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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.

PaddleSpeech won the NAACL2022 Best Demo Award, please check out our paper on Arxiv.

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。

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, CLI, Server, and 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 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 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、PP-TTS、PP-VPR
• 👏🏻 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 and TTS 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.

Community

• Scan the QR code below with your Wechat, you can access to official technical exchange group and get the bonus ( more than 20GB learning materials, such as papers, codes and videos ) and the live link of the lessons. Look forward to your participation.

Installation

We strongly recommend our users to install PaddleSpeech in Linux with python>=3.7 and paddlepaddle>=2.3.1. 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

Speaker Verification

paddlespeech vector --task spk --input input_16k.wav

Automatic Speech Recognition

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

• web demo for Automatic Speech Recognition is integrated to Huggingface Spaces with Gradio. See Demo: ASR Demo

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

• web demo for Text to Speech is integrated to Huggingface Spaces with Gradio. See Demo: TTS Demo

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
  

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

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.

Quick Start Server

Developers can have a try of our speech server with PaddleSpeech Server Command Line.

Start server

paddlespeech_server start --config_file ./paddlespeech/server/conf/application.yaml

Access Speech Recognition Services

paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input input_16k.wav

Access Text to Speech Services

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

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

Quick Start Streaming Server

Developers can have a try of streaming asr and streaming tts 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 and streaming tts

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	Example
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	Example
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 + Tacotron2	AISHELL-3	ge2e-tacotron2-aishell3
	GE2E + FastSpeech2	AISHELL-3	ge2e-fastspeech2-aishell3
End-to-End	VITS	CSMSC	VITS-csmsc

Audio Classification

Task	Dataset	Model Type	Example
Audio Classification	ESC-50	PANN	pann-esc50

Speaker Verification

Task	Dataset	Model Type	Example
Speaker Verification	VoxCeleb12	ECAPA-TDNN	ecapa-tdnn-voxceleb12

Punctuation Restoration

Task	Dataset	Model Type	Example
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.

• Installation
• Quick Start
• Some Demos
• Tutorials
  • Automatic Speech Recognition
    • Introduction
    • Data Preparation
    • Ngram LM
  • Text-to-Speech
    • Introduction
    • Advanced Usage
    • Chinese Rule Based Text Frontend
    • Test Audio Samples
  • Speaker Verification
    • Audio Searching
    • Speaker Verification
  • Audio Classification
  • Speech Translation
  • Speech Server
• Released Models
  • Speech-to-Text
  • Text-to-Speech
  • Audio Classification
  • Speaker Verification
  • Punctuation Restoration
• Community
• Welcome to contribute
• License

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.

⭐ Examples

• PaddleBoBo: Use PaddleSpeech TTS to generate virtual human voice.

• PaddleSpeech Demo Video

• VTuberTalk: Use PaddleSpeech TTS and ASR to clone voice from videos.

Citation

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

@inproceedings{zhang2022paddlespeech,
    title = {PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit},
    author = {Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, dianhai yu, Yanjun Ma, Liang Huang},
    booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations},
    year = {2022},
    publisher = {Association for Computational Linguistics},
}

@inproceedings{zheng2021fused,
  title={Fused acoustic and text encoding for multimodal bilingual pretraining and speech translation},
  author={Zheng, Renjie and Chen, Junkun and Ma, Mingbo and Huang, Liang},
  booktitle={International Conference on Machine Learning},
  pages={12736--12746},
  year={2021},
  organization={PMLR}
}

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

• Many thanks to yeyupiaoling/PPASR/PaddlePaddle-DeepSpeech/VoiceprintRecognition-PaddlePaddle/AudioClassification-PaddlePaddle for years of attention, constructive advice and great help.
• Many thanks to mymagicpower for the Java implementation of ASR upon short and long audio files.
• Many thanks to JiehangXie/PaddleBoBo for developing Virtual Uploader(VUP)/Virtual YouTuber(VTuber) with PaddleSpeech TTS function.
• Many thanks to 745165806/PaddleSpeechTask for contributing Punctuation Restoration model.
• Many thanks to kslz for supplementary Chinese documents.
• Many thanks to awmmmm for contributing fastspeech2 aishell3 conformer pretrained model.
• Many thanks to phecda-xu/PaddleDubbing for developing a dubbing tool with GUI based on PaddleSpeech TTS model.
• Many thanks to jerryuhoo/VTuberTalk for developing a GUI tool based on PaddleSpeech TTS and code for making datasets from videos based on PaddleSpeech ASR.
• Many thanks to vpegasus/xuesebot for developing a rasa chatbot,which is able to speak and listen thanks to PaddleSpeech.
• Many thanks to chenkui164/FastASR for the C++ inference implementation of PaddleSpeech ASR.

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.