Repository for paper "Non-intrusive speech intelligibility prediction from discrete latent representations"

Overview

Non-Intrusive Speech Intelligibility Prediction from Discrete Latent Representations

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

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

Usage

VQ-CPC Training

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)

Latent Dataset Generation

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`

Latent Plotting

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

STOI Predictor Training

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)

Predictor Evaluation

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)

Overall Evaluation

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

Configuration

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.

Checkpoints

Pretrained checkpoints for all models can be found here

Citation

TODO: add citation once paper published / arXiv-ed :)

Owner
Alex McKinney
Final-year student at Durham University. Interested in generative models and unsupervised representation learning.
Alex McKinney
PyTorch implementation of Off-policy Learning in Two-stage Recommender Systems

Off-Policy-2-Stage This repo provides a PyTorch implementation of the MovieLens experiments for the following paper: Off-policy Learning in Two-stage

Jiaqi Ma 25 Dec 12, 2022
Attack classification models with transferability, black-box attack; unrestricted adversarial attacks on imagenet

Attack classification models with transferability, black-box attack; unrestricted adversarial attacks on imagenet, CVPR2021 安全AI挑战者计划第六期:ImageNet无限制对抗攻击 决赛第四名(team name: Advers)

51 Dec 01, 2022
HyperPose is a library for building high-performance custom pose estimation applications.

HyperPose is a library for building high-performance custom pose estimation applications.

TensorLayer Community 1.2k Jan 04, 2023
Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images

Lung Segmentation (2D) Repository features UNet inspired architecture used for segmenting lungs on chest X-Ray images. Demo See the application of the

163 Sep 21, 2022
这是一个unet-pytorch的源码,可以训练自己的模型

Unet:U-Net: Convolutional Networks for Biomedical Image Segmentation目标检测模型在Pytorch当中的实现 目录 性能情况 Performance 所需环境 Environment 注意事项 Attention 文件下载 Downl

Bubbliiiing 567 Jan 05, 2023
Source code for the BMVC-2021 paper "SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation".

SimReg: A Simple Regression Based Framework for Self-supervised Knowledge Distillation Source code for the paper "SimReg: Regression as a Simple Yet E

9 Oct 15, 2022
Neural Scene Flow Fields using pytorch-lightning, with potential improvements

nsff_pl Neural Scene Flow Fields using pytorch-lightning. This repo reimplements the NSFF idea, but modifies several operations based on observation o

AI葵 178 Dec 21, 2022
Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec

Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec This repo

Building and Urban Data Science (BUDS) Group 5 Dec 02, 2022
QueryFuzz implements a metamorphic testing approach to test Datalog engines.

Datalog is a popular query language with applications in several domains. Like any complex piece of software, Datalog engines may contain bugs. The mo

34 Sep 10, 2022
Joint Versus Independent Multiview Hashing for Cross-View Retrieval[J] (IEEE TCYB 2021, PyTorch Code)

Thanks to the low storage cost and high query speed, cross-view hashing (CVH) has been successfully used for similarity search in multimedia retrieval. However, most existing CVH methods use all view

4 Nov 19, 2022
source code for https://arxiv.org/abs/2005.11248 "Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics"

Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics This work will be published in Nature Biomedical

International Business Machines 71 Nov 15, 2022
PyArmadillo: an alternative approach to linear algebra in Python

PyArmadillo is a linear algebra library for the Python language, with an emphasis on ease of use.

Terry Zhuo 58 Oct 11, 2022
MILK: Machine Learning Toolkit

MILK: MACHINE LEARNING TOOLKIT Machine Learning in Python Milk is a machine learning toolkit in Python. Its focus is on supervised classification with

Luis Pedro Coelho 610 Dec 14, 2022
Recognize Handwritten Digits using Deep Learning on the browser itself.

MNIST on the Web An attempt to predict MNIST handwritten digits from my PyTorch model from the browser (client-side) and not from the server, with the

Harjyot Bagga 7 May 28, 2022
This library contains a Tensorflow implementation of the paper Stability Analysis of Unfolded WMMSE for Power Allocation

UWMMSE-stability Tensorflow implementation of Stability Analysis of UWMMSE Overview This library contains a Tensorflow implementation of the paper Sta

Arindam Chowdhury 1 Nov 16, 2022
Incorporating Transformer and LSTM to Kalman Filter with EM algorithm

Deep learning based state estimation: incorporating Transformer and LSTM to Kalman Filter with EM algorithm Overview Kalman Filter requires the true p

zshicode 57 Dec 27, 2022
Rethinking the Importance of Implementation Tricks in Multi-Agent Reinforcement Learning

RIIT Our open-source code for RIIT: Rethinking the Importance of Implementation Tricks in Multi-AgentReinforcement Learning. We implement and standard

405 Jan 06, 2023
Official Repository for our ECCV2020 paper: Imbalanced Continual Learning with Partitioning Reservoir Sampling

Imbalanced Continual Learning with Partioning Reservoir Sampling This repository contains the official PyTorch implementation and the dataset for our

Chris Dongjoo Kim 40 Sep 18, 2022
Official re-implementation of the Calibrated Adversarial Refinement model described in the paper Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Official re-implementation of the Calibrated Adversarial Refinement model described in the paper Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Elias Kassapis 31 Nov 22, 2022
A fast Protein Chain / Ligand Extractor and organizer.

Are you tired of using visualization software, or full blown suites just to separate protein chains / ligands ? Are you tired of organizing the mess o

Amine Abdz 9 Nov 06, 2022