asteroid.data.librimix_dataset module¶

class asteroid.data.librimix_dataset.LibriMix(csv_dir, task='sep_clean', sample_rate=16000, n_src=2, segment=3, return_id=False)[source]¶

Bases: sphinx.ext.autodoc.importer._MockObject

Dataset class for LibriMix source separation tasks.

Parameters:

csv_dir (str) – The path to the metadata file.
task (str) –
One of 'enh_single', 'enh_both', 'sep_clean' or 'sep_noisy' :
- 'enh_single' for single speaker speech enhancement.
- 'enh_both' for multi speaker speech enhancement.
- 'sep_clean' for two-speaker clean source separation.
- 'sep_noisy' for two-speaker noisy source separation.
sample_rate (int) – The sample rate of the sources and mixtures.
n_src (int) – The number of sources in the mixture.
segment (int, optional) – The desired sources and mixtures length in s.

References: [1] “LibriMix: An Open-Source Dataset for Generalizable Speech Separation”, Cosentino et al. 2020.

dataset_name = 'LibriMix'[source]¶

classmethod loaders_from_mini(batch_size=4, **kwargs)[source]¶

Downloads MiniLibriMix and returns train and validation DataLoader.

Parameters:	batch_size (int) – Batch size of the Dataloader. Only DataLoader param. To have more control on Dataloader, call mini_from_download and instantiate the DatalLoader. **kwargs – keyword arguments to pass the LibriMix, see __init__. The kwargs will be fed to both the training set and validation set.
Returns:	train_loader, val_loader – training and validation DataLoader out of LibriMix Dataset.

Examples

>>> from asteroid.data import LibriMix
>>> train_loader, val_loader = LibriMix.loaders_from_mini(
>>>     task='sep_clean', batch_size=4
>>> )

classmethod mini_from_download(**kwargs)[source]¶

Downloads MiniLibriMix and returns train and validation Dataset. If you want to instantiate the Dataset by yourself, call mini_download that returns the path to the path to the metadata files.

Parameters:	**kwargs – keyword arguments to pass the LibriMix, see __init__. The kwargs will be fed to both the training set and validation set
Returns:	train_set, val_set – training and validation instances of LibriMix (data.Dataset).

Examples

>>> from asteroid.data import LibriMix
>>> train_set, val_set = LibriMix.mini_from_download(task='sep_clean')

static mini_download()[source]¶

Downloads MiniLibriMix from Zenodo in current directory

Returns:	The path to the metadata directory.

get_infos()[source]¶

Get dataset infos (for publishing models).

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