scoreq 0.0.1

A Python package for advanced speech quality assessment using the SCOREQ model

SCOREQ: Speech Contrastive Regression for Quality Assessment

SCOREQ is a framework for speech quality assessment based on pre-training the encoder with the SCOREQ loss.

This repo provides four speech quality metrics trained with the SCOREQ framework.

Domain	Usage Mode	Prediction
Natural speech	No-reference	Mean Opinion Score
Natural speech	Non-matching reference, full-reference	Euclidean distance clean speech
Synthetic speech	No-reference	Mean Opinion Score
Synthetic speech	Non-matching reference	Euclidean distance clean speech

Installation

SCOREQ is hosted on PyPi. It can be installed in your Python environment with the following command

pip install scoreq

The expected sampling rate is 16 kHz. The script automatically resamples audio with different sampling rates. SCOREQ models accept variable input length.

First run

The PyTorch weights are hosted on Zenodo. The first run might be slower due to model download.

Using SCOREQ

SCOREQ can be used in 2 modes and for 2 domains by setting the arguments data_domain and mode.

Using SCOREQ from the command line

Domain	Usage Mode	CLI
Natural speech	No-reference	python -m scoreq data_domain natural mode nr /path/to/test_audio
Natural speech	Non-matching reference, full-reference	python -m scoreq data_domain natural mode ref /path/to/test_audio --ref_path /path/to/ref_audio
Synthetic speech	No-reference	python -m scoreq data_domain synthetic mode nr /path/to/test_audio
Synthetic speech	Non-matching reference	python -m scoreq data_domain synthetic mode ref /path/to/test_audio --ref_path /path/to/ref_audio

Using SCOREQ inside Python

Inside python you first need to import the package. Examples using wav files provided in the data directory.

import scoreq

# Predict quality of natural speech in NR mode
nr_scoreq = scoreq.Scoreq(data_domain='natural', mode='nr')
pred_mos = nr_scoreq.predict(test_path='./data/opus.wav', ref_path=None)

# Predict quality of natural speech in REF mode
ref_scoreq = scoreq.Scoreq(data_domain='natural', mode='ref')
pred_distance = ref_scoreq.predict(test_path='./data/opus.wav', ref_path='./data/ref.wav')

# Predict quality of synthetic speech in NR mode
nr_scoreq = scoreq.Scoreq(data_domain='synthetic', mode='nr')
pred_mos = nr_scoreq.predict(test_path='./data/opus.wav', ref_path=None)

# Predict quality of synthetic speech in REF mode
ref_scoreq = scoreq.Scoreq(data_domain='synthetic', mode='ref')
pred_distance = ref_scoreq.predict(test_path='./data/opus.wav', ref_path='./data/ref.wav')

Other

We provide the best model for each domain-mode pair.

Use mode=ref for both non-matching reference or full-reference. This is affected by the clean speech used as input.

If you pass the clean counterpart, the metric will run in full-reference mode. If you pass any clean speech, the metric will run in non-matching reference mode.

Full-reference mode is expected to be used only for natural speech, where the clean copy is available.

SCOREQ learns a distance and it expects clean speech as non-matching reference. The model has not been evaluated for other non-matching references.

Paper (available soon)

The SCOREQ code is licensed under MIT license. Dependencies of the project are available under separate license terms.

Copyright © 2024 Alessandro Ragano