WV-MOS
MOS score prediction by fine-tuned wav2vec2.0 model
Keywords: MOS-Net, MB-Net, PESQ, STOI, speech quality
Getting started
The package installation was tested with python3.9
pip install git+https://github.com/AndreevP/wvmos
Inference
from wvmos import get_wvmos
model = get_wvmos(cuda=True)
mos = model.calculate_one("path/to/wav/file") # infer MOS score for one audio
mos = model.calculate_dir("path/to/dir/with/wav/files", mean=True) # infer average MOS score across .wav files in directory
Citation and Acknowledgment
This work was done for the deep learning course in Skolteh university by Pavel Andreev, Nikolay Patakin, Oleg Desheulin, Alexander Kagan and Arthur Bulanbaev. More details are described in paper https://arxiv.org/abs/2203.13086
Metadata
Release files for wvmos 1.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| wvmos-1.0.tar.gz | 3.2 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| wvmos-1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.5 kB
Release files / wvmos-1.0.tar.gz
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| Tags | Python 3 |
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