Predict subjective speech score with only 2 lines of code, with various MOS prediction systems.
predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True)
score = predictor(wave, sr)
# tensor([3.7730]), good quality speech!Predict naturalness (Naturalness Mean-Opinion-Score) of your audio by UTMOS:
import torch
import librosa
wave, sr = librosa.load("<your_audio>.wav", sr=None, mono=True)
predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True)
score = predictor(torch.from_numpy(wave).unsqueeze(0), sr)
# tensor([3.7730])SpeechMOS use torch.hub built-in model loader, so no needs of library import😉
(As general dependencies, SpeechMOS requires Python=>3.8, torch and torchaudio.)
First, instantiate a MOS predictor with model specifier string:
import torch
predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "<model_specifier>", trust_repo=True)Then, pass tensor of speeches :: (Batch, Time):
waves_tensor = torch.rand((2, 16000)) # Two speeches, each 1 sec (sr=16,000)
score = predictor(waves_tensor, sr=16000)
# tensor([2.0321, 2.0943])Returned scores :: (Batch,) are each speech's predicted MOS.
If you hope MOS average over speeches (e.g. for TTS model evaluation), just average them:
average_score = score.mean().item()
# 2.0632This repository is reimplementation collection of various MOS prediction systems.
Currently we provide below models:
| Model | specifier | paper |
|---|---|---|
| UTMOS strong | utmos22_strong |
Saeki (2022) |
- UTMOS