pescador_torch.py

import numpy as np
import pescador
import torch.utils.data

np.random.seed(42)


nb_tracks = 100
track_length = 30
excerpt_length = 10
excerpt_hop = 2
batch_size = 5

# define audio tracks as (nb_samples, nb_features)
tracks = [np.random.random((track_length, 1)) for i in range(nb_tracks)]


class TrackData(torch.utils.data.Dataset):
    def __init__(self, tracks):
        self.tracks = tracks
        self.streams = [pescador.Streamer(excerpt_gen, fn) for fn in tracks]
        self.mux = pescador.tuples(
            pescador.StochasticMux(
                self.streams, nb_tracks, rate=None, mode='exhaustive'
            ),
            'X', 'X'
        )

    def __len__(self):
        return len(self.tracks) * (track_length // excerpt_hop) - excerpt_length

    def __iter__(self):
        return self.mux.iterate()

    def __getitem__(self, idx):
        return next(self.mux)


def excerpt_gen(data):
    for i in range(0, data.shape[0] - excerpt_length, excerpt_hop):
        yield dict(X=data[i:i+excerpt_length, :])


dataset = TrackData(tracks)

train_loader = torch.utils.data.DataLoader(
    dataset,
    batch_size=batch_size
)
for batch, (X, y) in enumerate(train_loader):
    print(X.mean())

pescador_vanilla.py

import numpy as np
import pescador

np.random.seed(42)

nb_tracks = 100
track_length = 30
excerpt_length = 10
excerpt_hop = 2
batch_size = 5

# define audio tracks as (nb_samples, nb_features)
tracks = [np.random.random((track_length, 1)) for i in range(nb_tracks)]


# yield excerpts from audio tracks
def excerpt_gen(data):
    for i in range(0, data.shape[0] - excerpt_length, excerpt_hop):
        yield dict(X=data[i:i+excerpt_length, :])


# set up track streamers
streams = [pescador.Streamer(excerpt_gen, track) for track in tracks]
# randomly sample from streamers
mux = pescador.StochasticMux(streams, nb_tracks, rate=None, mode='exhaustive')
buffered_sample_gen = pescador.buffer_stream(mux, batch_size)

# iterate over data
for batch in buffered_sample_gen:
    print(batch['X'].mean())