sentences = [SENTENCES[i]]
max_N = len(SENTENCES[i])
L = torch.from_numpy(get_test_data(sentences, max_N))
zeros = torch.from_numpy(np.zeros((1, hp.n_mels, 1), np.float32))
Y = zeros
A = None
for t in tqdm(range(hp.max_T)):
_, Y_t, A = text2mel(L, Y, monotonic_attention=True)
Y = torch.cat((zeros, Y_t), -1)
_, attention = torch.max(A[0, :, -1], 0)
attention = attention.item()
if L[0, attention] == vocab.index('E'): # EOS
break
_, Z = ssrn(Y)
Y = Y.cpu().detach().numpy()
A = A.cpu().detach().numpy()
Z = Z.cpu().detach().numpy()
save_to_png('samples/samples_bea_sleepiness_3k/%d-att.png' % (i + 1),
A[0, :, :])
save_to_png('samples/samples_bea_sleepiness_3k/%d-mel.png' % (i + 1),
Y[0, :, :])
save_to_png('samples/samples_bea_sleepiness_3k/%d-mag.png' % (i + 1),
Z[0, :, :])
save_to_wav(Z[0, :, :].T,
'samples/samples_bea_sleepiness_3k/%d-wav.wav' % (i + 1))
sys.exit(1)
# synthetize by one by one because there is a batch processing bug!
for i in range(len(SENTENCES)):
sentences = [SENTENCES[i]]
max_N = len(SENTENCES[i])
L = torch.from_numpy(get_test_data(sentences, max_N))
zeros = torch.from_numpy(np.zeros((1, hp.n_mels, 1), np.float32))
Y = zeros
A = None
for t in tqdm(range(hp.max_T)):
_, Y_t, A = text2mel(L, Y, monotonic_attention=True)
Y = torch.cat((zeros, Y_t), -1)
_, attention = torch.max(A[0, :, -1], 0)
attention = attention.item()
if L[0, attention] == vocab.index('E'): # EOS
break
_, Z = ssrn(Y)
Y = Y.cpu().detach().numpy()
A = A.cpu().detach().numpy()
Z = Z.cpu().detach().numpy()
save_to_png('samples/%d-att.png' % (i + 1), A[0, :, :])
save_to_png('samples/%d-mel.png' % (i + 1), Y[0, :, :])
save_to_png('samples/%d-mag.png' % (i + 1), Z[0, :, :])
save_to_wav(Z[0, :, :].T, 'samples/%d-wav.wav' % (i + 1))
max_N = len(SENTENCES[i])
L = torch.from_numpy(get_test_data(sentences, max_N))
zeros = torch.from_numpy(np.zeros((1, hp.n_mels, 1), np.float32))
Y = zeros
A = None
for t in tqdm(range(hp.max_T)):
_, Y_t, A = text2mel(L, Y, monotonic_attention=True)
Y = torch.cat((zeros, Y_t), -1)
_, attention = torch.max(A[0, :, -1], 0)
attention = attention.item()
if L[0, attention] == vocab.index('E'): # EOS
break
_, Z = ssrn(Y)
Y = Y.cpu().detach().numpy()
A = A.cpu().detach().numpy()
Z = Z.cpu().detach().numpy()
#print("Z", Z[0, :, :])
save_dir = hp.synthesize_samples_dir
file_name = save_dir + '/' + t2mel_step_str + "_" + ssrn_step_str
os.makedirs(file_name, exist_ok=True)
save_to_png(file_name + '/%d-att.png' % (i + 1), A[0, :, :])
save_to_png(file_name + '/%d-mel.png' % (i + 1), Y[0, :, :])
save_to_png(file_name + '/%d-mag.png' % (i + 1), Z[0, :, :])
save_to_wav(Z[0, :, :].T, file_name + '/%d-wav.wav' % (i + 1))