timtoronto634 / cola_pytorch

COLA contrastive pre-training method implemented in PyTorch

COLA

(Unofficial)-Re-Impementation of the model archiecture of CONTRASTIVE LEARNING OF GENERAL-PURPOSE AUDIO REPRESENTATIONS in PyTorch.

Paper: CONTRASTIVE LEARNING OF GENERAL-PURPOSE AUDIO REPRESENTATIONS

Paper's Official Code (In tensorflow): tf-code

Install

git clone https://github.com/CVxTz/COLA_pytorch
cd COLA_pytorch
python -m pip install .

Run

# Data download: download fma data and metadata


wget -c https://os.unil.cloud.switch.ch/fma/fma_metadata.zip
wget -c https://os.unil.cloud.switch.ch/fma/fma_small.zip
wget -c https://os.unil.cloud.switch.ch/fma/fma_large.zip


# Data preparation : prepare json with fma_small labels and pre-compute mel-spectrograms and save them as .npy

python supervised_examples/prepare_data.py --metadata_path "/media/ml/data_ml/fma_metadata/"
python audio_encoder/audio_processing.py --mp3_path "/media/ml/data_ml/fma_large/"
python audio_encoder/audio_processing.py --mp3_path "/media/ml/data_ml/fma_small/"

# Training

# Train with COLA

python audio_encoder/train_encoder.py --mp3_path "/media/ml/data_ml/fma_large/"

# Train Supervised

python supervised_examples/cnn_genre_classification.py --metadata_path "/media/ml/data_ml/fma_metadata/" \
  --mp3_path "/media/ml/data_ml/fma_small/"

python supervised_examples/cnn_genre_classification.py --metadata_path "/media/ml/data_ml/fma_metadata/" \
  --mp3_path "/media/ml/data_ml/fma_small/" \
  --encoder_path "models/encoder.ckpt"

Data

FMA Data