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.
- 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.
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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