Metadata-Version: 2.4
Name: whisqa
Version: 0.1.4
Summary: Whisper-based non-intrusive speech quality assessment
Project-URL: Homepage, https://github.com/jwr1995/whisqa
Project-URL: Repository, https://github.com/jwr1995/whisqa
Author: William Ravenscroft, George Close
Maintainer-email: William Ravenscroft <jwravenscroft@gmail.com>
License: MIT License
        
        Copyright (c)  2026 George Close
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
Keywords: MOS,assessment,audio,quality,speech,whisper
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Requires-Dist: numpy>=1.24
Requires-Dist: soundfile>=0.12
Requires-Dist: torch>=2.0
Requires-Dist: torchaudio>=2.0
Requires-Dist: transformers>=4.35
Provides-Extra: dev
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: pytest-mock; extra == 'dev'
Description-Content-Type: text/markdown

# whisqa: a PyPI package for the WhiSQA Non-Intrusive Speech Quality Prediction Model

[![PyPI](https://img.shields.io/pypi/v/whisqa)](https://pypi.org/project/whisqa/)

This project is a fork of the original WhiSQA project repackaged for PyPI.

WhiSQA is a non-intrusive MOS (ITU-T P.835) speech quality predictor.

Original repo: https://github.com/leto19/WhiSQA

## Improvements

- **`pip install whisqa`** — fully installable package; no manual cloning or dependency wrangling.
- **Bundled weights** — the frozen Whisper encoder weights (~350 MB per checkpoint) are stripped down to head-only files of 11 MB and 13 MB that ship inside the wheel. The [strip script](scripts/strip_whisper_weights.py) is kept in the repo for full reproducibility.
- **Flexible input** — `predict()` accepts file paths, NumPy arrays, and Torch tensors, making it easy to integrate into existing pipelines without writing audio to disk.
- **Auto-resampling** — any sample rate is accepted; audio is resampled to 16 kHz automatically with a suppressible warning.
- **CLI** — score a file from the terminal with `whisqa speech.wav`.
- **Simple API** — `predict()` and `load_model()` with type hints, docstrings, and unit tests.

## Install

```bash
pip install whisqa
```

> **First-run note:** `load_model()` downloads `openai/whisper-small` (~240 MB) from HuggingFace Hub and caches it in `~/.cache/huggingface/`. The WhiSQA head weights ship inside the package and require no download.

## Usage

```python
import whisqa

# File path — WAV or any format supported by libsndfile
whisqa.predict("speech.wav")
# → {'mos': 3.82}

# NumPy array
import numpy as np
whisqa.predict(np.zeros(16000, dtype=np.float32), sample_rate=16000)

# Torch tensor — drop straight in from your existing pipeline
import torch
whisqa.predict(torch.zeros(1, 16000), sample_rate=16000)

# All five P.835 dimensions
whisqa.predict("speech.wav", model_type="multi")
# → {'mos': 3.82, 'noisiness': 4.10, 'coloration': 3.55, 'discontinuity': 4.20, 'loudness': 3.90}

# Efficient repeated inference — load once, score many
model = whisqa.load_model("single")
for f in my_files:
    print(whisqa.predict(f, model=model))
```

```bash
# CLI
whisqa speech.wav
whisqa speech.wav --model multi
whisqa speech.wav --no-warn-resample
```

## Citation

[![arXiv](https://img.shields.io/badge/arXiv-2508.02210-b31b1b.svg)](https://arxiv.org/abs/2508.02210)

If you use WhiSQA in your work, please cite the original authors:

```bibtex
@inproceedings{close2025whisqa,
  title     = {{WhiSQA}: Non-Intrusive Speech Quality Prediction Using {Whisper} Encoder Features},
  author    = {Close, George and Hong, Kris Y. and Hain, Thomas and Goetze, Stefan},
  booktitle = {Speech and Computer -- 27th International Conference, {SPECOM} 2025,
               Szeged, Hungary, October 13--15, 2025, Proceedings, Part {I}},
  editor    = {Karpov, Alexey and Gosztolya, G{\'a}bor},
  series    = {Lecture Notes in Computer Science},
  volume    = {16187},
  pages     = {39--51},
  publisher = {Springer},
  year      = {2025},
  doi       = {10.1007/978-3-032-07956-5_3},
}
```
