dr-costas / coala

COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations

COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations

This is the repository for the method presented in the paper: "COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations" by X. Favory, K. Drossos, T. Virtanen, and X. Serra. (arXiv)

Install python dependencies

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Reproduce the results of the paper

Training the embedding models

If you want to train the embeddings from scratch, you will need to download the dataset from this Zenodo page and place the hdf5 files in the hdf5_ds/ directory. Then you can launch the training of an embedding model by running for instance:

python train_dual_ae.py 'configs/dual_ae_c.json'

The config file may be edited for instance to select which device to use for training ('cuda' or 'cpu').

Downtream classification tasks

If you want to re-compute the classification accuracies on the downstream tasks, you will need to:

1. download the three datasets:

   • Urban Sound 8K
   • GTZAN Genre Collection and its fault-filtered splited version.
   • NSynth

2. place their content into the directory data/ as following:

   data
   └─── UrbanSound8K
   │     └─── audio
   │     └─── metadata
   └─── GTZAN
   │     └─── genres
   │     └─── test_filtered.txt
   │     └─── train_filtered.txt
   └─── nsynth
        └─── nsynth-train
              └─── audio_selected
        └───  nsynth-test
   

   keeping existing sub-directories as they are for each dataset. However, for NSynth, you will have to manually create the audio_selected/ folder and put there the files that are listed in the values of the dictionary stored in json/nsynth_selected_sounds_per_class.json.

3. compute the embeddings with the pre-trained (or re-trained) embedding models runing the encode.py script. This will store the embedding files into the data/embedding/ directory.

Use the pre-trained embedding models

You can use the embedding models on your own data. You will need to create your own script, but the idea is simple. Here is a simple example to extract embedding chunks given an audio file:

from encode import return_loaded_model, extract_audio_embedding_chunks
from models_t1000 import AudioEncoder

model = return_loaded_model(AudioEncoder, 'saved_models/dual_ae_c/audio_encoder_epoch_200.pt')
embedding, _ = extract_audio_embedding_chunks(model, '<path/to/audio/file>')

---

   __         __            
 /"  "\     /"  "\                            _    
(  (\  )___(  /)  )                          | |    
 \               /             ___ ___   __ _| | __ _ 
 /               \            / __/ _ \ / _` | |/ _` |
/    () ___ ()    \          | (_| (_) | (_| | | (_| |
|      (   )      |           \___\___/ \__,_|_|\__,_|
 \      \_/      /           
   \...__!__.../