(简体中文|[English](./README.md))
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< h3 >
< a href = "#quick-start" > 快速开始 < / a >
| < a href = "#quick-start-server" > 快速使用服务 < / a >
| < a href = "#documents" > 教程文档 < / a >
| < a href = "#model-list" > 模型列表 < / a >
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**PaddleSpeech** 是基于飞桨 [PaddlePaddle ](https://github.com/PaddlePaddle/Paddle ) 的语音方向的开源模型库，用于语音和音频中的各种关键任务的开发，包含大量基于深度学习前沿和有影响力的模型，一些典型的应用示例如下：
##### 语音识别
< div align = "center" >
< table style = "width:100%" >
< thead >
< tr >
< th > 输入音频 < / th >
< th width = "550" > 识别结果 < / 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 >
##### 语音翻译 (英译中)
< div align = "center" >
< table style = "width:100%" >
< thead >
< tr >
< th > 输入音频 < / th >
< th width = "550" > 翻译结果 < / 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 >
##### 语音合成
< div align = "center" >
< table style = "width:100%" >
< thead >
< tr >
< th width = "550" > 输入文本< / th >
< th > 合成音频< / 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， ， < / 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 >
更多合成音频，可以参考 [PaddleSpeech 语音合成音频示例 ](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html )。
##### 标点恢复
< div align = "center" >
< table style = "width:100%" >
< thead >
< tr >
< th width = "390" > 输入文本 < / th >
< th width = "390" > 输出文本 < / 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 >
### ⭐ 应用案例
- **[PaddleBoBo](https://github.com/JiehangXie/PaddleBoBo): 使用 PaddleSpeech 的语音合成模块生成虚拟人的声音。**
< 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 示例视频 ](https://paddlespeech.readthedocs.io/en/latest/demo_video.html )
- **[VTuberTalk](https://github.com/jerryuhoo/VTuberTalk): 使用 PaddleSpeech 的语音合成和语音识别从视频中克隆人声。**
< div align = "center" >
< img src = "https://raw.githubusercontent.com/jerryuhoo/VTuberTalk/main/gui/gui.png" width = "500px" / >
< / div >
### 🔥 热门活动
- 2021.12.21~12.24
4 日直播课: 深度解读 PaddleSpeech 语音技术!
** 直播回放与课件资料: https://aistudio.baidu.com/aistudio/education/group/info/25130**
### 特性
本项目采用了易用、高效、灵活以及可扩展的实现，旨在为工业应用、学术研究提供更好的支持，实现的功能包含训练、推断以及测试模块，以及部署过程，主要包括
- 📦 ** 易用性**: 安装门槛低，可使用 [CLI ](#quick-start ) 快速开始。
- 🏆 ** 对标 SoTA**: 提供了高速、轻量级模型，且借鉴了最前沿的技术。
- 💯 ** 基于规则的中文前端**: 我们的前端包含文本正则化和字音转换（ ） ，
- **多种工业界以及学术界主流功能支持**:
- 🛎️ 典型音频任务: 本工具包提供了音频任务如音频分类、语音翻译、自动语音识别、文本转语音、语音合成等任务的实现。
- 🔬 主流模型及数据集: 本工具包实现了参与整条语音任务流水线的各个模块，并且采用了主流数据集如 LibriSpeech、LJSpeech、AIShell、CSMSC， 模型列表 ](#model-list )。
- 🧩 级联模型应用: 作为传统语音任务的扩展，我们结合了自然语言处理、计算机视觉等任务，实现更接近实际需求的产业级应用。
### 近期更新
<!-- -
2021.12.14: We would like to have an online courses to introduce basics and research of speech, as well as code practice with `paddlespeech` . Please pay attention to our [Calendar ](https://www.paddlepaddle.org.cn/live ).
--->
- 👏🏻 2022.03.28: PaddleSpeech Server 上线! 覆盖了声音分类、语音识别、以及语音合成。
- 👏🏻 2022.03.28: PaddleSpeech CLI 上线声纹验证。
- 🤗 2021.12.14: Our PaddleSpeech [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: PaddleSpeech CLI 上线！覆盖了声音分类、语音识别、语音翻译（英译中）以及语音合成。
### 技术交流群
微信扫描二维码（好友申请通过后回复【语音】）加入官方交流群，获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
< div align = "center" >
< img src = "https://raw.githubusercontent.com/yt605155624/lanceTest/main/images/wechat_4.jpg" width = "300" / >
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## 安装
我们强烈建议用户在 **Linux** 环境下，*3.7* 以上版本的 *python* 上安装 PaddleSpeech。
目前为止，**Linux** 支持声音分类、语音识别、语音合成和语音翻译四种功能，**Mac OSX、 Windows** 下暂不支持语音翻译功能。 想了解具体安装细节，可以参考[安装文档](./docs/source/install_cn.md)。
## 快速开始
安装完成后，开发者可以通过命令行快速开始，改变 `--input` 可以尝试用自己的音频或文本测试。
**声音分类**
```shell
paddlespeech cls --input input.wav
```
**声纹识别**
```shell
paddlespeech vector --task spk --input input_16k.wav
```
**语音识别**
```shell
paddlespeech asr --lang zh --input input_16k.wav
```
**语音翻译** (English to Chinese)
```shell
paddlespeech st --input input_16k.wav
```
**语音合成**
```shell
paddlespeech tts --input "你好，欢迎使用百度飞桨深度学习框架！" --output output.wav
```
- 语音合成的 web demo 已经集成进了 [Huggingface Spaces ](https://huggingface.co/spaces ). 请参考: [TTS Demo ](https://huggingface.co/spaces/akhaliq/paddlespeech )
**文本后处理**
- 标点恢复
```bash
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
```
**批处理**
```
echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts
```
**Shell管道**
ASR + Punc:
```
paddlespeech asr --input ./zh.wav | paddlespeech text --task punc
```
更多命令行命令请参考 [demos ](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/demos )
> Note: 如果需要训练或者微调，请查看[语音识别](./docs/source/asr/quick_start.md)，
## 快速使用服务
安装完成后，开发者可以通过命令行快速使用服务。
**启动服务**
```shell
paddlespeech_server start --config_file ./paddlespeech/server/conf/application.yaml
```
**访问语音识别服务**
```shell
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input input_16k.wav
```
**访问语音合成服务**
```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
```
**访问音频分类服务**
```shell
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav
```
更多服务相关的命令行使用信息，请参考 [demos ](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/demos/speech_server )
## 模型列表
PaddleSpeech 支持很多主流的模型，并提供了预训练模型，详情请见[模型列表](./docs/source/released_model.md)。
PaddleSpeech 的 ** 语音转文本** 包含语音识别声学模型、语音识别语言模型和语音翻译, 详情如下：
< table style = "width:100%" >
< thead >
< tr >
< th > 语音转文本模块类型< / th >
< th > 数据集< / th >
< th > 模型种类< / th >
< th > 链接< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td rowspan = "4" > 语音识别< / 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 > 对齐< / td >
< td > THCHS30< / td >
< td > MFA< / td >
< td >
< a href = ".examples/thchs30/align0" > mfa-thchs30< / a >
< / td >
< / tr >
< tr >
< td rowspan = "1" > 语言模型< / td >
< td colspan = "2" > Ngram 语言模型< / td >
< td >
< a href = "./examples/other/ngram_lm" > kenlm< / a >
< / td >
< / tr >
< tr >
< td rowspan = "2" > 语音翻译（英译中）< / 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 = "语音合成模型" > < / a >
PaddleSpeech 的 ** 语音合成** 主要包含三个模块：文本前端、声学模型和声码器。声学模型和声码器模型如下：
< table >
< thead >
< tr >
< th > 语音合成模块类型 < / th >
< th > 模型种类 < / th >
< th > 数据集 < / th >
< th > 链接 < / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > 文本前端< / td >
< td colspan = "2" >   < / td >
< td >
< a href = "./examples/other/tn" > tn< / a > / < a href = "./examples/other/g2p" > g2p< / a >
< / td >
< / tr >
< tr >
< td rowspan = "4" > 声学模型< / 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< / 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 >
< / td >
< / tr >
< tr >
< td rowspan = "6" > 声码器< / 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" > 声音克隆< / td >
< td > GE2E< / td >
< td > Librispeech, etc.< / td >
< td >
< a href = "./examples/other/ge2e" > ge2e< / a >
< / td >
< / tr >
< tr >
< td > GE2E + Tactron2< / td >
< td > AISHELL-3< / td >
< td >
< a href = "./examples/aishell3/vc0" > ge2e-tactron2-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 >
< / tbody >
< / table >
< a name = "声音分类模型" > < / a >
**声音分类**
< table style = "width:100%" >
< thead >
< tr >
< th > 任务 < / th >
< th > 数据集 < / th >
< th > 模型种类 < / th >
< th > 链接< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > 声音分类< / td >
< td > ESC-50< / td >
< td > PANN< / td >
< td >
< a href = "./examples/esc50/cls0" > pann-esc50< / a >
< / td >
< / tr >
< / tbody >
< / table >
**声纹识别**
< table style = "width:100%" >
< thead >
< tr >
< th > Task < / th >
< th > Dataset < / th >
< th > Model Type < / th >
< th > Link < / 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 >
**标点恢复**
< table style = "width:100%" >
< thead >
< tr >
< th > 任务 < / th >
< th > 数据集 < / th >
< th > 模型种类 < / th >
< th > 链接 < / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > 标点恢复< / td >
< td > IWLST2012_zh< / td >
< td > Ernie Linear< / td >
< td >
< a href = "./examples/iwslt2012/punc0" > iwslt2012-punc0< / a >
< / td >
< / tr >
< / tbody >
< / table >
## 教程文档
对于 PaddleSpeech 的所关注的任务，以下指南有助于帮助开发者快速入门，了解语音相关核心思想。
- [下载安装 ](./docs/source/install_cn.md )
- [快速开始 ](#快速开始 )
- Notebook基础教程
- [声音分类 ](./docs/tutorial/cls/cls_tutorial.ipynb )
- [语音识别 ](./docs/tutorial/asr/tutorial_transformer.ipynb )
- [语音翻译 ](./docs/tutorial/st/st_tutorial.ipynb )
- [声音合成 ](./docs/tutorial/tts/tts_tutorial.ipynb )
- [示例Demo ](./demos/README.md )
- 进阶文档
- [语音识别自定义训练 ](./docs/source/asr/quick_start.md )
- [简介 ](./docs/source/asr/models_introduction.md )
- [数据准备 ](./docs/source/asr/data_preparation.md )
- [Ngram 语言模型 ](./docs/source/asr/ngram_lm.md )
- [语音合成自定义训练 ](./docs/source/tts/quick_start.md )
- [简介 ](./docs/source/tts/models_introduction.md )
- [进阶用法 ](./docs/source/tts/advanced_usage.md )
- [中文文本前端 ](./docs/source/tts/zh_text_frontend.md )
- [测试语音样本 ](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html )
- [声音分类 ](./demos/audio_tagging/README_cn.md )
- [声纹识别 ](./demos/speaker_verification/README_cn.md )
- [语音翻译 ](./demos/speech_translation/README_cn.md )
- [模型列表 ](#模型列表 )
- [语音识别 ](#语音识别模型 )
- [语音合成 ](#语音合成模型 )
- [声音分类 ](#声音分类模型 )
- [技术交流群 ](#技术交流群 )
- [欢迎贡献 ](#欢迎贡献 )
- [License ](#License )
语音合成模块最初被称为 [Parakeet ](https://github.com/PaddlePaddle/Parakeet )，现在与此仓库合并。如果您对该任务的学术研究感兴趣，请参阅 [TTS 研究概述 ](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/docs/source/tts#overview )。此外，[模型介绍](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/tts/models_introduction.md) 是了解语音合成流程的一个很好的指南。
## 引用
要引用 PaddleSpeech 进行研究，请使用以下格式进行引用。
```text
@misc {ppspeech2021,
title={PaddleSpeech, a toolkit for audio processing based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleSpeech}},
year={2021}
}
@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 = "欢迎贡献" > < / a >
## 参与 PaddleSpeech 的开发
热烈欢迎您在[Discussions](https://github.com/PaddlePaddle/PaddleSpeech/discussions) 中提交问题，并在[Issues](https://github.com/PaddlePaddle/PaddleSpeech/issues) 中指出发现的 bug。此外，
### 贡献者
< p align = "center" >
< 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 >
## 致谢
- 非常感谢 [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) 多年来的关注和建议，以及在诸多问题上的帮助。
- 非常感谢 [mymagicpower ](https://github.com/mymagicpower ) 采用PaddleSpeech 对 ASR 的[短语音](https://github.com/mymagicpower/AIAS/tree/main/3_audio_sdks/asr_sdk)及[长语音](https://github.com/mymagicpower/AIAS/tree/main/3_audio_sdks/asr_long_audio_sdk)进行 Java 实现。
- 非常感谢 [JiehangXie ](https://github.com/JiehangXie )/[PaddleBoBo](https://github.com/JiehangXie/PaddleBoBo) 采用 PaddleSpeech 语音合成功能实现 Virtual Uploader(VUP)/Virtual YouTuber(VTuber) 虚拟主播。
- 非常感谢 [745165806 ](https://github.com/745165806 )/[PaddleSpeechTask](https://github.com/745165806/PaddleSpeechTask) 贡献标点重建相关模型。
- 非常感谢 [kslz ](https://github.com/kslz ) 补充中文文档。
- 非常感谢 [awmmmm ](https://github.com/awmmmm ) 提供 fastspeech2 aishell3 conformer 预训练模型。
- 非常感谢 [phecda-xu ](https://github.com/phecda-xu )/[PaddleDubbing](https://github.com/phecda-xu/PaddleDubbing) 基于 PaddleSpeech 的 TTS 模型搭建带 GUI 操作界面的配音工具。
- 非常感谢 [jerryuhoo ](https://github.com/jerryuhoo )/[VTuberTalk](https://github.com/jerryuhoo/VTuberTalk) 基于 PaddleSpeech 的 TTS GUI 界面和基于 ASR 制作数据集的相关代码。
此外， references ](./docs/source/reference.md )。
## License
PaddleSpeech 在 [Apache-2.0 许可 ](./LICENSE ) 下提供。
