Cadenza Challenge: CAD2-Task1
A NonCausal Bassoon/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_Bassoon_NonCausal"
).cpu()
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Inference Providers
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This model is not currently available via any of the supported third-party Inference Providers, and
the HF Inference API does not support transformers models with pipeline type audio-to-audio