(简体中文|[English](./README.md))
< p align = "center" >
< img src = "./docs/images/PaddleSpeech_logo.png" / >
< / p >
< p align = "center" >
< a href = "./LICENSE" > < img src = "https://img.shields.io/badge/license-Apache%202-red.svg" > < / a >
< a href = "https://github.com/PaddlePaddle/PaddleSpeech/releases" > < img src = "https://img.shields.io/github/v/release/PaddlePaddle/PaddleSpeech?color=ffa" > < / a >
< a href = "support os" > < img src = "https://img.shields.io/badge/os-linux%2C%20win%2C%20mac-pink.svg" > < / a >
< a href = "" > < img src = "https://img.shields.io/badge/python-3.7+-aff.svg" > < / a >
< a href = "https://github.com/PaddlePaddle/PaddleSpeech/graphs/contributors" > < img src = "https://img.shields.io/github/contributors/PaddlePaddle/PaddleSpeech?color=9ea" > < / a >
< a href = "https://github.com/PaddlePaddle/PaddleSpeech/commits" > < img src = "https://img.shields.io/github/commit-activity/m/PaddlePaddle/PaddleSpeech?color=3af" > < / a >
< a href = "https://github.com/PaddlePaddle/PaddleSpeech/issues" > < img src = "https://img.shields.io/github/issues/PaddlePaddle/PaddleSpeech?color=9cc" > < / a >
< a href = "https://github.com/PaddlePaddle/PaddleSpeech/stargazers" > < img src = "https://img.shields.io/github/stars/PaddlePaddle/PaddleSpeech?color=ccf" > < / a >
< a href = "=https://pypi.org/project/paddlespeech/" > < img src = "https://img.shields.io/pypi/dm/PaddleSpeech" > < / a >
< a href = "=https://pypi.org/project/paddlespeech/" > < img src = "https://static.pepy.tech/badge/paddlespeech" > < / a >
< a href = "https://huggingface.co/spaces" > < img src = "https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue" > < / a >
< / p >
< div align = "center" >
< h4 >
< a href = "#安装" > 安装 < / a >
| < a href = "#快速开始" > 快速开始 < / a >
| < a href = "#教程文档" > 教程文档 < / a >
| < a href = "#模型列表" > 模型列表 < / a >
| < a href = "https://aistudio.baidu.com/aistudio/education/group/info/25130" > AIStudio 课程 < / a >
| < a href = "https://arxiv.org/abs/2205.12007" > NAACL2022 论文 < / a >
| < a href = "https://gitee.com/paddlepaddle/PaddleSpeech" > Gitee
< / h4 >
< / div >
------------------------------------------------------------------------------------
**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：
**PaddleSpeech** 荣获 [NAACL2022 Best Demo Award ](https://2022.naacl.org/blog/best-demo-award/ ), 请访问 [Arxiv ](https://arxiv.org/abs/2205.12007 ) 论文。
### 效果展示
##### 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 >
### 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。
- 🏆 ** 流式 ASR 和 TTS 系统**：工业级的端到端流式识别、流式合成系统。
- 💯 ** 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、声纹识别、KWS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。
### 近期活动
❗️重磅❗️飞桨智慧金融行业系列直播课
✅ 覆盖智能风控、智能运维、智能营销、智能客服四大金融主流场景
📆 9月6日-9月29日每周二、四19:00
+ 智慧金融行业深入洞察
+ 8节理论+实践精品直播课
+ 10+真实产业场景范例教学及实践
+ 更有免费算力+结业证书等礼品等你来拿
扫码报名码住直播链接，与行业精英深度交流
< div align = "center" >
< img src = "https://user-images.githubusercontent.com/30135920/188431897-a02f028f-dd13-41e8-8ff6-749468cdc850.jpg" width = "200" / >
< / div >
### 近期更新
- 🔥 2022.09.26: 新增 Voice Cloning, TTS finetune 和 ERNIE-SAT 到 [PaddleSpeech 网页应用 ](./demos/speech_web )。
- ⚡ 2022.09.09: 新增基于 ECAPA-TDNN 声纹模型的 AISHELL-3 Voice Cloning [示例 ](./examples/aishell3/vc2 )。
- ⚡ 2022.08.25: 发布 TTS [finetune ](./examples/other/tts_finetune/tts3 ) 示例。
- 🔥 2022.08.22: 新增 ERNIE-SAT 模型: [ERNIE-SAT-vctk ](./examples/vctk/ernie_sat )、[ERNIE-SAT-aishell3](./examples/aishell3/ernie_sat)、[ERNIE-SAT-zh_en](./examples/aishell3_vctk/ernie_sat)。
- 🔥 2022.08.15: 将 [g2pW ](https://github.com/GitYCC/g2pW ) 引入 TTS 中文文本前端。
- 🔥 2022.08.09: 发布[中英文混合 TTS](./examples/zh_en_tts/tts3)。
- ⚡ 2022.08.03: TTS CLI 新增 ONNXRuntime 推理方式。
- 🎉 2022.07.18: 发布 VITS 模型: [VITS-csmsc ](./examples/csmsc/vits )、[VITS-aishell3](./examples/aishell3/vits)、[VITS-VC](./examples/aishell3/vits-vc)。
- 🎉 2022.06.22: 所有 TTS 模型支持了 ONNX 格式。
- 🍀 2022.06.17: 新增 [PaddleSpeech 网页应用 ](./demos/speech_web )。
- 👑 2022.05.13: PaddleSpeech 发布 [PP-ASR ](./docs/source/asr/PPASR_cn.md ) 流式语音识别系统、[PP-TTS](./docs/source/tts/PPTTS_cn.md) 流式语音合成系统、[PP-VPR](docs/source/vpr/PPVPR_cn.md) 全链路声纹识别系统
- 👏🏻 2022.05.06: PaddleSpeech Streaming Server 上线！覆盖了语音识别（标点恢复、时间戳）和语音合成。
- 👏🏻 2022.05.06: PaddleSpeech Server 上线！覆盖了声音分类、语音识别、语音合成、声纹识别，标点恢复。
- 👏🏻 2022.03.28: PaddleSpeech CLI 覆盖声音分类、语音识别、语音翻译（英译中）、语音合成和声纹验证。
- 🤗 2021.12.14: PaddleSpeech [ASR ](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR ) 和 [TTS ](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS ) 可在 Hugging Face Spaces 上体验！
- 👏🏻 2021.12.10: PaddleSpeech CLI 支持语音分类, 语音识别, 语音翻译（英译中）和语音合成。
### 🔥 加入技术交流群获取入群福利
- 3 日直播课链接: 深度解读 PP-TTS、PP-ASR、PP-VPR 三项核心语音系统关键技术
- 20G 学习大礼包：视频课程、前沿论文与学习资料
微信扫描二维码关注公众号，点击“马上报名”填写问卷加入官方交流群，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://user-images.githubusercontent.com/23690325/169763015-cbd8e28d-602c-4723-810d-dbc6da49441e.jpg" width = "200" / >
< / div >
< a name = "安装" > < / a >
## 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。
### 相关依赖
+ gcc >= 4.8.5
+ paddlepaddle >= 2.3.1
+ python >= 3.7
+ linux(推荐), mac, windows
PaddleSpeech 依赖于 paddlepaddle，
```shell
pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
```
PaddleSpeech 快速安装方式有两种，一种是 pip 安装，一种是源码编译（推荐）。
### pip 安装
```shell
pip install pytest-runner
pip install paddlespeech
```
### 源码编译
```shell
git clone https://github.com/PaddlePaddle/PaddleSpeech.git
cd PaddleSpeech
pip install pytest-runner
pip install .
```
更多关于安装问题，如 conda 环境， ， ， #2150 ](https://github.com/PaddlePaddle/PaddleSpeech/issues/2150 ) 上留言以及查找相关问题
< a name = "快速开始" > < / a >
## 快速开始
安装完成后，开发者可以通过命令行或者 Python 快速开始，命令行模式下改变 `--input` 可以尝试用自己的音频或文本测试，支持 16k wav 格式音频。
你也可以在 `aistudio` 中快速体验 👉🏻[一键预测，快速上手 Speech 开发任务](https://aistudio.baidu.com/aistudio/projectdetail/4353348?sUid=2470186& shared=1& ts=1660878142250)。
测试音频示例下载
```shell
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
```
### 语音识别
< details > < summary >   （点击可展开）开源中文语音识别< / summary >
命令行一键体验
```shell
paddlespeech asr --lang zh --input zh.wav
```
Python API 一键预测
```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 >
### 语音合成
< details > < summary >   开源中文语音合成< / summary >
输出 24k 采样率wav格式音频
命令行一键体验
```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 一键预测
```python
>>> from paddlespeech.cli.tts.infer import TTSExecutor
>>> tts = TTSExecutor()
>>> tts(text="今天天气十分不错。", 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/KPatrick/PaddleSpeechTTS )
< / details >
### 声音分类
< details > < summary >   适配多场景的开放领域声音分类工具< / summary >
基于 AudioSet 数据集 527 个类别的声音分类模型
命令行一键体验
```shell
paddlespeech cls --input zh.wav
```
python API 一键预测
```python
>>> from paddlespeech.cli.cls.infer import CLSExecutor
>>> cls = CLSExecutor()
>>> result = cls(audio_file="zh.wav")
>>> print(result)
Speech 0.9027186632156372
```
< / details >
### 声纹提取
< details > < summary >   工业级声纹提取工具< / summary >
命令行一键体验
```shell
paddlespeech vector --task spk --input zh.wav
```
Python API 一键预测
```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 >
### 标点恢复
< details > < summary >   一键恢复文本标点， < / summary >
命令行一键体验
```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 一键预测
```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 >
### 语音翻译
< details > < summary >   端到端英译中语音翻译工具< / summary >
使用预编译的 kaldi 相关工具，只支持在 Ubuntu 系统中体验
命令行一键体验
```shell
paddlespeech st --input en.wav
```
python API 一键预测
```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 = "快速使用服务" > < / a >
## 快速使用服务
安装完成后，开发者可以通过命令行一键启动语音识别，语音合成，音频分类等多种服务。
你可以在 AI Studio 中快速体验：[SpeechServer 一键部署](https://aistudio.baidu.com/aistudio/projectdetail/4354592?sUid=2470186& shared=1& ts=1660878208266)
**启动服务**
```shell
paddlespeech_server start --config_file ./demos/speech_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 )
< a name = "快速使用流式服务" > < / a >
## 快速使用流式服务
开发者可以尝试 [流式 ASR ](./demos/streaming_asr_server/README.md ) 和 [流式 TTS ](./demos/streaming_tts_server/README.md ) 服务.
**启动流式 ASR 服务**
```
paddlespeech_server start --config_file ./demos/streaming_asr_server/conf/application.yaml
```
**访问流式 ASR 服务**
```
paddlespeech_client asr_online --server_ip 127.0.0.1 --port 8090 --input input_16k.wav
```
**启动流式 TTS 服务**
```
paddlespeech_server start --config_file ./demos/streaming_tts_server/conf/tts_online_application.yaml
```
**访问流式 TTS 服务**
```
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
```
更多信息参看： [流式 ASR ](./demos/streaming_asr_server/README.md ) 和 [流式 TTS ](./demos/streaming_tts_server/README.md )
< a name = "模型列表" > < / a >
## 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)。
< a name = "语音识别模型" > < / a >
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 > 模型类型< / th >
< th > 脚本< / 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 > 模型类型 < / th >
< th > 数据集 < / th >
< th > 脚本 < / 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 = "5" > 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 / ZH_EN / finetune< / 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 > / < a href = "./examples/other/tts_finetune/tts3" > fastspeech2-finetune< / a >
< / td >
< / tr >
< tr >
< td > ERNIE-SAT< / td >
< td > VCTK / AISHELL-3 / ZH_EN< / td >
< td >
< a href = "./examples/vctk/ernie_sat" > ERNIE-SAT-vctk< / a > / < a href = "./examples/aishell3/ernie_sat" > ERNIE-SAT-aishell3< / a > / < a href = "./examples/aishell3_vctk/ernie_sat" > ERNIE-SAT-zh_en< / 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 = "5" > 声音克隆< / td >
< td > GE2E< / td >
< td > Librispeech, etc.< / td >
< td >
< a href = "./examples/other/ge2e" > GE2E< / a >
< / td >
< / tr >
< tr >
< td > SV2TTS (GE2E + Tacotron2)< / td >
< td > AISHELL-3< / td >
< td >
< a href = "./examples/aishell3/vc0" > VC0< / a >
< / td >
< / tr >
< tr >
< td > SV2TTS (GE2E + FastSpeech2)< / td >
< td > AISHELL-3< / td >
< td >
< a href = "./examples/aishell3/vc1" > VC1< / a >
< / td >
< / tr >
< tr >
< td > SV2TTS (ECAPA-TDNN + FastSpeech2)< / td >
< td > AISHELL-3< / td >
< td >
< a href = "./examples/aishell3/vc2" > VC2< / a >
< / td >
< / tr >
< tr >
< td > GE2E + VITS< / td >
< td > AISHELL-3< / td >
< td >
< a href = "./examples/aishell3/vits-vc" > VITS-VC< / a >
< / td >
< / tr >
< tr >
< td rowspan = "3" > 端到端< / td >
< td > VITS< / td >
< td > CSMSC / AISHELL-3< / td >
< td >
< a href = "./examples/csmsc/vits" > VITS-csmsc< / a > / < a href = "./examples/aishell3/vits" > VITS-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 > 模型类型 < / th >
< th > 脚本< / 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 >
< a name = "声纹识别模型" > < / a >
**声纹识别**
< table style = "width:100%" >
< thead >
< tr >
< th > 任务 < / th >
< th > 数据集 < / th >
< th > 模型类型 < / th >
< th > 脚本 < / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > 声纹识别< / td >
< td > VoxCeleb1/2< / td >
< td > ECAPA-TDNN< / td >
< td >
< a href = "./examples/voxceleb/sv0" > ecapa-tdnn-voxceleb12< / a >
< / td >
< / tr >
< / tbody >
< / table >
< a name = "说话人日志模型" > < / a >
**说话人日志**
< table style = "width:100%" >
< thead >
< tr >
< th > 任务 < / th >
< th > 数据集 < / th >
< th > 模型类型 < / th >
< th > 脚本 < / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > 说话人日志< / td >
< td > AMI< / td >
< td > ECAPA-TDNN + AHC / SC< / td >
< td >
< a href = "./examples/ami/sd0" > ecapa-tdnn-ami< / a >
< / td >
< / tr >
< / tbody >
< / table >
< a name = "标点恢复模型" > < / a >
**标点恢复**
< table style = "width:100%" >
< thead >
< tr >
< th > 任务 < / th >
< th > 数据集 < / th >
< th > 模型类型 < / th >
< th > 脚本 < / 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 >
< a name = "教程文档" > < / a >
## 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 )
- 声纹识别
- [声纹识别 ](./demos/speaker_verification/README_cn.md )
- [音频检索 ](./demos/audio_searching/README_cn.md )
- [声音分类 ](./demos/audio_tagging/README_cn.md )
- [语音翻译 ](./demos/speech_translation/README_cn.md )
- [服务化部署 ](./demos/speech_server/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 )
- [声纹识别 ](#声纹识别模型 )
- [说话人日志 ](#说话人日志模型 )
- [标点恢复 ](#标点恢复模型 )
- [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。
## ⭐ 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 >
## 引用
要引用 PaddleSpeech 进行研究，请使用以下格式进行引用。
```text
@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 = "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?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/Jackwaterveg" > < img src = "https://avatars.githubusercontent.com/u/87408988?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/yt605155624" > < img src = "https://avatars.githubusercontent.com/u/24568452?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/Honei" > < img src = "https://avatars.githubusercontent.com/u/11361692?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/KPatr1ck" > < img src = "https://avatars.githubusercontent.com/u/22954146?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/kuke" > < img src = "https://avatars.githubusercontent.com/u/3064195?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/lym0302" > < img src = "https://avatars.githubusercontent.com/u/34430015?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/SmileGoat" > < img src = "https://avatars.githubusercontent.com/u/56786796?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/xinghai-sun" > < img src = "https://avatars.githubusercontent.com/u/7038341?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/pkuyym" > < img src = "https://avatars.githubusercontent.com/u/5782283?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/LittleChenCc" > < img src = "https://avatars.githubusercontent.com/u/10339970?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/qingen" > < img src = "https://avatars.githubusercontent.com/u/3139179?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/D-DanielYang" > < img src = "https://avatars.githubusercontent.com/u/23690325?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/Mingxue-Xu" > < img src = "https://avatars.githubusercontent.com/u/92848346?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/745165806" > < img src = "https://avatars.githubusercontent.com/u/20623194?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/jerryuhoo" > < img src = "https://avatars.githubusercontent.com/u/24245709?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/WilliamZhang06" > < img src = "https://avatars.githubusercontent.com/u/97937340?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/chrisxu2016" > < img src = "https://avatars.githubusercontent.com/u/18379485?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/iftaken" > < img src = "https://avatars.githubusercontent.com/u/30135920?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/lfchener" > < img src = "https://avatars.githubusercontent.com/u/6771821?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/BarryKCL" > < img src = "https://avatars.githubusercontent.com/u/48039828?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/mmglove" > < img src = "https://avatars.githubusercontent.com/u/38800877?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/gongel" > < img src = "https://avatars.githubusercontent.com/u/24390500?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/luotao1" > < img src = "https://avatars.githubusercontent.com/u/6836917?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/wanghaoshuang" > < img src = "https://avatars.githubusercontent.com/u/7534971?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/kslz" > < img src = "https://avatars.githubusercontent.com/u/54951765?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/JiehangXie" > < img src = "https://avatars.githubusercontent.com/u/51190264?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/david-95" > < img src = "https://avatars.githubusercontent.com/u/15189190?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/THUzyt21" > < img src = "https://avatars.githubusercontent.com/u/91456992?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/buchongyu2" > < img src = "https://avatars.githubusercontent.com/u/29157444?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/iclementine" > < img src = "https://avatars.githubusercontent.com/u/16222986?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/phecda-xu" > < img src = "https://avatars.githubusercontent.com/u/46859427?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/freeliuzc" > < img src = "https://avatars.githubusercontent.com/u/23568094?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/ZeyuChen" > < img src = "https://avatars.githubusercontent.com/u/1371212?s=60&v=4" width = 75 height = 75 > < / a >
< a href = "https://github.com/ccrrong" > < img src = "https://avatars.githubusercontent.com/u/101700995?s=60&v=4" width = 75 height = 75 > < / a >
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< / p >
## 致谢
- 非常感谢 [HighCWu ](https://github.com/HighCWu ) 新增 [VITS-aishell3 ](./examples/aishell3/vits ) 和 [VITS-VC ](./examples/aishell3/vits-vc ) 代码示例。
- 非常感谢 [david-95 ](https://github.com/david-95 ) 修复句尾多标点符号出错的问题，贡献补充多条程序和数据。
- 非常感谢 [BarryKCL ](https://github.com/BarryKCL ) 基于 [G2PW ](https://github.com/GitYCC/g2pW ) 对 TTS 中文文本前端的优化。
- 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。
- 非常感谢 [vpegasus ](https://github.com/vpegasus )/[xuesebot](https://github.com/vpegasus/xuesebot) 基于 PaddleSpeech 的 ASR 与 TTS 设计的可听、说对话机器人。
- 非常感谢 [chenkui164 ](https://github.com/chenkui164 )/[FastASR](https://github.com/chenkui164/FastASR) 对 PaddleSpeech 的 ASR 进行 C++ 推理实现。
此外， references ](./docs/source/reference.md )。
## License
PaddleSpeech 在 [Apache-2.0 许可 ](./LICENSE ) 下提供。
