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UTokyo-SaruLab MOS Prediction System
| Date | Stars |
|---|---|
| 2026-07-24 | 355 |
| 2026-07-25 | 356 |
| 2026-07-28 | 356 |
| 2026-07-30 | 356 |
| 2026-08-06 | 356 |
Today
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0.0
growth rate 0.00%/day
<p align="center">
<img src="docs/image/utmosv2.PNG" alt="utmosv2" width=500>
</p>
<h1 align="center">
UTMOSv2: UTokyo-SaruLab MOS Prediction System
<a href="https://github.com/sarulab-speech/UTMOSv2">
<img width="94%" height="5px" src="docs/image/titleLine.svg">
</a>
</h1>
<p align="center">
🎤✨ Official implementation of ✨🎤<br>
“<a href="http://arxiv.org/abs/2409.09305">The T05 System for The VoiceMOS Challenge 2024:</a><br>
<a href="http://arxiv.org/abs/2409.09305">Transfer Learning from Deep Image Classifier to Naturalness MOS Prediction of High-Quality Synthetic Speech</a>”<br>
🏅🎉 accepted by IEEE Spoken Language Technology Workshop (SLT) 2024. 🎉🏅
</p>
<p align="center">
ꔫ･-･ꔫ･-･ꔫ･-･ꔫ･-･ꔫ･-･ꔫ･-･ꔫ･-･ꔫ
</p>
<p align="center">
✨  UTMOSv2 achieved 1st place in 7 out of 16 metrics  ✨<br>
✨🏆    and 2nd place in the remaining 9 metrics    🏆✨<br>
✨    in the <a href="https://sites.google.com/view/voicemos-challenge/past-challenges/voicemos-challenge-2024">VoiceMOS Challenge 2024</a> Track1!    ✨
</p>
<div align="center">
<a target="_blank" href="https://www.python.org">
<img src="https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue" alt="Python"/>
</a>
</div>
<div align="center">
<a target="_blank" href="https://huggingface.co/spaces/sarulab-speech/UTMOSv2">
<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue" alt="Hugging Face Spaces"/>
</a>
<a target="_blank" href="https://colab.research.google.com/github/sarulab-speech/UTMOSv2/blob/main/quickstart.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
</div>
<div align="center">
<a target="_blank" href="http://arxiv.org/abs/2409.09305">
<img src="https://img.shields.io/badge/arXiv-2409.09305-b31b1b.svg" alt="arXiv"/>
</a>
<a target="_blank" href="https://ieeexplore.ieee.org/document/10832315">
<img src="https://img.shields.io/badge/IEEE%20Xplore-10832315-blue.svg" alt="poster"/>
</a>
<a target="_blank" href="https://github.com/sarulab-speech/UTMOSv2/blob/main/poster.pdf">
<img src="https://img.shields.io/badge/IEEE%20SLT%202024-Poster-blue.svg" alt="poster"/>
</a>
</div>
<br>
<h2 align="left">
<div>🚀 Quick Prediction</div>
<a href="https://github.com/sarulab-speech/UTMOSv2/tree/main?tab=readme-ov-file#---quick-prediction--------">
<img width="85%" height="6px" src="docs/image/line2.svg">
</a>
</h2>
✨ You can easily use the pretrained UTMOSv2 model!
<h3 align="center">
<div>🛠️ Using in your Python code 🛠️</div>
<a href="https://github.com/sarulab-speech/UTMOSv2/tree/doc-user-friendly-api?tab=readme-ov-file#--%EF%B8%8F-using-in-your-python-code-%EF%B8%8F--------">
<img width="70%" height="6px" src="docs/image/line3.svg">
</a>
</h3>
<div align="center">
✨⚡️ With the UTMOSv2 library, you can easily integrate it into your Python code, ⚡️✨<br>
✨ allowing you to quickly create models and make predictions with minimal effort!! ✨
</div>
<br>
If you want to make predictions using the UTMOSv2 library, follow these steps:
1. Install the UTMOSv2 library from GitHub
```bash
uv add git+https://github.com/sarulab-speech/UTMOSv2.git
# If you're using pip:
# pip install git+https://github.com/sarulab-speech/UTMOSv2.git
```
2. Make predictions
- To predict the MOS of a tensor or array already loaded in memory:
```python
import utmosv2
model = utmosv2.create_model(pretrained=True)
# data: torch.Tensor or np.ndarray with shape (batch_size, sequence_length) or (sequence_length,)
# sr: Sampling rate of the input audio data. If not provided, it defaults to 16000 Hz.
mos = model.predict(data=data, sr=16000) # Returns a torch.Tensor or np.ndarray with shape (batch_size,) or (1,)
```
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matched fp:6a8cdcb05fce425d, topic:deep-learning