Introduction to SpeechMOS
SpeechMOS is a versatile tool designed to predict the subjective quality of speech with remarkable simplicity and efficiency. It offers a variety of Mean Opinion Score (MOS) prediction systems, allowing users to assess the naturalness of speech using just two lines of code.
Quick Overview
The primary function of SpeechMOS is to predict how natural or high-quality a piece of audio sounds. It achieves this through advanced deep learning models that can be accessed easily using the torch.hub feature. Users only need a basic understanding of Python and PyTorch to get started.
Here's an example of how easy it is to use:
import torch
import librosa
# Load your audio file
wave, sr = librosa.load("<your_audio>.wav", sr=None, mono=True)
# Load the SpeechMOS model
predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "utmos22_strong", trust_repo=True)
# Get the MOS score
score = predictor(torch.from_numpy(wave).unsqueeze(0), sr)
# The score, e.g., tensor([3.7730]), represents good quality speech.
Features and Usage
Easy Implementation
SpeechMOS can be implemented with minimal setup. There's no need for additional library imports beyond PyTorch and Torchaudio, making it accessible for users with standard Python software setups (Python version 3.8 or higher).
Steps to Use
-
Instantiate a Predictor: Use the model specifier to load the MOS predictor.
import torch predictor = torch.hub.load("tarepan/SpeechMOS:v1.2.0", "<model_specifier>", trust_repo=True) -
Pass Speech Tensor: Prepare your audio data as a tensor and pass it to the predictor.
waves_tensor = torch.rand((2, 16000)) # Two audio clips, each 1 second long with a sample rate of 16,000 score = predictor(waves_tensor, sr=16000) # Example output: tensor([2.0321, 2.0943]) -
Obtain and Evaluate Scores: The returned scores indicate each clip's predicted MOS. For overall evaluation, you can calculate the average score.
average_score = score.mean().item() # Example result: 2.0632
Available Models
The repository hosts implementations of various MOS prediction models. As of now, it offers:
- UTMOS Strong: Identified by the specifier
utmos22_strong, based on Saeki's 2022 research paper (link).
Acknowledgements
SpeechMOS is built upon the strong foundations laid by the UTMOS project. For more detailed insights into the UTMOS framework, users can refer to the original UTMOS repository and the accompanying research paper.
SpeechMOS simplifies the process of evaluating speech quality, making it an invaluable tool for developers and researchers working with speech technologies. With its user-friendly implementation and accurate predictions, it caters to the growing demand for high-quality speech assessment tools in various applications, such as voice synthesis and recognition systems.