A PyTorch implementation of Contrastive Predictive Coding (CPC)

This repository contains a pipeline for training and evaluating contrastive predictive coding (CPC, see e.g. https://arxiv.org/abs/1807.03748) models, which has been implemented using PyTorch.

The present CPC implementation has been (partially) used in the following publication: E. Vaaras, M. Airaksinen, and O. Räsänen, "Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition," in Proc. INTERSPEECH 2022.

If you use the present code or its derivatives, please cite the repository URL and/or the aforementioned publication.

Repository contents

• conf_train_cpc_model.py and conf_train_cpc_model_orig_implementation.py: Example configuration files for training baseline models.
• cpc_data_loader.py: A file containing two example data loaders, one for raw audio and another one that converts raw audio into log-mel features.
• cpc_loss.py: A file containing two different variations of the InfoNCE loss used in CPC models.
• cpc_model.py: A file containing the actual CPC model implementation, i.e. the CPC encoder, autoregressive model, and post-net.
• cpc_test_bench.py: A superscript that can be used to train and evaluate different variations of CPC models and their hyperparameters at once.
• py_conf_file_into_text.py: A script for converting .py configuration files into lists of text that can be used for printing or writing the configuration file contents into a text file.
• train_cpc_model.py: A script for training and evaluating a single CPC model.

Note: By default, the data loaders in cpc_data_loader.py assume that you have a directory named wav_files containing WAV files in the same directory as you have the script cpc_data_loader.py. Of course, you can change this option in the configuration file, e.g. with

params_train_dataset = {'file_dir': <your_directory>}

where <your_directory> is the directory containing WAV files.

Examples of how to use the code

How to train a single CPC model with one hyperparameter configuration:

You can either use the command

python train_cpc_model.py

or

python train_cpc_model.py <configuration_file>

in order to train a single CPC model with one hyperparameter configuration. Using the former of these options requires having a configuration file named conf_train_cpc_model.py in the same directory as the file train_cpc_model.py. In the latter option, <configuration_file> is a .py configuration file containing the hyperparameters you want to use.

How to train multiple CPC models with different hyperparameter configurations:

In order to train multiple CPC models with different hyperparameter configurations, you can use the command

python cpc_test_bench.py <dir_to_configuration_files>

where <dir_to_configuration_files> is a directory containing at least one .py configuration file. This script runs through the CPC model training (train_cpc_model.py) with each of the configuration files located in <dir_to_configuration_files>, one at a time.

How to train the CPC model from the original paper:

Using the command

python train_cpc_model.py conf_train_cpc_model_orig_implementation.py

you can train the CPC model from the original CPC paper with your own WAV files.