This is the official implementation of "DNSMOS Pro: A Reduced-Size DNN for Probabilistic MOS of Speech". DNSMOS Pro is a model that takes as input a speech clip, and outputs a Gaussian mean opinion score (MOS) distribution.
Authors: Fredrik Cumlin, Xinyu Liang
Emails: fcumlin@gmail.com, hopeliang990504@gmail.com
There are three pretrained DNSMOS Pro ready to be used, trained on three datasets respectively. For inference, one can do the following (all paths are relative to this directory):
import numpy as np
import torch
import utils # Python file containing the STFT.
model = torch.jit.load('runs/test_nisqa/model_best.pt', map_location=torch.device('cpu'))
samples = np.ones(160_000)
# Defaults in `utils.stft` correspond to training values.
spec = torch.FloatTensor(utils.stft(samples))
with torch.no_grad():
prediction = model(spec[None, None, ...])
mean = prediction[:, 0]
variance = prediction[:, 1]
print(f'{mean=}, {variance=}')
The mean can be used as a scalar prediction of MOS. Recommended input duration is 10 s, and should be of 16 kHz sample rate.
Installation with pip:
pip install -r requirements.txt
pip install torch==2.1.0+cu118 --index-url https://download.pytorch.org/whl/cu118
The framework is Gin configurable; specifying model and dataset is done with a Gin config. See examples in configs/*.gin.
Example launch:
python train.py --gin_path "configs/vcc2018.gin" --save_path "runs/VCC2018"