recipes.cad2.task1.ConvTasNet.local.musdb18_dataset module

class recipes.cad2.task1.ConvTasNet.local.musdb18_dataset.Compose(transforms)[source]

Bases: object

Composes several augmentation transforms. :param augmentations: list of augmentations to compose.

class recipes.cad2.task1.ConvTasNet.local.musdb18_dataset.MUSDB18Dataset(root: str, sources=None, targets=None, mix_background=False, suffix='.wav', split='train', subset=None, exclude_tracks=None, segment=None, samples_per_track=1, random_segments=False, random_track_mix=False, source_augmentations=<function MUSDB18Dataset.<lambda>>, sample_rate=44100)[source]

Bases: Dataset

MUSDB18 music separation dataset

The dataset consists of 150 full lengths music tracks (~10h duration) of different genres along with their isolated stems:

drums, bass, vocals and others.

Out-of-the-box, asteroid does only support MUSDB18-HQ which comes as uncompressed WAV files. To use the MUSDB18, please convert it to WAV first:

Note

The datasets are hosted on Zenodo and require that users request access, since the tracks can only be used for academic purposes. We manually check this requests.

This dataset asssumes music tracks in (sub)folders where each folder has a fixed number of sources (defaults to 4). For each track, a list of sources and a common suffix can be specified. A linear mix is performed on the fly by summing up the sources

Due to the fact that all tracks comprise the exact same set of sources, random track mixing can be used can be used, where sources from different tracks are mixed together.

Folder Structure:
>>> #train/1/vocals.wav ---------|
>>> #train/1/drums.wav ----------+--> input (mix), output[target]
>>> #train/1/bass.wav -----------|
>>> #train/1/other.wav ---------/
Parameters:

  • root (str) – Root path of dataset

  • sources (list of str, optional) – List of source names that composes the mixture. Defaults to MUSDB18 4 stem scenario: vocals, drums, bass, other.

  • targets (list or None, optional) –

    List of source names to be used as targets. If None, a dict with the 4 stems is returned.

    If e.g [vocals, drums], a tensor with stacked vocals and drums is returned instead of a dict. Defaults to None.

  • suffix (str, optional) – Filename suffix, defaults to .wav.

  • split (str, optional) – Dataset subfolder, defaults to train.

  • subset (list of str, optional) – Selects a specific of list of tracks to be loaded, defaults to None (loads all tracks).

  • segment (float, optional) – Duration of segments in seconds, defaults to None which loads the full-length audio tracks.

  • samples_per_track (int, optional) – Number of samples yielded from each track, can be used to increase dataset size, defaults to 1.

  • random_segments (boolean, optional) – Enables random offset for track segments.

  • boolean (random_track_mix) – enables mixing of random sources from different tracks to assemble mix.

  • source_augmentations (list of callable) – list of augmentation function names, defaults to no-op augmentations (input = output)

  • sample_rate (int, optional) – Samplerate of files in dataset.

root

Root path of dataset

Type:

str

sources

List of source names. Defaults to MUSDB18 4 stem scenario: vocals, drums, bass, other.

Type:

list of str, optional

suffix

Filename suffix, defaults to .wav.

Type:

str, optional

split

Dataset subfolder, defaults to train.

Type:

str, optional

subset

Selects a specific of list of tracks to be loaded, defaults to None (loads all tracks).

Type:

list of str, optional

segment

Duration of segments in seconds, defaults to None which loads the full-length audio tracks.

Type:

float, optional

samples_per_track

Number of samples yielded from each track, can be used to increase dataset size, defaults to 1.

Type:

int, optional

random_segments

Enables random offset for track segments.

Type:

boolean, optional

random_track_mix boolean

enables mixing of random sources from different tracks to assemble mix.

source_augmentations

list of augmentation function names, defaults to no-op augmentations (input = output)

Type:

list of callable

sample_rate

Samplerate of files in dataset.

Type:

int, optional

tracks

List of track metadata

Type:

list of Dict

References

“The 2018 Signal Separation Evaluation Campaign” Stoter et al. 2018.

Notes

This is a modified versions of the MUSDB18 dataset from the version in Asteroid. It extend the Asteroid version to allow for targets `vocals` and `background`. The background is the sum of all sources except vocals. This is useful for training models that separate vocals from background.

dataset_name = 'MUSDB18'
get_infos()[source]

Get dataset infos (for publishing models).

Returns:

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

get_tracks()[source]

Loads input and output tracks

recipes.cad2.task1.ConvTasNet.local.musdb18_dataset.augment_channelswap(audio)[source]

Randomly swap channels of stereo sources

recipes.cad2.task1.ConvTasNet.local.musdb18_dataset.augment_gain(audio, low=0.25, high=1.25)[source]

Applies a random gain to each source between low and high