Spaces:
Running
Running
File size: 9,128 Bytes
67c46fd |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 |
# Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita)
# Licensed under the MIT license.
#
# This module is for computing audio features
import numpy as np
import librosa
def get_input_dim(
frame_size,
context_size,
transform_type,
):
if transform_type.startswith("logmel23"):
frame_size = 23
elif transform_type.startswith("logmel"):
frame_size = 40
else:
fft_size = 1 << (frame_size - 1).bit_length()
frame_size = int(fft_size / 2) + 1
input_dim = (2 * context_size + 1) * frame_size
return input_dim
def transform(Y, transform_type=None, dtype=np.float32):
"""Transform STFT feature
Args:
Y: STFT
(n_frames, n_bins)-shaped np.complex array
transform_type:
None, "log"
dtype: output data type
np.float32 is expected
Returns:
Y (numpy.array): transformed feature
"""
Y = np.abs(Y)
if not transform_type:
pass
elif transform_type == "log":
Y = np.log(np.maximum(Y, 1e-10))
elif transform_type == "logmel":
n_fft = 2 * (Y.shape[1] - 1)
sr = 16000
n_mels = 40
mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
Y = np.dot(Y**2, mel_basis.T)
Y = np.log10(np.maximum(Y, 1e-10))
elif transform_type == "logmel23":
n_fft = 2 * (Y.shape[1] - 1)
sr = 8000
n_mels = 23
mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
Y = np.dot(Y**2, mel_basis.T)
Y = np.log10(np.maximum(Y, 1e-10))
elif transform_type == "logmel23_mn":
n_fft = 2 * (Y.shape[1] - 1)
sr = 8000
n_mels = 23
mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
Y = np.dot(Y**2, mel_basis.T)
Y = np.log10(np.maximum(Y, 1e-10))
mean = np.mean(Y, axis=0)
Y = Y - mean
elif transform_type == "logmel23_swn":
n_fft = 2 * (Y.shape[1] - 1)
sr = 8000
n_mels = 23
mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
Y = np.dot(Y**2, mel_basis.T)
Y = np.log10(np.maximum(Y, 1e-10))
# b = np.ones(300)/300
# mean = scipy.signal.convolve2d(Y, b[:, None], mode='same')
# simple 2-means based threshoding for mean calculation
powers = np.sum(Y, axis=1)
th = (np.max(powers) + np.min(powers)) / 2.0
for i in range(10):
th = (np.mean(powers[powers >= th]) + np.mean(powers[powers < th])) / 2
mean = np.mean(Y[powers > th, :], axis=0)
Y = Y - mean
elif transform_type == "logmel23_mvn":
n_fft = 2 * (Y.shape[1] - 1)
sr = 8000
n_mels = 23
mel_basis = librosa.filters.mel(sr, n_fft, n_mels)
Y = np.dot(Y**2, mel_basis.T)
Y = np.log10(np.maximum(Y, 1e-10))
mean = np.mean(Y, axis=0)
Y = Y - mean
std = np.maximum(np.std(Y, axis=0), 1e-10)
Y = Y / std
else:
raise ValueError("Unknown transform_type: %s" % transform_type)
return Y.astype(dtype)
def subsample(Y, T, subsampling=1):
"""Frame subsampling"""
Y_ss = Y[::subsampling]
T_ss = T[::subsampling]
return Y_ss, T_ss
def splice(Y, context_size=0):
"""Frame splicing
Args:
Y: feature
(n_frames, n_featdim)-shaped numpy array
context_size:
number of frames concatenated on left-side
if context_size = 5, 11 frames are concatenated.
Returns:
Y_spliced: spliced feature
(n_frames, n_featdim * (2 * context_size + 1))-shaped
"""
Y_pad = np.pad(Y, [(context_size, context_size), (0, 0)], "constant")
Y_spliced = np.lib.stride_tricks.as_strided(
np.ascontiguousarray(Y_pad),
(Y.shape[0], Y.shape[1] * (2 * context_size + 1)),
(Y.itemsize * Y.shape[1], Y.itemsize),
writeable=False,
)
return Y_spliced
def stft(data, frame_size=1024, frame_shift=256):
"""Compute STFT features
Args:
data: audio signal
(n_samples,)-shaped np.float32 array
frame_size: number of samples in a frame (must be a power of two)
frame_shift: number of samples between frames
Returns:
stft: STFT frames
(n_frames, n_bins)-shaped np.complex64 array
"""
# round up to nearest power of 2
fft_size = 1 << (frame_size - 1).bit_length()
# HACK: The last frame is ommited
# as librosa.stft produces such an excessive frame
if len(data) % frame_shift == 0:
return librosa.stft(
data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift
).T[:-1]
else:
return librosa.stft(
data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift
).T
def _count_frames(data_len, size, shift):
# HACK: Assuming librosa.stft(..., center=True)
n_frames = 1 + int(data_len / shift)
if data_len % shift == 0:
n_frames = n_frames - 1
return n_frames
def get_frame_labels(
kaldi_obj, rec, start=0, end=None, frame_size=1024, frame_shift=256, n_speakers=None
):
"""Get frame-aligned labels of given recording
Args:
kaldi_obj (KaldiData)
rec (str): recording id
start (int): start frame index
end (int): end frame index
None means the last frame of recording
frame_size (int): number of frames in a frame
frame_shift (int): number of shift samples
n_speakers (int): number of speakers
if None, the value is given from data
Returns:
T: label
(n_frames, n_speakers)-shaped np.int32 array
"""
filtered_segments = kaldi_obj.segments[kaldi_obj.segments["rec"] == rec]
speakers = np.unique(
[kaldi_obj.utt2spk[seg["utt"]] for seg in filtered_segments]
).tolist()
if n_speakers is None:
n_speakers = len(speakers)
es = end * frame_shift if end is not None else None
data, rate = kaldi_obj.load_wav(rec, start * frame_shift, es)
n_frames = _count_frames(len(data), frame_size, frame_shift)
T = np.zeros((n_frames, n_speakers), dtype=np.int32)
if end is None:
end = n_frames
for seg in filtered_segments:
speaker_index = speakers.index(kaldi_obj.utt2spk[seg["utt"]])
start_frame = np.rint(seg["st"] * rate / frame_shift).astype(int)
end_frame = np.rint(seg["et"] * rate / frame_shift).astype(int)
rel_start = rel_end = None
if start <= start_frame and start_frame < end:
rel_start = start_frame - start
if start < end_frame and end_frame <= end:
rel_end = end_frame - start
if rel_start is not None or rel_end is not None:
T[rel_start:rel_end, speaker_index] = 1
return T
def get_labeledSTFT(
kaldi_obj,
rec,
start,
end,
frame_size,
frame_shift,
n_speakers=None,
use_speaker_id=False,
):
"""Extracts STFT and corresponding labels
Extracts STFT and corresponding diarization labels for
given recording id and start/end times
Args:
kaldi_obj (KaldiData)
rec (str): recording id
start (int): start frame index
end (int): end frame index
frame_size (int): number of samples in a frame
frame_shift (int): number of shift samples
n_speakers (int): number of speakers
if None, the value is given from data
Returns:
Y: STFT
(n_frames, n_bins)-shaped np.complex64 array,
T: label
(n_frmaes, n_speakers)-shaped np.int32 array.
"""
data, rate = kaldi_obj.load_wav(rec, start * frame_shift, end * frame_shift)
Y = stft(data, frame_size, frame_shift)
filtered_segments = kaldi_obj.segments[rec]
# filtered_segments = kaldi_obj.segments[kaldi_obj.segments['rec'] == rec]
speakers = np.unique(
[kaldi_obj.utt2spk[seg["utt"]] for seg in filtered_segments]
).tolist()
if n_speakers is None:
n_speakers = len(speakers)
T = np.zeros((Y.shape[0], n_speakers), dtype=np.int32)
if use_speaker_id:
all_speakers = sorted(kaldi_obj.spk2utt.keys())
S = np.zeros((Y.shape[0], len(all_speakers)), dtype=np.int32)
for seg in filtered_segments:
speaker_index = speakers.index(kaldi_obj.utt2spk[seg["utt"]])
if use_speaker_id:
all_speaker_index = all_speakers.index(kaldi_obj.utt2spk[seg["utt"]])
start_frame = np.rint(seg["st"] * rate / frame_shift).astype(int)
end_frame = np.rint(seg["et"] * rate / frame_shift).astype(int)
rel_start = rel_end = None
if start <= start_frame and start_frame < end:
rel_start = start_frame - start
if start < end_frame and end_frame <= end:
rel_end = end_frame - start
if rel_start is not None or rel_end is not None:
T[rel_start:rel_end, speaker_index] = 1
if use_speaker_id:
S[rel_start:rel_end, all_speaker_index] = 1
if use_speaker_id:
return Y, T, S
else:
return Y, T
|