ConvTasNet Clarinet 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 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: 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_Clarinet_Causal" ).cpu() ```