English | [简体中文](README_ch.md)

# PaddleSpeech



<p align="center">
  <img src="./docs/images/PaddleSpeech_log.png" />
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<div align="center">  

  <h3>
  <a href="https://github.com/Mingxue-Xu/DeepSpeech#quick-start"> Quick Start </a>
  | <a href="https://github.com/Mingxue-Xu/DeepSpeech#tutorials"> Tutorials </a>
  | <a href="https://github.com/Mingxue-Xu/DeepSpeech#model-list"> Models List </a>

</div>

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**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for two critical tasks in Speech - **Automatic Speech Recognition (ASR)** and **Text-To-Speech Synthesis (TTS)**, with modules involving state-of-art and influential models.

Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing module, and deployment. Besides, this toolkit also features at:
- **Fast and Light-weight**: we provide a high-speed and ultra-lightweight model that is convenient for industrial deployment.
- **Rule-based Chinese frontend**: our frontend contains Text Normalization (TN) and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi). Moreover, we use self-defined linguistic rules to adapt Chinese context.
- **Varieties of Functions that Vitalize Research**:
  - *Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of both ASR and TTS, and uses datasets like LibriSpeech, LJSpeech, AIShell, etc. See also [model lists](#models-list) for more details.
  - *Support of ASR streaming and non-streaming data*: This toolkit contains non-streaming/streaming models like [DeepSpeech2](http://proceedings.mlr.press/v48/amodei16.pdf), [Transformer](https://arxiv.org/abs/1706.03762), [Conformer](https://arxiv.org/abs/2005.08100) and [U2](https://arxiv.org/pdf/2012.05481.pdf).

Let's install PaddleSpeech with only a few lines of code!

>Note: The official name is still deepspeech. 2021/10/26

``` shell
# 1. Install essential libraries and paddlepaddle first.
# install prerequisites
sudo apt-get install -y sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev libsndfile1
# `pip install paddlepaddle-gpu` instead if you are using GPU.
pip install paddlepaddle

# 2.Then install PaddleSpeech.
git clone https://github.com/PaddlePaddle/DeepSpeech.git
cd DeepSpeech
pip install -e .
```


## Table of Contents

The contents of this README is as follow:
- [Alternative Installation](#installation)
- [Quick Start](#quick-start)
- [Models List](#models-list)
- [Tutorials](#tutorials)
- [FAQ and Contributing](#faq-and-contributing)
- [License](#license)
- [Acknowledgement](#acknowledgement)

## Alternative Installation

The base environment in this page is  
- Ubuntu 16.04
- python>=3.7
- paddlepaddle==2.1.2

If you want to set up PaddleSpeech in other environment, please see the [ASR installation](docs/source/asr/install.md) and [TTS installation](docs/source/tts/install.md) documents for all the alternatives.

## Quick Start

> Note: `ckptfile` should be replaced by real path that represents files or folders later. Similarly, `exp/default` is the folder that contains the pretrained models.

Try a tiny ASR DeepSpeech2 model training on toy set of LibriSpeech:

```shell
cd examples/tiny/s0/
# source the environment
source path.sh
# prepare librispeech dataset
bash local/data.sh
# evaluate your ckptfile model file
bash local/test.sh conf/deepspeech2.yaml ckptfile offline
```

For TTS, try FastSpeech2 on LJSpeech:
- Download LJSpeech-1.1 from the [ljspeech official website](https://keithito.com/LJ-Speech-Dataset/) and our prepared durations for fastspeech2 [ljspeech_alignment](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz).
- Assume your path to the dataset is `~/datasets/LJSpeech-1.1` and `./ljspeech_alignment` accordingly, preprocess your data and then use our pretrained model to synthesize:
```shell
bash ./local/preprocess.sh conf/default.yaml
bash ./local/synthesize_e2e.sh conf/default.yaml exp/default ckptfile
```



If you want to try more functions like training and tuning, please see [ASR getting started](docs/source/asr/getting_started.md) and [TTS Basic Use](/docs/source/tts/basic_usage.md).

## Models List



PaddleSpeech ASR supports a lot of mainstream models, which are summarized as follow. For more information, please refer to [ASR Models](./docs/source/asr/released_model.md).

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The current hyperlinks redirect to [Previous Parakeet](https://github.com/PaddlePaddle/Parakeet/tree/develop/examples).
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<table>
  <thead>
    <tr>
      <th>ASR Module Type</th>
      <th>Dataset</th>
      <th>Model Type</th>
      <th>Link</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="6">Acoustic Model</td>
      <td rowspan="4" >Aishell</td>
      <td >2 Conv + 5 LSTM layers with only forward direction    </td>
      <td>
      <a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds_online.5rnn.debug.tar.gz">Ds2 Online Aishell Model</a>
      </td>
    </tr>
    <tr>
      <td>2 Conv + 3 bidirectional GRU layers</td>
      <td>
      <a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s0/aishell.s0.ds2.offline.cer6p65.release.tar.gz">Ds2 Offline Aishell Model</a>
      </td>
    </tr>
    <tr>
      <td>Encoder:Conformer, Decoder:Transformer, Decoding method: Attention + CTC</td>
      <td>
      <a href = "https://deepspeech.bj.bcebos.com/release2.1/aishell/s1/aishell.release.tar.gz">Conformer Offline Aishell Model</a>
      </td>
    </tr>
    <tr>
      <td >Encoder:Conformer, Decoder:Transformer, Decoding method: Attention</td>
      <td>
      <a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz">Conformer Librispeech Model</a>
      </td>
    </tr>
      <tr>
      <td rowspan="2"> Librispeech</td>
      <td>Encoder:Conformer, Decoder:Transformer, Decoding method: Attention</td>
      <td> <a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/conformer.release.tar.gz">Conformer Librispeech Model</a> </td>
    </tr>
    <tr>
      <td>Encoder:Transformer, Decoder:Transformer, Decoding method: Attention</td>
      <td>
      <a href = "https://deepspeech.bj.bcebos.com/release2.1/librispeech/s1/transformer.release.tar.gz">Transformer Librispeech Model</a>
      </td>
    </tr>
   <tr>
      <td rowspan="3">Language Model</td>
      <td >CommonCrawl(en.00)</td>
      <td >English Language Model</td>
      <td>
      <a href = "https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm">English Language Model</a>
      </td>
    </tr>
    <tr>
      <td rowspan="2">Baidu Internal Corpus</td>
      <td>Mandarin Language Model Small</td>
      <td>
      <a href = "https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm">Mandarin Language Model Small</a>
      </td>
    </tr>
    <tr>
      <td >Mandarin Language Model Large</td>
      <td>
      <a href = "https://deepspeech.bj.bcebos.com/zh_lm/zhidao_giga.klm">Mandarin Language Model Large</a>
      </td>
    </tr>
  </tbody>
</table>


PaddleSpeech TTS mainly contains three modules: *Text Frontend*, *Acoustic Model* and *Vocoder*. Acoustic Model and Vocoder models are listed as follow:

<table>
  <thead>
    <tr>
      <th>TTS Module Type</th>
      <th>Model Type</th>
      <th>Dataset</th>
      <th>Link</th>
    </tr>
  </thead>
  <tbody>
    <tr>
    <td> Text Frontend</td>
    <td colspan="2"> &emsp; </td>
    <td>
    <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/text_frontend">chinese-fronted</a>
    </td>
    </tr>
    <tr>
      <td rowspan="7">Acoustic Model</td>
      <td >Tacotron2</td>
      <td rowspan="2" >LJSpeech</td>
      <td>
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/tts0">tacotron2-vctk</a>
      </td>
    </tr>
    <tr>
      <td>TransformerTTS</td>
      <td>
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/tts1">transformer-ljspeech</a>
      </td>
    </tr>
    <tr>
      <td>SpeedySpeech</td>
      <td>CSMSC</td>
      <td >
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/tts2">speedyspeech-csmsc</a>
      </td>
    </tr>
    <tr>
      <td rowspan="4">FastSpeech2</td>
      <td>AISHELL-3</td>
      <td>
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/aishell3/tts3">fastspeech2-aishell3</a>
      </td>
    </tr>
    <tr>
      <td>VCTK</td>
      <td> <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/vctk/tts3">fastspeech2-vctk</a> </td>
    </tr>
    <tr>
      <td>LJSpeech</td>
      <td> <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/tts3">fastspeech2-ljspeech</a> </td>
    </tr>
    <tr>
      <td>CSMSC</td>
      <td>
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/tts3">fastspeech2-csmsc</a>
      </td>
    </tr>
   <tr>
      <td rowspan="4">Vocoder</td>
      <td >WaveFlow</td>
      <td >LJSpeech</td>
      <td>
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/voc0">waveflow-ljspeech</a>
      </td>
    </tr>
    <tr>
      <td rowspan="3">Parallel WaveGAN</td>
      <td >LJSpeech</td>
      <td>
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/ljspeech/voc1">PWGAN-ljspeech</a>
      </td>
    </tr>
    <tr>
      <td >VCTK</td>
      <td>
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/vctk/voc1">PWGAN-vctk</a>
      </td>
    </tr>
    <tr>
      <td >CSMSC</td>
      <td>
      <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/csmsc/voc1">PWGAN-csmsc</a>
      </td>
    </tr>
    <tr>
    <td rowspan="2">Voice Cloning</td>
    <td>GE2E</td>
    <td >AISHELL-3, etc.</td>
    <td>
    <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/other/ge2e">ge2e</a>
    </td>
    </tr>
    <tr>
    <td>GE2E + Tactron2</td>
    <td>AISHELL-3</td>
    <td>
    <a href = "https://github.com/PaddlePaddle/DeepSpeech/tree/develop/examples/aishell3/vc0">ge2e-tactron2-aishell3</a>
    </td>
    </td>
    </tr>
  </tbody>
</table>


## Tutorials

Normally, [Speech SoTA](https://paperswithcode.com/area/speech) gives you an overview of the hot academic topics in speech. If you want to focus on the two tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.

The original ASR module is based on [Baidu's DeepSpeech](https://arxiv.org/abs/1412.5567) which is an independent product named [DeepSpeech](https://deepspeech.readthedocs.io). However, the toolkit aligns almost all the SoTA modules in the pipeline. Specifically, these modules are

* [Data Prepration](docs/source/asr/data_preparation.md)  
* [Data Augmentation](docs/source/asr/augmentation.md)  
* [Ngram LM](docs/source/asr/ngram_lm.md)  
* [Benchmark](docs/source/asr/benchmark.md)  
* [Relased Model](docs/source/asr/released_model.md)  

The TTS module is originally called [Parakeet](https://github.com/PaddlePaddle/Parakeet), and now merged with DeepSpeech. If you are interested in academic research about this function, please see [TTS research overview](https://github.com/PaddlePaddle/DeepSpeech/tree/develop/docs/source/tts#overview). Also, [this document](https://paddleparakeet.readthedocs.io/en/latest/released_models.html) is a good guideline for the pipeline components.


## FAQ and Contributing

You are warmly welcome to submit questions in [discussions](https://github.com/PaddlePaddle/DeepSpeech/discussions) and bug reports in [issues](https://github.com/PaddlePaddle/DeepSpeech/issues)! Also, we highly appreciate if you would like to contribute to this project!

## License

PaddleSpeech is provided under the [Apache-2.0 License](./LICENSE).

## Acknowledgement

PaddleSpeech depends on a lot of open source repos. See [references](docs/source/asr/reference.md) for more information.
