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ConvTasNet Clarinet NonCausal

A NonCausal Clarinet/Others separation model for the CAD2-Task2 baseline system.

Quellehuggingface.co/cadenzachallenge/ConvTasNet_Clarinet_NonCausal/tree/dbc1545e78091ada2cd989ef70d5c3b8effa3713
ArtOtherVersionRevision dbc1545

Modellquelle

Art
Other
Dateigröße
25,2 MB
Version
vRevision dbc1545
Aktualisiert
19. Sept. 2026
Downloads
6
Aufgaben
Audio
Quelle
Hugging Face

Quellenbeschreibung

Cadenza Challenge: CAD2-Task1

A NonCausal Clarinet/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_Clarinet_NonCausal"
).cpu()

Quellen

1 Quelle
Hugging FacePrimärVersion dbc1545e78091ada2cd989ef70d5c3b8effa3713
huggingface.co/cadenzachallenge/ConvTasNet_Clarinet_NonCausal/tree/dbc1545e78091ada2cd989ef70d5c3b8effa3713

Verifiziert 19. Sept.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 25,2 MB · SHA-256 cc7a814261a1…65f8 · Hugging Face

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Quellenauszüge

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Readme19. Sept.huggingface.co/cadenzachallenge/ConvTasNet_Clarinet_NonCausal

--- 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 Clarinet/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_Clarinet_NonCausal" ).cpu() ```

Note19. Sept.huggingface.co/cadenzachallenge/ConvTasNet_Clarinet_NonCausal

Source context: 6 downloads · 0 likes · Pipeline audio-to-audio · Library transformers · Repo cadenzachallenge/ConvTasNet_Clarinet_NonCausal