dynamic_source_separator.py

import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin
from huggingface_hub import ModelCard

from tasnet import ConvTasNetStereo


class DynamicSourceSeparator(torch.nn.Module, PyTorchModelHubMixin):
    def __init__(self, pre_trained_models):
        super(DynamicSourceSeparator, self).__init__()
        self.models = nn.ModuleDict(pre_trained_models)

    def forward(self, mixture, indicator):
        separated_sources = {}
        for instrument, active in indicator.items():
            if active:
                model = self.models[instrument]
                est_source = model(mixture)
                separated_sources[instrument] = est_source[:, 0, :, :]
            else:
                separated_sources[instrument] = torch.zeros_like(mixture)
        return separated_sources