Refactor egs & add licences

refactor egs & add licences

.clang_format.hook

-#!/usr/bin/env bash
-set -e
-
-readonly VERSION="3.9"
-
-version=$(clang-format -version)
-
-if ! [[ $version == *"$VERSION"* ]]; then
-    echo "clang-format version check failed."
-    echo "a version contains '$VERSION' is needed, but get '$version'"
-    echo "you can install the right version, and make an soft-link to '\$PATH' env"
-    exit -1
-fi
-
-clang-format $@

.pre-commit-config.yaml

@@ -30,6 +30,12 @@
     -   id: clang-format
         name: clang-format
         description: Format files with ClangFormat
-        entry: bash .clang_format.hook -i
+        entry: bash .pre-commit-hooks/clang-format.hook -i
         language: system
         files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|cuh|proto)$
+    -   id: copyright_checker
+        name: copyright_checker
+        entry: python .pre-commit-hooks/copyright-check.hook
+        language: system
+        files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto|py)$
+        exclude: (?=decoders/swig).*(\.cpp|\.h)$
\ No newline at end of file

.pre-commit-hooks/clang-format.hook

+#!/usr/bin/env bash
+set -e
+
+readonly VERSION="3.9"
+
+version=$(clang-format -version)
+
+# if ! [[ $version == *"$VERSION"* ]]; then
+#     echo "clang-format version check failed."
+#     echo "a version contains '$VERSION' is needed, but get '$version'"
+#     echo "you can install the right version, and make an soft-link to '\$PATH' env"
+#     exit -1
+# fi
+
+clang-format $@

.pre-commit-hooks/copyright-check.hook

+# Copyright (c) 2020 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 io, re
+import sys, os
+import subprocess
+import platform
+
+COPYRIGHT = '''
+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.
+'''
+
+LANG_COMMENT_MARK = None
+
+NEW_LINE_MARK = None
+
+COPYRIGHT_HEADER = None
+
+if platform.system() == "Windows":
+    NEW_LINE_MARK = "\r\n"
+else:
+    NEW_LINE_MARK = '\n'
+    COPYRIGHT_HEADER = COPYRIGHT.split(NEW_LINE_MARK)[1]
+    p = re.search('(\d{4})', COPYRIGHT_HEADER).group(0)
+    process = subprocess.Popen(["date", "+%Y"], stdout=subprocess.PIPE)
+    date, err = process.communicate()
+    date = date.decode("utf-8").rstrip("\n")
+    COPYRIGHT_HEADER = COPYRIGHT_HEADER.replace(p, date)
+
+
+def generate_copyright(template, lang='C'):
+    if lang == 'Python':
+        LANG_COMMENT_MARK = '#'
+    else:
+        LANG_COMMENT_MARK = "//"
+
+    lines = template.split(NEW_LINE_MARK)
+    BLANK = " "
+    ans = LANG_COMMENT_MARK + BLANK + COPYRIGHT_HEADER + NEW_LINE_MARK
+    for lino, line in enumerate(lines):
+        if lino == 0 or lino == 1 or lino == len(lines) - 1: continue
+        if len(line)  == 0:
+            BLANK = ""
+        else:
+            BLANK = " "
+        ans += LANG_COMMENT_MARK + BLANK + line + NEW_LINE_MARK
+
+    return ans + "\n"
+
+
+def lang_type(filename):
+    if filename.endswith(".py"):
+        return "Python"
+    elif filename.endswith(".h"):
+        return "C"
+    elif filename.endswith(".c"):
+        return "C"
+    elif filename.endswith(".hpp"):
+        return "C"
+    elif filename.endswith(".cc"):
+        return "C"
+    elif filename.endswith(".cpp"):
+        return "C"
+    elif filename.endswith(".cu"):
+        return "C"
+    elif filename.endswith(".cuh"):
+        return "C"
+    elif filename.endswith(".go"):
+        return "C"
+    elif filename.endswith(".proto"):
+        return "C"
+    else:
+        print("Unsupported filetype %s", filename)
+        exit(0)
+
+
+PYTHON_ENCODE = re.compile("^[ \t\v]*#.*?coding[:=][ \t]*([-_.a-zA-Z0-9]+)")
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(
+        description='Checker for copyright declaration.')
+    parser.add_argument('filenames', nargs='*', help='Filenames to check')
+    args = parser.parse_args(argv)
+
+    retv = 0
+    for filename in args.filenames:
+        fd = io.open(filename, encoding="utf-8")
+        first_line = fd.readline()
+        second_line = fd.readline()
+        if "COPYRIGHT (C)" in first_line.upper(): continue
+        if first_line.startswith("#!") or PYTHON_ENCODE.match(
+                second_line) != None or PYTHON_ENCODE.match(first_line) != None:
+            continue
+        original_contents = io.open(filename, encoding="utf-8").read()
+        new_contents = generate_copyright(
+            COPYRIGHT, lang_type(filename)) + original_contents
+        print('Auto Insert Copyright Header {}'.format(filename))
+        retv = 1
+        with io.open(filename, 'w') as output_file:
+            output_file.write(new_contents)
+
+    return retv
+
+
+if __name__ == '__main__':
+    exit(main())
\ No newline at end of file

README.md

@@ -6,21 +6,17 @@
 
 ## Table of Contents
 - [Installation](#installation)
-- [Running in Docker Container](#running-in-docker-container)
 - [Getting Started](#getting-started)
 - [Data Preparation](#data-preparation)
 - [Training a Model](#training-a-model)
-- [Data Augmentation Pipeline](#data-augmentation-pipeline)
 - [Inference and Evaluation](#inference-and-evaluation)
 - [Hyper-parameters Tuning](#hyper-parameters-tuning)
-- [Training for Mandarin Language](#training-for-mandarin-language)
 - [Trying Live Demo with Your Own Voice](#trying-live-demo-with-your-own-voice)
-- [Released Models](#released-models)
 - [Experiments and Benchmarks](#experiments-and-benchmarks)
+- [Released Models](#released-models)
 - [Questions and Help](#questions-and-help)
 
 
-
 ## Installation
 
 To avoid the trouble of environment setup, [running in Docker container](#running-in-docker-container) is highly recommended. Otherwise follow the guidelines below to install the dependencies manually.
@@ -105,41 +101,16 @@ Let's take a tiny sampled subset of [LibriSpeech dataset](http://www.openslr.org
     ```
 
     Notice that this is only a toy example with a tiny sampled subset of LibriSpeech. If you would like to try with the complete dataset (would take several days for training), please go to `examples/librispeech` instead.
-- Prepare the data
-
-    ```bash
-    sh run_data.sh
-    ```
-
-    `run_data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `./dataset/librispeech` and the corresponding manifest files generated in `./data/tiny` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
-- Train your own ASR model
-
-    ```bash
-    sh run_train.sh
-    ```
-
-    `run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `./checkpoints/tiny`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
-- Case inference with an existing model
-
-    ```bash
-    sh run_infer.sh
-    ```
-
-    `run_infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
-
+- Source env
+    
     ```bash
-    sh run_infer_golden.sh
+    source path.sh
     ```
-- Evaluate an existing model
+    Set `MAIN_ROOT` as project dir.
+- Main entrypoint
 
     ```bash
-    sh run_test.sh
-    ```
-
-    `run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
-
-    ```bash
-    sh run_test_golden.sh
+    bash run.sh
     ```
 
 More detailed information are provided in the following sections. Wish you a happy journey with the *DeepSpeech2 on PaddlePaddle* ASR engine!
@@ -158,7 +129,7 @@ More detailed information are provided in the following sections. Wish you a hap
 
 To use your custom data, you only need to generate such manifest files to summarize the dataset. Given such summarized manifests, training, inference and all other modules can be aware of where to access the audio files, as well as their meta data including the transcription labels.
 
-For how to generate such manifest files, please refer to `data/librispeech/librispeech.py`, which will download data and generate manifest files for LibriSpeech dataset.
+For how to generate such manifest files, please refer to `examples/librispeech/local/librispeech.py`, which will download data and generate manifest files for LibriSpeech dataset.
 
 ### Compute Mean & Stddev for Normalizer
 
@@ -168,11 +139,11 @@ To perform z-score normalization (zero-mean, unit stddev) upon audio features, w
 python3 tools/compute_mean_std.py \
 --num_samples 2000 \
 --specgram_type linear \
---manifest_path data/librispeech/manifest.train \
---output_path data/librispeech/mean_std.npz
+--manifest_path examples/librispeech/data/manifest.train \
+--output_path examples/librispeech/data/mean_std.npz
 ```
 
-It will compute the mean and standard deviatio of power spectrum feature with 2000 random sampled audio clips listed in `data/librispeech/manifest.train` and save the results to `data/librispeech/mean_std.npz` for further usage.
+It will compute the mean and standard deviatio of power spectrum feature with 2000 random sampled audio clips listed in `examples/librispeech/data/manifest.train` and save the results to `examples/librispeech/data/mean_std.npz` for further usage.
 
 
 ### Build Vocabulary
@@ -182,18 +153,18 @@ A vocabulary of possible characters is required to convert the transcription int
 ```bash
 python3 tools/build_vocab.py \
 --count_threshold 0 \
---vocab_path data/librispeech/eng_vocab.txt \
---manifest_paths data/librispeech/manifest.train
+--vocab_path examples/librispeech/data/eng_vocab.txt \
+--manifest_paths examples/librispeech/data/manifest.train
 ```
 
-It will write a vocabuary file `data/librispeeech/eng_vocab.txt` with all transcription text in `data/librispeech/manifest.train`, without vocabulary truncation (`--count_threshold 0`).
+It will write a vocabuary file `examples/librispeech/data/eng_vocab.txt` with all transcription text in `examples/librispeech/data/manifest.train`, without vocabulary truncation (`--count_threshold 0`).
 
 ### More Help
 
 For more help on arguments:
 
 ```bash
-python3 data/librispeech/librispeech.py --help
+python3 examples/librispeech/local/librispeech.py --help
 python3 tools/compute_mean_std.py --help
 python3 tools/build_vocab.py --help
 ```
@@ -226,10 +197,10 @@ For more help on arguments:
 ```bash
 python3 train.py --help
 ```
-or refer to `example/librispeech/run_train.sh`.
+or refer to `example/librispeech/local/run_train.sh`.
 
 
-## Data Augmentation Pipeline
+### Data Augmentation Pipeline
 
 Data augmentation has often been a highly effective technique to boost the deep learning performance. We augment our speech data by synthesizing new audios with small random perturbation (label-invariant transformation) added upon raw audios. You don't have to do the syntheses on your own, as it is already embedded into the data provider and is done on the fly, randomly for each epoch during training.
 
@@ -265,6 +236,12 @@ For other configuration examples, please refer to `conf/augmenatation.config.exa
 
 Be careful when utilizing the data augmentation technique, as improper augmentation will do harm to the training, due to the enlarged train-test gap.
 
+
+### Training for Mandarin Language
+
+The key steps of training for Mandarin language are same to that of English language and we have also provided an example for Mandarin training with Aishell in ```examples/aishell/local```. As mentioned above, please execute ```sh run_data.sh```, ```sh run_train.sh```, ```sh run_test.sh``` and ```sh run_infer.sh``` to do data preparation, training, testing and inference correspondingly. We have also prepared a pre-trained model (downloaded by ./models/aishell/download_model.sh) for users to try with ```sh run_infer_golden.sh``` and ```sh run_test_golden.sh```. Notice that, different from English LM, the Mandarin LM is character-based and please run ```tools/tune.py``` to find an optimal setting.
+
+
 ## Inference and Evaluation
 
 ### Prepare Language Model
@@ -322,7 +299,7 @@ For more help on arguments:
 ```
 python3 infer.py --help
 ```
-or refer to `example/librispeech/run_infer.sh`.
+or refer to `example/librispeech/local/run_infer.sh`.
 
 ### Evaluate a Model
 
@@ -347,7 +324,7 @@ For more help on arguments:
 ```bash
 python3 test.py --help
 ```
-or refer to `example/librispeech/run_test.sh`.
+or refer to `example/librispeech/local/run_test.sh`.
 
 ## Hyper-parameters Tuning
 
@@ -387,11 +364,8 @@ After tuning, you can reset $\alpha$ and $\beta$ in the inference and evaluation
 ```bash
 python3 tune.py --help
 ```
-or refer to `example/librispeech/run_tune.sh`.
+or refer to `example/librispeech/local/run_tune.sh`.
 
-## Training for Mandarin Language
-
-The key steps of training for Mandarin language are same to that of English language and we have also provided an example for Mandarin training with Aishell in ```examples/aishell```. As mentioned above, please execute ```sh run_data.sh```, ```sh run_train.sh```, ```sh run_test.sh``` and ```sh run_infer.sh``` to do data preparation, training, testing and inference correspondingly. We have also prepared a pre-trained model (downloaded by ./models/aishell/download_model.sh) for users to try with ```sh run_infer_golden.sh``` and ```sh run_test_golden.sh```. Notice that, different from English LM, the Mandarin LM is character-based and please run ```tools/tune.py``` to find an optimal setting.
 
 ## Trying Live Demo with Your Own Voice
 
@@ -429,7 +403,7 @@ Now, in the client console, press the `whitespace` key, hold, and start speaking
 
 Notice that `deploy/demo_client.py` must be run on a machine with a microphone device, while `deploy/demo_server.py` could be run on one without any audio recording hardware, e.g. any remote server machine. Just be careful to set the `host_ip` and `host_port` argument with the actual accessible IP address and port, if the server and client are running with two separate machines. Nothing should be done if they are running on one single machine.
 
-Please also refer to `examples/deploy_demo/run_english_demo_server.sh`, which will first download a pre-trained English model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/mandarin/run_demo_client.sh`, you can speak English to test it. If you would like to try some other models, just update `--model_path` argument in the script.  
+Please also refer to `examples/deploy_demo/run_english_demo_server.sh`, which will first download a pre-trained English model (trained with 3000 hours of internal speech data) and then start the demo server with the model. With running `examples/deploy_demo/run_demo_client.sh`, you can speak English to test it. If you would like to try some other models, just update `--model_path` argument in the script.  
 
 For more help on arguments:
 
@@ -438,24 +412,6 @@ python3 deploy/demo_server.py --help
 python3 deploy/demo_client.py --help
 ```
 
-## Released Models
-
-#### Speech Model Released
-
-Language  | Model Name | Training Data | Hours of Speech
-:-----------: | :------------: | :----------: |  -------:
-English  | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
-English  | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model_fluid.tar.gz) | Baidu Internal English Dataset | 8628 h
-Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_fluid.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
-Mandarin | [BaiduCN1.2k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_cn1.2k_model_fluid.tar.gz) | Baidu Internal Mandarin Dataset | 1204 h
-
-#### Language Model Released
-
-Language Model | Training Data | Token-based | Size | Descriptions
-:-------------:| :------------:| :-----: | -----: | :-----------------
-[English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) |  [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie'  binary with '-a 22 -q 8 -b 8'
-[Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings
-[Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings
 
 ## Experiments and Benchmarks
 
@@ -494,6 +450,27 @@ We compare the training time with 1, 2, 4, 8 Tesla V100 GPUs (with a subset of L
 
 `tools/profile.sh` provides such a profiling tool.
 
+
+## Released Models
+
+#### Speech Model Released
+
+Language  | Model Name | Training Data | Hours of Speech
+:-----------: | :------------: | :----------: |  -------:
+English  | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
+English  | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model_fluid.tar.gz) | Baidu Internal English Dataset | 8628 h
+Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_fluid.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
+Mandarin | [BaiduCN1.2k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_cn1.2k_model_fluid.tar.gz) | Baidu Internal Mandarin Dataset | 1204 h
+
+#### Language Model Released
+
+Language Model | Training Data | Token-based | Size | Descriptions
+:-------------:| :------------:| :-----: | -----: | :-----------------
+[English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) |  [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie'  binary with '-a 22 -q 8 -b 8'
+[Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings
+[Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings
+
+
 ## Questions and Help
 
 You are welcome to submit questions and bug reports in [Github Issues](https://github.com/PaddlePaddle/DeepSpeech/issues). You are also welcome to contribute to this project.

README_cn.md

@@ -7,17 +7,14 @@
 
 ## 目录
 - [安装](#安装)
-- [在 Docker 容器上运行](#在Docker容器上运行)
 - [开始](#开始)
 - [数据准备](#数据准备)
 - [训练模型](#训练模型)
-- [数据增强流水线](#数据增强流水线)
 - [推断和评价](#推断和评价)
 - [超参数调整](#超参数调整)
-- [训练汉语语言](#训练汉语语言)
 - [用自己的声音尝试现场演示](#用自己的声音尝试现场演示)
-- [发布模型](#发布模型)
 - [试验和基准](#试验和基准)
+- [发布模型](#发布模型)
 - [问题和帮助](#问题和帮助)
 
 ## 安装
@@ -102,42 +99,16 @@ python3 -m pip install paddlepaddle-gpu==1.8.0.post107
     cd examples/tiny
     ```
 
-    注意这仅仅是 LibriSpeech 一个小数据集的例子。如果你想尝试完整的数据集（可能需要花好几天来训练模型），请使用这个路径`examples/librispeech`。  
-- 准备数据
-
-    ```bash
-    sh run_data.sh
-    ```
-
-    运行`run_data.sh`脚本将会下载数据集，产出 manifests 文件，收集一些归一化需要的统计信息并建立词表。当数据准备完成之后，下载完的数据（仅有 LibriSpeech 一部分）在`dataset/librispeech`中；其对应的 manifest 文件，均值标准差和词表文件在`./data/tiny`中。在第一次执行的时候一定要执行这个脚本，在接下来所有的实验中我们都会用到这个数据集。  
-- 训练你自己的 ASR 模型
-
-    ```bash
-    sh run_train.sh
-    ```
-
-    `run_train.sh`将会启动训练任务，训练日志会打印到终端，并且模型每个 epoch 的 checkpoint 都会保存到`./checkpoints/tiny`目录中。这些 checkpoint 可以用来恢复训练，推断，评价和部署。
-- 用已有的模型进行案例推断
-
-    ```bash
-    sh run_infer.sh
-    ```
-
-    `run_infer.sh`将会利用训练好的模型展现一些（默认 10 个）样本语音到文本的解码结果。由于当前模型只使用了 LibriSpeech 一部分数据集训练，因此性能可能不会太好。为了看到更好模型上的表现，你可以下载一个已训练好的模型（用完整的 LibriSpeech 训练了好几天）来做推断。
-
-    ```bash
-    sh run_infer_golden.sh
-    ```
-- 评价一个已经存在的模型
+    注意这仅仅是 LibriSpeech 一个小数据集的例子。如果你想尝试完整的数据集（可能需要花好几天来训练模型），请使用这个路径`examples/librispeech`。   
+- 设置环境变量
 
     ```bash
-    sh run_test.sh
+    source path.sh
     ```
-
-    `run_test.sh`能够利用误字率（或字符错误率）来评价模型。类似的，你可以下载一个完全训练好的模型来测试它的性能：
+- 入口脚本
 
     ```bash
-    sh run_test_golden.sh
+    bash run.sh 
     ```
 
 更多细节会在接下来的章节中阐述。祝你在*DeepSpeech2*ASR引擎学习中过得愉快！
@@ -156,7 +127,7 @@ python3 -m pip install paddlepaddle-gpu==1.8.0.post107
 
 如果你要使用自定义数据，你只需要按照以上格式生成自己的 manifest 文件即可。给定 manifest 文件，训练、推断以及其它所有模块都能够访问到音频数据以及对应的时长和标签数据。
 
-关于如何生成 manifest 文件，请参考`data/librispeech/librispeech.py`。该脚本将会下载 LibriSpeech 数据集并生成 manifest 文件。
+关于如何生成 manifest 文件，请参考`examples/librispeech/local/librispeech.py`。该脚本将会下载 LibriSpeech 数据集并生成 manifest 文件。
 
 ### 计算均值和标准差用于归一化
 
@@ -166,11 +137,11 @@ python3 -m pip install paddlepaddle-gpu==1.8.0.post107
 python3 tools/compute_mean_std.py \
 --num_samples 2000 \
 --specgram_type linear \
---manifest_path data/librispeech/manifest.train \
---output_path data/librispeech/mean_std.npz
+--manifest_path examples/librispeech/data/manifest.train \
+--output_path examples/librispeech/data/mean_std.npz
 ```
 
-以上这段代码会计算在`data/librispeech/manifest.train`路径中，2000 个随机采样的语音频谱特征的均值和标准差，并将结果保存在`data/librispeech/mean_std.npz`中，方便以后使用。
+以上这段代码会计算在`examples/librispeech/data/manifest.train`路径中，2000 个随机采样的语音频谱特征的均值和标准差，并将结果保存在`examples/librispeech/data/mean_std.npz`中，方便以后使用。
 
 ### 建立词表
 
@@ -179,22 +150,24 @@ python3 tools/compute_mean_std.py \
 ```bash
 python3 tools/build_vocab.py \
 --count_threshold 0 \
---vocab_path data/librispeech/eng_vocab.txt \
---manifest_paths data/librispeech/manifest.train
+--vocab_path examples/librispeech/data/eng_vocab.txt \
+--manifest_paths examples/librispeech/data/manifest.train
 ```
 
-它将`data/librispeech/manifest.train`目录中的所有录音文本写入词表文件`data/librispeeech/eng_vocab.txt`，并且没有词汇截断(`--count_threshold 0`)。
+它将`examples/librispeech/data/manifest.train`目录中的所有录音文本写入词表文件`examples/librispeeech/data/eng_vocab.txt`，并且没有词汇截断(`--count_threshold 0`)。
 
 ### 更多帮助
 
 获得更多帮助：
 
 ```bash
-python3 data/librispeech/librispeech.py --help
+python3 examples/librispeech/local/librispeech.py --help
 python3 tools/compute_mean_std.py --help
 python3 tools/build_vocab.py --help
 ```
 
+
+
 ## 训练模型
 
 `train.py`是训练模块的主要调用者。使用示例如下。
@@ -224,10 +197,10 @@ python3 tools/build_vocab.py --help
 ```bash
 python3 train.py --help
 ```
-或参考 `example/librispeech/run_train.sh`.
+或参考 `example/librispeech/local/run_train.sh`.
 
 
-## 数据增强流水线
+### 数据增强流水线
 
 数据增强是用来提升深度学习性能的非常有效的技术。我们通过在原始音频中添加小的随机扰动（标签不变转换）获得新音频来增强我们的语音数据。你不必自己合成，因为数据增强已经嵌入到数据生成器中并且能够即时完成，在训练模型的每个epoch中随机合成音频。
 
@@ -263,6 +236,12 @@ python3 train.py --help
 
 使用数据增强技术时要小心，由于扩大了训练和测试集的差异，不恰当的增强会对训练模型不利，导致训练和预测的差距增大。
 
+### 训练普通话语言
+
+普通话语言训练与英语训练的关键步骤相同，我们提供了一个使用 Aishell 进行普通话训练的例子```examples/aishell```。如上所述，请执行```sh run_data.sh```, ```sh run_train.sh```, ```sh run_test.sh```和```sh run_infer.sh```做相应的数据准备，训练，测试和推断。我们还准备了一个预训练过的模型（执行./models/aishell/download_model.sh下载）供用户使用```run_infer_golden.sh```和```run_test_golden.sh```来。请注意，与英语语言模型不同，普通话语言模型是基于汉字的，请运行```tools/tune.py```来查找最佳设置。
+
+
+
 ## 推断和评价
 
 ### 准备语言模型
@@ -321,7 +300,7 @@ bash download_lm_ch.sh
 ```
 python3 infer.py --help
 ```
-或参考`example/librispeech/run_infer.sh`.
+或参考`example/librispeech/local/run_infer.sh`.
 
 ### 评估模型
 
@@ -346,7 +325,9 @@ python3 infer.py --help
 ```bash
 python3 test.py --help
 ```
-或参考`example/librispeech/run_test.sh`.
+或参考`example/librispeech/local/run_test.sh`.
+
+
 
 ## 超参数调整
 
@@ -386,11 +367,8 @@ python3 test.py --help
 ```bash
 python3 tune.py --help
 ```
-或参考`example/librispeech/run_tune.sh`.
-
-## 训练普通话语言
+或参考`example/librispeech/local/run_tune.sh`.
 
-普通话语言训练与英语训练的关键步骤相同，我们提供了一个使用 Aishell 进行普通话训练的例子```examples/aishell```。如上所述，请执行```sh run_data.sh```, ```sh run_train.sh```, ```sh run_test.sh```和```sh run_infer.sh```做相应的数据准备，训练，测试和推断。我们还准备了一个预训练过的模型（执行./models/aishell/download_model.sh下载）供用户使用```run_infer_golden.sh```和```run_test_golden.sh```来。请注意，与英语语言模型不同，普通话语言模型是基于汉字的，请运行```tools/tune.py```来查找最佳设置。
 
 ## 用自己的声音尝试现场演示
 
@@ -428,7 +406,7 @@ python3 -u deploy/demo_client.py \
 
 请注意，`deploy/demo_client.py`必须在带麦克风设备的机器上运行，而`deploy/demo_server.py`可以在没有任何录音硬件的情况下运行，例如任何远程服务器机器。如果服务器和客户端使用两台独立的机器运行，只需要注意将`host_ip`和`host_port`参数设置为实际可访问的IP地址和端口。如果它们在单台机器上运行，则不用作任何处理。
 
-请参考`examples/deploy_demo/run_english_demo_server.sh`，它将首先下载一个预先训练过的英语模型（用3000小时的内部语音数据训练），然后用模型启动演示服务器。通过运行`examples/mandarin/run_demo_client.sh`，你可以说英语来测试它。如果您想尝试其他模型，只需更新脚本中的`--model_path`参数即可。
+请参考`examples/deploy_demo/run_english_demo_server.sh`，它将首先下载一个预先训练过的英语模型（用3000小时的内部语音数据训练），然后用模型启动演示服务器。通过运行`examples/deploy_demo/run_demo_client.sh`，你可以说英语来测试它。如果您想尝试其他模型，只需更新脚本中的`--model_path`参数即可。
 
 获得更多帮助：
 
@@ -437,24 +415,6 @@ python3 deploy/demo_server.py --help
 python3 deploy/demo_client.py --help
 ```
 
-## 发布模型
-
-#### 语音模型发布
-
-语种  | 模型名 | 训练数据 | 语音时长
-:-----------: | :------------: | :----------: |  -------:
-English  | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
-English  | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model_fluid.tar.gz) | Baidu Internal English Dataset | 8628 h
-Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_fluid.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
-Mandarin | [BaiduCN1.2k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_cn1.2k_model_fluid.tar.gz) | Baidu Internal Mandarin Dataset | 1204 h
-
-#### 语言模型发布
-
-语言模型 | 训练数据 | 基于的字符 | 大小 | 描述
-:-------------:| :------------:| :-----: | -----: | :-----------------
-[English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) |  [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie'  binary with '-a 22 -q 8 -b 8'
-[Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings
-[Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings
 
 ## 实验和baseline
 
@@ -494,6 +454,27 @@ Baidu Internal Testset  |   12.64
 
 `tools/profile.sh`提供了上述分析工具.
 
+
+## 发布模型
+
+#### 语音模型发布
+
+语种  | 模型名 | 训练数据 | 语音时长
+:-----------: | :------------: | :----------: |  -------:
+English  | [LibriSpeech Model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_model_fluid.tar.gz) | [LibriSpeech Dataset](http://www.openslr.org/12/) | 960 h
+English  | [BaiduEN8k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_en8k_model_fluid.tar.gz) | Baidu Internal English Dataset | 8628 h
+Mandarin | [Aishell Model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_fluid.tar.gz) | [Aishell Dataset](http://www.openslr.org/33/) | 151 h
+Mandarin | [BaiduCN1.2k Model](https://deepspeech.bj.bcebos.com/demo_models/baidu_cn1.2k_model_fluid.tar.gz) | Baidu Internal Mandarin Dataset | 1204 h
+
+#### 语言模型发布
+
+语言模型 | 训练数据 | 基于的字符 | 大小 | 描述
+:-------------:| :------------:| :-----: | -----: | :-----------------
+[English LM](https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm) |  [CommonCrawl(en.00)](http://web-language-models.s3-website-us-east-1.amazonaws.com/ngrams/en/deduped/en.00.deduped.xz) | Word-based | 8.3 GB | Pruned with 0 1 1 1 1; <br/> About 1.85 billion n-grams; <br/> 'trie'  binary with '-a 22 -q 8 -b 8'
+[Mandarin LM Small](https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm) | Baidu Internal Corpus | Char-based | 2.8 GB | Pruned with 0 1 2 4 4; <br/> About 0.13 billion n-grams; <br/> 'probing' binary with default settings
+[Mandarin LM Large](https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm) | Baidu Internal Corpus | Char-based | 70.4 GB | No Pruning; <br/> About 3.7 billion n-grams; <br/> 'probing' binary with default settings
+
+
 ## 问题和帮助
 
 欢迎您在[Github问题](https://github.com/PaddlePaddle/models/issues)中提交问题和bug。也欢迎您为这个项目做出贡献。

data/noise/chime3_background.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.
 """Prepare CHiME3 background data.
 
 Download, unpack and create manifest files.

data/voxforge/voxforge.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.
 """Prepare VoxForge dataset
 
 Download, unpack and create manifest files.

data_utils/__init__.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.

data_utils/audio.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.
 """Contains the audio segment class."""
 
 import numpy as np

data_utils/augmentor/__init__.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.

data_utils/augmentor/augmentation.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.
 """Contains the data augmentation pipeline."""
 
 import json

data_utils/augmentor/base.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.
 """Contains the abstract base class for augmentation models."""
 
 from abc import ABCMeta, abstractmethod

data_utils/augmentor/impulse_response.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.
 """Contains the impulse response augmentation model."""
 
 from data_utils.augmentor.base import AugmentorBase

data_utils/augmentor/noise_perturb.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.
 """Contains the noise perturb augmentation model."""
 
 from data_utils.augmentor.base import AugmentorBase

data_utils/augmentor/online_bayesian_normalization.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.
 """Contain the online bayesian normalization augmentation model."""
 
 from data_utils.augmentor.base import AugmentorBase

data_utils/augmentor/resample.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.
 """Contain the resample augmentation model."""
 
 from data_utils.augmentor.base import AugmentorBase

data_utils/augmentor/shift_perturb.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.
 """Contains the volume perturb augmentation model."""
 
 from data_utils.augmentor.base import AugmentorBase

data_utils/augmentor/speed_perturb.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.
 """Contain the speech perturbation augmentation model."""
 
 from data_utils.augmentor.base import AugmentorBase

data_utils/augmentor/volume_perturb.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.
 """Contains the volume perturb augmentation model."""
 
 from data_utils.augmentor.base import AugmentorBase

data_utils/data.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.
 """Contains data generator for orgnaizing various audio data preprocessing
 pipeline and offering data reader interface of PaddlePaddle requirements.
 """

data_utils/featurizer/__init__.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.

data_utils/featurizer/audio_featurizer.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.
 """Contains the audio featurizer class."""
 
 import numpy as np

data_utils/featurizer/speech_featurizer.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.
 """Contains the speech featurizer class."""
 
 from data_utils.featurizer.audio_featurizer import AudioFeaturizer

data_utils/featurizer/text_featurizer.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.
 """Contains the text featurizer class."""
 
 import os

data_utils/normalizer.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.
 """Contains feature normalizers."""
 
 import numpy as np

data_utils/speech.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.
 """Contains the speech segment class."""
 
 import numpy as np

data_utils/utility.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.
 """Contains data helper functions."""
 
 import json

decoders/__init__.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.

decoders/decoders_deprecated.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.
 """Contains various CTC decoders."""
 
 from itertools import groupby

decoders/scorer_deprecated.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.
 """External Scorer for Beam Search Decoder."""
 
 import os

decoders/swig/__init__.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.

decoders/swig/_init_paths.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.
 """Set up paths for DS2"""
 
 import os.path

decoders/swig/setup.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.
 """Script to build and install decoder package."""
 
 from setuptools import setup, Extension, distutils

decoders/swig_wrapper.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.
 """Wrapper for various CTC decoders in SWIG."""
 
 import swig_decoders

decoders/tests/test_decoders.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.
 """Test decoders."""
 
 import unittest

deploy/_init_paths.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.
 """Set up paths for DS2"""
 
 import os.path

deploy/demo_client.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.
 """Client-end for the ASR demo."""
 import keyboard
 import struct

deploy/demo_server.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.
 """Server-end for the ASR demo."""
 import os
 import time

data/aishell/aishell.py → examples/aishell/local/aishell.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.
 """Prepare Aishell mandarin dataset
 
 Download, unpack and create manifest files.

examples/aishell/run_data.sh → examples/aishell/local/run_data.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
+mkdir -p data
 
 # download data, generate manifests
-PYTHONPATH=.:$PYTHONPATH python3 data/aishell/aishell.py \
---manifest_prefix='data/aishell/manifest' \
---target_dir='./dataset/aishell'
+PYTHONPATH=.:$PYTHONPATH python3 local/aishell.py \
+--manifest_prefix="data/manifest" \
+--target_dir="${MAIN_ROOT}/dataset/aishell"
 
 if [ $? -ne 0 ]; then
     echo "Prepare Aishell failed. Terminated."
@@ -14,10 +14,10 @@ fi
 
 
 # build vocabulary
-python3 tools/build_vocab.py \
+python3 ${MAIN_ROOT}/tools/build_vocab.py \
 --count_threshold=0 \
---vocab_path='data/aishell/vocab.txt' \
---manifest_paths 'data/aishell/manifest.train' 'data/aishell/manifest.dev'
+--vocab_path="data/vocab.txt" \
+--manifest_paths "data/manifest.train" "data/manifest.dev"
 
 if [ $? -ne 0 ]; then
     echo "Build vocabulary failed. Terminated."
@@ -26,11 +26,11 @@ fi
 
 
 # compute mean and stddev for normalizer
-python3 tools/compute_mean_std.py \
---manifest_path='data/aishell/manifest.train' \
+python3 ${MAIN_ROOT}/tools/compute_mean_std.py \
+--manifest_path="data/manifest.train" \
 --num_samples=2000 \
---specgram_type='linear' \
---output_path='data/aishell/mean_std.npz'
+--specgram_type="linear" \
+--output_path="data/mean_std.npz"
 
 if [ $? -ne 0 ]; then
     echo "Compute mean and stddev failed. Terminated."

examples/aishell/run_infer.sh → examples/aishell/local/run_infer.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
 
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_ch.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -13,7 +12,7 @@ cd - > /dev/null
 
 # infer
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u infer.py \
+python3 -u ${MAIN_ROOT}/infer.py \
 --num_samples=10 \
 --beam_size=300 \
 --num_proc_bsearch=8 \
@@ -27,14 +26,14 @@ python3 -u infer.py \
 --use_gru=True \
 --use_gpu=True \
 --share_rnn_weights=False \
---infer_manifest='data/aishell/manifest.test' \
---mean_std_path='data/aishell/mean_std.npz' \
---vocab_path='data/aishell/vocab.txt' \
---model_path='checkpoints/aishell/step_final' \
---lang_model_path='models/lm/zh_giga.no_cna_cmn.prune01244.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='cer' \
---specgram_type='linear'
+--infer_manifest="data/manifest.test" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--model_path="checkpoints/step_final" \
+--lang_model_path="${MAIN_ROOT}/models/lm/zh_giga.no_cna_cmn.prune01244.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="cer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in inference!"

examples/aishell/run_infer_golden.sh → examples/aishell/local/run_infer_golden.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_ch.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -12,7 +10,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/aishell > /dev/null
+cd ${MAIN_ROOT}/models/aishell > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -22,7 +20,7 @@ cd - > /dev/null
 
 # infer
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u infer.py \
+python3 -u ${MAIN_ROOT}/infer.py \
 --num_samples=10 \
 --beam_size=300 \
 --num_proc_bsearch=8 \
@@ -36,14 +34,14 @@ python3 -u infer.py \
 --use_gru=True \
 --use_gpu=False \
 --share_rnn_weights=False \
---infer_manifest='data/aishell/manifest.test' \
---mean_std_path='models/aishell/mean_std.npz' \
---vocab_path='models/aishell/vocab.txt' \
---model_path='models/aishell' \
---lang_model_path='models/lm/zh_giga.no_cna_cmn.prune01244.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='cer' \
---specgram_type='linear'
+--infer_manifest="data/manifest.test" \
+--mean_std_path="${MAIN_ROOT}/models/aishell/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/aishell/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/aishell" \
+--lang_model_path="${MAIN_ROOT}/models/lm/zh_giga.no_cna_cmn.prune01244.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="cer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in inference!"

examples/aishell/run_test.sh → examples/aishell/local/run_test.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_ch.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -13,7 +11,7 @@ cd - > /dev/null
 
 # evaluate model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u test.py \
+python3 -u ${MAIN_ROOT}/test.py \
 --batch_size=128 \
 --beam_size=300 \
 --num_proc_bsearch=8 \
@@ -27,14 +25,14 @@ python3 -u test.py \
 --use_gru=True \
 --use_gpu=True \
 --share_rnn_weights=False \
---test_manifest='data/aishell/manifest.test' \
---mean_std_path='data/aishell/mean_std.npz' \
---vocab_path='data/aishell/vocab.txt' \
---model_path='checkpoints/aishell/step_final' \
---lang_model_path='models/lm/zh_giga.no_cna_cmn.prune01244.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='cer' \
---specgram_type='linear'
+--test_manifest="data/manifest.test" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--model_path="checkpoints/step_final" \
+--lang_model_path="${MAIN_ROOT}/models/lm/zh_giga.no_cna_cmn.prune01244.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="cer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in evaluation!"

examples/aishell/run_test_golden.sh → examples/aishell/local/run_test_golden.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_ch.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -12,7 +10,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/aishell > /dev/null
+cd ${MAIN_ROOT}/models/aishell > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -22,7 +20,7 @@ cd - > /dev/null
 
 # evaluate model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u test.py \
+python3 -u ${MAIN_ROOT}/test.py \
 --batch_size=128 \
 --beam_size=300 \
 --num_proc_bsearch=8 \
@@ -36,14 +34,14 @@ python3 -u test.py \
 --use_gru=True \
 --use_gpu=True \
 --share_rnn_weights=False \
---test_manifest='data/aishell/manifest.test' \
---mean_std_path='models/aishell/mean_std.npz' \
---vocab_path='models/aishell/vocab.txt' \
---model_path='models/aishell' \
---lang_model_path='models/lm/zh_giga.no_cna_cmn.prune01244.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='cer' \
---specgram_type='linear'
+--test_manifest="data/manifest.test" \
+--mean_std_path="${MAIN_ROOT}/models/aishell/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/aishell/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/aishell" \
+--lang_model_path="${MAIN_ROOT}/models/lm/zh_giga.no_cna_cmn.prune01244.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="cer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in evaluation!"

examples/aishell/run_train.sh → examples/aishell/local/run_train.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # train model
 # if you wish to resume from an exists model, uncomment --init_from_pretrained_model
 export FLAGS_sync_nccl_allreduce=0
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u train.py \
+python3 -u ${MAIN_ROOT}/train.py \
 --batch_size=64 \
 --num_epoch=50 \
 --num_conv_layers=2 \
@@ -24,14 +22,14 @@ python3 -u train.py \
 --use_gpu=True \
 --is_local=True \
 --share_rnn_weights=False \
---train_manifest='data/aishell/manifest.train' \
---dev_manifest='data/aishell/manifest.dev' \
---mean_std_path='data/aishell/mean_std.npz' \
---vocab_path='data/aishell/vocab.txt' \
---output_model_dir='./checkpoints/aishell' \
---augment_conf_path='conf/augmentation.config' \
---specgram_type='linear' \
---shuffle_method='batch_shuffle_clipped' \
+--train_manifest="data/manifest.train" \
+--dev_manifest="data/manifest.dev" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--output_model_dir="./checkpoints" \
+--augment_conf_path="${MAIN_ROOT}/conf/augmentation.config" \
+--specgram_type="linear" \
+--shuffle_method="batch_shuffle_clipped" \
 
 if [ $? -ne 0 ]; then
     echo "Failed in training!"

examples/aishell/path.sh

+export MAIN_ROOT=${PWD}/../../
+
+export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
+export LC_ALL=C
+
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8 
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}

examples/aishell/run.sh

+#!/bin/bash
+
+source path.sh
+
+# prepare data
+bash ./local/run_data.sh
+
+# test pretrain model
+bash ./local/run_test_golden.sh
+
+# test pretain model
+bash ./local/run_infer_golden.sh
+
+# train model
+bash ./local/run_train.sh
+
+# test model
+bash ./local/run_test.sh
+
+# infer model
+bash ./local/run_infer.sh

examples/baidu_en8k/path.sh

+export MAIN_ROOT=${PWD}/../../
+
+export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
+export LC_ALL=C
+
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8 
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}

examples/baidu_en8k/run_infer_golden.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
+source path.sh
 
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -12,7 +12,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/baidu_en8k > /dev/null
+cd ${MAIN_ROOT}/models/baidu_en8k > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -22,7 +22,7 @@ cd - > /dev/null
 
 # infer
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u infer.py \
+python3 -u ${MAIN_ROOT}/infer.py \
 --num_samples=10 \
 --beam_size=500 \
 --num_proc_bsearch=5 \
@@ -36,14 +36,14 @@ python3 -u infer.py \
 --use_gru=True \
 --use_gpu=False \
 --share_rnn_weights=False \
---infer_manifest='data/librispeech/manifest.test-clean' \
---mean_std_path='models/baidu_en8k/mean_std.npz' \
---vocab_path='models/baidu_en8k/vocab.txt' \
---model_path='models/baidu_en8k' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--infer_manifest="${MAIN_ROOT}/examples/librispeech/data/manifest.test-clean" \
+--mean_std_path="${MAIN_ROOT}/models/baidu_en8k/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/baidu_en8k/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/baidu_en8k" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in inference!"

examples/baidu_en8k/run_test_golden.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
+source path.sh
 
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -12,7 +12,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/baidu_en8k > /dev/null
+cd ${MAIN_ROOT}/models/baidu_en8k > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -22,7 +22,7 @@ cd - > /dev/null
 
 # evaluate model
 CUDA_VISIBLE_DEVICES=0,1,2,3 \
-python3 -u test.py \
+python3 -u ${MAIN_ROOT}/test.py \
 --batch_size=128 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -37,14 +37,14 @@ python3 -u test.py \
 --use_gru=True \
 --use_gpu=False \
 --share_rnn_weights=False \
---test_manifest='data/librispeech/manifest.test-clean' \
---mean_std_path='models/baidu_en8k/mean_std.npz' \
---vocab_path='models/baidu_en8k/vocab.txt' \
---model_path='models/baidu_en8k' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--test_manifest="data/manifest.test-clean" \
+--mean_std_path="${MAIN_ROOT}/models/baidu_en8k/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/baidu_en8k/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/baidu_en8k" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in evaluation!"

examples/deploy_demo/path.sh

+export MAIN_ROOT=${PWD}/../../
+
+export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
+export LC_ALL=C
+
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8 
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}

examples/deploy_demo/run_demo_client.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
+source path.sh
 
 # start demo client
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u deploy/demo_client.py \
---host_ip='localhost' \
+python3 -u ${MAIN_ROOT}/deploy/demo_client.py \
+--host_ip="localhost" \
 --host_port=8086 \
 
 if [ $? -ne 0 ]; then

examples/deploy_demo/run_english_demo_server.sh

 #! /usr/bin/env bash
 # TODO: replace the model with a mandarin model
 
-cd ../.. > /dev/null
+source path.sh
 
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -13,7 +13,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/baidu_en8k > /dev/null
+cd ${MAIN_ROOT}/models/baidu_en8k > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -23,8 +23,8 @@ cd - > /dev/null
 
 # start demo server
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u deploy/demo_server.py \
---host_ip='localhost' \
+python3 -u ${MAIN_ROOT}/deploy/demo_server.py \
+--host_ip="localhost" \
 --host_port=8086 \
 --num_conv_layers=2 \
 --num_rnn_layers=3 \
@@ -36,14 +36,14 @@ python3 -u deploy/demo_server.py \
 --use_gru=True \
 --use_gpu=True \
 --share_rnn_weights=False \
---speech_save_dir='demo_cache' \
---warmup_manifest='data/tiny/manifest.test-clean' \
---mean_std_path='models/baidu_en8k/mean_std.npz' \
---vocab_path='models/baidu_en8k/vocab.txt' \
---model_path='models/baidu_en8k' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---specgram_type='linear'
+--speech_save_dir="demo_cache" \
+--warmup_manifest="${MAIN_ROOT}/examples/tiny/data/manifest.test-clean" \
+--mean_std_path="${MAIN_ROOT}/models/baidu_en8k/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/baidu_en8k/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/baidu_en8k" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in starting demo server!"

data/librispeech/librispeech.py → examples/librispeech/local/librispeech.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.
 """Prepare Librispeech ASR datasets.
 
 Download, unpack and create manifest files.

examples/librispeech/run_data.sh → examples/librispeech/local/run_data.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
+mkdir -p data
 
 # download data, generate manifests
-PYTHONPATH=.:$PYTHONPATH python3 data/librispeech/librispeech.py \
---manifest_prefix='data/librispeech/manifest' \
---target_dir='./dataset/librispeech' \
---full_download='True'
+PYTHONPATH=.:$PYTHONPATH python3 local/librispeech.py \
+--manifest_prefix="data/manifest" \
+--target_dir="${MAIN_ROOT}/dataset/librispeech" \
+--full_download="True"
 
 if [ $? -ne 0 ]; then
     echo "Prepare LibriSpeech failed. Terminated."
     exit 1
 fi
 
-cat data/librispeech/manifest.train-* | shuf > data/librispeech/manifest.train
+cat data/manifest.train-* | shuf > data/manifest.train
 
 
 # build vocabulary
-python3 tools/build_vocab.py \
+python3 ${MAIN_ROOT}/tools/build_vocab.py \
 --count_threshold=0 \
---vocab_path='data/librispeech/vocab.txt' \
---manifest_paths='data/librispeech/manifest.train'
+--vocab_path="data/vocab.txt" \
+--manifest_paths="data/manifest.train"
 
 if [ $? -ne 0 ]; then
     echo "Build vocabulary failed. Terminated."
@@ -29,11 +29,11 @@ fi
 
 
 # compute mean and stddev for normalizer
-python3 tools/compute_mean_std.py \
---manifest_path='data/librispeech/manifest.train' \
+python3 ${MAIN_ROOT}/tools/compute_mean_std.py \
+--manifest_path="data/manifest.train" \
 --num_samples=2000 \
---specgram_type='linear' \
---output_path='data/librispeech/mean_std.npz'
+--specgram_type="linear" \
+--output_path="data/mean_std.npz"
 
 if [ $? -ne 0 ]; then
     echo "Compute mean and stddev failed. Terminated."

examples/tiny/run_infer.sh → examples/librispeech/local/run_infer.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -13,7 +11,7 @@ cd - > /dev/null
 
 # infer
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u infer.py \
+python3 -u ${MAIN_ROOT}/infer.py \
 --num_samples=10 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -27,14 +25,14 @@ python3 -u infer.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---infer_manifest='data/tiny/manifest.test-clean' \
---mean_std_path='data/tiny/mean_std.npz' \
---vocab_path='data/tiny/vocab.txt' \
---model_path='./checkpoints/tiny/step_final' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--infer_manifest="data/manifest.test-clean" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--model_path="checkpoints/step_final" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in inference!"

examples/tiny/run_infer_golden.sh → examples/librispeech/local/run_infer_golden.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -12,7 +10,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/librispeech > /dev/null
+cd ${MAIN_ROOT}/models/librispeech > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -22,7 +20,7 @@ cd - > /dev/null
 
 # infer
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u infer.py \
+python3 -u ${MAIN_ROOT}/infer.py \
 --num_samples=10 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -36,14 +34,14 @@ python3 -u infer.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---infer_manifest='data/tiny/manifest.test-clean' \
---mean_std_path='models/librispeech/mean_std.npz' \
---vocab_path='models/librispeech/vocab.txt' \
---model_path='models/librispeech' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--infer_manifest="data/manifest.test-clean" \
+--mean_std_path="${MAIN_ROOT}/models/librispeech/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/librispeech/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/librispeech" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in inference!"

examples/tiny/run_test.sh → examples/librispeech/local/run_test.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -13,7 +11,7 @@ cd - > /dev/null
 
 # evaluate model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u test.py \
+python3 -u ${MAIN_ROOT}/test.py \
 --batch_size=128 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -27,14 +25,14 @@ python3 -u test.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---test_manifest='data/tiny/manifest.test-clean' \
---mean_std_path='data/tiny/mean_std.npz' \
---vocab_path='data/tiny/vocab.txt' \
---model_path='checkpoints/tiny/step_final' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--test_manifest="data/manifest.test-clean" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--model_path="checkpoints/step_final" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in evaluation!"

examples/tiny/run_test_golden.sh → examples/librispeech/local/run_test_golden.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -12,7 +10,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/librispeech > /dev/null
+cd ${MAIN_ROOT}/models/librispeech > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -22,7 +20,7 @@ cd - > /dev/null
 
 # evaluate model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u test.py \
+python3 -u ${MAIN_ROOT}/test.py \
 --batch_size=128 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -36,14 +34,14 @@ python3 -u test.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---test_manifest='data/tiny/manifest.test-clean' \
---mean_std_path='models/librispeech/mean_std.npz' \
---vocab_path='models/librispeech/vocab.txt' \
---model_path='models/librispeech' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--test_manifest="data/manifest.test-clean" \
+--mean_std_path="${MAIN_ROOT}/models/librispeech/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/librispeech/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/librispeech" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in evaluation!"

examples/librispeech/run_train.sh → examples/librispeech/local/run_train.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # train model
 # if you wish to resume from an exists model, uncomment --init_from_pretrained_model
 export FLAGS_sync_nccl_allreduce=0
 
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u train.py \
+python3 -u ${MAIN_ROOT}/train.py \
 --batch_size=20 \
 --num_epoch=50 \
 --num_conv_layers=2 \
@@ -25,14 +23,14 @@ python3 -u train.py \
 --use_gpu=True \
 --is_local=True \
 --share_rnn_weights=True \
---train_manifest='data/librispeech/manifest.train' \
---dev_manifest='data/librispeech/manifest.dev-clean' \
---mean_std_path='data/librispeech/mean_std.npz' \
---vocab_path='data/librispeech/vocab.txt' \
---output_model_dir='./checkpoints/libri' \
---augment_conf_path='conf/augmentation.config' \
---specgram_type='linear' \
---shuffle_method='batch_shuffle_clipped' \
+--train_manifest="data/manifest.train" \
+--dev_manifest="data/manifest.dev-clean" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--output_model_dir="./checkpoints/libri" \
+--augment_conf_path="${MAIN_ROOT}/conf/augmentation.config" \
+--specgram_type="linear" \
+--shuffle_method="batch_shuffle_clipped" \
 
 if [ $? -ne 0 ]; then
     echo "Failed in training!"

examples/librispeech/run_tune.sh → examples/librispeech/local/run_tune.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # grid-search for hyper-parameters in language model
 CUDA_VISIBLE_DEVICES=0,1,2,3 \
-python3 -u tools/tune.py \
+python3 -u ${MAIN_ROOT}tools/tune.py \
 --num_batches=-1 \
 --batch_size=128 \
 --beam_size=500 \
@@ -23,13 +21,13 @@ python3 -u tools/tune.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---tune_manifest='data/librispeech/manifest.dev-clean' \
---mean_std_path='data/librispeech/mean_std.npz' \
---vocab_path='models/librispeech/vocab.txt' \
---model_path='models/librispeech' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---error_rate_type='wer' \
---specgram_type='linear'
+--tune_manifest="data/manifest.dev-clean" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/librispeech/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/librispeech" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in tuning!"

examples/librispeech/path.sh

+export MAIN_ROOT=${PWD}/../../
+
+export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
+export LC_ALL=C
+
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8 
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}

examples/librispeech/run.sh

+#!/bin/bash
+
+source path.sh
+
+# prepare data
+bash ./local/run_data.sh
+
+# test pretrain model
+bash ./local/run_test_golden.sh
+
+# test pretain model
+bash ./local/run_infer_golden.sh
+
+# train model
+bash ./local/run_train.sh
+
+# test model
+bash ./local/run_test.sh
+
+# infer model
+bash ./local/run_infer.sh
+
+# tune model
+bash ./local/run_tune.sh

examples/tiny/README.md

+# Tiny Example
+
+1. `source path.sh`
+2. `bash run.sh`
+
+## Steps
+- Prepare the data
+
+    ```bash
+    sh local/run_data.sh
+    ```
+
+    `run_data.sh` will download dataset, generate manifests, collect normalizer's statistics and build vocabulary. Once the data preparation is done, you will find the data (only part of LibriSpeech) downloaded in `${MAIN_ROOT}/dataset/librispeech` and the corresponding manifest files generated in `${PWD}/data` as well as a mean stddev file and a vocabulary file. It has to be run for the very first time you run this dataset and is reusable for all further experiments.
+- Train your own ASR model
+
+    ```bash
+    sh local/run_train.sh
+    ```
+
+    `run_train.sh` will start a training job, with training logs printed to stdout and model checkpoint of every pass/epoch saved to `${PWD}/checkpoints`. These checkpoints could be used for training resuming, inference, evaluation and deployment.
+- Case inference with an existing model
+
+    ```bash
+    sh local/run_infer.sh
+    ```
+
+    `run_infer.sh` will show us some speech-to-text decoding results for several (default: 10) samples with the trained model. The performance might not be good now as the current model is only trained with a toy subset of LibriSpeech. To see the results with a better model, you can download a well-trained (trained for several days, with the complete LibriSpeech) model and do the inference:
+
+    ```bash
+    sh local/run_infer_golden.sh
+    ```
+- Evaluate an existing model
+
+    ```bash
+    sh local/run_test.sh
+    ```
+
+    `run_test.sh` will evaluate the model with Word Error Rate (or Character Error Rate) measurement. Similarly, you can also download a well-trained model and test its performance:
+
+    ```bash
+    sh local/run_test_golden.sh
+    ```
\ No newline at end of file

examples/tiny/run_data.sh → examples/tiny/local/run_data.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # prepare folder
-if [ ! -e data/tiny ]; then
-    mkdir data/tiny
+if [ ! -e data ]; then
+    mkdir data
 fi
 
 # download data, generate manifests
-PYTHONPATH=.:$PYTHONPATH python3 data/librispeech/librispeech.py \
---manifest_prefix='data/tiny/manifest' \
---target_dir='./dataset/librispeech' \
---full_download='False'
+PYTHONPATH=.:$PYTHONPATH python3 ../librispeech/local/librispeech.py \
+--manifest_prefix="data/manifest" \
+--target_dir="${MAIN_ROOT}/dataset/librispeech" \
+--full_download="False"
 
 if [ $? -ne 0 ]; then
     echo "Prepare LibriSpeech failed. Terminated."
     exit 1
 fi
 
-head -n 64 data/tiny/manifest.dev-clean  > data/tiny/manifest.tiny
+head -n 64 data/manifest.dev-clean  > data/manifest.tiny
 
 # build vocabulary
-python3 tools/build_vocab.py \
+python3 ${MAIN_ROOT}/tools/build_vocab.py \
 --count_threshold=0 \
---vocab_path='data/tiny/vocab.txt' \
---manifest_paths='data/tiny/manifest.tiny'
+--vocab_path="data/vocab.txt" \
+--manifest_paths="data/manifest.tiny"
 
 if [ $? -ne 0 ]; then
     echo "Build vocabulary failed. Terminated."
@@ -33,11 +31,11 @@ fi
 
 
 # compute mean and stddev for normalizer
-python3 tools/compute_mean_std.py \
---manifest_path='data/tiny/manifest.tiny' \
+python3 ${MAIN_ROOT}/tools/compute_mean_std.py \
+--manifest_path="data/manifest.tiny" \
 --num_samples=64 \
---specgram_type='linear' \
---output_path='data/tiny/mean_std.npz'
+--specgram_type="linear" \
+--output_path="data/mean_std.npz"
 
 if [ $? -ne 0 ]; then
     echo "Compute mean and stddev failed. Terminated."

examples/librispeech/run_infer.sh → examples/tiny/local/run_infer.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd $MAIN_ROOT/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -13,7 +11,7 @@ cd - > /dev/null
 
 # infer
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u infer.py \
+python3 -u $MAIN_ROOT/infer.py \
 --num_samples=10 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -27,14 +25,14 @@ python3 -u infer.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---infer_manifest='data/librispeech/manifest.test-clean' \
---mean_std_path='data/librispeech/mean_std.npz' \
---vocab_path='data/librispeech/vocab.txt' \
---model_path='checkpoints/libri/step_final' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--infer_manifest="data/manifest.test-clean" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--model_path="checkpoints/step_final" \
+--lang_model_path="$MAIN_ROOT/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in inference!"

examples/librispeech/run_infer_golden.sh → examples/tiny/local/run_infer_golden.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd ${MAIN_ROOT}/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -12,7 +10,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/librispeech > /dev/null
+cd ${MAIN_ROOT}/models/librispeech > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -22,7 +20,7 @@ cd - > /dev/null
 
 # infer
 CUDA_VISIBLE_DEVICES=0 \
-python3 -u infer.py \
+python3 -u ${MAIN_ROOT}/infer.py \
 --num_samples=10 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -36,14 +34,14 @@ python3 -u infer.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---infer_manifest='data/librispeech/manifest.test-clean' \
---mean_std_path='models/librispeech/mean_std.npz' \
---vocab_path='models/librispeech/vocab.txt' \
---model_path='models/librispeech' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--infer_manifest="data/manifest.test-clean" \
+--mean_std_path="${MAIN_ROOT}/models/librispeech/mean_std.npz" \
+--vocab_path="${MAIN_ROOT}/models/librispeech/vocab.txt" \
+--model_path="${MAIN_ROOT}/models/librispeech" \
+--lang_model_path="${MAIN_ROOT}/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in inference!"

examples/librispeech/run_test.sh → examples/tiny/local/run_test.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd $MAIN_ROOT/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -13,7 +11,7 @@ cd - > /dev/null
 
 # evaluate model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u test.py \
+python3 -u $MAIN_ROOT/test.py \
 --batch_size=128 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -27,14 +25,14 @@ python3 -u test.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---test_manifest='data/librispeech/manifest.test-clean' \
---mean_std_path='data/librispeech/mean_std.npz' \
---vocab_path='data/librispeech/vocab.txt' \
---model_path='checkpoints/libri/step_final' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--test_manifest="data/manifest.test-clean" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--model_path="checkpoints/step_final" \
+--lang_model_path="$MAIN_ROOT/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in evaluation!"

examples/librispeech/run_test_golden.sh → examples/tiny/local/run_test_golden.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # download language model
-cd models/lm > /dev/null
+cd $MAIN_ROOT/models/lm > /dev/null
 bash download_lm_en.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -12,7 +10,7 @@ cd - > /dev/null
 
 
 # download well-trained model
-cd models/librispeech > /dev/null
+cd $MAIN_ROOT/models/librispeech > /dev/null
 bash download_model.sh
 if [ $? -ne 0 ]; then
     exit 1
@@ -22,7 +20,7 @@ cd - > /dev/null
 
 # evaluate model
 CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
-python3 -u test.py \
+python3 -u $MAIN_ROOT/test.py \
 --batch_size=128 \
 --beam_size=500 \
 --num_proc_bsearch=8 \
@@ -36,14 +34,14 @@ python3 -u test.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---test_manifest='data/librispeech/manifest.test-clean' \
---mean_std_path='models/librispeech/mean_std.npz' \
---vocab_path='models/librispeech/vocab.txt' \
---model_path='models/librispeech' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---decoding_method='ctc_beam_search' \
---error_rate_type='wer' \
---specgram_type='linear'
+--test_manifest="data/manifest.test-clean" \
+--mean_std_path="$MAIN_ROOT/models/librispeech/mean_std.npz" \
+--vocab_path="$MAIN_ROOT/models/librispeech/vocab.txt" \
+--model_path="$MAIN_ROOT/models/librispeech" \
+--lang_model_path="$MAIN_ROOT/models/lm/common_crawl_00.prune01111.trie.klm" \
+--decoding_method="ctc_beam_search" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in evaluation!"

examples/tiny/run_train.sh → examples/tiny/local/run_train.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # train model
 # if you wish to resume from an exists model, uncomment --init_from_pretrained_model
 export FLAGS_sync_nccl_allreduce=0
 CUDA_VISIBLE_DEVICES=0,1,2,3 \
-python3 -u train.py \
+python3 -u ${MAIN_ROOT}/train.py \
 --batch_size=4 \
 --num_epoch=20 \
 --num_conv_layers=2 \
@@ -24,14 +22,14 @@ python3 -u train.py \
 --use_gpu=True \
 --is_local=True \
 --share_rnn_weights=True \
---train_manifest='data/tiny/manifest.tiny' \
---dev_manifest='data/tiny/manifest.tiny' \
---mean_std_path='data/tiny/mean_std.npz' \
---vocab_path='data/tiny/vocab.txt' \
---output_model_dir='./checkpoints/tiny' \
---augment_conf_path='conf/augmentation.config' \
---specgram_type='linear' \
---shuffle_method='batch_shuffle_clipped' \
+--train_manifest="data/manifest.tiny" \
+--dev_manifest="data/manifest.tiny" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--output_model_dir="./checkpoints/" \
+--augment_conf_path="${MAIN_ROOT}/conf/augmentation.config" \
+--specgram_type="linear" \
+--shuffle_method="batch_shuffle_clipped" \
 
 if [ $? -ne 0 ]; then
     echo "Failed in training!"

examples/tiny/run_tune.sh → examples/tiny/local/run_tune.sh

 #! /usr/bin/env bash
 
-cd ../.. > /dev/null
-
 # grid-search for hyper-parameters in language model
 CUDA_VISIBLE_DEVICES=0,1,2,3 \
-python3 -u tools/tune.py \
+python3 -u $MAIN_ROOT/tools/tune.py \
 --num_batches=-1 \
 --batch_size=128 \
 --beam_size=500 \
@@ -23,13 +21,13 @@ python3 -u tools/tune.py \
 --use_gru=False \
 --use_gpu=True \
 --share_rnn_weights=True \
---tune_manifest='data/tiny/manifest.dev-clean' \
---mean_std_path='data/tiny/mean_std.npz' \
---vocab_path='data/tiny/vocab.txt' \
---model_path='models/librispeech' \
---lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm' \
---error_rate_type='wer' \
---specgram_type='linear'
+--tune_manifest="data/manifest.dev-clean" \
+--mean_std_path="data/mean_std.npz" \
+--vocab_path="data/vocab.txt" \
+--model_path="$MAIN_ROOT/models/librispeech" \
+--lang_model_path="$MAIN_ROOT/models/lm/common_crawl_00.prune01111.trie.klm" \
+--error_rate_type="wer" \
+--specgram_type="linear"
 
 if [ $? -ne 0 ]; then
     echo "Failed in tuning!"

examples/tiny/path.sh

+export MAIN_ROOT=${PWD}/../../
+
+export PATH=${MAIN_ROOT}:${PWD}/tools:${PATH}
+export LC_ALL=C
+
+# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8 
+export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}

examples/tiny/run.sh

+#!/bin/bash
+
+source path.sh
+
+# prepare data
+bash ./local/run_data.sh
+
+# test pretrain model
+bash ./local/run_test_golden.sh
+
+# test pretain model
+bash ./local/run_infer_golden.sh
+
+# train model
+bash ./local/run_train.sh
+
+# test model
+bash ./local/run_test.sh
+
+# infer model
+bash ./local/run_infer.sh
+
+# tune model
+bash ./local/run_tune.sh

infer.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.
 """Inferer for DeepSpeech2 model."""
 
 import sys

model_utils/__init__.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.

model_utils/model.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.
 """Contains DeepSpeech2 model."""
 
 import sys

model_utils/network.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 collections
 import paddle.fluid as fluid
 import numpy as np

test.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.
 """Evaluation for DeepSpeech2 model."""
 
 import argparse

tools/_init_paths.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.
 """Set up paths for DS2"""
 
 import os.path

tools/build_vocab.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.
 """Build vocabulary from manifest files.
 
 Each item in vocabulary file is a character.

tools/compute_mean_std.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.
 """Compute mean and std for feature normalizer, and save to file."""
 
 import argparse

tools/tune.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.
 """Beam search parameters tuning for DeepSpeech2 model."""
 
 import sys

train.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.
 """Trainer for DeepSpeech2 model."""
 
 import argparse

utils/__init__.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.

utils/error_rate.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.
 """This module provides functions to calculate error rate in different level.
 e.g. wer for word-level, cer for char-level.
 """

utils/tests/test_error_rate.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.
 """Test error rate."""
 
 import unittest

utils/utility.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.
 """Contains common utility functions."""
 
 import distutils.util