title	emoji	colorFrom	colorTo	sdk	sdk_version	app_file	pinned
VoiceRestore	🔊	yellow	green	gradio	5.0.0b3	app.py	false

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

Build - using Gradio 🟠

!git lfs install
!git clone https://github.com/jadechoghari/VoiceRestore-demo
%cd VoiceRestore
!pip install -r requirements.txt
!python app.py

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")

Example

Degraded Input:

Degraded Input Audio

Your browser does not support the audio element.

---

Restored (steps=32, cfg=1.0):

Your browser does not support the audio element.

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