def convert(catalog, dat_file, n_quant, sample_rate=16000):
"""
Convert all input data and save a dat file
"""
import librosa
if n_quant <= 2**8:
snd_dtype = np.uint8
elif n_quant <= 2**15:
snd_dtype = np.int16
else:
snd_dtype = np.int32
n_mel_chan = None
speaker_ids = set(id for id, __ in catalog)
speaker_id_map = dict((v, k) for k, v in enumerate(speaker_ids))
snd_data = np.empty((0), dtype=snd_dtype)
samples = []
for (voice_id, snd_path) in catalog:
snd, _ = librosa.load(snd_path, sample_rate)
snd_mu = util.mu_encode_np(snd, n_quant).astype(snd_dtype)
wav_b = len(snd_data)
wav_e = wav_b + len(snd_mu)
snd_data.resize(wav_e)
snd_data[wav_b:wav_e] = snd_mu
samples.append(
SpokenSample(
voice_index=speaker_id_map[voice_id],
wav_b=wav_b,
wav_e=wav_e,
# mel_b=mel_b, mel_e=mel_e,
file_path=snd_path))
if len(samples) % 100 == 0:
print('Converted {} files of {}.'.format(len(samples),
len(catalog),
file=stderr))
stderr.flush()
with open(dat_file, 'wb') as dat_fh:
state = {
'samples': samples,
'snd_dtype': snd_dtype,
'snd_data': snd_data
}
pickle.dump(state, dat_fh)
def convert(catalog,
pfx,
n_quant,
sample_rate=16000,
win_sz=400,
hop_sz=160,
n_mels=80,
n_mfcc=13):
mfcc_proc = mfcc.ProcessWav(sample_rate, win_sz, hop_sz, n_mels, n_mfcc)
if n_quant <= 2**8:
snd_dtype = np.uint8
elif n_quant <= 2**15:
snd_dtype = np.int16
else:
snd_dtype = np.int32
snd_file = pfx + '.dat'
ind_file = pfx + '.ind'
mel_file = pfx + '.mel'
ind = {'voice_id': [], 'n_snd_elem': [], 'n_mel_elem': [], 'snd_path': []}
n_snd_elem = 0
n_mel_elem = 0
n_mel_chan = None
with open(snd_file, 'wb') as snd_fh, open(mel_file, 'wb') as mel_fh:
for (voice_id, snd_path) in catalog:
snd, _ = librosa.load(snd_path, sample_rate)
snd_mu = util.mu_encode_np(snd, n_quant).astype(snd_dtype)
# mel: C, T (n_mels, n_timesteps)
# reshape to T, C and flatten
mel = mfcc_proc.func(snd)
if n_mel_chan is None:
n_mel_chan = mel.shape[0]
mel = mel.transpose((1, 0)).flatten()
snd_fh.write(snd_mu.data)
mel_fh.write(mel.data)
ind['voice_id'].append(voice_id)
ind['n_snd_elem'].append(snd.size)
ind['n_mel_elem'].append(mel.size)
ind['snd_path'].append(snd_path)
if len(ind['voice_id']) % 100 == 0:
print('Converted {} files of {}.'.format(len(ind['voice_id']),
len(catalog),
file=stderr))
stderr.flush()
n_snd_elem += snd.size
n_mel_elem += mel.size
with open(ind_file, 'wb') as ind_fh:
index = {
'window_size': win_sz,
'hop_size': hop_sz,
'n_snd_elem': n_snd_elem,
'n_mel_elem': n_mel_elem,
'n_mel_chan': n_mel_chan,
'snd_dtype': snd_dtype,
'n_quant': n_quant
}
index.update(ind)
pickle.dump(index, ind_fh)