visqol-python 3.3.3

ViSQOL - Virtual Speech Quality Objective Listener (Pure Python)

ViSQOL (Python)

A pure Python implementation of Google's ViSQOL (Virtual Speech Quality Objective Listener) v3.3.3 for objective audio/speech quality assessment.

ViSQOL compares a reference audio signal with a degraded version and outputs a MOS-LQO (Mean Opinion Score - Listening Quality Objective) score on a scale of 1.0 – 5.0.

Features

• Two modes: Audio mode (music/general audio at 48 kHz) and Speech mode (speech at 16 kHz)
• High accuracy: 11/11 conformance tests pass against the official C++ implementation
  • Audio mode: 9/10 tests produce identical MOS scores (diff = 0.000000), 1 test diff = 0.000117
  • Speech mode: diff = 0.006715
• Pure Python: no C/C++ compilation required
• Minimal dependencies: only 4 pip packages (numpy, scipy, soundfile, libsvm-official)
• Faster than real-time: Audio RTF ≈ 0.71x, Speech RTF ≈ 0.38x

Installation

pip install numpy scipy soundfile libsvm-official

Or install as a package:

git clone https://github.com/talker93/visqol-python.git
cd visqol-python
pip install -e .

Quick Start

Python API

from visqol import VisqolApi

# Audio mode (default) - for music and general audio
api = VisqolApi()
api.create(mode="audio")
result = api.measure("reference.wav", "degraded.wav")
print(f"MOS-LQO: {result.moslqo:.4f}")

# Speech mode - for speech signals
api = VisqolApi()
api.create(mode="speech")
result = api.measure("ref_speech.wav", "deg_speech.wav")
print(f"MOS-LQO: {result.moslqo:.4f}")

Using NumPy Arrays

import numpy as np
import soundfile as sf
from visqol import VisqolApi

ref, sr = sf.read("reference.wav")
deg, _  = sf.read("degraded.wav")

api = VisqolApi()
api.create(mode="audio")
result = api.measure_from_arrays(ref, deg, sample_rate=sr)
print(f"MOS-LQO: {result.moslqo:.4f}")

Command Line

# Audio mode (default)
python -m visqol -r reference.wav -d degraded.wav

# Speech mode
python -m visqol -r reference.wav -d degraded.wav --speech_mode

# Verbose output (per-patch details)
python -m visqol -r reference.wav -d degraded.wav -v

CLI options:

Flag	Description
-r, --reference	Path to reference WAV file (required)
-d, --degraded	Path to degraded WAV file (required)
--speech_mode	Use speech mode (16 kHz, polynomial mapping)
--model	Custom SVR model file path (audio mode only)
--search_window	Search window radius (default: 60)
--verbose, -v	Show detailed per-patch results

Output

The measure() method returns a SimilarityResult object with:

Field	Description
moslqo	MOS-LQO score (1.0 – 5.0)
vnsim	Mean NSIM across all patches
fvnsim	Per-frequency-band mean NSIM
fstdnsim	Per-frequency-band std of NSIM
fvdegenergy	Per-frequency-band degraded energy
patch_sims	List of per-patch similarity details

Modes

Audio Mode (default)

• Target sample rate: 48 kHz
• 32 Gammatone frequency bands (50 Hz – 15 000 Hz)
• Quality mapping: SVR (Support Vector Regression) model
• Best for: music, environmental audio, codecs

Speech Mode

• Target sample rate: 16 kHz
• 32 Gammatone frequency bands (50 Hz – 8 000 Hz)
• Quality mapping: exponential polynomial fit
• VAD (Voice Activity Detection) based patch selection
• Best for: speech, VoIP, telephony

Performance

Measured on Apple M-series, Python 3.13:

Mode	Avg RTF	Typical Time
Audio (48 kHz)	0.71x	7 – 12 s per file pair
Speech (16 kHz)	0.38x	~1 s per file pair

RTF (Real-Time Factor) < 1.0 means faster than real-time.

Project Structure

visqol-python/
├── visqol/                    # Main package
│   ├── __init__.py            # Package exports
│   ├── api.py                 # Public API
│   ├── visqol_manager.py      # Pipeline orchestrator
│   ├── visqol_core.py         # Core algorithm
│   ├── audio_utils.py         # Audio I/O & SPL normalization
│   ├── signal_utils.py        # Envelope, cross-correlation
│   ├── analysis_window.py     # Hann window
│   ├── gammatone.py           # ERB + Gammatone filterbank + spectrogram
│   ├── patch_creator.py       # Patch creation (Image + VAD modes)
│   ├── patch_selector.py      # DP-based optimal patch matching
│   ├── alignment.py           # Global alignment via cross-correlation
│   ├── nsim.py                # NSIM similarity metric
│   ├── quality_mapper.py      # SVR & exponential quality mapping
│   └── __main__.py            # CLI entry point
├── model/                     # Bundled SVR model
│   └── libsvm_nu_svr_model.txt
├── tests/                     # Conformance tests
│   ├── test_conformance.py
│   └── test_quick.py
├── setup.py
├── requirements.txt
├── LICENSE
└── README.md

Conformance Test Results

Tested against the official C++ ViSQOL v3.3.3 expected values:

Test Case	Mode	Expected MOS	Python MOS	Δ
strauss_lp35	Audio	1.3889	1.3889	0.000000
steely_lp7	Audio	2.2502	2.2502	0.000000
sopr_256aac	Audio	4.6823	4.6823	0.000000
ravel_128opus	Audio	4.4651	4.4651	0.000000
moonlight_128aac	Audio	4.6843	4.6843	0.000000
harpsichord_96mp3	Audio	4.2237	4.2237	0.000000
guitar_64aac	Audio	4.3497	4.3497	0.000000
glock_48aac	Audio	4.3325	4.3325	0.000000
contrabassoon_24aac	Audio	2.3469	2.3468	0.000117
castanets_identity	Audio	4.7321	4.7321	0.000000
speech_CA01	Speech	3.3745	3.3678	0.006715

References

• Google ViSQOL (C++) — the original implementation this project is ported from
• Hines, A., Gillen, E., Kelly, D., Skoglund, J., Kokaram, A., & Harte, N. (2015). ViSQOLAudio: An Objective Audio Quality Metric for Low Bitrate Codecs. The Journal of the Acoustical Society of America.
• Chinen, M., Lim, F. S., Skoglund, J., Gureev, N., O'Gorman, F., & Hines, A. (2020). ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric. 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX).

License

Apache License 2.0. See LICENSE for details.

This project is a Python port of Google's ViSQOL, which is also licensed under Apache 2.0.