def plot_before_after_transformation(dataset, entonema, wav):
if not wav.endswith('.wav'):
return
path = '{}/{}/{}'.format(dataset, entonema, wav)
ybefore = get_pitch_decompy_values(path,
remove_silencess=False,
interpolate=False)
ynew = get_pitch_decompy_values(path,
remove_silencess=True,
interpolate=True)
fig, axarr = plt.subplots(2)
axarr[0].grid()
axarr[0].plot(ybefore)
axarr[0].set_ylabel('No trans')
axarr[0].set_xlabel('len = {} entropy = {}'.format(
len(ybefore), entropy(ybefore)))
ax = plt.axis
axarr[1].grid()
axarr[1].plot(ynew)
axarr[1].set_ylabel('With trans')
axarr[1].set_xlabel('len = {} entropy = {}'.format(
len(ynew), entropy(ynew)))
fig.subplots_adjust(hspace=0.5)
plt.axis = ax
dir = './temp/{}'.format(entonema)
if not os.path.exists(dir):
os.mkdir(dir)
plt.savefig('{}/{}_{}.png'.format(dir, entonema, wav.replace('.wav', '')))
def on_browse_button_released(self):
#path = unicode(QFileDialog.getOpenFileName(self))
path = QFileDialog.getOpenFileName(self)[0]
name = os.path.basename(path)
if not name.endswith('.wav'):
self.track_label.setText(u"{} no es un archivo .wav".format(name))
return
self.player.setMedia(QtMultimedia.QMediaContent(QUrl.fromLocalFile(path)))
self.track_label.setText(name)
self.canvas.plot_spectrogram(path)
print('path = {}'.format(path))
curve = get_pitch_decompy_values(path, remove_silencess = True, interpolate = False)
vector, _ = naive_vectorizer(curve, true_features = self.cols)
X = [vector]
X = self.scaler.transform(X)
predicted = self.clf.predict(X)[0]
print('predicted = {}'.format(predicted))
self.entonema_label.setText(
u"Predicción de entonema: {}".format(predicted)
)
def plot_spline_interpolation(dataset, entonema, wav):
print('Hola!')
if not wav.endswith('.wav'):
return
curve_path = '{}/{}/{}'.format(dataset, entonema, wav)
curve = get_pitch_decompy_values(curve_path,
remove_silencess=True,
interpolate=False)
ynew = spline_interpolation(curve)
x = np.arange(0, len(curve))
plt.figure()
plt.scatter(x,
curve,
s=np.pi,
label='Original_pitch entropy = {}'.format(entropy(curve)))
plt.plot(x,
ynew,
label='Spline_interpolation entropy = {}'.format(entropy(ynew)),
alpha=0.4)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.legend(loc='upper center',
fancybox=True,
shadow=True,
bbox_to_anchor=(0.5, 1.13))
plt.grid()
#plt.title('Cubic-spline interpolation\n\n entonema = {} wav = {}'.format(entonema, wav))
#plt.show()
dir = './temp/{}'.format(entonema)
if not os.path.exists(dir):
os.mkdir(dir)
plt.savefig('{}/{}_{}.png'.format(dir, entonema, wav.replace('.wav', '')))
def make_excels(folder,
entonema,
vectoricer,
excels_path,
max_audio_by_folder=1000,
processer=True,
remove_silencess=True,
interpolate=True):
#input('processer = {} silencess = {}'.format(processer, remove_silencess))
processer = simply_add_vector_processer if processer else by_level_cD_processer
#folder = os.path.join(dataset, entonema, wav)
if os.path.isdir(folder):
data, archivos, start = [], 0, time.time()
dataframe = pd.DataFrame([])
for augmentation in os.listdir(folder)[:max_audio_by_folder]:
if augmentation.endswith('.wav'):
curve = get_pitch_decompy_values(
'{}/{}'.format(folder, augmentation),
remove_silencess=remove_silencess,
interpolate=interpolate)
dataframe = processer(dataframe, vectoricer, curve,
augmentation)
archivos += 1
#print(' augmentation = {} vector_len = {}'.format(augmentation, len(vector)))
#print(':)')
'''
except:
print('deprecated = {} folder = {}'.format(augmentation, os.path.basename(folder)))
#print(' augmentation deprecated dataset = {} entonema {} wav = {} augmentattion = {}'.format(dataset, entonema, wav, augmentation))
'''
dataframe['entonema'] = str(entonema)
dataframe = dataframe.fillna(value=0)
write_excel(dataframe, '{}/{}_{}.xlsx'.format(excels_path, entonema,
''))
def info(self, dataset, entonema, wav, curve = []):
wav_path = '{}/{}/{}'.format(dataset,entonema,wav)
if len(curve) == 0:
curve = get_pitch_decompy_values(wav_path, \
remove_silencess = self.silences, interpolate = self.interpolate)
pendt = pendiente_pitch(curve)
spectrumm = spectrum(curve)
if(len(spectrumm) > self.max): self.max = len(spectrumm)
return [pendt] + spectrumm.tolist()
def sol(dataset, entonema, wav, *args):
if wav.endswith('.wav'):
curve_path = '{}/{}/{}'.format(dataset, entonema, wav)
curve = get_pitch_decompy_values(curve_path,
remove_silencess=args[0],
interpolate=args[1])
if len(curve) > 0:
return function(curve, entonema, wav, *args[2:])
else:
print('Error {} needs a .wav'.format(str(function)))
def info(self, dataset, entonema, wav, curve = []):
wav_path = '{}/{}/{}'.format(dataset,entonema,wav)
if len(curve) == 0:
curve = get_pitch_decompy_values(wav_path, \
remove_silencess = self.silences, interpolate = self.interpolate)
entr_pitch = [round(entropy(curve), ndigits=2)]
cA_entr = []
cD_entr = []
conv = []
descp = get_cD(curve, 'db5', level = 4)
for (cA, cD) in descp:
cA_entr.append(round(entropy(cA), ndigits=2))
cD_entr.append(round(entropy(cD), ndigits=2))
rec = pywt.waverec((cA,cD), 'db5')
cv = convolve(curve,rec, mode='valid')[0]
conv.append(round(cv,ndigits=2))
return entr_pitch + cA_entr + cD_entr + conv