-
Notifications
You must be signed in to change notification settings - Fork 9
/
utils.py
193 lines (165 loc) · 5.81 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
import numpy as np
import h5py
import os
from scipy.io import wavfile
import librosa
import librosa.filters
def read_hdf5(hdf5_name, hdf5_path='feats'):
"""Read hdf5 dataset.
Args:
hdf5_name (str): Filename of hdf5 file.
hdf5_path (str): Dataset name in hdf5 file.
Return:
any: Dataset values.
"""
if not os.path.exists(hdf5_name):
raise Exception(f"There is no such a hdf5 file ({hdf5_name}).")
sys.exit(1)
hdf5_file = h5py.File(hdf5_name, "r")
if hdf5_path not in hdf5_file:
raise Exception(f"There is no such a data in hdf5 file. ({hdf5_path})")
sys.exit(1)
hdf5_data = hdf5_file[hdf5_path][()]
hdf5_file.close()
return hdf5_data
def write_hdf5(hdf5_name, hdf5_path, write_data, is_overwrite=True):
"""Write dataset to hdf5.
Args:
hdf5_name (str): Hdf5 dataset filename.
hdf5_path (str): Dataset path in hdf5.
write_data (ndarray): Data to write.
is_overwrite (bool): Whether to overwrite dataset.
"""
# convert to numpy array
write_data = np.array(write_data)
# check folder existence
folder_name, _ = os.path.split(hdf5_name)
if not os.path.exists(folder_name) and len(folder_name) != 0:
os.makedirs(folder_name)
# check hdf5 existence
if os.path.exists(hdf5_name):
# if already exists, open with r+ mode
hdf5_file = h5py.File(hdf5_name, "r+")
# check dataset existence
if hdf5_path in hdf5_file:
if is_overwrite:
#raise Exception("Dataset in hdf5 file already exists. "
# "recreate dataset in hdf5.")
hdf5_file.__delitem__(hdf5_path)
else:
#logging.error("Dataset in hdf5 file already exists. "
# "if you want to overwrite, please set is_overwrite = True.")
hdf5_file.close()
sys.exit(1)
else:
# if not exists, open with w mode
hdf5_file = h5py.File(hdf5_name, "w")
# write data to hdf5
hdf5_file.create_dataset(hdf5_path, data=write_data)
hdf5_file.flush()
hdf5_file.close()
def to_categorical(y, num_classes=None):
"""Converts a class vector (integers) to binary class matrix.
E.g. for use with categorical_crossentropy.
# Arguments
y: class vector to be converted into a matrix
(integers from 0 to num_classes).
num_classes: total number of classes.
# Returns
A binary matrix representation of the input. The classes axis
is placed last.
From Keras np_utils
"""
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=np.float32)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
fmin=80
fmax=7600
hop_size=240
win_length=1024
fft_size=1024
highpass_cutoff=70.0
min_level_db=-100
sample_rate=24000
window='hann'
silence_threshold=2
num_mels=80
def start_and_end_indices(quantized, silence_threshold=2):
for start in range(quantized.size):
if abs(quantized[start] - 127) > silence_threshold:
break
for end in range(quantized.size - 1, 1, -1):
if abs(quantized[end] - 127) > silence_threshold:
break
assert abs(quantized[start] - 127) > silence_threshold
assert abs(quantized[end] - 127) > silence_threshold
return start, end
def _normalize(S):
return np.clip((S - min_level_db) / -min_level_db, 0, 1)
def _denormalize(S):
return (np.clip(S, 0, 1) * -min_level_db) + min_level_db
def _build_mel_basis():
if fmax is not None:
assert fmax <= sample_rate // 2
return librosa.filters.mel(sample_rate, fft_size,
fmin=fmin, fmax=fmax,
n_mels=num_mels)
def _linear_to_mel(spectrogram):
_mel_basis = None
#global _mel_basis
if _mel_basis is None:
_mel_basis = _build_mel_basis()
return np.dot(_mel_basis, spectrogram)
def _stft(y, pad_mode="constant"):
# use constant padding (defaults to zeros) instead of reflection padding
return librosa.stft(y=y, n_fft=fft_size, hop_length=hop_size,
win_length=win_length, window=window,
pad_mode=pad_mode)
def logmelspectrogram(y, pad_mode="reflect"):
"""Same log-melspectrogram computation as espnet
https://github.com/espnet/espnet
from espnet.transform.spectrogram import logmelspectrogram
"""
D = _stft(y, pad_mode=pad_mode)
S = _linear_to_mel(np.abs(D))
S = np.log10(np.maximum(S, 1e-10))
return S
def trim(quantized):
start, end = start_and_end_indices(quantized, silence_threshold)
return quantized[start:end]
def load_wav(path):
sr, x = wavfile.read(path)
signed_int16_max = 2**15
if x.dtype == np.int16:
x = x.astype(np.float32) / signed_int16_max
if sr != sample_rate:
x = librosa.resample(x, sr, sample_rate)
x = np.clip(x, -1.0, 1.0)
return x
def low_cut_filter(x, fs, cutoff=70):
"""APPLY LOW CUT FILTER.
https://github.com/kan-bayashi/PytorchWaveNetVocoder
Args:
x (ndarray): Waveform sequence.
fs (int): Sampling frequency.
cutoff (float): Cutoff frequency of low cut filter.
Return:
ndarray: Low cut filtered waveform sequence.
"""
nyquist = fs // 2
norm_cutoff = cutoff / nyquist
from scipy.signal import firwin, lfilter
# low cut filter
fil = firwin(255, norm_cutoff, pass_zero=False)
lcf_x = lfilter(fil, 1, x)
return lcf_x