VoiceRestore: Flow-Matching Transformers for Speech Recording Quality Restoration
VoiceRestore is a cutting-edge speech restoration model designed to significantly enhance the quality of degraded voice recordings. Leveraging flow-matching transformers, this model excels at addressing a wide range of audio imperfections commonly found in speech, including background noise, reverberation, distortion, and signal loss.
It is based on this repo & demo of audio restorations: VoiceRestore
Usage - using Transformers 🤗
!git lfs install
!git clone https://huggingface.co/jadechoghari/VoiceRestore
%cd VoiceRestore
!pip install -r requirements.txt
from transformers import AutoModel
# path to the model folder (on colab it's as follows)
checkpoint_path = "/content/VoiceRestore"
model = AutoModel.from_pretrained(checkpoint_path, trust_remote_code=True)
model("test_input.wav", "test_output.wav")
#add short=False if audio is > 10 seconds
model("long.mp3", "long_output.mp3", short=False)
Example
Degraded Input:
Degraded Input Audio
Restored (steps=32, cfg=1.0):
Restored audio - 16 steps, strength 0.5:
Key Features
- Universal Restoration: The model can handle any level and type of voice recording degradation. Pure magic.
- Easy to Use: Simple interface for processing degraded audio files.
- Pretrained Model: Includes a 301 million parameter transformer model with pre-trained weights. (Model is still in the process of training, there will be further checkpoint updates)
Model Details
- Architecture: Flow-matching transformer
- Parameters: 300M+ parameters
- Input: Degraded speech audio (various formats supported)
- Output: Restored speech
Limitations and Future Work
- Current model is optimized for speech; may not perform optimally on music or other audio types.
- Ongoing research to improve performance on extreme degradations.
- Future updates may include real-time processing capabilities.
Citation
If you use VoiceRestore in your research, please cite our paper:
@article{kirdey2024voicerestore,
title={VoiceRestore: Flow-Matching Transformers for Speech Recording Quality Restoration},
author={Kirdey, Stanislav},
journal={arXiv},
year={2024}
}
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Based on the E2-TTS implementation by Lucidrains
- Special thanks to the open-source community for their invaluable contributions.
- Credits: This repository is based on the E2-TTS implementation by Lucidrains