https://github.com/talker93/visqol-python
A pure Python implementation of Google's ViSQOL (Virtual Speech Quality Objective Listener) for objective audio/speech quality assessment.
https://github.com/talker93/visqol-python
audio-analysis audio-codec audio-processing audio-quality mos numba objective-metric perceptual-audio pesq polqa speech-quality tflite visqol
Last synced: 17 days ago
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A pure Python implementation of Google's ViSQOL (Virtual Speech Quality Objective Listener) for objective audio/speech quality assessment.
- Host: GitHub
- URL: https://github.com/talker93/visqol-python
- Owner: talker93
- License: apache-2.0
- Created: 2026-03-23T04:47:41.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-05-26T04:36:13.000Z (18 days ago)
- Last Synced: 2026-05-26T06:28:52.644Z (18 days ago)
- Topics: audio-analysis, audio-codec, audio-processing, audio-quality, mos, numba, objective-metric, perceptual-audio, pesq, polqa, speech-quality, tflite, visqol
- Language: Python
- Homepage: https://pypi.org/project/visqol-python/
- Size: 994 KB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-python-scientific-audio - visqol-python - python) [:package:](https://pypi.org/project/visqol-python/) - Port of Google's ViSQOL audio/speech quality metric (MOS-LQO) that installs without Bazel. (Audio Related Packages)
README
# ViSQOL (Python)
[](https://pypi.org/project/visqol-python/)
[](https://github.com/talker93/visqol-python/actions/workflows/ci.yml)
[](https://pypi.org/project/visqol-python/)
[](LICENSE)
A pure Python implementation of [Google's ViSQOL](https://github.com/google/visqol) (Virtual Speech Quality Objective Listener) 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**: 12/12 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 (polynomial): diff = 0.001057
- Speech mode (lattice TFLite): diff = 0.002341
- **Two speech quality mappers** matching C++ ViSQOL:
- **Lattice (default)** — deep-lattice TFLite network (`--use_lattice_model=true` in C++); requires the optional `[lattice]` extra
- **Polynomial (fallback)** — legacy exponential fit (`--use_lattice_model=false` in C++)
- **Pure Python**: no C/C++ compilation required (the optional `[lattice]` extra adds the Google `ai-edge-litert` TFLite runtime as a binary wheel)
- **Minimal dependencies**: 4 core pip packages (`numpy`, `scipy`, `soundfile`, `libsvm-official`)
- **Optional Numba acceleration**: `pip install visqol-python[accel]` for JIT-compiled Gammatone filterbank (parallel) and a fused NSIM + DP patch matching kernel
- **Optional pyFFTW backend**: `pip install visqol-python[fftw]` routes alignment / xcorr FFTs through FFTW3 — **~16× overall speedup**, RTF 0.036 (vs C++ estimate 0.093)
- **Batch & parallel evaluation**: `measure_batch(parallel=True)` for multi-process execution across CPU cores
- **Fully typed**: PEP 561 `py.typed`, strict mypy, ruff-enforced code style
## Installation
```bash
pip install visqol-python
```
For **C++-default-equivalent speech mode** (deep-lattice TFLite mapper):
```bash
pip install visqol-python[lattice] # requires Python ≥ 3.10
```
For **Numba-accelerated** Gammatone filtering and the fused NSIM + DP kernel:
```bash
pip install visqol-python[accel]
```
For **FFTW3-backed alignment FFTs** via pyFFTW:
```bash
pip install visqol-python[fftw]
```
Install everything (lattice + numba + fftw):
```bash
pip install visqol-python[all]
```
Or install from source:
```bash
git clone https://github.com/talker93/visqol-python.git
cd visqol-python
pip install -e ".[dev]"
```
> **Note on speech mode parity**: Without the `[lattice]` extra, speech mode falls back to the polynomial mapping (equivalent to running C++ ViSQOL with `--use_lattice_model=false`). The polynomial can over-predict MOS by 1–2 points on degraded speech vs the C++ default. Install `[lattice]` whenever you need numbers that line up with the C++ default behaviour (see [issue #1](https://github.com/talker93/visqol-python/issues/1)).
## Quick Start
### Python API
```python
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
```python
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}")
```
### Batch Evaluation
```python
from visqol import VisqolApi
api = VisqolApi()
api.create(mode="audio")
file_pairs = [
("ref1.wav", "deg1.wav"),
("ref2.wav", "deg2.wav"),
("ref3.wav", "deg3.wav"),
]
# Sequential with progress callback
results = api.measure_batch(
file_pairs,
progress_callback=lambda done, total: print(f"{done}/{total}"),
)
# Multi-process parallel (uses all CPU cores)
results = api.measure_batch(file_pairs, parallel=True, max_workers=4)
for pair, result in zip(file_pairs, results):
if isinstance(result, Exception):
print(f"{pair}: FAILED — {result}")
else:
print(f"{pair}: MOS-LQO = {result.moslqo:.4f}")
```
### Command Line
```bash
# 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) |
| `--no_lattice_model` | Speech mode: disable lattice TFLite mapper, use polynomial fallback |
| `--lattice_model` | Custom path to lattice `.tflite` model (speech mode) |
| `--unscaled_speech` | Don't scale polynomial speech MOS to 5.0 (polynomial only) |
| `--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**
- 21 Gammatone frequency bands (50 Hz – 8 000 Hz)
- VAD (Voice Activity Detection) based patch selection
- Quality mapping (choose one):
- **Deep-lattice TFLite (default)** — same mapper as C++ ViSQOL's default `--use_lattice_model=true`; requires `pip install visqol-python[lattice]`
- **Exponential polynomial (fallback)** — same as C++ `--use_lattice_model=false`; used automatically when the lattice runtime is not installed
- Toggle from Python: `api.create(mode="speech", use_lattice_model=False)`
- Toggle from CLI: `--no_lattice_model`
- Best for: speech, VoIP, telephony
## Performance
Measured on Apple M-series, Python 3.13, audio mode on the `guitar48_stereo` 12.5 s conformance case (3-run average):
| Configuration | RTF | Typical Time | Speedup vs pure Python |
|---|---|---|---|
| Pure Python + NumPy/SciPy | 0.58 | ~7 s | 1.0× |
| + `[accel]` (Numba JIT) | 0.067 | ~0.84 s | 8.7× |
| + `[accel] [fftw]` (Numba + FFTW3) | **0.036** | **~0.45 s** | **16×** |
> RTF (Real-Time Factor) < 1.0 means faster than real-time.
> With Numba + pyFFTW the Python implementation runs at **2.6× the C++ estimated speed** (C++ RTF ≈ 0.093).
Stage-level breakdown of the v3.6.0 fully-accelerated path:
| Stage | Time | % |
|---|---|---|
| Gammatone filterbank | 0.179 s | 40% |
| DP Patch matching (fused NSIM kernel) | 0.131 s | 29% |
| Global alignment (pyFFTW rfft/irfft) | 0.091 s | 20% |
| Fine alignment + NSIM | 0.043 s | 10% |
| Other (SPL, postproc, SVR, …) | 0.003 s | < 1% |
## Project Structure
```
visqol-python/
├── visqol/ # Main package
│ ├── __init__.py # Package exports & version
│ ├── api.py # Public API (VisqolApi)
│ ├── 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
│ ├── numba_accel.py # Optional Numba JIT kernels (DP, NSIM, Gammatone)
│ ├── __main__.py # CLI entry point
│ ├── py.typed # PEP 561 type marker
│ └── model/ # Bundled SVR model
│ └── libsvm_nu_svr_model.txt
├── tests/ # Tests & benchmarks (pytest)
│ ├── conftest.py # Shared fixtures & CLI options
│ ├── test_quick.py # Smoke tests (no external data needed)
│ ├── test_conformance.py # Full conformance tests (needs testdata)
│ ├── test_parallel_correctness.py # Numba parallel correctness tests
│ └── bench_*.py # Performance benchmarks
├── .github/workflows/
│ ├── ci.yml # CI: lint + type-check + matrix test (Python × NumPy)
│ └── publish.yml # Auto-publish to PyPI on tag push
├── pyproject.toml # Package metadata & build config
├── CHANGELOG.md
├── CONTRIBUTING.md
├── LICENSE
└── README.md
```
## Conformance Test Results
Tested against the [official C++ ViSQOL v3.3.3](https://github.com/google/visqol) 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 (polynomial) | Speech | 3.3745 | 3.3756 | 0.001057 |
| speech_CA01 (lattice) | Speech | 3.3130 | 3.3153 | 0.002341 |
Both speech values come from running the C++ ViSQOL binary directly with the corresponding `--use_lattice_model` flag, so they represent ground-truth parity targets.
## References
- [Google ViSQOL (C++)](https://github.com/google/visqol) — 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](LICENSE) for details.
This project is a Python port of [Google's ViSQOL](https://github.com/google/visqol), which is also licensed under Apache 2.0.