def eval(self, batch):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seqs = [np.asarray(text_to_sequence(text, cleaner_names)) for text in batch]
input_lengths = [len(seq) for seq in seqs]
seqs = self._prepare_inputs(seqs)
feed_dict = {
self.model.inputs: seqs,
self.model.input_lengths: np.asarray(input_lengths, dtype=np.int32),
}
linears, stop_tokens = self.session.run([self.linear_outputs, self.stop_token_prediction], feed_dict=feed_dict)
#Get Mel/Linear lengths for the entire batch from stop_tokens predictions
target_lengths = self._get_output_lengths(stop_tokens)
#Take off the batch wise padding
linears = [linear[:target_length, :] for linear, target_length in zip(linears, target_lengths)]
assert len(linears) == len(batch)
#save wav (linear -> wav)
results = []
for i, linear in enumerate(linears):
linear_wav = self.session.run(self.linear_wav_outputs, feed_dict={self.linear_spectrograms: linear})
wav = audio.inv_preemphasis(linear_wav, hparams.preemphasis)
results.append(wav)
return np.concatenate(results)
def eval(self, text):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seqs = [np.asarray(text_to_sequence(text, cleaner_names))]
input_lengths = [len(seq) for seq in seqs]
feed_dict = {
self.model.inputs: seqs,
self.model.input_lengths: np.asarray(input_lengths, dtype=np.int32),
}
linear_wavs = self.session.run(self.linear_wav_outputs, feed_dict=feed_dict)
wav = audio.inv_preemphasis(linear_wavs, hparams.preemphasis)
out = io.BytesIO()
audio.save_wav(wav, out ,sr=hparams.sample_rate)
return out.getvalue()
def eval(self, text):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seqs = [np.asarray(text_to_sequence(text, cleaner_names))]
input_lengths = [len(seq) for seq in seqs]
size_per_device = len(seqs) // self._hparams.tacotron_num_gpus
#Pad inputs according to each GPU max length
input_seqs = None
split_infos = []
for i in range(self._hparams.tacotron_num_gpus):
device_input = seqs[size_per_device * i:size_per_device * (i + 1)]
device_input, max_seq_len = self._prepare_inputs(device_input)
input_seqs = np.concatenate(
(input_seqs, device_input),
axis=1) if input_seqs is not None else device_input
split_infos.append([max_seq_len, 0, 0, 0])
feed_dict = {
self.inputs: input_seqs,
self.input_lengths: np.asarray(input_lengths, dtype=np.int32),
}
feed_dict[self.split_infos] = np.asarray(split_infos, dtype=np.int32)
#linear_wavs = self.session.run(self.linear_wav_outputs, feed_dict=feed_dict)
linear_wavs, linears, mels, alignments, stop_tokens = self.session.run(
[
self.linear_wav_outputs, self.linear_outputs, self.mel_outputs,
self.alignments, self.stop_token_prediction
],
feed_dict=feed_dict)
linear_wavs = [
linear_wav for gpu_linear_wav in linear_wavs
for linear_wav in gpu_linear_wav
]
wav = audio.inv_preemphasis(linear_wavs, hparams.preemphasis)
#audio.save_wav(wav, 'wavs/wav-1-linear.wav', sr=hparams.sample_rate)
out = io.BytesIO()
audio.save_wav(wav, out, sr=hparams.sample_rate)
return out.getvalue(), wav
def synthesize(self, texts, basenames, out_dir, log_dir, mel_filenames):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
#[-max, max] or [0,max]
T2_output_range = (
-hparams.max_abs_value,
hparams.max_abs_value) if hparams.symmetric_mels else (
0, hparams.max_abs_value)
#Repeat last sample until number of samples is dividable by the number of GPUs (last run scenario)
while len(texts) % hparams.tacotron_synthesis_batch_size != 0:
texts.append(texts[-1])
basenames.append(basenames[-1])
if mel_filenames is not None:
mel_filenames.append(mel_filenames[-1])
assert 0 == len(texts) % self._hparams.tacotron_num_gpus
seqs = [
np.asarray(text_to_sequence(text, cleaner_names)) for text in texts
]
input_lengths = [len(seq) for seq in seqs]
size_per_device = len(seqs) // self._hparams.tacotron_num_gpus
#Pad inputs according to each GPU max length
input_seqs = None
split_infos = []
for i in range(self._hparams.tacotron_num_gpus):
device_input = seqs[size_per_device * i:size_per_device * (i + 1)]
device_input, max_seq_len = self._prepare_inputs(device_input)
input_seqs = np.concatenate(
(input_seqs, device_input),
axis=1) if input_seqs is not None else device_input
split_infos.append([max_seq_len, 0, 0, 0])
feed_dict = {
self.inputs: input_seqs,
self.input_lengths: np.asarray(input_lengths, dtype=np.int32),
}
if self.gta:
np_targets = [
np.load(mel_filename) for mel_filename in mel_filenames
]
target_lengths = [len(np_target) for np_target in np_targets]
#pad targets according to each GPU max length
target_seqs = None
for i in range(self._hparams.tacotron_num_gpus):
device_target = np_targets[size_per_device *
i:size_per_device * (i + 1)]
device_target, max_target_len = self._prepare_targets(
device_target, self._hparams.outputs_per_step)
target_seqs = np.concatenate(
(target_seqs, device_target),
axis=1) if target_seqs is not None else device_target
split_infos[i][
1] = max_target_len #Not really used but setting it in case for future development maybe?
feed_dict[self.targets] = target_seqs
assert len(np_targets) == len(texts)
feed_dict[self.split_infos] = np.asarray(split_infos, dtype=np.int32)
if self.gta or not hparams.predict_linear:
mels, alignments, stop_tokens = self.session.run(
[
self.mel_outputs, self.alignments,
self.stop_token_prediction
],
feed_dict=feed_dict)
#Linearize outputs (n_gpus -> 1D)
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [
align for gpu_aligns in alignments for align in gpu_aligns
]
stop_tokens = [
token for gpu_token in stop_tokens for token in gpu_token
]
if not self.gta:
#Natural batch synthesis
#Get Mel lengths for the entire batch from stop_tokens predictions
target_lengths = self._get_output_lengths(stop_tokens)
#Take off the batch wise padding
mels = [
mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)
]
assert len(mels) == len(texts)
else:
linears, mels, alignments, stop_tokens = self.session.run(
[
self.linear_outputs, self.mel_outputs, self.alignments,
self.stop_token_prediction
],
feed_dict=feed_dict)
#Linearize outputs (1D arrays)
linears = [
linear for gpu_linear in linears for linear in gpu_linear
]
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [
align for gpu_aligns in alignments for align in gpu_aligns
]
stop_tokens = [
token for gpu_token in stop_tokens for token in gpu_token
]
#Natural batch synthesis
#Get Mel/Linear lengths for the entire batch from stop_tokens predictions
target_lengths = self._get_output_lengths(stop_tokens)
#Take off the batch wise padding
mels = [
mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)
]
linears = [
linear[:target_length, :]
for linear, target_length in zip(linears, target_lengths)
]
linears = np.clip(linears, T2_output_range[0], T2_output_range[1])
assert len(mels) == len(linears) == len(texts)
mels = np.clip(mels, T2_output_range[0], T2_output_range[1])
if basenames is None:
#Generate wav and read it
if hparams.GL_on_GPU:
wav = self.session.run(
self.GLGPU_mel_outputs,
feed_dict={self.GLGPU_mel_inputs: mels[0]})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_mel_spectrogram(mels[0].T, hparams)
audio.save_wav(wav, 'temp.wav',
sr=hparams.sample_rate) #Find a better way
if platform.system() == 'Linux':
#Linux wav reader
os.system('aplay temp.wav')
elif platform.system() == 'Windows':
#windows wav reader
os.system('start /min mplay32 /play /close temp.wav')
else:
raise RuntimeError(
'Your OS type is not supported yet, please add it to "tacotron/synthesizer.py, line-165" and feel free to make a Pull Request ;) Thanks!'
)
return
saved_mels_paths = []
speaker_ids = []
for i, mel in enumerate(mels):
#Get speaker id for global conditioning (only used with GTA generally)
if hparams.gin_channels > 0:
raise RuntimeError(
'Please set the speaker_id rule in line 99 of tacotron/synthesizer.py to allow for global condition usage later.'
)
speaker_id = '<no_g>' #set the rule to determine speaker id. By using the file basename maybe? (basenames are inside "basenames" variable)
speaker_ids.append(
speaker_id
) #finish by appending the speaker id. (allows for different speakers per batch if your model is multispeaker)
else:
speaker_id = '<no_g>'
speaker_ids.append(speaker_id)
# Write the spectrogram to disk
# Note: outputs mel-spectrogram files and target ones have same names, just different folders
mel_filename = os.path.join(out_dir,
'mel-{}.npy'.format(basenames[i]))
np.save(mel_filename, mel, allow_pickle=False)
saved_mels_paths.append(mel_filename)
if log_dir is not None:
#save wav (mel -> wav)
if hparams.GL_on_GPU:
wav = self.session.run(
self.GLGPU_mel_outputs,
feed_dict={self.GLGPU_mel_inputs: mel})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_mel_spectrogram(mel.T, hparams)
audio.save_wav(wav,
os.path.join(
log_dir,
'wavs/wav-{}-mel.wav'.format(basenames[i])),
sr=hparams.sample_rate)
#save alignments
plot.plot_alignment(alignments[i],
os.path.join(
log_dir,
'plots/alignment-{}.png'.format(
basenames[i])),
title='{}'.format(texts[i]),
split_title=True,
max_len=target_lengths[i])
#save mel spectrogram plot
plot.plot_spectrogram(
mel,
os.path.join(log_dir,
'plots/mel-{}.png'.format(basenames[i])),
title='{}'.format(texts[i]),
split_title=True)
if hparams.predict_linear:
#save wav (linear -> wav)
if hparams.GL_on_GPU:
wav = self.session.run(
self.GLGPU_lin_outputs,
feed_dict={self.GLGPU_lin_inputs: linears[i]})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_linear_spectrogram(
linears[i].T, hparams)
audio.save_wav(wav,
os.path.join(
log_dir,
'wavs/wav-{}-linear.wav'.format(
basenames[i])),
sr=hparams.sample_rate)
#save linear spectrogram plot
plot.plot_spectrogram(linears[i],
os.path.join(
log_dir,
'plots/linear-{}.png'.format(
basenames[i])),
title='{}'.format(texts[i]),
split_title=True,
auto_aspect=True)
return saved_mels_paths, speaker_ids
def train(log_dir, args, hparams):
save_dir = os.path.join(log_dir, 'taco_pretrained')
plot_dir = os.path.join(log_dir, 'plots')
wav_dir = os.path.join(log_dir, 'wavs')
mel_dir = os.path.join(log_dir, 'mel-spectrograms')
eval_dir = os.path.join(log_dir, 'eval-dir')
eval_plot_dir = os.path.join(eval_dir, 'plots')
eval_wav_dir = os.path.join(eval_dir, 'wavs')
tensorboard_dir = os.path.join(log_dir, 'tacotron_events')
meta_folder = os.path.join(log_dir, 'metas')
os.makedirs(save_dir, exist_ok=True)
os.makedirs(plot_dir, exist_ok=True)
os.makedirs(wav_dir, exist_ok=True)
os.makedirs(mel_dir, exist_ok=True)
os.makedirs(eval_dir, exist_ok=True)
os.makedirs(eval_plot_dir, exist_ok=True)
os.makedirs(eval_wav_dir, exist_ok=True)
os.makedirs(tensorboard_dir, exist_ok=True)
os.makedirs(meta_folder, exist_ok=True)
checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt')
input_path = os.path.join(args.base_dir, args.tacotron_input)
if hparams.predict_linear:
linear_dir = os.path.join(log_dir, 'linear-spectrograms')
os.makedirs(linear_dir, exist_ok=True)
log('Checkpoint path: {}'.format(checkpoint_path))
log('Loading training data from: {}'.format(input_path))
log('Using model: {}'.format(args.model))
log(hparams_debug_string())
#Start by setting a seed for repeatability
tf.set_random_seed(hparams.tacotron_random_seed)
#Set up data feeder
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
feeder = Feeder(coord, input_path, hparams)
#Set up model:
global_step = tf.Variable(0, name='global_step', trainable=False)
model, stats = model_train_mode(args, feeder, hparams, global_step)
eval_model = model_test_mode(args, feeder, hparams, global_step)
#Embeddings metadata
char_embedding_meta = os.path.join(meta_folder, 'CharacterEmbeddings.tsv')
if not os.path.isfile(char_embedding_meta):
with open(char_embedding_meta, 'w', encoding='utf-8') as f:
for symbol in symbols:
if symbol == ' ':
symbol = '\\s' #For visual purposes, swap space with \s
f.write('{}\n'.format(symbol))
char_embedding_meta = char_embedding_meta.replace(log_dir, '..')
#Potential Griffin-Lim GPU setup
if hparams.GL_on_GPU:
GLGPU_mel_inputs = tf.placeholder(tf.float32, (None, hparams.num_mels),
name='GLGPU_mel_inputs')
GLGPU_lin_inputs = tf.placeholder(tf.float32, (None, hparams.num_freq),
name='GLGPU_lin_inputs')
GLGPU_mel_outputs = audio.inv_mel_spectrogram_tensorflow(
GLGPU_mel_inputs, hparams)
GLGPU_lin_outputs = audio.inv_linear_spectrogram_tensorflow(
GLGPU_lin_inputs, hparams)
#Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=20)
log('Tacotron training set to a maximum of {} steps'.format(
args.tacotron_train_steps))
#Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
#Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph)
sess.run(tf.global_variables_initializer())
#saved model restoring
if args.restore:
# Restore saved model if the user requested it, default = True
try:
checkpoint_state = tf.train.get_checkpoint_state(save_dir)
if (checkpoint_state
and checkpoint_state.model_checkpoint_path):
log('Loading checkpoint {}'.format(
checkpoint_state.model_checkpoint_path),
slack=True)
ckpt = tf.train.load_checkpoint(
checkpoint_state.model_checkpoint_path)
variables = list(
ckpt.get_variable_to_shape_map().keys())
#print('=====================PRINTING VARS===============================')
#print(variables)
#drop_source_layers = ['Tacotron_model/inference/inputs_embedding','Tacotron_model/Tacotron_model/inference/inputs_embedding/Adam_1','Tacotron_model/Tacotron_model/inference/inputs_embedding/Adam']
#for v in tf.global_variables():
# if not any(layer in v.op.name for layer in drop_source_layers):
# print('Loading', v.op.name)
# v.load(ckpt.get_tensor(v.op.name), session=sess)
# Initialize all variables needed for DS, but not loaded from ckpt
#init_op = tf.variables_initializer([v for v in tf.global_variables() if any(layer in v.op.name for layer in drop_source_layers)])
#sess.run(init_op)
saver.restore(sess,
checkpoint_state.model_checkpoint_path)
else:
log('No model to load at {}'.format(save_dir),
slack=True)
saver.save(sess,
checkpoint_path,
global_step=global_step)
except tf.errors.OutOfRangeError as e:
log('Cannot restore checkpoint: {}'.format(e), slack=True)
else:
log('Starting new training!', slack=True)
saver.save(sess, checkpoint_path, global_step=global_step)
#initializing feeder
feeder.start_threads(sess)
#Training loop
while not coord.should_stop() and step < args.tacotron_train_steps:
start_time = time.time()
step, loss, opt = sess.run(
[global_step, model.loss, model.optimize])
time_window.append(time.time() - start_time)
loss_window.append(loss)
message = 'Step {:7d} [{:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}]'.format(
step, time_window.average, loss, loss_window.average)
log(message,
end='\r',
slack=(step % args.checkpoint_interval == 0))
if np.isnan(loss):
log('Loss exploded to {:.5f} at step {}'.format(
loss, step))
raise Exception('Loss exploded')
if step % args.summary_interval == 0:
log('\nWriting summary at step {}'.format(step))
summary_writer.add_summary(sess.run(stats), step)
if step % args.eval_interval == 0:
#Run eval and save eval stats
log('\nRunning evaluation at step {}'.format(step))
eval_losses = []
before_losses = []
after_losses = []
stop_token_losses = []
linear_losses = []
linear_loss = None
if hparams.predict_linear:
for i in tqdm(range(feeder.test_steps)):
eloss, before_loss, after_loss, stop_token_loss, linear_loss, mel_p, mel_t, t_len, align, lin_p, lin_t = sess.run(
[
eval_model.tower_loss[0],
eval_model.tower_before_loss[0],
eval_model.tower_after_loss[0],
eval_model.tower_stop_token_loss[0],
eval_model.tower_linear_loss[0],
eval_model.tower_mel_outputs[0][0],
eval_model.tower_mel_targets[0][0],
eval_model.tower_targets_lengths[0][0],
eval_model.tower_alignments[0][0],
eval_model.tower_linear_outputs[0][0],
eval_model.tower_linear_targets[0][0],
])
eval_losses.append(eloss)
before_losses.append(before_loss)
after_losses.append(after_loss)
stop_token_losses.append(stop_token_loss)
linear_losses.append(linear_loss)
linear_loss = sum(linear_losses) / len(linear_losses)
if hparams.GL_on_GPU:
wav = sess.run(GLGPU_lin_outputs,
feed_dict={GLGPU_lin_inputs: lin_p})
wav = audio.inv_preemphasis(
wav, hparams.preemphasis, hparams.preemphasize)
else:
wav = audio.inv_linear_spectrogram(
lin_p.T, hparams)
audio.save_wav(
wav,
os.path.join(
eval_wav_dir,
'step-{}-eval-wave-from-linear.wav'.format(
step)),
sr=hparams.sample_rate)
else:
for i in tqdm(range(feeder.test_steps)):
eloss, before_loss, after_loss, stop_token_loss, mel_p, mel_t, t_len, align = sess.run(
[
eval_model.tower_loss[0],
eval_model.tower_before_loss[0],
eval_model.tower_after_loss[0],
eval_model.tower_stop_token_loss[0],
eval_model.tower_mel_outputs[0][0],
eval_model.tower_mel_targets[0][0],
eval_model.tower_targets_lengths[0][0],
eval_model.tower_alignments[0][0]
])
eval_losses.append(eloss)
before_losses.append(before_loss)
after_losses.append(after_loss)
stop_token_losses.append(stop_token_loss)
eval_loss = sum(eval_losses) / len(eval_losses)
before_loss = sum(before_losses) / len(before_losses)
after_loss = sum(after_losses) / len(after_losses)
stop_token_loss = sum(stop_token_losses) / len(
stop_token_losses)
log('Saving eval log to {}..'.format(eval_dir))
#Save some log to monitor model improvement on same unseen sequence
if hparams.GL_on_GPU:
wav = sess.run(GLGPU_mel_outputs,
feed_dict={GLGPU_mel_inputs: mel_p})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_mel_spectrogram(mel_p.T, hparams)
audio.save_wav(
wav,
os.path.join(
eval_wav_dir,
'step-{}-eval-wave-from-mel.wav'.format(step)),
sr=hparams.sample_rate)
plot.plot_alignment(
align,
os.path.join(eval_plot_dir,
'step-{}-eval-align.png'.format(step)),
title='{}, {}, step={}, loss={:.5f}'.format(
args.model, time_string(), step, eval_loss),
max_len=t_len // hparams.outputs_per_step)
plot.plot_spectrogram(
mel_p,
os.path.join(
eval_plot_dir,
'step-{}-eval-mel-spectrogram.png'.format(step)),
title='{}, {}, step={}, loss={:.5f}'.format(
args.model, time_string(), step, eval_loss),
target_spectrogram=mel_t,
max_len=t_len)
if hparams.predict_linear:
plot.plot_spectrogram(
lin_p,
os.path.join(
eval_plot_dir,
'step-{}-eval-linear-spectrogram.png'.format(
step)),
title='{}, {}, step={}, loss={:.5f}'.format(
args.model, time_string(), step, eval_loss),
target_spectrogram=lin_t,
max_len=t_len,
auto_aspect=True)
log('Eval loss for global step {}: {:.3f}'.format(
step, eval_loss))
log('Writing eval summary!')
add_eval_stats(summary_writer, step, linear_loss,
before_loss, after_loss, stop_token_loss,
eval_loss)
if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps or step == 300:
#Save model and current global step
saver.save(sess, checkpoint_path, global_step=global_step)
log('\nSaving alignment, Mel-Spectrograms and griffin-lim inverted waveform..'
)
if hparams.predict_linear:
input_seq, mel_prediction, linear_prediction, alignment, target, target_length, linear_target = sess.run(
[
model.tower_inputs[0][0],
model.tower_mel_outputs[0][0],
model.tower_linear_outputs[0][0],
model.tower_alignments[0][0],
model.tower_mel_targets[0][0],
model.tower_targets_lengths[0][0],
model.tower_linear_targets[0][0],
])
#save predicted linear spectrogram to disk (debug)
linear_filename = 'linear-prediction-step-{}.npy'.format(
step)
np.save(os.path.join(linear_dir, linear_filename),
linear_prediction.T,
allow_pickle=False)
#save griffin lim inverted wav for debug (linear -> wav)
if hparams.GL_on_GPU:
wav = sess.run(GLGPU_lin_outputs,
feed_dict={
GLGPU_lin_inputs:
linear_prediction
})
wav = audio.inv_preemphasis(
wav, hparams.preemphasis, hparams.preemphasize)
else:
wav = audio.inv_linear_spectrogram(
linear_prediction.T, hparams)
audio.save_wav(
wav,
os.path.join(
wav_dir,
'step-{}-wave-from-linear.wav'.format(step)),
sr=hparams.sample_rate)
#Save real and predicted linear-spectrogram plot to disk (control purposes)
plot.plot_spectrogram(
linear_prediction,
os.path.join(
plot_dir,
'step-{}-linear-spectrogram.png'.format(step)),
title='{}, {}, step={}, loss={:.5f}'.format(
args.model, time_string(), step, loss),
target_spectrogram=linear_target,
max_len=target_length,
auto_aspect=True)
else:
input_seq, mel_prediction, alignment, target, target_length = sess.run(
[
model.tower_inputs[0][0],
model.tower_mel_outputs[0][0],
model.tower_alignments[0][0],
model.tower_mel_targets[0][0],
model.tower_targets_lengths[0][0],
])
#save predicted mel spectrogram to disk (debug)
mel_filename = 'mel-prediction-step-{}.npy'.format(step)
np.save(os.path.join(mel_dir, mel_filename),
mel_prediction.T,
allow_pickle=False)
#save griffin lim inverted wav for debug (mel -> wav)
if hparams.GL_on_GPU:
wav = sess.run(
GLGPU_mel_outputs,
feed_dict={GLGPU_mel_inputs: mel_prediction})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_mel_spectrogram(
mel_prediction.T, hparams)
audio.save_wav(
wav,
os.path.join(wav_dir,
'step-{}-wave-from-mel.wav'.format(step)),
sr=hparams.sample_rate)
#save alignment plot to disk (control purposes)
plot.plot_alignment(
alignment,
os.path.join(plot_dir,
'step-{}-align.png'.format(step)),
title='{}, {}, step={}, loss={:.5f}'.format(
args.model, time_string(), step, loss),
max_len=target_length // hparams.outputs_per_step)
#save real and predicted mel-spectrogram plot to disk (control purposes)
plot.plot_spectrogram(
mel_prediction,
os.path.join(
plot_dir,
'step-{}-mel-spectrogram.png'.format(step)),
title='{}, {}, step={}, loss={:.5f}'.format(
args.model, time_string(), step, loss),
target_spectrogram=target,
max_len=target_length)
log('Input at step {}: {}'.format(
step, sequence_to_text(input_seq)))
if step % args.embedding_interval == 0 or step == args.tacotron_train_steps or step == 1:
#Get current checkpoint state
checkpoint_state = tf.train.get_checkpoint_state(save_dir)
#Update Projector
log('\nSaving Model Character Embeddings visualization..')
add_embedding_stats(summary_writer,
[model.embedding_table.name],
[char_embedding_meta],
checkpoint_state.model_checkpoint_path)
log('Tacotron Character embeddings have been updated on tensorboard!'
)
log('Tacotron training complete after {} global steps!'.format(
args.tacotron_train_steps),
slack=True)
return save_dir
except Exception as e:
log('Exiting due to exception: {}'.format(e), slack=True)
traceback.print_exc()
coord.request_stop(e)
def synthesize(self, texts, speaker_id, basenames, out_dir, log_dir,
mel_filenames):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seqs = [
np.asarray(text_to_sequence(text, cleaner_names)) for text in texts
]
speaker_id_list = []
input_lengths = [len(seq) for seq in seqs]
seqs = self._prepare_inputs(seqs)
feed_dict = {
self.model.inputs: seqs,
self.model.input_lengths: np.asarray(input_lengths,
dtype=np.int32),
}
if self.gta:
np_targets = [
np.load(mel_filename) for mel_filename in mel_filenames
]
target_lengths = [len(np_target) for np_target in np_targets]
padded_targets = self._prepare_targets(
np_targets, self._hparams.outputs_per_step)
feed_dict[self.model.mel_targets] = padded_targets.reshape(
len(np_targets), -1, hparams.num_mels)
if self.gta or not hparams.predict_linear:
mels, alignments = self.session.run(
[self.mel_outputs, self.alignments], feed_dict=feed_dict)
if self.gta:
mels = [
mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)
] #Take off the reduction factor padding frames for time consistency with wavenet
assert len(mels) == len(np_targets)
else:
linears, mels, alignments, stop_tokens = self.session.run(
[
self.linear_outputs, self.mel_outputs, self.alignments,
self.stop_token_prediction
],
feed_dict=feed_dict)
#Get Mel/Linear lengths for the entire batch from stop_tokens predictions
target_lengths = self._get_output_lengths(stop_tokens)
#Take off the batch wise padding
mels = [
mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)
]
linears = [
linear[:target_length, :]
for linear, target_length in zip(linears, target_lengths)
]
assert len(mels) == len(linears) == len(texts)
# if basenames is None:
# #Generate wav and read it
# wav = audio.inv_mel_spectrogram(mels.T, hparams)
# audio.save_wav(wav, 'temp.wav', hparams) #Find a better way
# chunk = 512
# f = wave.open('temp.wav', 'rb')
# p = pyaudio.PyAudio()
# stream = p.open(format=p.get_format_from_width(f.getsampwidth()),
# channels=f.getnchannels(),
# rate=f.getframerate(),
# output=True)
# data = f.readframes(chunk)
# while data:
# stream.write(data)
# data=f.readframes(chunk)
# stream.stop_stream()
# stream.close()
# p.terminate()
# return
saved_mels_paths = []
speaker_ids = []
for i, mel in enumerate(mels):
#Get speaker id for global conditioning (only used with GTA generally)
speaker_id = '<no_g>'
speaker_ids.append(speaker_id)
# Write the spectrogram to disk
# Note: outputs mel-spectrogram files and target ones have same names, just different folders
mel_filename = os.path.join(out_dir, '{}.npy'.format(basenames[i]))
np.save(mel_filename, mel.T, allow_pickle=False)
saved_mels_paths.append(mel_filename)
if log_dir is not None:
#save wav (mel -> wav)
wav = audio.inv_mel_spectrogram(mel.T, hparams)
audio.save_wav(
wav,
os.path.join(log_dir,
'wavs/wav-{}-mel.wav'.format(basenames[i])),
hparams)
#save alignments
plot.plot_alignment(alignments[i],
os.path.join(
log_dir,
'plots/alignment-{}.png'.format(
basenames[i])),
info='{}'.format(texts[i]),
split_title=True)
#save mel spectrogram plot
plot.plot_spectrogram(
mel,
os.path.join(log_dir,
'plots/mel-{}.png'.format(basenames[i])),
info='{}'.format(texts[i]),
split_title=True)
if hparams.predict_linear and not self.gta:
#save wav (linear -> wav)
linear_wav = self.session.run(
self.linear_wav_outputs,
feed_dict={self.linear_spectrograms: linears[i]})
wav = audio.inv_preemphasis(linear_wav,
hparams.preemphasis)
audio.save_wav(
wav,
os.path.join(log_dir,
'wavs/wav-{}-linear.wav'.format(i)),
hparams)
#save mel spectrogram plot
plot.plot_spectrogram(linears[i],
os.path.join(
log_dir,
'plots/linear-{}.png'.format(
basenames[i])),
info='{}'.format(texts[i]),
split_title=True,
auto_aspect=True)
return saved_mels_paths, speaker_ids
def synthesize_detail(self, texts, basenames, out_dir, log_dir, mel_filenames, spknm=''):
hparams = self._hparams
# Repeat last sample until number of samples is dividable by the number of GPUs (last run scenario)
while len(texts) % hparams.tacotron_synthesis_batch_size != 0:
texts.append(texts[-1])
basenames.append(basenames[-1])
if mel_filenames is not None:
mel_filenames.append(mel_filenames[-1])
assert 0 == len(texts) % self._hparams.tacotron_num_gpus
#cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
#seqs = [np.asarray(text_to_sequence(text, cleaner_names))
# for text in texts]
seqs = [np.asarray(phoneme_str_to_seq(text))
for text in texts]
if self._hparams.multispeaker:
if isinstance(spknm, str):
spkid = np.load(spknm)
else:
spkid = spknm
spkids = [spkid for text in texts]
input_lengths = [len(seq) for seq in seqs]
size_per_device = len(seqs) // self._hparams.tacotron_num_gpus
# Pad inputs according to each GPU max length
input_seqs = None
split_infos = []
spkid_seqs = None
lang_mask_seqs = None
for i in range(self._hparams.tacotron_num_gpus):
device_input = seqs[size_per_device*i: size_per_device*(i+1)]
device_spkids = spkids[size_per_device*i: size_per_device *
(i+1)] if self._hparams.multispeaker else None
device_lang_mask = [np.asarray(list(seq_to_cnen_mask(
text_seq)), dtype=np.int32) for text_seq in device_input]
device_input, max_seq_len = self._prepare_inputs(device_input)
device_lang_mask, max_lang_seq_len = self._prepare_lang_masks(
device_lang_mask)
input_seqs = np.concatenate(
(input_seqs, device_input), axis=1) if input_seqs is not None else device_input
spkid_seqs = np.concatenate(
(spkid_seqs, device_spkids), axis=1) if spkid_seqs is not None else device_spkids
lang_mask_seqs = np.concatenate(
(lang_mask_seqs, device_lang_mask), axis=1) if lang_mask_seqs is not None else device_lang_mask
split_infos.append([max_seq_len, 0, 0, 0, 256, max_lang_seq_len])
feed_dict = {
self.inputs: input_seqs,
self.input_lengths: np.asarray(input_lengths, dtype=np.int32),
}
if self._hparams.multispeaker:
feed_dict[self.spkid_embeddings] = spkid_seqs
if self._hparams.add_lang_space:
feed_dict[self.language_masks] = lang_mask_seqs
if self.gta or self._hparams.tacotron_style_transfer or self._hparams.style_vae or self._hparams.reference_encoder:
np_targets = [np.load(mel_filename)
for mel_filename in mel_filenames]
target_lengths = [len(np_target) for np_target in np_targets]
# pad targets according to each GPU max length
target_seqs = None
for i in range(self._hparams.tacotron_num_gpus):
device_target = np_targets[size_per_device *
i: size_per_device*(i+1)]
device_target, max_target_len = self._prepare_targets(
device_target, self._hparams.outputs_per_step)
target_seqs = np.concatenate(
(target_seqs, device_target), axis=1) if target_seqs is not None else device_target
# Not really used but setting it in case for future development maybe?
split_infos[i][1] = max_target_len
feed_dict[self.targets] = target_seqs
feed_dict[self.targets_lengths] = target_lengths
assert len(np_targets) == len(texts)
feed_dict[self.split_infos] = np.asarray(split_infos, dtype=np.int32)
if self.gta or not hparams.predict_linear:
if self._hparams.tfdbg:
self.session = tf_debug.LocalCLIDebugWrapperSession(
self.session)
mels, alignments, stop_tokens = self.session.run(
[self.mel_outputs, self.alignments, self.stop_token_prediction], feed_dict=feed_dict)
# Linearize outputs (1D arrays)
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [
align for gpu_aligns in alignments for align in gpu_aligns]
stop_tokens = [
token for gpu_token in stop_tokens for token in gpu_token]
if not self.gta:
# Natural batch synthesis
# Get Mel lengths for the entire batch from stop_tokens predictions
target_lengths = self._get_output_lengths(stop_tokens)
# Take off the batch wise padding
mels = [mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)]
assert len(mels) == len(texts)
return mels, alignments, None
else:
if self._hparams.tfdbg:
self.session = tf_debug.LocalCLIDebugWrapperSession(
self.session)
linear_wavs, linears, mels, alignments, stop_tokens = self.session.run(
[self.linear_wav_outputs, self.linear_outputs, self.mel_outputs, self.alignments, self.stop_token_prediction], feed_dict=feed_dict)
print('linear_wavs: ', linear_wavs.shape)
#tf.summary.FileWriter('tflogs', graph=tf.get_default_graph())
# Linearize outputs (1D arrays)
# below line is wrong, since we are inputing only the first GPU's result to inv_spec, it will not have the array of wavs, but only the pure wav
#linear_wavs = [linear_wav for gpu_linear_wav in linear_wavs for linear_wav in gpu_linear_wav]
#print('linear_wavs2: ', len(linear_wavs))
print('SHAPES: linears: %s, mels: %s, alignments: %s, stop_tokens: %s' % (str(np.array(linears).shape), str(np.array(mels).shape), str(np.array(alignments).shape), str(np.array(stop_tokens).shape)))
linears = [
linear for gpu_linear in linears for linear in gpu_linear]
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [
align for gpu_aligns in alignments for align in gpu_aligns]
stop_tokens = [
token for gpu_token in stop_tokens for token in gpu_token]
print('SHAPES: linears: %s, mels: %s, alignments: %s, stop_tokens: %s' % (str(np.array(linears).shape), str(np.array(mels).shape), str(np.array(alignments).shape), str(np.array(stop_tokens).shape)))
stop_indicator = []
for token in stop_tokens:
ind_arr = np.where(token > 0.5)
if len(ind_arr) > 0 and len(ind_arr[0]) > 0:
stop_indicator.append(ind_arr[0][0])
else:
stop_indicator.append(9999)
# Natural batch synthesis
# Get Mel/Linear lengths for the entire batch from stop_tokens predictions
# target_lengths = self._get_output_lengths(stop_tokens)
target_lengths = [9999] * len(mels)
# Take off the batch wise padding
print("len(mels) linears, texts", len(mels), mels[0].shape, len(linears), linears[0].shape, len(
texts), len(stop_tokens), stop_tokens[0].shape, stop_indicator)
mels = [mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)]
linears = [linear[:target_length, :]
for linear, target_length in zip(linears, target_lengths)]
print("len(mels) linears, texts", len(mels),
mels[0].shape, len(linears), linears[0].shape, len(texts))
assert len(mels) == len(linears) == len(texts)
if basenames is None:
#plot.plot_spectrogram(linears[0], os.path.join(
# 'tools', 'tmp-linear-spectrogram.png'), auto_aspect=True)
#plot.plot_alignment(
# alignments[0], os.path.join('tools', 'tmp-align.png'))
#np.save(os.path.join('tools', 'linear.npy'), linears[0])
#np.save(os.path.join('tools', 'aligns.npy'), alignments)
wavs = audio.inv_preemphasis(linear_wavs, hparams.preemphasis)
cut_wavs=[]
for i, wav in enumerate(wavs):
wav = wav[:stop_indicator[i]*hparams.hop_size]
cut_wavs.append(wav)
#audio.save_wav(wav, 'tools/wav_res/temp_%02d.wav' %
# i, sr=hparams.sample_rate)
# Generate wav and read it
#wav = audio.inv_preemphasis(linear_wavs, hparams.preemphasis)
#wav = np.array(linear_wavs)
#print('wav.shape: ', wav.shape)
#wav = audio.inv_mel_spectrogram(mels[0].T, hparams)
# audio.save_wav(wav, 'temp.wav', sr=hparams.sample_rate) #Find a better way
#chunk = 512
#f = wave.open('temp.wav', 'rb')
#p = pyaudio.PyAudio()
# stream = p.open(format=p.get_format_from_width(f.getsampwidth()),
# channels=f.getnchannels(),
# rate=f.getframerate(),
# output=True)
#data = f.readframes(chunk)
# while data:
# stream.write(data)
# data=f.readframes(chunk)
# stream.stop_stream()
# stream.close()
# p.terminate()
# return None,None
return cut_wavs, hparams.sample_rate, (mels, linears, alignments)
saved_mels_paths = []
speaker_ids = []
for i, mel in enumerate(mels):
# Get speaker id for global conditioning (only used with GTA generally)
if hparams.gin_channels > 0:
raise RuntimeError(
'Please set the speaker_id rule in line 99 of tacotron/synthesizer.py to allow for global condition usage later.')
# set the rule to determine speaker id. By using the file basename maybe? (basenames are inside "basenames" variable)
speaker_id = '<no_g>'
# finish by appending the speaker id. (allows for different speakers per batch if your model is multispeaker)
speaker_ids.append(speaker_id)
else:
speaker_id = '<no_g>'
speaker_ids.append(speaker_id)
# Write the spectrogram to disk
# Note: outputs mel-spectrogram files and target ones have same names, just different folders
mel_filename = os.path.join(
out_dir, 'mel-{}.npy'.format(basenames[i]))
np.save(mel_filename, mel, allow_pickle=False)
saved_mels_paths.append(mel_filename)
if log_dir is not None:
# save wav (mel -> wav)
# wav = audio.inv_mel_spectrogram(mel.T, hparams)
# audio.save_wav(wav, os.path.join(log_dir, 'wavs/wav-{}-mel.wav'.format(basenames[i])), sr=hparams.sample_rate)
# save alignments
# plot.plot_alignment(alignments[i], os.path.join(log_dir, 'plots/alignment-{}.png'.format(basenames[i])),
# title='{}'.format(texts[i]), split_title=True, max_len=target_lengths[i])
# save mel spectrogram plot
# plot.plot_spectrogram(mel, os.path.join(log_dir, 'plots/mel-{}.png'.format(basenames[i])),
# title='{}'.format(texts[i]), split_title=True)
if hparams.predict_linear:
# save wav (linear -> wav)
wav = audio.inv_preemphasis(
linear_wavs, hparams.preemphasis)
audio.save_wav(wav, os.path.join(
log_dir, 'wavs/wav-{}-linear.wav'.format(basenames[i])), sr=hparams.sample_rate)
# save linear spectrogram plot
# plot.plot_spectrogram(linears[i], os.path.join(log_dir, 'plots/linear-{}.png'.format(basenames[i])),
# title='{}'.format(texts[i]), split_title=True, auto_aspect=True)
return saved_mels_paths, speaker_ids
def synthesize(self, texts, basenames, out_dir, log_dir, mel_filenames):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
#Repeat last sample until number of samples is dividable by the number of GPUs (last run scenario)
while len(texts) % hparams.tacotron_synthesis_batch_size != 0:
texts.append(texts[-1])
basenames.append(basenames[-1])
if mel_filenames is not None:
mel_filenames.append(mel_filenames[-1])
assert 0 == len(texts) % self._hparams.tacotron_num_gpus
seqs = [np.asarray(text_to_sequence(text, cleaner_names)) for text in texts]
input_lengths = [len(seq) for seq in seqs]
size_per_device = len(seqs) // self._hparams.tacotron_num_gpus
#Pad inputs according to each GPU max length
input_seqs = None
split_infos = []
for i in range(self._hparams.tacotron_num_gpus):
device_input = seqs[size_per_device*i: size_per_device*(i+1)]
device_input, max_seq_len = self._prepare_inputs(device_input)
input_seqs = np.concatenate((input_seqs, device_input), axis=1) if input_seqs is not None else device_input
split_infos.append([max_seq_len, 0, 0, 0])
feed_dict = {
self.inputs: input_seqs,
self.input_lengths: np.asarray(input_lengths, dtype=np.int32),
}
if self.gta:
np_targets = [np.load(mel_filename) for mel_filename in mel_filenames]
target_lengths = [len(np_target) for np_target in np_targets]
#pad targets according to each GPU max length
target_seqs = None
for i in range(self._hparams.tacotron_num_gpus):
device_target = np_targets[size_per_device*i: size_per_device*(i+1)]
device_target, max_target_len = self._prepare_targets(device_target, self._hparams.outputs_per_step)
target_seqs = np.concatenate((target_seqs, device_target), axis=1) if target_seqs is not None else device_target
split_infos[i][1] = max_target_len #Not really used but setting it in case for future development maybe?
feed_dict[self.targets] = target_seqs
assert len(np_targets) == len(texts)
feed_dict[self.split_infos] = np.asarray(split_infos, dtype=np.int32)
if self.gta or not hparams.predict_linear:
mels, alignments, stop_tokens = self.session.run([self.mel_outputs, self.alignments, self.stop_token_prediction], feed_dict=feed_dict)
#Linearize outputs (1D arrays)
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [align for gpu_aligns in alignments for align in gpu_aligns]
stop_tokens = [token for gpu_token in stop_tokens for token in gpu_token]
if not self.gta:
#Natural batch synthesis
#Get Mel lengths for the entire batch from stop_tokens predictions
target_lengths = self._get_output_lengths(stop_tokens)
#Take off the batch wise padding
mels = [mel[:target_length, :] for mel, target_length in zip(mels, target_lengths)]
assert len(mels) == len(texts)
else:
linear_wavs, linears, mels, alignments, stop_tokens = self.session.run([self.linear_wav_outputs, self.linear_outputs, self.mel_outputs, self.alignments, self.stop_token_prediction], feed_dict=feed_dict)
#Linearize outputs (1D arrays)
linear_wavs = [linear_wav for gpu_linear_wav in linear_wavs for linear_wav in gpu_linear_wav]
linears = [linear for gpu_linear in linears for linear in gpu_linear]
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [align for gpu_aligns in alignments for align in gpu_aligns]
stop_tokens = [token for gpu_token in stop_tokens for token in gpu_token]
#Natural batch synthesis
#Get Mel/Linear lengths for the entire batch from stop_tokens predictions
# target_lengths = self._get_output_lengths(stop_tokens)
target_lengths = [9999]
#Take off the batch wise padding
mels = [mel[:target_length, :] for mel, target_length in zip(mels, target_lengths)]
linears = [linear[:target_length, :] for linear, target_length in zip(linears, target_lengths)]
assert len(mels) == len(linears) == len(texts)
if basenames is None:
#Generate wav and read it
wav = audio.inv_mel_spectrogram(mels.T, hparams)
audio.save_wav(wav, 'temp.wav', sr=hparams.sample_rate) #Find a better way
chunk = 512
f = wave.open('temp.wav', 'rb')
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(f.getsampwidth()),
channels=f.getnchannels(),
rate=f.getframerate(),
output=True)
data = f.readframes(chunk)
while data:
stream.write(data)
data=f.readframes(chunk)
stream.stop_stream()
stream.close()
p.terminate()
return
saved_mels_paths = []
speaker_ids = []
for i, mel in enumerate(mels):
#Get speaker id for global conditioning (only used with GTA generally)
if hparams.gin_channels > 0:
raise RuntimeError('Please set the speaker_id rule in line 99 of tacotron/synthesizer.py to allow for global condition usage later.')
speaker_id = '<no_g>' #set the rule to determine speaker id. By using the file basename maybe? (basenames are inside "basenames" variable)
speaker_ids.append(speaker_id) #finish by appending the speaker id. (allows for different speakers per batch if your model is multispeaker)
else:
speaker_id = '<no_g>'
speaker_ids.append(speaker_id)
# Write the spectrogram to disk
# Note: outputs mel-spectrogram files and target ones have same names, just different folders
mel_filename = os.path.join(out_dir, 'mel-{}.npy'.format(basenames[i]))
np.save(mel_filename, mel, allow_pickle=False)
saved_mels_paths.append(mel_filename)
if log_dir is not None:
#save wav (mel -> wav)
# wav = audio.inv_mel_spectrogram(mel.T, hparams)
# audio.save_wav(wav, os.path.join(log_dir, 'wavs/wav-{}-mel.wav'.format(basenames[i])), sr=hparams.sample_rate)
#save alignments
# plot.plot_alignment(alignments[i], os.path.join(log_dir, 'plots/alignment-{}.png'.format(basenames[i])),
# title='{}'.format(texts[i]), split_title=True, max_len=target_lengths[i])
#save mel spectrogram plot
# plot.plot_spectrogram(mel, os.path.join(log_dir, 'plots/mel-{}.png'.format(basenames[i])),
# title='{}'.format(texts[i]), split_title=True)
if hparams.predict_linear:
#save wav (linear -> wav)
wav = audio.inv_preemphasis(linear_wavs, hparams.preemphasis)
audio.save_wav(wav, os.path.join(log_dir, 'wavs/wav-{}-linear.wav'.format(basenames[i])), sr=hparams.sample_rate)
#save linear spectrogram plot
# plot.plot_spectrogram(linears[i], os.path.join(log_dir, 'plots/linear-{}.png'.format(basenames[i])),
# title='{}'.format(texts[i]), split_title=True, auto_aspect=True)
return saved_mels_paths, speaker_ids
def synthesize(self,
texts,
basenames,
out_dir,
log_dir,
mel_filenames,
basenames_refs=None,
mel_ref_filenames_emt=None,
mel_ref_filenames_spk=None,
emb_only=False,
emt_labels_synth=None,
spk_labels_synth=None):
hparams = self._hparams
# [-max, max] or [0,max]
T2_output_range = (
-hparams.max_abs_value,
hparams.max_abs_value) if hparams.symmetric_mels else (
0, hparams.max_abs_value)
basenames, basenames_refs, input_seqs, input_lengths, split_infos, mel_ref_seqs_emt, mel_ref_seqs_spk,\
emt_labels_synth, spk_labels_synth = filenames_to_inputs(hparams, texts, basenames, mel_filenames,
basenames_refs, mel_ref_filenames_emt,
mel_ref_filenames_spk, emt_labels_synth,
spk_labels_synth)
feed_dict = {
self.inputs: input_seqs,
self.input_lengths: input_lengths,
self.mel_refs_emt: mel_ref_seqs_emt,
self.mel_refs_spk: mel_ref_seqs_spk,
self.spk_labels: spk_labels_synth,
self.emt_labels: emt_labels_synth,
self.split_infos: split_infos
}
# if self.gta:
# np_targets = [np.load(mel_filename) for mel_filename in mel_filenames]
# target_lengths = [len(np_target) for np_target in np_targets]
#
# #pad targets according to each GPU max length
# target_seqs = None
# for i in range(self._hparams.tacotron_num_gpus):
# device_target = np_targets[size_per_device*i: size_per_device*(i+1)]
# device_target, max_target_len = self._prepare_targets(device_target, self._hparams.outputs_per_step, target_pad=self._target_pad)
# target_seqs = np.concatenate((target_seqs, device_target), axis=1) if target_seqs is not None else device_target
# split_infos[i][1] = max_target_len #Not really used but setting it in case for future development maybe?
#
# feed_dict[self.targets] = target_seqs
# assert len(np_targets) == len(texts)
# feed_dict[self.split_infos] = np.asarray(split_infos, dtype=np.int32)
if emb_only:
if self.args.emt_attn:
return (self.session.run([
self.model.tower_refnet_out_emt[0],
self.model.tower_refnet_out_spk[0],
self.model.tower_refnet_outputs_mel_out_emt[0],
self.model.tower_refnet_outputs_mel_out_spk[0],
self.model.tower_context_emt[0]
],
feed_dict=feed_dict))
else:
return (self.session.run([
self.model.tower_refnet_out_emt[0],
self.model.tower_refnet_out_spk[0],
self.model.tower_refnet_outputs_mel_out_emt[0],
self.model.tower_refnet_outputs_mel_out_spk[0],
tf.constant(1.)
],
feed_dict=feed_dict))
if self.gta or not hparams.predict_linear:
if self.args.attn == 'style_tokens':
mels, alignments, stop_tokens = self.session.run(
[
self.mel_outputs, self.alignments,
self.stop_token_prediction
],
feed_dict=feed_dict)
else:
mels, alignments, stop_tokens, refnet_emt,\
ref_emt, alignments_emt = self.session.run([self.mel_outputs,self.alignments,self.stop_token_prediction,
self.model.tower_refnet_out_emt[0],self.model.tower_ref_mel_emt[0],
self.model.tower_alignments_emt],#self.model.tower_context_emt[0],#self.model.tower_refnet_out_spk[0]],
feed_dict=feed_dict)
# import pandas as pd
# df_cont = pd.DataFrame(cont[0])
# df_cont.to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\test\cont.csv')
# pd.DataFrame(refnet_spk).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\test\r_spk.csv')
# raise
# print(refnet_emt[:,0:5])
# print(refnet_spk[:,0:5])
# for i,(m1,m2,m3) in enumerate(zip(mels[0],ref_emt,ref_spk)):
# np.save('../eval/mels_save/{}_mel.npy'.format(i),m1)
# np.save('../eval/mels_save/{}_ref_emt.npy'.format(i), m2)
# np.save('../eval/mels_save/{}_ref_spk.npy'.format(i), m3)
# time.sleep(.5)
# raise
#Linearize outputs (n_gpus -> 1D)
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [
align for gpu_aligns in alignments for align in gpu_aligns
]
stop_tokens = [
token for gpu_token in stop_tokens for token in gpu_token
]
if self.args.emt_attn and not (self.args.attn == 'style_tokens'):
alignments_emt = [
align_emt for gpu_aligns_emt in alignments_emt
for align_emt in gpu_aligns_emt
]
if not self.gta:
#Natural batch synthesis
#Get Mel lengths for the entire batch from stop_tokens predictions
target_lengths = get_output_lengths(stop_tokens)
#Take off the batch wise padding
mels = [
mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)
]
assert len(mels) == len(texts)
else:
linears, mels, alignments, stop_tokens = self.session.run(
[
self.linear_outputs, self.mel_outputs, self.alignments,
self.stop_token_prediction
],
feed_dict=feed_dict)
#Linearize outputs (1D arrays)
linears = [
linear for gpu_linear in linears for linear in gpu_linear
]
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [
align for gpu_aligns in alignments for align in gpu_aligns
]
stop_tokens = [
token for gpu_token in stop_tokens for token in gpu_token
]
#Natural batch synthesis
#Get Mel/Linear lengths for the entire batch from stop_tokens predictions
target_lengths = get_output_lengths(stop_tokens)
#Take off the batch wise padding
mels = [
mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)
]
linears = [
linear[:target_length, :]
for linear, target_length in zip(linears, target_lengths)
]
linears = np.clip(linears, T2_output_range[0], T2_output_range[1])
assert len(mels) == len(linears) == len(texts)
mels = [
np.clip(m, T2_output_range[0], T2_output_range[1]) for m in mels
]
if basenames is None:
#Generate wav and read it
if hparams.GL_on_GPU:
wav = self.session.run(
self.GLGPU_mel_outputs,
feed_dict={self.GLGPU_mel_inputs: mels[0]})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_mel_spectrogram(mels[0].T, hparams)
audio.save_wav(wav, 'temp.wav',
sr=hparams.sample_rate) #Find a better way
if platform.system() == 'Linux':
#Linux wav reader
os.system('aplay temp.wav')
elif platform.system() == 'Windows':
#windows wav reader
os.system('start /min mplay32 /play /close temp.wav')
else:
raise RuntimeError(
'Your OS type is not supported yet, please add it to "tacotron/synthesizer.py, line-165" and feel free to make a Pull Request ;) Thanks!'
)
return
saved_mels_paths = []
speaker_ids = []
for i, mel in enumerate(mels):
#Get speaker id for global conditioning (only used with GTA generally)
if hparams.gin_channels > 0:
raise RuntimeError(
'Please set the speaker_id rule in line 99 of tacotron/synthesizer.py to allow for global condition usage later.'
)
speaker_id = '<no_g>' #set the rule to determine speaker id. By using the file basename maybe? (basenames are inside "basenames" variable)
speaker_ids.append(
speaker_id
) #finish by appending the speaker id. (allows for different speakers per batch if your model is multispeaker)
else:
speaker_id = '<no_g>'
speaker_ids.append(speaker_id)
if log_dir is not None:
os.makedirs(os.path.join(log_dir, 'wavs'), exist_ok=True)
os.makedirs(os.path.join(log_dir, 'plots'), exist_ok=True)
os.makedirs(os.path.join(log_dir, 'mels'), exist_ok=True)
mel_filename = os.path.join(
out_dir, 'mels',
'mel-{}_{}.npy'.format(basenames[i], basenames_refs[i]))
np.save(mel_filename, mel, allow_pickle=False)
#save wav (mel -> wav)
if hparams.GL_on_GPU:
wav = self.session.run(
self.GLGPU_mel_outputs,
feed_dict={self.GLGPU_mel_inputs: mel})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_mel_spectrogram(mel.T, hparams)
#add silence to make ending of file more noticeable
wav = np.append(
np.append(np.zeros(int(.5 * hparams.sample_rate)), wav),
np.zeros(int(.5 * hparams.sample_rate)))
audio.save_wav(wav,
os.path.join(
log_dir, 'wavs/wav-{}_{}.wav'.format(
basenames[i], basenames_refs[i])),
sr=hparams.sample_rate)
#save alignments
plot.plot_alignment(alignments[i],
os.path.join(
log_dir,
'plots/alignment-{}_{}.png'.format(
basenames[i], basenames_refs[i])),
title='{}'.format(texts[i]),
split_title=True,
max_len=target_lengths[i])
if self.args.emt_attn and self.args.attn == 'simple':
plot.plot_alignment(
alignments_emt[i],
os.path.join(
log_dir, 'plots/alignment_emt-{}_{}.png'.format(
basenames[i], basenames_refs[i])),
title='{}'.format(texts[i]),
split_title=True,
max_len=target_lengths[i])
#save mel spectrogram plot
plot.plot_spectrogram(mel,
os.path.join(
log_dir,
'plots/mel-{}_{}.png'.format(
basenames[i],
basenames_refs[i])),
title='{}'.format(texts[i]),
split_title=True)
print("Finished saving {}_{}".format(basenames[i],
basenames_refs[i]))
if hparams.predict_linear:
#save wav (linear -> wav)
if hparams.GL_on_GPU:
wav = self.session.run(
self.GLGPU_lin_outputs,
feed_dict={self.GLGPU_lin_inputs: linears[i]})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_linear_spectrogram(
linears[i].T, hparams)
audio.save_wav(wav,
os.path.join(
log_dir,
'wavs/wav-{}-linear_{}.wav'.format(
basenames[i], basenames_refs[i])),
sr=hparams.sample_rate)
#save linear spectrogram plot
plot.plot_spectrogram(linears[i],
os.path.join(
log_dir,
'plots/linear-{}_{}.png'.format(
basenames[i],
basenames_refs[i])),
title='{}'.format(texts[i]),
split_title=True,
auto_aspect=True)
return saved_mels_paths, speaker_ids
def synthesize(self, texts, basenames, out_dir, log_dir, mel_filenames):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seqs = [
np.asarray(text_to_sequence(text, cleaner_names)) for text in texts
]
input_lengths = [len(seq) for seq in seqs]
seqs = self._prepare_inputs(seqs)
feed_dict = {
self.model.inputs: seqs,
self.model.input_lengths: np.asarray(input_lengths,
dtype=np.int32),
}
if self.gta:
np_targets = [
np.load(mel_filename) for mel_filename in mel_filenames
]
target_lengths = [len(np_target) for np_target in np_targets]
padded_targets = self._prepare_targets(
np_targets, self._hparams.outputs_per_step)
feed_dict[self.model.mel_targets] = padded_targets.reshape(
len(np_targets), -1, 80)
if self.gta or not hparams.predict_linear:
mels, alignments = self.session.run(
[self.mel_outputs, self.alignments], feed_dict=feed_dict)
if self.gta:
mels = [
mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)
] #Take off the reduction factor padding frames for time consistency with wavenet
assert len(mels) == len(np_targets)
else:
linear_wavs, linears, mels, alignments = self.session.run(
[
self.linear_wav_outputs, self.linear_outputs,
self.mel_outputs, self.alignments
],
feed_dict=feed_dict)
if basenames is None:
#Generate wav and read it
wav = audio.inv_mel_spectrogram(mels.T, hparams)
audio.save_wav(wav, 'temp.wav',
sr=hparams.sample_rate) #Find a better way
chunk = 512
f = wave.open('temp.wav', 'rb')
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(f.getsampwidth()),
channels=f.getnchannels(),
rate=f.getframerate(),
output=True)
data = f.readframes(chunk)
while data:
stream.write(data)
data = f.readframes(chunk)
stream.stop_stream()
stream.close()
p.terminate()
return
saved_mels_paths = []
speaker_ids = []
for i, mel in enumerate(mels):
#Get speaker id for global conditioning (only used with GTA generally)
if hparams.gin_channels > 0:
raise RuntimeError(
'Please set the speaker_id rule in line 99 of tacotron/synthesizer.py to allow for global condition usage later.'
)
speaker_id = '<no_g>' #set the rule to determine speaker id. By using the file basename maybe? (basenames are inside "basenames" variable)
speaker_ids.append(
speaker_id
) #finish by appending the speaker id. (allows for different speakers per batch if your model is multispeaker)
else:
speaker_id = '<no_g>'
speaker_ids.append(speaker_id)
# Write the spectrogram to disk
# Note: outputs mel-spectrogram files and target ones have same names, just different folders
mel_filename = os.path.join(out_dir,
'mel-{:03d}.npy'.format(basenames[i]))
np.save(mel_filename, mel, allow_pickle=False)
saved_mels_paths.append(mel_filename)
if log_dir is not None:
#save wav (mel -> wav)
# wav = audio.inv_mel_spectrogram(mel.T, hparams)
# audio.save_wav(wav, os.path.join(log_dir, 'wavs/wav-{:03d}-mel.wav'.format(basenames[i])), sr=hparams.sample_rate)
#save alignments
# plot.plot_alignment(alignments[i], os.path.join(log_dir, 'plots/alignment-{:03d}.png'.format(basenames[i])),
# info='{}'.format(texts[i]), split_title=True)
#save mel spectrogram plot
# plot.plot_spectrogram(mel, os.path.join(log_dir, 'plots/mel-{:03d}.png'.format(basenames[i])),
# info='{}'.format(texts[i]), split_title=True)
if hparams.predict_linear:
#save wav (linear -> wav)
wav = audio.inv_preemphasis(linear_wavs,
hparams.preemphasis)
audio.save_wav(wav,
os.path.join(
log_dir,
'wavs/wav-{:03d}-linear.wav'.format(
basenames[i])),
sr=hparams.sample_rate)
#save mel spectrogram plot
# plot.plot_spectrogram(linears[i], os.path.join(log_dir, 'plots/linear-{:03d}.png'.format(basenames[i])),
# info='{}'.format(texts[i]), split_title=True, auto_aspect=True)
return saved_mels_paths, speaker_ids
def my_synthesize(self, texts, basenames, out_dir):
hparams = self._hparams
# [-max, max] or [0,max]
T2_output_range = (
-hparams.max_abs_value,
hparams.max_abs_value) if hparams.symmetric_mels else (
0, hparams.max_abs_value)
# Repeat last sample until number of samples is dividable by the number of GPUs (last run scenario)
while len(texts) % hparams.tacotron_synthesis_batch_size != 0:
texts.append(texts[-1])
basenames.append(basenames[-1])
assert 0 == len(texts) % self._hparams.tacotron_num_gpus
seqs = [np.asarray(text_to_sequence(text)) for text in texts]
input_lengths = [len(seq) for seq in seqs]
size_per_device = len(seqs) // self._hparams.tacotron_num_gpus
# Pad inputs according to each GPU max length
input_seqs = None
split_infos = []
for i in range(self._hparams.tacotron_num_gpus):
device_input = seqs[size_per_device * i:size_per_device * (i + 1)]
device_input, max_seq_len = self._prepare_inputs(device_input)
input_seqs = np.concatenate(
(input_seqs, device_input),
axis=1) if input_seqs is not None else device_input
split_infos.append([max_seq_len, 0, 0, 0])
feed_dict = {
self.inputs: input_seqs,
self.input_lengths: np.asarray(input_lengths, dtype=np.int32),
}
feed_dict[self.split_infos] = np.asarray(split_infos, dtype=np.int32)
linears, mels, alignments, stop_tokens, dur_predicts = self.session.run(
[
self.linear_outputs, self.mel_outputs, self.alignments,
self.stop_token_prediction, self.dur_predict
],
feed_dict=feed_dict)
# Linearize outputs (1D arrays)
linears = [linear for gpu_linear in linears for linear in gpu_linear]
mels = [mel for gpu_mels in mels for mel in gpu_mels]
alignments = [
align for gpu_aligns in alignments for align in gpu_aligns
]
stop_tokens = [
token for gpu_token in stop_tokens for token in gpu_token
]
dur_predicts = [dur for gpu_dur in dur_predicts for dur in gpu_dur]
# Natural batch synthesis
# Get Mel/Linear lengths for the entire batch from stop_tokens predictions
target_lengths = self._get_output_lengths(stop_tokens)
# Take off the batch wise padding
alignments = [
alignment[:, :target_length]
for alignment, target_length in zip(alignments, target_lengths)
]
mels = [
mel[:target_length, :]
for mel, target_length in zip(mels, target_lengths)
]
linears = [
linear[:target_length, :]
for linear, target_length in zip(linears, target_lengths)
]
# linears = np.clip(linears, T2_output_range[0], T2_output_range[1])
assert len(mels) == len(linears) == len(texts)
# mels = np.clip(mels, T2_output_range[0], T2_output_range[1])
out_wav_dir = os.path.join(out_dir, 'wav')
out_align_dir = os.path.join(out_dir, 'align')
os.makedirs(out_wav_dir, exist_ok=True)
os.makedirs(out_align_dir, exist_ok=True)
for i, mel in enumerate(mels):
wav_dir = os.path.join(out_wav_dir, basenames[i] + '.wav')
align_dir = os.path.join(out_align_dir, basenames[i] + '.npy')
wav = self.session.run(
self.GLGPU_lin_outputs,
feed_dict={self.GLGPU_lin_inputs: linears[i]})
wav = audio.inv_preemphasis(wav, hparams.preemphasis,
hparams.preemphasize)
audio.save_wav(wav, wav_dir, sr=hparams.sample_rate)
np.save(align_dir, alignments[i])
def train(log_dir, args, hparams):
save_dir = os.path.join(log_dir, 'taco_pretrained')
plot_dir = os.path.join(log_dir, 'plots')
wav_dir = os.path.join(log_dir, 'wavs')
mel_dir = os.path.join(log_dir, 'mel-spectrograms')
eval_dir = os.path.join(log_dir, 'eval-dir')
eval_plot_dir = os.path.join(eval_dir, 'plots')
eval_wav_dir = os.path.join(eval_dir, 'wavs')
tensorboard_dir = os.path.join(log_dir, 'tacotron_events')
meta_folder = os.path.join(log_dir, 'metas')
os.makedirs(save_dir, exist_ok=True)
os.makedirs(plot_dir, exist_ok=True)
os.makedirs(wav_dir, exist_ok=True)
os.makedirs(mel_dir, exist_ok=True)
os.makedirs(eval_dir, exist_ok=True)
os.makedirs(eval_plot_dir, exist_ok=True)
os.makedirs(eval_wav_dir, exist_ok=True)
os.makedirs(tensorboard_dir, exist_ok=True)
os.makedirs(meta_folder, exist_ok=True)
checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt')
input_path = os.path.join(args.base_dir, args.tacotron_input)
if hparams.predict_linear:
linear_dir = os.path.join(log_dir, 'linear-spectrograms')
os.makedirs(linear_dir, exist_ok=True)
log('Checkpoint path: {}'.format(checkpoint_path))
log('Loading training data from: {}'.format(input_path))
log('Using model: {}'.format(args.model))
log(hparams_debug_string())
#Start by setting a seed for repeatability
tf.set_random_seed(hparams.tacotron_random_seed)
#Set up data feeder
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
feeder = Feeder(coord, input_path, hparams, args)
#Set up model:
global_step = tf.Variable(0, name='global_step', trainable=False)
model, stats = model_train_mode(args, feeder, hparams, global_step)
eval_model = model_test_mode(args, hparams, model)
# if args.TEST:
# for v in tf.global_variables():
# print(v)
#Embeddings metadata
char_embedding_meta = os.path.join(meta_folder, 'CharacterEmbeddings.tsv')
if not os.path.isfile(char_embedding_meta):
with open(char_embedding_meta, 'w', encoding='utf-8') as f:
for symbol in symbols:
if symbol == ' ':
symbol = '\\s' #For visual purposes, swap space with \s
f.write('{}\n'.format(symbol))
char_embedding_meta = char_embedding_meta.replace(log_dir, '..')
#Potential Griffin-Lim GPU setup
if hparams.GL_on_GPU:
GLGPU_mel_inputs = tf.placeholder(tf.float32, (None, hparams.num_mels),
name='GLGPU_mel_inputs')
GLGPU_lin_inputs = tf.placeholder(tf.float32, (None, hparams.num_freq),
name='GLGPU_lin_inputs')
GLGPU_mel_outputs = audio.inv_mel_spectrogram_tensorflow(
GLGPU_mel_inputs, hparams)
GLGPU_lin_outputs = audio.inv_linear_spectrogram_tensorflow(
GLGPU_lin_inputs, hparams)
#Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
loss_bef_window = ValueWindow(100)
loss_aft_window = ValueWindow(100)
loss_stop_window = ValueWindow(100)
loss_reg_window = ValueWindow(100)
loss_emt_window = ValueWindow(100)
loss_spk_window = ValueWindow(100)
loss_orthog_window = ValueWindow(100)
loss_up_emt_window = ValueWindow(100)
loss_up_spk_window = ValueWindow(100)
loss_mo_up_emt_window = ValueWindow(100)
loss_mo_up_spk_window = ValueWindow(100)
if args.nat_gan:
d_loss_t_window = ValueWindow(100)
d_loss_p_window = ValueWindow(100)
d_loss_up_window = ValueWindow(100)
g_loss_p_window = ValueWindow(100)
g_loss_up_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=args.max_to_keep)
if args.opt_ref_no_mo and not (args.restart_optimizer_r):
print(
"WILL ATTEMPT TO RESTORE OPTIMIZER R - SET ARGS.RESTART_OPTIMIZER_R IF RETRAINING A MODEL THAT DIDN'T HAVE THE OPTIMIZER R"
)
assert (not (args.restart_nat_gan_d and args.restore_nat_gan_d_sep))
var_list = tf.global_variables()
var_list = [v for v in var_list if not ('pretrained' in v.name)]
var_list = [
v for v in var_list
if not ('nat_gan' in v.name or 'optimizer_n' in v.name)
] if (args.restart_nat_gan_d or args.restore_nat_gan_d_sep) else var_list
var_list = [
v for v in var_list
if not ('optimizer_r' in v.name or 'optimizer_3' in v.name)
] if args.restart_optimizer_r else var_list
saver_restore = tf.train.Saver(var_list=var_list)
if args.unpaired and args.pretrained_emb_disc:
saver_restore_emt_disc = tf.train.Saver(var_list=[
v for v in tf.global_variables()
if ('pretrained_ref_enc_emt' in v.name)
])
saver_restore_spk_disc = tf.train.Saver(var_list=[
v for v in tf.global_variables()
if ('pretrained_ref_enc_spk' in v.name)
])
elif args.unpaired and args.pretrained_emb_disc_all:
saver_restore_emt_disc = tf.train.Saver(var_list=[
v for v in tf.global_variables() if ('refnet_emt' in v.name)
])
saver_restore_spk_disc = tf.train.Saver(var_list=[
v for v in tf.global_variables() if ('refnet_spk' in v.name)
])
if args.nat_gan:
saver_nat_gan = tf.train.Saver(var_list=[
v for v in tf.global_variables()
if ('nat_gan' in v.name or 'optimizer_n' in v.name)
])
save_dir_nat_gan = r'nat_gan/pretrained_model'
log('Tacotron training set to a maximum of {} steps'.format(
args.tacotron_train_steps))
if hparams.tacotron_fine_tuning:
print('FINE TUNING SET TO TRUE - MAKE SURE THIS IS WHAT YOU WANT!')
#Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
eval_feed_dict, emt_labels, spk_labels, \
basenames, basenames_refs = get_eval_feed_dict(hparams, args.synth_metadata_filename,
eval_model, args.input_dir, args.flip_spk_emt)
#Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(tensorboard_dir, sess.graph)
# for x in tf.global_variables():
# print(x)
sess.run(tf.global_variables_initializer())
#saved model restoring
if args.restore:
# Restore saved model if the user requested it, default = True
try:
checkpoint_state = tf.train.get_checkpoint_state(save_dir)
if (checkpoint_state
and checkpoint_state.model_checkpoint_path):
log('Loading checkpoint {}'.format(
checkpoint_state.model_checkpoint_path),
slack=True)
saver_restore.restore(
sess, checkpoint_state.model_checkpoint_path)
else:
raise ValueError(
'No model to load at {}'.format(save_dir))
except tf.errors.OutOfRangeError as e:
log('Cannot restore checkpoint: {}'.format(e), slack=True)
else:
log('Starting new training!', slack=True)
saver.save(sess, checkpoint_path, global_step=global_step)
if args.unpaired and (args.pretrained_emb_disc
or args.pretrained_emb_disc_all):
save_dir_emt = r'spk_disc/pretrained_model_emt_disc'
checkpoint_state_emt = tf.train.get_checkpoint_state(
save_dir_emt)
saver_restore_emt_disc.restore(
sess, checkpoint_state_emt.model_checkpoint_path)
log('Loaded Emotion Discriminator from checkpoint {}'.format(
checkpoint_state_emt.model_checkpoint_path),
slack=True)
save_dir_spk = r'spk_disc/pretrained_model_spk_disc'
checkpoint_state_spk = tf.train.get_checkpoint_state(
save_dir_spk)
saver_restore_spk_disc.restore(
sess, checkpoint_state_spk.model_checkpoint_path)
log('Loaded Speaker Discriminator from checkpoint {}'.format(
checkpoint_state_spk.model_checkpoint_path),
slack=True)
if args.nat_gan and args.restore_nat_gan_d_sep:
checkpoint_state_nat_gan = tf.train.get_checkpoint_state(
save_dir_nat_gan)
saver_nat_gan.restore(
sess, checkpoint_state_nat_gan.model_checkpoint_path)
log('Loaded Nat Gan Discriminator from checkpoint {}'.format(
checkpoint_state_nat_gan.model_checkpoint_path),
slack=True)
#initializing feeder
feeder.start_threads(sess)
#Training loop
while not coord.should_stop() and step < args.tacotron_train_steps:
start_time = time.time()
# vars = [global_step, model.loss, model.optimize,model.before_loss, model.after_loss,model.stop_token_loss,
# model.regularization_loss,model.style_emb_loss_emt, model.style_emb_loss_spk, model.style_emb_orthog_loss]
# out = [step, loss, opt, bef, aft, stop, reg, loss_emt, loss_spk, loss_orthog]
# message = 'Step {:7d} {:.3f} sec/step, loss={:.5f}, avg_loss={:.5f}, bef={:.5f}, aft={:.5f}, stop={:.5f},' \
# 'reg={:.5f}, emt={:.5f}, spk={:.5f}, orthog={:.5f}'.format(step, time_window.average, loss, loss_window.average,
# loss_bef_window.average, loss_aft_window.average,
# loss_stop_window.average, loss_reg_window.average,
# loss_emt_window.average, loss_spk_window.average,
# loss_orthog_window.average)
# if args.unpaired:
# vars += [model.style_emb_loss_up_emt, model.style_emb_loss_up_spk,model.style_emb_loss_mel_out_up_emt, model.style_emb_loss_mel_out_up_spk]
# out += [loss_up_emt, loss_up_spk, loss_mo_up_emt, loss_mo_up_spk]
# message += ' up_emt={:.5f}, up_spk={:.5f}, mo_up_emt={:.5f}, mo_up_spk={:.5f}]'.format(loss_up_emt_window.average,
# loss_up_spk_window.average,
# loss_mo_up_emt_window.average,
# loss_mo_up_spk_window.average)
# if False:
# vars += [model.tower_style_emb_logit_emt[0], model.tower_emt_labels[0],model.tower_style_emb_logit_up_emt[0],
# model.tower_emt_up_labels[0],model.tower_spk_labels[0]]
# out += [emt_logit, emt_labels, emt_up_logit, emt_up_labels, spk_labels]
#
# out = sess.run([vars])
if args.nat_gan and (args.restart_nat_gan_d
or not (args.restore)) and step == 0:
log("Will start with Training Nat GAN Discriminator",
end='\r')
disc_epochs = 300 if args.unpaired else 200
disc_epochs = 0 if args.TEST else disc_epochs
for i in range(disc_epochs + 1):
d_loss_t, d_loss_p, d_loss_up,\
d_loss_t_emt, d_loss_p_emt, d_loss_up_emt, \
d_loss_t_spk, d_loss_p_spk, d_loss_up_spk, \
opt_n = sess.run([model.d_loss_targ, model.d_loss_p, model.d_loss_up,
model.d_loss_targ_emt, model.d_loss_p_emt, model.d_loss_up_emt,
model.d_loss_targ_spk, model.d_loss_p_spk, model.d_loss_up_spk,
model.optimize_n])
message = 'step: {}, d_loss_t={:.5f}, d_loss_p ={:.5f}, d_loss_up ={:.5f},' \
' d_loss_t_emt={:.5f}, d_loss_p_emt ={:.5f}, d_loss_up_emt ={:.5f},' \
' d_loss_t_spk={:.5f}, d_loss_p_spk ={:.5f}, d_loss_up_spk ={:.5f}'.format(i, d_loss_t, d_loss_p, d_loss_up,
d_loss_t_emt, d_loss_p_emt, d_loss_up_emt,
d_loss_t_spk, d_loss_p_spk, d_loss_up_spk)
log(message, end='\r')
os.makedirs(r'nat_gan', exist_ok=True)
os.makedirs(r'nat_gan/pretrained_model', exist_ok=True)
checkpoint_path_nat_gan = os.path.join(
save_dir_nat_gan, 'nat_gan_model.ckpt')
saver_nat_gan.save(sess,
checkpoint_path_nat_gan,
global_step=i)
if args.nat_gan:
d_loss_t, d_loss_p, d_loss_up, opt_n = sess.run([
model.d_loss_targ, model.d_loss_p, model.d_loss_up,
model.optimize_n
])
if args.unpaired:
step, tfr, loss, opt, bef, aft, stop, reg, loss_emt, loss_spk, loss_orthog, \
loss_up_emt, loss_up_spk, loss_mo_up_emt, loss_mo_up_spk, g_loss_p, g_loss_up, mels, opt_r\
= sess.run([global_step, model.ratio, model.loss, model.optimize,model.before_loss, model.after_loss,model.stop_token_loss,
model.regularization_loss, model.style_emb_loss_emt, model.style_emb_loss_spk, model.style_emb_orthog_loss,
model.style_emb_loss_up_emt, model.style_emb_loss_up_spk,model.style_emb_loss_mel_out_up_emt,
model.style_emb_loss_mel_out_up_spk,model.g_loss_p, model.g_loss_up, model.tower_mel_outputs[0], model.optimize_r])
else:
step, tfr, loss, opt, bef, aft, stop, reg, loss_emt, loss_spk, loss_orthog, \
loss_up_emt, loss_up_spk, loss_mo_up_emt, loss_mo_up_spk, g_loss_p, g_loss_up, mels,dec_out,opt_r = sess.run([global_step, model.helper._ratio, model.loss,
model.optimize, model.before_loss, model.after_loss, model.stop_token_loss,
model.regularization_loss, model.style_emb_loss_emt, model.style_emb_loss_spk, model.style_emb_orthog_loss,
model.style_emb_loss_up_emt, model.style_emb_loss_up_spk,model.style_emb_loss_mel_out_up_emt,
model.style_emb_loss_mel_out_up_spk, model.g_loss_p, model.g_loss_up, model.tower_mel_outputs[0],model.tower_decoder_output[0],model.optimize_r])
# step, loss, opt, bef, aft, stop, reg, loss_emt, loss_spk, loss_orthog, \
# loss_up_emt, loss_up_spk, loss_mo_up_emt, loss_mo_up_spk, g_loss_p, g_loss_up, mels,ref_emt,ref_spk,ref_up_emt,ref_up_spk,emb,enc_out,enc_out_up,\
# stop_pred, targ, inp, inp_len,targ_len,stop_targ,mels_up,dec_out,dec_out_up,opt_r\
# = sess.run([global_step, model.loss, model.optimize,model.before_loss, model.after_loss,model.stop_token_loss,
# model.regularization_loss, model.style_emb_loss_emt, model.style_emb_loss_spk, model.style_emb_orthog_loss,
# model.style_emb_loss_up_emt, model.style_emb_loss_up_spk,model.style_emb_loss_mel_out_up_emt,
# model.style_emb_loss_mel_out_up_spk,model.g_loss_p, model.g_loss_up, model.tower_mel_outputs[0],
# model.tower_refnet_out_emt[0],model.tower_refnet_out_spk[0],model.tower_refnet_out_up_emt[0],model.tower_refnet_out_up_spk[0],
# model.tower_embedded_inputs[0], model.tower_encoder_outputs[0],model.tower_encoder_outputs_up[0],model.tower_stop_token_prediction[0],
# model.tower_mel_targets[0],model.tower_inputs[0],model.tower_input_lengths[0],model.tower_targets_lengths[0],
# model.tower_stop_token_targets[0],model.tower_mel_outputs_up[0],model.tower_decoder_output[0],model.tower_decoder_output_up[0],model.optimize_r])
#
# if args.save_output_vars:
# import pandas as pd
# pd.DataFrame(emb[:, 0, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\emb.csv')
# pd.DataFrame(enc_out[:, 0, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\enc_out.csv')
# pd.DataFrame(enc_out_up[:, 0, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\enc_out_up.csv')
# pd.DataFrame(stop_pred[:, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\stop.csv')
# pd.DataFrame(targ[:, 0, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\targ.csv')
# pd.DataFrame(inp[:, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\inp.csv')
# pd.DataFrame(inp_len[:]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\inp_len.csv')
# pd.DataFrame(targ_len[:]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\targ_len.csv')
# pd.DataFrame(stop_targ[:, :]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\stop_targ.csv')
# pd.DataFrame(mels_up[:, 0, 0:5]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\mels_up.csv')
# pd.DataFrame(dec_out_up[:, 0, 0:5]).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\dec_out_up.csv')
if args.save_output_vars:
import pandas as pd
pd.DataFrame(mels[:, 0, 0:5]).to_csv(
r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\mels.csv'
)
pd.DataFrame(dec_out[:, 0, 0:5]).to_csv(
r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\dec_out.csv'
)
# import pandas as pd
# print(emt_logit.shape, emt_labels.shape)
# if len(emt_logit.shape)>2:
# emt_logit = emt_logit.squeeze(1)
# emt_up_logit = emt_up_logit.squeeze(1)
# emt_labels = emt_labels.reshape(-1,1)
# emt_up_labels = emt_up_labels.reshape(-1, 1)
# spk_labels = spk_labels.reshape(-1, 1)
# df = np.concatenate((emt_logit,emt_labels,spk_labels,emt_up_logit,emt_up_labels),axis=1)
# print(emt_labels)
# print(emt_logit)
# print(emt_up_labels)
# print(emt_up_logit)
#
# pd.DataFrame(df).to_csv(r'C:\Users\t-mawhit\Documents\code\Tacotron-2\eval\mels_save\emt_logit_.001_up_10k.csv')
# raise
time_window.append(time.time() - start_time)
loss_window.append(loss)
loss_bef_window.append(bef)
loss_aft_window.append(aft)
loss_stop_window.append(stop)
loss_reg_window.append(reg)
loss_emt_window.append(loss_emt)
loss_spk_window.append(loss_spk)
loss_orthog_window.append(loss_orthog)
loss_up_emt_window.append(loss_up_emt)
loss_up_spk_window.append(loss_up_spk)
loss_mo_up_emt_window.append(loss_mo_up_emt)
loss_mo_up_spk_window.append(loss_mo_up_spk)
if args.nat_gan:
d_loss_t_window.append(d_loss_t)
d_loss_p_window.append(d_loss_p)
d_loss_up_window.append(d_loss_up)
g_loss_p_window.append(g_loss_p)
g_loss_up_window.append(g_loss_up)
message = 'Step {:7d} {:.3f} sec/step, tfr={:.3f}, loss={:.5f}, avg_loss={:.5f}, bef={:.5f}, aft={:.5f}, stop={:.5f}, reg={:.5f}'.format(
step, time_window.average, tfr, loss, loss_window.average,
loss_bef_window.average, loss_aft_window.average,
loss_stop_window.average, loss_reg_window.average)
if args.emt_attn:
message += ' emt={:.5f}, spk={:.5f}, spk_l2={:.5f}'.format(
loss_emt_window.average, loss_spk_window.average,
loss_orthog_window.average)
else:
message += ' emt={:.5f}, spk={:.5f}, orthog={:.5f},'.format(
loss_emt_window.average, loss_spk_window.average,
loss_orthog_window.average)
if args.unpaired:
message += ' up_emt={:.5f}, up_spk={:.5f}, mo_up_emt={:.5f}, mo_up_spk={:.5f}'.format(
loss_up_emt_window.average, loss_up_spk_window.average,
loss_mo_up_emt_window.average,
loss_mo_up_spk_window.average)
if args.nat_gan:
message += ' d_loss_t={:.5f}, d_loss_p ={:.5f}, d_loss_up ={:.5f}, g_loss_p ={:.5f}, g_loss_up ={:.5f}'.format(
d_loss_t_window.average, d_loss_p_window.average,
d_loss_up_window.average, g_loss_p_window.average,
g_loss_up_window.average)
log(message,
end='\r',
slack=(step % args.checkpoint_interval == 0))
if np.isnan(loss) or loss > 100.:
log('Loss exploded to {:.5f} at step {}'.format(
loss, step))
raise Exception('Loss exploded')
if step % args.summary_interval == 0:
log('\nWriting summary at step {}'.format(step))
summary_writer.add_summary(sess.run(stats), step)
# if step % args.eval_interval == 0:
# #Run eval and save eval stats
# log('\nRunning evaluation and saving model at step {}'.format(step))
# saver.save(sess, checkpoint_path, global_step=global_step)
#
# eval_losses = []
# before_losses = []
# after_losses = []
# stop_token_losses = []
# linear_losses = []
# linear_loss = None
#
# if hparams.predict_linear:
# for i in tqdm(range(feeder.test_steps)):
# eloss, before_loss, after_loss, stop_token_loss, linear_loss, mel_p, mel_t, t_len, align, lin_p, lin_t = sess.run([
# eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0],
# eval_model.tower_stop_token_loss[0], eval_model.tower_linear_loss[0], eval_model.tower_mel_outputs[0][0],
# eval_model.tower_mel_targets[0][0], eval_model.tower_targets_lengths[0][0],
# eval_model.tower_alignments[0][0], eval_model.tower_linear_outputs[0][0],
# eval_model.tower_linear_targets[0][0],
# ])
# eval_losses.append(eloss)
# before_losses.append(before_loss)
# after_losses.append(after_loss)
# stop_token_losses.append(stop_token_loss)
# linear_losses.append(linear_loss)
# linear_loss = sum(linear_losses) / len(linear_losses)
#
# if hparams.GL_on_GPU:
# wav = sess.run(GLGPU_lin_outputs, feed_dict={GLGPU_lin_inputs: lin_p})
# wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize)
# else:
# wav = audio.inv_linear_spectrogram(lin_p.T, hparams)
# audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-wave-from-linear.wav'.format(step)), sr=hparams.sample_rate)
#
# else:
# for i in tqdm(range(feeder.test_steps)):
# eloss, before_loss, after_loss, stop_token_loss, input_seq, mel_p, mel_t, t_len, align = sess.run([
# eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0],
# eval_model.tower_stop_token_loss[0],eval_model.tower_inputs[0][0], eval_model.tower_mel_outputs[0][0],
# eval_model.tower_mel_targets[0][0],
# eval_model.tower_targets_lengths[0][0], eval_model.tower_alignments[0][0]
# ])
# eval_losses.append(eloss)
# before_losses.append(before_loss)
# after_losses.append(after_loss)
# stop_token_losses.append(stop_token_loss)
#
# eval_loss = sum(eval_losses) / len(eval_losses)
# before_loss = sum(before_losses) / len(before_losses)
# after_loss = sum(after_losses) / len(after_losses)
# stop_token_loss = sum(stop_token_losses) / len(stop_token_losses)
#
# # log('Saving eval log to {}..'.format(eval_dir))
# #Save some log to monitor model improvement on same unseen sequence
# if hparams.GL_on_GPU:
# wav = sess.run(GLGPU_mel_outputs, feed_dict={GLGPU_mel_inputs: mel_p})
# wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize)
# else:
# wav = audio.inv_mel_spectrogram(mel_p.T, hparams)
# audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate)
#
# input_seq = sequence_to_text(input_seq)
# plot.plot_alignment(align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)),
# title='{}, {}, step={}, loss={:.5f}\n{}'.format(args.model, time_string(), step, eval_loss, input_seq),
# max_len=t_len // hparams.outputs_per_step)
# plot.plot_spectrogram(mel_p, os.path.join(eval_plot_dir, 'step-{}-eval-mel-spectrogram.png'.format(step)),
# title='{}, {}, step={}, loss={:.5f}\n{}'.format(args.model, time_string(), step, eval_loss,input_seq), target_spectrogram=mel_t,
# max_len=t_len)
#
# if hparams.predict_linear:
# plot.plot_spectrogram(lin_p, os.path.join(eval_plot_dir, 'step-{}-eval-linear-spectrogram.png'.format(step)),
# title='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, eval_loss), target_spectrogram=lin_t,
# max_len=t_len, auto_aspect=True)
#
# log('Step {:7d} [eval loss: {:.3f}, before loss: {:.3f}, after loss: {:.3f}, stop loss: {:.3f}]'.format(step, eval_loss, before_loss, after_loss, stop_token_loss))
# # log('Writing eval summary!')
# add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss, stop_token_loss, eval_loss)
if step % args.checkpoint_interval == 0 or step == args.tacotron_train_steps or step == 300:
#Save model and current global step
saver.save(sess, checkpoint_path, global_step=global_step)
log('\nSaved model at step {}'.format(step))
if step % args.eval_interval == 0:
if hparams.predict_linear:
raise ValueError('predict linear not implemented')
# input_seq, mel_prediction, linear_prediction, alignment, target, target_length, linear_target = sess.run([
# model.tower_inputs[0][0],
# model.tower_mel_outputs[0][0],
# model.tower_linear_outputs[0][0],
# model.tower_alignments[0][0],
# model.tower_mel_targets[0][0],
# model.tower_targets_lengths[0][0],
# model.tower_linear_targets[0][0],
# ])
#
# #save predicted linear spectrogram to disk (debug)
# linear_filename = 'linear-prediction-step-{}.npy'.format(step)
# np.save(os.path.join(linear_dir, linear_filename), linear_prediction.T, allow_pickle=False)
#
# #save griffin lim inverted wav for debug (linear -> wav)
# if hparams.GL_on_GPU:
# wav = sess.run(GLGPU_lin_outputs, feed_dict={GLGPU_lin_inputs: linear_prediction})
# wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize)
# else:
# wav = audio.inv_linear_spectrogram(linear_prediction.T, hparams)
# audio.save_wav(wav, os.path.join(wav_dir, 'step-{}-wave-from-linear.wav'.format(step)), sr=hparams.sample_rate)
#
# #Save real and predicted linear-spectrogram plot to disk (control purposes)
# plot.plot_spectrogram(linear_prediction, os.path.join(plot_dir, 'step-{}-linear-spectrogram.png'.format(step)),
# title='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, loss), target_spectrogram=linear_target,
# max_len=target_length, auto_aspect=True)
else:
input_seqs, mels, alignments,\
stop_tokens = sess.run([eval_model.tower_inputs,
eval_model.tower_mel_outputs,
eval_model.tower_alignments,
eval_model.tower_stop_token_prediction],
feed_dict=eval_feed_dict)
# num_evals = len(input_seqs) if False else 1
# for i in range(num_evals):
# input_seq = input_seqs[i]
# mel_prediction = mel_predictions[i]
# alignment = alignments[i]
# target = targets[i]
# target_length = target_lengths[i]
# emt = emts[i]
# spk = spks[i]
# if args.emt_attn and args.attn=='simple':
# alignment_emt = alignments_emt[0][i]
# Linearize outputs (n_gpus -> 1D)
inp = [
inp for gpu_inp in input_seqs for inp in gpu_inp
]
mels = [mel for gpu_mels in mels for mel in gpu_mels]
# targets = [target for gpu_targets in targets for target in gpu_targets]
alignments = [
align for gpu_aligns in alignments
for align in gpu_aligns
]
stop_tokens = [
token for gpu_token in stop_tokens
for token in gpu_token
]
try:
target_lengths = get_output_lengths(stop_tokens)
# Take off the batch wise padding
mels = [
mel[:target_length, :]
for mel, target_length in zip(
mels, target_lengths)
]
T2_output_range = (
-hparams.max_abs_value, hparams.max_abs_value
) if hparams.symmetric_mels else (
0, hparams.max_abs_value)
mels = [
np.clip(m, T2_output_range[0],
T2_output_range[1]) for m in mels
]
folder_bucket = 'step_{}'.format(step // 500)
folder_wavs_save = os.path.join(
wav_dir, folder_bucket)
folder_plot_save = os.path.join(
plot_dir, folder_bucket)
os.makedirs(folder_wavs_save, exist_ok=True)
os.makedirs(folder_plot_save, exist_ok=True)
for i, (mel, align, basename,
basename_ref) in enumerate(
zip(mels, alignments, basenames,
basenames_refs)):
#save griffin lim inverted wav for debug (mel -> wav)
if hparams.GL_on_GPU:
wav = sess.run(
GLGPU_mel_outputs,
feed_dict={GLGPU_mel_inputs: mel})
wav = audio.inv_preemphasis(
wav, hparams.preemphasis,
hparams.preemphasize)
else:
wav = audio.inv_mel_spectrogram(
mel.T, hparams)
audio.save_wav(
wav,
os.path.join(
folder_wavs_save,
'step_{}_wav_{}_{}_{}.wav'.format(
step, i, basename, basename_ref)),
sr=hparams.sample_rate)
input_seq = sequence_to_text(inp[i])
#save alignment plot to disk (control purposes)
try:
plot.plot_alignment(
align,
os.path.join(
folder_plot_save,
'step_{}_wav_{}_{}_{}_align.png'.
format(step, i, basename,
basename_ref)),
title='{}, {}, step={}\n{}'.format(
args.model, time_string(), step,
input_seq),
max_len=target_lengths[i] //
hparams.outputs_per_step)
except:
print("failed to plot alignment")
try:
#save real and predicted mel-spectrogram plot to disk (control purposes)
plot.plot_spectrogram(
mel,
os.path.join(
folder_plot_save,
'step-{}-{}-mel-spectrogram.png'.
format(step, i)),
title='{}, {}, step={}\n{}'.format(
args.model, time_string(), step,
input_seq))
# target_spectrogram=targets[i],
# max_len=target_lengths[i])
except:
print("failed to plot spectrogram")
log('Saved synthesized samples for step {}'.format(
step),
end='\r')
except:
print("Couldn't synthesize samples")
# log('Input at step {}: {}'.format(step, input_seq), end='\r')
# if step % args.embedding_interval == 0 or step == args.tacotron_train_steps or step == 1:
# #Get current checkpoint state
# checkpoint_state = tf.train.get_checkpoint_state(save_dir)
#
# #Update Projector
# log('\nSaving Model Character Embeddings visualization..')
# add_embedding_stats(summary_writer, [model.embedding_table.name], [char_embedding_meta], checkpoint_state.model_checkpoint_path)
# log('Tacotron Character embeddings have been updated on tensorboard!')
log('Tacotron training complete after {} global steps!'.format(
args.tacotron_train_steps),
slack=True)
return save_dir
except Exception as e:
log('Exiting due to exception: {}'.format(e), slack=True)
traceback.print_exc()
coord.request_stop(e)
