First of all you need to download the repository:

git clone https://github.com/retir/dla_project1
cd dla_project1

Then install necessary dependencies:

pip install -r ./requirements.txt
pip install https://github.com/kpu/kenlm/archive/master.zip

If you want to use pretrained model, you can load it as follow:

python3 downloader.py

Нou can download only the files you need by listing them via , in flag --files (as example --files='bpe_token_500,arpa_5_gram'). Also you can rewrite already loaded files using flag --hard, in other case loading such a files will be skipped.

To calculate metrics use follows:

python3 calc_metr.py -r path/to/model.pt -c path/to/config.json

where model.pt means pretrained model, and config.json is a config with datasets, metrics and beam search parameters in it (you can find example at hw_asr/configs/eval.json). It is necessary that the folder with the model contains the config.json file - the config on which the model was trained.

Pretrained model from downloader.py was learnd with config deepspeech.json (hw_asr/configs/deepspeech.json). To learn model use follow:

python3 train.py -c path/to/config.json

You can find more detailed report about experiments at wandb report

This repository is based on a heavily modified fork of pytorch-template repository.

You can use this project with docker. Quick start:

docker build -t my_hw_asr_image . 
docker run \
   --gpus '"device=0"' \
   -it --rm \
   -v /path/to/local/storage/dir:/repos/asr_project_template/data/datasets \
   -e WANDB_API_KEY=<your_wandb_api_key> \
	my_hw_asr_image python -m unittest 

Notes:

• -v /out/of/container/path:/inside/container/path -- bind mount a path, so you wouldn't have to download datasets at the start of every docker run.
• -e WANDB_API_KEY=<your_wandb_api_key> -- set envvar for wandb (if you want to use it). You can find your API key here: https://wandb.ai/authorize