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README.md
浏览文件 @ df3f975e
([简体中文](./README_cn.md)|English)
<p align="center">
<img src="./docs/images/PaddleSpeech_logo.png" />
</p>
<div align="center">
<h3>
<a href="#quick-start"> Quick Start </a>
| <a href="#quick-start-server"> Quick Start Server </a>
| <a href="#documents"> Documents </a>
| <a href="#model-list"> Models List </a>
</div>
------------------------------------------------------------------------------------
<p align="center">
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-red.svg"></a>
......@@ -28,6 +19,20 @@
<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">
<h3>
| <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>
|
</br>
| <a href="#documents"> Documents </a>
| <a href="#model-list"> Models List </a>
|
</h3>
</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.
......@@ -142,47 +147,40 @@ For more synthesized audios, please refer to [PaddleSpeech Text-to-Speech sample
</div>
### ⭐ 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>
### 🔥 Hot Activities
- 2021.12.21~12.24
4 Days Live Courses: Depth interpretation of PaddleSpeech!
**Courses videos and related materials: https://aistudio.baidu.com/aistudio/education/group/info/25130**
### 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, and [CLI](#quick-start) is available to quick-start your journey.
- 📦 **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 Audio Classification, Speech Translation, Automatic Speech Recognition, Text-to-Speech Synthesis, etc.
- 📦 **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`.
### 🔥 Hot Activities
<!---
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 is available for Audio Classification, Automatic Speech Recognition and Text-to-Speech.
- 👏🏻 2022.03.28: PaddleSpeech CLI is available for Speaker Verification.
- 🤗 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 is available for Audio Classification, Automatic Speech Recognition, Speech Translation (English to Chinese) and Text-to-Speech.
- 2021.12.21~12.24
4 Days Live Courses: Depth interpretation of PaddleSpeech!
**Courses videos and related materials: https://aistudio.baidu.com/aistudio/education/group/info/25130**
### Community
- Scan the QR code below with your Wechat (reply【语音】after your friend's application is approved), you can access to official technical exchange group. Look forward to your participation.
......@@ -196,6 +194,7 @@ Via the easy-to-use, efficient, flexible and scalable implementation, our vision
We strongly recommend our users to install PaddleSpeech in **Linux** with *python>=3.7*.
Up to now, **Linux** supports CLI for the all our tasks, **Mac OSX** and **Windows** only supports PaddleSpeech CLI for Audio Classification, Speech-to-Text and Text-to-Speech. To install `PaddleSpeech`, please see [installation](./docs/source/install.md).
<a name="quickstart"></a>
## Quick Start
......@@ -238,7 +237,7 @@ paddlespeech tts --input "你好，欢迎使用飞桨深度学习框架！" --ou
**Batch Process**
```
echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts
```
```
**Shell Pipeline**
- ASR + Punctuation Restoration
......@@ -257,16 +256,19 @@ If you want to try more functions like training and tuning, please have a look a
Developers can have a try of our speech server with [PaddleSpeech Server Command Line](./paddlespeech/server/README.md).
**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
```
......@@ -280,6 +282,37 @@ 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
......@@ -296,7 +329,7 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
<th>Speech-to-Text Module Type</th>
<th>Dataset</th>
<th>Model Type</th>
<th>Link</th>
<th>Example</th>
</tr>
</thead>
<tbody>
......@@ -371,7 +404,7 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
<th> Text-to-Speech Module Type </th>
<th> Model Type </th>
<th> Dataset </th>
<th> Link </th>
<th> Example </th>
</tr>
</thead>
<tbody>
......@@ -489,7 +522,7 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
<th> Task </th>
<th> Dataset </th>
<th> Model Type </th>
<th> Link </th>
<th> Example </th>
</tr>
</thead>
<tbody>
......@@ -514,7 +547,7 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
<th> Task </th>
<th> Dataset </th>
<th> Model Type </th>
<th> Link </th>
<th> Example </th>
</tr>
</thead>
<tbody>
......@@ -539,7 +572,7 @@ PaddleSpeech supports a series of most popular models. They are summarized in [r
<th> Task </th>
<th> Dataset </th>
<th> Model Type </th>
<th> Link </th>
<th> Example </th>
</tr>
</thead>
<tbody>
......@@ -589,6 +622,21 @@ Normally, [Speech SoTA](https://paperswithcode.com/area/speech), [Audio SoTA](ht
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.
......@@ -655,7 +703,6 @@ You are warmly welcome to submit questions in [discussions](https://github.com/P
## Acknowledgement
- 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.
......
README_cn.md
浏览文件 @ df3f975e
......@@ -2,26 +2,45 @@
<p align="center">
<img src="./docs/images/PaddleSpeech_logo.png" />
</p>
<div align="center">
<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>
</div>
------------------------------------------------------------------------------------
<p align="center">
<a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-red.svg"></a>
<a href="support os"><img src="https://img.shields.io/badge/os-linux-yellow.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">
<h3>
<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>
</br>
<a href="#documents"> Documents </a>
| <a href="#model-list"> Models List </a>
</h3>
</div>
------------------------------------------------------------------------------------
<div align="center">
<h3>
<a href="#quick-start"> 快速开始 </a>
| <a href="#quick-start-server"> 快速使用服务 </a>
| <a href="#quick-start-streaming-server"> 快速使用流式服务 </a>
| <a href="#documents"> 教程文档 </a>
| <a href="#model-list"> 模型列表 </a>
</div>
<!---
from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readmes-readable.md
......@@ -31,6 +50,8 @@ from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readme
4.What is the goal of this project?
-->
**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
......@@ -57,7 +78,6 @@ from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readme
</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>
......@@ -143,47 +163,39 @@ from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readme
</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。
- 🏆 **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（G2P）。此外，我们使用自定义语言规则来适应中文语境。
- **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。
- 🛎️ 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，详情请见 [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。
- 👑 2022.05.13: PaddleSpeech 发布 [PP-ASR](./docs/source/asr/PPASR_cn.md)、[PP-TTS](./docs/source/tts/PPTTS_cn.md)、[PP-VPR](docs/source/vpr/PPVPR_cn.md)
- 👏🏻 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，声纹验证。
- 🤗 2021.12.14: PaddleSpeech [ASR](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR) and [TTS](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS) Demos on Hugging Face Spaces are available!
### 🔥 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**
### 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。
......@@ -192,11 +204,13 @@ from https://github.com/18F/open-source-guide/blob/18f-pages/pages/making-readme
<img src="https://raw.githubusercontent.com/yt605155624/lanceTest/main/images/wechat_4.jpg" width = "300" />
</div>
## 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。
......@@ -232,7 +246,7 @@ paddlespeech tts --input "你好，欢迎使用百度飞桨深度学习框架！
**批处理**
```
echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts
```
```
**Shell管道**
ASR + Punc:
......@@ -269,6 +283,38 @@ 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="quickstartstreamingserver"></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="modulelist"></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)。
......@@ -282,8 +328,8 @@ PaddleSpeech 的 **语音转文本** 包含语音识别声学模型、语音识
<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>
<th>模型类型</th>
<th>脚本</th>
</tr>
</thead>
<tbody>
......@@ -356,9 +402,9 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
<thead>
<tr>
<th> 语音合成模块类型 </th>
<th> 模型种类 </th>
<th> 模型类型 </th>
<th> 数据集 </th>
<th> 链接 </th>
<th> 脚本 </th>
</tr>
</thead>
<tbody>
......@@ -474,8 +520,8 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
<tr>
<th> 任务 </th>
<th> 数据集 </th>
<th> 模型种类 </th>
<th> 链接</th>
<th> 模型类型 </th>
<th> 脚本</th>
</tr>
</thead>
<tbody>
......@@ -498,10 +544,10 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
<table style="width:100%">
<thead>
<tr>
<th> Task </th>
<th> Dataset </th>
<th> Model Type </th>
<th> Link </th>
<th> 任务 </th>
<th> 数据集 </th>
<th> 模型类型 </th>
<th> 脚本 </th>
</tr>
</thead>
<tbody>
......@@ -525,8 +571,8 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
<tr>
<th> 任务 </th>
<th> 数据集 </th>
<th> 模型种类 </th>
<th> 链接 </th>
<th> 模型类型 </th>
<th> 脚本 </th>
</tr>
</thead>
<tbody>
......@@ -582,6 +628,21 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
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。
......@@ -658,6 +719,7 @@ PaddleSpeech 的 **语音合成** 主要包含三个模块：文本前端、声
- 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，PaddleSpeech 依赖于许多开源存储库。有关更多信息，请参阅 [references](./docs/source/reference.md)。
## License
......
paddleaudio/paddleaudio/datasets/__init__.py → audio/paddleaudio/datasets/__init__.py
浏览文件 @ df3f975e
......@@ -13,6 +13,7 @@
# limitations under the License.
from .esc50 import ESC50
from .gtzan import GTZAN
from .hey_snips import HeySnips
from .rirs_noises import OpenRIRNoise
from .tess import TESS
from .urban_sound import UrbanSound8K
......
paddleaudio/paddleaudio/datasets/dataset.py → audio/paddleaudio/datasets/dataset.py
浏览文件 @ df3f975e
......@@ -17,6 +17,8 @@ import numpy as np
import paddle
from ..backends import load as load_audio
from ..compliance.kaldi import fbank as kaldi_fbank
from ..compliance.kaldi import mfcc as kaldi_mfcc
from ..compliance.librosa import melspectrogram
from ..compliance.librosa import mfcc
......@@ -24,6 +26,8 @@ feat_funcs = {
'raw': None,
'melspectrogram': melspectrogram,
'mfcc': mfcc,
'kaldi_fbank': kaldi_fbank,
'kaldi_mfcc': kaldi_mfcc,
}
......@@ -73,16 +77,24 @@ class AudioClassificationDataset(paddle.io.Dataset):
feat_func = feat_funcs[self.feat_type]
record = {}
record['feat'] = feat_func(
waveform, sample_rate,
**self.feat_config) if feat_func else waveform
if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
waveform = paddle.to_tensor(waveform).unsqueeze(0) # (C, T)
record['feat'] = feat_func(
waveform=waveform, sr=self.sample_rate, **self.feat_config)
else:
record['feat'] = feat_func(
waveform, sample_rate,
**self.feat_config) if feat_func else waveform
record['label'] = label
return record
def __getitem__(self, idx):
record = self._convert_to_record(idx)
return np.array(record['feat']).transpose(), np.array(
record['label'], dtype=np.int64)
if self.feat_type in ['kaldi_fbank', 'kaldi_mfcc']:
return self.keys[idx], record['feat'], record['label']
else:
return np.array(record['feat']).transpose(), np.array(
record['label'], dtype=np.int64)
def __len__(self):
return len(self.files)
audio/paddleaudio/datasets/hey_snips.py 0 → 100644
浏览文件 @ df3f975e
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import json
import os
from typing import List
from typing import Tuple
from .dataset import AudioClassificationDataset
__all__ = ['HeySnips']
class HeySnips(AudioClassificationDataset):
meta_info = collections.namedtuple('META_INFO',
('key', 'label', 'duration', 'wav'))
def __init__(self,
data_dir: os.PathLike,
mode: str='train',
feat_type: str='kaldi_fbank',
sample_rate: int=16000,
**kwargs):
self.data_dir = data_dir
files, labels = self._get_data(mode)
super(HeySnips, self).__init__(
files=files,
labels=labels,
feat_type=feat_type,
sample_rate=sample_rate,
**kwargs)
def _get_meta_info(self, mode) -> List[collections.namedtuple]:
ret = []
with open(os.path.join(self.data_dir, '{}.json'.format(mode)),
'r') as f:
data = json.load(f)
for item in data:
sample = collections.OrderedDict()
if item['duration'] > 0:
sample['key'] = item['id']
sample['label'] = 0 if item['is_hotword'] == 1 else -1
sample['duration'] = item['duration']
sample['wav'] = os.path.join(self.data_dir,
item['audio_file_path'])
ret.append(self.meta_info(*sample.values()))
return ret
def _get_data(self, mode: str) -> Tuple[List[str], List[int]]:
meta_info = self._get_meta_info(mode)
files = []
labels = []
self.keys = []
self.durations = []
for sample in meta_info:
key, target, duration, wav = sample
files.append(wav)
labels.append(int(target))
self.keys.append(key)
self.durations.append(float(duration))
return files, labels
paddleaudio/setup.py → audio/setup.py
浏览文件 @ df3f975e
......@@ -19,7 +19,7 @@ from setuptools.command.install import install
from setuptools.command.test import test
# set the version here
VERSION = '0.2.1'
VERSION = '0.0.0'
# Inspired by the example at https://pytest.org/latest/goodpractises.html
......
paddleaudio/tests/backends/soundfile/test_io.py → audio/tests/backends/soundfile/test_io.py
浏览文件 @ df3f975e
......@@ -16,9 +16,9 @@ import os
import unittest
import numpy as np
import paddleaudio
import soundfile as sf
import paddleaudio
from ..base import BackendTest
......
paddleaudio/tests/benchmark/log_melspectrogram.py → audio/tests/benchmark/log_melspectrogram.py
浏览文件 @ df3f975e
......@@ -17,11 +17,10 @@ import urllib.request
import librosa
import numpy as np
import paddle
import paddleaudio
import torch
import torchaudio
import paddleaudio
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
if not os.path.isfile(os.path.basename(wav_url)):
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
......
paddleaudio/tests/benchmark/melspectrogram.py → audio/tests/benchmark/melspectrogram.py
浏览文件 @ df3f975e
......@@ -17,11 +17,10 @@ import urllib.request
import librosa
import numpy as np
import paddle
import paddleaudio
import torch
import torchaudio
import paddleaudio
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
if not os.path.isfile(os.path.basename(wav_url)):
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
......
paddleaudio/tests/benchmark/mfcc.py → audio/tests/benchmark/mfcc.py
浏览文件 @ df3f975e
......@@ -17,11 +17,10 @@ import urllib.request
import librosa
import numpy as np
import paddle
import paddleaudio
import torch
import torchaudio
import paddleaudio
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
if not os.path.isfile(os.path.basename(wav_url)):
urllib.request.urlretrieve(wav_url, os.path.basename(wav_url))
......
paddleaudio/tests/features/base.py → audio/tests/features/base.py
浏览文件 @ df3f975e
......@@ -17,7 +17,6 @@ import urllib.request
import numpy as np
import paddle
from paddleaudio import load
wav_url = 'https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav'
......
paddleaudio/tests/features/test_istft.py → audio/tests/features/test_istft.py
浏览文件 @ df3f975e
......@@ -15,9 +15,9 @@ import unittest
import numpy as np
import paddle
from paddleaudio.functional.window import get_window
from .base import FeatTest
from paddleaudio.functional.window import get_window
from paddlespeech.s2t.transform.spectrogram import IStft
from paddlespeech.s2t.transform.spectrogram import Stft
......
paddleaudio/tests/features/test_kaldi.py → audio/tests/features/test_kaldi.py
浏览文件 @ df3f975e
......@@ -15,10 +15,10 @@ import unittest
import numpy as np
import paddle
import paddleaudio
import torch
import torchaudio
import paddleaudio
from .base import FeatTest
......
paddleaudio/tests/features/test_librosa.py → audio/tests/features/test_librosa.py
浏览文件 @ df3f975e
......@@ -16,11 +16,11 @@ import unittest
import librosa
import numpy as np
import paddle
import paddleaudio
from .base import FeatTest
from paddleaudio.functional.window import get_window
from .base import FeatTest
class TestLibrosa(FeatTest):
def initParmas(self):
......
paddleaudio/tests/features/test_log_melspectrogram.py → audio/tests/features/test_log_melspectrogram.py
浏览文件 @ df3f975e
......@@ -15,8 +15,8 @@ import unittest
import numpy as np
import paddle
import paddleaudio
from .base import FeatTest
from paddlespeech.s2t.transform.spectrogram import LogMelSpectrogram
......
paddleaudio/tests/features/test_spectrogram.py → audio/tests/features/test_spectrogram.py
浏览文件 @ df3f975e
......@@ -15,8 +15,8 @@ import unittest
import numpy as np
import paddle
import paddleaudio
from .base import FeatTest
from paddlespeech.s2t.transform.spectrogram import Spectrogram
......
paddleaudio/tests/features/test_stft.py → audio/tests/features/test_stft.py
浏览文件 @ df3f975e
......@@ -15,9 +15,9 @@ import unittest
import numpy as np
import paddle
from paddleaudio.functional.window import get_window
from .base import FeatTest
from paddleaudio.functional.window import get_window
from paddlespeech.s2t.transform.spectrogram import Stft
......
demos/README.md
浏览文件 @ df3f975e
......@@ -11,6 +11,7 @@ The directory containes many speech applications in multi scenarios.
* punctuation_restoration - restore punctuation from raw text
* speech recogintion - recognize text of an audio file
* speech server - Server for Speech Task, e.g. ASR,TTS,CLS
* streaming asr server - receive audio stream from websocket, and recognize to transcript.
* speech translation - end to end speech translation
* story talker - book reader based on OCR and TTS
* style_fs2 - multi style control for FastSpeech2 model
......
demos/README_cn.md
浏览文件 @ df3f975e
......@@ -11,6 +11,7 @@
* 标点恢复 - 通常作为语音识别的文本后处理任务，为一段无标点的纯文本添加相应的标点符号。
* 语音识别 - 识别一段音频中包含的语音文字。
* 语音服务 - 离线语音服务，包括ASR、TTS、CLS等
* 流式语音识别服务 - 流式输入语音数据流识别音频中的文字
* 语音翻译 - 实时识别音频中的语言，并同时翻译成目标语言。
* 会说话的故事书 - 基于 OCR 和语音合成的会说话的故事书。
* 个性化语音合成 - 基于 FastSpeech2 模型的个性化语音合成。
......
demos/audio_content_search/README.md 0 → 100644
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([简体中文](./README_cn.md)|English)
# ACS (Audio Content Search)
## Introduction
ACS, or Audio Content Search, refers to the problem of getting the key word time stamp from automatically transcribe spoken language (speech-to-text).
This demo is an implementation of obtaining the keyword timestamp in the text from a given audio file. It can be done by a single command or a few lines in python using `PaddleSpeech`.
Now, the search word in demo is:
```
我
康
```
## Usage
### 1. Installation
see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
You can choose one way from meduim and hard to install paddlespeech.
The dependency refers to the requirements.txt
### 2. Prepare Input File
The input of this demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
Here are sample files for this demo that can be downloaded:
```bash
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
```
### 3. Usage
- Command Line(Recommended)
```bash
# Chinese
paddlespeech_client acs --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
```
Usage:
```bash
paddlespeech asr --help
```
Arguments:
- `input`(required): Audio file to recognize.
- `server_ip`: the server ip.
- `port`: the server port.
- `lang`: the language type of the model. Default: `zh`.
- `sample_rate`: Sample rate of the model. Default: `16000`.
- `audio_format`: The audio format.
Output:
```bash
[2022-05-15 15:00:58,185] [ INFO] - acs http client start
[2022-05-15 15:00:58,185] [ INFO] - endpoint: http://127.0.0.1:8490/paddlespeech/asr/search
[2022-05-15 15:01:03,220] [ INFO] - acs http client finished
[2022-05-15 15:01:03,221] [ INFO] - ACS result: {'transcription': '我认为跑步最重要的就是给我带来了身体健康', 'acs': [{'w': '我', 'bg': 0, 'ed': 1.6800000000000002}, {'w': '我', 'bg': 2.1, 'ed': 4.28}, {'w': '康', 'bg': 3.2, 'ed': 4.92}]}
[2022-05-15 15:01:03,221] [ INFO] - Response time 5.036084 s.
```
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import ACSClientExecutor
acs_executor = ACSClientExecutor()
res = acs_executor(
input='./zh.wav',
server_ip="127.0.0.1",
port=8490,)
print(res)
```
Output:
```bash
[2022-05-15 15:08:13,955] [ INFO] - acs http client start
[2022-05-15 15:08:13,956] [ INFO] - endpoint: http://127.0.0.1:8490/paddlespeech/asr/search
[2022-05-15 15:08:19,026] [ INFO] - acs http client finished
{'transcription': '我认为跑步最重要的就是给我带来了身体健康', 'acs': [{'w': '我', 'bg': 0, 'ed': 1.6800000000000002}, {'w': '我', 'bg': 2.1, 'ed': 4.28}, {'w': '康', 'bg': 3.2, 'ed': 4.92}]}
```
demos/audio_content_search/README_cn.md 0 → 100644
浏览文件 @ df3f975e
(简体中文|[English](./README.md))
# 语音内容搜索
## 介绍
语音内容搜索是一项用计算机程序获取转录语音内容关键词时间戳的技术。
这个 demo 是一个从给定音频文件获取其文本中关键词时间戳的实现，它可以通过使用 `PaddleSpeech` 的单个命令或 python 中的几行代码来实现。
当前示例中检索词是
```
我
康
```
## 使用方法
### 1. 安装
请看[安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install_cn.md)。
你可以从 medium，hard 三中方式中选择一种方式安装。
依赖参见 requirements.txt
### 2. 准备输入
这个 demo 的输入应该是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。
可以下载此 demo 的示例音频：
```bash
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
```
### 3. 使用方法
- 命令行 (推荐使用)
```bash
# 中文
paddlespeech_client acs --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
```
使用方法：
```bash
paddlespeech acs --help
```
参数：
- `input`(必须输入)：用于识别的音频文件。
- `server_ip`: 服务的ip。
- `port`：服务的端口。
- `lang`：模型语言，默认值：`zh`。
- `sample_rate`：音频采样率，默认值：`16000`。
- `audio_format`: 音频的格式。
输出：
```bash
[2022-05-15 15:00:58,185] [ INFO] - acs http client start
[2022-05-15 15:00:58,185] [ INFO] - endpoint: http://127.0.0.1:8490/paddlespeech/asr/search
[2022-05-15 15:01:03,220] [ INFO] - acs http client finished
[2022-05-15 15:01:03,221] [ INFO] - ACS result: {'transcription': '我认为跑步最重要的就是给我带来了身体健康', 'acs': [{'w': '我', 'bg': 0, 'ed': 1.6800000000000002}, {'w': '我', 'bg': 2.1, 'ed': 4.28}, {'w': '康', 'bg': 3.2, 'ed': 4.92}]}
[2022-05-15 15:01:03,221] [ INFO] - Response time 5.036084 s.
```
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import ACSClientExecutor
acs_executor = ACSClientExecutor()
res = acs_executor(
input='./zh.wav',
server_ip="127.0.0.1",
port=8490,)
print(res)
```
输出：
```bash
[2022-05-15 15:08:13,955] [ INFO] - acs http client start
[2022-05-15 15:08:13,956] [ INFO] - endpoint: http://127.0.0.1:8490/paddlespeech/asr/search
[2022-05-15 15:08:19,026] [ INFO] - acs http client finished
{'transcription': '我认为跑步最重要的就是给我带来了身体健康', 'acs': [{'w': '我', 'bg': 0, 'ed': 1.6800000000000002}, {'w': '我', 'bg': 2.1, 'ed': 4.28}, {'w': '康', 'bg': 3.2, 'ed': 4.92}]}
```
demos/audio_content_search/acs_clinet.py 0 → 100644
浏览文件 @ df3f975e
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from paddlespeech.cli.log import logger
from paddlespeech.server.utils.audio_handler import ASRHttpHandler
def main(args):
logger.info("asr http client start")
audio_format = "wav"
sample_rate = 16000
lang = "zh"
handler = ASRHttpHandler(
server_ip=args.server_ip, port=args.port, endpoint=args.endpoint)
res = handler.run(args.wavfile, audio_format, sample_rate, lang)
# res = res['result']
logger.info(f"the final result: {res}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="audio content search client")
parser.add_argument(
'--server_ip', type=str, default='127.0.0.1', help='server ip')
parser.add_argument('--port', type=int, default=8090, help='server port')
parser.add_argument(
"--wavfile",
action="store",
help="wav file path ",
default="./16_audio.wav")
parser.add_argument(
'--endpoint',
type=str,
default='/paddlespeech/asr/search',
help='server endpoint')
args = parser.parse_args()
main(args)
demos/audio_content_search/conf/acs_application.yaml 0 → 100644
浏览文件 @ df3f975e
#################################################################################
# SERVER SETTING #
#################################################################################
host: 0.0.0.0
port: 8490
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['acs_python']
# protocol = ['http'] (only one can be selected).
# http only support offline engine type.
protocol: 'http'
engine_list: ['acs_python']
#################################################################################
# ENGINE CONFIG #
#################################################################################
################################### ACS #########################################
################### acs task: engine_type: python ###############################
acs_python:
task: acs
asr_protocol: 'websocket' # 'websocket'
offset: 1.0 # second
asr_server_ip: 127.0.0.1
asr_server_port: 8390
lang: 'zh'
word_list: "./conf/words.txt"
sample_rate: 16000
device: 'cpu' # set 'gpu:id' or 'cpu'
demos/audio_content_search/conf/words.txt 0 → 100644
浏览文件 @ df3f975e
我
康
\ No newline at end of file
demos/audio_content_search/conf/ws_conformer_application.yaml 0 → 100644
浏览文件 @ df3f975e
#################################################################################
# SERVER SETTING #
#################################################################################
host: 0.0.0.0
port: 8390
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_online']
# protocol = ['websocket'] (only one can be selected).
# websocket only support online engine type.
protocol: 'websocket'
engine_list: ['asr_online']
#################################################################################
# ENGINE CONFIG #
#################################################################################
################################### ASR #########################################
################### speech task: asr; engine_type: online #######################
asr_online:
model_type: 'conformer_online_multicn'
am_model: # the pdmodel file of am static model [optional]
am_params: # the pdiparams file of am static model [optional]
lang: 'zh'
sample_rate: 16000
cfg_path:
decode_method: 'attention_rescoring'
force_yes: True
device: 'cpu' # cpu or gpu:id
am_predictor_conf:
device: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
chunk_buffer_conf:
window_n: 7 # frame
shift_n: 4 # frame
window_ms: 25 # ms
shift_ms: 10 # ms
sample_rate: 16000
sample_width: 2
demos/audio_content_search/conf/ws_conformer_wenetspeech_application.yaml 0 → 100644
浏览文件 @ df3f975e
# This is the parameter configuration file for PaddleSpeech Serving.
#################################################################################
# SERVER SETTING #
#################################################################################
host: 0.0.0.0
port: 8390
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_online']
# protocol = ['websocket'] (only one can be selected).
# websocket only support online engine type.
protocol: 'websocket'
engine_list: ['asr_online']
#################################################################################
# ENGINE CONFIG #
#################################################################################
################################### ASR #########################################
################### speech task: asr; engine_type: online #######################
asr_online:
model_type: 'conformer_online_wenetspeech'
am_model: # the pdmodel file of am static model [optional]
am_params: # the pdiparams file of am static model [optional]
lang: 'zh'
sample_rate: 16000
cfg_path:
decode_method:
force_yes: True
device: 'cpu' # cpu or gpu:id
decode_method: "attention_rescoring"
am_predictor_conf:
device: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
chunk_buffer_conf:
window_n: 7 # frame
shift_n: 4 # frame
window_ms: 25 # ms
shift_ms: 10 # ms
sample_rate: 16000
sample_width: 2
demos/audio_content_search/run.sh 0 → 100755
浏览文件 @ df3f975e
export CUDA_VISIBLE_DEVICE=0,1,2,3
# we need the streaming asr server
nohup python3 streaming_asr_server.py --config_file conf/ws_conformer_application.yaml > streaming_asr.log 2>&1 &
# start the acs server
nohup paddlespeech_server start --config_file conf/acs_application.yaml > acs.log 2>&1 &
demos/audio_searching/README.md
浏览文件 @ df3f975e
......@@ -167,8 +167,8 @@ Then to start the system server, and it provides HTTP backend services.
[2022-03-26 22:54:08,633] [ INFO] - embedding size: (192,)
Extracting feature from audio No. 2 , 20 audios in total
...
2022-03-26 22:54:15,892 ｜ INFO ｜ main.py ｜ load_audios ｜ 85 ｜ Successfully loaded data, total count: 20
2022-03-26 22:54:15,908 ｜ INFO ｜ main.py ｜ count_audio ｜ 148 ｜ Successfully count the number of data!
2022-03-26 22:54:15,892 ｜ INFO ｜ audio_search.py ｜ load_audios ｜ 85 ｜ Successfully loaded data, total count: 20
2022-03-26 22:54:15,908 ｜ INFO ｜ audio_search.py ｜ count_audio ｜ 148 ｜ Successfully count the number of data!
[2022-03-26 22:54:15,916] [ INFO] - checking the aduio file format......
[2022-03-26 22:54:15,916] [ INFO] - The sample rate is 16000
[2022-03-26 22:54:15,916] [ INFO] - The audio file format is right
......@@ -183,12 +183,12 @@ Then to start the system server, and it provides HTTP backend services.
[2022-03-26 22:54:15,924] [ INFO] - feats shape:[1, 80, 53], lengths shape: [1]
[2022-03-26 22:54:16,051] [ INFO] - embedding size: (192,)
...
2022-03-26 22:54:16,086 ｜ INFO ｜ main.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/test.wav, score 100.0
2022-03-26 22:54:16,087 ｜ INFO ｜ main.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_chopping.wav, score 29.182177782058716
2022-03-26 22:54:16,087 ｜ INFO ｜ main.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_cut_into_body.wav, score 22.73637056350708
2022-03-26 22:54:16,086 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/test.wav, score 100.0
2022-03-26 22:54:16,087 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_chopping.wav, score 29.182177782058716
2022-03-26 22:54:16,087 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_cut_into_body.wav, score 22.73637056350708
...
2022-03-26 22:54:16,088 ｜ INFO ｜ main.py ｜ search_local_audio ｜ 136 ｜ Successfully searched similar audio!
2022-03-26 22:54:17,164 ｜ INFO ｜ main.py ｜ drop_tables ｜ 160 ｜ Successfully drop tables in Milvus and MySQL!
2022-03-26 22:54:16,088 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 136 ｜ Successfully searched similar audio!
2022-03-26 22:54:17,164 ｜ INFO ｜ audio_search.py ｜ drop_tables ｜ 160 ｜ Successfully drop tables in Milvus and MySQL!
```
- GUI test (Optional)
......
demos/audio_searching/README_cn.md
浏览文件 @ df3f975e
......@@ -169,8 +169,8 @@ ffce340b3790 minio/minio:RELEASE.2020-12-03T00-03-10Z "/usr/bin/docker-ent…"
[2022-03-26 22:54:08,633] [ INFO] - embedding size: (192,)
Extracting feature from audio No. 2 , 20 audios in total
...
2022-03-26 22:54:15,892 ｜ INFO ｜ main.py ｜ load_audios ｜ 85 ｜ Successfully loaded data, total count: 20
2022-03-26 22:54:15,908 ｜ INFO ｜ main.py ｜ count_audio ｜ 148 ｜ Successfully count the number of data!
2022-03-26 22:54:15,892 ｜ INFO ｜ audio_search.py ｜ load_audios ｜ 85 ｜ Successfully loaded data, total count: 20
2022-03-26 22:54:15,908 ｜ INFO ｜ audio_search.py ｜ count_audio ｜ 148 ｜ Successfully count the number of data!
[2022-03-26 22:54:15,916] [ INFO] - checking the aduio file format......
[2022-03-26 22:54:15,916] [ INFO] - The sample rate is 16000
[2022-03-26 22:54:15,916] [ INFO] - The audio file format is right
......@@ -185,12 +185,12 @@ ffce340b3790 minio/minio:RELEASE.2020-12-03T00-03-10Z "/usr/bin/docker-ent…"
[2022-03-26 22:54:15,924] [ INFO] - feats shape:[1, 80, 53], lengths shape: [1]
[2022-03-26 22:54:16,051] [ INFO] - embedding size: (192,)
...
2022-03-26 22:54:16,086 ｜ INFO ｜ main.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/test.wav, score 100.0
2022-03-26 22:54:16,087 ｜ INFO ｜ main.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_chopping.wav, score 29.182177782058716
2022-03-26 22:54:16,087 ｜ INFO ｜ main.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_cut_into_body.wav, score 22.73637056350708
2022-03-26 22:54:16,086 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/test.wav, score 100.0
2022-03-26 22:54:16,087 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_chopping.wav, score 29.182177782058716
2022-03-26 22:54:16,087 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 132 ｜ search result http://testserver/data?audio_path=./example_audio/knife_cut_into_body.wav, score 22.73637056350708
...
2022-03-26 22:54:16,088 ｜ INFO ｜ main.py ｜ search_local_audio ｜ 136 ｜ Successfully searched similar audio!
2022-03-26 22:54:17,164 ｜ INFO ｜ main.py ｜ drop_tables ｜ 160 ｜ Successfully drop tables in Milvus and MySQL!
2022-03-26 22:54:16,088 ｜ INFO ｜ audio_search.py ｜ search_local_audio ｜ 136 ｜ Successfully searched similar audio!
2022-03-26 22:54:17,164 ｜ INFO ｜ audio_search.py ｜ drop_tables ｜ 160 ｜ Successfully drop tables in Milvus and MySQL!
```
- 前端测试（可选）
......
demos/audio_searching/src/operations/load.py
浏览文件 @ df3f975e
......@@ -26,8 +26,9 @@ def get_audios(path):
"""
supported_formats = [".wav", ".mp3", ".ogg", ".flac", ".m4a"]
return [
item for sublist in [[os.path.join(dir, file) for file in files]
for dir, _, files in list(os.walk(path))]
item
for sublist in [[os.path.join(dir, file) for file in files]
for dir, _, files in list(os.walk(path))]
for item in sublist if os.path.splitext(item)[1] in supported_formats
]
......
demos/audio_searching/src/test_vpr_search.py
浏览文件 @ df3f975e
......@@ -73,7 +73,9 @@ def test_data(spk: str):
"""
Get the audio file by spk_id in MySQL
"""
response = client.get("/vpr/data?spk_id=" + spk)
response = client.get(
"/vpr/data",
json={"spk_id": spk}, )
assert response.status_code == 200
......@@ -81,7 +83,9 @@ def test_del(spk: str):
"""
Delete the record in MySQL by spk_id
"""
response = client.post("/vpr/del?spk_id=" + spk)
response = client.post(
"/vpr/del",
json={"spk_id": spk}, )
assert response.status_code == 200
......
demos/audio_searching/src/vpr_search.py
浏览文件 @ df3f975e
......@@ -17,6 +17,7 @@ import uvicorn
from config import UPLOAD_PATH
from fastapi import FastAPI
from fastapi import File
from fastapi import Form
from fastapi import UploadFile
from logs import LOGGER
from mysql_helpers import MySQLHelper
......@@ -49,10 +50,12 @@ if not os.path.exists(UPLOAD_PATH):
@app.post('/vpr/enroll')
async def vpr_enroll(table_name: str=None,
spk_id: str=None,
spk_id: str=Form(...),
audio: UploadFile=File(...)):
# Enroll the uploaded audio with spk-id into MySQL
try:
if not spk_id:
return {'status': False, 'msg': "spk_id can not be None"}
# Save the upload data to server.
content = await audio.read()
audio_path = os.path.join(UPLOAD_PATH, audio.filename)
......@@ -63,7 +66,7 @@ async def vpr_enroll(table_name: str=None,
return {'status': True, 'msg': "Successfully enroll data!"}
except Exception as e:
LOGGER.error(e)
return {'status': False, 'msg': e}, 400
return {'status': False, 'msg': e}
@app.post('/vpr/enroll/local')
......@@ -128,9 +131,12 @@ async def vpr_recog_local(request: Request,
@app.post('/vpr/del')
async def vpr_del(table_name: str=None, spk_id: str=None):
async def vpr_del(table_name: str=None, spk_id: dict=None):
# Delete a record by spk_id in MySQL
try:
spk_id = spk_id['spk_id']
if not spk_id:
return {'status': False, 'msg': "spk_id can not be None"}
do_delete(table_name, spk_id, MYSQL_CLI)
LOGGER.info("Successfully delete a record by spk_id in MySQL")
return {'status': True, 'msg': "Successfully delete data!"}
......@@ -156,9 +162,12 @@ async def vpr_list(table_name: str=None):
@app.get('/vpr/data')
async def vpr_data(
table_name: str=None,
spk_id: str=None, ):
spk_id: dict=None, ):
# Get the audio file from path by spk_id in MySQL
try:
spk_id = spk_id['spk_id']
if not spk_id:
return {'status': False, 'msg': "spk_id can not be None"}
audio_path = do_get(table_name, spk_id, MYSQL_CLI)
LOGGER.info(f"Successfully get audio path {audio_path}!")
return FileResponse(audio_path)
......
demos/custom_streaming_asr/README.md 0 → 100644
浏览文件 @ df3f975e
([简体中文](./README_cn.md)|English)
# Customized Auto Speech Recognition
## introduction
In some cases, we need to recognize the specific rare words with high accuracy. eg: address recognition in navigation apps. customized ASR can slove those issues.
this demo is customized for expense account, which need to recognize rare address.
* G with slot: 打车到 "address_slot"。
![](https://ai-studio-static-online.cdn.bcebos.com/28d9ef132a7f47a895a65ae9e5c4f55b8f472c9f3dd24be8a2e66e0b88b173a4)
* this is address slot wfst, you can add the address which want to recognize.
![](https://ai-studio-static-online.cdn.bcebos.com/47c89100ef8c465bac733605ffc53d76abefba33d62f4d818d351f8cea3c8fe2)
* after replace operation, G = fstreplace(G_with_slot, address_slot), we will get the customized graph.
![](https://ai-studio-static-online.cdn.bcebos.com/60a3095293044f10b73039ab10c7950d139a6717580a44a3ba878c6e74de402b)
## Usage
### 1. Installation
install paddle:2.2.2 docker.
```
sudo docker pull registry.baidubce.com/paddlepaddle/paddle:2.2.2
sudo docker run --privileged --net=host --ipc=host -it --rm -v $PWD:/paddle --name=paddle_demo_docker registry.baidubce.com/paddlepaddle/paddle:2.2.2 /bin/bash
```
### 2. demo
* run websocket_server.sh. This script will download resources and libs, and launch the service.
```
cd /paddle
bash websocket_server.sh
```
this script run in two steps:
1. download the resources.tar.gz, those direcotries will be found in resource directory.
model: acustic model
graph: the decoder graph (TLG.fst)
lib: some libs
bin: binary
data: audio and wav.scp
2. websocket_server_main launch the service.
some params:
port: the service port
graph_path: the decoder graph path
model_path: acustic model path
please refer other params in those files:
PaddleSpeech/speechx/speechx/decoder/param.h
PaddleSpeech/speechx/examples/ds2_ol/websocket/websocket_server_main.cc
* In other terminal, run script websocket_client.sh, the client will send data and get the results.
```
bash websocket_client.sh
```
websocket_client_main will launch the client, the wav_scp is the wav set, port is the server service port.
* result:
In the log of client, you will see the message below:
```
0513 10:58:13.827821 41768 recognizer_test_main.cc:56] wav len (sample): 70208
I0513 10:58:13.884493 41768 feature_cache.h:52] set finished
I0513 10:58:24.247171 41768 paddle_nnet.h:76] Tensor neml: 10240
I0513 10:58:24.247249 41768 paddle_nnet.h:76] Tensor neml: 10240
LOG ([5.5.544~2-f21d7]:main():decoder/recognizer_test_main.cc:90) the result of case_10 is 五月十二日二十二点三十六分加班打车回家四十一元
```
\ No newline at end of file
demos/custom_streaming_asr/README_cn.md 0 → 100644
浏览文件 @ df3f975e
(简体中文|[English](./README.md))
# 定制化语音识别演示
## 介绍
在一些场景中，识别系统需要高精度的识别一些稀有词，例如导航软件中地名识别。而通过定制化识别可以满足这一需求。
这个 demo 是打车报销单的场景识别，需要识别一些稀有的地名，可以通过如下操作实现。
* G with slot: 打车到 "address_slot"。
![](https://ai-studio-static-online.cdn.bcebos.com/28d9ef132a7f47a895a65ae9e5c4f55b8f472c9f3dd24be8a2e66e0b88b173a4)
* 这是 address slot wfst, 可以添加一些需要识别的地名.
![](https://ai-studio-static-online.cdn.bcebos.com/47c89100ef8c465bac733605ffc53d76abefba33d62f4d818d351f8cea3c8fe2)
* 通过 replace 操作, G = fstreplace(G_with_slot, address_slot), 最终可以得到定制化的解码图。
![](https://ai-studio-static-online.cdn.bcebos.com/60a3095293044f10b73039ab10c7950d139a6717580a44a3ba878c6e74de402b)
## 使用方法
### 1. 配置环境
安装paddle:2.2.2 docker镜像。
```
sudo docker pull registry.baidubce.com/paddlepaddle/paddle:2.2.2
sudo docker run --privileged --net=host --ipc=host -it --rm -v $PWD:/paddle --name=paddle_demo_docker registry.baidubce.com/paddlepaddle/paddle:2.2.2 /bin/bash
```
### 2. 演示
* 运行如下命令，完成相关资源和库的下载和服务启动。
```
cd /paddle
bash websocket_server.sh
```
上面脚本完成了如下两个功能：
1. 完成 resource.tar.gz 下载，解压后,会在 resource 中发现如下目录：
model: 声学模型
graph: 解码构图
lib: 相关库
bin: 运行程序
data: 语音数据
2. 通过 websocket_server_main 来启动服务。
这里简单的介绍几个参数:
port 是服务端口，
graph_path 用来指定解码图文件，
其他参数说明可参见代码：
PaddleSpeech/speechx/speechx/decoder/param.h
PaddleSpeech/speechx/examples/ds2_ol/websocket/websocket_server_main.cc
* 在另一个终端中， 通过 client 发送数据，得到结果。运行如下命令：
```
bash websocket_client.sh
```
通过 websocket_client_main 来启动 client 服务，其中 wav_scp 是发送的语音句子集合，port 为服务端口。
* 结果：
client 的 log 中可以看到如下类似的结果
```
0513 10:58:13.827821 41768 recognizer_test_main.cc:56] wav len (sample): 70208
I0513 10:58:13.884493 41768 feature_cache.h:52] set finished
I0513 10:58:24.247171 41768 paddle_nnet.h:76] Tensor neml: 10240
I0513 10:58:24.247249 41768 paddle_nnet.h:76] Tensor neml: 10240
LOG ([5.5.544~2-f21d7]:main():decoder/recognizer_test_main.cc:90) the result of case_10 is 五月十二日二十二点三十六分加班打车回家四十一元
```
demos/custom_streaming_asr/path.sh 0 → 100644
浏览文件 @ df3f975e
export LD_LIBRARY_PATH=$PWD/resource/lib
export PATH=$PATH:$PWD/resource/bin
demos/custom_streaming_asr/setup_docker.sh 0 → 100644
浏览文件 @ df3f975e
sudo nvidia-docker run --privileged --net=host --ipc=host -it --rm -v $PWD:/paddle --name=paddle_demo_docker registry.baidubce.com/paddlepaddle/paddle:2.2.2 /bin/bash
demos/custom_streaming_asr/websocket_client.sh 0 → 100755
浏览文件 @ df3f975e
#!/bin/bash
set +x
set -e
. path.sh
# input
data=$PWD/data
# output
wav_scp=wav.scp
export GLOG_logtostderr=1
# websocket client
websocket_client_main \
--wav_rspecifier=scp:$data/$wav_scp \
--streaming_chunk=0.36 \
--port=8881
demos/custom_streaming_asr/websocket_server.sh 0 → 100755
浏览文件 @ df3f975e
#!/bin/bash
set +x
set -e
export GLOG_logtostderr=1
. path.sh
#test websocket server
model_dir=./resource/model
graph_dir=./resource/graph
cmvn=./data/cmvn.ark
#paddle_asr_online/resource.tar.gz
if [ ! -f $cmvn ]; then
wget -c https://paddlespeech.bj.bcebos.com/s2t/paddle_asr_online/resource.tar.gz
tar xzfv resource.tar.gz
ln -s ./resource/data .
fi
websocket_server_main \
--cmvn_file=$cmvn \
--streaming_chunk=0.1 \
--use_fbank=true \
--model_path=$model_dir/avg_10.jit.pdmodel \
--param_path=$model_dir/avg_10.jit.pdiparams \
--model_cache_shapes="5-1-2048,5-1-2048" \
--model_output_names=softmax_0.tmp_0,tmp_5,concat_0.tmp_0,concat_1.tmp_0 \
--word_symbol_table=$graph_dir/words.txt \
--graph_path=$graph_dir/TLG.fst --max_active=7500 \
--port=8881 \
--acoustic_scale=12
demos/speaker_verification/README.md
浏览文件 @ df3f975e
......@@ -14,7 +14,7 @@ see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/doc
You can choose one way from easy, meduim and hard to install paddlespeech.
### 2. Prepare Input File
The input of this demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
The input of this cli demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
Here are sample files for this demo that can be downloaded:
```bash
......
demos/speaker_verification/README_cn.md
浏览文件 @ df3f975e
......@@ -4,16 +4,16 @@
## 介绍
声纹识别是一项用计算机程序自动提取说话人特征的技术。
这个 demo 是一个从给定音频文件提取说话人特征，它可以通过使用 `PaddleSpeech` 的单个命令或 python 中的几行代码来实现。
这个 demo 是从一个给定音频文件中提取说话人特征，它可以通过使用 `PaddleSpeech` 的单个命令或 python 中的几行代码来实现。
## 使用方法
### 1. 安装
请看[安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install_cn.md)。
你可以从 easy，medium，hard 三中方式中选择一种方式安装。
你可以从easy medium，hard 三种方式中选择一种方式安装。
### 2. 准备输入
这个 demo 的输入应该是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。
声纹cli demo 的输入应该是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。
可以下载此 demo 的示例音频：
```bash
......
demos/speech_recognition/README.md
浏览文件 @ df3f975e
......@@ -24,13 +24,13 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- Command Line(Recommended)
```bash
# Chinese
paddlespeech asr --input ./zh.wav
paddlespeech asr --input ./zh.wav -v
# English
paddlespeech asr --model transformer_librispeech --lang en --input ./en.wav
paddlespeech asr --model transformer_librispeech --lang en --input ./en.wav -v
# Chinese ASR + Punctuation Restoration
paddlespeech asr --input ./zh.wav | paddlespeech text --task punc
paddlespeech asr --input ./zh.wav -v | paddlespeech text --task punc -v
```
(It doesn't matter if package `paddlespeech-ctcdecoders` is not found, this package is optional.)
(If you don't want to see the log information, you can remove "-v". Besides, it doesn't matter if package `paddlespeech-ctcdecoders` is not found, this package is optional.)
Usage:
```bash
......@@ -45,6 +45,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`.
- `yes`: No additional parameters required. Once set this parameter, it means accepting the request of the program by default, which includes transforming the audio sample rate. Default: `False`.
- `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
- `verbose`: Show the log information.
Output:
```bash
......@@ -84,8 +85,12 @@ Here is a list of pretrained models released by PaddleSpeech that can be used by
| Model | Language | Sample Rate
| :--- | :---: | :---: |
| conformer_wenetspeech| zh| 16k
| transformer_librispeech| en| 16k
| conformer_wenetspeech | zh | 16k
| conformer_online_multicn | zh | 16k
| conformer_aishell | zh | 16k
| conformer_online_aishell | zh | 16k
| transformer_librispeech | en | 16k
| deepspeech2online_wenetspeech | zh | 16k
| deepspeech2offline_aishell| zh| 16k
| deepspeech2online_aishell | zh | 16k
|deepspeech2offline_librispeech|en| 16k
| deepspeech2offline_librispeech | en | 16k
demos/speech_recognition/README_cn.md
浏览文件 @ df3f975e
......@@ -22,13 +22,13 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- 命令行 (推荐使用)
```bash
# 中文
paddlespeech asr --input ./zh.wav
paddlespeech asr --input ./zh.wav -v
# 英文
paddlespeech asr --model transformer_librispeech --lang en --input ./en.wav
paddlespeech asr --model transformer_librispeech --lang en --input ./en.wav -v
# 中文 + 标点恢复
paddlespeech asr --input ./zh.wav | paddlespeech text --task punc
paddlespeech asr --input ./zh.wav -v | paddlespeech text --task punc -v
```
(如果显示 `paddlespeech-ctcdecoders` 这个 python 包没有找到的 Error，没有关系，这个包是非必须的。)
(如果不想显示 log 信息，可以不使用"-v", 另外如果显示 `paddlespeech-ctcdecoders` 这个 python 包没有找到的 Error，没有关系，这个包是非必须的。)
使用方法：
```bash
......@@ -43,6 +43,7 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
- `ckpt_path`：模型参数文件，若不设置则下载预训练模型使用，默认值：`None`。
- `yes`；不需要设置额外的参数，一旦设置了该参数，说明你默认同意程序的所有请求，其中包括自动转换输入音频的采样率。默认值：`False`。
- `device`：执行预测的设备，默认值：当前系统下 paddlepaddle 的默认 device。
- `verbose`: 如果使用，显示 logger 信息。
输出：
```bash
......@@ -82,7 +83,11 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
| 模型 | 语言 | 采样率
| :--- | :---: | :---: |
| conformer_wenetspeech | zh | 16k
| conformer_online_multicn | zh | 16k
| conformer_aishell | zh | 16k
| conformer_online_aishell | zh | 16k
| transformer_librispeech | en | 16k
| deepspeech2online_wenetspeech | zh | 16k
| deepspeech2offline_aishell| zh| 16k
| deepspeech2online_aishell | zh | 16k
| deepspeech2offline_librispeech | en | 16k
demos/speech_server/README.md
浏览文件 @ df3f975e
......@@ -10,7 +10,7 @@ This demo is an implementation of starting the voice service and accessing the s
### 1. Installation
see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
It is recommended to use **paddlepaddle 2.2.1** or above.
It is recommended to use **paddlepaddle 2.2.2** or above.
You can choose one way from meduim and hard to install paddlespeech.
### 2. Prepare config File
......@@ -18,6 +18,7 @@ The configuration file can be found in `conf/application.yaml` .
Among them, `engine_list` indicates the speech engine that will be included in the service to be started, in the format of `<speech task>_<engine type>`.
At present, the speech tasks integrated by the service include: asr (speech recognition), tts (text to sppech) and cls (audio classification).
Currently the engine type supports two forms: python and inference (Paddle Inference)
**Note:** If the service can be started normally in the container, but the client access IP is unreachable, you can try to replace the `host` address in the configuration file with the local IP address.
The input of ASR client demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.
......@@ -83,6 +84,9 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
### 4. ASR Client Usage
**Note:** The response time will be slightly longer when using the client for the first time
- Command Line (Recommended)
If `127.0.0.1` is not accessible, you need to use the actual service IP address.
```
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
```
......@@ -131,6 +135,9 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
### 5. TTS Client Usage
**Note:** The response time will be slightly longer when using the client for the first time
- Command Line (Recommended)
If `127.0.0.1` is not accessible, you need to use the actual service IP address
```bash
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
```
......@@ -191,6 +198,9 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
### 6. CLS Client Usage
**Note:** The response time will be slightly longer when using the client for the first time
- Command Line (Recommended)
If `127.0.0.1` is not accessible, you need to use the actual service IP address.
```
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
```
......@@ -235,6 +245,172 @@ wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespee
```
### 7. Speaker Verification Client Usage
#### 7.1 Extract speaker embedding
**Note:** The response time will be slightly longer when using the client for the first time
- Command Line (Recommended)
If `127.0.0.1` is not accessible, you need to use the actual service IP address.
``` bash
paddlespeech_client vector --task spk --server_ip 127.0.0.1 --port 8090 --input 85236145389.wav
```
Usage:
``` bash
paddlespeech_client vector --help
```
Arguments:
* server_ip: server ip. Default: 127.0.0.1
* port: server port. Default: 8090
* input(required): Input text to generate.
* task: the task of vector, can be use 'spk' or 'score。Default is 'spk'。
* enroll: enroll audio
* test: test audio
Output:
```bash
[2022-05-08 00:18:44,249] [ INFO] - vector http client start
[2022-05-08 00:18:44,250] [ INFO] - the input audio: 85236145389.wav
[2022-05-08 00:18:44,250] [ INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector
[2022-05-08 00:18:44,250] [ INFO] - http://127.0.0.1:8590/paddlespeech/vector
[2022-05-08 00:18:44,406] [ INFO] - The vector: {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'vec': [1.421751856803894, 5.626245498657227, -5.342077255249023, 1.1773887872695923, 3.3080549240112305, 1.7565933465957642, 5.167886257171631, 10.806358337402344, -3.8226819038391113, -5.614140033721924, 2.6238479614257812, -0.8072972893714905, 1.9635076522827148, -7.312870025634766, 0.011035939678549767, -9.723129272460938, 0.6619706153869629, -6.976806163787842, 10.213476181030273, 7.494769096374512, 2.9105682373046875, 3.8949244022369385, 3.799983501434326, 7.106168746948242, 16.90532875061035, -7.149388313293457, 8.733108520507812, 3.423006296157837, -4.831653594970703, -11.403363227844238, 11.232224464416504, 7.127461910247803, -4.282842636108398, 2.452359437942505, -5.130749702453613, -18.17766761779785, -2.6116831302642822, -11.000344276428223, -6.731433391571045, 1.6564682722091675, 0.7618281245231628, 1.125300407409668, -2.0838370323181152, 4.725743293762207, -8.782588005065918, -3.5398752689361572, 3.8142364025115967, 5.142068862915039, 2.1620609760284424, 4.09643030166626, -6.416214942932129, 12.747446060180664, 1.9429892301559448, -15.15294361114502, 6.417416095733643, 16.09701156616211, -9.716667175292969, -1.9920575618743896, -3.36494779586792, -1.8719440698623657, 11.567351341247559, 3.6978814601898193, 11.258262634277344, 7.442368507385254, 9.183408737182617, 4.528149127960205, -1.2417854070663452, 4.395912170410156, 6.6727728843688965, 5.88988733291626, 7.627128601074219, -0.6691966652870178, -11.889698028564453, -9.20886516571045, -7.42740535736084, -3.777663230895996, 6.917238712310791, -9.848755836486816, -2.0944676399230957, -5.1351165771484375, 0.4956451654434204, 9.317537307739258, -5.914181232452393, -1.809860348701477, -0.11738915741443634, -7.1692705154418945, -1.057827353477478, -5.721670627593994, -5.117385387420654, 16.13765525817871, -4.473617076873779, 7.6624321937561035, -0.55381840467453, 9.631585121154785, -6.470459461212158, -8.548508644104004, 4.371616840362549, -0.7970245480537415, 4.4789886474609375, -2.975860834121704, 3.2721822261810303, 2.838287830352783, 5.134591102600098, -9.19079875946045, -0.5657302737236023, -4.8745832443237305, 2.3165574073791504, -5.984319686889648, -2.1798853874206543, 0.3554139733314514, -0.3178512752056122, 9.493552207946777, 2.1144471168518066, 4.358094692230225, -12.089824676513672, 8.451693534851074, -7.925466537475586, 4.624246597290039, 4.428936958312988, 18.69200897216797, -2.6204581260681152, -5.14918851852417, -0.3582090139389038, 8.488558769226074, 4.98148775100708, -9.326835632324219, -2.2544219493865967, 6.641760349273682, 1.2119598388671875, 10.977124214172363, 16.555034637451172, 3.3238420486450195, 9.551861763000488, -1.6676981449127197, -0.7953944206237793, -8.605667114257812, -0.4735655188560486, 2.674196243286133, -5.359177112579346, -2.66738224029541, 0.6660683155059814, 15.44322681427002, 4.740593433380127, -3.472534418106079, 11.592567443847656, -2.0544962882995605, 1.736127495765686, -8.265326499938965, -9.30447769165039, 5.406829833984375, -1.518022894859314, -7.746612548828125, -6.089611053466797, 0.07112743705511093, -0.3490503430366516, -8.64989185333252, -9.998957633972168, -2.564845085144043, -0.5399947762489319, 2.6018123626708984, -0.3192799389362335, -1.8815255165100098, -2.0721492767333984, -3.410574436187744, -8.29980754852295, 1.483638048171997, -15.365986824035645, -8.288211822509766, 3.884779930114746, -3.4876468181610107, 7.362999439239502, 0.4657334089279175, 3.1326050758361816, 12.438895225524902, -1.8337041139602661, 4.532927989959717, 2.7264339923858643, 10.14534854888916, -6.521963596343994, 2.897155523300171, -3.392582654953003, 5.079153060913086, 7.7597246170043945, 4.677570819854736, 5.845779895782471, 2.402411460876465, 7.7071051597595215, 3.9711380004882812, -6.39003849029541, 6.12687873840332, -3.776029348373413, -11.118121147155762]}}
[2022-05-08 00:18:44,406] [ INFO] - Response time 0.156481 s.
```
* Python API
``` python
from paddlespeech.server.bin.paddlespeech_client import VectorClientExecutor
vectorclient_executor = VectorClientExecutor()
res = vectorclient_executor(
input="85236145389.wav",
server_ip="127.0.0.1",
port=8090,
task="spk")
print(res)
```
Output:
``` bash
{'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'vec': [1.421751856803894, 5.626245498657227, -5.342077255249023, 1.1773887872695923, 3.3080549240112305, 1.7565933465957642, 5.167886257171631, 10.806358337402344, -3.8226819038391113, -5.614140033721924, 2.6238479614257812, -0.8072972893714905, 1.9635076522827148, -7.312870025634766, 0.011035939678549767, -9.723129272460938, 0.6619706153869629, -6.976806163787842, 10.213476181030273, 7.494769096374512, 2.9105682373046875, 3.8949244022369385, 3.799983501434326, 7.106168746948242, 16.90532875061035, -7.149388313293457, 8.733108520507812, 3.423006296157837, -4.831653594970703, -11.403363227844238, 11.232224464416504, 7.127461910247803, -4.282842636108398, 2.452359437942505, -5.130749702453613, -18.17766761779785, -2.6116831302642822, -11.000344276428223, -6.731433391571045, 1.6564682722091675, 0.7618281245231628, 1.125300407409668, -2.0838370323181152, 4.725743293762207, -8.782588005065918, -3.5398752689361572, 3.8142364025115967, 5.142068862915039, 2.1620609760284424, 4.09643030166626, -6.416214942932129, 12.747446060180664, 1.9429892301559448, -15.15294361114502, 6.417416095733643, 16.09701156616211, -9.716667175292969, -1.9920575618743896, -3.36494779586792, -1.8719440698623657, 11.567351341247559, 3.6978814601898193, 11.258262634277344, 7.442368507385254, 9.183408737182617, 4.528149127960205, -1.2417854070663452, 4.395912170410156, 6.6727728843688965, 5.88988733291626, 7.627128601074219, -0.6691966652870178, -11.889698028564453, -9.20886516571045, -7.42740535736084, -3.777663230895996, 6.917238712310791, -9.848755836486816, -2.0944676399230957, -5.1351165771484375, 0.4956451654434204, 9.317537307739258, -5.914181232452393, -1.809860348701477, -0.11738915741443634, -7.1692705154418945, -1.057827353477478, -5.721670627593994, -5.117385387420654, 16.13765525817871, -4.473617076873779, 7.6624321937561035, -0.55381840467453, 9.631585121154785, -6.470459461212158, -8.548508644104004, 4.371616840362549, -0.7970245480537415, 4.4789886474609375, -2.975860834121704, 3.2721822261810303, 2.838287830352783, 5.134591102600098, -9.19079875946045, -0.5657302737236023, -4.8745832443237305, 2.3165574073791504, -5.984319686889648, -2.1798853874206543, 0.3554139733314514, -0.3178512752056122, 9.493552207946777, 2.1144471168518066, 4.358094692230225, -12.089824676513672, 8.451693534851074, -7.925466537475586, 4.624246597290039, 4.428936958312988, 18.69200897216797, -2.6204581260681152, -5.14918851852417, -0.3582090139389038, 8.488558769226074, 4.98148775100708, -9.326835632324219, -2.2544219493865967, 6.641760349273682, 1.2119598388671875, 10.977124214172363, 16.555034637451172, 3.3238420486450195, 9.551861763000488, -1.6676981449127197, -0.7953944206237793, -8.605667114257812, -0.4735655188560486, 2.674196243286133, -5.359177112579346, -2.66738224029541, 0.6660683155059814, 15.44322681427002, 4.740593433380127, -3.472534418106079, 11.592567443847656, -2.0544962882995605, 1.736127495765686, -8.265326499938965, -9.30447769165039, 5.406829833984375, -1.518022894859314, -7.746612548828125, -6.089611053466797, 0.07112743705511093, -0.3490503430366516, -8.64989185333252, -9.998957633972168, -2.564845085144043, -0.5399947762489319, 2.6018123626708984, -0.3192799389362335, -1.8815255165100098, -2.0721492767333984, -3.410574436187744, -8.29980754852295, 1.483638048171997, -15.365986824035645, -8.288211822509766, 3.884779930114746, -3.4876468181610107, 7.362999439239502, 0.4657334089279175, 3.1326050758361816, 12.438895225524902, -1.8337041139602661, 4.532927989959717, 2.7264339923858643, 10.14534854888916, -6.521963596343994, 2.897155523300171, -3.392582654953003, 5.079153060913086, 7.7597246170043945, 4.677570819854736, 5.845779895782471, 2.402411460876465, 7.7071051597595215, 3.9711380004882812, -6.39003849029541, 6.12687873840332, -3.776029348373413, -11.118121147155762]}}
```
#### 7.2 Get the score between speaker audio embedding
**Note:** The response time will be slightly longer when using the client for the first time
- Command Line (Recommended)
If `127.0.0.1` is not accessible, you need to use the actual service IP address.
``` bash
paddlespeech_client vector --task score --server_ip 127.0.0.1 --port 8090 --enroll 85236145389.wav --test 123456789.wav
```
Usage:
``` bash
paddlespeech_client vector --help
```
Arguments:
* server_ip: server ip. Default: 127.0.0.1
* port: server port. Default: 8090
* input(required): Input text to generate.
* task: the task of vector, can be use 'spk' or 'score。If get the score, this must be 'score' parameter.
* enroll: enroll audio
* test: test audio
Output:
``` bash
[2022-05-09 10:28:40,556] [ INFO] - vector score http client start
[2022-05-09 10:28:40,556] [ INFO] - enroll audio: 85236145389.wav, test audio: 123456789.wav
[2022-05-09 10:28:40,556] [ INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector/score
[2022-05-09 10:28:40,731] [ INFO] - The vector score is: {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'score': 0.4292638897895813}}
[2022-05-09 10:28:40,731] [ INFO] - The vector: None
[2022-05-09 10:28:40,731] [ INFO] - Response time 0.175514 s.
```
* Python API
``` python
from paddlespeech.server.bin.paddlespeech_client import VectorClientExecutor
vectorclient_executor = VectorClientExecutor()
res = vectorclient_executor(
input=None,
enroll_audio="85236145389.wav",
test_audio="123456789.wav",
server_ip="127.0.0.1",
port=8090,
task="score")
print(res)
```
Output:
``` bash
[2022-05-09 10:34:54,769] [ INFO] - vector score http client start
[2022-05-09 10:34:54,771] [ INFO] - enroll audio: 85236145389.wav, test audio: 123456789.wav
[2022-05-09 10:34:54,771] [ INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector/score
[2022-05-09 10:34:55,026] [ INFO] - The vector score is: {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'score': 0.4292638897895813}}
```
### 8. Punctuation prediction
**Note:** The response time will be slightly longer when using the client for the first time
- Command Line (Recommended)
If `127.0.0.1` is not accessible, you need to use the actual service IP address.
``` bash
paddlespeech_client text --server_ip 127.0.0.1 --port 8090 --input "我认为跑步最重要的就是给我带来了身体健康"
```
Usage:
```bash
paddlespeech_client text --help
```
Arguments:
- `server_ip`: server ip. Default: 127.0.0.1
- `port`: server port. Default: 8090
- `input`(required): Input text to get punctuation.
Output:
```bash
[2022-05-09 18:19:04,397] [ INFO] - The punc text: 我认为跑步最重要的就是给我带来了身体健康。
[2022-05-09 18:19:04,397] [ INFO] - Response time 0.092407 s.
```
- Python API
```python
from paddlespeech.server.bin.paddlespeech_client import TextClientExecutor
textclient_executor = TextClientExecutor()
res = textclient_executor(
input="我认为跑步最重要的就是给我带来了身体健康",
server_ip="127.0.0.1",
port=8090,)
print(res)
```
Output:
```bash
我认为跑步最重要的就是给我带来了身体健康。
```
## Models supported by the service
### ASR model
Get all models supported by the ASR service via `paddlespeech_server stats --task asr`, where static models can be used for paddle inference inference.
......@@ -244,3 +420,9 @@ Get all models supported by the TTS service via `paddlespeech_server stats --tas
### CLS model
Get all models supported by the CLS service via `paddlespeech_server stats --task cls`, where static models can be used for paddle inference inference.
### Vector model
Get all models supported by the TTS service via `paddlespeech_server stats --task vector`, where static models can be used for paddle inference inference.
### Text model
Get all models supported by the CLS service via `paddlespeech_server stats --task text`, where static models can be used for paddle inference inference.
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#!/bin/bash
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
# If `127.0.0.1` is not accessible, you need to use the actual service IP address.
paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav
demos/speech_server/cls_client.sh
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#!/bin/bash
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
# If `127.0.0.1` is not accessible, you need to use the actual service IP address.
paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav --topk 1
demos/speech_server/conf/application.yaml
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# This is the parameter configuration file for PaddleSpeech Serving.
# This is the parameter configuration file for PaddleSpeech Offline Serving.
#################################################################################
# SERVER SETTING #
#################################################################################
host: 127.0.0.1
host: 0.0.0.0
port: 8090
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference']
engine_list: ['asr_python', 'tts_python', 'cls_python']
# task choices = ['asr_python', 'asr_inference', 'tts_python', 'tts_inference', 'cls_python', 'cls_inference']
protocol: 'http'
engine_list: ['asr_python', 'tts_python', 'cls_python', 'text_python', 'vector_python']
#################################################################################
......@@ -135,3 +135,26 @@ cls_inference:
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
################################### Text #########################################
################### text task: punc; engine_type: python #######################
text_python:
task: punc
model_type: 'ernie_linear_p3_wudao'
lang: 'zh'
sample_rate: 16000
cfg_path: # [optional]
ckpt_path: # [optional]
vocab_file: # [optional]
device: # set 'gpu:id' or 'cpu'
################################### Vector ######################################
################### Vector task: spk; engine_type: python #######################
vector_python:
task: spk
model_type: 'ecapatdnn_voxceleb12'
sample_rate: 16000
cfg_path: # [optional]
ckpt_path: # [optional]
device: # set 'gpu:id' or 'cpu'
demos/speech_server/tts_client.sh
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#!/bin/bash
# If `127.0.0.1` is not accessible, you need to use the actual service IP address.
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
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# This is the parameter configuration file for PaddleSpeech Serving.
#################################################################################
# SERVER SETTING #
#################################################################################
host: 0.0.0.0
port: 8090
# The task format in the engin_list is: <speech task>_<engine type>
# task choices = ['asr_online']
# protocol = ['websocket'] (only one can be selected).
# websocket only support online engine type.
protocol: 'websocket'
engine_list: ['asr_online']
#################################################################################
# ENGINE CONFIG #
#################################################################################
################################### ASR #########################################
################### speech task: asr; engine_type: online #######################
asr_online:
model_type: 'conformer_online_wenetspeech'
am_model: # the pdmodel file of am static model [optional]
am_params: # the pdiparams file of am static model [optional]
lang: 'zh'
sample_rate: 16000
cfg_path:
decode_method:
force_yes: True
device: 'cpu' # cpu or gpu:id
decode_method: "attention_rescoring"
am_predictor_conf:
device: # set 'gpu:id' or 'cpu'
switch_ir_optim: True
glog_info: False # True -> print glog
summary: True # False -> do not show predictor config
chunk_buffer_conf:
window_n: 7 # frame
shift_n: 4 # frame
window_ms: 25 # ms
shift_ms: 10 # ms
sample_rate: 16000
sample_width: 2
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