prev_w = w_s
cond = c_mb
print("Completed sampling after %i steps" % fixed_steps)
completed = np.array(completed).transpose(1, 0, 2)
rlookup = {v: k for k, v in vocabulary.items()}
all_strings = []
for yi in y:
ex_str = "".join([rlookup[c] for c in np.argmax(yi, axis=1)])
all_strings.append(ex_str)
for i in range(len(completed)):
ex = completed[i]
ex_str = "".join(
[rlookup[c] for c in np.argmax(cond[:, i], axis=1)])
s = "gen_%s_%i.wav" % (ex_str, i)
ii = reconstruct(ex)
wavfile.write(s, fs, soundsc(ii))
if ex_str in all_strings:
inds = [
n for n, s in enumerate(all_strings) if ex_str == s
]
ind = inds[0]
it = reconstruct(X[ind])
s = "orig_%s_%i.wav" % (ex_str, i)
wavfile.write(s, fs, soundsc(it))
valid_itr.reset()
print("Sampling complete, exiting...")
sys.exit()
else:
print("No plotting arguments, starting training mode!")
X_sym = tensor.tensor3("X_sym")
prev_h2 = h2_s
prev_h3 = h3_s
prev_kappa = k_s
prev_w = w_s
cond = c_mb
print("Completed sampling after %i steps" % fixed_steps)
# Minibatch size first
completed = np.array(completed).transpose(1, 0, 2)
rlookup = {v: k for k, v in vocabulary.items()}
for i in range(len(completed)):
ex = completed[i]
ex_str = "".join([rlookup[c]
for c in np.argmax(cond[:, i], axis=1)])
s = "gen_%s_%i.wav" % (ex_str, i)
ii = reconstruct(ex)
wavfile.write(s, fs, soundsc(ii))
valid_itr.reset()
print("Sampling complete, exiting...")
sys.exit()
else:
print("No plotting arguments, starting training mode!")
X_sym = tensor.tensor3("X_sym")
X_sym.tag.test_value = X_mb[:cut_len]
X_mask_sym = tensor.matrix("X_mask_sym")
X_mask_sym.tag.test_value = X_mb_mask[:cut_len]
c_sym = tensor.tensor3("c_sym")
c_sym.tag.test_value = c_mb
c_mask_sym = tensor.matrix("c_mask_sym")
c_mask_sym.tag.test_value = c_mb_mask
bias_sym = tensor.scalar("bias_sym")
# mb, length
completed = np.array(completed)[:, 0, :]
completed = completed.transpose(1, 0)
# all samples would be range(len(completed))
for i in range(10):
ex = completed[i].ravel()
s = "gen_%i.wav" % (i)
ex = ex.astype("float32")
"""
ex -= ex.min()
ex /= ex.max()
ex -= 0.5
ex *= 0.95
wavfile.write(s, fs, ex)
"""
wavfile.write(s, fs, soundsc(ex))
print("Sampling complete, exiting...")
sys.exit()
else:
print("No plotting arguments, starting training mode!")
X_sym = tensor.tensor3("X_sym")
X_sym.tag.test_value = X_mb[:cut_len]
X_mask_sym = tensor.matrix("X_mask_sym")
X_mask_sym.tag.test_value = X_mb_mask[:cut_len]
init_h1_i = tensor.matrix("init_h1")
init_h1_i.tag.test_value = np_zeros((minibatch_size, n_hid))
init_h2_i = tensor.matrix("init_h2")
init_h2_i.tag.test_value = np_zeros((minibatch_size, n_hid))
pred_srnn.load_weights(args.sample)
print(pred_srnn.model().summary())
try:
from keras.utils import plot_model
plot_model(pred_srnn.top_tier_model_predictor, to_file='top_model.png')
plot_model(pred_srnn.mid_tier_model_predictor, to_file='mid_model.png')
plot_model(pred_srnn.slow_tier_model_predictor,
to_file='slow_model.png')
except:
print('failed to plot models to png')
pass
w = pred_srnn.sample(4 * args.samplerate, random_state, args.debug)
fs = args.samplerate
wavfile.write("generated.wav", fs, soundsc(w))
exit(0)
frame_size = 1
bliz_train = Blizzard_dataset(
minibatch_size=minibatch_size,
wav_folder_path='./blizzard/{}_parts/'.format(args.exp),
prompt_path='./blizzard/{}_parts/prompts.txt'.format(args.exp),
preproc_fn=wav_to_qbins_frames,
frame_size=frame_size,
fraction_range=[0, train_stop_index],
thread_cnt=1)
bliz_valid = Blizzard_dataset(
minibatch_size=minibatch_size,
wav_folder_path='./blizzard/{}_parts/'.format(args.exp),
prompt_path='./blizzard/{}_parts/prompts.txt'.format(args.exp),
# mb, length
completed = np.array(completed)[:, 0, :]
completed = completed.transpose(1, 0)
# all samples would be range(len(completed))
for i in range(10):
ex = completed[i].ravel()
s = "gen_%i.wav" % (i)
ex = ex.astype("float32")
"""
ex -= ex.min()
ex /= ex.max()
ex -= 0.5
ex *= 0.95
wavfile.write(s, fs, ex)
"""
wavfile.write(s, fs, soundsc(ex))
print("Sampling complete, exiting...")
sys.exit()
else:
print("No plotting arguments, starting training mode!")
X_sym = tensor.tensor3("X_sym")
X_sym.tag.test_value = X_mb[:cut_len]
X_mask_sym = tensor.matrix("X_mask_sym")
X_mask_sym.tag.test_value = X_mb_mask[:cut_len]
init_h1_i = tensor.matrix("init_h1")
init_h1_i.tag.test_value = np_zeros((minibatch_size, n_hid))
init_h2_i = tensor.matrix("init_h2")
init_h2_i.tag.test_value = np_zeros((minibatch_size, n_hid))
