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build/lib.linux-x86_64-2.7/musicnet/dataset.py
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# -*- coding: utf-8 -*-
#
# Authors: Dmitriy Serdyuk
import itertools
import numpy
from six.moves import range
from itertools import chain
from scipy import fft
from scipy.signal import stft
FS = 44100 # samples/second
DEFAULT_WINDOW_SIZE = 2048 # fourier window size
OUTPUT_SIZE = 128 # number of distinct notes
STRIDE = 512 # samples between windows
WPS = FS / float(512) # windows/second
class MusicNet(object):
def __init__(self, filename, in_memory=True, window_size=4096,
output_size=84, feature_size=1024, sample_freq=11000,
complex_=False, fourier=False, stft=False, fast_load=False,
rng=None, seed=123):
if not in_memory:
raise NotImplementedError
self.filename = filename
self.window_size = window_size
self.output_size = output_size
self.feature_size = feature_size
self.sample_freq = sample_freq
self.complex_ = complex_
self.fourier = fourier
self.stft = stft
self.fast_load = fast_load
if rng is not None:
self.rng = rng
else:
self.rng = numpy.random.RandomState(seed)
self._train_data = {}
self._valid_data = {}
self._test_data = {}
self._loaded = False
self._eval_sets = {}
def splits(self):
with open(self.filename, 'rb') as f:
# This should be fast
all_inds = numpy.load(f).keys()
test_inds = ['2303', '2382', '1819']
valid_inds = ['2131', '2384', '1792',
'2514', '2567', '1876']
train_inds = [ind for ind in all_inds
if ind not in test_inds and ind not in test_inds]
return train_inds, valid_inds, test_inds
@classmethod
def note_to_class(cls, note):
return note - 21
@property
def train_data(self):
if self._train_data == {}:
self.load()
return self._train_data
@property
def valid_data(self):
if self._valid_data == {}:
self.load()
return self._valid_data
@property
def test_data(self):
if self._test_data == {}:
self.load()
return self._test_data
def load(self, filename=None, reload=False):
if filename is None:
filename = self.filename
if self._loaded and not reload:
return
with open(filename, 'rb') as f:
train_inds, valid_inds, test_inds = self.splits()
data_file = numpy.load(f)
if self.fast_load:
train_inds = train_inds[:6]
train_data = {}
for ind in chain(train_inds, valid_inds, test_inds):
train_data[ind] = data_file[ind]
else:
train_data = dict(data_file)
# test set
test_data = {}
for ind in test_inds:
if ind in train_data:
test_data[ind] = train_data.pop(ind)
# valid set
valid_data = {}
for ind in valid_inds:
valid_data[ind] = train_data.pop(ind)
self._train_data = train_data
self._valid_data = valid_data
self._test_data = test_data
def construct_eval_set(self, data, step=128):
n_files = len(data)
pos_per_file = 7500
features = numpy.empty([n_files * pos_per_file, self.window_size])
outputs = numpy.zeros([n_files * pos_per_file, self.output_size])
features_ind = 0
labels_ind = 1
for i, ind in enumerate(data):
print(ind)
audio = data[ind][features_ind]
for j in range(pos_per_file):
if j % 1000 == 0:
print(j)
# start from one second to give us some wiggle room for larger
# segments
index = self.sample_freq + j * step
features[pos_per_file * i + j] = audio[index:
index + self.window_size]
# label stuff that's on in the center of the window
s = int((index + self.window_size / 2))
for label in data[ind][labels_ind][s]:
note = label.data[1]
outputs[pos_per_file * i + j, self.note_to_class(note)] = 1
return features, outputs
@property
def feature_dim(self):
dummy_features = numpy.zeros((1, self.window_size, 1))
dummy_output = numpy.zeros((1, self.output_size))
dummy_features, _ = self.aggregate_raw_batch(
dummy_features, dummy_output)
return dummy_features.shape[1:]
def aggregate_raw_batch(self, features, output):
"""Aggregate batch.
All post processing goes here.
Parameters:
-----------
features : 3D float tensor
Input tensor
output : 2D integer tensor
Output classes
"""
channels = 2 if self.complex_ else 1
features_out = numpy.zeros(
[features.shape[0], self.window_size, channels])
if self.fourier:
if self.complex_:
data = fft(features, axis=1)
features_out[:, :, 0] = numpy.real(data[:, :, 0])
features_out[:, :, 1] = numpy.imag(data[:, :, 0])
else:
data = numpy.abs(fft(features, axis=1))
features_out = data
elif self.stft:
_, _, data = stft(features, nperseg=120, noverlap=60, axis=1)
length = data.shape[1]
n_feats = data.shape[3]
if self.complex_:
features_out = numpy.zeros(
[len(self.train_data), length, n_feats * 2])
features_out[:, :, :n_feats] = numpy.real(data)
features_out[:, :, n_feats:] = numpy.imag(data)
else:
features_out = numpy.abs(data[:, :, 0, :])
else:
features_out = features
return features_out, output
def train_iterator(self):
features = numpy.zeros([len(self.train_data), self.window_size])
while True:
output = numpy.zeros([len(self.train_data), self.output_size])
for j, ind in enumerate(self.train_data):
s = self.rng.randint(
self.window_size / 2,
len(self.train_data[ind][0]) - self.window_size / 2)
data = self.train_data[ind][0][s - self.window_size / 2:
s + self.window_size / 2]
features[j, :] = data
for label in self.train_data[ind][1][s]:
note = label.data[1]
output[j, self.note_to_class(note)] = 1
yield self.aggregate_raw_batch(features[:, :, None], output)
def eval_set(self, set_name):
if not self._eval_sets:
for name in ['valid', 'test']:
data = self.valid_data if name == 'valid' else self.test_data
x, y = self.construct_eval_set(data)
x, y = self.aggregate_raw_batch(x[:, :, None], y)
self._eval_sets[name] = (x, y)
return self._eval_sets[set_name]
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