import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt
import numpy as np
import librosa
import scipy.io.wavfile
def plot_data(data, fn, figsize=(12, 4)):
fig, axes = plt.subplots(1, len(data), figsize=figsize)
for i in range(len(data)):
if len(data) == 1:
ax = axes
else:
ax = axes[i]
g = ax.imshow(data[i], aspect='auto', origin='bottom',
interpolation='none')
plt.colorbar(g, ax=ax)
plt.savefig(fn)
def levenshteinDistance(s1, s2):
if len(s1) > len(s2):
s1, s2 = s2, s1
distances = list(range(len(s1) + 1))
for i2, c2 in enumerate(s2):
distances_ = [i2+1]
for i1, c1 in enumerate(s1):
if c1 == c2:
distances_.append(distances[i1])
else:
distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1])))
distances = distances_
return distances[-1]
def recover_wav(mel, wav_path, mel_mean_std, ismel=False,
n_fft = 2048,win_length=800, hop_length=200):
if ismel:
mean, std = np.load(mel_mean_std)
else:
mean, std = np.load(mel_mean_std.replace('mel','spec'))
mean = mean[:,None]
std = std[:,None]
mel = 1.2 * mel * std + mean
mel = np.exp(mel)
if ismel:
filters = librosa.filters.mel(sr=16000, n_fft=2048, n_mels=80)
inv_filters = np.linalg.pinv(filters)
spec = np.dot(inv_filters, mel)
else:
spec = mel
def _griffin_lim(stftm_matrix, shape, max_iter=50):
y = np.random.random(shape)
for i in range(max_iter):
stft_matrix = librosa.core.stft(y, n_fft=n_fft, win_length=win_length, hop_length=hop_length)
stft_matrix = stftm_matrix * stft_matrix / np.abs(stft_matrix)
y = librosa.core.istft(stft_matrix, win_length=win_length, hop_length=hop_length)
return y
shape = spec.shape[1] * hop_length - hop_length + 1
y = _griffin_lim(spec, shape)
scipy.io.wavfile.write(wav_path, 16000, y)
return y
