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    PaddleSpeech is an open-source toolkit on PaddlePaddle platform for a variety of critical tasks in speech, with the state-of-art and influential models.

    Input Audio Recognition Result

    I knocked at the door on the ancient side of the building.

    Input Text Synthetic Audio
    Life was like a box of chocolates, you never know what you're gonna get.

    For more synthesized audios, please refer to PaddleSpeech Text-to-Speech samples.

    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:

    • Fast and Light-weight: we provide high-speed and ultra-lightweight models that are convenient for industrial deployment.
    • 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 Speech Translation, Automatic Speech Recognition, Text-to-Speech Synthesis, Voice Cloning, 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 application of traditional audio tasks, we combine the workflows of aforementioned tasks with other fields like Natural language processing (NLP), like Punctuation Restoration.


    The base environment in this page is

    • Ubuntu 16.04
    • python>=3.7
    • paddlepaddle>=2.2.0

    If you want to set up PaddleSpeech in other environment, please see the installation documents for all the alternatives.

    Quick Start

    Developers can have a try of our model with only a few lines of code.

    A tiny DeepSpeech2 Speech-to-Text model training on toy set of LibriSpeech:

    cd examples/tiny/s0/
    # source the environment
    source ../../../utils/
    # prepare data
    bash ./local/
    # train model, all `ckpt` under `exp` dir, if you use paddlepaddle-gpu, you can set CUDA_VISIBLE_DEVICES before the train script
    ./local/ conf/deepspeech2.yaml deepspeech2 offline
    # avg n best model to get the test model, in this case, n = 1 best exp/deepspeech2/checkpoints 1
    # evaluate the test model
    ./local/ conf/deepspeech2.yaml exp/deepspeech2/checkpoints/avg_1 offline

    For Text-to-Speech, try pretrained FastSpeech2 + Parallel WaveGAN on CSMSC:

    cd examples/csmsc/tts3
    # download the pretrained models and unaip them
    # source the environment
    # run end-to-end synthesize
    FLAGS_allocator_strategy=naive_best_fit \
    FLAGS_fraction_of_gpu_memory_to_use=0.01 \
    python3 ${BIN_DIR}/ \
      --fastspeech2-config=fastspeech2_nosil_baker_ckpt_0.4/default.yaml \
      --fastspeech2-checkpoint=fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz \
      --fastspeech2-stat=fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy \
      --pwg-config=pwg_baker_ckpt_0.4/pwg_default.yaml \
      --pwg-checkpoint=pwg_baker_ckpt_0.4/pwg_snapshot_iter_400000.pdz \
      --pwg-stat=pwg_baker_ckpt_0.4/pwg_stats.npy \
      --text=${BIN_DIR}/../sentences.txt \
      --output-dir=exp/default/test_e2e \
      --inference-dir=exp/default/inference \

    If you want to try more functions like training and tuning, please see Speech-to-Text Quick Start and Text-to-Speech Quick Start.

    Model List

    PaddleSpeech supports a series of most popular models, summarized in released models with available pretrained models.

    Speech-to-Text module contains Acoustic Model and Language Model, with the following details:

    Speech-to-Text Module Type Dataset Model Type Link
    Acoustic Model 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
    Alignment THCHS30 MFA mfa-thchs30
    Language Model Ngram Language Model kenlm
    TIMIT Unified Streaming & Non-streaming Two-pass u2-timit

    PaddleSpeech Text-to-Speech 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 tacotron2-ljspeech
    Transformer TTS 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
    Voice Cloning GE2E AISHELL-3, etc. ge2e
    GE2E + Tactron2 AISHELL-3 ge2e-tactron2-aishell3
    GE2E + FastSpeech2 AISHELL-3 ge2e-fastspeech2-aishell3


    Normally, Speech SoTA gives you an overview of the hot academic topics in speech. To focus on the tasks in PaddleSpeech, you will find the following guidelines are helpful to grasp the core ideas.

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

    FAQ and Contributing

    You are warmly welcome to submit questions in discussions and bug reports in issues! Also, we highly appreciate if you would like to contribute to this project!


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

    title={PaddleSpeech, a toolkit for audio processing based on PaddlePaddle.},
    author={PaddlePaddle Authors},
    howpublished = {\url{}},

    License and Acknowledge

    PaddleSpeech is provided under the Apache-2.0 License.

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


    A PaddlePaddle implementation of DeepSpeech2 architecture for ASR.

    发行版本 4

    DeepSpeech v2.1.1


    贡献者 38



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