Voice Recognition

ESPnet – end-to-end speech processing toolkit

ESPnet is an end-to-end speech processing toolkit, mainly focuses on end-to-end speech recognition and end-to-end text-to-speech.

ESPnet uses chainer as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments.

ESPnet is free and open source software.

Features include:

  • Hybrid CTC/attention based end-to-end ASR:
    • Fast/accurate training with CTC/attention multitask training.
    • CTC/attention joint decoding to boost monotonic alignment decoding.
  • Encoder: VGG-like CNN + BiRNN (LSTM/GRU), sub-sampling BiRNN (LSTM/GRU) or Transformer.
  • Attention: Dot product, location-aware attention, variants of multihead.
  • Incorporate RNNLM/LSTMLM trained only with text data.
  • Batch GPU decoding.
  • Transducer based end-to-end ASR:
    • Available: RNN-Transducer, Transformer-Transducer, Transformer/RNN-Transducer.
    • Support attention extension and VGG-Transformer (encoder).
  • Tacotron2 based end-to-end TTS.
  • Transformer based end-to-end TTS.
  • Feed-forward Transformer (a.k.a. FastSpeech) based end-to-end TTS.
  • Transformer based end-to-end ST.
  • Transformer based end-to-end MT.
  • Flexible network architecture thanks to chainer and pytorch.
  • Kaldi style complete recipe:
    • Support numbers of ASR recipes (WSJ, Switchboard, CHiME-4/5, Librispeech, TED, CSJ, AMI, HKUST, Voxforge, REVERB, etc).
    • Support numbers of TTS recipes with a similar manner to the ASR recipe (LJSpeech, LibriTTS, M-AILABS, etc).
    • Support numbers of ST recipes (Fisher-CallHome Spanish, Libri-trans, IWSLT’18, How2, Must-C, Mboshi-French, etc).
    • Support numbers of MT recipes (IWSLT’16, the above ST recipes etc).
    • Support speech separation and recognition recipe (WSJ-2mix).
  • State-of-the-art performance in several ASR benchmarks (comparable/superior to hybrid DNN/HMM and CTC).
  • State-of-the-art performance in several ST benchmarks (comparable/superior to cascaded ASR and MT).
  • Flexible front-end processing thanks to kaldiio and HDF5 support.
  • Tensorboard based monitoring.

Website: espnet.github.io/espnet
Support: GitHub Code Repository
Developer: Tomoki Hayashi, Hirofumi Inaguma, Naoyuki Kamo, Shigeki Karita, and many contributors
License: Apache License 2.0

ESPnet is written in Python. Learn Python with our recommended free books and free tutorials.

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