def load(n=0, cut=0, use_delta=False, timesteps=100, shift=10):
# pre-processing of data:
# - downsample data from 44.1KHz to 16KHz: python downsample.py
# - extract audio features: sudo ./ahocoder.sh
# - extract derivatives of audio features: sudo ./extractdelta.sh
data = sp.load_ahocoder_data(n, use_delta=use_delta)
interp_data = []
vuv_data = []
for sample in data:
sample = sample[cut:sample.shape[0]-cut, :]
vuv = sp.get_vuv_flag(sample, use_delta=use_delta)
sample = sp.interp_uv(sample, use_delta=use_delta)
interp_data.append(sample)
vuv_data.append(vuv)
data = sp.split_samples(interp_data, timesteps, shift)
vuv = sp.split_samples(vuv_data, timesteps, shift)
interp_data = vuv_data = None # free some space
data = np.dstack(data).transpose((2, 0, 1))
vuv = np.dstack(vuv).transpose((2, 0, 1))
data = np.concatenate((data, vuv), -1)
return data
print("Shift: " + str(shift))
print("Batch size: " + str(batch_size))
print("Epochs: " + str(nb_epoch))
print("Prediction length: " + str(pred_len))
print("Generation length: " + str(gen_len))
print("Cut: " + str(cut))
print("Use delta: " + str(use_delta))
print("M: " + str(M))
print("Patience: " + str(patience))
print("Load data")
if gen_speech:
data = load(nb_rawsamples, cut, use_delta, timesteps, shift)
else:
data = sp.load_spectrogram_data(nb_rawsamples)
data = sp.split_samples(data, timesteps, shift)
data = np.dstack(data).transpose((2, 0, 1))
# vuv = np.expand_dims(np.ones(data.shape[0:2]), 2)
# data = np.concatenate((data, vuv), -1)
(X_train, y_train), (X_test, y_test) = sp.train_test_split(data, pred_len)
nb_samples, timesteps, input_dim = X_train.shape
data = None # free some space
print("Preprocess data")
# exclude vuv bit from normalization
if gen_speech or M is not None:
X_train[:, :, :-1], mu, sigma = preprocess(X_train[:, :, :-1])
y_train[:, :, :-1], _, _ = preprocess(y_train[:, :, :-1], mu, sigma)
X_test[:, :, :-1], _, _ = preprocess(X_test[:, :, :-1], mu, sigma)
y_test[:, :, :-1], _, _ = preprocess(y_test[:, :, :-1], mu, sigma)
