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data_generator.py
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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.
"""
import json
import numpy as np
import random
from python_speech_features import mfcc
import librosa
import scipy.io.wavfile as wav
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from utils import calc_feat_dim, spectrogram_from_file, text_to_int_sequence
from utils import conv_output_length
RNG_SEED = 123
class AudioGenerator():
def __init__(self, step=10, window=20, max_freq=8000, mfcc_dim=13,
minibatch_size=20, desc_file=None, spectrogram=True, max_duration=10.0,
sort_by_duration=False):
"""
Params:
step (int): Step size in milliseconds between windows (for spectrogram ONLY)
window (int): FFT window size in milliseconds (for spectrogram ONLY)
max_freq (int): Only FFT bins corresponding to frequencies between
[0, max_freq] are returned (for spectrogram ONLY)
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.mfcc_dim = mfcc_dim
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.cur_train_index = 0
self.cur_valid_index = 0
self.cur_test_index = 0
self.max_duration=max_duration
self.minibatch_size = minibatch_size
self.spectrogram = spectrogram
self.sort_by_duration = sort_by_duration
def get_batch(self, partition):
""" Obtain a batch of train, validation, or test data
"""
if partition == 'train':
audio_paths = self.train_audio_paths
cur_index = self.cur_train_index
texts = self.train_texts
elif partition == 'valid':
audio_paths = self.valid_audio_paths
cur_index = self.cur_valid_index
texts = self.valid_texts
elif partition == 'test':
audio_paths = self.test_audio_paths
cur_index = self.test_valid_index
texts = self.test_texts
else:
raise Exception("Invalid partition. "
"Must be train/validation")
features = [self.normalize(self.featurize(a)) for a in
audio_paths[cur_index:cur_index+self.minibatch_size]]
# calculate necessary sizes
max_length = max([features[i].shape[0]
for i in range(0, self.minibatch_size)])
max_string_length = max([len(texts[cur_index+i])
for i in range(0, self.minibatch_size)])
# initialize the arrays
X_data = np.zeros([self.minibatch_size, max_length,
self.feat_dim*self.spectrogram + self.mfcc_dim*(not self.spectrogram)])
labels = np.ones([self.minibatch_size, max_string_length]) * 28
input_length = np.zeros([self.minibatch_size, 1])
label_length = np.zeros([self.minibatch_size, 1])
for i in range(0, self.minibatch_size):
# calculate X_data & input_length
feat = features[i]
input_length[i] = feat.shape[0]
X_data[i, :feat.shape[0], :] = feat
# calculate labels & label_length
label = np.array(text_to_int_sequence(texts[cur_index+i]))
labels[i, :len(label)] = label
label_length[i] = len(label)
# return the arrays
outputs = {'ctc': np.zeros([self.minibatch_size])}
inputs = {'the_input': X_data,
'the_labels': labels,
'input_length': input_length,
'label_length': label_length
}
return (inputs, outputs)
def shuffle_data_by_partition(self, partition):
""" Shuffle the training or validation data
"""
if partition == 'train':
self.train_audio_paths, self.train_durations, self.train_texts = shuffle_data(
self.train_audio_paths, self.train_durations, self.train_texts)
elif partition == 'valid':
self.valid_audio_paths, self.valid_durations, self.valid_texts = shuffle_data(
self.valid_audio_paths, self.valid_durations, self.valid_texts)
else:
raise Exception("Invalid partition. "
"Must be train/validation")
def sort_data_by_duration(self, partition):
""" Sort the training or validation sets by (increasing) duration
"""
if partition == 'train':
self.train_audio_paths, self.train_durations, self.train_texts = sort_data(
self.train_audio_paths, self.train_durations, self.train_texts)
elif partition == 'valid':
self.valid_audio_paths, self.valid_durations, self.valid_texts = sort_data(
self.valid_audio_paths, self.valid_durations, self.valid_texts)
else:
raise Exception("Invalid partition. "
"Must be train/validation")
def next_train(self):
""" Obtain a batch of training data
"""
while True:
ret = self.get_batch('train')
self.cur_train_index += self.minibatch_size
if self.cur_train_index >= len(self.train_texts) - self.minibatch_size:
self.cur_train_index = 0
self.shuffle_data_by_partition('train')
yield ret
def next_valid(self):
""" Obtain a batch of validation data
"""
while True:
ret = self.get_batch('valid')
self.cur_valid_index += self.minibatch_size
if self.cur_valid_index >= len(self.valid_texts) - self.minibatch_size:
self.cur_valid_index = 0
self.shuffle_data_by_partition('valid')
yield ret
def next_test(self):
""" Obtain a batch of test data
"""
while True:
ret = self.get_batch('test')
self.cur_test_index += self.minibatch_size
if self.cur_test_index >= len(self.test_texts) - self.minibatch_size:
self.cur_test_index = 0
yield ret
def load_train_data(self, desc_file='train_corpus.json'):
self.load_metadata_from_desc_file(desc_file, 'train')
self.fit_train()
if self.sort_by_duration:
self.sort_data_by_duration('train')
def load_validation_data(self, desc_file='valid_corpus.json'):
self.load_metadata_from_desc_file(desc_file, 'validation')
if self.sort_by_duration:
self.sort_data_by_duration('valid')
def load_test_data(self, desc_file='test_corpus.json'):
self.load_metadata_from_desc_file(desc_file, 'test')
def load_metadata_from_desc_file(self, desc_file, partition):
""" Read metadata from a JSON-line 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'
"""
audio_paths, durations, texts = [], [], []
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']) > self.max_duration:
continue
audio_paths.append(spec['key'])
durations.append(float(spec['duration']))
texts.append(spec['text'])
except Exception as e:
# Change to (KeyError, ValueError) or
# (KeyError,json.decoder.JSONDecodeError), depending on
# json module version
print('Error reading line #{}: {}'
.format(line_num, json_line))
if partition == 'train':
self.train_audio_paths = audio_paths
self.train_durations = durations
self.train_texts = texts
elif partition == 'validation':
self.valid_audio_paths = audio_paths
self.valid_durations = durations
self.valid_texts = texts
elif partition == 'test':
self.test_audio_paths = audio_paths
self.test_durations = durations
self.test_texts = texts
else:
raise Exception("Invalid partition to load metadata. "
"Must be train/validation/test")
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 featurize(self, audio_clip):
""" For a given audio clip, calculate the corresponding feature
Params:
audio_clip (str): Path to the audio clip
"""
if self.spectrogram:
return spectrogram_from_file(
audio_clip, step=self.step, window=self.window,
max_freq=self.max_freq)
else:
(rate, sig) = wav.read(audio_clip)
return mfcc(sig, rate, numcep=self.mfcc_dim)
def normalize(self, feature, eps=1e-14):
""" Center a feature using the mean and std
Params:
feature (numpy.ndarray): Feature to normalize
"""
return (feature - self.feats_mean) / (self.feats_std + eps)
def shuffle_data(audio_paths, durations, texts):
""" Shuffle the data (called after making a complete pass through
training or validation data during the training process)
Params:
audio_paths (list): Paths to audio clips
durations (list): Durations of utterances for each audio clip
texts (list): Sentences uttered in each audio clip
"""
p = np.random.permutation(len(audio_paths))
audio_paths = [audio_paths[i] for i in p]
durations = [durations[i] for i in p]
texts = [texts[i] for i in p]
return audio_paths, durations, texts
def sort_data(audio_paths, durations, texts):
""" Sort the data by duration
Params:
audio_paths (list): Paths to audio clips
durations (list): Durations of utterances for each audio clip
texts (list): Sentences uttered in each audio clip
"""
p = np.argsort(durations).tolist()
audio_paths = [audio_paths[i] for i in p]
durations = [durations[i] for i in p]
texts = [texts[i] for i in p]
return audio_paths, durations, texts
def vis_train_features(index=6):
""" Visualizing the data point in the training set at the supplied index
"""
# obtain spectrogram
audio_gen = AudioGenerator(spectrogram=True)
audio_gen.load_train_data()
vis_audio_path = audio_gen.train_audio_paths[index]
vis_spectrogram_feature = audio_gen.normalize(audio_gen.featurize(vis_audio_path))
# obtain mfcc
audio_gen = AudioGenerator(spectrogram=False)
audio_gen.load_train_data()
vis_mfcc_feature = audio_gen.normalize(audio_gen.featurize(vis_audio_path))
# obtain text label
vis_text = audio_gen.train_texts[index]
# obtain raw audio
vis_raw_audio, _ = librosa.load(vis_audio_path)
# print total number of training examples
print('There are %d total training examples.' % len(audio_gen.train_audio_paths))
# return labels for plotting
return vis_text, vis_raw_audio, vis_mfcc_feature, vis_spectrogram_feature, vis_audio_path
def plot_raw_audio(vis_raw_audio):
# plot the raw audio signal
fig = plt.figure(figsize=(12,3))
ax = fig.add_subplot(111)
steps = len(vis_raw_audio)
ax.plot(np.linspace(1, steps, steps), vis_raw_audio)
plt.title('Audio Signal')
plt.xlabel('Time')
plt.ylabel('Amplitude')
plt.show()
def plot_mfcc_feature(vis_mfcc_feature):
# plot the MFCC feature
fig = plt.figure(figsize=(12,5))
ax = fig.add_subplot(111)
im = ax.imshow(vis_mfcc_feature, cmap=plt.cm.jet, aspect='auto')
plt.title('Normalized MFCC')
plt.ylabel('Time')
plt.xlabel('MFCC Coefficient')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)
ax.set_xticks(np.arange(0, 13, 2), minor=False);
plt.show()
def plot_spectrogram_feature(vis_spectrogram_feature):
# plot the normalized spectrogram
fig = plt.figure(figsize=(12,5))
ax = fig.add_subplot(111)
im = ax.imshow(vis_spectrogram_feature, cmap=plt.cm.jet, aspect='auto')
plt.title('Normalized Spectrogram')
plt.ylabel('Time')
plt.xlabel('Frequency')
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)
plt.show()