A PyTorch implementation of Speech Transformer, an End-to-End ASR with Transformer network on Mandarin Chinese.
A PyTorch implementation of Speech Transformer [1], an end-to-end automatic speech recognition with Transformer network, which directly converts acoustic features to character sequence using a single nueral network.
pip install -r requirements.txt
cd tools; make KALDI=/path/to/kaldi
egs/aishell/run.sh
, download aishell dataset for free.$ cd egs/aishell
# Modify aishell data path to your path in the begining of run.sh
$ bash run.sh
That's all!
You can change 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
.
Workflow of egs/aishell/run.sh
:
egs/aishell/run.sh
provide example usage.
# Set PATH and PYTHONPATH
$ cd egs/aishell/; . ./path.sh
# Train
$ train.py -h
# Decode
$ recognize.py -h
If you want to visualize your loss, you can use visdom to do that:
$ visdom
.$ bash run.sh --visdom 1 --visdom_id "<any-string>"
or $ train.py ... --visdom 1 --vidsdom_id "<any-string>"
.<your-remote-server-ip>:8097
, egs, 127.0.0.1:8097
.<any-string>
in Environment
to see your loss.
$ bash run.sh --continue_from <model-path>
When happened in training, try to reduce batch_size
. $ bash run.sh --batch_size <lower-value>
.
Model | CER | Config |
---|---|---|
LSTMP | 9.85 | 4x(1024-512). See kaldi-ktnet1 |
Listen, Attend and Spell | 13.2 | See Listen-Attend-Spell's egs/aishell/run.sh |
SpeechTransformer | 12.8 | See egs/aishell/run.sh |