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data_generator.py
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"""
Defines a class that is used to featurize audio clips, and provide
them to the network for training or testing.
"""
from __future__ import absolute_import, division, print_function
import json
import logging
import numpy as np
import random
import keras.backend as K
from keras.preprocessing.sequence import pad_sequences
from concurrent.futures import ThreadPoolExecutor, wait
from utils import calc_feat_dim, spectrogram_from_file, text_to_int_sequence
from arpabets import arpabet_to_int_sequence
RNG_SEED = 123
logger = logging.getLogger(__name__)
class DataGenerator(object):
def __init__(self, step=10, window=20, max_freq=8000, desc_file=None,
use_arpabets=False, use_durations=False):
"""
Params:
step (int): Step size in milliseconds between windows
window (int): FFT window size in milliseconds
max_freq (int): Only FFT bins corresponding to frequencies between
[0, max_freq] are returned
desc_file (str, optional): Path to a JSON-line file that contains
labels and paths to the audio files. If this is None, then
load metadata right away
"""
self.feat_dim = calc_feat_dim(window, max_freq)
self.feats_mean = np.zeros((self.feat_dim,))
self.feats_std = np.ones((self.feat_dim,))
self.rng = random.Random(RNG_SEED)
if desc_file is not None:
self.load_metadata_from_desc_file(desc_file)
self.step = step
self.window = window
self.max_freq = max_freq
self.use_arpabets = use_arpabets
self.use_durations = use_durations
def featurize(self, audio_clip):
""" For a given audio clip, calculate the log of its Fourier Transform
Params:
audio_clip(str): Path to the audio clip
"""
return spectrogram_from_file(
audio_clip, step=self.step, window=self.window,
max_freq=self.max_freq)
def load_metadata_from_desc_file(self, desc_file, partition='train',
max_duration=10.0):
""" Read metadata from the description file
(possibly takes long, depending on the filesize)
Params:
desc_file (str): Path to a JSON-line file that contains labels and
paths to the audio files
partition (str): One of 'train', 'validation' or 'test'
max_duration (float): In seconds, the maximum duration of
utterances to train or test on
"""
logger.info('Reading description file: {} for partition: {}'
.format(desc_file, partition))
audio_paths, durations, texts, arpabets = [], [], [], []
with open(desc_file) as json_line_file:
for line_num, json_line in enumerate(json_line_file):
try:
spec = json.loads(json_line)
if float(spec['duration']) > max_duration:
continue
audio_paths.append(spec['key'])
durations.append(float(spec['duration']))
texts.append(spec['text'])
if self.use_arpabets:
arpabets.append(spec['arpabet'])
except Exception as e:
# Change to (KeyError, ValueError) or
# (KeyError,json.decoder.JSONDecodeError), depending on
# json module version
logger.warn('Error reading line #{}: {}'
.format(line_num, json_line))
logger.warn(str(e))
if not self.use_arpabets:
arpabets = [''] * len(audio_paths)
if partition == 'train':
self.train_audio_paths = audio_paths
self.train_durations = durations
self.train_texts = texts
self.train_arpabets = arpabets
elif partition == 'validation':
self.val_audio_paths = audio_paths
self.val_durations = durations
self.val_texts = texts
self.val_arpabets = arpabets
elif partition == 'test':
self.test_audio_paths = audio_paths
self.test_durations = durations
self.test_texts = texts
self.test_arpabets = arpabets
else:
raise Exception("Invalid partition to load metadata. "
"Must be train/validation/test")
def load_train_data(self, desc_file, max_duration=10.0):
self.load_metadata_from_desc_file(desc_file, 'train', max_duration)
def load_test_data(self, desc_file):
self.load_metadata_from_desc_file(desc_file, 'test')
def load_validation_data(self, desc_file):
self.load_metadata_from_desc_file(desc_file, 'validation')
@staticmethod
def sort_by_duration(durations, audio_paths, texts, arpabets):
x = sorted(zip(durations, audio_paths, texts, arpabets))
if K.backend() == 'theano':
x.reverse()
return zip(*x)
def normalize(self, feature, eps=1e-14):
return (feature - self.feats_mean) / (self.feats_std + eps)
def prepare_minibatch(self, audio_paths, texts, durations, arpabets):
""" Featurize a minibatch of audio, zero pad them and return a dictionary
Params:
audio_paths (list(str)): List of paths to audio files
texts (list(str)): List of texts corresponding to the audio files
Returns:
dict: See below for contents
"""
assert len(audio_paths) == len(texts),\
"Inputs and outputs to the network must be of the same number"
# Features is a list of (timesteps, feature_dim) arrays
# Calculate the features for each audio clip, as the log of the
# Fourier Transform of the audio
features = [self.featurize(a) for a in audio_paths]
input_lengths = [f.shape[0] for f in features]
max_length = max(input_lengths)
feature_dim = features[0].shape[1]
mb_size = len(features)
# Pad all the inputs so that they are all the same length
x = np.zeros((mb_size, max_length, feature_dim))
y = []
label_lengths = []
for i in range(mb_size):
feat = features[i]
feat = self.normalize(feat) # Center using means and std
x[i, :feat.shape[0], :] = feat
label = text_to_int_sequence(texts[i])
y.append(label)
label_lengths.append(len(label))
y = pad_sequences(y, maxlen=len(max(texts, key=len)), dtype='int32',
padding='post', truncating='post', value=-1)
res = {
'x': x, # (0-padded features of shape(mb_size,timesteps,feat_dim)
'y': y, # list(int) Flattened labels (integer sequences)
'texts': texts, # list(str) Original texts
'input_lengths': input_lengths, # list(int) Length of each input
'label_lengths': label_lengths # list(int) Length of each label
# 'durations' [if use_durations] list(float) Duration of each sample
# 'phonemes'[if use_arpabets] list(int) Flattened arpabet ints
}
if self.use_durations:
res['durations'] = durations
if self.use_arpabets:
arpints, arpaint_lengths = [], []
for i in range(mb_size):
arpaint_seq = arpabet_to_int_sequence(arpabets[i])
arpints.append(arpaint_seq)
arpaint_lengths.append(len(arpaint_seq))
maxlen = len(max(arpints, key=len))
res['phonemes'] = pad_sequences(arpints, maxlen=maxlen,
dtype='int32', padding='post',
truncating='post', value=-1)
res['phoneme_lengths'] = arpaint_lengths
return res
def iterate(self, audio_paths, texts, minibatch_size, durations=[],
arpabets=[], max_iters=None, parallel=True):
if max_iters is not None:
k_iters = max_iters
else:
k_iters = int(np.ceil(len(audio_paths) / minibatch_size))
logger.info("Iters: {}".format(k_iters))
if parallel:
pool = ThreadPoolExecutor(1) # Run a single I/O thread in parallel
future = pool.submit(self.prepare_minibatch,
audio_paths[:minibatch_size],
texts[:minibatch_size],
durations[:minibatch_size],
arpabets[:minibatch_size])
else:
minibatch = self.prepare_minibatch(audio_paths[:minibatch_size],
texts[:minibatch_size],
durations[:minibatch_size],
arpabets[:minibatch_size])
start = minibatch_size
for i in range(k_iters - 1):
if parallel:
wait([future])
minibatch = future.result()
# While the current minibatch is being consumed, prepare the
# next
future = pool.submit(self.prepare_minibatch,
audio_paths[start:start+minibatch_size],
texts[start:start+minibatch_size],
durations[start:start+minibatch_size],
arpabets[start:start+minibatch_size])
yield minibatch
if not parallel:
minibatch = self.prepare_minibatch(
audio_paths[start:start+minibatch_size],
texts[start:start+minibatch_size],
durations[start:start+minibatch_size],
arpabets[start:start+minibatch_size]
)
start += minibatch_size
# Wait on the last minibatch
if parallel:
wait([future])
minibatch = future.result()
yield minibatch
else:
yield minibatch
def iterate_train(self, minibatch_size=16, sort_by_duration=False,
shuffle=True):
if sort_by_duration and shuffle:
shuffle = False
logger.warn("Both sort_by_duration and shuffle were set to True. "
"Setting shuffle to False")
durations, audio_paths, texts, arpabets = (self.train_durations,
self.train_audio_paths,
self.train_texts,
self.train_arpabets)
if shuffle:
temp = zip(durations, audio_paths, texts, arpabets)
self.rng.shuffle(temp)
durations, audio_paths, texts, arpabets = zip(*temp)
if sort_by_duration:
logger.info('Sorting training samples by duration')
if getattr(self, '_sorted_data', None) is None:
self._sorted_data = DataGenerator.sort_by_duration(
durations, audio_paths, texts, arpabets)
durations, audio_paths, texts, arpabets = self._sorted_data
return self.iterate(audio_paths, texts, minibatch_size, durations,
arpabets)
def iterate_test(self, minibatch_size=16):
return self.iterate(self.test_audio_paths, self.test_texts,
minibatch_size, self.test_durations)
def iterate_validation(self, minibatch_size=16):
return self.iterate(self.val_audio_paths, self.val_texts,
minibatch_size, self.val_durations,
self.val_arpabets)
def fit_train(self, k_samples=100):
""" Estimate the mean and std of the features from the training set
Params:
k_samples (int): Use this number of samples for estimation
"""
k_samples = min(k_samples, len(self.train_audio_paths))
samples = self.rng.sample(self.train_audio_paths, k_samples)
feats = [self.featurize(s) for s in samples]
feats = np.vstack(feats)
self.feats_mean = np.mean(feats, axis=0)
self.feats_std = np.std(feats, axis=0)
def reload_norm(self, dataset):
""" Set mean and std of features from previous calculations
Params:
dataset (str)
"""
if dataset == '860-1000':
self.feats_std = np.array([
4.25136062, 3.8713157, 4.27721627, 4.79254968, 5.047769,
5.00917253, 4.92034587, 4.95192179, 4.99958183, 4.98448796,
4.93224872, 4.85590985, 4.78577772, 4.70706027, 4.62677301,
4.54424163, 4.455477, 4.38643766, 4.32992825, 4.28711064,
4.24306676, 4.24044366, 4.23590435, 4.21825687, 4.19820567,
4.17238816, 4.12828632, 3.903265, 3.88530966, 4.10232629,
4.15094822, 4.14674498, 4.13922566, 4.13210467, 4.12067026,
4.10835004, 4.09651096, 4.08038286, 4.06577381, 4.04688416,
4.01817645, 4.02679759, 4.02986556, 4.03453092, 4.04160862,
4.04830856, 4.0602057, 4.0771961, 4.09297194, 4.11034371,
4.11758663, 4.12095657, 4.11906109, 4.1155184, 4.10200893,
4.08276379, 4.08628075, 4.08675451, 4.07840435, 4.06359915,
4.04148782, 4.06030573, 4.06159643, 4.0473447, 4.03310411,
4.02725498, 4.02498171, 4.02632823, 4.02484766, 4.02769822,
4.02489051, 4.02088211, 4.02309526, 4.01872619, 4.01964194,
4.02153504, 4.02851296, 4.02778547, 4.0279664, 4.02255787,
4.00012165, 4.01658932, 3.93528177, 3.89534593, 4.017947,
4.03439452, 4.03349856, 4.03254631, 4.03193693, 4.0297471,
4.02667958, 4.02249605, 4.01419366, 4.01364902, 4.01290134,
4.01051293, 4.0089972, 4.00612032, 4.00165361, 3.98616987,
3.96209925, 3.98299328, 3.99713713, 3.99335162, 3.99078871,
3.98656532, 3.98739388, 3.98590306, 3.99035434, 3.98769832,
3.96287722, 3.97156738, 3.9831056, 3.97919869, 3.9740908,
3.96782821, 3.96331332, 3.95866512, 3.94998412, 3.92881555,
3.90712036, 3.92175492, 3.92782247, 3.92540498, 3.92125062,
3.91851146, 3.91551745, 3.90756256, 3.90717957, 3.90416995,
3.8988367, 3.89860032, 3.89073579, 3.8857745, 3.88896128,
3.88531893, 3.87826128, 3.84122702, 3.80223124, 3.84022503,
3.83359076, 3.85137669, 3.85647565, 3.85543909, 3.85545835,
3.85710792, 3.85664199, 3.85589913, 3.85612751, 3.85606959,
3.84913531, 3.84287049, 3.83940561, 3.84250948, 3.83669538,
3.83200409, 3.83749335, 3.8358794, 3.83973577, 3.83577876,
3.88315269])
self.feats_mean = np.array([
-19.9190417, -17.87074816, -17.18417253, -16.60615722,
-16.47524177, -16.78722456, -17.22669022, -17.37899149,
-17.43706583, -17.56693628, -17.78871635, -18.10035868,
-18.49648794, -18.90612757, -19.25623952, -19.51816016,
-19.7352671, -19.91201681, -20.07744978, -20.23590349,
-20.36163737, -20.49420555, -20.59985973, -20.68975368,
-20.76943646, -20.81912058, -20.83019722, -20.71162806,
-20.69917742, -20.80050996, -20.80537402, -20.81130023,
-20.8206865, -20.84339359, -20.87898781, -20.9239178,
-20.97779635, -21.03484084, -21.11069463, -21.18509355,
-21.23770916, -21.31738037, -21.37270682, -21.41930211,
-21.4509242, -21.47946454, -21.50444787, -21.51825131,
-21.51594888, -21.51341618, -21.50983657, -21.52451875,
-21.55498166, -21.59505743, -21.63528843, -21.67396515,
-21.73275922, -21.79585578, -21.84556578, -21.87028101,
-21.87358228, -21.92915795, -21.98821111, -22.04652423,
-22.10257075, -22.14423208, -22.17747545, -22.21208271,
-22.24781483, -22.28908871, -22.33593842, -22.37691381,
-22.42626383, -22.46079106, -22.48787287, -22.50766501,
-22.53586539, -22.56786281, -22.59582998, -22.62581144,
-22.64886813, -22.71750843, -22.7279599, -22.7583583,
-22.87805837, -22.9381045, -22.98543052, -23.03572058,
-23.09462637, -23.15124978, -23.20831305, -23.26313514,
-23.3142818, -23.3671435, -23.41988972, -23.47336085,
-23.52725449, -23.57844801, -23.63283689, -23.67411968,
-23.69833849, -23.77693613, -23.83653636, -23.87643816,
-23.91009798, -23.94392507, -23.98188049, -24.0217485,
-24.05824361, -24.09093447, -24.1127368, -24.15196192,
-24.18803859, -24.21636283, -24.24457024, -24.27694599,
-24.3077978, -24.33267521, -24.35511158, -24.3535704,
-24.33095828, -24.37981144, -24.41567347, -24.42952345,
-24.42856408, -24.43132484, -24.44428014, -24.46147466,
-24.48292062, -24.50661327, -24.53453117, -24.56691744,
-24.58867348, -24.61431382, -24.6420865, -24.66867143,
-24.70376135, -24.71120825, -24.70337552, -24.74880836,
-24.74934962, -24.8100975, -24.85395296, -24.88296942,
-24.90949419, -24.93485977, -24.96167982, -24.98794161,
-25.01261378, -25.04233288, -25.06206523, -25.0873588,
-25.11614341, -25.1463879, -25.16707626, -25.18893841,
-25.22892228, -25.26932148, -25.31942099, -25.34896047,
-26.51045921])
else:
raise ValueError