asteroid.data.sms_wsj_dataset module

class asteroid.data.sms_wsj_dataset.SmsWsjDataset(json_path, target, dset, sample_rate=8000, single_channel=True, segment=4.0, nondefault_nsrc=None, normalize_audio=False)

Bases: sphinx.ext.autodoc.importer._MockObject

Dataset class for SMS WSJ source separation.

Parameters:

• json_path (str) – The path to the sms_wsj json file.
• target (str) –

  One of 'source', 'early' or 'image'.

  • 'source' non reverberant clean targets signals.
  • 'early' early reverberation target signals.
  • 'image' reverberant target signals
• dset (str) – train_si284 for train, cv_dev93 for validation and test_eval92 for test
• sample_rate (int, optional) – The sampling rate of the wav files.
• segment (float, optional) – Length of the segments used for training, in seconds. If None, use full utterances (e.g. for test).
• single_channel (bool) – if False all channels are used if True only a random channel is used during training and the first channel during test
• nondefault_nsrc (int, optional) – Number of sources in the training targets.
• normalize_audio (bool) – If True then both sources and the mixture are normalized with the standard deviation of the mixture.

References

“SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition”, Drude et al. 2019

dataset_name = 'SMS_WSJ'

__getitem__(idx)

Gets a mixture/sources pair. :returns: mixture, vstack([source_arrays])

get_infos()

Get dataset infos (for publishing models).

Returns:dict, dataset infos with keys dataset, task and target.