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PaddleSpeech/docs/source/introduction.md

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PaddleSpeech

What is PaddleSpeech?

PaddleSpeech is an open-source toolkit on the PaddlePaddle platform for two critical tasks in Speech - Speech-to-Text (Automatic Speech Recognition, ASR) and Text-to-Speech Synthesis (TTS), with modules involving state-of-art and influential models.

What can PaddleSpeech do?

Speech-to-Text

PaddleSpeech ASR mainly consists of components below:

  • Implementation of models and commonly used neural network layers.
  • Dataset abstraction and common data preprocessing pipelines.
  • Ready-to-run experiments.

PaddleSpeech ASR provides you with a complete ASR pipeline, including:

  • Data Preparation
    • Build vocabulary
    • Compute Cepstral mean and variance normalization (CMVN)
    • Featrue extraction
      • linear
      • fbank (also support kaldi feature)
      • mfcc
  • Acoustic Models
    • Deepspeech2 (Streaming and Non-Streaming)
    • Transformer (Streaming and Non-Streaming)
    • Conformer (Streaming and Non-Streaming)
  • Decoder
    • ctc greedy search (used in DeepSpeech2, Transformer and Conformer)
    • ctc beam search (used in DeepSpeech2, Transformer and Conformer)
    • attention decoding (used in Transformer and Conformer)
    • attention rescoring (used in Transformer and Conformer)

Speech-to-Text helps you train the ASR model very simply.

Text-to-Speech

TTS mainly consists of components below:

  • Implementation of models and commonly used neural network layers.
  • Dataset abstraction and common data preprocessing pipelines.
  • Ready-to-run experiments.

PaddleSpeech TTS provides you with a complete TTS pipeline, including:

  • Text FrontEnd
    • Rule based Chinese frontend.
  • Acoustic Models
    • FastSpeech2
    • SpeedySpeech
    • TransformerTTS
    • Tacotron2
  • Vocoders
    • Multi Band MelGAN
    • Parallel WaveGAN
    • WaveFlow
  • Voice Cloning
    • Transfer Learning from Speaker Verification to Multispeaker Text-to-Speech Synthesis
    • GE2E

Text-to-Speech helps you to train TTS models with simple commands.