def compute_spect(wav):
# compute spectrogram
D = pySTFT(wav).T
D_mel = np.dot(D, mel_basis)
D_db = 20 * np.log10(np.maximum(min_level, D_mel)) - 16
S = (D_db + 100) / 100
S = S[np.newaxis, :, :]
if S.shape[1] <= 192:
S, _ = pad_seq_to_2(S, 192)
uttr = torch.from_numpy(S.astype(np.float32)).to(device)
return uttr
def _processing_data(hparams, full_path, spk_label, spk_emb, gender, npz_name,
pbar, i):
if gender == 'M':
lo, hi = 50, 250
elif gender == 'F':
lo, hi = 100, 600
else:
raise ValueError
prng = RandomState(int(random.random()))
x, fs = librosa.load(full_path, sr=hparams.sample_rate)
assert fs == hparams.sample_rate
if x.shape[0] % hparams.hop_size == 0:
x = np.concatenate((x, np.array([1e-06])), axis=0)
y = signal.filtfilt(b, a, x)
wav = y * 0.96 + (prng.rand(y.shape[0]) - 0.5) * 1e-06
# compute spectrogram
D = pySTFT(wav).T
D_mel = np.dot(D, mel_basis)
D_db = 20 * np.log10(np.maximum(min_level, D_mel)) - hparams.ref_level_db
S = (D_db + 100) / 100
# extract f0
f0_rapt = sptk.rapt(wav.astype(np.float32) * 32768,
fs,
hparams.hop_size,
min=lo,
max=hi,
otype=2)
index_nonzero = (f0_rapt != -1e10)
mean_f0, std_f0 = np.mean(f0_rapt[index_nonzero]), np.std(
f0_rapt[index_nonzero])
f0_norm = speaker_normalization(f0_rapt, index_nonzero, mean_f0, std_f0)
assert len(S) == len(f0_rapt)
data = {
'mel': S.astype(np.float32),
'f0': f0_norm.astype(np.float32),
'spk_label': spk_label
}
if spk_emb is not None:
data['spk_emb'] = spk_emb
np.savez(npz_name, **data)
pbar.update(i)
try:
x, fs = sf.read(wav_path)
except Exception as e:
# print("Error on {}".format(basename))
print(e) if 'System error' not in str(
e
) else None # preprocessed dir can have no wav file due to the lenght constraint.
continue
# assert fs == 16000
if x.shape[0] % 256 == 0:
x = np.concatenate((x, np.array([1e-06])), axis=0)
y = signal.filtfilt(b, a, x)
wav = y * 0.96 + (prng.rand(y.shape[0]) - 0.5) * 1e-06
# compute spectrogram
D = pySTFT(wav).T
D_mel = np.dot(D, mel_basis)
D_db = 20 * np.log10(np.maximum(min_level, D_mel)) - 16
S = (D_db + 100) / 100
# extract f0
f0_rapt = sptk.rapt(wav.astype(np.float32) * 32768,
fs,
256,
min=lo,
max=hi,
otype=2)
index_nonzero = (f0_rapt != -1e10)
mean_f0, std_f0 = np.mean(f0_rapt[index_nonzero]), np.std(
f0_rapt[index_nonzero])
f0_norm = speaker_normalization(f0_rapt, index_nonzero, mean_f0,
