README.md

([简体中文](./README_cn.md)|English)
<p align="center">
  <img src="./docs/images/PaddleSpeech_logo.png" />
</p>

<p align="center">
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<div align="center">  
<h4>
    <a href="#quick-start"> Quick Start </a>
  | <a href="#quick-start-server"> Quick Start Server </a>
  | <a href="#quick-start-streaming-server"> Quick Start Streaming Server</a>
  | <a href="#documents"> Documents </a>
  | <a href="#model-list"> Models List </a>
  | <a href="https://aistudio.baidu.com/aistudio/education/group/info/25130"> AIStudio Courses </a>
  | <a href="https://arxiv.org/abs/2205.12007"> NAACL2022 Best Demo Award Paper </a>
  | <a href="https://gitee.com/paddlepaddle/PaddleSpeech"> Gitee </a>
</h4>
</div>

------------------------------------------------------------------------------------

**PaddleSpeech** is an open-source toolkit on [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) platform for a variety of critical tasks in speech and audio, with the state-of-art and influential models. 

**PaddleSpeech** won the [NAACL2022 Best Demo Award](https://2022.naacl.org/blog/best-demo-award/), please check out our paper on [Arxiv](https://arxiv.org/abs/2205.12007).

##### Speech Recognition

<div align = "center">
<table style="width:100%">
  <thead>
    <tr>
      <th> Input Audio  </th>
      <th width="550"> Recognition Result  </th>
    </tr>
  </thead>
  <tbody>
   <tr>
      <td align = "center">
      <a href="https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav" rel="nofollow">
            <img align="center" src="./docs/images/audio_icon.png" width="200 style="max-width: 100%;"></a><br>
      </td>
      <td >I knocked at the door on the ancient side of the building.</td>
    </tr>
    <tr>
      <td align = "center">
      <a href="https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav" rel="nofollow">
            <img align="center" src="./docs/images/audio_icon.png" width="200" style="max-width: 100%;"></a><br>
      </td>
      <td>我认为跑步最重要的就是给我带来了身体健康。</td>
    </tr>
  </tbody>
</table>

</div>

##### Speech Translation (English to Chinese)

<div align = "center">
<table style="width:100%">
  <thead>
    <tr>
      <th> Input Audio  </th>
      <th width="550"> Translations Result  </th>
    </tr>
  </thead>
  <tbody>
   <tr>
      <td align = "center">
      <a href="https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav" rel="nofollow">
            <img align="center" src="./docs/images/audio_icon.png" width="200 style="max-width: 100%;"></a><br>
      </td>
      <td >我 在 这栋 建筑 的 古老 门上 敲门。</td>
    </tr>
  </tbody>
</table>

</div>

##### Text-to-Speech
<div align = "center">
<table style="width:100%">
  <thead>
    <tr>
      <th width="550" > Input Text</th>
      <th>Synthetic Audio</th>
    </tr>
  </thead>
  <tbody>
   <tr>
      <td >Life was like a box of chocolates, you never know what you're gonna get.</td>
      <td align = "center">
      <a href="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/tacotron2_ljspeech_waveflow_samples_0.2/sentence_1.wav" rel="nofollow">
            <img align="center" src="./docs/images/audio_icon.png" width="200" style="max-width: 100%;"></a><br>
      </td>
    </tr>
    <tr>
      <td >早上好，今天是2020/10/29，最低温度是-3°C。</td>
      <td align = "center">
      <a href="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/parakeet_espnet_fs2_pwg_demo/tn_g2p/parakeet/001.wav" rel="nofollow">
            <img align="center" src="./docs/images/audio_icon.png" width="200" style="max-width: 100%;"></a><br>
      </td>
    </tr>
    <tr>
      <td >季姬寂，集鸡，鸡即棘鸡。棘鸡饥叽，季姬及箕稷济鸡。鸡既济，跻姬笈，季姬忌，急咭鸡，鸡急，继圾几，季姬急，即籍箕击鸡，箕疾击几伎，伎即齑，鸡叽集几基，季姬急极屐击鸡，鸡既殛，季姬激，即记《季姬击鸡记》。</td>
      <td align = "center">
      <a href="https://paddlespeech.bj.bcebos.com/Parakeet/docs/demos/jijiji.wav" rel="nofollow">
            <img align="center" src="./docs/images/audio_icon.png" width="200" style="max-width: 100%;"></a><br>
      </td>
    </tr>
  </tbody>
</table>

</div>

For more synthesized audios, please refer to [PaddleSpeech Text-to-Speech samples](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html).

##### Punctuation Restoration
<div align = "center">
<table style="width:100%">
  <thead>
    <tr>
      <th width="390"> Input Text </th>
      <th width="390"> Output Text </th>
    </tr>
  </thead>
  <tbody>
   <tr>
      <td>今天的天气真不错啊你下午有空吗我想约你一起去吃饭</td>
      <td>今天的天气真不错啊！你下午有空吗？我想约你一起去吃饭。</td>
    </tr>
  </tbody>
</table>

</div>


### Features

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 modules, and deployment process. To be more specific, this toolkit features at:
- 📦  **Ease of Use**: low barriers to install, [CLI](#quick-start), [Server](#quick-start-server), and [Streaming Server](#quick-start-streaming-server) is available to quick-start your journey.
- 🏆  **Align to the State-of-the-Art**: we provide high-speed and ultra-lightweight models, and also cutting-edge technology. 
- 🏆  **Streaming ASR and TTS System**: we provide production ready streaming asr and streaming tts system.
- 💯  **Rule-based Chinese frontend**: our frontend contains Text Normalization 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 both Industrial and Academia**:
  - 🛎️  *Implementation of critical audio tasks*: this toolkit contains audio functions like  Automatic Speech Recognition, Text-to-Speech Synthesis, Speaker Verfication, KeyWord Spotting, Audio Classification, and Speech Translation, etc.
  - 🔬  *Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of the speech tasks, and uses mainstream datasets like LibriSpeech, LJSpeech, AIShell, CSMSC, etc. See also [model list](#model-list) for more details.
  - 🧩  *Cascaded models application*: as an extension of the typical traditional audio tasks, we combine the workflows of the aforementioned tasks with other fields like Natural language processing (NLP) and Computer Vision (CV).

### Recent Update
- 👑 2022.05.13: Release [PP-ASR](./docs/source/asr/PPASR.md)、[PP-TTS](./docs/source/tts/PPTTS.md)、[PP-VPR](docs/source/vpr/PPVPR.md)
- 👏🏻  2022.05.06: `Streaming ASR` with `Punctuation Restoration` and `Token Timestamp`.
- 👏🏻  2022.05.06: `Server` is available for `Speaker Verification`, and `Punctuation Restoration`.
- 👏🏻  2022.04.28: `Streaming Server` is available for `Automatic Speech Recognition` and `Text-to-Speech`.
- 👏🏻  2022.03.28: `Server` is available for `Audio Classification`, `Automatic Speech Recognition` and `Text-to-Speech`.
- 👏🏻  2022.03.28: `CLI` is available for `Speaker Verification`.
- 🤗  2021.12.14: [ASR](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR) and [TTS](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS) Demos on Hugging Face Spaces are available!
- 👏🏻  2021.12.10: `CLI` is available for `Audio Classification`, `Automatic Speech Recognition`, `Speech Translation (English to Chinese)` and `Text-to-Speech`.


### Community
- Scan the QR code below with your Wechat, you can access to official technical exchange group and get the bonus ( more than 20GB learning materials, such as papers, codes and videos ) and the live link of the lessons. Look forward to your participation.

<div align="center">
<img src="https://user-images.githubusercontent.com/23690325/169763015-cbd8e28d-602c-4723-810d-dbc6da49441e.jpg"  width = "200"  />
</div>

## Installation

We strongly recommend our users to install PaddleSpeech in **Linux** with *python>=3.7* and *paddlepaddle>=2.3.1*.

### **Dependency Introduction**

+ gcc >= 4.8.5
+ paddlepaddle >= 2.3.1
+ python >= 3.7
+ OS support:  Linux(recommend), Windows, Mac OSX

PaddleSpeech depends on paddlepaddle. For installation, please refer to the official website of [paddlepaddle](https://www.paddlepaddle.org.cn/en) and choose according to your own machine. Here is an example of the cpu version.

```bash
pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
```

There are two quick installation methods for PaddleSpeech, one is pip installation, and the other is source code compilation (recommended).
### pip install

```shell
pip install pytest-runner
pip install paddlespeech
```

### source code compilation

```shell
git clone https://github.com/PaddlePaddle/PaddleSpeech.git
cd PaddleSpeech
pip install pytest-runner
pip install .
```

For more installation problems, such as conda environment, librosa-dependent, gcc problems, kaldi installation, etc., you can refer to this [installation document](./docs/source/install.md). If you encounter problems during installation, you can leave a message on [#2150](https://github.com/PaddlePaddle/PaddleSpeech/issues/2150) and find related problems


<a name="quickstart"></a>
## Quick Start

Developers can have a try of our models with [PaddleSpeech Command Line](./paddlespeech/cli/README.md) or Python. Change `--input` to test your own audio/text and support 16k wav format audio.

**You can also quickly experience it in AI Studio 👉🏻 [PaddleSpeech API Demo](https://aistudio.baidu.com/aistudio/projectdetail/4353348?sUid=2470186&shared=1&ts=1660876445786)**


Test audio sample download

```shell
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
```

### Automatic Speech Recognition

<details><summary>&emsp;（Click to expand）Open Source Speech Recognition</summary>

**command line experience**

```shell
paddlespeech asr --lang zh --input zh.wav
```

**Python API experience**

```python
>>> from paddlespeech.cli.asr.infer import ASRExecutor
>>> asr = ASRExecutor()
>>> result = asr(audio_file="zh.wav")
>>> print(result)
我认为跑步最重要的就是给我带来了身体健康
```
</details>

### Text-to-Speech

<details><summary>&emsp;Open Source Speech Synthesis</summary>

Output 24k sample rate wav format audio


**command line experience**

```shell
paddlespeech tts --input "你好，欢迎使用百度飞桨深度学习框架！" --output output.wav
```

**Python API experience**

```python
>>> from paddlespeech.cli.tts.infer import TTSExecutor
>>> tts = TTSExecutor()
>>> tts(text="今天天气十分不错。", output="output.wav")
```
- You can experience in [Huggingface Spaces](https://huggingface.co/spaces) [TTS Demo](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS)

</details>

### Audio Classification

<details><summary>&emsp;An open-domain sound classification tool</summary>

Sound classification model based on 527 categories of AudioSet dataset

**command line experience**

```shell
paddlespeech cls --input zh.wav
```

**Python API experience**

```python
>>> from paddlespeech.cli.cls.infer import CLSExecutor
>>> cls = CLSExecutor()
>>> result = cls(audio_file="zh.wav")
>>> print(result)
Speech 0.9027186632156372
```

</details>

### Voiceprint Extraction

<details><summary>&emsp;Industrial-grade voiceprint extraction tool</summary>

**command line experience**

```shell
paddlespeech vector --task spk --input zh.wav
```

**Python API experience**

```python
>>> from paddlespeech.cli.vector import VectorExecutor
>>> vec = VectorExecutor()
>>> result = vec(audio_file="zh.wav")
>>> print(result) # 187维向量
[ -0.19083306   9.474295   -14.122263    -2.0916545    0.04848729
   4.9295826    1.4780062    0.3733844   10.695862     3.2697146
  -4.48199     -0.6617882   -9.170393   -11.1568775   -1.2358263 ...]
```

</details>

### Punctuation Restoration

<details><summary>&emsp;Quick recovery of text punctuation, works with ASR models</summary>

**command line experience**

```shell
paddlespeech text --task punc --input 今天的天气真不错啊你下午有空吗我想约你一起去吃饭
```

**Python API experience**

```python
>>> from paddlespeech.cli.text.infer import TextExecutor
>>> text_punc = TextExecutor()
>>> result = text_punc(text="今天的天气真不错啊你下午有空吗我想约你一起去吃饭")
今天的天气真不错啊！你下午有空吗？我想约你一起去吃饭。
```

</details>

### Speech Translation

<details><summary>&emsp;End-to-end English to Chinese Speech Translation Tool</summary>

Use pre-compiled kaldi related tools, only support experience in Ubuntu system

**command line experience**

```shell
paddlespeech st --input en.wav
```

**Python API experience**

```python
>>> from paddlespeech.cli.st.infer import STExecutor
>>> st = STExecutor()
>>> result = st(audio_file="en.wav")
['我 在 这栋 建筑 的 古老 门上 敲门 。']
```

</details>


<a name="quickstartserver"></a>
## Quick Start Server

Developers can have a try of our speech server with [PaddleSpeech Server Command Line](./paddlespeech/server/README.md).

**You can try it quickly in AI Studio (recommend): [SpeechServer](https://aistudio.baidu.com/aistudio/projectdetail/4354592?sUid=2470186&shared=1&ts=1660877827034)**

**Start server**     

```shell
paddlespeech_server start --config_file ./paddlespeech/server/conf/application.yaml
```

**Access Speech Recognition Services**     

```shell
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input input_16k.wav
```

**Access Text to Speech Services**     

```shell
paddlespeech_client tts --server_ip 127.0.0.1 --port 8090 --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
```

**Access Audio Classification Services**     
```shell
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav
```


For more information about server command lines, please see: [speech server demos](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/demos/speech_server)


<a name="quickstartstreamingserver"></a>
## Quick Start Streaming Server

Developers can have a try of  [streaming asr](./demos/streaming_asr_server/README.md) and [streaming tts](./demos/streaming_tts_server/README.md) server.

**Start Streaming Speech Recognition Server**

```
paddlespeech_server start --config_file ./demos/streaming_asr_server/conf/application.yaml
```

**Access Streaming Speech Recognition Services**     

```
paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8090 --input input_16k.wav
```

**Start Streaming Text to Speech  Server**

```
paddlespeech_server start --config_file ./demos/streaming_tts_server/conf/tts_online_application.yaml
```

**Access Streaming Text to Speech Services**     

```
paddlespeech_client tts_online --server_ip 127.0.0.1 --port 8092 --protocol http --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav
```

For more information please see:  [streaming asr](./demos/streaming_asr_server/README.md) and [streaming tts](./demos/streaming_tts_server/README.md) 

<a name="ModelList"></a>

## Model List

PaddleSpeech supports a series of most popular models. They are summarized in [released models](./docs/source/released_model.md) and attached with available pretrained models.

<a name="SpeechToText"></a>

**Speech-to-Text** contains *Acoustic Model*, *Language Model*, and *Speech Translation*, with the following details:

<table style="width:100%">
  <thead>
    <tr>
      <th>Speech-to-Text Module Type</th>
      <th>Dataset</th>
      <th>Model Type</th>
      <th>Example</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="4">Speech Recogination</td>
      <td rowspan="2" >Aishell</td>
      <td >DeepSpeech2 RNN + Conv based Models</td>
      <td>
      <a href = "./examples/aishell/asr0">deepspeech2-aishell</a>
      </td>
    </tr>
    <tr>
      <td>Transformer based Attention Models </td>
      <td>
      <a href = "./examples/aishell/asr1">u2.transformer.conformer-aishell</a>
      </td>
    </tr>
    <tr>
      <td> Librispeech</td>
      <td>Transformer based Attention Models </td>
      <td>
      <a href = "./examples/librispeech/asr0">deepspeech2-librispeech</a> / <a href = "./examples/librispeech/asr1">transformer.conformer.u2-librispeech</a>  / <a href = "./examples/librispeech/asr2">transformer.conformer.u2-kaldi-librispeech</a>
      </td>
      </td>
    </tr>
  <tr>
      <td>TIMIT</td>
      <td>Unified Streaming & Non-streaming Two-pass</td>
      <td>
    <a href = "./examples/timit/asr1"> u2-timit</a>
      </td>
  </tr>
  <tr>
  <td>Alignment</td>
  <td>THCHS30</td>
  <td>MFA</td>
  <td>
  <a href = ".examples/thchs30/align0">mfa-thchs30</a>
  </td>
  </tr>
   <tr>
      <td rowspan="1">Language Model</td>
      <td colspan = "2">Ngram Language Model</td>
      <td>
      <a href = "./examples/other/ngram_lm">kenlm</a>
      </td>
    </tr>
  <tr>
      <td rowspan="2">Speech Translation (English to Chinese)</td> 
      <td rowspan="2">TED En-Zh</td>
      <td>Transformer + ASR MTL</td>
      <td>
      <a href = "./examples/ted_en_zh/st0">transformer-ted</a>
      </td>
  </tr>
  <tr>
      <td>FAT + Transformer + ASR MTL</td>
      <td>
      <a href = "./examples/ted_en_zh/st1">fat-st-ted</a>
      </td>
  </tr>
  </tbody>
</table>

<a name="TextToSpeech"></a>

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

<table>
  <thead>
    <tr>
      <th> Text-to-Speech Module Type </th>
      <th> Model Type </th>
      <th> Dataset </th>
      <th> Example </th>
    </tr>
  </thead>
  <tbody>
    <tr>
    <td> Text Frontend </td>
    <td colspan="2"> &emsp; </td>
    <td>
    <a href = "./examples/other/tn">tn</a> / <a href = "./examples/other/g2p">g2p</a>
    </td>
    </tr>
    <tr>
      <td rowspan="4">Acoustic Model</td>
      <td>Tacotron2</td>
      <td>LJSpeech / CSMSC</td>
      <td>
      <a href = "./examples/ljspeech/tts0">tacotron2-ljspeech</a> / <a href = "./examples/csmsc/tts0">tacotron2-csmsc</a>
      </td>
    </tr>
    <tr>
      <td>Transformer TTS</td>
      <td>LJSpeech</td>
      <td>
      <a href = "./examples/ljspeech/tts1">transformer-ljspeech</a>
      </td>
    </tr>
    <tr>
      <td>SpeedySpeech</td>
      <td>CSMSC</td>
      <td >
      <a href = "./examples/csmsc/tts2">speedyspeech-csmsc</a>
      </td>
    </tr>
    <tr>
      <td>FastSpeech2</td>
      <td>LJSpeech / VCTK / CSMSC / AISHELL-3 / ZH_EN</td>
      <td>
      <a href = "./examples/ljspeech/tts3">fastspeech2-ljspeech</a> / <a href = "./examples/vctk/tts3">fastspeech2-vctk</a> / <a href = "./examples/csmsc/tts3">fastspeech2-csmsc</a> / <a href = "./examples/aishell3/tts3">fastspeech2-aishell3</a> / <a href = "./examples/zh_en_tts/tts3">fastspeech2-zh_en</a>
      </td>
    </tr>
   <tr>
      <td rowspan="6">Vocoder</td>
      <td >WaveFlow</td>
      <td >LJSpeech</td>
      <td>
      <a href = "./examples/ljspeech/voc0">waveflow-ljspeech</a>
      </td>
    </tr>
    <tr>
      <td >Parallel WaveGAN</td>
      <td >LJSpeech / VCTK / CSMSC / AISHELL-3</td>
      <td>
      <a href = "./examples/ljspeech/voc1">PWGAN-ljspeech</a> / <a href = "./examples/vctk/voc1">PWGAN-vctk</a> / <a href = "./examples/csmsc/voc1">PWGAN-csmsc</a> /  <a href = "./examples/aishell3/voc1">PWGAN-aishell3</a>
      </td>
    </tr>
    <tr>
      <td >Multi Band MelGAN</td>
      <td >CSMSC</td>
      <td>
      <a href = "./examples/csmsc/voc3">Multi Band MelGAN-csmsc</a> 
      </td>
    </tr> 
    <tr>
      <td >Style MelGAN</td>
      <td >CSMSC</td>
      <td>
      <a href = "./examples/csmsc/voc4">Style MelGAN-csmsc</a> 
      </td>
    </tr>
    <tr>
      <td >HiFiGAN</td>
      <td >LJSpeech / VCTK / CSMSC / AISHELL-3</td>
      <td>
      <a href = "./examples/ljspeech/voc5">HiFiGAN-ljspeech</a> / <a href = "./examples/vctk/voc5">HiFiGAN-vctk</a> / <a href = "./examples/csmsc/voc5">HiFiGAN-csmsc</a> / <a href = "./examples/aishell3/voc5">HiFiGAN-aishell3</a>
      </td>
    </tr>
    <tr>
      <td >WaveRNN</td>
      <td >CSMSC</td>
      <td>
      <a href = "./examples/csmsc/voc6">WaveRNN-csmsc</a>
      </td>
    </tr>
    <tr>
      <td rowspan="3">Voice Cloning</td>
      <td>GE2E</td>
      <td >Librispeech, etc.</td>
      <td>
      <a href = "./examples/other/ge2e">ge2e</a>
      </td>
    </tr>
    <tr>
      <td>GE2E + Tacotron2</td>
      <td>AISHELL-3</td>
      <td>
      <a href = "./examples/aishell3/vc0">ge2e-tacotron2-aishell3</a>
      </td>
    </tr>
    <tr>
      <td>GE2E + FastSpeech2</td>
      <td>AISHELL-3</td>
      <td>
      <a href = "./examples/aishell3/vc1">ge2e-fastspeech2-aishell3</a>
      </td>
    </tr>
     <tr>
      <td rowspan="3">End-to-End</td>
      <td>VITS</td>
      <td >CSMSC</td>
      <td>
      <a href = "./examples/csmsc/vits">VITS-csmsc</a>
      </td>
    </tr>
  </tbody>
</table>

<a name="AudioClassification"></a>

**Audio Classification**

<table style="width:100%">
  <thead>
    <tr>
      <th> Task </th>
      <th> Dataset </th>
      <th> Model Type </th>
      <th> Example </th>
    </tr>
  </thead>
  <tbody>
  <tr>
      <td>Audio Classification</td>
      <td>ESC-50</td>
      <td>PANN</td>
      <td>
      <a href = "./examples/esc50/cls0">pann-esc50</a>
      </td>
    </tr>
  </tbody>
</table>

<a name="SpeakerVerification"></a>

**Speaker Verification**

<table style="width:100%">
  <thead>
    <tr>
      <th> Task </th>
      <th> Dataset </th>
      <th> Model Type </th>
      <th> Example </th>
    </tr>
  </thead>
  <tbody>
  <tr>
      <td>Speaker Verification</td>
      <td>VoxCeleb12</td>
      <td>ECAPA-TDNN</td>
      <td>
      <a href = "./examples/voxceleb/sv0">ecapa-tdnn-voxceleb12</a>
      </td>
    </tr>
  </tbody>
</table>

<a name="PunctuationRestoration"></a>

**Punctuation Restoration**

<table style="width:100%">
  <thead>
    <tr>
      <th> Task </th>
      <th> Dataset </th>
      <th> Model Type </th>
      <th> Example </th>
    </tr>
  </thead>
  <tbody>
  <tr>
      <td>Punctuation Restoration</td>
      <td>IWLST2012_zh</td>
      <td>Ernie Linear</td>
      <td>
      <a href = "./examples/iwslt2012/punc0">iwslt2012-punc0</a>
      </td>
    </tr>
  </tbody>
</table>

## Documents

Normally, [Speech SoTA](https://paperswithcode.com/area/speech), [Audio SoTA](https://paperswithcode.com/area/audio) and [Music SoTA](https://paperswithcode.com/area/music) give you an overview of the hot academic topics in the related area. To focus on the tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.

- [Installation](./docs/source/install.md)
- [Quick Start](#quickstart)
- [Some Demos](./demos/README.md)
- Tutorials
  - [Automatic Speech Recognition](./docs/source/asr/quick_start.md)
    - [Introduction](./docs/source/asr/models_introduction.md)
    - [Data Preparation](./docs/source/asr/data_preparation.md)
    - [Ngram LM](./docs/source/asr/ngram_lm.md)
  - [Text-to-Speech](./docs/source/tts/quick_start.md)
    - [Introduction](./docs/source/tts/models_introduction.md)
    - [Advanced Usage](./docs/source/tts/advanced_usage.md)
    - [Chinese Rule Based Text Frontend](./docs/source/tts/zh_text_frontend.md)
    - [Test Audio Samples](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html)
  - Speaker Verification
    - [Audio Searching](./demos/audio_searching/README.md)
    - [Speaker Verification](./demos/speaker_verification/README.md)
  - [Audio Classification](./demos/audio_tagging/README.md)
  - [Speech Translation](./demos/speech_translation/README.md)
  - [Speech Server](./demos/speech_server/README.md)
- [Released Models](./docs/source/released_model.md)
  - [Speech-to-Text](#SpeechToText)
  - [Text-to-Speech](#TextToSpeech)
  - [Audio Classification](#AudioClassification)
  - [Speaker Verification](#SpeakerVerification)
  - [Punctuation Restoration](#PunctuationRestoration)
- [Community](#Community)
- [Welcome to contribute](#contribution)
- [License](#License)

The Text-to-Speech module is originally called [Parakeet](https://github.com/PaddlePaddle/Parakeet), and now merged with this repository. If you are interested in academic research about this task, please see [TTS research overview](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/docs/source/tts#overview). Also, [this document](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/tts/models_introduction.md) is a good guideline for the pipeline components.


## ⭐ Examples
- **[PaddleBoBo](https://github.com/JiehangXie/PaddleBoBo): Use PaddleSpeech TTS to generate virtual human voice.**
  
<div align="center"><a href="https://www.bilibili.com/video/BV1cL411V71o?share_source=copy_web"><img src="https://ai-studio-static-online.cdn.bcebos.com/06fd746ab32042f398fb6f33f873e6869e846fe63c214596ae37860fe8103720" / width="500px"></a></div>

- [PaddleSpeech Demo Video](https://paddlespeech.readthedocs.io/en/latest/demo_video.html)

- **[VTuberTalk](https://github.com/jerryuhoo/VTuberTalk): Use PaddleSpeech TTS and ASR to clone voice from videos.**

<div align="center">
<img src="https://raw.githubusercontent.com/jerryuhoo/VTuberTalk/main/gui/gui.png"  width = "500px"  />
</div>


## Citation

To cite PaddleSpeech for research, please use the following format.
```tex
@inproceedings{zhang2022paddlespeech,
    title = {PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit},
    author = {Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, dianhai yu, Yanjun Ma, Liang Huang},
    booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations},
    year = {2022},
    publisher = {Association for Computational Linguistics},
}

@inproceedings{zheng2021fused,
  title={Fused acoustic and text encoding for multimodal bilingual pretraining and speech translation},
  author={Zheng, Renjie and Chen, Junkun and Ma, Mingbo and Huang, Liang},
  booktitle={International Conference on Machine Learning},
  pages={12736--12746},
  year={2021},
  organization={PMLR}
}
```

<a name="contribution"></a>
## Contribute to PaddleSpeech

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

### Contributors
<p align="center">
<a href="https://github.com/david-95"><img src="https://avatars.githubusercontent.com/u/15189190?v=4" width=75 height=75></a>
<a href="https://github.com/zh794390558"><img src="https://avatars.githubusercontent.com/u/3038472?v=4" width=75 height=75></a>
<a href="https://github.com/Jackwaterveg"><img src="https://avatars.githubusercontent.com/u/87408988?v=4" width=75 height=75></a>
<a href="https://github.com/yt605155624"><img src="https://avatars.githubusercontent.com/u/24568452?v=4" width=75 height=75></a>
<a href="https://github.com/kuke"><img src="https://avatars.githubusercontent.com/u/3064195?v=4" width=75 height=75></a>
<a href="https://github.com/xinghai-sun"><img src="https://avatars.githubusercontent.com/u/7038341?v=4" width=75 height=75></a>
<a href="https://github.com/pkuyym"><img src="https://avatars.githubusercontent.com/u/5782283?v=4" width=75 height=75></a>
<a href="https://github.com/KPatr1ck"><img src="https://avatars.githubusercontent.com/u/22954146?v=4" width=75 height=75></a>
<a href="https://github.com/LittleChenCc"><img src="https://avatars.githubusercontent.com/u/10339970?v=4" width=75 height=75></a>
<a href="https://github.com/745165806"><img src="https://avatars.githubusercontent.com/u/20623194?v=4" width=75 height=75></a>
<a href="https://github.com/Mingxue-Xu"><img src="https://avatars.githubusercontent.com/u/92848346?v=4" width=75 height=75></a>
<a href="https://github.com/chrisxu2016"><img src="https://avatars.githubusercontent.com/u/18379485?v=4" width=75 height=75></a>
<a href="https://github.com/lfchener"><img src="https://avatars.githubusercontent.com/u/6771821?v=4" width=75 height=75></a>
<a href="https://github.com/luotao1"><img src="https://avatars.githubusercontent.com/u/6836917?v=4" width=75 height=75></a>
<a href="https://github.com/wanghaoshuang"><img src="https://avatars.githubusercontent.com/u/7534971?v=4" width=75 height=75></a>
<a href="https://github.com/gongel"><img src="https://avatars.githubusercontent.com/u/24390500?v=4" width=75 height=75></a>
<a href="https://github.com/mmglove"><img src="https://avatars.githubusercontent.com/u/38800877?v=4" width=75 height=75></a>
<a href="https://github.com/iclementine"><img src="https://avatars.githubusercontent.com/u/16222986?v=4" width=75 height=75></a>
<a href="https://github.com/ZeyuChen"><img src="https://avatars.githubusercontent.com/u/1371212?v=4" width=75 height=75></a>
<a href="https://github.com/AK391"><img src="https://avatars.githubusercontent.com/u/81195143?v=4" width=75 height=75></a>
<a href="https://github.com/qingqing01"><img src="https://avatars.githubusercontent.com/u/7845005?v=4" width=75 height=75></a>
<a href="https://github.com/ericxk"><img src="https://avatars.githubusercontent.com/u/4719594?v=4" width=75 height=75></a>
<a href="https://github.com/kvinwang"><img src="https://avatars.githubusercontent.com/u/6442159?v=4" width=75 height=75></a>
<a href="https://github.com/jiqiren11"><img src="https://avatars.githubusercontent.com/u/82639260?v=4" width=75 height=75></a>
<a href="https://github.com/AshishKarel"><img src="https://avatars.githubusercontent.com/u/58069375?v=4" width=75 height=75></a>
<a href="https://github.com/chesterkuo"><img src="https://avatars.githubusercontent.com/u/6285069?v=4" width=75 height=75></a>
<a href="https://github.com/tensor-tang"><img src="https://avatars.githubusercontent.com/u/21351065?v=4" width=75 height=75></a>
<a href="https://github.com/hysunflower"><img src="https://avatars.githubusercontent.com/u/52739577?v=4" width=75 height=75></a>  
<a href="https://github.com/wwhu"><img src="https://avatars.githubusercontent.com/u/6081200?v=4" width=75 height=75></a>
<a href="https://github.com/lispc"><img src="https://avatars.githubusercontent.com/u/2833376?v=4" width=75 height=75></a>
<a href="https://github.com/jerryuhoo"><img src="https://avatars.githubusercontent.com/u/24245709?v=4" width=75 height=75></a>
<a href="https://github.com/harisankarh"><img src="https://avatars.githubusercontent.com/u/1307053?v=4" width=75 height=75></a>
<a href="https://github.com/Jackiexiao"><img src="https://avatars.githubusercontent.com/u/18050469?v=4" width=75 height=75></a>
<a href="https://github.com/limpidezza"><img src="https://avatars.githubusercontent.com/u/71760778?v=4" width=75 height=75></a>
</p>

## Acknowledgement
-  Many thanks to [david-95](https://github.com/david-95) improved TTS, fixed multi-punctuation bug, and contributed to multiple program and data. 
- Many thanks to [BarryKCL](https://github.com/BarryKCL) improved TTS Chinses frontend based on [G2PW](https://github.com/GitYCC/g2pW)
- Many thanks to [yeyupiaoling](https://github.com/yeyupiaoling)/[PPASR](https://github.com/yeyupiaoling/PPASR)/[PaddlePaddle-DeepSpeech](https://github.com/yeyupiaoling/PaddlePaddle-DeepSpeech)/[VoiceprintRecognition-PaddlePaddle](https://github.com/yeyupiaoling/VoiceprintRecognition-PaddlePaddle)/[AudioClassification-PaddlePaddle](https://github.com/yeyupiaoling/AudioClassification-PaddlePaddle) for years of attention, constructive advice and great help.
- Many thanks to [mymagicpower](https://github.com/mymagicpower) for the Java implementation of ASR upon [short](https://github.com/mymagicpower/AIAS/tree/main/3_audio_sdks/asr_sdk) and [long](https://github.com/mymagicpower/AIAS/tree/main/3_audio_sdks/asr_long_audio_sdk) audio files.
- Many thanks to [JiehangXie](https://github.com/JiehangXie)/[PaddleBoBo](https://github.com/JiehangXie/PaddleBoBo) for developing Virtual Uploader(VUP)/Virtual YouTuber(VTuber) with PaddleSpeech TTS function.
- Many thanks to [745165806](https://github.com/745165806)/[PaddleSpeechTask](https://github.com/745165806/PaddleSpeechTask) for contributing Punctuation Restoration model.
- Many thanks to [kslz](https://github.com/745165806) for supplementary Chinese documents.
- Many thanks to [awmmmm](https://github.com/awmmmm) for contributing fastspeech2 aishell3 conformer pretrained model.
- Many thanks to [phecda-xu](https://github.com/phecda-xu)/[PaddleDubbing](https://github.com/phecda-xu/PaddleDubbing) for developing a dubbing tool with GUI based on PaddleSpeech TTS model.
- Many thanks to [jerryuhoo](https://github.com/jerryuhoo)/[VTuberTalk](https://github.com/jerryuhoo/VTuberTalk) for developing a GUI tool based on PaddleSpeech TTS and code for making datasets from videos based on PaddleSpeech ASR.
- Many thanks to [vpegasus](https://github.com/vpegasus)/[xuesebot](https://github.com/vpegasus/xuesebot) for developing a rasa chatbot,which is able to speak and listen thanks to PaddleSpeech.
- Many thanks to [chenkui164](https://github.com/chenkui164)/[FastASR](https://github.com/chenkui164/FastASR) for the C++ inference implementation of PaddleSpeech ASR.

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

<a name="License"></a>
## License

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