Merge branch 'develop' of https://github.com/PaddlePaddle/DeepSpeech into new_config

50ceca9d · huangyuxin · 35ca7f6e · fb238d83 · 50ceca9d · 50ceca9d
43 changed file
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
+# Changelog
--- a/README.md
+++ b/README.md
@@ -171,7 +171,7 @@ Via the easy-to-use, efficient, flexible and scalable implementation, our vision
 - 👏🏻  2021.12.10: PaddleSpeech CLI is available for Audio Classification, Automatic Speech Recognition, Speech Translation (English to Chinese) and Text-to-Speech.
 ### Community
- Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.
+- Scan the QR code below with your Wechat (reply【语音】after your friend's application is approved), you can access to official technical exchange group. Look forward to your participation.
 <div align="center">
 <img src="https://raw.githubusercontent.com/yt605155624/lanceTest/main/images/wechat_4.jpg"  width = "300"  />

--- a/README_cn.md
+++ b/README_cn.md
@@ -175,7 +175,7 @@ from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readme
 - 👏🏻 2021.12.10: PaddleSpeech CLI 上线！覆盖了声音分类、语音识别、语音翻译（英译中）以及语音合成。
 ### 技术交流群
-微信扫描二维码加入官方交流群，获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
+微信扫描二维码（好友申请通过后回复【语音】）加入官方交流群，获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
 <div align="center">
 <img src="https://raw.githubusercontent.com/yt605155624/lanceTest/main/images/wechat_4.jpg"  width = "300"  />

--- a/docs/source/install.md
+++ b/docs/source/install.md
@@ -6,7 +6,7 @@ There are 3 ways to use `PaddleSpeech`. According to the degree of difficulty, t
 |:---- |:----------------------------------------------------------- |:----|
 | Easy     | (1) Use command-line functions of PaddleSpeech. <br> (2) Experience PaddleSpeech on Ai Studio. | Linux, Mac(not support M1 chip)，Windows |
 | Medium     | Support major functions ，such as using the` ready-made `examples and using PaddleSpeech to train your model.                                           | Linux |
-| Hard     | Support full function of Paddlespeech，including training n-gram language model, Montreal-Forced-Aligner, and so on. And you are more able to be a developer! | Ubuntu |
+| Hard     | Support full function of Paddlespeech, including using join ctc decoder with kaldi, training n-gram language model, Montreal-Forced-Aligner, and so on. And you are more able to be a developer! | Ubuntu |
 ## Prerequisites
 - Python >= 3.7
@@ -52,12 +52,19 @@ sudo apt install build-essential
 conda install -y -c gcc_linux-64=8.4.0 gxx_linux-64=8.4.0
 ```
 ### Install PaddleSpeech 
-You can use the following command:
+Some users may fail to install `kaldiio` due to the default download source, you can install `pytest-runner` at first；
+```bash
+pip install pytest-runner -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+Then you can use the following commands:
 ```bash
 pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
 pip install paddlespeech -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
 > If you encounter problem with downloading **nltk_data** while using paddlespeech, it maybe due to your poor network, we suggest you download the [nltk_data](https://paddlespeech.bj.bcebos.com/Parakeet/tools/nltk_data.tar.gz) provided by us, and extract it to your `${HOME}`.
+> If you fail to install paddlespeech-ctcdecoders, it doesn't matter.
 ## Medium: Get the Major Functions (Support Linux)
 If you want to get the major function of  `paddlespeech`, you need to do following steps:
 ### Git clone PaddleSpeech
@@ -117,6 +124,8 @@ python3 -m pip install paddlepaddle-gpu==2.2.0 -i https://mirror.baidu.com/pypi/
 ### Install PaddleSpeech 
 You can install  `paddlespeech`  by the following command，then you can use the `ready-made` examples in `paddlespeech` :
 ```bash
+# Some users may fail to install `kaldiio` due to the default download source, you can install `pytest-runner` at first；
+pip install pytest-runner -i https://pypi.tuna.tsinghua.edu.cn/simple
 # Make sure you are in the root directory of PaddleSpeech
 pip install . -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
@@ -182,8 +191,11 @@ conda activate tools/venv
 conda install -y -c conda-forge sox libsndfile swig bzip2 libflac bc
 ```
 ### Install PaddlePaddle
+Some users may fail to install `kaldiio` due to the default download source, you can install `pytest-runner` at first；
+```bash
+pip install pytest-runner -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
 Make sure you have GPU and the paddlepaddle version is right. For example, for CUDA 10.2, CuDNN7.5 install paddle 2.2.0:
 ```bash
 python3 -m pip install paddlepaddle-gpu==2.2.0 -i https://mirror.baidu.com/pypi/simple
 ```

--- a/docs/source/install_cn.md
+++ b/docs/source/install_cn.md
@@ -5,7 +5,7 @@
 | :--- | :----------------------------------------------------------- | :------------------ |
 | 简单 | (1) 使用 PaddleSpeech 的命令行功能. <br> (2) 在 Aistudio上体验 PaddleSpeech. | Linux, Mac(不支持M1芯片)，Windows |
 | 中等 | 支持 PaddleSpeech 主要功能，比如使用已有 examples 中的模型和使用 PaddleSpeech 来训练自己的模型. | Linux               |
-| 困难 | 支持 PaddleSpeech 的各项功能，包含训练语言模型,使用强制对齐等。并且你更能成为一名开发者！ | Ubuntu              |
+| 困难 | 支持 PaddleSpeech 的各项功能，包含结合kaldi使用 join ctc decoder 方式解码，训练语言模型,使用强制对齐等。并且你更能成为一名开发者！ | Ubuntu              |
 ## 先决条件
 - Python >= 3.7
 - 最新版本的 PaddlePaddle (请看 [安装向导](https://www.paddlepaddle.org.cn/documentation/docs/en/beginners_guide/index_en.html))
@@ -49,12 +49,19 @@ sudo apt install build-essential
 conda install -y -c gcc_linux-64=8.4.0 gxx_linux-64=8.4.0
 ```
 ### 安装 PaddleSpeech
-你可以使用如下命令：
+部分用户系统由于默认源的问题，安装中会出现kaldiio安转出错的问题，建议首先安装pytest-runner:
+```bash
+pip install pytest-runner -i https://pypi.tuna.tsinghua.edu.cn/simple 
+```
+然后你可以使用如下命令：
 ```bash
 pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
 pip install paddlespeech -i https://pypi.tuna.tsinghua.edu.cn/simple 
 ```
 > 如果您在使用 paddlespeech 的过程中遇到关于下载 **nltk_data** 的问题，可能是您的网络不佳，我们建议您下载我们提供的 [nltk_data](https://paddlespeech.bj.bcebos.com/Parakeet/tools/nltk_data.tar.gz) 并解压缩到您的 `${HOME}` 目录下。
+> 如果出现 paddlespeech-ctcdecoders 无法安装的问题，无须担心，这不影响使用。
 ## 中等： 获取主要功能（支持 Linux）
 如果你想要使用 `paddlespeech` 的主要功能。你需要完成以下几个步骤
 ### Git clone PaddleSpeech
@@ -111,6 +118,8 @@ python3 -m pip install paddlepaddle-gpu==2.2.0 -i https://mirror.baidu.com/pypi/
 ### 安装 PaddleSpeech
 最后安装 `paddlespeech`，这样你就可以使用 `paddlespeech`中已有的 examples：
 ```bash
+# 部分用户系统由于默认源的问题，安装中会出现kaldiio安转出错的问题，建议首先安装pytest-runner:
+pip install pytest-runner -i https://pypi.tuna.tsinghua.edu.cn/simple 
 # 请确保目前处于PaddleSpeech项目的根目录
 pip install . -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```
@@ -176,6 +185,11 @@ conda install -y -c conda-forge sox libsndfile swig bzip2 libflac bc
 python3 -m pip install paddlepaddle-gpu==2.2.0 -i https://mirror.baidu.com/pypi/simple
 ```
 ### 用开发者模式安装 PaddleSpeech
+部分用户系统由于默认源的问题，安装中会出现kaldiio安转出错的问题，建议首先安装pytest-runner:
+```bash
+pip install pytest-runner -i https://pypi.tuna.tsinghua.edu.cn/simple 
+```
+然后安装 PaddleSpeech：
 ```bash
 pip install -e .[develop] -i https://pypi.tuna.tsinghua.edu.cn/simple
 ```

--- a/docs/topic/gan_vocoder/gan_vocoder.ipynb
+++ b/docs/topic/gan_vocoder/gan_vocoder.ipynb
@@ -21,7 +21,11 @@
    "|FB-RAWs|Filter Bank Random Window Discriminators|\n",
    "\n",
    "<br></br>\n",
-    "csmsc 数据集上 GAN Vocoder 整体对比\n",
+    "csmsc 数据集上 GAN Vocoder 整体对比如下， \n ",
+    "\n",
+    "测试机器：1 x Tesla V100-32G 40 core Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz\n ",
+    "\n",
+    "测试环境：Python 3.7.0, paddlepaddle 2.2.0\n",
    "\n",
    "Model|Date|Input|Generator<br>Loss|Discriminator<br>Loss|Need<br>Finetune|Training<br>Steps|Finetune<br>Steps|Batch<br>Size|ips<br>(gen only)<br>(gen + dis)|Static Model<br>Size (gen)|RTF<br>(GPU)|\n",
    ":-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|\n",

--- a/docs/topic/package_release/python_package_release.md
+++ b/docs/topic/package_release/python_package_release.md
+# 发包方法
+## conda 代替系统依赖
+conda可以用来代替一些 apt-get 安装的系统依赖，这样可以让项目适用于除了 ubuntu 以外的系统。
+使用 conda 可以安装 sox, libsndfile，swig等 paddlespeech 需要的依赖：
+```bash
+conda install -y -c conda-forge sox libsndfile
+```
+部分系统会缺少libbzip2库，这个 paddlespeech 也是需要的，这也可以用 conda 安装：
+```bash
+conda install -y -c bzip2
+```
+conda也可以安装linux的C++的依赖：
+```bash
+conda install -y -c gcc_linux-64=8.4.0 gxx_linux-64=8.4.0
+```
+#### 剩余问题：使用conda环境编译kenlm失败。目前在conda环境下编译kenlm会出现链接失败的问题
+目前知道需要的依赖：
+```bash
+conda install -c conda-forge eigen boost cmake
+```
+## python 编包方法
+#### 创建 pypi的账号
+创建 pypi 账号
+#### 下载 twine
+```
+pip install twine
+```
+#### python 编包
+编写好python包的setup.py, 然后使用如下命令编wheel包：
+```bash
+python setup.py bdist_wheel
+```
+如果要编源码包，用如下命令：
+```bash
+python setup.py sdist
+```
+#### 上传包
+```bash
+twine upload dist/wheel包
+```
+输入账号和密码后就可以上传wheel包了
+#### 关于python 包的发包信息
+主要可以参考这个[文档](https://packaging.python.org/en/latest/guides/distributing-packages-using-setuptools/?highlight=find_packages)
+## Manylinux 降低含有 C++ 依赖的 pip 包的 glibc 依赖
+为了让有C++依赖的 pip wheel 包可以适用于更多的 linux 系统，需要降低其本身的 glibc 的依赖。这就需要让 pip wheel 包在 manylinux 的 docker 下编包。关于查看系统的 glibc 版本，可以使用命令：`ldd --version`。
+### Manylinux
+关于Many Linux，主要可以参考 Github 项目的说明[ github many linux](https://github.com/pypa/manylinux)。
+manylinux1 支持 Centos5以上， manylinux2010 支持 Centos 6 以上，manylinux2014 支持Centos 7 以上。
+目前使用 manylinux2010 基本可以满足所有的 linux 生产环境需求。（不建议使用manylinux1，系统较老，难度较大）
+### 拉取 manylinux2010
+```bash
+docker pull quay.io/pypa/manylinux1_x86_64
+```
+### 使用 manylinux2010
+启动 manylinux2010 docker。
+```bash
+docker run -it xxxxxx
+```
+在 Many Linux 2010 的docker环境自带 swig 和各种类型的 python 版本。这里注意不要自己下载conda 来安装环境来编译 pip 包，要用 docker 本身的环境来编包。
+设置python：
+```bash
+export PATH="/opt/python/cp37-cp37m/bin/:$PATH"
+#export PATH="/opt/python/cp38-cp38/bin/:$PATH"
+#export PATH="/opt/python/cp39-cp39/bin/:$PATH"
+```
+随后正常编包，编包后需要使用 [auditwheel](https://github.com/pypa/auditwheel) 来降低编好的wheel包的版本。
+显示 wheel 包的 glibc 依赖版本
+```bash
+auditwheel show wheel包
+```
+降低 wheel包的版本
+```bash
+auditwheel repair wheel包
+```
+## 区分 install 模式和 develop 模式
+可以在setup.py 中划分 install 的依赖（基本依赖）和 develop 的依赖 （开发者额外依赖）。 setup_info 中 `install_requires` 设置 install 的依赖，而在 `extras_require` 中设置 `develop` key为 develop的依赖。
+普通安装可以使用：
+```bash
+pip install . 
+```
+另外使用 pip 安装已发的包也是使用普通安装的：
+```
+pip install paddlespeech
+```
+而开发者可以使用如下方式安装，这样不仅会安装install的依赖，也会安装develop的依赖， 即：最后安装的依赖=install依赖 + develop依赖：
+```bash
+pip install -e .[develop]
+```
+## python 包的动态安装
+可以使用 pip包来实现动态安装：
+```python
+import pip
+if int(pip.__version__.split('.')[0]) > 9:
+        from pip._internal import main
+    else:
+        from pip import main
+    main(['install', package_name])
+```
--- a/examples/csmsc/voc3/finetune.sh
+++ b/examples/csmsc/voc3/finetune.sh
@@ -15,11 +15,13 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
        --fastspeech2-stat=fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
        --dur-file=durations.txt \
        --output-dir=dump_finetune \
-        --phones-dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
+        --phones-dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt \
+        --dataset=baker \
+        --rootdir=~/datasets/BZNSYP/
 fi
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    python3 local/link_wav.py \
+    python3 link_wav.py \
        --old-dump-dir=dump \
        --dump-dir=dump_finetune
 fi

--- a/examples/csmsc/voc5/finetune.sh
+++ b/examples/csmsc/voc5/finetune.sh
@@ -15,11 +15,13 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
        --fastspeech2-stat=fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
        --dur-file=durations.txt \
        --output-dir=dump_finetune \
-        --phones-dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
+        --phones-dict=fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt \
+        --dataset=baker \
+        --rootdir=~/datasets/BZNSYP/
 fi
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    python3 local/link_wav.py \
+    python3 link_wav.py \
        --old-dump-dir=dump \
        --dump-dir=dump_finetune
 fi

--- a/examples/csmsc/voc5/local/link_wav.py
+++ b/examples/csmsc/voc5/local/link_wav.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 operator import itemgetter
-from pathlib import Path
-import jsonlines
-import numpy as np
-def main():
-    # parse config and args
-    parser = argparse.ArgumentParser(
-        description="Preprocess audio and then extract features .")
-    parser.add_argument(
-        "--old-dump-dir",
-        default=None,
-        type=str,
-        help="directory to dump feature files.")
-    parser.add_argument(
-        "--dump-dir",
-        type=str,
-        required=True,
-        help="directory to finetune dump feature files.")
-    args = parser.parse_args()
-    old_dump_dir = Path(args.old_dump_dir).expanduser()
-    old_dump_dir = old_dump_dir.resolve()
-    dump_dir = Path(args.dump_dir).expanduser()
-    # use absolute path
-    dump_dir = dump_dir.resolve()
-    dump_dir.mkdir(parents=True, exist_ok=True)
-    assert old_dump_dir.is_dir()
-    assert dump_dir.is_dir()
-    for sub in ["train", "dev", "test"]:
-        # 把 old_dump_dir 里面的 *-wave.npy 软连接到 dump_dir 的对应位置
-        output_dir = dump_dir / sub
-        output_dir.mkdir(parents=True, exist_ok=True)
-        results = []
-        for name in os.listdir(output_dir / "raw"):
-            # 003918_feats.npy
-            utt_id = name.split("_")[0]
-            mel_path = output_dir / ("raw/" + name)
-            gen_mel = np.load(mel_path)
-            wave_name = utt_id + "_wave.npy"
-            wav = np.load(old_dump_dir / sub / ("raw/" + wave_name))
-            os.symlink(old_dump_dir / sub / ("raw/" + wave_name),
-                       output_dir / ("raw/" + wave_name))
-            num_sample = wav.shape[0]
-            num_frames = gen_mel.shape[0]
-            wav_path = output_dir / ("raw/" + wave_name)
-            record = {
-                "utt_id": utt_id,
-                "num_samples": num_sample,
-                "num_frames": num_frames,
-                "feats": str(mel_path),
-                "wave": str(wav_path),
-            }
-            results.append(record)
-        results.sort(key=itemgetter("utt_id"))
-        with jsonlines.open(output_dir / "raw/metadata.jsonl", 'w') as writer:
-            for item in results:
-                writer.write(item)
-if __name__ == "__main__":
-    main()
--- a/examples/other/1xt2x/src_deepspeech2x/models/ds2/deepspeech2.py
+++ b/examples/other/1xt2x/src_deepspeech2x/models/ds2/deepspeech2.py
@@ -149,13 +149,13 @@ class DeepSpeech2Model(nn.Layer):
        """Compute Model loss
        Args:
-            audio (Tenosr): [B, T, D]
+            audio (Tensor): [B, T, D]
            audio_len (Tensor): [B]
            text (Tensor): [B, U]
            text_len (Tensor): [B]
        Returns:
-            loss (Tenosr): [1]
+            loss (Tensor): [1]
        """
        eouts, eouts_len = self.encoder(audio, audio_len)
        loss = self.decoder(eouts, eouts_len, text, text_len)

--- a/examples/vctk/tts3/README.md
+++ b/examples/vctk/tts3/README.md
@@ -95,16 +95,16 @@ optional arguments:
 ### Synthesizing
 We use [parallel wavegan](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/vctk/voc1) as the neural vocoder.
-Download pretrained parallel wavegan model from [pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.5.zip)and unzip it.
+Download pretrained parallel wavegan model from [pwg_vctk_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip) and unzip it.
 ```bash
-unzip pwg_vctk_ckpt_0.5.zip
+unzip pwg_vctk_ckpt_0.1.1.zip
 ```
 Parallel WaveGAN checkpoint contains files listed below.
 ```text
-pwg_vctk_ckpt_0.5
+pwg_vctk_ckpt_0.1.1
-├── pwg_default.yaml               # default config used to train parallel wavegan
+├── default.yaml                   # default config used to train parallel wavegan
-├── pwg_snapshot_iter_1000000.pdz  # generator parameters of parallel wavegan
+├── snapshot_iter_1500000.pdz      # generator parameters of parallel wavegan
-└── pwg_stats.npy                  # statistics used to normalize spectrogram when training parallel wavegan
+└── feats_stats.npy                # statistics used to normalize spectrogram when training parallel wavegan
 ```
 `./local/synthesize.sh` calls `${BIN_DIR}/../synthesize.py`, which can synthesize waveform from `metadata.jsonl`.
 ```bash

--- a/examples/vctk/tts3/local/synthesize.sh
+++ b/examples/vctk/tts3/local/synthesize.sh
@@ -12,9 +12,9 @@ python3 ${BIN_DIR}/../synthesize.py \
    --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
    --am_stat=dump/train/speech_stats.npy \
    --voc=pwgan_vctk \
-    --voc_config=pwg_vctk_ckpt_0.5/pwg_default.yaml \
+    --voc_config=pwg_vctk_ckpt_0.1.1/default.yaml  \
-    --voc_ckpt=pwg_vctk_ckpt_0.5/pwg_snapshot_iter_1000000.pdz \
+    --voc_ckpt=pwg_vctk_ckpt_0.1.1/snapshot_iter_1500000.pdz \
-    --voc_stat=pwg_vctk_ckpt_0.5/pwg_stats.npy \
+    --voc_stat=pwg_vctk_ckpt_0.1.1/feats_stats.npy \
    --test_metadata=dump/test/norm/metadata.jsonl \
    --output_dir=${train_output_path}/test \
    --phones_dict=dump/phone_id_map.txt \

--- a/examples/vctk/tts3/local/synthesize_e2e.sh
+++ b/examples/vctk/tts3/local/synthesize_e2e.sh
@@ -12,9 +12,9 @@ python3 ${BIN_DIR}/../synthesize_e2e.py \
    --am_ckpt=${train_output_path}/checkpoints/${ckpt_name} \
    --am_stat=dump/train/speech_stats.npy \
    --voc=pwgan_vctk \
-    --voc_config=pwg_vctk_ckpt_0.5/pwg_default.yaml  \
+    --voc_config=pwg_vctk_ckpt_0.1.1/default.yaml  \
-    --voc_ckpt=pwg_vctk_ckpt_0.5/pwg_snapshot_iter_1000000.pdz \
+    --voc_ckpt=pwg_vctk_ckpt_0.1.1/snapshot_iter_1500000.pdz \
-    --voc_stat=pwg_vctk_ckpt_0.5/pwg_stats.npy \
+    --voc_stat=pwg_vctk_ckpt_0.1.1/feats_stats.npy \
    --lang=en \
    --text=${BIN_DIR}/../sentences_en.txt \
    --output_dir=${train_output_path}/test_e2e \

--- a/examples/vctk/voc1/README.md
+++ b/examples/vctk/voc1/README.md
@@ -132,15 +132,15 @@ optional arguments:
 5. `--ngpu` is the number of gpus to use, if ngpu == 0, use cpu.
 ## Pretrained Model
-Pretrained models can be downloaded here [pwg_vctk_ckpt_0.5.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.5.zip).
+Pretrained models can be downloaded here [pwg_vctk_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip).
 Parallel WaveGAN checkpoint contains files listed below.
 ```text
-pwg_vctk_ckpt_0.5
+pwg_vctk_ckpt_0.1.1
-├── pwg_default.yaml               # default config used to train parallel wavegan
+├── default.yaml                   # default config used to train parallel wavegan
-├── pwg_snapshot_iter_1000000.pdz  # generator parameters of parallel wavegan
+├── snapshot_iter_1500000.pdz      # generator parameters of parallel wavegan
-└── pwg_stats.npy                  # statistics used to normalize spectrogram when training parallel wavegan
+└── feats_stats.npy                # statistics used to normalize spectrogram when training parallel wavegan
 ```
 ## Acknowledgement
 We adapted some code from https://github.com/kan-bayashi/ParallelWaveGAN.
--- a/examples/vctk/voc1/conf/default.yaml
+++ b/examples/vctk/voc1/conf/default.yaml
@@ -70,7 +70,7 @@ lambda_adv: 4.0  # Loss balancing coefficient.
 ###########################################################
 #                  DATA LOADER SETTING                    #
 ###########################################################
-batch_size: 8              # Batch size.
+batch_size: 6              # Batch size.
 batch_max_steps: 24000     # Length of each audio in batch. Make sure dividable by n_shift.
 num_workers: 2             # Number of workers in DataLoader.
@@ -100,7 +100,7 @@ discriminator_grad_norm: 1 # Discriminator's gradient norm.
 #                    INTERVAL SETTING                     #
 ###########################################################
 discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator.
-train_max_steps: 1000000                # Number of training steps.
+train_max_steps: 1500000                # Number of training steps.
 save_interval_steps: 5000               # Interval steps to save checkpoint.
 eval_interval_steps: 1000               # Interval steps to evaluate the network.

--- a/paddleaudio/CHANGELOG.md
+++ b/paddleaudio/CHANGELOG.md
+# Changelog
--- a/paddlespeech/cli/asr/infer.py
+++ b/paddlespeech/cli/asr/infer.py
@@ -31,6 +31,7 @@ from ..log import logger
 from ..utils import cli_register
 from ..utils import download_and_decompress
 from ..utils import MODEL_HOME
+from ..utils import stats_wrapper
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.transform.transformation import Transformation
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
@@ -425,6 +426,7 @@ class ASRExecutor(BaseExecutor):
            logger.exception(e)
            return False
+    @stats_wrapper
    def __call__(self,
                 audio_file: os.PathLike,
                 model: str='conformer_wenetspeech',

--- a/paddlespeech/cli/cls/infer.py
+++ b/paddlespeech/cli/cls/infer.py
@@ -26,6 +26,7 @@ from ..log import logger
 from ..utils import cli_register
 from ..utils import download_and_decompress
 from ..utils import MODEL_HOME
+from ..utils import stats_wrapper
 from paddleaudio import load
 from paddleaudio.features import LogMelSpectrogram
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
@@ -245,6 +246,7 @@ class CLSExecutor(BaseExecutor):
            logger.exception(e)
            return False
+    @stats_wrapper
    def __call__(self,
                 audio_file: os.PathLike,
                 model: str='panns_cnn14',

--- a/paddlespeech/cli/st/infer.py
+++ b/paddlespeech/cli/st/infer.py
@@ -30,6 +30,7 @@ from ..log import logger
 from ..utils import cli_register
 from ..utils import download_and_decompress
 from ..utils import MODEL_HOME
+from ..utils import stats_wrapper
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 from paddlespeech.s2t.utils.utility import UpdateConfig
@@ -334,6 +335,7 @@ class STExecutor(BaseExecutor):
            logger.exception(e)
            return False
+    @stats_wrapper
    def __call__(self,
                 audio_file: os.PathLike,
                 model: str='fat_st_ted',

--- a/paddlespeech/cli/text/infer.py
+++ b/paddlespeech/cli/text/infer.py
@@ -26,6 +26,7 @@ from ..log import logger
 from ..utils import cli_register
 from ..utils import download_and_decompress
 from ..utils import MODEL_HOME
+from ..utils import stats_wrapper
 __all__ = ['TextExecutor']
@@ -272,6 +273,7 @@ class TextExecutor(BaseExecutor):
            logger.exception(e)
            return False
+    @stats_wrapper
    def __call__(
            self,
            text: str,

--- a/paddlespeech/cli/tts/infer.py
+++ b/paddlespeech/cli/tts/infer.py
@@ -29,6 +29,7 @@ from ..log import logger
 from ..utils import cli_register
 from ..utils import download_and_decompress
 from ..utils import MODEL_HOME
+from ..utils import stats_wrapper
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 from paddlespeech.t2s.frontend import English
 from paddlespeech.t2s.frontend.zh_frontend import Frontend
@@ -155,15 +156,15 @@ pretrained_models = {
    },
    "pwgan_vctk-en": {
        'url':
-        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.5.zip',
+        'https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_vctk_ckpt_0.1.1.zip',
        'md5':
-        '322ca688aec9b127cec2788b65aa3d52',
+        'b3da1defcde3e578be71eb284cb89f2c',
        'config':
-        'pwg_default.yaml',
+        'default.yaml',
        'ckpt':
-        'pwg_snapshot_iter_1000000.pdz',
+        'snapshot_iter_1500000.pdz',
        'speech_stats':
-        'pwg_stats.npy',
+        'feats_stats.npy',
    },
    # mb_melgan
    "mb_melgan_csmsc-zh": {
@@ -645,6 +646,7 @@ class TTSExecutor(BaseExecutor):
            logger.exception(e)
            return False
+    @stats_wrapper
    def __call__(self,
                 text: str,
                 am: str='fastspeech2_csmsc',

--- a/paddlespeech/cli/utils.py
+++ b/paddlespeech/cli/utils.py
@@ -11,22 +11,36 @@
 # 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 inspect
+import json
 import os
 import tarfile
+import threading
+import time
+import uuid
 import zipfile
 from typing import Any
 from typing import Dict
+import paddle
+import paddleaudio
+import requests
+import yaml
 from paddle.framework import load
 from . import download
+from .. import __version__
 from .entry import commands
+requests.adapters.DEFAULT_RETRIES = 3
 __all__ = [
    'cli_register',
    'get_command',
    'download_and_decompress',
    'load_state_dict_from_url',
+    'stats_wrapper',
 ]
@@ -101,6 +115,13 @@ def download_and_decompress(archive: Dict[str, str], path: str) -> os.PathLike:
        if not os.path.isdir(uncompress_path):
            download._decompress(filepath)
    else:
+        StatsWorker(
+            task='download',
+            version=__version__,
+            extra_info={
+                'download_url': archive['url'],
+                'paddle_version': paddle.__version__
+            }).start()
        uncompress_path = download.get_path_from_url(archive['url'], path,
                                                     archive['md5'])
@@ -146,3 +167,171 @@ def _get_sub_home(directory):
 PPSPEECH_HOME = _get_paddlespcceh_home()
 MODEL_HOME = _get_sub_home('models')
+CONF_HOME = _get_sub_home('conf')
+def _md5(text: str):
+    '''Calculate the md5 value of the input text.'''
+    md5code = hashlib.md5(text.encode())
+    return md5code.hexdigest()
+class ConfigCache:
+    def __init__(self):
+        self._data = {}
+        self._initialize()
+        self.file = os.path.join(CONF_HOME, 'cache.yaml')
+        if not os.path.exists(self.file):
+            self.flush()
+            return
+        with open(self.file, 'r') as file:
+            try:
+                cfg = yaml.load(file, Loader=yaml.FullLoader)
+                self._data.update(cfg)
+            except:
+                self.flush()
+    @property
+    def cache_info(self):
+        return self._data['cache_info']
+    def _initialize(self):
+        # Set default configuration values.
+        cache_info = _md5(str(uuid.uuid1())[-12:]) + "-" + str(int(time.time()))
+        self._data['cache_info'] = cache_info
+    def flush(self):
+        '''Flush the current configuration into the configuration file.'''
+        with open(self.file, 'w') as file:
+            cfg = json.loads(json.dumps(self._data))
+            yaml.dump(cfg, file)
+stats_api = "http://paddlepaddle.org.cn/paddlehub/stat"
+cache_info = ConfigCache().cache_info
+class StatsWorker(threading.Thread):
+    def __init__(self,
+                 task="asr",
+                 model=None,
+                 version=__version__,
+                 extra_info={}):
+        threading.Thread.__init__(self)
+        self._task = task
+        self._model = model
+        self._version = version
+        self._extra_info = extra_info
+    def run(self):
+        params = {
+            'task': self._task,
+            'version': self._version,
+            'from': 'ppspeech'
+        }
+        if self._model:
+            params['model'] = self._model
+        self._extra_info.update({
+            'cache_info': cache_info,
+        })
+        params.update({"extra": json.dumps(self._extra_info)})
+        try:
+            requests.get(stats_api, params)
+        except Exception:
+            pass
+        return
+def _note_one_stat(cls_name, params={}):
+    task = cls_name.replace('Executor', '').lower()  # XXExecutor
+    extra_info = {
+        'paddle_version': paddle.__version__,
+    }
+    if 'model' in params:
+        model = params['model']
+    else:
+        model = None
+    if 'audio_file' in params:
+        try:
+            _, sr = paddleaudio.load(params['audio_file'])
+        except Exception:
+            sr = -1
+    if task == 'asr':
+        extra_info.update({
+            'lang': params['lang'],
+            'inp_sr': sr,
+            'model_sr': params['sample_rate'],
+        })
+    elif task == 'st':
+        extra_info.update({
+            'lang':
+            params['src_lang'] + '-' + params['tgt_lang'],
+            'inp_sr':
+            sr,
+            'model_sr':
+            params['sample_rate'],
+        })
+    elif task == 'tts':
+        model = params['am']
+        extra_info.update({
+            'lang': params['lang'],
+            'vocoder': params['voc'],
+        })
+    elif task == 'cls':
+        extra_info.update({
+            'inp_sr': sr,
+        })
+    elif task == 'text':
+        extra_info.update({
+            'sub_task': params['task'],
+            'lang': params['lang'],
+        })
+    else:
+        return
+    StatsWorker(
+        task=task,
+        model=model,
+        version=__version__,
+        extra_info=extra_info, ).start()
+def _parse_args(func, *args, **kwargs):
+    # FullArgSpec(args, varargs, varkw, defaults, kwonlyargs, kwonlydefaults, annotations)
+    argspec = inspect.getfullargspec(func)
+    keys = argspec[0]
+    if keys[0] == 'self':  # Remove self pointer.
+        keys = keys[1:]
+    default_values = argspec[3]
+    values = [None] * (len(keys) - len(default_values))
+    values.extend(list(default_values))
+    params = dict(zip(keys, values))
+    for idx, v in enumerate(args):
+        params[keys[idx]] = v
+    for k, v in kwargs.items():
+        params[k] = v
+    return params
+def stats_wrapper(executor_func):
+    def _warpper(self, *args, **kwargs):
+        try:
+            _note_one_stat(
+                type(self).__name__, _parse_args(executor_func, *args,
+                                                 **kwargs))
+        except Exception:
+            pass
+        return executor_func(self, *args, **kwargs)
+    return _warpper
--- a/paddlespeech/s2t/decoders/ctcdecoder/scorer_deprecated.py
+++ b/paddlespeech/s2t/decoders/ctcdecoder/scorer_deprecated.py
@@ -62,7 +62,7 @@ class Scorer(object):
        """Evaluation function, gathering all the different scores
        and return the final one.
-        :param sentence: The input sentence for evalutation
+        :param sentence: The input sentence for evaluation
        :type sentence: str
        :param log: Whether return the score in log representation.
        :type log: bool

--- a/paddlespeech/s2t/decoders/ctcdecoder/swig/ctc_beam_search_decoder.cpp
+++ b/paddlespeech/s2t/decoders/ctcdecoder/swig/ctc_beam_search_decoder.cpp
@@ -183,7 +183,7 @@ std::vector<std::pair<double, std::string>> ctc_beam_search_decoder(
    std::sort(
        prefixes.begin(), prefixes.begin() + num_prefixes, prefix_compare);
-    // compute aproximate ctc score as the return score, without affecting the
+    // compute approximate ctc score as the return score, without affecting the
    // return order of decoding result. To delete when decoder gets stable.
    for (size_t i = 0; i < beam_size && i < prefixes.size(); ++i) {
        double approx_ctc = prefixes[i]->score;

--- a/paddlespeech/s2t/decoders/ctcdecoder/swig/decoder_utils.cpp
+++ b/paddlespeech/s2t/decoders/ctcdecoder/swig/decoder_utils.cpp
@@ -26,7 +26,7 @@ std::vector<std::pair<size_t, float>> get_pruned_log_probs(
    for (size_t i = 0; i < prob_step.size(); ++i) {
        prob_idx.push_back(std::pair<int, double>(i, prob_step[i]));
    }
-    // pruning of vacobulary
+    // pruning of vocabulary
    size_t cutoff_len = prob_step.size();
    if (cutoff_prob < 1.0 || cutoff_top_n < cutoff_len) {
        std::sort(prob_idx.begin(),

--- a/paddlespeech/s2t/decoders/ctcdecoder/swig/scorer.cpp
+++ b/paddlespeech/s2t/decoders/ctcdecoder/swig/scorer.cpp
@@ -223,7 +223,7 @@ void Scorer::fill_dictionary(bool add_space) {
     * This gets rid of "epsilon" transitions in the FST.
     * These are transitions that don't require a string input to be taken.
-     * Getting rid of them is necessary to make the FST determinisitc, but
+     * Getting rid of them is necessary to make the FST deterministic, but
     * can greatly increase the size of the FST
     */
    fst::RmEpsilon(&dictionary);

--- a/paddlespeech/s2t/decoders/scorers/ctc.py
+++ b/paddlespeech/s2t/decoders/scorers/ctc.py
@@ -154,7 +154,7 @@ class CTCPrefixScorer(BatchPartialScorerInterface):
        Args:
            state: The states of hyps
-        Returns: exteded state
+        Returns: extended state
        """
        new_state = []

--- a/paddlespeech/s2t/decoders/scorers/ctc_prefix_score.py
+++ b/paddlespeech/s2t/decoders/scorers/ctc_prefix_score.py
@@ -11,7 +11,7 @@ class CTCPrefixScorePD():
    which is based on Algorithm 2 in WATANABE et al.
    "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
-    but extended to efficiently compute the label probablities for multiple
+    but extended to efficiently compute the label probabilities for multiple
    hypotheses simultaneously
    See also Seki et al. "Vectorized Beam Search for CTC-Attention-Based
    Speech Recognition," In INTERSPEECH (pp. 3825-3829), 2019.
@@ -272,7 +272,7 @@ class CTCPrefixScore():
    which is based on Algorithm 2 in WATANABE et al.
    "HYBRID CTC/ATTENTION ARCHITECTURE FOR END-TO-END SPEECH RECOGNITION,"
-    but extended to efficiently compute the probablities of multiple labels
+    but extended to efficiently compute the probabilities of multiple labels
    simultaneously
    """

--- a/paddlespeech/s2t/exps/u2/model.py
+++ b/paddlespeech/s2t/exps/u2/model.py
@@ -238,7 +238,9 @@ class U2Trainer(Trainer):
                preprocess_conf=config.preprocess_config,
                n_iter_processes=config.num_workers,
                subsampling_factor=1,
-                num_encs=1)
+                num_encs=1,
+                dist_sampler=False,
+                shortest_first=False)
            self.valid_loader = BatchDataLoader(
                json_file=config.dev_manifest,
@@ -257,7 +259,9 @@ class U2Trainer(Trainer):
                preprocess_conf=config.preprocess_config,
                n_iter_processes=config.num_workers,
                subsampling_factor=1,
-                num_encs=1)
+                num_encs=1,
+                dist_sampler=False,
+                shortest_first=False)
            logger.info("Setup train/valid Dataloader!")
        else:
            decode_batch_size = config.get('decode', dict()).get(

--- a/paddlespeech/s2t/io/dataloader.py
+++ b/paddlespeech/s2t/io/dataloader.py
@@ -78,7 +78,8 @@ class BatchDataLoader():
                 load_aux_input: bool=False,
                 load_aux_output: bool=False,
                 num_encs: int=1,
-                 dist_sampler: bool=False):
+                 dist_sampler: bool=False,
+                 shortest_first: bool=False):
        self.json_file = json_file
        self.train_mode = train_mode
        self.use_sortagrad = sortagrad == -1 or sortagrad > 0
@@ -97,6 +98,7 @@ class BatchDataLoader():
        self.load_aux_input = load_aux_input
        self.load_aux_output = load_aux_output
        self.dist_sampler = dist_sampler
+        self.shortest_first = shortest_first
        # read json data
        with jsonlines.open(json_file, 'r') as reader:
@@ -113,7 +115,7 @@ class BatchDataLoader():
            maxlen_out,
            minibatches,  # for debug
            min_batch_size=mini_batch_size,
-            shortest_first=self.use_sortagrad,
+            shortest_first=self.shortest_first or self.use_sortagrad,
            count=batch_count,
            batch_bins=batch_bins,
            batch_frames_in=batch_frames_in,
@@ -149,13 +151,13 @@ class BatchDataLoader():
                                        self.reader)
        if self.dist_sampler:
-            self.sampler = DistributedBatchSampler(
+            self.batch_sampler = DistributedBatchSampler(
                dataset=self.dataset,
                batch_size=1,
                shuffle=not self.use_sortagrad if self.train_mode else False,
                drop_last=False, )
        else:
-            self.sampler = BatchSampler(
+            self.batch_sampler = BatchSampler(
                dataset=self.dataset,
                batch_size=1,
                shuffle=not self.use_sortagrad if self.train_mode else False,
@@ -163,7 +165,7 @@ class BatchDataLoader():
        self.dataloader = DataLoader(
            dataset=self.dataset,
-            batch_sampler=self.sampler,
+            batch_sampler=self.batch_sampler,
            collate_fn=batch_collate,
            num_workers=self.n_iter_processes, )
@@ -194,5 +196,6 @@ class BatchDataLoader():
        echo += f"load_aux_input: {self.load_aux_input}, "
        echo += f"load_aux_output: {self.load_aux_output}, "
        echo += f"dist_sampler: {self.dist_sampler}, "
+        echo += f"shortest_first: {self.shortest_first}, "
        echo += f"file: {self.json_file}"
        return echo
--- a/paddlespeech/s2t/models/ds2/deepspeech2.py
+++ b/paddlespeech/s2t/models/ds2/deepspeech2.py
@@ -151,13 +151,13 @@ class DeepSpeech2Model(nn.Layer):
        """Compute Model loss
        Args:
-            audio (Tenosr): [B, T, D]
+            audio (Tensors): [B, T, D]
            audio_len (Tensor): [B]
            text (Tensor): [B, U]
            text_len (Tensor): [B]
        Returns:
-            loss (Tenosr): [1]
+            loss (Tensor): [1]
        """
        eouts, eouts_len = self.encoder(audio, audio_len)
        loss = self.decoder(eouts, eouts_len, text, text_len)

--- a/paddlespeech/s2t/models/ds2_online/deepspeech2.py
+++ b/paddlespeech/s2t/models/ds2_online/deepspeech2.py
@@ -279,13 +279,13 @@ class DeepSpeech2ModelOnline(nn.Layer):
        """Compute Model loss
        Args:
-            audio (Tenosr): [B, T, D]
+            audio (Tensor): [B, T, D]
            audio_len (Tensor): [B]
            text (Tensor): [B, U]
            text_len (Tensor): [B]
        Returns:
-            loss (Tenosr): [1]
+            loss (Tensor): [1]
        """
        eouts, eouts_len, final_state_h_box, final_state_c_box = self.encoder(
            audio, audio_len, None, None)

--- a/paddlespeech/s2t/models/u2/u2.py
+++ b/paddlespeech/s2t/models/u2/u2.py
@@ -680,8 +680,8 @@ class U2BaseModel(ASRInterface, nn.Layer):
        """u2 decoding.
        Args:
-            feats (Tenosr): audio features, (B, T, D)
+            feats (Tensor): audio features, (B, T, D)
-            feats_lengths (Tenosr): (B)
+            feats_lengths (Tensor): (B)
            text_feature (TextFeaturizer): text feature object.
            decoding_method (str): decoding mode, e.g.
                    'attention', 'ctc_greedy_search',

--- a/paddlespeech/s2t/models/u2_st/u2_st.py
+++ b/paddlespeech/s2t/models/u2_st/u2_st.py
@@ -478,8 +478,8 @@ class U2STBaseModel(nn.Layer):
        """u2 decoding.
        Args:
-            feats (Tenosr): audio features, (B, T, D)
+            feats (Tensor): audio features, (B, T, D)
-            feats_lengths (Tenosr): (B)
+            feats_lengths (Tensor): (B)
            text_feature (TextFeaturizer): text feature object.
            decoding_method (str): decoding mode, e.g.
                    'fullsentence',

--- a/paddlespeech/s2t/modules/ctc.py
+++ b/paddlespeech/s2t/modules/ctc.py
@@ -39,10 +39,6 @@ except ImportError:
    except Exception as e:
        logger.info("paddlespeech_ctcdecoders not installed!")
-#try:
-#except Exception as e:
-#    logger.info("ctcdecoder not installed!")
 __all__ = ['CTCDecoder']
@@ -85,10 +81,10 @@ class CTCDecoderBase(nn.Layer):
        Args:
            hs_pad (Tensor): batch of padded hidden state sequences (B, Tmax, D)
            hlens (Tensor): batch of lengths of hidden state sequences (B)
-            ys_pad (Tenosr): batch of padded character id sequence tensor (B, Lmax)
+            ys_pad (Tensor): batch of padded character id sequence tensor (B, Lmax)
            ys_lens (Tensor): batch of lengths of character sequence (B)
        Returns:
-            loss (Tenosr): ctc loss value, scalar.
+            loss (Tensor): ctc loss value, scalar.
        """
        logits = self.ctc_lo(self.dropout(hs_pad))
        loss = self.criterion(logits, ys_pad, hlens, ys_lens)
@@ -256,8 +252,8 @@ class CTCDecoder(CTCDecoderBase):
        """ctc decoding with probs.
        Args:
-            probs (Tenosr): activation after softmax
+            probs (Tensor): activation after softmax
-            logits_lens (Tenosr): audio output lens
+            logits_lens (Tensor): audio output lens
            vocab_list ([type]): [description]
            decoding_method ([type]): [description]
            lang_model_path ([type]): [description]

--- a/paddlespeech/s2t/modules/mask.py
+++ b/paddlespeech/s2t/modules/mask.py
@@ -54,7 +54,7 @@ def make_pad_mask(lengths: paddle.Tensor) -> paddle.Tensor:
                 [0, 0, 0, 1, 1],
                 [0, 0, 1, 1, 1]]
    """
-    # (TODO: Hui Zhang): jit not support Tenosr.dim() and Tensor.ndim
+    # (TODO: Hui Zhang): jit not support Tensor.dim() and Tensor.ndim
    # assert lengths.dim() == 1
    batch_size = int(lengths.shape[0])
    max_len = int(lengths.max())

--- a/paddlespeech/s2t/training/scheduler.py
+++ b/paddlespeech/s2t/training/scheduler.py
@@ -67,18 +67,19 @@ class WarmupLR(LRScheduler):
        super().__init__(learning_rate, last_epoch, verbose)
    def __repr__(self):
-        return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})"
+        return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps}, lr={self.base_lr}, last_epoch={self.last_epoch})"
    def get_lr(self):
+        # self.last_epoch start from zero
        step_num = self.last_epoch + 1
        return self.base_lr * self.warmup_steps**0.5 * min(
            step_num**-0.5, step_num * self.warmup_steps**-1.5)
    def set_step(self, step: int=None):
        '''
-        It will update the learning rate in optimizer according to current ``epoch`` .  
+        It will update the learning rate in optimizer according to current ``epoch`` .
        The new learning rate will take effect on next ``optimizer.step`` .
        Args:
            step (int, None): specify current epoch. Default: None. Auto-increment from last_epoch=-1.
        Returns:
@@ -94,7 +95,7 @@ class ConstantLR(LRScheduler):
        learning_rate (float): The initial learning rate. It is a python float number.
        last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
        verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
    Returns:
        ``ConstantLR`` instance to schedule learning rate.
    """

--- a/paddlespeech/s2t/training/trainer.py
+++ b/paddlespeech/s2t/training/trainer.py
@@ -222,7 +222,7 @@ class Trainer():
            batch_sampler = self.train_loader.batch_sampler
            if isinstance(batch_sampler, paddle.io.DistributedBatchSampler):
                logger.debug(
-                    f"train_loader.batch_sample set epoch: {self.epoch}")
+                    f"train_loader.batch_sample.set_epoch: {self.epoch}")
                batch_sampler.set_epoch(self.epoch)
    def before_train(self):

--- a/paddlespeech/s2t/utils/dynamic_import.py
+++ b/paddlespeech/s2t/utils/dynamic_import.py
@@ -57,7 +57,7 @@ def filter_valid_args(args: Dict[Text, Any], valid_keys: List[Text]):
    return new_args
-def filter_out_tenosr(args: Dict[Text, Any]):
+def filter_out_tensor(args: Dict[Text, Any]):
    return {key: val for key, val in args.items() if not has_tensor(val)}
@@ -65,5 +65,5 @@ def instance_class(module_class, args: Dict[Text, Any]):
    valid_keys = inspect.signature(module_class).parameters.keys()
    new_args = filter_valid_args(args, valid_keys)
    logger.info(
-        f"Instance: {module_class.__name__} {filter_out_tenosr(new_args)}.")
+        f"Instance: {module_class.__name__} {filter_out_tensor(new_args)}.")
    return module_class(**new_args)
--- a/paddlespeech/t2s/exps/fastspeech2/gen_gta_mel.py
+++ b/paddlespeech/t2s/exps/fastspeech2/gen_gta_mel.py
@@ -21,6 +21,8 @@ import numpy as np
 import paddle
 import yaml
 from yacs.config import CfgNode
+from tqdm import tqdm
+import os
 from paddlespeech.t2s.datasets.preprocess_utils import get_phn_dur
 from paddlespeech.t2s.datasets.preprocess_utils import merge_silence
@@ -30,6 +32,8 @@ from paddlespeech.t2s.modules.normalizer import ZScore
 def evaluate(args, fastspeech2_config):
+    rootdir = Path(args.rootdir).expanduser()
+    assert rootdir.is_dir()
    # construct dataset for evaluation
    with open(args.phones_dict, "r") as f:
@@ -41,9 +45,16 @@ def evaluate(args, fastspeech2_config):
    for phn, id in phn_id:
        phone_dict[phn] = int(id)
+    if args.speaker_dict:
+        with open(args.speaker_dict, 'rt') as f:
+            spk_id_list = [line.strip().split() for line in f.readlines()]
+            spk_num = len(spk_id_list)
+    else:
+        spk_num=None
    odim = fastspeech2_config.n_mels
    model = FastSpeech2(
-        idim=vocab_size, odim=odim, **fastspeech2_config["model"])
+        idim=vocab_size, odim=odim, **fastspeech2_config["model"], spk_num=spk_num)
    model.set_state_dict(
        paddle.load(args.fastspeech2_checkpoint)["main_params"])
@@ -65,7 +76,34 @@ def evaluate(args, fastspeech2_config):
    sentences, speaker_set = get_phn_dur(args.dur_file)
    merge_silence(sentences)
-    for i, utt_id in enumerate(sentences):
+    if args.dataset == "baker":
+        wav_files = sorted(list((rootdir / "Wave").rglob("*.wav")))
+        # split data into 3 sections
+        num_train = 9800
+        num_dev = 100
+        train_wav_files = wav_files[:num_train]
+        dev_wav_files = wav_files[num_train:num_train + num_dev]
+        test_wav_files = wav_files[num_train + num_dev:]
+    elif args.dataset == "aishell3":
+        sub_num_dev = 5
+        wav_dir = rootdir / "train" / "wav"
+        train_wav_files = []
+        dev_wav_files = []
+        test_wav_files = []
+        for speaker in os.listdir(wav_dir):
+            wav_files = sorted(list((wav_dir / speaker).rglob("*.wav")))
+            if len(wav_files) > 100:
+                train_wav_files += wav_files[:-sub_num_dev * 2]
+                dev_wav_files += wav_files[-sub_num_dev * 2:-sub_num_dev]
+                test_wav_files += wav_files[-sub_num_dev:]
+            else:
+                train_wav_files += wav_files
+    train_wav_files = [os.path.basename(str(str_path)) for str_path in train_wav_files]
+    dev_wav_files = [os.path.basename(str(str_path)) for str_path in dev_wav_files]
+    test_wav_files = [os.path.basename(str(str_path)) for str_path in test_wav_files]
+    for i, utt_id in enumerate(tqdm(sentences)):
        phones = sentences[utt_id][0]
        durations = sentences[utt_id][1]
        speaker = sentences[utt_id][2]
@@ -82,21 +120,30 @@ def evaluate(args, fastspeech2_config):
        phone_ids = [phone_dict[phn] for phn in phones]
        phone_ids = paddle.to_tensor(np.array(phone_ids))
+        if args.speaker_dict:
+            speaker_id = int([item[1] for item in spk_id_list if speaker == item[0]][0])    
+            speaker_id = paddle.to_tensor(speaker_id)
+        else:
+            speaker_id = None
        durations = paddle.to_tensor(np.array(durations))
        # 生成的和真实的可能有 1, 2 帧的差距，但是 batch_fn 会修复
        # split data into 3 sections
-        if args.dataset == "baker":
-            num_train = 9800
+        wav_path = utt_id + ".wav"
-            num_dev = 100
-        if i in range(0, num_train):
+        if wav_path in train_wav_files:
            sub_output_dir = output_dir / ("train/raw")
-        elif i in range(num_train, num_train + num_dev):
+        elif wav_path in dev_wav_files:
            sub_output_dir = output_dir / ("dev/raw")
-        else:
+        elif wav_path in test_wav_files:
            sub_output_dir = output_dir / ("test/raw")
        sub_output_dir.mkdir(parents=True, exist_ok=True)
        with paddle.no_grad():
-            mel = fastspeech2_inference(phone_ids, durations=durations)
+            mel = fastspeech2_inference(phone_ids, durations=durations, spk_id=speaker_id)
        np.save(sub_output_dir / (utt_id + "_feats.npy"), mel)
@@ -109,6 +156,8 @@ def main():
        default="baker",
        type=str,
        help="name of dataset, should in {baker, ljspeech, vctk} now")
+    parser.add_argument(
+        "--rootdir", default=None, type=str, help="directory to dataset.")
    parser.add_argument(
        "--fastspeech2-config", type=str, help="fastspeech2 config file.")
    parser.add_argument(
@@ -126,6 +175,12 @@ def main():
        type=str,
        default="phone_id_map.txt",
        help="phone vocabulary file.")
+    parser.add_argument(
+        "--speaker-dict",
+        type=str,
+        default=None,
+        help="speaker id map file.")
    parser.add_argument(
        "--dur-file", default=None, type=str, help="path to durations.txt.")

--- a/paddlespeech/t2s/frontend/zh_normalization/char_convert.py
+++ b/paddlespeech/t2s/frontend/zh_normalization/char_convert.py
@@ -14,7 +14,7 @@
 # limitations under the License.
 """Traditional and simplified Chinese conversion, a simplified character may correspond to multiple traditional characters.
 """
-simplified_charcters = 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+simplified_charcters = 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 traditional_characters = 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--- a/examples/csmsc/voc3/local/link_wav.py
+++ b/examples/csmsc/voc3/local/link_wav.py
@@ -18,7 +18,7 @@ from pathlib import Path
 import jsonlines
 import numpy as np
+from tqdm import tqdm
 def main():
    # parse config and args
@@ -52,9 +52,9 @@ def main():
        output_dir = dump_dir / sub
        output_dir.mkdir(parents=True, exist_ok=True)
        results = []
-        for name in os.listdir(output_dir / "raw"):
+        files = os.listdir(output_dir / "raw")
-            # 003918_feats.npy
+        for name in tqdm(files):
-            utt_id = name.split("_")[0]
+            utt_id = name.split("_feats.npy")[0]
            mel_path = output_dir / ("raw/" + name)
            gen_mel = np.load(mel_path)
            wave_name = utt_id + "_wave.npy"