README.md

    (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.

    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》。
    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.

    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

    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>=3.8 and paddlepaddle>=2.5.0.

    Dependency Introduction

    • gcc >= 4.8.5
    • paddlepaddle >= 2.5.0
    • python >= 3.8
    • 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

    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

    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")

    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
    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

    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.

    Examples

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

    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{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}
    }

    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

    License

    PaddleSpeech is provided under the Apache-2.0 License.

    Stargazers over time

    Stargazers over time

    项目简介

    Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/PaddlePaddle/DeepSpeech

    发行版本 15

    PaddleSpeech r1.4.1

    全部发行版

    贡献者 99

    全部贡献者

    开发语言

    • Python 69.6 %
    • C++ 23.3 %
    • Shell 2.8 %
    • Perl 2.0 %
    • C 1.1 %