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.

• 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.

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.

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

Train

$ train.py -h

Decode

$ recognize.py -h

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

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.

[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)