Intro
The demucs model in the ICASSP 2024 Cadenza Challenge is an innovative sound separation technique that efficiently separates pristine audio tracks from audio mixtures using deep learning algorithms. demucs models utilize complex neural network structures, including encoder-decoder architectures and attentional mechanisms, to improve the quality and accuracy of the audio during the separation process. The demucs model utilizes complex neural network structures including encoder-decoder architectures and attention mechanisms to improve audio quality and accuracy during the separation process. The model has demonstrated exceptional performance in a variety of areas including post-processing of music production, audio analysis, and music information retrieval, bringing breakthroughs in the field of music technology.
Usage
from modelscope import snapshot_download
model_dir = snapshot_download('monetjoe/hdemucs_high_musdbhq')
Maintenance
git clone [email protected]:monetjoe/hdemucs_high_musdbhq
cd hdemucs_high_musdbhq
Mirror
https://www.modelscope.cn/models/monetjoe/hdemucs_high_musdbhq