DL-AUDIO homework. More details on model, train configuration and results examples located in wandb report
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├── checkpoints <-- Move here pretrained model checkpoint and corresponding `config.json`.
├── data <-- Directory with train, test and validation datasets
├── notebooks <-- Example notebooks
│ └── speaker-separation.ipynb <-- Example how to use this repository in kaggle.
└── ss <-- Module with model, trainer, dataloader and etc.
First download the datasets:
cd data
wget https://us.openslr.org/resources/12/test-clean.tar.gz
tar -xf test-clean.tar.gz
mkdir librispeech
mv LibriSpeech/test-clean librispeech/
rm -r LibriSpeech
cd data
wget https://us.openslr.org/resources/12/train-clean-100.tar.gz
tar -xf train-clean-100.tar.gz
mkdir librispeech
mv LibriSpeech/train-clean-100 librispeech/
rm -r LibriSpeech
cd ..
Now train the model:
python train.py --config ss/configs/spexp.json
Put file checkpoint-epoch95.pth
from https://drive.google.com/drive/folders/1qcMY2sN1mK1AYI27BXjoy4bOomjPJ14c?usp=sharing into checkpoints/exp3/checkpoint-epoch95.pth
and run test :
python test.py --config test_configs/public.json --resume checkpoints/exp3/checkpoint-epoch95.pth