metadata
library_name: transformers
pipeline_tag: audio-to-audio
tags:
- signal-processing
license: apache-2.0
Dasheng Denoiser
Official PyTorch inference code for the Interspeech 2025 paper:
Efficient Speech Enhancement via Embeddings from Pre-trained Generative Audioencoders
Installation and Usage
uv pip install transformers torch torchaudio einops
import torch
import torchaudio
from transformers import AutoModel
model = AutoModel.from_pretrained("mispeech/dasheng-denoiser", trust_remote_code=True)
model.eval()
# Load audio file (only 16kHz supported!)
audio, sr = torchaudio.load("path/to/audio.wav")
with torch.no_grad(), torch.autocast(device_type='cuda'):
enhanced = model(audio)
torchaudio.save("enhanced_audio.wav", enhanced, sr)
Acknowledgements
We referred to Dasheng and Vocos to implement this.
Citation
@inproceedings{xingwei2025dashengdenoiser,
title={Efficient Speech Enhancement via Embeddings from Pre-trained Generative Audioencoders},
author={Xingwei Sun, Heinrich Dinkel, Yadong Niu, Linzhang Wang, Junbo Zhang, Jian Luan},
booktitle={Interspeech 2025},
year={2025}
}