def _get_wav_and_melspec(wav_file, length, is_training=True):
'''
the range of values of wav is [-1, 1].
'''
wav = read_wav(wav_file, sr=hp.signal.sr)
wav = trim_wav(wav)
# divide wav into chunks that have the given length and one is randomly selected in training, but first chunk in generation.
n_clips = math.ceil(len(wav) / length) if is_training else 1
idx = random.randrange(n_clips)
start, end = length * idx, length * (idx + 1)
wav = wav[start:end]
assert (len(wav) <= length)
wav = fix_length(wav, length) # padding in case of last chunk.
melspec = wav2melspec_db(wav,
sr=hp.signal.sr,
n_fft=hp.signal.n_fft,
win_length=hp.signal.win_length,
hop_length=hp.signal.hop_length,
n_mels=hp.signal.n_mels,
min_db=hp.signal.min_db,
max_db=hp.signal.max_db)
wav = np.expand_dims(wav, -1)
return wav, melspec.astype(np.float32)
def get_random_wav_and_label(self, tar_wavfiles, ntar_wavfiles):
"""
:return: wav: raw wave. float32. shape=(t, ),
label: 1 if target, 0 otherwise. int32.
melspec: mel-spectrogram. float32. shape=(t, n_mels)
"""
wavfiles, label = (
tar_wavfiles,
self.tar_labels) if np.random.sample(1) <= self.tar_ratio else (
ntar_wavfiles, self.ntar_labels)
wavfile = wavfiles[np.random.randint(0, len(wavfiles))]
if type(wavfile) == bytes:
wavfile = wavfile.decode()
if wavfile.endswith('arr'): # pyarrow format
wav = read_wav_from_arr(wavfile)
else:
wav = read_wav(wavfile, sr=hp.signal.sr)
wav = trim_wav(wav)
wav = crop_random_wav(wav, self.length)
wav = augment_volume(wav)
wav = fix_length(wav, self.length) # padding
melspec = wav2melspec_db(wav,
sr=hp.signal.sr,
n_fft=hp.signal.n_fft,
win_length=hp.signal.win_length,
hop_length=hp.signal.hop_length,
n_mels=hp.signal.n_mels,
min_db=hp.signal.min_db,
max_db=hp.signal.max_db)
melspec = np.float32(melspec)
label = np.float32(label)
return wav, melspec, label
def do_task(nthreads, audio):
print 'Thread-{} start.\n'.format(nthreads)
try:
while True:
src_path, tar_path = audio.next()
wav, sr = librosa.load(src_path)
wav = trim_wav(wav)
write_wav(wav, sr, tar_path)
except StopIteration:
print 'Thread-{} done.\n'.format(nthreads)
def get_random_wav(self, wavfile):
"""
:param: wavfile: a raw wave file.
:return: wav: raw wave. float32. shape=(t, ),
melspec: mel-spectrogram. float32. shape=(t, n_mels),
wavfile: the raw wave file.
"""
wav = read_wav(wavfile, sr=hp.signal.sr)
wav = trim_wav(wav)
wav = fix_length(wav, self.length) # crop from the beginning.
melspec = wav2melspec_db(wav,
sr=hp.signal.sr,
n_fft=hp.signal.n_fft,
win_length=hp.signal.win_length,
hop_length=hp.signal.hop_length,
n_mels=hp.signal.n_mels,
min_db=hp.signal.min_db,
max_db=hp.signal.max_db)
melspec = np.float32(melspec)
return wav, melspec, wavfile
def _get_wav_and_melspec(wav_file, length=None, is_training=True):
wav = read_wav(wav_file, sr=hp.signal.sr)
wav = trim_wav(wav)
if length:
n_clips = math.ceil(len(wav) / length) if is_training else 1
idx = random.randrange(n_clips)
start, end = length * idx, length * (idx + 1)
wav = wav[start:end]
assert (len(wav) <= length)
wav = fix_length(wav, length) # padding
melspec = wav2melspec_db(wav,
sr=hp.signal.sr,
n_fft=hp.signal.n_fft,
win_length=hp.signal.win_length,
hop_length=hp.signal.hop_length,
n_mels=hp.signal.n_mels,
min_db=hp.signal.min_db,
max_db=hp.signal.max_db)
wav = np.expand_dims(wav, -1)
return wav, melspec.astype(np.float32)