(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.
| 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。 |
| Input Audio | Translations Result |
|---|---|
|
| 我 在 这栋 建筑 的 古老 门上 敲门。 |
| 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》。 |
|
| 大家好，我是 parrot 虚拟老师，我们来读一首诗，我与春风皆过客，I and the spring breeze are passing by，你携秋水揽星河，you take the autumn water to take the galaxy。 |
|
| 宜家唔系事必要你讲，但系你所讲嘅说话将会变成呈堂证供。 |
|
| 各个国家有各个国家嘅国歌 |
|
For more synthesized audios, please refer to PaddleSpeech Text-to-Speech samples.
| 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。 |
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:
PaddleSpeech Streaming Server is available for Streaming ASR with Punctuation Restoration and Token Timestamp and Text-to-Speech.PaddleSpeech Server is available for Audio Classification, Automatic Speech Recognition and Text-to-Speech, Speaker Verification and Punctuation Restoration.PaddleSpeech CLI is available for Speaker Verification.PaddleSpeech CLI is available for Audio Classification, Automatic Speech Recognition, Speech Translation (English to Chinese) and Text-to-Speech.
We strongly recommend our users to install PaddleSpeech in Linux with python>=3.8 and paddlepaddle<=2.5.1. Some new versions of Paddle do not have support for adaptation in PaddleSpeech, so currently only versions 2.5.1 and earlier can be supported.
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
You can also specify the version of paddlepaddle or install the develop version.
# install 2.4.1 version. Note, 2.4.1 is just an example, please follow the minimum dependency of paddlepaddle for your selection
pip install paddlepaddle==2.4.1 -i https://mirror.baidu.com/pypi/simple
# install develop version
pip install paddlepaddle==0.0.0 -f https://www.paddlepaddle.org.cn/whl/linux/cpu-mkl/develop.html
There are two quick installation methods for PaddleSpeech, one is pip installation, and the other is source code compilation (recommended).
pip install pytest-runner
pip install paddlespeech
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
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
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
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")
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
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 ...]
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。
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 。']
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
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
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 | |
| DiffSinger | Opencpop | DiffSinger-opencpop | |
| Vocoder | WaveFlow | LJSpeech | waveflow-ljspeech |
| Parallel WaveGAN | LJSpeech / VCTK / CSMSC / AISHELL-3 / Opencpop | PWGAN-ljspeech / PWGAN-vctk / PWGAN-csmsc / PWGAN-aishell3 / PWGAN-opencpop | |
| Multi Band MelGAN | CSMSC | Multi Band MelGAN-csmsc | |
| Style MelGAN | CSMSC | Style MelGAN-csmsc | |
| HiFiGAN | LJSpeech / VCTK / CSMSC / AISHELL-3 / Opencpop | HiFiGAN-ljspeech / HiFiGAN-vctk / HiFiGAN-csmsc / HiFiGAN-aishell3 / HiFiGAN-opencpop | |
| 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 |
Keyword Spotting
| Task | Dataset | Model Type | Example |
|---|---|---|---|
| Keyword Spotting | hey-snips | MDTC | mdtc-hey-snips |
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 |
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.
VTuberTalk: Use PaddleSpeech TTS and ASR to clone voice from videos.
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{pmlr-v162-bai22d,
title = {{A}$^3${T}: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing},
author = {Bai, He and Zheng, Renjie and Chen, Junkun and Ma, Mingbo and Li, Xintong and Huang, Liang},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {1399--1411},
year = {2022},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/bai22d/bai22d.pdf},
url = {https://proceedings.mlr.press/v162/bai22d.html},
}
@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}
}
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!
PaddleSpeech is provided under the Apache-2.0 License.
FAQs
Speech tools and models based on Paddlepaddle
We found that paddlespeech demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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