def process_file(wav_dir,
vad_dir,
out_dir,
file_name,
model,
wav_suffix='.wav',
vad_suffix='.lab.gz'):
""" Extract i-vector from wav file.
:param wav_dir: directory with wav files
:type wav_dir: str
:param vad_dir: directory with vad files
:type vad_dir: str
:param out_dir: output directory
:type out_dir: str
:param file_name: name of the file
:type file_name: str
:param model: input models for i-vector extraction
:type model: tuple
:param wav_suffix: suffix of wav files
:type wav_suffix: str
:param vad_suffix: suffix of vad files
:type vad_suffix
"""
loginfo('[wav2ivec.process_file] Processing file {} ...'.format(file_name))
ubm_weights, ubm_means, ubm_covs, ubm_norm, gmm_model, numg, dimf, v, mvvt = model
wav = os.path.join(wav_dir, file_name) + wav_suffix
rate, sig = read(wav)
if len(sig.shape) != 1:
raise GeneralException(
'[wav2ivec.process_file] Expected mono as input audio.')
if rate != 8000:
logwarning(
'[wav2ivec.process_file] '
'The input file is expected to be in 8000 Hz, got {} Hz instead, resampling.'
.format(rate))
sig = signal.resample(sig, 8000)
if ADDDITHER > 0.0:
loginfo('[wav2ivec.process_file] Adding dither ...')
sig = features.add_dither(sig, ADDDITHER)
fea = get_mfccs(sig)
vad, n_regions, n_frames = get_vad(vad_dir, file_name, vad_suffix, sig,
fea)
fea = fea[vad, ...]
w = get_ivec(fea, numg, dimf, gmm_model, ubm_means, ubm_norm, v, mvvt)
if w is not None:
Tools.mkdir_p(os.path.join(out_dir, os.path.dirname(file_name)))
np.save(os.path.join(out_dir, file_name), w)
def process_file(wav_dir,
vad_dir,
out_dir,
file_name,
model,
min_size,
max_size,
tolerance,
wav_suffix='.wav',
vad_suffix='.lab.gz'):
""" Extract i-vectors from wav file.
:param wav_dir: directory with wav files
:type wav_dir: str
:param vad_dir: directory with vad files
:type vad_dir: str
:param out_dir: output directory
:type out_dir: str
:param file_name: name of the file
:type file_name: str
:param model: input models for i-vector extraction
:type model: tuple
:param min_size: minimal size of window in ms
:type min_size: int
:param max_size: maximal size of window in ms
:type max_size: int
:param tolerance: accept given number of frames as speech even when it
is marked as silence
:type tolerance: int
:param wav_suffix: suffix of wav files
:type wav_suffix: str
:param vad_suffix: suffix of vad files
:type vad_suffix
"""
loginfo('[wav2ivecs.process_file] Processing file {} '
'...'.format(file_name))
_, ubm_means, _, ubm_norm, gmm_model, numg, dimf, v, mvvt = model
ubm_means = model[1]
ubm_norm = model[3]
gmm_model = model[4]
if len(file_name.split()) > 1:
file_name = file_name.split()[0]
wav = os.path.join(wav_dir, file_name) + wav_suffix
rate = get_sr(wav)
#
loginfo(wav)
#
if rate != 8000:
logwarning('[wav2ivec.process_file] '
'The input file is expected to be in 8000 Hz, got {} Hz '
'instead, resampling.'.format(rate))
rate, sig = af_to_array(wav, target_sr=8000)
if ADDDITHER > 0.0: # TODO: Where is this defined?
#loginfo('[wav2ivecs.process_file] Adding dither ...')
sig = features.add_dither(sig, ADDDITHER)
fea = get_mfccs(sig)
vad, n_regions, n_frames = get_vad(vad_dir, file_name, vad_suffix, sig,
fea)
ivec_set = IvecSet()
ivec_set.name = file_name
for seg in get_segments(vad, min_size, max_size, tolerance):
start, end = get_num_segments(seg[0]), get_num_segments(seg[1])
w = get_ivec(fea[seg[0]:seg[1] + 1], numg, dimf, gmm_model, ubm_means,
ubm_norm, v, mvvt)
if w is None:
continue
ivec_set.add(w, start, end)
Tools.mkdir_p(os.path.join(out_dir, os.path.dirname(file_name)))
ivec_set.save(os.path.join(out_dir, '{}.pkl'.format(file_name)))
def process_file(wav_dir,
vad_dir,
out_dir,
file_name,
model,
min_size,
max_size,
tolerance,
wav_suffix='.wav',
vad_suffix='.lab.gz'):
""" Extract i-vectors from wav file.
:param wav_dir: directory with wav files
:type wav_dir: str
:param vad_dir: directory with vad files
:type vad_dir: str
:param out_dir: output directory
:type out_dir: str
:param file_name: name of the file
:type file_name: str
:param model: input models for i-vector extraction
:type model: tuple
:param min_size: minimal size of window in ms
:type min_size: int
:param max_size: maximal size of window in ms
:type max_size: int
:param tolerance: accept given number of frames as speech even when it is marked as silence
:type tolerance: int
:param wav_suffix: suffix of wav files
:type wav_suffix: str
:param vad_suffix: suffix of vad files
:type vad_suffix
"""
loginfo('[wav2ivecs.process_file] Processing file {} ...'.format(
file_name.split()[0]))
ubm_weights, ubm_means, ubm_covs, ubm_norm, gmm_model, numg, dimf, v, mvvt = model
if len(file_name.split()) > 1:
file_name = file_name.split()[0]
wav = os.path.join(wav_dir, file_name) + wav_suffix
rate, sig = read(wav)
if len(sig.shape) != 1:
raise GeneralException(
'[wav2ivec.process_file] Expected mono as input audio.')
if rate != 8000:
logwarning(
'[wav2ivecs.process_file] '
'The input file is expected to be in 8000 Hz, got {} Hz instead, resampling.'
.format(rate))
sig = signal.resample(sig, 8000)
if ADDDITHER > 0.0:
loginfo('[wav2ivecs.process_file] Adding dither ...')
sig = features.add_dither(sig, ADDDITHER)
fea = get_mfccs(sig)
vad, n_regions, n_frames = get_vad(vad_dir, file_name, vad_suffix, sig,
fea)
ivec_set = IvecSet()
ivec_set.name = file_name
for seg in get_segments(vad, min_size, max_size, tolerance):
start, end = get_num_segments(seg[0]), get_num_segments(seg[1])
loginfo(
'[wav2ivecs.process_file] Processing speech segment from {} ms to {} ms ...'
.format(start, end))
if seg[0] > fea.shape[0] - 1 or seg[1] > fea.shape[0] - 1:
logwarning(
'[norm.process_file] Unexpected features dimensionality - check VAD input or audio.'
)
continue
w = get_ivec(fea[seg[0]:seg[1]], numg, dimf, gmm_model, ubm_means,
ubm_norm, v, mvvt)
if w is None:
continue
ivec_set.add(w, start, end)
Tools.mkdir_p(os.path.join(out_dir, os.path.dirname(file_name)))
ivec_set.save(os.path.join(out_dir, '{}.pkl'.format(file_name)))
