41 KiB
(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.
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. |
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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)
Text-to-Speech
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
- 🔥 2022.09.26: Add Voice Cloning, TTS finetune, and ERNIE-SAT in PaddleSpeech Web Demo.
- ⚡ 2022.09.09: Add AISHELL-3 Voice Cloning example with ECAPA-TDNN speaker encoder.
- ⚡ 2022.08.25: Release TTS finetune example.
- 🔥 2022.08.22: Add ERNIE-SAT models: ERNIE-SAT-vctk、ERNIE-SAT-aishell3、ERNIE-SAT-zh_en.
- 🔥 2022.08.15: Add g2pW into TTS Chinese Text Frontend.
- 🔥 2022.08.09: Release Chinese English mixed TTS.
- ⚡ 2022.08.03: Add ONNXRuntime infer for TTS CLI.
- 🎉 2022.07.18: Release VITS: VITS-csmsc、VITS-aishell3、VITS-VC.
- 🎉 2022.06.22: All TTS models support ONNX format.
- 🍀 2022.06.17: Add PaddleSpeech Web Demo.
- 👑 2022.05.13: Release PP-ASR、PP-TTS、PP-VPR.
- 👏🏻 2022.05.06:
PaddleSpeech Streaming Server
is available forStreaming ASR
withPunctuation Restoration
andToken Timestamp
andText-to-Speech
. - 👏🏻 2022.05.06:
PaddleSpeech Server
is available forAudio Classification
,Automatic Speech Recognition
andText-to-Speech
,Speaker Verification
andPunctuation Restoration
. - 👏🏻 2022.03.28:
PaddleSpeech CLI
is available forSpeaker Verification
. - 🤗 2021.12.14: ASR and TTS Demos on Hugging Face Spaces are available!
- 👏🏻 2021.12.10:
PaddleSpeech CLI
is available forAudio Classification
,Automatic Speech Recognition
,Speech Translation (English to Chinese)
andText-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.
![](https://user-images.githubusercontent.com/23690325/169763015-cbd8e28d-602c-4723-810d-dbc6da49441e.jpg)
Installation
We strongly recommend our users to install PaddleSpeech in Linux with python>=3.7 and paddlepaddle>=2.3.1.
Dependency Introduction
- gcc >= 4.8.5
- paddlepaddle >= 2.3.1
- python >= 3.7
- OS support: Linux(recommend), Windows, Mac OSX
PaddleSpeech depends on paddlepaddle. For installation, please refer to the official website of paddlepaddle and choose according to your own machine. Here is an example of the cpu version.
pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
There are two quick installation methods for PaddleSpeech, one is pip installation, and the other is source code compilation (recommended).
pip install
pip install pytest-runner
pip install paddlespeech
source code compilation
git clone https://github.com/PaddlePaddle/PaddleSpeech.git
cd PaddleSpeech
pip install pytest-runner
pip install .
For more installation problems, such as conda environment, librosa-dependent, gcc problems, kaldi installation, etc., you can refer to this installation document. If you encounter problems during installation, you can leave a message on #2150 and find related problems
Quick Start
Developers can have a try of our models with PaddleSpeech Command Line or Python. Change --input
to test your own audio/text and support 16k wav format audio.
You can also quickly experience it in AI Studio 👉🏻 PaddleSpeech API Demo
Test audio sample download
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav
Automatic Speech Recognition
(Click to expand)Open Source Speech Recognition
command line experience
paddlespeech asr --lang zh --input zh.wav
Python API experience
>>> from paddlespeech.cli.asr.infer import ASRExecutor
>>> asr = ASRExecutor()
>>> result = asr(audio_file="zh.wav")
>>> print(result)
我认为跑步最重要的就是给我带来了身体健康
Text-to-Speech
Open Source Speech Synthesis
Output 24k sample rate wav format audio
command line experience
paddlespeech tts --input "你好,欢迎使用百度飞桨深度学习框架!" --output output.wav
Python API experience
>>> from paddlespeech.cli.tts.infer import TTSExecutor
>>> tts = TTSExecutor()
>>> tts(text="今天天气十分不错。", output="output.wav")
- You can experience in Huggingface Spaces TTS Demo
Audio Classification
An open-domain sound classification tool
Sound classification model based on 527 categories of AudioSet dataset
command line experience
paddlespeech cls --input zh.wav
Python API experience
>>> from paddlespeech.cli.cls.infer import CLSExecutor
>>> cls = CLSExecutor()
>>> result = cls(audio_file="zh.wav")
>>> print(result)
Speech 0.9027186632156372
Voiceprint Extraction
Industrial-grade voiceprint extraction tool
command line experience
paddlespeech vector --task spk --input zh.wav
Python API experience
>>> from paddlespeech.cli.vector import VectorExecutor
>>> vec = VectorExecutor()
>>> result = vec(audio_file="zh.wav")
>>> print(result) # 187维向量
[ -0.19083306 9.474295 -14.122263 -2.0916545 0.04848729
4.9295826 1.4780062 0.3733844 10.695862 3.2697146
-4.48199 -0.6617882 -9.170393 -11.1568775 -1.2358263 ...]
Punctuation Restoration
Quick recovery of text punctuation, works with ASR models
command line experience
paddlespeech text --task punc --input 今天的天气真不错啊你下午有空吗我想约你一起去吃饭
Python API experience
>>> from paddlespeech.cli.text.infer import TextExecutor
>>> text_punc = TextExecutor()
>>> result = text_punc(text="今天的天气真不错啊你下午有空吗我想约你一起去吃饭")
今天的天气真不错啊!你下午有空吗?我想约你一起去吃饭。
Speech Translation
End-to-end English to Chinese Speech Translation Tool
Use pre-compiled kaldi related tools, only support experience in Ubuntu system
command line experience
paddlespeech st --input en.wav
Python API experience
>>> from paddlespeech.cli.st.infer import STExecutor
>>> st = STExecutor()
>>> result = st(audio_file="en.wav")
['我 在 这栋 建筑 的 古老 门上 敲门 。']
Quick Start Server
Developers can have a try of our speech server with PaddleSpeech Server Command Line.
You can try it quickly in AI Studio (recommend): SpeechServer
Start server
paddlespeech_server start --config_file ./demos/speech_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 / ZH_EN / finetune | fastspeech2-ljspeech / fastspeech2-vctk / fastspeech2-csmsc / fastspeech2-aishell3 / fastspeech2-zh_en / fastspeech2-finetune | |
ERNIE-SAT | VCTK / AISHELL-3 / ZH_EN | ERNIE-SAT-vctk / ERNIE-SAT-aishell3 / ERNIE-SAT-zh_en | |
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 |
SV2TTS (GE2E + Tacotron2) | AISHELL-3 | VC0 | |
SV2TTS (GE2E + FastSpeech2) | AISHELL-3 | VC1 | |
SV2TTS (ECAPA-TDNN + FastSpeech2) | AISHELL-3 | VC2 | |
GE2E + VITS | AISHELL-3 | VITS-VC | |
End-to-End | VITS | CSMSC / AISHELL-3 | VITS-csmsc / VITS-aishell3 |
Audio Classification
Task | Dataset | Model Type | Example |
---|---|---|---|
Audio Classification | ESC-50 | PANN | pann-esc50 |
Speaker Verification
Task | Dataset | Model Type | Example |
---|---|---|---|
Speaker Verification | VoxCeleb1/2 | ECAPA-TDNN | ecapa-tdnn-voxceleb12 |
Speaker Diarization
Task | Dataset | Model Type | Example |
---|---|---|---|
Speaker Diarization | AMI | ECAPA-TDNN + AHC / SC | ecapa-tdnn-ami |
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
- Released Models
- 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.
-
VTuberTalk: Use PaddleSpeech TTS and ASR to clone voice from videos.
![](https://raw.githubusercontent.com/jerryuhoo/VTuberTalk/main/gui/gui.png)
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 HighCWu for adding VITS-aishell3 and VITS-VC examples.
- Many thanks to david-95 improved TTS, fixed multi-punctuation bug, and contributed to multiple program and data.
- Many thanks to BarryKCL improved TTS Chinses frontend based on G2PW.
- 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.