from dynamic_source_separator import DynamicSourceSeparator

model = DynamicSourceSeparator.from_pretrained(
    "cadenzachallenge/Dynamic_Source_Separator_Causal"
).cpu()

Description

Audio source separation model used in Sytem T002 for Cadenza2 Task2 Challenge

The model is a finetune of the 8 ConvTasNet models from the Task2 baseline. The training optimised the estimated sources and the recosntructed mixture

$$ Loss = \sum_{}^{Sources}(L_1(estimated~~source, ref~~source)) + L_1(reconstructed~~mixture, original~~mixture) $$

Python

def dynamic_masked_loss(mixture, separated_sources, ground_truth_sources, indicator):
    # Reconstruction Loss
    reconstruction = sum(separated_sources.values())
    reconstruction_loss = nn.L1Loss()(reconstruction, mixture)
    # Separation Loss
    separation_loss = 0
    for instrument, active in indicator.items():
        if active:
            separation_loss += nn.L1Loss()(
                separated_sources[instrument], ground_truth_sources[instrument]
            )
    return reconstruction_loss + separation_loss

Model and T002 recipe are shared in Clarity toolkit