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PaddleSpeech/README.md

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(简体中文|English)

PaddleSpeech is an open-source toolkit on PaddlePaddle 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.

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: Use PaddleSpeech TTS to generate virtual human voice.

🔥 Hot Activities

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 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 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

  • 🤗 2021.12.14: Our PaddleSpeech ASR and TTS 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.

Quick Start

Developers can have a try of our models with PaddleSpeech Command Line. Change --input to test your own audio/text.

Audio Classification

paddlespeech cls --input input.wav

Automatic Speech Recognition

paddlespeech asr --lang zh --input input_16k.wav

Speech Translation (English to Chinese)

(not support for Mac and Windows now)

paddlespeech st --input input_16k.wav

Text-to-Speech

paddlespeech tts --input "你好，欢迎使用飞桨深度学习框架！" --output output.wav

Text Postprocessing

  • Punctuation Restoration 
    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

For more command lines, please see: demos

If you want to try more functions like training and tuning, please have a look at Speech-to-Text Quick Start and Text-to-Speech Quick Start.

Model List

PaddleSpeech supports a series of most popular models. They are summarized in released models 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 AISHELL-3 / VCTK / LJSpeech / CSMSC fastspeech2-aishell3 / fastspeech2-vctk / fastspeech2-ljspeech / fastspeech2-csmsc
Vocoder WaveFlow LJSpeech waveflow-ljspeech
Parallel WaveGAN LJSpeech / VCTK / CSMSC PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc
Multi Band MelGAN CSMSC Multi Band MelGAN-csmsc
Style MelGAN CSMSC Style MelGAN-csmsc
HiFiGAN CSMSC HiFiGAN-csmsc
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

Punctuation Restoration

Task Dataset Model Type Link
Punctuation Restoration IWLST2012_zh Ernie Linear iwslt2012-punc0

Documents

Normally, Speech SoTA, Audio SoTA and Music SoTA 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.

The Text-to-Speech module is originally called Parakeet, and now merged with this repository. If you are interested in academic research about this task, please see TTS research overview. Also, this document is a good guideline for the pipeline components.

Citation

To cite PaddleSpeech for research, please use the following format.

@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 and bug reports in issues! Also, we highly appreciate if you are willing to contribute to this project!

Contributors

Acknowledgement

Besides, PaddleSpeech depends on a lot of open source repositories. See references for more information.

License

PaddleSpeech is provided under the Apache-2.0 License.