotFoundError:
         """Empty implementation of DataLoader when paddle is not available."""
 
         pass
-
-try:
-    from paddle import Tensor as PaddleTensor
-except ModuleNotFoundError:
-
-    class TorchTensor:
-        """Empty implementation of PaddleTensor when paddle is not available."""
-
-        pass

paddlespeech/audio/streamdata/pipeline.py

@@ -3,15 +3,12 @@
 # See the LICENSE file for licensing terms (BSD-style).
 # Modified from https://github.com/webdataset/webdataset
 #%%
-import copy, os, random, sys, time
-from dataclasses import dataclass
+import copy
+import sys
 from itertools import islice
-from typing import List
 
-import braceexpand, yaml
-
-from .handlers import reraise_exception
-from .paddle_utils import DataLoader, IterableDataset
+from .paddle_utils import DataLoader
+from .paddle_utils import IterableDataset
 from .utils import PipelineStage
 
 
@@ -22,8 +19,7 @@ def add_length_method(obj):
     Combined = type(
         obj.__class__.__name__ + "_Length",
         (obj.__class__, IterableDataset),
-        {"__len__": length},
-    )
+        {"__len__": length}, )
     obj.__class__ = Combined
     return obj
 

paddlespeech/audio/streamdata/shardlists.py

@@ -4,28 +4,30 @@
 # This file is part of the WebDataset library.
 # See the LICENSE file for licensing terms (BSD-style).
 #
-
 # Modified from https://github.com/webdataset/webdataset
-
 """Train PyTorch models directly from POSIX tar archive.
 
 Code works locally or over HTTP connections.
 """
-
-import os, random, sys, time
-from dataclasses import dataclass, field
+import os
+import random
+import sys
+import time
+from dataclasses import dataclass
+from dataclasses import field
 from itertools import islice
 from typing import List
 
-import braceexpand, yaml
+import braceexpand
+import yaml
 
 from . import utils
+from ..utils.log import Logger
 from .filters import pipelinefilter
 from .paddle_utils import IterableDataset
+logger = Logger(__name__)
 
 
-from ..utils.log import Logger
-logger = Logger(__name__)
 def expand_urls(urls):
     if isinstance(urls, str):
         urllist = urls.split("::")
@@ -64,7 +66,8 @@ class SimpleShardList(IterableDataset):
 
 
 def split_by_node(src, group=None):
-    rank, world_size, worker, num_workers = utils.paddle_worker_info(group=group)
+    rank, world_size, worker, num_workers = utils.paddle_worker_info(
+        group=group)
     logger.info(f"world_size:{world_size}, rank:{rank}")
     if world_size > 1:
         for s in islice(src, rank, None, world_size):
@@ -75,9 +78,11 @@ def split_by_node(src, group=None):
 
 
 def single_node_only(src, group=None):
-    rank, world_size, worker, num_workers = utils.paddle_worker_info(group=group)
+    rank, world_size, worker, num_workers = utils.paddle_worker_info(
+        group=group)
     if world_size > 1:
-        raise ValueError("input pipeline needs to be reconfigured for multinode training")
+        raise ValueError(
+            "input pipeline needs to be reconfigured for multinode training")
     for s in src:
         yield s
 
@@ -104,7 +109,8 @@ def resampled_(src, n=sys.maxsize):
     rng = random.Random(seed)
     print("# resampled loading", file=sys.stderr)
     items = list(src)
-    print(f"# resampled got {len(items)} samples, yielding {n}", file=sys.stderr)
+    print(
+        f"# resampled got {len(items)} samples, yielding {n}", file=sys.stderr)
     for i in range(n):
         yield rng.choice(items)
 
@@ -118,7 +124,9 @@ def non_empty(src):
         yield s
         count += 1
     if count == 0:
-        raise ValueError("pipeline stage received no data at all and this was declared as an error")
+        raise ValueError(
+            "pipeline stage received no data at all and this was declared as an error"
+        )
 
 
 @dataclass
@@ -138,10 +146,6 @@ def expand(s):
     return os.path.expanduser(os.path.expandvars(s))
 
 
-class MultiShardSample(IterableDataset):
-    def __init__(self, fname):
-        """Construct a shardlist from multiple sources using a YAML spec."""
-        self.epoch = -1
 class MultiShardSample(IterableDataset):
     def __init__(self, fname):
         """Construct a shardlist from multiple sources using a YAML spec."""
@@ -156,20 +160,23 @@ class MultiShardSample(IterableDataset):
         else:
             with open(fname) as stream:
                 spec = yaml.safe_load(stream)
-        assert set(spec.keys()).issubset(set("prefix datasets buckets".split())), list(spec.keys())
+        assert set(spec.keys()).issubset(
+            set("prefix datasets buckets".split())), list(spec.keys())
         prefix = expand(spec.get("prefix", ""))
         self.sources = []
         for ds in spec["datasets"]:
-            assert set(ds.keys()).issubset(set("buckets name shards resample choose".split())), list(
-                ds.keys()
-            )
+            assert set(ds.keys()).issubset(
+                set("buckets name shards resample choose".split())), list(
+                    ds.keys())
             buckets = ds.get("buckets", spec.get("buckets", []))
             if isinstance(buckets, str):
                 buckets = [buckets]
             buckets = [expand(s) for s in buckets]
             if buckets == []:
                 buckets = [""]
-            assert len(buckets) == 1, f"{buckets}: FIXME support for multiple buckets unimplemented"
+            assert len(
+                buckets
+            ) == 1, f"{buckets}: FIXME support for multiple buckets unimplemented"
             bucket = buckets[0]
             name = ds.get("name", "@" + bucket)
             urls = ds["shards"]
@@ -177,15 +184,19 @@ class MultiShardSample(IterableDataset):
                 urls = [urls]
             # urls = [u for url in urls for u in braceexpand.braceexpand(url)]
             urls = [
-                prefix + os.path.join(bucket, u) for url in urls for u in braceexpand.braceexpand(expand(url))
+                prefix + os.path.join(bucket, u)
+                for url in urls for u in braceexpand.braceexpand(expand(url))
             ]
             resample = ds.get("resample", -1)
             nsample = ds.get("choose", -1)
             if nsample > len(urls):
-                raise ValueError(f"perepoch {nsample} must be no greater than the number of shards")
+                raise ValueError(
+                    f"perepoch {nsample} must be no greater than the number of shards"
+                )
             if (nsample > 0) and (resample > 0):
                 raise ValueError("specify only one of perepoch or choose")
-            entry = MSSource(name=name, urls=urls, perepoch=nsample, resample=resample)
+            entry = MSSource(
+                name=name, urls=urls, perepoch=nsample, resample=resample)
             self.sources.append(entry)
             print(f"# {name} {len(urls)} {nsample}", file=sys.stderr)
 
@@ -203,7 +214,7 @@ class MultiShardSample(IterableDataset):
                 # sample without replacement
                 l = list(source.urls)
                 self.rng.shuffle(l)
-                l = l[: source.perepoch]
+                l = l[:source.perepoch]
             else:
                 l = list(source.urls)
             result += l
@@ -227,12 +238,11 @@ class ResampledShards(IterableDataset):
     """An iterable dataset yielding a list of urls."""
 
     def __init__(
-        self,
-        urls,
-        nshards=sys.maxsize,
-        worker_seed=None,
-        deterministic=False,
-    ):
+            self,
+            urls,
+            nshards=sys.maxsize,
+            worker_seed=None,
+            deterministic=False, ):
         """Sample shards from the shard list with replacement.
 
         :param urls: a list of URLs as a Python list or brace notation string
@@ -252,7 +262,8 @@ class ResampledShards(IterableDataset):
         if self.deterministic:
             seed = utils.make_seed(self.worker_seed(), self.epoch)
         else:
-            seed = utils.make_seed(self.worker_seed(), self.epoch, os.getpid(), time.time_ns(), os.urandom(4))
+            seed = utils.make_seed(self.worker_seed(), self.epoch,
+                                   os.getpid(), time.time_ns(), os.urandom(4))
         if os.environ.get("WDS_SHOW_SEED", "0") == "1":
             print(f"# ResampledShards seed {seed}")
         self.rng = random.Random(seed)

paddlespeech/audio/streamdata/tariterators.py

@@ -3,13 +3,12 @@
 # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
 # This file is part of the WebDataset library.
 # See the LICENSE file for licensing terms (BSD-style).
-
 # Modified from https://github.com/webdataset/webdataset
 # Modified from wenet(https://github.com/wenet-e2e/wenet)
-
 """Low level iteration functions for tar archives."""
-
-import random, re, tarfile
+import random
+import re
+import tarfile
 
 import braceexpand
 
@@ -27,6 +26,7 @@ import numpy as np
 
 AUDIO_FORMAT_SETS = set(['flac', 'mp3', 'm4a', 'ogg', 'opus', 'wav', 'wma'])
 
+
 def base_plus_ext(path):
     """Split off all file extensions.
 
@@ -47,12 +47,8 @@ def valid_sample(sample):
 
     :param sample: sample to be checked
     """
-    return (
-        sample is not None
-        and isinstance(sample, dict)
-        and len(list(sample.keys())) > 0
-        and not sample.get("__bad__", False)
-    )
+    return (sample is not None and isinstance(sample, dict) and
+            len(list(sample.keys())) > 0 and not sample.get("__bad__", False))
 
 
 # FIXME: UNUSED
@@ -79,16 +75,16 @@ def url_opener(data, handler=reraise_exception, **kw):
             sample.update(stream=stream)
             yield sample
         except Exception as exn:
-            exn.args = exn.args + (url,)
+            exn.args = exn.args + (url, )
             if handler(exn):
                 continue
             else:
                 break
 
 
-def tar_file_iterator(
-    fileobj, skip_meta=r"__[^/]*__($|/)", handler=reraise_exception
-):
+def tar_file_iterator(fileobj,
+                      skip_meta=r"__[^/]*__($|/)",
+                      handler=reraise_exception):
     """Iterate over tar file, yielding filename, content pairs for the given tar stream.
 
     :param fileobj: byte stream suitable for tarfile
@@ -103,11 +99,8 @@ def tar_file_iterator(
                 continue
             if fname is None:
                 continue
-            if (
-                "/" not in fname
-                and fname.startswith(meta_prefix)
-                and fname.endswith(meta_suffix)
-            ):
+            if ("/" not in fname and fname.startswith(meta_prefix) and
+                    fname.endswith(meta_suffix)):
                 # skipping metadata for now
                 continue
             if skip_meta is not None and re.match(skip_meta, fname):
@@ -118,8 +111,10 @@ def tar_file_iterator(
             assert pos > 0
             prefix, postfix = name[:pos], name[pos + 1:]
             if postfix == 'wav':
-                waveform, sample_rate = paddlespeech.audio.load(stream.extractfile(tarinfo), normal=False)
-                result = dict(fname=prefix, wav=waveform, sample_rate = sample_rate)
+                waveform, sample_rate = paddlespeech.audio.load(
+                    stream.extractfile(tarinfo), normal=False)
+                result = dict(
+                    fname=prefix, wav=waveform, sample_rate=sample_rate)
             else:
                 txt = stream.extractfile(tarinfo).read().decode('utf8').strip()
                 result = dict(fname=prefix, txt=txt)
@@ -128,16 +123,17 @@ def tar_file_iterator(
             stream.members = []
         except Exception as exn:
             if hasattr(exn, "args") and len(exn.args) > 0:
-                exn.args = (exn.args[0] + " @ " + str(fileobj),) + exn.args[1:]
+                exn.args = (exn.args[0] + " @ " + str(fileobj), ) + exn.args[1:]
             if handler(exn):
                 continue
             else:
                 break
     del stream
 
-def tar_file_and_group_iterator(
-    fileobj, skip_meta=r"__[^/]*__($|/)", handler=reraise_exception
-):
+
+def tar_file_and_group_iterator(fileobj,
+                                skip_meta=r"__[^/]*__($|/)",
+                                handler=reraise_exception):
     """ Expand a stream of open tar files into a stream of tar file contents.
         And groups the file with same prefix
 
@@ -167,8 +163,11 @@ def tar_file_and_group_iterator(
                 if postfix == 'txt':
                     example['txt'] = file_obj.read().decode('utf8').strip()
                 elif postfix in AUDIO_FORMAT_SETS:
-                    waveform, sample_rate = paddlespeech.audio.load(file_obj, normal=False)
-                    waveform = paddle.to_tensor(np.expand_dims(np.array(waveform),0), dtype=paddle.float32)
+                    waveform, sample_rate = paddlespeech.audio.load(
+                        file_obj, normal=False)
+                    waveform = paddle.to_tensor(
+                        np.expand_dims(np.array(waveform), 0),
+                        dtype=paddle.float32)
 
                     example['wav'] = waveform
                     example['sample_rate'] = sample_rate
@@ -176,19 +175,21 @@ def tar_file_and_group_iterator(
                     example[postfix] = file_obj.read()
             except Exception as exn:
                 if hasattr(exn, "args") and len(exn.args) > 0:
-                    exn.args = (exn.args[0] + " @ " + str(fileobj),) + exn.args[1:]
+                    exn.args = (exn.args[0] + " @ " + str(fileobj),
+                                ) + exn.args[1:]
                 if handler(exn):
                     continue
                 else:
                     break
                 valid = False
-              #  logging.warning('error to parse {}'.format(name))
+            #  logging.warning('error to parse {}'.format(name))
         prev_prefix = prefix
     if prev_prefix is not None:
         example['fname'] = prev_prefix
         yield example
     stream.close()
 
+
 def tar_file_expander(data, handler=reraise_exception):
     """Expand a stream of open tar files into a stream of tar file contents.
 
@@ -200,9 +201,8 @@ def tar_file_expander(data, handler=reraise_exception):
             assert isinstance(source, dict)
             assert "stream" in source
             for sample in tar_file_iterator(source["stream"]):
-                assert (
-                    isinstance(sample, dict) and "data" in sample and "fname" in sample
-                )
+                assert (isinstance(sample, dict) and "data" in sample and
+                        "fname" in sample)
                 sample["__url__"] = url
                 yield sample
         except Exception as exn:
@@ -213,8 +213,6 @@ def tar_file_expander(data, handler=reraise_exception):
                 break
 
 
-
-
 def tar_file_and_group_expander(data, handler=reraise_exception):
     """Expand a stream of open tar files into a stream of tar file contents.
 
@@ -226,9 +224,8 @@ def tar_file_and_group_expander(data, handler=reraise_exception):
             assert isinstance(source, dict)
             assert "stream" in source
             for sample in tar_file_and_group_iterator(source["stream"]):
-                assert (
-                    isinstance(sample, dict) and "wav" in sample and "txt" in sample and "fname" in sample
-                )
+                assert (isinstance(sample, dict) and "wav" in sample and
+                        "txt" in sample and "fname" in sample)
                 sample["__url__"] = url
                 yield sample
         except Exception as exn:
@@ -239,7 +236,11 @@ def tar_file_and_group_expander(data, handler=reraise_exception):
                 break
 
 
-def group_by_keys(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None):
+def group_by_keys(data,
+                  keys=base_plus_ext,
+                  lcase=True,
+                  suffixes=None,
+                  handler=None):
     """Return function over iterator that groups key, value pairs into samples.
 
     :param keys: function that splits the key into key and extension (base_plus_ext)
@@ -254,8 +255,8 @@ def group_by_keys(data, keys=base_plus_ext, lcase=True, suffixes=None, handler=N
             print(
                 prefix,
                 suffix,
-                current_sample.keys() if isinstance(current_sample, dict) else None,
-            )
+                current_sample.keys()
+                if isinstance(current_sample, dict) else None, )
         if prefix is None:
             continue
         if lcase:

paddlespeech/audio/streamdata/utils.py

@@ -4,22 +4,23 @@
 # This file is part of the WebDataset library.
 # See the LICENSE file for licensing terms (BSD-style).
 #
-
 # Modified from https://github.com/webdataset/webdataset
-
 """Miscellaneous utility functions."""
-
 import importlib
 import itertools as itt
 import os
 import re
 import sys
-from typing import Any, Callable, Iterator, Optional, Union
+from typing import Any
+from typing import Callable
+from typing import Iterator
+from typing import Union
 
 from ..utils.log import Logger
 
 logger = Logger(__name__)
 
+
 def make_seed(*args):
     seed = 0
     for arg in args:
@@ -37,7 +38,7 @@ def identity(x: Any) -> Any:
     return x
 
 
-def safe_eval(s: str, expr: str = "{}"):
+def safe_eval(s: str, expr: str="{}"):
     """Evaluate the given expression more safely."""
     if re.sub("[^A-Za-z0-9_]", "", s) != s:
         raise ValueError(f"safe_eval: illegal characters in: '{s}'")
@@ -54,9 +55,9 @@ def lookup_sym(sym: str, modules: list):
     return None
 
 
-def repeatedly0(
-    loader: Iterator, nepochs: int = sys.maxsize, nbatches: int = sys.maxsize
-):
+def repeatedly0(loader: Iterator,
+                nepochs: int=sys.maxsize,
+                nbatches: int=sys.maxsize):
     """Repeatedly returns batches from a DataLoader."""
     for epoch in range(nepochs):
         for sample in itt.islice(loader, nbatches):
@@ -69,12 +70,11 @@ def guess_batchsize(batch: Union[tuple, list]):
 
 
 def repeatedly(
-    source: Iterator,
-    nepochs: int = None,
-    nbatches: int = None,
-    nsamples: int = None,
-    batchsize: Callable[..., int] = guess_batchsize,
-):
+        source: Iterator,
+        nepochs: int=None,
+        nbatches: int=None,
+        nsamples: int=None,
+        batchsize: Callable[..., int]=guess_batchsize, ):
     """Repeatedly yield samples from an iterator."""
     epoch = 0
     batch = 0
@@ -93,6 +93,7 @@ def repeatedly(
         if nepochs is not None and epoch >= nepochs:
             return
 
+
 def paddle_worker_info(group=None):
     """Return node and worker info for PyTorch and some distributed environments."""
     rank = 0
@@ -116,7 +117,7 @@ def paddle_worker_info(group=None):
     else:
         try:
             from paddle.io import get_worker_info
-            worker_info = paddle.io.get_worker_info()
+            worker_info = get_worker_info()
             if worker_info is not None:
                 worker = worker_info.id
                 num_workers = worker_info.num_workers
@@ -126,6 +127,7 @@ def paddle_worker_info(group=None):
 
     return rank, world_size, worker, num_workers
 
+
 def paddle_worker_seed(group=None):
     """Compute a distinct, deterministic RNG seed for each worker and node."""
     rank, world_size, worker, num_workers = paddle_worker_info(group=group)

paddlespeech/audio/streamdata/writer.py

@@ -5,18 +5,24 @@
 # See the LICENSE file for licensing terms (BSD-style).
 # Modified from https://github.com/webdataset/webdataset
 #
-
 """Classes and functions for writing tar files and WebDataset files."""
-
-import io, json, pickle, re, tarfile, time
-from typing import Any, Callable, Optional, Union
+import io
+import json
+import pickle
+import re
+import tarfile
+import time
+from typing import Any
+from typing import Callable
+from typing import Optional
+from typing import Union
 
 import numpy as np
 
 from . import gopen
 
 
-def imageencoder(image: Any, format: str = "PNG"):  # skipcq: PYL-W0622
+def imageencoder(image: Any, format: str="PNG"):  # skipcq: PYL-W0622
     """Compress an image using PIL and return it as a string.
 
     Can handle float or uint8 images.
@@ -67,6 +73,7 @@ def bytestr(data: Any):
         return data.encode("ascii")
     return str(data).encode("ascii")
 
+
 def paddle_dumps(data: Any):
     """Dump data into a bytestring using paddle.dumps.
 
@@ -82,6 +89,7 @@ def paddle_dumps(data: Any):
     paddle.save(data, stream)
     return stream.getvalue()
 
+
 def numpy_dumps(data: np.ndarray):
     """Dump data into a bytestring using numpy npy format.
 
@@ -139,9 +147,8 @@ def add_handlers(d, keys, value):
 def make_handlers():
     """Create a list of handlers for encoding data."""
     handlers = {}
-    add_handlers(
-        handlers, "cls cls2 class count index inx id", lambda x: str(x).encode("ascii")
-    )
+    add_handlers(handlers, "cls cls2 class count index inx id",
+                 lambda x: str(x).encode("ascii"))
     add_handlers(handlers, "txt text transcript", lambda x: x.encode("utf-8"))
     add_handlers(handlers, "html htm", lambda x: x.encode("utf-8"))
     add_handlers(handlers, "pyd pickle", pickle.dumps)
@@ -152,7 +159,8 @@ def make_handlers():
     add_handlers(handlers, "json jsn", lambda x: json.dumps(x).encode("utf-8"))
     add_handlers(handlers, "mp msgpack msg", mp_dumps)
     add_handlers(handlers, "cbor", cbor_dumps)
-    add_handlers(handlers, "jpg jpeg img image", lambda data: imageencoder(data, "jpg"))
+    add_handlers(handlers, "jpg jpeg img image",
+                 lambda data: imageencoder(data, "jpg"))
     add_handlers(handlers, "png", lambda data: imageencoder(data, "png"))
     add_handlers(handlers, "pbm", lambda data: imageencoder(data, "pbm"))
     add_handlers(handlers, "pgm", lambda data: imageencoder(data, "pgm"))
@@ -192,7 +200,8 @@ def encode_based_on_extension(sample: dict, handlers: dict):
     :param handlers: handlers for encoding
     """
     return {
-        k: encode_based_on_extension1(v, k, handlers) for k, v in list(sample.items())
+        k: encode_based_on_extension1(v, k, handlers)
+        for k, v in list(sample.items())
     }
 
 
@@ -258,15 +267,14 @@ class TarWriter:
     """
 
     def __init__(
-        self,
-        fileobj,
-        user: str = "bigdata",
-        group: str = "bigdata",
-        mode: int = 0o0444,
-        compress: Optional[bool] = None,
-        encoder: Union[None, bool, Callable] = True,
-        keep_meta: bool = False,
-    ):
+            self,
+            fileobj,
+            user: str="bigdata",
+            group: str="bigdata",
+            mode: int=0o0444,
+            compress: Optional[bool]=None,
+            encoder: Union[None, bool, Callable]=True,
+            keep_meta: bool=False, ):
         """Create a tar writer.
 
         :param fileobj: stream to write data to
@@ -330,8 +338,7 @@ class TarWriter:
                 continue
             if not isinstance(v, (bytes, bytearray, memoryview)):
                 raise ValueError(
-                    f"{k} doesn't map to a bytes after encoding ({type(v)})"
-                )
+                    f"{k} doesn't map to a bytes after encoding ({type(v)})")
         key = obj["__key__"]
         for k in sorted(obj.keys()):
             if k == "__key__":
@@ -349,7 +356,8 @@ class TarWriter:
             ti.uname = self.user
             ti.gname = self.group
             if not isinstance(v, (bytes, bytearray, memoryview)):
-                raise ValueError(f"converter didn't yield bytes: {k}, {type(v)}")
+                raise ValueError(
+                    f"converter didn't yield bytes: {k}, {type(v)}")
             stream = io.BytesIO(v)
             self.tarstream.addfile(ti, stream)
             total += ti.size
@@ -360,14 +368,13 @@ class ShardWriter:
     """Like TarWriter but splits into multiple shards."""
 
     def __init__(
-        self,
-        pattern: str,
-        maxcount: int = 100000,
-        maxsize: float = 3e9,
-        post: Optional[Callable] = None,
-        start_shard: int = 0,
-        **kw,
-    ):
+            self,
+            pattern: str,
+            maxcount: int=100000,
+            maxsize: float=3e9,
+            post: Optional[Callable]=None,
+            start_shard: int=0,
+            **kw, ):
         """Create a ShardWriter.
 
         :param pattern: output file pattern
@@ -400,8 +407,7 @@ class ShardWriter:
                 self.fname,
                 self.count,
                 "%.1f GB" % (self.size / 1e9),
-                self.total,
-            )
+                self.total, )
         self.shard += 1
         stream = open(self.fname, "wb")
         self.tarstream = TarWriter(stream, **self.kw)
@@ -413,11 +419,8 @@ class ShardWriter:
 
         :param obj: sample to be written
         """
-        if (
-            self.tarstream is None
-            or self.count >= self.maxcount
-            or self.size >= self.maxsize
-        ):
+        if (self.tarstream is None or self.count >= self.maxcount or
+                self.size >= self.maxsize):
             self.next_stream()
         size = self.tarstream.write(obj)
         self.count += 1

paddlespeech/audio/text/text_featurizer.py

@@ -17,6 +17,7 @@ from typing import Union
 
 import sentencepiece as spm
 
+from ..utils.log import Logger
 from .utility import BLANK
 from .utility import EOS
 from .utility import load_dict
@@ -24,7 +25,6 @@ from .utility import MASKCTC
 from .utility import SOS
 from .utility import SPACE
 from .utility import UNK
-from ..utils.log import Logger
 
 logger = Logger(__name__)
 

paddlespeech/audio/transform/perturb.py

@@ -12,15 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # Modified from espnet(https://github.com/espnet/espnet)
+import io
+import os
+
+import h5py
 import librosa
 import numpy
+import numpy as np
 import scipy
 import soundfile
 
-import io
-import os
-import h5py
-import numpy as np
 
 class SoundHDF5File():
     """Collecting sound files to a HDF5 file
@@ -109,6 +110,7 @@ class SoundHDF5File():
     def close(self):
         self.file.close()
 
+
 class SpeedPerturbation():
     """SpeedPerturbation
 
@@ -558,4 +560,3 @@ class RIRConvolve():
                 [scipy.convolve(x, r, mode="same") for r in rir], axis=-1)
         else:
             return scipy.convolve(x, rir, mode="same")
-

paddlespeech/audio/transform/spec_augment.py

@@ -14,6 +14,7 @@
 # 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
 

paddlespeech/cli/executor.py

@@ -191,7 +191,7 @@ class BaseExecutor(ABC):
                 line = line.strip()
                 if not line:
                     continue
-                k, v = line.split() # space or \t
+                k, v = line.split()  # space or \t
                 job_contents[k] = v
         return job_contents
 

paddlespeech/s2t/__init__.py

@@ -114,6 +114,7 @@ if not hasattr(paddle.Tensor, 'new_full'):
     paddle.Tensor.new_full = new_full
     paddle.static.Variable.new_full = new_full
 
+
 def contiguous(xs: paddle.Tensor) -> paddle.Tensor:
     return xs
 

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

@@ -20,8 +20,8 @@ import paddle
 import soundfile
 from yacs.config import CfgNode
 
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
-from paddlespeech.s2t.io.collator import SpeechCollator
 from paddlespeech.s2t.models.ds2 import DeepSpeech2Model
 from paddlespeech.s2t.training.cli import default_argument_parser
 from paddlespeech.s2t.utils import mp_tools
@@ -38,24 +38,24 @@ class DeepSpeech2Tester_hub():
         self.args = args
         self.config = config
         self.audio_file = args.audio_file
-        self.collate_fn_test = SpeechCollator.from_config(config)
-        self._text_featurizer = TextFeaturizer(
-            unit_type=config.unit_type, vocab=None)
 
-    def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
-        result_transcripts = self.model.decode(
-            audio,
-            audio_len,
-            vocab_list,
-            decoding_method=cfg.decoding_method,
-            lang_model_path=cfg.lang_model_path,
-            beam_alpha=cfg.alpha,
-            beam_beta=cfg.beta,
-            beam_size=cfg.beam_size,
-            cutoff_prob=cfg.cutoff_prob,
-            cutoff_top_n=cfg.cutoff_top_n,
-            num_processes=cfg.num_proc_bsearch)
+        self.preprocess_conf = config.preprocess_config
+        self.preprocess_args = {"train": False}
+        self.preprocessing = Transformation(self.preprocess_conf)
+
+        self.text_feature = TextFeaturizer(
+            unit_type=config.unit_type,
+            vocab=config.vocab_filepath,
+            spm_model_prefix=config.spm_model_prefix)
+        paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
 
+    def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
+        decode_batch_size = cfg.decode_batch_size
+        self.model.decoder.init_decoder(
+            decode_batch_size, vocab_list, cfg.decoding_method,
+            cfg.lang_model_path, cfg.alpha, cfg.beta, cfg.beam_size,
+            cfg.cutoff_prob, cfg.cutoff_top_n, cfg.num_proc_bsearch)
+        result_transcripts = self.model.decode(audio, audio_len)
         return result_transcripts
 
     @mp_tools.rank_zero_only
@@ -64,16 +64,23 @@ class DeepSpeech2Tester_hub():
         self.model.eval()
         cfg = self.config
         audio_file = self.audio_file
-        collate_fn_test = self.collate_fn_test
-        audio, _ = collate_fn_test.process_utterance(
-            audio_file=audio_file, transcript=" ")
-        audio_len = audio.shape[0]
-        audio = paddle.to_tensor(audio, dtype='float32')
-        audio_len = paddle.to_tensor(audio_len)
-        audio = paddle.unsqueeze(audio, axis=0)
-        vocab_list = collate_fn_test.vocab_list
+
+        audio, sample_rate = soundfile.read(
+            self.audio_file, dtype="int16", always_2d=True)
+
+        audio = audio[:, 0]
+        logger.info(f"audio shape: {audio.shape}")
+
+        # fbank
+        feat = self.preprocessing(audio, **self.preprocess_args)
+        logger.info(f"feat shape: {feat.shape}")
+
+        audio_len = paddle.to_tensor(feat.shape[0])
+        audio = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0)
+
         result_transcripts = self.compute_result_transcripts(
-            audio, audio_len, vocab_list, cfg.decode)
+            audio, audio_len, self.text_feature.vocab_list, cfg.decode)
+
         logger.info("result_transcripts: " + result_transcripts[0])
 
     def run_test(self):
@@ -109,11 +116,9 @@ class DeepSpeech2Tester_hub():
     def setup_model(self):
         config = self.config.clone()
         with UpdateConfig(config):
-            config.input_dim = self.collate_fn_test.feature_size
-            config.output_dim = self.collate_fn_test.vocab_size
-
+            config.input_dim = config.feat_dim
+            config.output_dim = self.text_feature.vocab_size
         model = DeepSpeech2Model.from_config(config)
-
         self.model = model
 
     def setup_checkpointer(self):

paddlespeech/s2t/exps/u2/model.py

@@ -25,8 +25,6 @@ import paddle
 from paddle import distributed as dist
 
 from paddlespeech.s2t.frontend.featurizer import TextFeaturizer
-from paddlespeech.s2t.io.dataloader import BatchDataLoader
-from paddlespeech.s2t.io.dataloader import StreamDataLoader
 from paddlespeech.s2t.io.dataloader import DataLoaderFactory
 from paddlespeech.s2t.models.u2 import U2Model
 from paddlespeech.s2t.training.optimizer import OptimizerFactory
@@ -109,7 +107,8 @@ class U2Trainer(Trainer):
     def valid(self):
         self.model.eval()
         if not self.use_streamdata:
-            logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
+            logger.info(
+                f"Valid Total Examples: {len(self.valid_loader.dataset)}")
         valid_losses = defaultdict(list)
         num_seen_utts = 1
         total_loss = 0.0
@@ -136,7 +135,8 @@ class U2Trainer(Trainer):
                 msg += "epoch: {}, ".format(self.epoch)
                 msg += "step: {}, ".format(self.iteration)
                 if not self.use_streamdata:
-                    msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
+                    msg += "batch: {}/{}, ".format(i + 1,
+                                                   len(self.valid_loader))
                 msg += ', '.join('{}: {:>.6f}'.format(k, v)
                                  for k, v in valid_dump.items())
                 logger.info(msg)
@@ -157,7 +157,8 @@ class U2Trainer(Trainer):
         self.before_train()
 
         if not self.use_streamdata:
-            logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
+            logger.info(
+                f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.n_epoch:
             with Timer("Epoch-Train Time Cost: {}"):
                 self.model.train()
@@ -225,14 +226,18 @@ class U2Trainer(Trainer):
         config = self.config.clone()
         self.use_streamdata = config.get("use_stream_data", False)
         if self.train:
-            self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args)
-            self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args)
+            self.train_loader = DataLoaderFactory.get_dataloader(
+                'train', config, self.args)
+            self.valid_loader = DataLoaderFactory.get_dataloader(
+                'valid', config, self.args)
             logger.info("Setup train/valid Dataloader!")
         else:
             decode_batch_size = config.get('decode', dict()).get(
                 'decode_batch_size', 1)
-            self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args)
-            self.align_loader = DataLoaderFactory.get_dataloader('align', config, self.args)
+            self.test_loader = DataLoaderFactory.get_dataloader('test', config,
+                                                                self.args)
+            self.align_loader = DataLoaderFactory.get_dataloader(
+                'align', config, self.args)
             logger.info("Setup test/align Dataloader!")
 
     def setup_model(self):

paddlespeech/s2t/exps/u2_kaldi/model.py

@@ -105,7 +105,8 @@ class U2Trainer(Trainer):
     def valid(self):
         self.model.eval()
         if not self.use_streamdata:
-            logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
+            logger.info(
+                f"Valid Total Examples: {len(self.valid_loader.dataset)}")
         valid_losses = defaultdict(list)
         num_seen_utts = 1
         total_loss = 0.0
@@ -133,7 +134,8 @@ class U2Trainer(Trainer):
                 msg += "epoch: {}, ".format(self.epoch)
                 msg += "step: {}, ".format(self.iteration)
                 if not self.use_streamdata:
-                    msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
+                    msg += "batch: {}/{}, ".format(i + 1,
+                                                   len(self.valid_loader))
                 msg += ', '.join('{}: {:>.6f}'.format(k, v)
                                  for k, v in valid_dump.items())
                 logger.info(msg)
@@ -153,7 +155,8 @@ class U2Trainer(Trainer):
 
         self.before_train()
         if not self.use_streamdata:
-            logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
+            logger.info(
+                f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.n_epoch:
             with Timer("Epoch-Train Time Cost: {}"):
                 self.model.train()
@@ -165,8 +168,8 @@ class U2Trainer(Trainer):
                         msg += "epoch: {}, ".format(self.epoch)
                         msg += "step: {}, ".format(self.iteration)
                         if not self.use_streamdata:
-                            msg += "batch : {}/{}, ".format(batch_index + 1,
-                                                        len(self.train_loader))
+                            msg += "batch : {}/{}, ".format(
+                                batch_index + 1, len(self.train_loader))
                         msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
                         msg += "data time: {:>.3f}s, ".format(dataload_time)
                         self.train_batch(batch_index, batch, msg)
@@ -204,21 +207,24 @@ class U2Trainer(Trainer):
         self.use_streamdata = config.get("use_stream_data", False)
         if self.train:
             config = self.config.clone()
-            self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args)
+            self.train_loader = DataLoaderFactory.get_dataloader(
+                'train', config, self.args)
             config = self.config.clone()
             config['preprocess_config'] = None
-            self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args)
+            self.valid_loader = DataLoaderFactory.get_dataloader(
+                'valid', config, self.args)
             logger.info("Setup train/valid Dataloader!")
         else:
             config = self.config.clone()
             config['preprocess_config'] = None
-            self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args)
+            self.test_loader = DataLoaderFactory.get_dataloader('test', config,
+                                                                self.args)
             config = self.config.clone()
             config['preprocess_config'] = None
-            self.align_loader = DataLoaderFactory.get_dataloader('align', config, self.args)
+            self.align_loader = DataLoaderFactory.get_dataloader(
+                'align', config, self.args)
             logger.info("Setup test/align Dataloader!")
 
-
     def setup_model(self):
         config = self.config
 

paddlespeech/s2t/exps/u2_st/model.py

@@ -121,7 +121,8 @@ class U2STTrainer(Trainer):
     def valid(self):
         self.model.eval()
         if not self.use_streamdata:
-            logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
+            logger.info(
+                f"Valid Total Examples: {len(self.valid_loader.dataset)}")
         valid_losses = defaultdict(list)
         num_seen_utts = 1
         total_loss = 0.0
@@ -155,7 +156,8 @@ class U2STTrainer(Trainer):
                 msg += "epoch: {}, ".format(self.epoch)
                 msg += "step: {}, ".format(self.iteration)
                 if not self.use_streamdata:
-                    msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
+                    msg += "batch: {}/{}, ".format(i + 1,
+                                                   len(self.valid_loader))
                 msg += ', '.join('{}: {:>.6f}'.format(k, v)
                                  for k, v in valid_dump.items())
                 logger.info(msg)
@@ -175,7 +177,8 @@ class U2STTrainer(Trainer):
 
         self.before_train()
         if not self.use_streamdata:
-            logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
+            logger.info(
+                f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.n_epoch:
             with Timer("Epoch-Train Time Cost: {}"):
                 self.model.train()
@@ -248,14 +251,16 @@ class U2STTrainer(Trainer):
         config['load_transcript'] = load_transcript
         self.use_streamdata = config.get("use_stream_data", False)
         if self.train:
-            self.train_loader = DataLoaderFactory.get_dataloader('train', config, self.args)
-            self.valid_loader = DataLoaderFactory.get_dataloader('valid', config, self.args)
+            self.train_loader = DataLoaderFactory.get_dataloader(
+                'train', config, self.args)
+            self.valid_loader = DataLoaderFactory.get_dataloader(
+                'valid', config, self.args)
             logger.info("Setup train/valid Dataloader!")
         else:
-            self.test_loader = DataLoaderFactory.get_dataloader('test', config, self.args)
+            self.test_loader = DataLoaderFactory.get_dataloader('test', config,
+                                                                self.args)
             logger.info("Setup test Dataloader!")
 
-
     def setup_model(self):
         config = self.config
         model_conf = config

paddlespeech/s2t/io/dataloader.py

@@ -22,17 +22,16 @@ import paddle
 from paddle.io import BatchSampler
 from paddle.io import DataLoader
 from paddle.io import DistributedBatchSampler
+from yacs.config import CfgNode
 
+import paddlespeech.audio.streamdata as streamdata
+from paddlespeech.audio.text.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.io.batchfy import make_batchset
 from paddlespeech.s2t.io.converter import CustomConverter
 from paddlespeech.s2t.io.dataset import TransformDataset
 from paddlespeech.s2t.io.reader import LoadInputsAndTargets
 from paddlespeech.s2t.utils.log import Log
 
-import paddlespeech.audio.streamdata as streamdata
-from paddlespeech.audio.text.text_featurizer import TextFeaturizer
-from yacs.config import CfgNode
-
 __all__ = ["BatchDataLoader", "StreamDataLoader"]
 
 logger = Log(__name__).getlog()
@@ -61,6 +60,7 @@ def batch_collate(x):
     """
     return x[0]
 
+
 def read_preprocess_cfg(preprocess_conf_file):
     augment_conf = dict()
     preprocess_cfg = CfgNode(new_allowed=True)
@@ -82,7 +82,8 @@ def read_preprocess_cfg(preprocess_conf_file):
             augment_conf['num_t_mask'] = process['n_mask']
             augment_conf['t_inplace'] = process['inplace']
             augment_conf['t_replace_with_zero'] = process['replace_with_zero']
-    return augment_conf 
+    return augment_conf
+
 
 class StreamDataLoader():
     def __init__(self,
@@ -95,12 +96,12 @@ class StreamDataLoader():
                  frame_length=25,
                  frame_shift=10,
                  dither=0.0,
-                 minlen_in: float=0.0, 
+                 minlen_in: float=0.0,
                  maxlen_in: float=float('inf'),
                  minlen_out: float=0.0,
                  maxlen_out: float=float('inf'),
                  resample_rate: int=16000,
-                 shuffle_size: int=10000, 
+                 shuffle_size: int=10000,
                  sort_size: int=1000,
                  n_iter_processes: int=1,
                  prefetch_factor: int=2,
@@ -116,11 +117,11 @@ class StreamDataLoader():
 
         text_featurizer = TextFeaturizer(unit_type, vocab_filepath)
         symbol_table = text_featurizer.vocab_dict
-        self.feat_dim = num_mel_bins 
-        self.vocab_size = text_featurizer.vocab_size 
-        
+        self.feat_dim = num_mel_bins
+        self.vocab_size = text_featurizer.vocab_size
+
         augment_conf = read_preprocess_cfg(preprocess_conf)
-        
+
         # The list of shard
         shardlist = []
         with open(manifest_file, "r") as f:
@@ -128,58 +129,68 @@ class StreamDataLoader():
                 shardlist.append(line.strip())
         world_size = 1
         try:
-            world_size = paddle.distributed.get_world_size() 
+            world_size = paddle.distributed.get_world_size()
         except Exception as e:
             logger.warninig(e)
-            logger.warninig("can not get world_size using paddle.distributed.get_world_size(), use world_size=1")
-        assert(len(shardlist) >= world_size, "the length of shard list should >= number of gpus/xpus/...")
+            logger.warninig(
+                "can not get world_size using paddle.distributed.get_world_size(), use world_size=1"
+            )
+        assert len(shardlist) >= world_size, \
+            "the length of shard list should >= number of gpus/xpus/..."
 
-        update_n_iter_processes = int(max(min(len(shardlist)/world_size - 1, self.n_iter_processes), 0))
+        update_n_iter_processes = int(
+            max(min(len(shardlist) / world_size - 1, self.n_iter_processes), 0))
         logger.info(f"update_n_iter_processes {update_n_iter_processes}")
         if update_n_iter_processes != self.n_iter_processes:
-            self.n_iter_processes = update_n_iter_processes         
+            self.n_iter_processes = update_n_iter_processes
             logger.info(f"change nun_workers to {self.n_iter_processes}")
 
         if self.dist_sampler:
             base_dataset = streamdata.DataPipeline(
-                streamdata.SimpleShardList(shardlist),
-                streamdata.split_by_node if train_mode else streamdata.placeholder(),
+                streamdata.SimpleShardList(shardlist), streamdata.split_by_node
+                if train_mode else streamdata.placeholder(),
                 streamdata.split_by_worker,
-                streamdata.tarfile_to_samples(streamdata.reraise_exception)
-            )
+                streamdata.tarfile_to_samples(streamdata.reraise_exception))
         else:
             base_dataset = streamdata.DataPipeline(
                 streamdata.SimpleShardList(shardlist),
                 streamdata.split_by_worker,
-                streamdata.tarfile_to_samples(streamdata.reraise_exception)
-            )
+                streamdata.tarfile_to_samples(streamdata.reraise_exception))
 
         self.dataset = base_dataset.append_list(
             streamdata.audio_tokenize(symbol_table),
-            streamdata.audio_data_filter(frame_shift=frame_shift, max_length=maxlen_in, min_length=minlen_in, token_max_length=maxlen_out, token_min_length=minlen_out),
+            streamdata.audio_data_filter(
+                frame_shift=frame_shift,
+                max_length=maxlen_in,
+                min_length=minlen_in,
+                token_max_length=maxlen_out,
+                token_min_length=minlen_out),
             streamdata.audio_resample(resample_rate=resample_rate),
-            streamdata.audio_compute_fbank(num_mel_bins=num_mel_bins, frame_length=frame_length, frame_shift=frame_shift, dither=dither),
-            streamdata.audio_spec_aug(**augment_conf) if train_mode else streamdata.placeholder(),  # num_t_mask=2, num_f_mask=2, max_t=40, max_f=30, max_w=80)
+            streamdata.audio_compute_fbank(
+                num_mel_bins=num_mel_bins,
+                frame_length=frame_length,
+                frame_shift=frame_shift,
+                dither=dither),
+            streamdata.audio_spec_aug(**augment_conf)
+            if train_mode else streamdata.placeholder(
+            ),  # num_t_mask=2, num_f_mask=2, max_t=40, max_f=30, max_w=80)
             streamdata.shuffle(shuffle_size),
             streamdata.sort(sort_size=sort_size),
             streamdata.batched(batch_size),
             streamdata.audio_padding(),
-            streamdata.audio_cmvn(cmvn_file)
-        )
+            streamdata.audio_cmvn(cmvn_file))
 
         if paddle.__version__ >= '2.3.2':
             self.loader = streamdata.WebLoader(
-                self.dataset, 
-                num_workers=self.n_iter_processes, 
-                prefetch_factor = self.prefetch_factor, 
-                batch_size=None
-            )
+                self.dataset,
+                num_workers=self.n_iter_processes,
+                prefetch_factor=self.prefetch_factor,
+                batch_size=None)
         else:
             self.loader = streamdata.WebLoader(
-                self.dataset, 
-                num_workers=self.n_iter_processes, 
-                batch_size=None
-            )
+                self.dataset,
+                num_workers=self.n_iter_processes,
+                batch_size=None)
 
     def __iter__(self):
         return self.loader.__iter__()
@@ -188,7 +199,9 @@ class StreamDataLoader():
         return self.__iter__()
 
     def __len__(self):
-        logger.info("Stream dataloader does not support calculate the length of the dataset")
+        logger.info(
+            "Stream dataloader does not support calculate the length of the dataset"
+        )
         return -1
 
 
@@ -347,7 +360,7 @@ class DataLoaderFactory():
                 config['train_mode'] = True
             elif mode == 'valid':
                 config['manifest'] = config.dev_manifest
-                config['train_mode'] = False  
+                config['train_mode'] = False
             elif model == 'test' or mode == 'align':
                 config['manifest'] = config.test_manifest
                 config['train_mode'] = False
@@ -358,30 +371,31 @@ class DataLoaderFactory():
                 config['maxlen_out'] = float('inf')
                 config['dist_sampler'] = False
             else:
-                raise KeyError("not valid mode type!!, please input one of 'train, valid, test, align'")
-            return StreamDataLoader(
-                    manifest_file=config.manifest,
-                    train_mode=config.train_mode,
-                    unit_type=config.unit_type,
-                    preprocess_conf=config.preprocess_config,
-                    batch_size=config.batch_size,
-                    num_mel_bins=config.feat_dim,
-                    frame_length=config.window_ms,
-                    frame_shift=config.stride_ms,
-                    dither=config.dither,
-                    minlen_in=config.minlen_in,
-                    maxlen_in=config.maxlen_in,
-                    minlen_out=config.minlen_out,
-                    maxlen_out=config.maxlen_out,
-                    resample_rate=config.resample_rate,
-                    shuffle_size=config.shuffle_size, 
-                    sort_size=config.sort_size,
-                    n_iter_processes=config.num_workers,
-                    prefetch_factor=config.prefetch_factor,
-                    dist_sampler=config.dist_sampler,
-                    cmvn_file=config.cmvn_file,
-                    vocab_filepath=config.vocab_filepath,
+                raise KeyError(
+                    "not valid mode type!!, please input one of 'train, valid, test, align'"
                 )
+            return StreamDataLoader(
+                manifest_file=config.manifest,
+                train_mode=config.train_mode,
+                unit_type=config.unit_type,
+                preprocess_conf=config.preprocess_config,
+                batch_size=config.batch_size,
+                num_mel_bins=config.feat_dim,
+                frame_length=config.window_ms,
+                frame_shift=config.stride_ms,
+                dither=config.dither,
+                minlen_in=config.minlen_in,
+                maxlen_in=config.maxlen_in,
+                minlen_out=config.minlen_out,
+                maxlen_out=config.maxlen_out,
+                resample_rate=config.resample_rate,
+                shuffle_size=config.shuffle_size,
+                sort_size=config.sort_size,
+                n_iter_processes=config.num_workers,
+                prefetch_factor=config.prefetch_factor,
+                dist_sampler=config.dist_sampler,
+                cmvn_file=config.cmvn_file,
+                vocab_filepath=config.vocab_filepath, )
         else:
             if mode == 'train':
                 config['manifest'] = config.train_manifest
@@ -411,7 +425,7 @@ class DataLoaderFactory():
                 config['train_mode'] = False
                 config['sortagrad'] = False
                 config['batch_size'] = config.get('decode', dict()).get(
-                'decode_batch_size', 1)
+                    'decode_batch_size', 1)
                 config['maxlen_in'] = float('inf')
                 config['maxlen_out'] = float('inf')
                 config['minibatches'] = 0
@@ -427,8 +441,10 @@ class DataLoaderFactory():
                 config['dist_sampler'] = False
                 config['shortest_first'] = False
             else:
-                raise KeyError("not valid mode type!!, please input one of 'train, valid, test, align'")
-            
+                raise KeyError(
+                    "not valid mode type!!, please input one of 'train, valid, test, align'"
+                )
+
             return BatchDataLoader(
                 json_file=config.manifest,
                 train_mode=config.train_mode,
@@ -450,4 +466,3 @@ class DataLoaderFactory():
                 num_encs=config.num_encs,
                 dist_sampler=config.dist_sampler,
                 shortest_first=config.shortest_first)
-           

paddlespeech/s2t/models/u2/u2.py

@@ -605,8 +605,8 @@ class U2BaseModel(ASRInterface, nn.Layer):
             xs: paddle.Tensor,
             offset: int,
             required_cache_size: int,
-            att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
-            cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
+            att_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
+            cnn_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """ Export interface for c++ call, give input chunk xs, and return
             output from time 0 to current chunk.

paddlespeech/s2t/models/u2_st/u2_st.py

@@ -18,7 +18,6 @@ Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recogni
 """
 import time
 from typing import Dict
-from typing import List
 from typing import Optional
 from typing import Tuple
 
@@ -26,6 +25,8 @@ import paddle
 from paddle import jit
 from paddle import nn
 
+from paddlespeech.audio.utils.tensor_utils import add_sos_eos
+from paddlespeech.audio.utils.tensor_utils import th_accuracy
 from paddlespeech.s2t.frontend.utility import IGNORE_ID
 from paddlespeech.s2t.frontend.utility import load_cmvn
 from paddlespeech.s2t.modules.cmvn import GlobalCMVN
@@ -38,8 +39,6 @@ from paddlespeech.s2t.modules.mask import subsequent_mask
 from paddlespeech.s2t.utils import checkpoint
 from paddlespeech.s2t.utils import layer_tools
 from paddlespeech.s2t.utils.log import Log
-from paddlespeech.audio.utils.tensor_utils import add_sos_eos
-from paddlespeech.audio.utils.tensor_utils import th_accuracy
 from paddlespeech.s2t.utils.utility import UpdateConfig
 
 __all__ = ["U2STModel", "U2STInferModel"]
@@ -401,8 +400,8 @@ class U2STBaseModel(nn.Layer):
             xs: paddle.Tensor,
             offset: int,
             required_cache_size: int,
-            att_cache: paddle.Tensor = paddle.zeros([0, 0, 0, 0]),
-            cnn_cache: paddle.Tensor = paddle.zeros([0, 0, 0, 0]),
+            att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
+            cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """ Export interface for c++ call, give input chunk xs, and return
             output from time 0 to current chunk.
@@ -435,8 +434,8 @@ class U2STBaseModel(nn.Layer):
             paddle.Tensor: new conformer cnn cache required for next chunk, with
                 same shape as the original cnn_cache.
         """
-        return self.encoder.forward_chunk(
-            xs, offset, required_cache_size, att_cache, cnn_cache)
+        return self.encoder.forward_chunk(xs, offset, required_cache_size,
+                                          att_cache, cnn_cache)
 
     # @jit.to_static
     def ctc_activation(self, xs: paddle.Tensor) -> paddle.Tensor:

paddlespeech/s2t/modules/align.py

@@ -11,9 +11,10 @@
 # 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 math
+
 import paddle
 from paddle import nn
-import math
 """
     To align the initializer between paddle and torch, 
     the API below are set defalut initializer with priority higger than global initializer.
@@ -81,10 +82,18 @@ class Linear(nn.Linear):
                  name=None):
         if weight_attr is None:
             if global_init_type == "kaiming_uniform":
-                weight_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
+                weight_attr = paddle.ParamAttr(
+                    initializer=nn.initializer.KaimingUniform(
+                        fan_in=None,
+                        negative_slope=math.sqrt(5),
+                        nonlinearity='leaky_relu'))
         if bias_attr is None:
             if global_init_type == "kaiming_uniform":
-                bias_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
+                bias_attr = paddle.ParamAttr(
+                    initializer=nn.initializer.KaimingUniform(
+                        fan_in=None,
+                        negative_slope=math.sqrt(5),
+                        nonlinearity='leaky_relu'))
         super(Linear, self).__init__(in_features, out_features, weight_attr,
                                      bias_attr, name)
 
@@ -104,10 +113,18 @@ class Conv1D(nn.Conv1D):
                  data_format='NCL'):
         if weight_attr is None:
             if global_init_type == "kaiming_uniform":
-                weight_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
+                weight_attr = paddle.ParamAttr(
+                    initializer=nn.initializer.KaimingUniform(
+                        fan_in=None,
+                        negative_slope=math.sqrt(5),
+                        nonlinearity='leaky_relu'))
         if bias_attr is None:
             if global_init_type == "kaiming_uniform":
-                bias_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
+                bias_attr = paddle.ParamAttr(
+                    initializer=nn.initializer.KaimingUniform(
+                        fan_in=None,
+                        negative_slope=math.sqrt(5),
+                        nonlinearity='leaky_relu'))
         super(Conv1D, self).__init__(
             in_channels, out_channels, kernel_size, stride, padding, dilation,
             groups, padding_mode, weight_attr, bias_attr, data_format)
@@ -128,10 +145,18 @@ class Conv2D(nn.Conv2D):
                  data_format='NCHW'):
         if weight_attr is None:
             if global_init_type == "kaiming_uniform":
-                weight_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
+                weight_attr = paddle.ParamAttr(
+                    initializer=nn.initializer.KaimingUniform(
+                        fan_in=None,
+                        negative_slope=math.sqrt(5),
+                        nonlinearity='leaky_relu'))
         if bias_attr is None:
             if global_init_type == "kaiming_uniform":
-                bias_attr = paddle.ParamAttr(initializer=nn.initializer.KaimingUniform(fan_in=None, negative_slope=math.sqrt(5), nonlinearity='leaky_relu'))
+                bias_attr = paddle.ParamAttr(
+                    initializer=nn.initializer.KaimingUniform(
+                        fan_in=None,
+                        negative_slope=math.sqrt(5),
+                        nonlinearity='leaky_relu'))
         super(Conv2D, self).__init__(
             in_channels, out_channels, kernel_size, stride, padding, dilation,
             groups, padding_mode, weight_attr, bias_attr, data_format)

paddlespeech/s2t/modules/attention.py

@@ -15,7 +15,6 @@
 # Modified from wenet(https://github.com/wenet-e2e/wenet)
 """Multi-Head Attention layer definition."""
 import math
-from typing import Optional
 from typing import Tuple
 
 import paddle
@@ -83,11 +82,12 @@ class MultiHeadedAttention(nn.Layer):
 
         return q, k, v
 
-    def forward_attention(self,
-            value: paddle.Tensor, 
+    def forward_attention(
+            self,
+            value: paddle.Tensor,
             scores: paddle.Tensor,
-            mask: paddle.Tensor = paddle.ones([0, 0, 0], dtype=paddle.bool),
-        ) -> paddle.Tensor:
+            mask: paddle.Tensor,  # paddle.ones([0, 0, 0], dtype=paddle.bool)
+    ) -> paddle.Tensor:
         """Compute attention context vector.
         Args:
             value (paddle.Tensor): Transformed value, size
@@ -108,7 +108,7 @@ class MultiHeadedAttention(nn.Layer):
         # When will `if mask.size(2) > 0` be False?
         # 1. onnx(16/-1, -1/-1, 16/0)
         # 2. jit (16/-1, -1/-1, 16/0, 16/4)
-        if paddle.shape(mask)[2] > 0: # time2 > 0
+        if paddle.shape(mask)[2] > 0:  # time2 > 0
             mask = mask.unsqueeze(1).equal(0)  # (batch, 1, *, time2)
             # for last chunk, time2 might be larger than scores.size(-1)
             mask = mask[:, :, :, :paddle.shape(scores)[-1]]
@@ -127,14 +127,15 @@ class MultiHeadedAttention(nn.Layer):
 
         return self.linear_out(x)  # (batch, time1, d_model)
 
-    def forward(self,
-                query: paddle.Tensor,
-                key: paddle.Tensor,
-                value: paddle.Tensor,
-                mask: paddle.Tensor = paddle.ones([0,0,0], dtype=paddle.bool),
-                pos_emb: paddle.Tensor = paddle.empty([0]),
-                cache: paddle.Tensor = paddle.zeros([0,0,0,0])
-                ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    def forward(
+            self,
+            query: paddle.Tensor,
+            key: paddle.Tensor,
+            value: paddle.Tensor,
+            mask: paddle.Tensor,  # paddle.ones([0,0,0], dtype=paddle.bool)
+            pos_emb: paddle.Tensor,  # paddle.empty([0])
+            cache: paddle.Tensor  # paddle.zeros([0,0,0,0])
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
         """Compute scaled dot product attention.
        Args:
             query (paddle.Tensor): Query tensor (#batch, time1, size).
@@ -243,14 +244,15 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
 
         return x
 
-    def forward(self,
-                query: paddle.Tensor,
-                key: paddle.Tensor,
-                value: paddle.Tensor,
-                mask: paddle.Tensor = paddle.ones([0,0,0], dtype=paddle.bool),
-                pos_emb: paddle.Tensor = paddle.empty([0]),
-                cache: paddle.Tensor = paddle.zeros([0,0,0,0])
-                ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    def forward(
+            self,
+            query: paddle.Tensor,
+            key: paddle.Tensor,
+            value: paddle.Tensor,
+            mask: paddle.Tensor,  # paddle.ones([0,0,0], dtype=paddle.bool)
+            pos_emb: paddle.Tensor,  # paddle.empty([0])
+            cache: paddle.Tensor  # paddle.zeros([0,0,0,0])
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
         """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
         Args:
             query (paddle.Tensor): Query tensor (#batch, time1, size).

paddlespeech/s2t/modules/conformer_convolution.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 # Modified from wenet(https://github.com/wenet-e2e/wenet)
 """ConvolutionModule definition."""
-from typing import Optional
 from typing import Tuple
 
 import paddle
@@ -106,11 +105,12 @@ class ConvolutionModule(nn.Layer):
         )
         self.activation = activation
 
-    def forward(self,
-                x: paddle.Tensor,
-                mask_pad: paddle.Tensor= paddle.ones([0,0,0], dtype=paddle.bool),
-                cache: paddle.Tensor= paddle.zeros([0,0,0]),
-                ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+    def forward(
+            self,
+            x: paddle.Tensor,
+            mask_pad: paddle.Tensor,  # paddle.ones([0,0,0], dtype=paddle.bool)
+            cache: paddle.Tensor  # paddle.zeros([0,0,0,0])
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
         """Compute convolution module.
         Args:
             x (paddle.Tensor): Input tensor (#batch, time, channels).
@@ -127,11 +127,11 @@ class ConvolutionModule(nn.Layer):
         x = x.transpose([0, 2, 1])  # [B, C, T]
 
         # mask batch padding
-        if paddle.shape(mask_pad)[2] > 0: # time > 0
+        if paddle.shape(mask_pad)[2] > 0:  # time > 0
             x = x.masked_fill(mask_pad, 0.0)
 
         if self.lorder > 0:
-            if paddle.shape(cache)[2] == 0: # cache_t == 0
+            if paddle.shape(cache)[2] == 0:  # cache_t == 0
                 x = nn.functional.pad(
                     x, [self.lorder, 0], 'constant', 0.0, data_format='NCL')
             else:
@@ -161,7 +161,7 @@ class ConvolutionModule(nn.Layer):
         x = self.pointwise_conv2(x)
 
         # mask batch padding
-        if paddle.shape(mask_pad)[2] > 0: # time > 0
+        if paddle.shape(mask_pad)[2] > 0:  # time > 0
             x = x.masked_fill(mask_pad, 0.0)
 
         x = x.transpose([0, 2, 1])  # [B, T, C]

paddlespeech/s2t/modules/decoder_layer.py

@@ -121,11 +121,16 @@ class DecoderLayer(nn.Layer):
 
         if self.concat_after:
             tgt_concat = paddle.cat(
-                (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0]), dim=-1)
+                (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask,
+                                       paddle.empty([0]),
+                                       paddle.zeros([0, 0, 0, 0]))[0]),
+                dim=-1)
             x = residual + self.concat_linear1(tgt_concat)
         else:
             x = residual + self.dropout(
-                self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)[0])
+                self.self_attn(tgt_q, tgt, tgt, tgt_q_mask,
+                               paddle.empty([0]), paddle.zeros([0, 0, 0, 0]))[
+                                   0])
         if not self.normalize_before:
             x = self.norm1(x)
 
@@ -134,11 +139,15 @@ class DecoderLayer(nn.Layer):
             x = self.norm2(x)
         if self.concat_after:
             x_concat = paddle.cat(
-                (x, self.src_attn(x, memory, memory, memory_mask)[0]), dim=-1)
+                (x, self.src_attn(x, memory, memory, memory_mask,
+                                  paddle.empty([0]),
+                                  paddle.zeros([0, 0, 0, 0]))[0]),
+                dim=-1)
             x = residual + self.concat_linear2(x_concat)
         else:
             x = residual + self.dropout(
-                self.src_attn(x, memory, memory, memory_mask)[0])
+                self.src_attn(x, memory, memory, memory_mask,
+                              paddle.empty([0]), paddle.zeros([0, 0, 0, 0]))[0])
         if not self.normalize_before:
             x = self.norm2(x)
 

paddlespeech/s2t/modules/encoder.py

@@ -14,8 +14,6 @@
 # limitations under the License.
 # Modified from wenet(https://github.com/wenet-e2e/wenet)
 """Encoder definition."""
-from typing import List
-from typing import Optional
 from typing import Tuple
 
 import paddle
@@ -177,7 +175,9 @@ class BaseEncoder(nn.Layer):
             decoding_chunk_size, self.static_chunk_size,
             num_decoding_left_chunks)
         for layer in self.encoders:
-            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad,
+                                          paddle.zeros([0, 0, 0, 0]),
+                                          paddle.zeros([0, 0, 0, 0]))
         if self.normalize_before:
             xs = self.after_norm(xs)
         # Here we assume the mask is not changed in encoder layers, so just
@@ -190,9 +190,9 @@ class BaseEncoder(nn.Layer):
             xs: paddle.Tensor,
             offset: int,
             required_cache_size: int,
-            att_cache: paddle.Tensor = paddle.zeros([0,0,0,0]),
-            cnn_cache: paddle.Tensor = paddle.zeros([0,0,0,0]),
-            att_mask: paddle.Tensor = paddle.ones([0,0,0], dtype=paddle.bool),
+            att_cache: paddle.Tensor,  # paddle.zeros([0,0,0,0])
+            cnn_cache: paddle.Tensor,  # paddle.zeros([0,0,0,0]),
+            att_mask: paddle.Tensor,  # paddle.ones([0,0,0], dtype=paddle.bool)
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """ Forward just one chunk
         Args:
@@ -227,7 +227,7 @@ class BaseEncoder(nn.Layer):
             xs = self.global_cmvn(xs)
 
         # before embed, xs=(B, T, D1), pos_emb=(B=1, T, D)
-        xs, pos_emb, _ = self.embed(xs, tmp_masks, offset=offset) 
+        xs, pos_emb, _ = self.embed(xs, tmp_masks, offset=offset)
         # after embed, xs=(B=1, chunk_size, hidden-dim)
 
         elayers = paddle.shape(att_cache)[0]
@@ -252,14 +252,17 @@ class BaseEncoder(nn.Layer):
             # att_cache[i:i+1] = (1, head, cache_t1, d_k*2)
             # cnn_cache[i:i+1] = (1, B=1, hidden-dim, cache_t2)
             xs, _, new_att_cache, new_cnn_cache = layer(
-                xs, att_mask, pos_emb,
-                att_cache=att_cache[i:i+1] if elayers > 0 else att_cache,
-                cnn_cache=cnn_cache[i:i+1] if paddle.shape(cnn_cache)[0] > 0 else cnn_cache,
-            )
+                xs,
+                att_mask,
+                pos_emb,
+                mask_pad=paddle.ones([0, 0, 0], dtype=paddle.bool),
+                att_cache=att_cache[i:i + 1] if elayers > 0 else att_cache,
+                cnn_cache=cnn_cache[i:i + 1]
+                if paddle.shape(cnn_cache)[0] > 0 else cnn_cache, )
             # new_att_cache = (1, head, attention_key_size, d_k*2)
             # new_cnn_cache = (B=1, hidden-dim, cache_t2)
-            r_att_cache.append(new_att_cache[:,:, next_cache_start:, :])
-            r_cnn_cache.append(new_cnn_cache.unsqueeze(0)) # add elayer dim
+            r_att_cache.append(new_att_cache[:, :, next_cache_start:, :])
+            r_cnn_cache.append(new_cnn_cache.unsqueeze(0))  # add elayer dim
 
         if self.normalize_before:
             xs = self.after_norm(xs)
@@ -270,7 +273,6 @@ class BaseEncoder(nn.Layer):
         r_cnn_cache = paddle.concat(r_cnn_cache, axis=0)
         return xs, r_att_cache, r_cnn_cache
 
-
     def forward_chunk_by_chunk(
             self,
             xs: paddle.Tensor,
@@ -315,8 +317,8 @@ class BaseEncoder(nn.Layer):
         num_frames = xs.shape[1]
         required_cache_size = decoding_chunk_size * num_decoding_left_chunks
 
-        att_cache: paddle.Tensor = paddle.zeros([0,0,0,0])
-        cnn_cache: paddle.Tensor = paddle.zeros([0,0,0,0])
+        att_cache: paddle.Tensor = paddle.zeros([0, 0, 0, 0])
+        cnn_cache: paddle.Tensor = paddle.zeros([0, 0, 0, 0])
 
         outputs = []
         offset = 0
@@ -326,7 +328,8 @@ class BaseEncoder(nn.Layer):
             chunk_xs = xs[:, cur:end, :]
 
             (y, att_cache, cnn_cache) = self.forward_chunk(
-                 chunk_xs, offset, required_cache_size, att_cache, cnn_cache)
+                chunk_xs, offset, required_cache_size, att_cache, cnn_cache,
+                paddle.ones([0, 0, 0], dtype=paddle.bool))
 
             outputs.append(y)
             offset += y.shape[1]

paddlespeech/s2t/modules/encoder_layer.py

@@ -76,9 +76,10 @@ class TransformerEncoderLayer(nn.Layer):
             x: paddle.Tensor,
             mask: paddle.Tensor,
             pos_emb: paddle.Tensor,
-            mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
-            att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
-            cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
+            mask_pad: paddle.
+            Tensor,  # paddle.ones([0, 0, 0], dtype=paddle.bool)
+            att_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
+            cnn_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """Compute encoded features.
         Args:
@@ -105,7 +106,8 @@ class TransformerEncoderLayer(nn.Layer):
         if self.normalize_before:
             x = self.norm1(x)
 
-        x_att, new_att_cache = self.self_attn(x, x, x, mask, cache=att_cache)
+        x_att, new_att_cache = self.self_attn(
+            x, x, x, mask, paddle.empty([0]), cache=att_cache)
 
         if self.concat_after:
             x_concat = paddle.concat((x, x_att), axis=-1)
@@ -193,9 +195,9 @@ class ConformerEncoderLayer(nn.Layer):
             x: paddle.Tensor,
             mask: paddle.Tensor,
             pos_emb: paddle.Tensor,
-            mask_pad: paddle.Tensor=paddle.ones([0, 0, 0], dtype=paddle.bool),
-            att_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
-            cnn_cache: paddle.Tensor=paddle.zeros([0, 0, 0, 0]),
+            mask_pad: paddle.Tensor,  #paddle.ones([0, 0, 0],dtype=paddle.bool)
+            att_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
+            cnn_cache: paddle.Tensor,  # paddle.zeros([0, 0, 0, 0])
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """Compute encoded features.
         Args:

paddlespeech/s2t/modules/initializer.py

@@ -11,7 +11,7 @@
 # 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 numpy as np
+
 
 class DefaultInitializerContext(object):
     """

paddlespeech/server/engine/asr/online/ctc_endpoint.py

@@ -102,8 +102,10 @@ class OnlineCTCEndpoint:
 
         assert self.num_frames_decoded >= self.trailing_silence_frames
         assert self.frame_shift_in_ms > 0
-        
-        decoding_something = (self.num_frames_decoded > self.trailing_silence_frames) and decoding_something
+
+        decoding_something = (
+            self.num_frames_decoded > self.trailing_silence_frames
+        ) and decoding_something
         utterance_length = self.num_frames_decoded * self.frame_shift_in_ms
         trailing_silence = self.trailing_silence_frames * self.frame_shift_in_ms
 

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

@@ -21,12 +21,12 @@ import paddle
 from numpy import float32
 from yacs.config import CfgNode
 
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
 from paddlespeech.resource import CommonTaskResource
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.modules.ctc import CTCDecoder
-from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.utils.utility import UpdateConfig
 from paddlespeech.server.engine.base_engine import BaseEngine
 from paddlespeech.server.utils import onnx_infer

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

@@ -21,10 +21,10 @@ import paddle
 from numpy import float32
 from yacs.config import CfgNode
 
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
 from paddlespeech.resource import CommonTaskResource
-from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.modules.ctc import CTCDecoder
 from paddlespeech.s2t.utils.utility import UpdateConfig

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

@@ -21,10 +21,10 @@ import paddle
 from numpy import float32
 from yacs.config import CfgNode
 
+from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.log import logger
 from paddlespeech.resource import CommonTaskResource
-from paddlespeech.audio.transform.transformation import Transformation
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.modules.ctc import CTCDecoder
 from paddlespeech.s2t.utils.tensor_utils import add_sos_eos
@@ -130,8 +130,8 @@ class PaddleASRConnectionHanddler:
 
         ## conformer
         # cache for conformer online
-        self.att_cache = paddle.zeros([0,0,0,0])
-        self.cnn_cache = paddle.zeros([0,0,0,0])
+        self.att_cache = paddle.zeros([0, 0, 0, 0])
+        self.cnn_cache = paddle.zeros([0, 0, 0, 0])
 
         self.encoder_out = None
         # conformer decoding state
@@ -474,9 +474,14 @@ class PaddleASRConnectionHanddler:
             # cur chunk
             chunk_xs = self.cached_feat[:, cur:end, :]
             # forward chunk
-            (y, self.att_cache, self.cnn_cache) = self.model.encoder.forward_chunk(
-                 chunk_xs, self.offset, required_cache_size,
-                 self.att_cache, self.cnn_cache)
+            (y, self.att_cache,
+             self.cnn_cache) = self.model.encoder.forward_chunk(
+                 chunk_xs,
+                 self.offset,
+                 required_cache_size,
+                 att_cache=self.att_cache,
+                 cnn_cache=self.cnn_cache,
+                 att_mask=paddle.ones([0, 0, 0], dtype=paddle.bool))
             outputs.append(y)
 
             # update the global offset, in decoding frame unit

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

@@ -66,12 +66,14 @@ class ASREngine(BaseEngine):
             )
             logger.error(e)
             return False
-        
-        self.executor._init_from_path(
-            model_type = self.config.model, lang = self.config.lang, sample_rate = self.config.sample_rate,
-            cfg_path = self.config.cfg_path, decode_method = self.config.decode_method,
-            ckpt_path = self.config.ckpt_path)
 
+        self.executor._init_from_path(
+            model_type=self.config.model,
+            lang=self.config.lang,
+            sample_rate=self.config.sample_rate,
+            cfg_path=self.config.cfg_path,
+            decode_method=self.config.decode_method,
+            ckpt_path=self.config.ckpt_path)
 
         logger.info("Initialize ASR server engine successfully on device: %s." %
                     (self.device))

paddlespeech/t2s/datasets/am_batch_fn.py

@@ -483,3 +483,58 @@ def vits_single_spk_batch_fn(examples):
         "speech": speech
     }
     return batch
+
+
+def vits_multi_spk_batch_fn(examples):
+    """
+    Returns:
+        Dict[str, Any]:
+            - text (Tensor): Text index tensor (B, T_text).
+            - text_lengths (Tensor): Text length tensor (B,).
+            - feats (Tensor): Feature tensor (B, T_feats, aux_channels).
+            - feats_lengths (Tensor): Feature length tensor (B,).
+            - speech (Tensor): Speech waveform tensor (B, T_wav).
+            - spk_id (Optional[Tensor]): Speaker index tensor (B,) or (B, 1).
+            - spk_emb (Optional[Tensor]): Speaker embedding tensor (B, spk_embed_dim).
+    """
+    # fields = ["text", "text_lengths", "feats", "feats_lengths", "speech", "spk_id"/"spk_emb"]
+    text = [np.array(item["text"], dtype=np.int64) for item in examples]
+    feats = [np.array(item["feats"], dtype=np.float32) for item in examples]
+    speech = [np.array(item["wave"], dtype=np.float32) for item in examples]
+    text_lengths = [
+        np.array(item["text_lengths"], dtype=np.int64) for item in examples
+    ]
+    feats_lengths = [
+        np.array(item["feats_lengths"], dtype=np.int64) for item in examples
+    ]
+
+    text = batch_sequences(text)
+    feats = batch_sequences(feats)
+    speech = batch_sequences(speech)
+
+    # convert each batch to paddle.Tensor
+    text = paddle.to_tensor(text)
+    feats = paddle.to_tensor(feats)
+    text_lengths = paddle.to_tensor(text_lengths)
+    feats_lengths = paddle.to_tensor(feats_lengths)
+
+    batch = {
+        "text": text,
+        "text_lengths": text_lengths,
+        "feats": feats,
+        "feats_lengths": feats_lengths,
+        "speech": speech
+    }
+    # spk_emb has a higher priority than spk_id
+    if "spk_emb" in examples[0]:
+        spk_emb = [
+            np.array(item["spk_emb"], dtype=np.float32) for item in examples
+        ]
+        spk_emb = batch_sequences(spk_emb)
+        spk_emb = paddle.to_tensor(spk_emb)
+        batch["spk_emb"] = spk_emb
+    elif "spk_id" in examples[0]:
+        spk_id = [np.array(item["spk_id"], dtype=np.int64) for item in examples]
+        spk_id = paddle.to_tensor(spk_id)
+        batch["spk_id"] = spk_id
+    return batch

paddlespeech/t2s/datasets/sampler.py

-import paddle
 import math
+
 import numpy as np
 from paddle.io import BatchSampler
 
+
 class ErnieSATSampler(BatchSampler):
     """Sampler that restricts data loading to a subset of the dataset.
     In such case, each process can pass a DistributedBatchSampler instance 
@@ -70,7 +71,7 @@ class ErnieSATSampler(BatchSampler):
         assert isinstance(drop_last, bool), \
                 "drop_last should be a boolean number"
 
-        from paddle.fluid.dygraph.parallel import ParallelEnv
+        from paddle.distributed import ParallelEnv
 
         if num_replicas is not None:
             assert isinstance(num_replicas, int) and num_replicas > 0, \
@@ -110,8 +111,8 @@ class ErnieSATSampler(BatchSampler):
                 subsampled_indices.extend(indices[i:i + self.batch_size])
 
             indices = indices[len(indices) - last_batch_size:]
-            subsampled_indices.extend(indices[
-                self.local_rank * last_local_batch_size:(
+            subsampled_indices.extend(
+                indices[self.local_rank * last_local_batch_size:(
                     self.local_rank + 1) * last_local_batch_size])
             return subsampled_indices
 

paddlespeech/t2s/exps/ernie_sat/train.py

@@ -25,7 +25,6 @@ from paddle import DataParallel
 from paddle import distributed as dist
 from paddle import nn
 from paddle.io import DataLoader
-from paddle.io import DistributedBatchSampler
 from paddle.optimizer import Adam
 from yacs.config import CfgNode
 

paddlespeech/t2s/exps/ernie_sat/utils.py

@@ -11,32 +11,35 @@
 # 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 hashlib
+import os
 from pathlib import Path
 from typing import Dict
 from typing import List
 from typing import Union
-import os
 
 import numpy as np
 import paddle
 import yaml
 from yacs.config import CfgNode
-import hashlib
-
 
 from paddlespeech.t2s.exps.syn_utils import get_am_inference
 from paddlespeech.t2s.exps.syn_utils import get_voc_inference
 
+
 def _get_user():
     return os.path.expanduser('~').split('/')[-1]
 
+
 def str2md5(string):
     md5_val = hashlib.md5(string.encode('utf8')).hexdigest()
     return md5_val
 
-def get_tmp_name(text:str):
+
+def get_tmp_name(text: str):
     return _get_user() + '_' + str(os.getpid()) + '_' + str2md5(text)
 
+
 def get_dict(dictfile: str):
     word2phns_dict = {}
     with open(dictfile, 'r') as fid:

paddlespeech/t2s/exps/syn_utils.py

@@ -298,8 +298,8 @@ def am_to_static(am_inference,
     am_name = am[:am.rindex('_')]
     am_dataset = am[am.rindex('_') + 1:]
     if am_name == 'fastspeech2':
-        if am_dataset in {"aishell3", "vctk", "mix"
-                          } and speaker_dict is not None:
+        if am_dataset in {"aishell3", "vctk",
+                          "mix"} and speaker_dict is not None:
             am_inference = jit.to_static(
                 am_inference,
                 input_spec=[
@@ -311,8 +311,8 @@ def am_to_static(am_inference,
                 am_inference, input_spec=[InputSpec([-1], dtype=paddle.int64)])
 
     elif am_name == 'speedyspeech':
-        if am_dataset in {"aishell3", "vctk", "mix"
-                          } and speaker_dict is not None:
+        if am_dataset in {"aishell3", "vctk",
+                          "mix"} and speaker_dict is not None:
             am_inference = jit.to_static(
                 am_inference,
                 input_spec=[

paddlespeech/t2s/exps/vits/synthesize.py

@@ -15,6 +15,7 @@ import argparse
 from pathlib import Path
 
 import jsonlines
+import numpy as np
 import paddle
 import soundfile as sf
 import yaml
@@ -23,6 +24,7 @@ from yacs.config import CfgNode
 
 from paddlespeech.t2s.datasets.data_table import DataTable
 from paddlespeech.t2s.models.vits import VITS
+from paddlespeech.t2s.utils import str2bool
 
 
 def evaluate(args):
@@ -40,8 +42,26 @@ def evaluate(args):
     print(config)
 
     fields = ["utt_id", "text"]
+    converters = {}
+
+    spk_num = None
+    if args.speaker_dict is not None:
+        print("multiple speaker vits!")
+        with open(args.speaker_dict, 'rt') as f:
+            spk_id = [line.strip().split() for line in f.readlines()]
+        spk_num = len(spk_id)
+        fields += ["spk_id"]
+    elif args.voice_cloning:
+        print("Evaluating voice cloning!")
+        fields += ["spk_emb"]
+    else:
+        print("single speaker vits!")
+    print("spk_num:", spk_num)
 
-    test_dataset = DataTable(data=test_metadata, fields=fields)
+    test_dataset = DataTable(
+        data=test_metadata,
+        fields=fields,
+        converters=converters, )
 
     with open(args.phones_dict, "r") as f:
         phn_id = [line.strip().split() for line in f.readlines()]
@@ -49,6 +69,7 @@ def evaluate(args):
     print("vocab_size:", vocab_size)
 
     odim = config.n_fft // 2 + 1
+    config["model"]["generator_params"]["spks"] = spk_num
 
     vits = VITS(idim=vocab_size, odim=odim, **config["model"])
     vits.set_state_dict(paddle.load(args.ckpt)["main_params"])
@@ -65,7 +86,15 @@ def evaluate(args):
         phone_ids = paddle.to_tensor(datum["text"])
         with timer() as t:
             with paddle.no_grad():
-                out = vits.inference(text=phone_ids)
+                spk_emb = None
+                spk_id = None
+                # multi speaker
+                if args.voice_cloning and "spk_emb" in datum:
+                    spk_emb = paddle.to_tensor(np.load(datum["spk_emb"]))
+                elif "spk_id" in datum:
+                    spk_id = paddle.to_tensor(datum["spk_id"])
+                out = vits.inference(
+                    text=phone_ids, sids=spk_id, spembs=spk_emb)
             wav = out["wav"]
             wav = wav.numpy()
             N += wav.size
@@ -90,6 +119,13 @@ def parse_args():
         '--ckpt', type=str, default=None, help='Checkpoint file of VITS.')
     parser.add_argument(
         "--phones_dict", type=str, default=None, help="phone vocabulary file.")
+    parser.add_argument(
+        "--speaker_dict", type=str, default=None, help="speaker id map file.")
+    parser.add_argument(
+        "--voice-cloning",
+        type=str2bool,
+        default=False,
+        help="whether training voice cloning model.")
     # other
     parser.add_argument(
         "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")

paddlespeech/t2s/exps/vits/synthesize_e2e.py

@@ -42,12 +42,23 @@ def evaluate(args):
     # frontend
     frontend = get_frontend(lang=args.lang, phones_dict=args.phones_dict)
 
+    spk_num = None
+    if args.speaker_dict is not None:
+        print("multiple speaker vits!")
+        with open(args.speaker_dict, 'rt') as f:
+            spk_id = [line.strip().split() for line in f.readlines()]
+        spk_num = len(spk_id)
+    else:
+        print("single speaker vits!")
+    print("spk_num:", spk_num)
+
     with open(args.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)
 
     odim = config.n_fft // 2 + 1
+    config["model"]["generator_params"]["spks"] = spk_num
 
     vits = VITS(idim=vocab_size, odim=odim, **config["model"])
     vits.set_state_dict(paddle.load(args.ckpt)["main_params"])
@@ -78,7 +89,10 @@ def evaluate(args):
                 flags = 0
                 for i in range(len(phone_ids)):
                     part_phone_ids = phone_ids[i]
-                    out = vits.inference(text=part_phone_ids)
+                    spk_id = None
+                    if spk_num is not None:
+                        spk_id = paddle.to_tensor(args.spk_id)
+                    out = vits.inference(text=part_phone_ids, sids=spk_id)
                     wav = out["wav"]
                     if flags == 0:
                         wav_all = wav
@@ -109,6 +123,13 @@ def parse_args():
         '--ckpt', type=str, default=None, help='Checkpoint file of VITS.')
     parser.add_argument(
         "--phones_dict", type=str, default=None, help="phone vocabulary file.")
+    parser.add_argument(
+        "--speaker_dict", type=str, default=None, help="speaker id map file.")
+    parser.add_argument(
+        '--spk_id',
+        type=int,
+        default=0,
+        help='spk id for multi speaker acoustic model')
     # other
     parser.add_argument(
         '--lang',

paddlespeech/t2s/exps/vits/train.py

@@ -28,6 +28,7 @@ from paddle.io import DistributedBatchSampler
 from paddle.optimizer import Adam
 from yacs.config import CfgNode
 
+from paddlespeech.t2s.datasets.am_batch_fn import vits_multi_spk_batch_fn
 from paddlespeech.t2s.datasets.am_batch_fn import vits_single_spk_batch_fn
 from paddlespeech.t2s.datasets.data_table import DataTable
 from paddlespeech.t2s.models.vits import VITS
@@ -43,6 +44,7 @@ from paddlespeech.t2s.training.extensions.visualizer import VisualDL
 from paddlespeech.t2s.training.optimizer import scheduler_classes
 from paddlespeech.t2s.training.seeding import seed_everything
 from paddlespeech.t2s.training.trainer import Trainer
+from paddlespeech.t2s.utils import str2bool
 
 
 def train_sp(args, config):
@@ -72,6 +74,23 @@ def train_sp(args, config):
         "wave": np.load,
         "feats": np.load,
     }
+    spk_num = None
+    if args.speaker_dict is not None:
+        print("multiple speaker vits!")
+        collate_fn = vits_multi_spk_batch_fn
+        with open(args.speaker_dict, 'rt') as f:
+            spk_id = [line.strip().split() for line in f.readlines()]
+        spk_num = len(spk_id)
+        fields += ["spk_id"]
+    elif args.voice_cloning:
+        print("Training voice cloning!")
+        collate_fn = vits_multi_spk_batch_fn
+        fields += ["spk_emb"]
+        converters["spk_emb"] = np.load
+    else:
+        print("single speaker vits!")
+        collate_fn = vits_single_spk_batch_fn
+    print("spk_num:", spk_num)
 
     # construct dataset for training and validation
     with jsonlines.open(args.train_metadata, 'r') as reader:
@@ -100,18 +119,16 @@ def train_sp(args, config):
         drop_last=False)
     print("samplers done!")
 
-    train_batch_fn = vits_single_spk_batch_fn
-
     train_dataloader = DataLoader(
         train_dataset,
         batch_sampler=train_sampler,
-        collate_fn=train_batch_fn,
+        collate_fn=collate_fn,
         num_workers=config.num_workers)
 
     dev_dataloader = DataLoader(
         dev_dataset,
         batch_sampler=dev_sampler,
-        collate_fn=train_batch_fn,
+        collate_fn=collate_fn,
         num_workers=config.num_workers)
     print("dataloaders done!")
 
@@ -121,6 +138,7 @@ def train_sp(args, config):
     print("vocab_size:", vocab_size)
 
     odim = config.n_fft // 2 + 1
+    config["model"]["generator_params"]["spks"] = spk_num
     model = VITS(idim=vocab_size, odim=odim, **config["model"])
     gen_parameters = model.generator.parameters()
     dis_parameters = model.discriminator.parameters()
@@ -240,6 +258,17 @@ def main():
         "--ngpu", type=int, default=1, help="if ngpu == 0, use cpu.")
     parser.add_argument(
         "--phones-dict", type=str, default=None, help="phone vocabulary file.")
+    parser.add_argument(
+        "--speaker-dict",
+        type=str,
+        default=None,
+        help="speaker id map file for multiple speaker model.")
+
+    parser.add_argument(
+        "--voice-cloning",
+        type=str2bool,
+        default=False,
+        help="whether training voice cloning model.")
 
     args = parser.parse_args()
 

paddlespeech/t2s/exps/vits/voice_cloning.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 argparse
+import os
+from pathlib import Path
+
+import librosa
+import numpy as np
+import paddle
+import soundfile as sf
+import yaml
+from yacs.config import CfgNode
+
+from paddlespeech.t2s.datasets.get_feats import LinearSpectrogram
+from paddlespeech.t2s.exps.syn_utils import get_frontend
+from paddlespeech.t2s.models.vits import VITS
+from paddlespeech.t2s.utils import str2bool
+from paddlespeech.vector.exps.ge2e.audio_processor import SpeakerVerificationPreprocessor
+from paddlespeech.vector.models.lstm_speaker_encoder import LSTMSpeakerEncoder
+
+
+def voice_cloning(args):
+
+    # Init body.
+    with open(args.config) as f:
+        config = CfgNode(yaml.safe_load(f))
+
+    print("========Args========")
+    print(yaml.safe_dump(vars(args)))
+    print("========Config========")
+    print(config)
+
+    # speaker encoder
+    spec_extractor = LinearSpectrogram(
+        n_fft=config.n_fft,
+        hop_length=config.n_shift,
+        win_length=config.win_length,
+        window=config.window)
+    p = SpeakerVerificationPreprocessor(
+        sampling_rate=16000,
+        audio_norm_target_dBFS=-30,
+        vad_window_length=30,
+        vad_moving_average_width=8,
+        vad_max_silence_length=6,
+        mel_window_length=25,
+        mel_window_step=10,
+        n_mels=40,
+        partial_n_frames=160,
+        min_pad_coverage=0.75,
+        partial_overlap_ratio=0.5)
+    print("Audio Processor Done!")
+
+    speaker_encoder = LSTMSpeakerEncoder(
+        n_mels=40, num_layers=3, hidden_size=256, output_size=256)
+    speaker_encoder.set_state_dict(paddle.load(args.ge2e_params_path))
+    speaker_encoder.eval()
+    print("GE2E Done!")
+
+    frontend = get_frontend(lang=args.lang, phones_dict=args.phones_dict)
+    print("frontend done!")
+
+    with open(args.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)
+
+    odim = config.n_fft // 2 + 1
+
+    vits = VITS(idim=vocab_size, odim=odim, **config["model"])
+    vits.set_state_dict(paddle.load(args.ckpt)["main_params"])
+    vits.eval()
+
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    input_dir = Path(args.input_dir)
+
+    if args.audio_path == "":
+        args.audio_path = None
+    if args.audio_path is None:
+        sentence = args.text
+        merge_sentences = True
+        add_blank = args.add_blank
+
+        if args.lang == 'zh':
+            input_ids = frontend.get_input_ids(
+                sentence, merge_sentences=merge_sentences, add_blank=add_blank)
+        elif args.lang == 'en':
+            input_ids = frontend.get_input_ids(
+                sentence, merge_sentences=merge_sentences)
+        phone_ids = input_ids["phone_ids"][0]
+    else:
+        wav, _ = librosa.load(str(args.audio_path), sr=config.fs)
+        feats = paddle.to_tensor(spec_extractor.get_linear_spectrogram(wav))
+
+        mel_sequences = p.extract_mel_partials(
+            p.preprocess_wav(args.audio_path))
+        with paddle.no_grad():
+            spk_emb_src = speaker_encoder.embed_utterance(
+                paddle.to_tensor(mel_sequences))
+
+    for name in os.listdir(input_dir):
+        utt_id = name.split(".")[0]
+        ref_audio_path = input_dir / name
+        mel_sequences = p.extract_mel_partials(p.preprocess_wav(ref_audio_path))
+        # print("mel_sequences: ", mel_sequences.shape)
+        with paddle.no_grad():
+            spk_emb = speaker_encoder.embed_utterance(
+                paddle.to_tensor(mel_sequences))
+        # print("spk_emb shape: ", spk_emb.shape)
+
+        with paddle.no_grad():
+            if args.audio_path is None:
+                out = vits.inference(text=phone_ids, spembs=spk_emb)
+            else:
+                out = vits.voice_conversion(
+                    feats=feats, spembs_src=spk_emb_src, spembs_tgt=spk_emb)
+            wav = out["wav"]
+
+        sf.write(
+            str(output_dir / (utt_id + ".wav")),
+            wav.numpy(),
+            samplerate=config.fs)
+        print(f"{utt_id} done!")
+    # Randomly generate numbers of 0 ~ 0.2, 256 is the dim of spk_emb
+    random_spk_emb = np.random.rand(256) * 0.2
+    random_spk_emb = paddle.to_tensor(random_spk_emb, dtype='float32')
+    utt_id = "random_spk_emb"
+    with paddle.no_grad():
+        if args.audio_path is None:
+            out = vits.inference(text=phone_ids, spembs=random_spk_emb)
+        else:
+            out = vits.voice_conversion(
+                feats=feats, spembs_src=spk_emb_src, spembs_tgt=random_spk_emb)
+        wav = out["wav"]
+    sf.write(
+        str(output_dir / (utt_id + ".wav")), wav.numpy(), samplerate=config.fs)
+    print(f"{utt_id} done!")
+
+
+def parse_args():
+    # parse args and config
+    parser = argparse.ArgumentParser(description="")
+    parser.add_argument(
+        '--config', type=str, default=None, help='Config of VITS.')
+    parser.add_argument(
+        '--ckpt', type=str, default=None, help='Checkpoint file of VITS.')
+    parser.add_argument(
+        "--phones_dict", type=str, default=None, help="phone vocabulary file.")
+    parser.add_argument(
+        "--text",
+        type=str,
+        default="每当你觉得，想要批评什么人的时候，你切要记着，这个世界上的人，并非都具备你禀有的条件。",
+        help="text to synthesize, a line")
+    parser.add_argument(
+        '--lang',
+        type=str,
+        default='zh',
+        help='Choose model language. zh or en')
+    parser.add_argument(
+        "--audio-path",
+        type=str,
+        default=None,
+        help="audio as content to synthesize")
+
+    parser.add_argument(
+        "--ge2e_params_path", type=str, help="ge2e params path.")
+
+    parser.add_argument(
+        "--ngpu", type=int, default=1, help="if ngpu=0, use cpu.")
+
+    parser.add_argument(
+        "--input-dir",
+        type=str,
+        help="input dir of *.wav, the sample rate will be resample to 16k.")
+    parser.add_argument("--output-dir", type=str, help="output dir.")
+
+    parser.add_argument(
+        "--add-blank",
+        type=str2bool,
+        default=True,
+        help="whether to add blank between phones")
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    if args.ngpu == 0:
+        paddle.set_device("cpu")
+    elif args.ngpu > 0:
+        paddle.set_device("gpu")
+    else:
+        print("ngpu should >= 0 !")
+
+    voice_cloning(args)
+
+
+if __name__ == "__main__":
+    main()

paddlespeech/t2s/frontend/g2pw/__init__.py

 from paddlespeech.t2s.frontend.g2pw.onnx_api import G2PWOnnxConverter
-

paddlespeech/t2s/frontend/mix_frontend.py

@@ -61,8 +61,11 @@ class MixFrontend():
             return False
 
     def is_end(self, before_char, after_char) -> bool:
-        if ((self.is_alphabet(before_char) or before_char == " ") and
-            (self.is_alphabet(after_char) or after_char == " ")):
+        flag = 0
+        for char in (before_char, after_char):
+            if self.is_alphabet(char) or char == " ":
+                flag += 1
+        if flag == 2:
             return True
         else:
             return False

paddlespeech/t2s/frontend/tone_sandhi.py

@@ -42,7 +42,7 @@ class ToneSandhi():
             '木头', '木匠', '朋友', '月饼', '月亮', '暖和', '明白', '时候', '新鲜', '故事', '收拾',
             '收成', '提防', '挖苦', '挑剔', '指甲', '指头', '拾掇', '拳头', '拨弄', '招牌', '招呼',
             '抬举', '护士', '折腾', '扫帚', '打量', '打算', '打扮', '打听', '打发', '扎实', '扁担',
-            '戒指', '懒得', '意识', '意思', '情形', '悟性', '怪物', '思量', '怎么', '念头', '念叨',
+            '戒指', '懒得', '意识', '意思', '悟性', '怪物', '思量', '怎么', '念头', '念叨', '别人',
             '快活', '忙活', '志气', '心思', '得罪', '张罗', '弟兄', '开通', '应酬', '庄稼', '干事',
             '帮手', '帐篷', '希罕', '师父', '师傅', '巴结', '巴掌', '差事', '工夫', '岁数', '屁股',
             '尾巴', '少爷', '小气', '小伙', '将就', '对头', '对付', '寡妇', '家伙', '客气', '实在',
@@ -60,7 +60,7 @@ class ToneSandhi():
             '邋遢', '费用', '冤家', '甜头', '介绍', '荒唐', '大人', '泥鳅', '幸福', '熟悉', '计划',
             '扑腾', '蜡烛', '姥爷', '照顾', '喉咙', '吉他', '弄堂', '蚂蚱', '凤凰', '拖沓', '寒碜',
             '糟蹋', '倒腾', '报复', '逻辑', '盘缠', '喽啰', '牢骚', '咖喱', '扫把', '惦记', '戏弄',
-            '将军', '别人'
+            '将军'
         }
         self.must_not_neural_tone_words = {
             '男子', '女子', '分子', '原子', '量子', '莲子', '石子', '瓜子', '电子', '人人', '虎虎',
@@ -84,7 +84,7 @@ class ToneSandhi():
             if j - 1 >= 0 and item == word[j - 1] and pos[0] in {"n", "v", "a"}:
                 finals[j] = finals[j][:-1] + "5"
         ge_idx = word.find("个")
-        if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
+        if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒滴哩哟喽啰耶喔诶":
             finals[-1] = finals[-1][:-1] + "5"
         elif len(word) >= 1 and word[-1] in "的地得":
             finals[-1] = finals[-1][:-1] + "5"
@@ -169,6 +169,7 @@ class ToneSandhi():
         return new_word_list
 
     def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
+
         if len(word) == 2 and self._all_tone_three(finals):
             finals[0] = finals[0][:-1] + "2"
         elif len(word) == 3:
@@ -346,6 +347,7 @@ class ToneSandhi():
 
     def modified_tone(self, word: str, pos: str,
                       finals: List[str]) -> List[str]:
+
         finals = self._bu_sandhi(word, finals)
         finals = self._yi_sandhi(word, finals)
         finals = self._neural_sandhi(word, pos, finals)

paddlespeech/t2s/models/vits/generator.py

@@ -522,6 +522,82 @@ class VITSGenerator(nn.Layer):
 
         return wav.squeeze(1), attn.squeeze(1), dur.squeeze(1)
 
+    def voice_conversion(
+            self,
+            feats: paddle.Tensor=None,
+            feats_lengths: paddle.Tensor=None,
+            sids_src: Optional[paddle.Tensor]=None,
+            sids_tgt: Optional[paddle.Tensor]=None,
+            spembs_src: Optional[paddle.Tensor]=None,
+            spembs_tgt: Optional[paddle.Tensor]=None,
+            lids: Optional[paddle.Tensor]=None, ) -> paddle.Tensor:
+        """Run voice conversion.
+        Args:
+            feats (Tensor): Feature tensor (B, aux_channels, T_feats,).
+            feats_lengths (Tensor): Feature length tensor (B,).
+            sids_src (Optional[Tensor]): Speaker index tensor of source feature (B,) or (B, 1).
+            sids_tgt (Optional[Tensor]): Speaker index tensor of target feature (B,) or (B, 1).
+            spembs_src (Optional[Tensor]): Speaker embedding tensor of source feature (B, spk_embed_dim).
+            spembs_tgt (Optional[Tensor]): Speaker embedding tensor of target feature (B, spk_embed_dim).
+            lids (Optional[Tensor]): Language index tensor (B,) or (B, 1).
+        Returns:
+            Tensor: Generated waveform tensor (B, T_wav).
+        """
+        # encoder
+        g_src = None
+        g_tgt = None
+        if self.spks is not None:
+            # (B, global_channels, 1)
+            g_src = self.global_emb(
+                paddle.reshape(sids_src, [-1])).unsqueeze(-1)
+            g_tgt = self.global_emb(
+                paddle.reshape(sids_tgt, [-1])).unsqueeze(-1)
+
+        if self.spk_embed_dim is not None:
+            # (B, global_channels, 1)
+            g_src_ = self.spemb_proj(
+                F.normalize(spembs_src.unsqueeze(0))).unsqueeze(-1)
+            if g_src is None:
+                g_src = g_src_
+            else:
+                g_src = g_src + g_src_
+
+            # (B, global_channels, 1)
+            g_tgt_ = self.spemb_proj(
+                F.normalize(spembs_tgt.unsqueeze(0))).unsqueeze(-1)
+            if g_tgt is None:
+                g_tgt = g_tgt_
+            else:
+                g_tgt = g_tgt + g_tgt_
+
+        if self.langs is not None:
+            # (B, global_channels, 1)
+            g_ = self.lang_emb(paddle.reshape(lids, [-1])).unsqueeze(-1)
+
+            if g_src is None:
+                g_src = g_
+            else:
+                g_src = g_src + g_
+
+            if g_tgt is None:
+                g_tgt = g_
+            else:
+                g_tgt = g_tgt + g_
+
+        # forward posterior encoder
+        z, m_q, logs_q, y_mask = self.posterior_encoder(
+            feats, feats_lengths, g=g_src)
+
+        # forward flow
+        # (B, H, T_feats)
+        z_p = self.flow(z, y_mask, g=g_src)
+
+        # decoder
+        z_hat = self.flow(z_p, y_mask, g=g_tgt, inverse=True)
+        wav = self.decoder(z_hat * y_mask, g=g_tgt)
+
+        return wav.squeeze(1)
+
     def _generate_path(self, dur: paddle.Tensor,
                        mask: paddle.Tensor) -> paddle.Tensor:
         """Generate path a.k.a. monotonic attention.

paddlespeech/t2s/models/vits/vits.py

@@ -381,7 +381,7 @@ class VITS(nn.Layer):
         if use_teacher_forcing:
             assert feats is not None
             feats = feats[None].transpose([0, 2, 1])
-            feats_lengths = paddle.to_tensor([paddle.shape(feats)[2]])
+            feats_lengths = paddle.to_tensor(paddle.shape(feats)[2])
             wav, att_w, dur = self.generator.inference(
                 text=text,
                 text_lengths=text_lengths,
@@ -406,3 +406,43 @@ class VITS(nn.Layer):
                 max_len=max_len, )
         return dict(
             wav=paddle.reshape(wav, [-1]), att_w=att_w[0], duration=dur[0])
+
+    def voice_conversion(
+            self,
+            feats: paddle.Tensor,
+            sids_src: Optional[paddle.Tensor]=None,
+            sids_tgt: Optional[paddle.Tensor]=None,
+            spembs_src: Optional[paddle.Tensor]=None,
+            spembs_tgt: Optional[paddle.Tensor]=None,
+            lids: Optional[paddle.Tensor]=None, ) -> paddle.Tensor:
+        """Run voice conversion.
+        Args:
+            feats (Tensor): Feature tensor (T_feats, aux_channels).
+            sids_src (Optional[Tensor]): Speaker index tensor of source feature (1,).
+            sids_tgt (Optional[Tensor]): Speaker index tensor of target feature (1,).
+            spembs_src (Optional[Tensor]): Speaker embedding tensor of source feature (spk_embed_dim,).
+            spembs_tgt (Optional[Tensor]): Speaker embedding tensor of target feature (spk_embed_dim,).
+            lids (Optional[Tensor]): Language index tensor (1,).
+        Returns:
+            Dict[str, Tensor]:
+                * wav (Tensor): Generated waveform tensor (T_wav,).
+        """
+        assert feats is not None
+        feats = feats[None].transpose([0, 2, 1])
+        feats_lengths = paddle.to_tensor(paddle.shape(feats)[2])
+
+        sids_none = sids_src is None and sids_tgt is None
+        spembs_none = spembs_src is None and spembs_tgt is None
+
+        assert not sids_none or not spembs_none
+
+        wav = self.generator.voice_conversion(
+            feats,
+            feats_lengths,
+            sids_src,
+            sids_tgt,
+            spembs_src,
+            spembs_tgt,
+            lids, )
+
+        return dict(wav=paddle.reshape(wav, [-1]))

paddlespeech/t2s/models/vits/vits_updater.py

@@ -111,6 +111,8 @@ class VITSUpdater(StandardUpdater):
                 text_lengths=batch["text_lengths"],
                 feats=batch["feats"],
                 feats_lengths=batch["feats_lengths"],
+                sids=batch.get("spk_id", None),
+                spembs=batch.get("spk_emb", None),
                 forward_generator=turn == "generator")
             # Generator
             if turn == "generator":
@@ -268,6 +270,8 @@ class VITSEvaluator(StandardEvaluator):
                 text_lengths=batch["text_lengths"],
                 feats=batch["feats"],
                 feats_lengths=batch["feats_lengths"],
+                sids=batch.get("spk_id", None),
+                spembs=batch.get("spk_emb", None),
                 forward_generator=turn == "generator")
             # Generator
             if turn == "generator":

paddlespeech/t2s/training/updaters/standard_updater.py

@@ -24,10 +24,11 @@ from paddle.nn import Layer
 from paddle.optimizer import Optimizer
 from timer import timer
 
+from paddlespeech.t2s.datasets.sampler import ErnieSATSampler
 from paddlespeech.t2s.training.reporter import report
 from paddlespeech.t2s.training.updater import UpdaterBase
 from paddlespeech.t2s.training.updater import UpdaterState
-from paddlespeech.t2s.datasets.sampler import ErnieSATSampler
+
 
 class StandardUpdater(UpdaterBase):
     """An example of over-simplification. Things may not be that simple, but

setup.py

@@ -77,12 +77,7 @@ base = [
     "pybind11",
 ]
 
-server = [
-    "fastapi",
-    "uvicorn",
-    "pattern_singleton",
-    "websockets" 
-]
+server = ["fastapi", "uvicorn", "pattern_singleton", "websockets"]
 
 requirements = {
     "install":
@@ -330,4 +325,4 @@ setup_info = dict(
     })
 
 with version_info():
-    setup(**setup_info,include_package_data=True)
+    setup(**setup_info, include_package_data=True)

speechx/examples/ds2_ol/onnx/local/onnx_infer_shape.py

@@ -490,18 +490,10 @@ class SymbolicShapeInference:
     def _onnx_infer_single_node(self, node):
         # skip onnx shape inference for some ops, as they are handled in _infer_*
         skip_infer = node.op_type in [
-            'If', 'Loop', 'Scan', 'SplitToSequence', 'ZipMap', \
-            # contrib ops
-
-
-
-
-            'Attention', 'BiasGelu', \
-            'EmbedLayerNormalization', \
-            'FastGelu', 'Gelu', 'LayerNormalization', \
-            'LongformerAttention', \
-            'SkipLayerNormalization', \
-            'PythonOp'
+            'If', 'Loop', 'Scan', 'SplitToSequence', 'ZipMap', 'Attention',
+            'BiasGelu', 'EmbedLayerNormalization', 'FastGelu', 'Gelu',
+            'LayerNormalization', 'LongformerAttention',
+            'SkipLayerNormalization', 'PythonOp'
         ]
 
         if not skip_infer:
@@ -514,8 +506,8 @@ class SymbolicShapeInference:
             if (get_opset(self.out_mp_) >= 9) and node.op_type in ['Unsqueeze']:
                 initializers = [
                     self.initializers_[name] for name in node.input
-                    if (name in self.initializers_ and
-                        name not in self.graph_inputs_)
+                    if (name in self.initializers_ and name not in
+                        self.graph_inputs_)
                 ]
 
             # run single node inference with self.known_vi_ shapes
@@ -601,8 +593,8 @@ class SymbolicShapeInference:
             for o in symbolic_shape_inference.out_mp_.graph.output
         ]
         subgraph_new_symbolic_dims = set([
-            d for s in subgraph_shapes if s for d in s
-            if type(d) == str and not d in self.symbolic_dims_
+            d for s in subgraph_shapes
+            if s for d in s if type(d) == str and not d in self.symbolic_dims_
         ])
         new_dims = {}
         for d in subgraph_new_symbolic_dims:
@@ -729,8 +721,9 @@ class SymbolicShapeInference:
                         for d, s in zip(sympy_shape[-rank:], strides)
                     ]
                     total_pads = [
-                        max(0, (k - s) if r == 0 else (k - r)) for k, s, r in
-                        zip(effective_kernel_shape, strides, residual)
+                        max(0, (k - s) if r == 0 else (k - r))
+                        for k, s, r in zip(effective_kernel_shape, strides,
+                                           residual)
                     ]
                 except TypeError:  # sympy may throw TypeError: cannot determine truth value of Relational
                     total_pads = [
@@ -1276,8 +1269,9 @@ class SymbolicShapeInference:
         if pads is not None:
             assert len(pads) == 2 * rank
             new_sympy_shape = [
-                d + pad_up + pad_down for d, pad_up, pad_down in
-                zip(sympy_shape, pads[:rank], pads[rank:])
+                d + pad_up + pad_down
+                for d, pad_up, pad_down in zip(sympy_shape, pads[:rank], pads[
+                    rank:])
             ]
             self._update_computed_dims(new_sympy_shape)
         else:
@@ -1590,8 +1584,8 @@ class SymbolicShapeInference:
                 scales = list(scales)
                 new_sympy_shape = [
                     sympy.simplify(sympy.floor(d * (end - start) * scale))
-                    for d, start, end, scale in
-                    zip(input_sympy_shape, roi_start, roi_end, scales)
+                    for d, start, end, scale in zip(input_sympy_shape,
+                                                    roi_start, roi_end, scales)
                 ]
                 self._update_computed_dims(new_sympy_shape)
             else:
@@ -2204,8 +2198,9 @@ class SymbolicShapeInference:
         # topological sort nodes, note there might be dead nodes so we check if all graph outputs are reached to terminate
         sorted_nodes = []
         sorted_known_vi = set([
-            i.name for i in list(self.out_mp_.graph.input) +
-            list(self.out_mp_.graph.initializer)
+            i.name
+            for i in list(self.out_mp_.graph.input) + list(
+                self.out_mp_.graph.initializer)
         ])
         if any([o.name in sorted_known_vi for o in self.out_mp_.graph.output]):
             # Loop/Scan will have some graph output in graph inputs, so don't do topological sort

tests/test_tipc/prepare.sh

@@ -22,7 +22,7 @@ model_name=$(func_parser_value "${lines[1]}")
 echo "model_name:"${model_name}
 trainer_list=$(func_parser_value "${lines[14]}")
 
-if [ ${MODE} = "benchmark_train" ];then
+if [[ ${MODE} = "benchmark_train" ]];then
     curPath=$(readlink -f "$(dirname "$0")")
     echo "curPath:"${curPath}    # /PaddleSpeech/tests/test_tipc
     cd ${curPath}/../..
@@ -36,10 +36,10 @@ if [ ${MODE} = "benchmark_train" ];then
     pip install jsonlines
     pip list
     cd -
-    if [ ${model_name} == "conformer" ]; then
+    if [[ ${model_name} == "conformer" ]]; then
         # set the URL for aishell_tiny dataset
         conformer_aishell_URL=${conformer_aishell_URL:-"None"}
-        if [ ${conformer_aishell_URL} == 'None' ];then
+        if [[ ${conformer_aishell_URL} == 'None' ]];then
             echo "please contact author to get the URL.\n"
             exit
 	    else
@@ -66,7 +66,7 @@ if [ ${MODE} = "benchmark_train" ];then
         sed -i "s#data/#test_tipc/conformer/benchmark_train/data/#g" ${curPath}/conformer/benchmark_train/conf/preprocess.yaml
     fi
 
-    if [ ${model_name} == "pwgan" ]; then
+    if [[ ${model_name} == "pwgan" ]]; then
         # 下载 csmsc 数据集并解压缩
         wget -nc https://weixinxcxdb.oss-cn-beijing.aliyuncs.com/gwYinPinKu/BZNSYP.rar
         mkdir -p BZNSYP
@@ -80,9 +80,10 @@ if [ ${MODE} = "benchmark_train" ];then
         python ../paddlespeech/t2s/exps/gan_vocoder/normalize.py --metadata=dump/test/raw/metadata.jsonl --dumpdir=dump/test/norm --stats=dump/train/feats_stats.npy
     fi
 
-    if [ ${model_name} == "mdtc" ]; then
+    if [[ ${model_name} == "mdtc" ]]; then
         # 下载 Snips 数据集并解压缩
-        wget -nc https://paddlespeech.bj.bcebos.com/datasets/hey_snips_kws_4.0.tar.gz.1 https://paddlespeech.bj.bcebos.com/datasets/hey_snips_https://paddlespeech.bj.bcebos.com/datasets/hey_snips_kws_4.0.tar.gz.2
+        wget https://paddlespeech.bj.bcebos.com/datasets/hey_snips_kws_4.0.tar.gz.1 
+	wget https://paddlespeech.bj.bcebos.com/datasets/hey_snips_kws_4.0.tar.gz.2
         cat hey_snips_kws_4.0.tar.gz.* > hey_snips_kws_4.0.tar.gz
         rm hey_snips_kws_4.0.tar.gz.*
         tar -xzf hey_snips_kws_4.0.tar.gz