Audio-to-Audio
Transformers
Safetensors
English
hearing loss
challenge
signal processing
source separation
audio
NonCausal
Instructions to use cadenzachallenge/ConvTasNet_Viola_NonCausal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cadenzachallenge/ConvTasNet_Viola_NonCausal with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cadenzachallenge/ConvTasNet_Viola_NonCausal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from cadenzachallenge/ConvTasNet_Viola_NonCausal: direct link, hf CLI and curl.
- Browser
- Download file 934 Bytes
-
https://huggingface.co/cadenzachallenge/ConvTasNet_Viola_NonCausal/resolve/main/README.md
- Command line
-
hf download hf://cadenzachallenge/ConvTasNet_Viola_NonCausal/README.md
-
curl -L -o README.md https://huggingface.co/cadenzachallenge/ConvTasNet_Viola_NonCausal/resolve/main/README.md
934 Bytes
metadata
language:
- en
license: apache-2.0
tags:
- hearing loss
- challenge
- signal processing
- source separation
- audio
- audio-to-audio
- NonCausal
Cadenza Challenge: CAD2-Task1
A NonCausal Viola/Others separation model for the CAD2-Task2 baseline system.
- Architecture: ConvTasNet (Kaituo XU) with multichannel support (Alexandre Defossez).
- Parameters:
- B: 256
- C: 2
- H: 512
- L: 20
- N: 256
- P: 3
- R: 3
- X: 8
- audio_channels: 2
- causal: false
- mask_nonlinear: relu
- norm_type: gLN
- training:
- sample_rate: 44100
- samples_per_track: 64
- segment: 5.0
- aggregate: 2
- batch_size: 4
- early_stop: true
- epochs: 200
Dataset
The model was trained using EnsembleSet and CadenzaWoodwind datasets.
How to use
from tasnet import ConvTasNetStereo
model = ConvTasNetStereo.from_pretrained(
"cadenzachallenge/ConvTasNet_Viola_NonCausal"
).cpu()