Official repository for paper "Non-Intrusive Speech Intelligibility Prediction from Discrete Latent Representations".

The paper can be found on arXiv here.

This public repository is a work in progress! Results here bear no resemblance to results in the paper!

We predict the intelligibility of binaural speech signals by first extracting latent representations from raw audio. Then, a lightweight predictor over these latent representations can be trained. This results in improved performance over predicting on spectral features of the audio, despite the feature extractor not being explicitly trained for this task. In certain cases, a single layer is sufficient for strong correlations between the predictions and the ground-truth scores.

This repository contains:

• vqcpc/ - Module for VQCPC model in PyTorch
• stoi/ - Module for Small and SeqPool predictor model in PyTorch
• data.py - File containing various PyTorch custom datasets
• main-vqcpc.py - Script for VQCPC training
• create-latents.py - Script for generating latent dataset from trained VQCPC
• plot-latents.py - Script for visualizing extracted latent representations
• main-stoi.py - Script for STOI predictor training
• main-test.py - Script for evaluating models
• compute-correlations.py - Script for computing metrics for many models
• checkpoints/ - trained checkpoints of VQCPC and STOI predictor models
• config/ - Directory containing various configuration files for experiments
• results/ - Directory containing official results from experiments
• dataset/ - Directory containing metadata files for the dataset
• data-generator/ - Directory containing dataset generation scripts (MATLAB)

All models are implemented in PyTorch. The training scripts are implemented using ptpt - a lightweight framework around PyTorch.

Visualisation of binaural waveform, predicted per-frame STOI, and latent representation:

Begin VQ-CPC training using the configuration defined in config.toml:

python main-vqcpc.py --cfg-path config-path.toml

Other useful arguments:

--resume            # resume from specified checkpoint
--no-save           # do not save training progress (useful for debugging)
--no-cuda           # do not try to access CUDA device (very slow)
--no-amp            # disable automatic mixed precision (if you encounter NaN)
--nb-workers        # number of workers for for data loading (default: 8)
--detect-anomaly    # detect autograd anomalies and terminate if encountered
--seed              # random seed (default: 12345)

Begin latent dataset generation using pre-trained VQCPC model-checkpoint.pt from dataset wav-dataset and output to latent-dataset using configuration defined in config.toml:

python create-latents.py model-checkpoint.pt wav-dataset latent-dataset --cfg-path config.toml

As above, but distributed across n processes with script rank r:

python create-latents.py model-checkpoint.pt wav-dataset latent-dataset --cfg-path config.toml --array-size n --array-rank r

Other useful arguments:

--no-cuda           # do not try to access CUDA device (very slow)
--no-amp            # disable automatic mixed precision (if you encounter NaN)
--no-tqdm           # disable progress bars
--detect-anomaly    # detect autograd anomalies and terminate if encountered
-n                  # alias for `--array-size`
-r                  # alias for `--array-rank`

Begin interactive VQCPC latent visualisation script using pre-trained model model-checkpoint.pt on dataset wav-dataset using configuration defined in config.toml:

python plot-latents.py model-checkpoint.pt wav-dataset --cfg-path config.toml

If you additionally have a pre-trained, per-frame STOI score predictor (not SeqPool predictor) you can specify the checkpoint stoi-checkpoint.pt and additional configuration stoi-config.toml, you can plot per-frame scores alongside the waveform and latent features:

python plot-latents.py model-checkpoint.pt wav-dataset --cfg-path config.toml --stoi stoi-checkpoint.pt --stoi-cfg stoi-config.toml

Other useful arguments:

--no-cuda           # do not try to access CUDA device (very slow)
--no-amp            # disable automatic mixed precision (if you encounter NaN)
--cmap              # define matplotlib colourmap
--style             # define matplotlib style

Begin intelligibility score predictor training script using configuration in config.toml:

python main-stoi.py --cfg-path config.toml

Other useful arguments:

--resume            # resume from specified checkpoint
--no-save           # do not save training progress (useful for debugging)
--no-cuda           # do not try to access CUDA device (very slow)
--no-amp            # disable automatic mixed precision (if you encounter NaN)
--nb-workers        # number of workers for for data loading (default: 8)
--detect-anomaly    # detect autograd anomalies and terminate if encountered
--seed              # random seed (default: 12345)

Begin evaluation of a pre-trained STOI score predictor using checkpoint stoi-checkpoint.pt on dataset dataset-root using configuration in stoi-config.toml:

python main-test.py stoi-checkpoint.pt dataset-root --cfg-path stoi-config.toml

Other useful arguments:

--no-save           # do not save training progress (useful for debugging)
--no-cuda           # do not try to access CUDA device (very slow)
--no-amp            # disable automatic mixed precision (if you encounter NaN)
--no-tqdm           # disable progress bars
--nb-workers        # number of workers for for data loading (default: 8)
--detect-anomaly    # detect autograd anomalies and terminate if encountered
--batch-size        # control dataloader batch size
--seed              # random seed (default: 12345)

Compare results from many results files produced by main-test.py based on dataset ground truth:

python compute-correlations.py ground-truth.csv pred-1.csv ... pred-n.csv --names pred-1 ... pred-n

Examples configurations for all experiments can be found here

We use toml files to define configurations. Each one consists of three sections:

• [trainer]: configuration options for ptpt.TrainerConfig.
• [data]: configuration options for the dataset.
• [vqcpc] or [stoi]: configuration options for the VQCPC and predictor models respectively.

Pretrained checkpoints for all models can be found here

Non-Intrusive Binaural Speech Intelligibility Prediction from Discrete Latent Representations

Alex F. McKinney, Benjamin Cauchi

@misc{mckinney2021nonintrusive,
      title={Non-Intrusive Binaural Speech Intelligibility Prediction from Discrete Latent Representations}, 
      author={Alex F. McKinney and Benjamin Cauchi},
      year={2021},
      eprint={2111.12531},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}