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Listen, Attend and Spell

A PyTorch implementation of Listen, Attend and Spell (LAS) [1], an end-to-end automatic speech recognition framework, which directly converts acoustic features to character sequence using only one nueral network.

Install

  • Python3 (Recommend Anaconda)
  • PyTorch 0.4.1+
  • Kaldi (Just for feature extraction)
  • pip install -r requirements.txt
  • cd tools; make KALDI=/path/to/kaldi
  • If you want to run egs/aishell/run.sh, download aishell dataset for free.

Usage

  1. $ cd egs/aishell and modify aishell data path to your path in run.sh.
  2. $ bash run.sh, that's all!

You can change hyper-parameter by $ bash run.sh --parameter_name parameter_value, egs, $ bash run.sh --stage 3. See parameter name in egs/aishell/run.sh before . utils/parse_options.sh.

More detail

$ cd egs/aishell/
$ . ./path.sh

Train

$ train.py -h

Decode

$ recognize.py -h

Workflow

Workflow of egs/aishell/run.sh:

  • Stage 0: Data Preparation
  • Stage 1: Feature Generation
  • Stage 2: Dictionary and Json Data Preparation
  • Stage 3: Network Training
  • Stage 4: Decoding

Visualize loss

If you want to visualize your loss, you can use visdom to do that:

  • Open a new terminal in your remote server (recommend tmux) and run $ visdom.
  • Open a new terminal and run $ bash run.sh --visdom 1 --visdom_id "<any-string>" or $ train.py ... --visdom 1 --vidsdom_id "<any-string>".
  • Open your browser and type <your-remote-server-ip>:8097, egs, 127.0.0.1:8097.
  • In visdom website, chose <any-string> in Environment to see your loss.

Results

Model CER Config
LSTMP 9.85 4x(1024-512)
Listen, Attend and Spell 13.2 See egs/aishell/run.sh

Reference

[1] W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in ICASSP 2016. (https://arxiv.org/abs/1508.01211v2)