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ConvTasNet Oboe Causal

A Causal Oboe/Others separation model for the CAD2-Task2 baseline system.

Sourcehuggingface.co/cadenzachallenge/ConvTasNet_Oboe_Causal/tree/6fd6040f03a99f176af97c45d0b118bde602c8f3
KindOtherVersionRevision 6fd6040

Model source

Kind
Other
File size
25.2 MB
Version
vRevision 6fd6040
Updated
Sep 19, 2026
Downloads
11
Tasks
Audio
Source
Hugging Face

Source description

Cadenza Challenge: CAD2-Task1

A Causal Oboe/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_Oboe_Causal"
).cpu()

Sources

1 source
Hugging FacePrimaryVersion 6fd6040f03a99f176af97c45d0b118bde602c8f3
huggingface.co/cadenzachallenge/ConvTasNet_Oboe_Causal/tree/6fd6040f03a99f176af97c45d0b118bde602c8f3

Verified Sep 19

Model artifacts

1 artifact

model.safetensors

safetensors · 25.2 MB · SHA-256 4558de516b1c…5cf6 · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 19huggingface.co/cadenzachallenge/ConvTasNet_Oboe_Causal

--- language: - en license: apache-2.0 tags: - hearing loss - challenge - signal processing - source separation - audio - audio-to-audio - Causal --- # Cadenza Challenge: CAD2-Task1 A Causal Oboe/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_Oboe_Causal" ).cpu() ```

NoteSep 19huggingface.co/cadenzachallenge/ConvTasNet_Oboe_Causal

Source context: 11 downloads · 0 likes · Pipeline audio-to-audio · Library transformers · Repo cadenzachallenge/ConvTasNet_Oboe_Causal