ASR project barebones

В do_everything.ipynb можно посмотреть на мои логи обучения и валидации.

В текст энкодере реализованы custom_ctc_beam_search (написанный руками бим сёрч с семинара) и ctc_beam_search (при помощи pyctcdecode).

Как запустить

Запускаем докер.

docker build -t asr .
docker run -it asr

Скачиваем языковую модель.

pip install https://github.com/kpu/kenlm/archive/master.zip
wget https://www.openslr.org/resources/11/3-gram.arpa.gz --no-check-certificate
gzip -d 3-gram.arpa.gz

Скачиваем чекпоинт модели.

wget https://downloader.disk.yandex.ru/disk/7a2747e39158f3e70bbb493407f3b1e0bc0f69e9d780cf19c4c7ec05f01db602/6376b7ff/8QS15owusqz-m1yNRn8_WS_okE79_hbaLxZm6TiDTjv1bV_hbzZmTIPnkE1aXuSNyg8NnWG9-6x-oxTaSLdxDA%3D%3D?uid=0&filename=model_best.pth&disposition=attachment&hash=ilgsfWA8OM84RYEizPtdxA%2BC3St4e5HXCV1FKmy6t4K7BBBjODfJC7b23vIzWYt/q/J6bpmRyOJonT3VoXnDag%3D%3D%3A&limit=0&content_type=application%2Fzip&owner_uid=1184656726&fsize=230184375&hid=579b5415d0a32fa41dd0cb2675e69544&media_type=compressed&tknv=v2

Запускаем инференс.

python3 test.py \
   --config hw_asr/configs/balrog_evaluation_test_other.json \
   --resume model_best.pth \
   --batch-size 64 \
   --jobs 4 \
   --beam-size 100 \
   --output output_other.json

python3 test.py \
   --config hw_asr/configs/balrog_evaluation_test_clean.json \
   --resume model_best.pth \
   --batch-size 64 \
   --jobs 4 \
   --beam-size 100 \
   --output output_clean.json

Обучение можно запустить командой

python3 train.py --config hw_asr/configs/balrog.json

Чтобы прогнать модель на тестах, запустите

python3 test.py \
   -c hw_asr/configs/balrog_evaluation_test_other.json \
   -r model_best.pth \
   -t test_data \
   -o test_result.json \
   -b 5

Recommended implementation order

You might be a little intimidated by the number of folders and classes. Try to follow this steps to gradually undestand the workflow.

Test hw_asr/tests/test_dataset.py and hw_asr/tests/test_config.py and make sure everythin works for you
Implement missing functions to fix tests in hw_asr\tests\test_text_encoder.py
Implement missing functions to fix tests in hw_asr\tests\test_dataloader.py
Implement functions in hw_asr\metric\utils.py
Implement missing function to run train.py with a baseline model
Write your own model and try to overfit it on a single batch
Implement ctc beam search and add metrics to calculate WER and CER over hypothesis obtained from beam search.
~~Pain and suffering~~ Implement your own models and train them. You've mastered this template when you can tune your experimental setup just by tuning configs.json file and running train.py
Don't forget to write a report about your work
Get hired by Google the next day

Before submitting

Make sure your projects run on a new machine after complemeting the installation guide or by running it in docker container.
Search project for # TODO: your code here and implement missing functionality

Make sure all tests work without errors

python -m unittest discover hw_asr/tests

Make sure test.py works fine and works as expected. You should create files default_test_config.json and your installation guide should download your model checpoint and configs in default_test_model/checkpoint.pth and default_test_model/config.json.
```
python test.py \
   -c default_test_config.json \
   -r default_test_model/checkpoint.pth \
   -t test_data \
   -o test_result.json
```
Use train.py for training

Credits

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

Docker

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

TODO

These barebones can use more tests. We highly encourage students to create pull requests to add more tests / new functionality. Current demands:

Tests for beam search
README section to describe folders
Notebook to show how to work with ConfigParser and config_parser.init_obj(...)