([简体中文](./README_cn.md)|English)

Quick Start | Quick Start Server | Documents | Models List

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**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. ##### Speech Recognition
Input Audio Recognition Result

I knocked at the door on the ancient side of the building.

我认为跑步最重要的就是给我带来了身体健康。
##### Speech Translation (English to Chinese)
Input Audio Translations Result

我 在 这栋 建筑 的 古老 门上 敲门。
##### Text-to-Speech
Input Text Synthetic Audio
Life was like a box of chocolates, you never know what you're gonna get.
早上好,今天是2020/10/29,最低温度是-3°C。
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》。
For more synthesized audios, please refer to [PaddleSpeech Text-to-Speech samples](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html). ##### Punctuation Restoration
Input Text Output 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。
### ⭐ Examples - **[PaddleBoBo](https://github.com/JiehangXie/PaddleBoBo): Use PaddleSpeech TTS to generate virtual human voice.**
- [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.**
### 🔥 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. - 🏆 **Align to the State-of-the-Art**: we provide high-speed and ultra-lightweight models, and also cutting-edge technology. - 💯 **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. - 🔬 *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 - 👏🏻 2022.03.28: PaddleSpeech Server is available for Audio Classification, Automatic Speech Recognition and Text-to-Speech. - 👏🏻 2022.03.28: PaddleSpeech CLI is available for Speaker Verfication. - 🤗 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. ### 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.
## Installation 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). ## Quick Start Developers can have a try of our models with [PaddleSpeech Command Line](./paddlespeech/cli/README.md). Change `--input` to test your own audio/text. **Audio Classification** ```shell paddlespeech cls --input input.wav ``` **Speaker Verification** ``` paddlespeech vector --task spk --input input_16k.wav ``` **Automatic Speech Recognition** ```shell paddlespeech asr --lang zh --input input_16k.wav ``` - web demo for Automatic Speech Recognition is integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See Demo: [ASR Demo](https://huggingface.co/spaces/KPatrick/PaddleSpeechASR) **Speech Translation** (English to Chinese) (not support for Mac and Windows now) ```shell paddlespeech st --input input_16k.wav ``` **Text-to-Speech** ```shell paddlespeech tts --input "你好,欢迎使用飞桨深度学习框架!" --output output.wav ``` - web demo for Text to Speech is integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See Demo: [TTS Demo](https://huggingface.co/spaces/KPatrick/PaddleSpeechTTS) **Text Postprocessing** - Punctuation Restoration ```bash paddlespeech text --task punc --input 今天的天气真不错啊你下午有空吗我想约你一起去吃饭 ``` **Batch Process** ``` echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts ``` **Shell Pipeline** - ASR + Punctuation Restoration ``` paddlespeech asr --input ./zh.wav | paddlespeech text --task punc ``` For more command lines, please see: [demos](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/demos) If you want to try more functions like training and tuning, please have a look at [Speech-to-Text Quick Start](./docs/source/asr/quick_start.md) and [Text-to-Speech Quick Start](./docs/source/tts/quick_start.md). ## Quick Start Server 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 ``` **Access Audio Classification Services** ```shell paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input input.wav ``` For more information about server command lines, please see: [speech server demos](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/demos/speech_server) ## Model List PaddleSpeech supports a series of most popular models. They are summarized in [released models](./docs/source/released_model.md) and attached with available pretrained models. **Speech-to-Text** contains *Acoustic Model*, *Language Model*, and *Speech Translation*, with the following details:
Speech-to-Text Module Type Dataset Model Type Link
Speech Recogination Aishell DeepSpeech2 RNN + Conv based Models deepspeech2-aishell
Transformer based Attention Models u2.transformer.conformer-aishell
Librispeech Transformer based Attention Models deepspeech2-librispeech / transformer.conformer.u2-librispeech / transformer.conformer.u2-kaldi-librispeech
TIMIT Unified Streaming & Non-streaming Two-pass u2-timit
Alignment THCHS30 MFA mfa-thchs30
Language Model Ngram Language Model kenlm
Speech Translation (English to Chinese) TED En-Zh Transformer + ASR MTL transformer-ted
FAT + Transformer + ASR MTL fat-st-ted
**Text-to-Speech** in PaddleSpeech mainly contains three modules: *Text Frontend*, *Acoustic Model* and *Vocoder*. Acoustic Model and Vocoder models are listed as follow:
Text-to-Speech Module Type Model Type Dataset Link
Text Frontend tn / g2p
Acoustic Model Tacotron2 LJSpeech / CSMSC tacotron2-ljspeech / tacotron2-csmsc
Transformer TTS LJSpeech transformer-ljspeech
SpeedySpeech CSMSC speedyspeech-csmsc
FastSpeech2 LJSpeech / VCTK / CSMSC / AISHELL-3 fastspeech2-ljspeech / fastspeech2-vctk / fastspeech2-csmsc / fastspeech2-aishell3
Vocoder WaveFlow LJSpeech waveflow-ljspeech
Parallel WaveGAN LJSpeech / VCTK / CSMSC / AISHELL-3 PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc / PWGAN-aishell3
Multi Band MelGAN CSMSC Multi Band MelGAN-csmsc
Style MelGAN CSMSC Style MelGAN-csmsc
HiFiGAN LJSpeech / VCTK / CSMSC / AISHELL-3 HiFiGAN-ljspeech / HiFiGAN-vctk / HiFiGAN-csmsc / HiFiGAN-aishell3
WaveRNN CSMSC WaveRNN-csmsc
Voice Cloning GE2E Librispeech, etc. ge2e
GE2E + Tactron2 AISHELL-3 ge2e-tactron2-aishell3
GE2E + FastSpeech2 AISHELL-3 ge2e-fastspeech2-aishell3
**Audio Classification**
Task Dataset Model Type Link
Audio Classification ESC-50 PANN pann-esc50
**Speaker Verification**
Task Dataset Model Type Link
Speaker Verification VoxCeleb12 ECAPA-TDNN ecapa-tdnn-voxceleb12
**Punctuation Restoration**
Task Dataset Model Type Link
Punctuation Restoration IWLST2012_zh Ernie Linear iwslt2012-punc0
## Documents Normally, [Speech SoTA](https://paperswithcode.com/area/speech), [Audio SoTA](https://paperswithcode.com/area/audio) and [Music SoTA](https://paperswithcode.com/area/music) give you an overview of the hot academic topics in the related area. To focus on the tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas. - [Installation](./docs/source/install.md) - [Quick Start](#quickstart) - [Some Demos](./demos/README.md) - Tutorials - [Automatic Speech Recognition](./docs/source/asr/quick_start.md) - [Introduction](./docs/source/asr/models_introduction.md) - [Data Preparation](./docs/source/asr/data_preparation.md) - [Ngram LM](./docs/source/asr/ngram_lm.md) - [Text-to-Speech](./docs/source/tts/quick_start.md) - [Introduction](./docs/source/tts/models_introduction.md) - [Advanced Usage](./docs/source/tts/advanced_usage.md) - [Chinese Rule Based Text Frontend](./docs/source/tts/zh_text_frontend.md) - [Test Audio Samples](https://paddlespeech.readthedocs.io/en/latest/tts/demo.html) - [Audio Classification](./demos/audio_tagging/README.md) - [Speaker Verification](./demos/speaker_verification/README.md) - [Speech Translation](./demos/speech_translation/README.md) - [Released Models](./docs/source/released_model.md) - [Community](#Community) - [Welcome to contribute](#contribution) - [License](#License) The Text-to-Speech module is originally called [Parakeet](https://github.com/PaddlePaddle/Parakeet), and now merged with this repository. If you are interested in academic research about this task, please see [TTS research overview](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/docs/source/tts#overview). Also, [this document](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/tts/models_introduction.md) is a good guideline for the pipeline components. ## Citation To cite PaddleSpeech for research, please use the following format. ```tex @misc{ppspeech2021, title={PaddleSpeech, a toolkit for audio processing based on PaddlePaddle.}, author={PaddlePaddle Authors}, howpublished = {\url{https://github.com/PaddlePaddle/PaddleSpeech}}, year={2021} } @inproceedings{zheng2021fused, title={Fused acoustic and text encoding for multimodal bilingual pretraining and speech translation}, author={Zheng, Renjie and Chen, Junkun and Ma, Mingbo and Huang, Liang}, booktitle={International Conference on Machine Learning}, pages={12736--12746}, year={2021}, organization={PMLR} } ``` ## Contribute to PaddleSpeech You are warmly welcome to submit questions in [discussions](https://github.com/PaddlePaddle/PaddleSpeech/discussions) and bug reports in [issues](https://github.com/PaddlePaddle/PaddleSpeech/issues)! Also, we highly appreciate if you are willing to contribute to this project! ### Contributors

## 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. - Many thanks to [745165806](https://github.com/745165806)/[PaddleSpeechTask](https://github.com/745165806/PaddleSpeechTask) for contributing Punctuation Restoration model. - Many thanks to [kslz](https://github.com/745165806) for supplementary Chinese documents. - Many thanks to [awmmmm](https://github.com/awmmmm) for contributing fastspeech2 aishell3 conformer pretrained model. - Many thanks to [phecda-xu](https://github.com/phecda-xu)/[PaddleDubbing](https://github.com/phecda-xu/PaddleDubbing) for developing a dubbing tool with GUI based on PaddleSpeech TTS model. - Many thanks to [jerryuhoo](https://github.com/jerryuhoo)/[VTuberTalk](https://github.com/jerryuhoo/VTuberTalk) for developing a GUI tool based on PaddleSpeech TTS and code for making datasets from videos based on PaddleSpeech ASR. Besides, PaddleSpeech depends on a lot of open source repositories. See [references](./docs/source/reference.md) for more information. ## License PaddleSpeech is provided under the [Apache-2.0 License](./LICENSE).