Edit model card

Cadenza Challenge: CAD2-Task1

A Causal Flute/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: true
    • mask_nonlinear: relu
    • norm_type: cLN
  • 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_Flute_Causal"
).cpu()
Downloads last month
168
Safetensors
Model size
6.6M params
Tensor type
F32
·
Inference Examples
Inference API (serverless) does not yet support transformers models for this pipeline type.