prepare_wsjmix, kwargs={ "datapath": hparams["data_folder"], "savepath": hparams["save_folder"], "n_spks": hparams["num_spks"], "skip_prep": hparams["skip_prep"], }, ) # Create dataset objects if hparams["dynamic_mixing"]: if hparams["num_spks"] == 2: from dynamic_mixing import dynamic_mix_data_prep # noqa train_data = dynamic_mix_data_prep(hparams) elif hparams["num_spks"] == 3: from dynamic_mixing import dynamic_mix_data_prep_3mix # noqa train_data = dynamic_mix_data_prep_3mix(hparams) else: raise ValueError( "The specified number of speakers is not supported.") _, valid_data, test_data = dataio_prep(hparams) else: train_data, valid_data, test_data = dataio_prep(hparams) # Brain class initialization separator = Separation( modules=hparams["modules"], opt_class=hparams["optimizer"],
) # adjust the base_folder_dm path hparams["base_folder_dm"] = ( os.path.normpath(hparams["base_folder_dm"]) + "_processed") else: print( "Using the existing processed folder on the same directory as base_folder_dm" ) hparams["base_folder_dm"] = ( os.path.normpath(hparams["base_folder_dm"]) + "_processed") train_data = dynamic_mix_data_prep( tr_csv=hparams["train_data"], data_root_folder=hparams["data_folder"], base_folder_dm=hparams["base_folder_dm"], sample_rate=hparams["sample_rate"], num_spks=hparams["num_spks"], max_training_signal_len=hparams["training_signal_len"], batch_size=hparams["dataloader_opts"]["batch_size"], num_workers=hparams["dataloader_opts"]["num_workers"], ) _, valid_data, test_data = dataio_prep(hparams) else: train_data, valid_data, test_data = dataio_prep(hparams) # Load pretrained model if pretrained_separator is present in the yaml if "pretrained_separator" in hparams: run_on_main(hparams["pretrained_separator"].collect_files) hparams["pretrained_separator"].load_collected() # Brain class initialization