MultiCMGAN+/+

Code for the paper: "MULTI-CMGAN+/+: LEVERAGING SPEECH QUALITY METRIC PREDICTION FOR SPEECH ENHANCEMENT TRAINED ON REFERENCE-FREE REAL-WORLD DATA" by George Close, William Ravenscroft, Thomas Hain, and Stefan Goetze

Data

Uses data format and dataloading from CHiME-7 UDASE task. See that task for data preperation guide.

Setup

Set variables in __config__.py to point to required training data

For the HuBERT representation, edit HuBERT_wrapper.py to point to the file hubert_base_ls960.pt

Conda environment for training is chime.yaml

Training

To train the framework

python3 train.py hparams/hyperparams_chime_bak_ovr_pesq_1.0.yaml

or use one of the other provided hyperparameter files.

Evaluation

Use the eval_cmgan.py script to evaluate a trained model. See the command line arguments for this script for details.