def run_synthesis(args, checkpoint_path, output_dir, sentences):
metadata_filename = os.path.join(args.input_dir, 'train.txt')
print(hparams_debug_string())
synth = Synthesizer()
synth.load(checkpoint_path, gta=args.GTA)
wav = load_wav(args.reference_audio)
reference_mel = melspectrogram(wav).transpose()
with open(metadata_filename, encoding='utf-8') as f:
metadata = [line.strip().split('|') for line in f]
frame_shift_ms = hparams.hop_size / hparams.sample_rate
hours = sum([int(x[4]) for x in metadata]) * frame_shift_ms / (3600)
print('Loaded metadata for {} examples ({:.2f} hours)'.format(len(metadata), hours))
if args.GTA==True:
synth_dir = os.path.join(output_dir, 'gta')
else:
synth_dir = os.path.join(output_dir, 'natural')
#Create output path if it doesn't exist
os.makedirs(synth_dir, exist_ok=True)
os.makedirs(os.path.join(synth_dir, 'wavs/'), exist_ok=True)
print('starting synthesis')
with open(os.path.join(synth_dir, 'map.txt'), 'w') as file:
#for i, meta in enumerate(tqdm(metadata)):
#text = meta[5]
for i, text in enumerate(tqdm(sentences)):
mel_output_filename = synth.synthesize(text=text, index=i+1, out_dir=synth_dir, log_dir=None, mel_filename=None, reference_mel=reference_mel)
mels = np.load(mel_output_filename)
wav = audio.inv_mel_spectrogram(mels.T)
audio.save_wav(wav, os.path.join(synth_dir, 'wavs/speech-wav-{:05d}-mel.wav'.format(i+1)))
with open(os.path.join(synth_dir, 'wavs/speech-wav-{:05d}.txt'.format(i+1)), 'w') as tf:
tf.write(text)
if hparams.predict_linear:
# save wav (linear -> wav)
wav = audio.inv_linear_spectrogram(linear.T)
audio.save_wav(wav, os.path.join(synth_dir, 'wavs/speech-wav-{:05d}-linear.wav'.format(i+1)))
#file.write('{}|{}|{}|{}\n'.format(text, mel_filename, mel_output_filename, wav_filename))
print('synthesized mel spectrograms at {}'.format(synth_dir))
def synthesize(self, texts):
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),
}
# linears, alignments= self.session.run([self.linear_outputs, self.alignments], feed_dict=feed_dict)
linears, stop_token= self.session.run([self.linear_outputs, self.stop_token_prediction], feed_dict=feed_dict)
wav = audio.inv_linear_spectrogram(linears[0].T, hparams)
return wav
def train(log_dir, args):
save_dir = os.path.join(log_dir, 'pretrained/')
checkpoint_path = os.path.join(save_dir, 'model.ckpt')
input_path = os.path.join(args.base_dir, args.input)
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')
os.makedirs(plot_dir, exist_ok=True)
os.makedirs(wav_dir, exist_ok=True)
os.makedirs(mel_dir, exist_ok=True)
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())
#Set up data feeder
coord = tf.train.Coordinator()
with tf.variable_scope('datafeeder') as scope:
feeder = Feeder(coord, input_path, hparams)
#Set up model:
step_count = 0
try:
#simple text file to keep count of global step
with open(os.path.join(log_dir, 'step_counter.txt'), 'r') as file:
step_count = int(file.read())
except:
print(
'no step_counter file found, assuming there is no saved checkpoint'
)
global_step = tf.Variable(step_count, name='global_step', trainable=False)
with tf.variable_scope('model') as scope:
model = create_model(args.model, hparams)
if hparams.predict_linear:
model.initialize(feeder.inputs, feeder.input_lengths,
feeder.mel_targets, feeder.token_targets,
feeder.linear_targets)
else:
model.initialize(feeder.inputs, feeder.input_lengths,
feeder.mel_targets, feeder.token_targets)
model.add_loss()
model.add_optimizer(global_step)
stats = add_stats(model)
#Book keeping
step = 0
save_step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=5)
#Memory allocation on the GPU as needed
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(log_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)
except tf.errors.OutOfRangeError as e:
log('Cannot restore checkpoint: {}'.format(e))
if (checkpoint_state and checkpoint_state.model_checkpoint_path):
log('Loading checkpoint {}'.format(
checkpoint_state.model_checkpoint_path))
saver.restore(sess, checkpoint_state.model_checkpoint_path)
else:
if not args.restore:
log('Starting new training!')
else:
log('No model to load at {}'.format(save_dir))
#initiating feeder
feeder.start_in_session(sess)
#Training loop
while not coord.should_stop():
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')
if loss > 100 or 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.checkpoint_interval == 0:
with open(os.path.join(log_dir, 'step_counter.txt'),
'w') as file:
file.write(str(step))
log('Saving checkpoint to: {}-{}'.format(
checkpoint_path, step))
saver.save(sess, checkpoint_path, global_step=step)
save_step = step
log('Saving alignment, Mel-Spectrograms and griffin-lim inverted waveform..'
)
if hparams.predict_linear:
input_seq, mel_prediction, linear_prediction, alignment, target = sess.run(
[
model.inputs[0],
model.mel_outputs[0],
model.linear_outputs[0],
model.alignments[0],
model.mel_targets[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)
wav = audio.inv_linear_spectrogram(linear_prediction.T)
audio.save_wav(
wav,
os.path.join(
wav_dir,
'step-{}-waveform-linear.wav'.format(step)))
else:
input_seq, mel_prediction, alignment, target = sess.run(
[
model.inputs[0],
model.mel_outputs[0],
model.alignments[0],
model.mel_targets[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)
wav = audio.inv_mel_spectrogram(mel_prediction.T)
audio.save_wav(
wav,
os.path.join(wav_dir,
'step-{}-waveform-mel.wav'.format(step)))
#save alignment plot to disk (control purposes)
plot.plot_alignment(
alignment,
os.path.join(plot_dir,
'step-{}-align.png'.format(step)),
info='{}, {}, step={}, loss={:.5f}'.format(
args.model, time_string(), step, loss))
#save real mel-spectrogram plot to disk (control purposes)
plot.plot_spectrogram(
target,
os.path.join(
plot_dir,
'step-{}-real-mel-spectrogram.png'.format(step)),
info='{}, {}, step={}, Real'.format(
args.model, time_string(), step, loss))
#save predicted mel-spectrogram plot to disk (control purposes)
plot.plot_spectrogram(
mel_prediction,
os.path.join(
plot_dir,
'step-{}-pred-mel-spectrogram.png'.format(step)),
info='{}, {}, step={}, loss={:.5}'.format(
args.model, time_string(), step, loss))
log('Input at step {}: {}'.format(
step, sequence_to_text(input_seq)))
except Exception as e:
log('Exiting due to exception: {}'.format(e), slack=True)
traceback.print_exc()
coord.request_stop(e)
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)
# with open("split_train.txt", "w") as file:
# for line in feeder._train_meta:
# for k in range(len(line)-1):
# file.write(line[k]+"|")
# file.write(line[-1]+"\n")
# with open("split_validation.txt", "w") as file:
# for line in feeder._test_meta:
# for k in range(len(line)-1):
# file.write(line[k]+"|")
# file.write(line[-1]+"\n")
# print("Feeder init done !")
# assert False
# 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)
# TODO Visualize embeddings
# Embeddings inputs 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, '..')
# # Embeddings speaker metadata
# speaker_embedding_meta = os.path.join(meta_folder, 'SpeakerEmbeddings.tsv')
# if not os.path.isfile(speaker_embedding_meta):
# with open(speaker_embedding_meta, 'w', encoding='utf-8') as f:
# f.write("Filename\tSpeaker\n")
# for description in feeder._metadata:
# f.write('{}\t{}\n'.format(description[1], description[-1]))
# speaker_embedding_meta = speaker_embedding_meta.replace(log_dir, '..')
# Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=5)
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)
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 loss > 100 or 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
speaker_losses = []
speaker_loss = None
eval_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]
]
if hparams.predict_linear:
eval_run.append(eval_model.tower_linear_loss[0])
eval_run.append(eval_model.tower_linear_outputs[0][0])
eval_run.append(eval_model.tower_linear_targets[0][0])
if hparams.tacotron_multi_speaker:
eval_run.append(eval_model.tower_speaker_loss[0])
for i in tqdm(range(feeder.test_steps)):
blob = sess.run(eval_run)
eloss = blob[0]
before_loss = blob[1]
after_loss = blob[2]
stop_token_loss = blob[3]
mel_p = blob[4]
mel_t = blob[5]
t_len = blob[6]
align = blob[7]
if hparams.predict_linear:
linear_loss = blob[8]
lin_p = blob[9]
lin_t = blob[10]
if hparams.tacotron_multi_speaker:
speaker_p = blob[11]
eval_losses.append(eloss)
before_losses.append(before_loss)
after_losses.append(after_loss)
stop_token_losses.append(stop_token_loss)
if hparams.predict_linear:
linear_losses.append(linear_loss)
if hparams.tacotron_multi_speaker:
speaker_losses.append(speaker_p)
if hparams.predict_linear:
linear_loss = sum(linear_losses) / len(linear_losses)
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)
if hparams.tacotron_multi_speaker:
speaker_loss = sum(speaker_losses) / len(
speaker_losses)
# 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)
# # print("len(eval_loss) : {}".format(len(eval_loss)))
# # print("len(before_losses) : {}".format(len(before_losses)))
# # print("len(after_losses) : {}".format(len(after_losses)))
# # print("len(stop_token_losses) : {}".format(len(stop_token_losses)))
# # print("len(linear_losses) : {}".format(len(linear_losses)))
# # print("division par : {}, dans hparams.predict_linear".format(len(linear_losses)))
# linear_loss = sum(linear_losses) / len(linear_losses)
#
# 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)
# print("len(eval_loss) : {}".format(len(eval_loss)))
# print("len(before_losses) : {}".format(len(before_losses)))
# print("len(after_losses) : {}".format(len(after_losses)))
# print("len(stop_token_losses) : {}".format(len(stop_token_losses)))
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
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, speaker_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)
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)
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)
# TODO Find a way to revert encoded IPA to original IPA or original text
# 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_backup state
# checkpoint_state = tf.train.get_checkpoint_state(save_dir)
checkpoint_state = tf.train.get_checkpoint_state(save_dir)
# TODO Visualize embeddings
#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)
# add_embedding_stats(summary_writer, [model.embedding_speaker.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,
basenames,
out_dir,
log_dir,
mel_filenames,
mel_reference_filenames=None):
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])
if mel_reference_filenames is not None:
mel_reference_filenames.append(mel_reference_filenames[-1])
assert 0 == len(texts) % self._hparams.tacotron_num_gpus
if not self._hparams.tacotron_phoneme_transcription:
seqs = [
np.asarray(text_to_sequence(text, cleaner_names))
for text in texts
]
else:
seqs = [
np.asarray(ipa_to_articulatory_sequence(text), dtype=np.int32)
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 mel_reference_filenames is not None:
np_references = [
np.load(mel_reference_filename)
for mel_reference_filename in mel_reference_filenames
]
# pad references according to each GPU max length
reference_seqs = None
for i in range(self._hparams.tacotron_num_gpus):
device_reference = np_references[size_per_device *
i:size_per_device * (i + 1)]
device_reference, max_reference_len = self._prepare_targets(
device_reference, self._hparams.outputs_per_step)
reference_seqs = np.concatenate(
(reference_seqs, device_reference),
axis=1) if reference_seqs is not None else device_reference
# split_infos[i][
# 1] = max_target_len # Not really used but setting it in case for future development maybe?
feed_dict[self.mel_references] = reference_seqs
# assert len(np_targets) == len(texts)
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:
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)
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_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 synthesize(self, text, index, out_dir, log_dir, mel_filename):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seq = text_to_sequence(text, cleaner_names)
feed_dict = {
self.model.inputs: [np.asarray(seq, dtype=np.int32)],
self.model.input_lengths: np.asarray([len(seq)], dtype=np.int32),
}
if self.gta:
feed_dict[self.model.mel_targets] = np.load(mel_filename).reshape(
1, -1, 80)
if self.gta or not hparams.predict_linear:
mels, alignment = self.session.run(
[self.mel_outputs, self.alignment], feed_dict=feed_dict)
else:
linear, mels, alignment = self.session.run(
[self.linear_outputs, self.mel_outputs, self.alignment],
feed_dict=feed_dict)
linear = linear.reshape(-1, hparams.num_freq)
mels = mels.reshape(
-1, hparams.num_mels) #Thanks to @imdatsolak for pointing this out
if index 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
#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 89 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 "index" variable)
else:
speaker_id = '<no_g>'
# 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(index))
np.save(mel_filename, mels, allow_pickle=False)
if log_dir is not None:
#save wav (mel -> wav)
wav = audio.inv_mel_spectrogram(mels.T, hparams)
audio.save_wav(wav,
os.path.join(log_dir,
'wavs/wav-{}-mel.wav'.format(index)),
sr=hparams.sample_rate)
if hparams.predict_linear:
#save wav (linear -> wav)
wav = audio.inv_linear_spectrogram(linear.T, hparams)
audio.save_wav(wav,
os.path.join(
log_dir,
'wavs/wav-{}-linear.wav'.format(index)),
sr=hparams.sample_rate)
#save alignments
plot.plot_alignment(alignment,
os.path.join(
log_dir,
'plots/alignment-{}.png'.format(index)),
info='{}'.format(text),
split_title=True)
#save mel spectrogram plot
plot.plot_spectrogram(mels,
os.path.join(
log_dir,
'plots/mel-{}.png'.format(index)),
info='{}'.format(text),
split_title=True)
return mel_filename, speaker_id
def synthesize(self, text, index, out_dir, log_dir, mel_filename):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seq = text_to_sequence(text, cleaner_names)
feed_dict = {
self.model.inputs: [np.asarray(seq, dtype=np.int32)],
self.model.input_lengths: np.asarray([len(seq)], dtype=np.int32),
}
if self.gta:
feed_dict[self.model.mel_targets] = np.load(mel_filename).reshape(1, -1, 80)
if self.gta or not hparams.predict_linear:
mels, alignment = self.session.run([self.mel_outputs, self.alignment], feed_dict=feed_dict)
else:
linear, mels, alignment = self.session.run([self.linear_outputs, self.mel_outputs, self.alignment], feed_dict=feed_dict)
linear = linear.reshape(-1, hparams.num_freq)
mels = mels.reshape(-1, hparams.num_mels) #Thanks to @imdatsolak for pointing this out
if index 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
# 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, 'speech-mel-{:05d}.npy'.format(index))
np.save(mel_filename, mels, allow_pickle=False)
if log_dir is not None:
#save wav (mel -> wav)
wav = audio.inv_mel_spectrogram(mels.T, hparams)
audio.save_wav(wav, os.path.join(log_dir, 'wavs/speech-wav-{:05d}-mel.wav'.format(index)), sr=hparams.sample_rate)
if hparams.predict_linear:
#save wav (linear -> wav)
wav = audio.inv_linear_spectrogram(linear.T, hparams)
audio.save_wav(wav, os.path.join(log_dir, 'wavs/speech-wav-{:05d}-linear.wav'.format(index)), sr=hparams.sample_rate)
#save alignments
plot.plot_alignment(alignment, os.path.join(log_dir, 'plots/speech-alignment-{:05d}.png'.format(index)),
info='{}'.format(text), split_title=True)
#save mel spectrogram plot
plot.plot_spectrogram(mels, os.path.join(log_dir, 'plots/speech-mel-{:05d}.png'.format(index)),
info='{}'.format(text), split_title=True)
return mel_filename
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/')
checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt')
input_path = os.path.join(args.base_dir, args.tacotron_input)
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')
os.makedirs(eval_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_plot_dir, exist_ok=True)
os.makedirs(eval_wav_dir, exist_ok=True)
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)
#Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=5)
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
#Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(log_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)
except tf.errors.OutOfRangeError as e:
log('Cannot restore checkpoint: {}'.format(e))
if (checkpoint_state and checkpoint_state.model_checkpoint_path):
log('Loading checkpoint {}'.format(checkpoint_state.model_checkpoint_path))
saver.restore(sess, checkpoint_state.model_checkpoint_path)
else:
if not args.restore:
log('Starting new training!')
else:
log('No model to load at {}'.format(save_dir))
#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')
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 = sess.run(
[eval_model.loss, eval_model.before_loss, eval_model.after_loss,
eval_model.stop_token_loss, eval_model.linear_loss, eval_model.mel_outputs[0],
eval_model.mel_targets[0], eval_model.targets_lengths[0],
eval_model.alignments[0], eval_model.linear_outputs[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)
wav = audio.inv_linear_spectrogram(lin_p.T, hparams)
audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-waveform-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.loss, eval_model.before_loss, eval_model.after_loss,
eval_model.stop_token_loss, eval_model.mel_outputs[0], eval_model.mel_targets[0],
eval_model.targets_lengths[0], eval_model.alignments[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
wav = audio.inv_mel_spectrogram(mel_p.T, hparams)
audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-waveform-mel.wav'.format(step)), sr=hparams.sample_rate)
plot.plot_alignment(align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)),
info='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, eloss),
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)),
info='{}, {}, step={}, loss={:.5}'.format(args.model, time_string(), step, eloss), target_spectrogram=mel_t,
max_len=t_len)
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:
#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 = sess.run([
model.inputs[0],
model.mel_outputs[0],
model.linear_outputs[0],
model.alignments[0],
model.mel_targets[0],
model.targets_lengths[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)
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)
else:
input_seq, mel_prediction, alignment, target, target_length = sess.run([model.inputs[0],
model.mel_outputs[0],
model.alignments[0],
model.mel_targets[0],
model.targets_lengths[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)
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)),
info='{}, {}, 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)),
info='{}, {}, step={}, loss={:.5}'.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)))
log('Tacotron training complete after {} global steps!'.format(args.tacotron_train_steps))
return save_dir
except Exception as e:
log('Exiting due to exception: {}'.format(e))
traceback.print_exc()
coord.request_stop(e)
def synthesize(self, text, index, out_dir, log_dir, mel_filename,
reference_mel):
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
seq = text_to_sequence(text, cleaner_names)
feed_dict = {
self.model.inputs: [np.asarray(seq, dtype=np.int32)],
self.model.input_lengths: np.asarray([len(seq)], dtype=np.int32)
}
if self.gta:
feed_dict[self.model.mel_targets] = np.load(mel_filename).reshape(
1, -1, 80)
feed_dict[self.model.reference_mel] = np.load(
mel_filename).reshape(1, -1, 80)
elif hparams.use_vae:
reference_mel = [np.asarray(reference_mel, dtype=np.float32)]
feed_dict[self.model.reference_mel] = reference_mel
if self.gta or not hparams.predict_linear:
mels, alignment = self.session.run(
[self.mel_outputs, self.alignment], feed_dict=feed_dict)
else:
linear, mels, alignment = self.session.run(
[self.linear_outputs, self.mel_outputs, self.alignment],
feed_dict=feed_dict)
linear = linear.reshape(-1, hparams.num_freq)
mels = mels.reshape(
-1, hparams.num_mels) #Thanks to @imdatsolak for pointing this out
# 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,
'speech-mel-{:05d}.npy'.format(index))
np.save(mel_filename, mels, allow_pickle=False)
if log_dir is not None:
#save wav (mel -> wav)
wav = audio.inv_mel_spectrogram(mels.T)
audio.save_wav(
wav,
os.path.join(log_dir,
'wavs/speech-wav-{:05d}-mel.wav'.format(index)))
if hparams.predict_linear:
#save wav (linear -> wav)
wav = audio.inv_linear_spectrogram(linear.T)
audio.save_wav(
wav,
os.path.join(
log_dir,
'wavs/speech-wav-{:05d}-linear.wav'.format(index)))
#save alignments
plot.plot_alignment(
alignment,
os.path.join(
log_dir,
'plots/speech-alignment-{:05d}.png'.format(index)),
info='{}'.format(text),
split_title=True)
#save mel spectrogram plot
plot.plot_spectrogram(
mels,
os.path.join(log_dir,
'plots/speech-mel-{:05d}.png'.format(index)),
info='{}'.format(text),
split_title=True)
return mel_filename
def synthesize(self, texts, speakers, basenames, out_dir, log_dir,
mel_filenames, reference_mels, Lf0s):
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])
speakers.append(speakers[-1])
if mel_filenames is not None:
mel_filenames.append(mel_filenames[-1])
if reference_mels is not None:
reference_mels.append(reference_mels[-1])
assert 0 == len(texts) % self._hparams.tacotron_num_gpus
seqs = texts #[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, 0, 0])
feed_dict = {
self.inputs: input_seqs,
self.input_lengths: np.asarray(input_lengths, dtype=np.int32),
self.speakers: np.asarray(speakers, 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)
if reference_mels is not None:
np_refs = [
np.asarray(reference_mel) for reference_mel in reference_mels
]
reference_lengths = [len(np_ref) for np_ref in np_refs]
ref_seqs = None
for i in range(self._hparams.tacotron_num_gpus):
device_ref = np_refs[size_per_device * i:size_per_device *
(i + 1)]
device_ref, max_ref_len = self._prepare_targets(
device_ref, self._hparams.outputs_per_step)
ref_seqs = np.concatenate(
(ref_seqs, device_ref),
axis=1) if ref_seqs is not None else device_ref
split_infos[i][-1] = max_ref_len
feed_dict[self.reference_mels] = ref_seqs
feed_dict[self.reference_lengths] = reference_lengths
assert len(np_refs) == len(texts)
##2020.7.24 加入lf0
if Lf0s is not None:
np_Lf0s = [np.asarray(Lf0) for Lf0 in Lf0s]
Lf0_lengths = [len(np_Lf0) for np_Lf0 in np_Lf0s]
Lf0_seqs = None
for i in range(self._hparams.tacotron_num_gpus):
device_Lf0 = np_Lf0s[size_per_device * i:size_per_device *
(i + 1)]
device_Lf0, max_Lf0_len = self._prepare_F0_inputs(
device_Lf0, max_seq_len) #保证不要因为分帧问题导致不一样的长度
#device_Lf0, max_Lf0_len = self._prepare_targets(device_Lf0, self._hparams.outputs_per_step)
Lf0_seqs = np.concatenate(
(Lf0_seqs, device_Lf0),
axis=1) if Lf0_seqs is not None else device_Lf0
split_infos[i][-1] = max_Lf0_len
feed_dict[self.Lf0s] = Lf0_seqs
assert len(np_Lf0s) == len(texts)
if Lf0_seqs.shape[-1] != 2:
print(2333)
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
target_lengths = [9999]
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)
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 = []
for i, mel in enumerate(mels):
# 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='speaker_id = {:d}'.format(speakers[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='speaker_id = {:d}'.format(speakers[i]),
split_title=True)
if hparams.predict_linear:
#save wav (linear -> wav)
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='speaker_id = {:d}'.format(speakers[i]),
split_title=True,
auto_aspect=True)
return saved_mels_paths
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
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)
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)
def spec2wav(linears, hparams, save_path):
wav = audio.inv_linear_spectrogram(linears.T, hparams)
audio.save_wav(wav, save_path, sr=hparams.sample_rate)
def synthesize(self, text, index, out_dir, log_dir, mel_filename, speaker_id):
hparams = self._hparams
cleaner_names = [x.strip() for x in hparams.cleaners.split(',')]
if is_korean_text(text):
text = normalize_number(text)
text = split_to_jamo(text, cleaner_names)
seq = text_to_sequence(text, cleaner_names)
feed_dict = {
self.model.inputs: [np.asarray(seq, dtype=np.int32)],
self.model.input_lengths: np.asarray([len(seq)], dtype=np.int32),
self.model.speaker_ids: np.asarray([speaker_id], dtype=np.int32)
}
if self.gta:
feed_dict[self.model.mel_targets] = np.load(mel_filename).reshape(1, -1, 80)
if self.gta or not hparams.predict_linear:
mels, alignment = self.session.run([self.mel_outputs, self.alignment], feed_dict=feed_dict)
else:
linear, mels, alignment = self.session.run([self.linear_outputs, self.mel_outputs, self.alignment],
feed_dict=feed_dict)
linear = linear.reshape(-1, hparams.num_freq)
mels = mels.reshape(-1, hparams.num_mels) # Thanks to @imdatsolak for pointing this out
if index 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
# 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, 'speech-mel-{:05d}.npy'.format(index))
np.save(mel_filename, mels, allow_pickle=False)
if log_dir is not None:
# save wav (mel -> wav)
wav = audio.inv_mel_spectrogram(mels.T, hparams)
audio.save_wav(wav, os.path.join(log_dir, 'wavs/speech-wav-{:05d}-mel.wav'.format(index)),
sr=hparams.sample_rate)
if hparams.predict_linear:
# save wav (linear -> wav)
wav = audio.inv_linear_spectrogram(linear.T, hparams)
audio.save_wav(wav, os.path.join(log_dir, 'wavs/speech-wav-{:05d}-linear.wav'.format(index)),
sr=hparams.sample_rate)
if is_korean_char(text):
text = j2h(text)
# save alignments
plot.plot_alignment(alignment, os.path.join(log_dir, 'plots/speech-alignment-{:05d}.png'.format(index)),
info='{}'.format(text), split_title=True)
# save mel spectrogram plot
plot.plot_spectrogram(mels, os.path.join(log_dir, 'plots/speech-mel-{:05d}.png'.format(index)),
info='{}'.format(text), split_title=True)
return mel_filename
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, 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:
linears, mels, alignments = self.session.run([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', 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])), 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)
# 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_linear_spectrogram(linears[i].T, hparams)
audio.save_wav(wav, os.path.join(log_dir, 'wavs/wav-{}-linear.wav'.format(basenames[i])), hparams.sample_rate)
# save mel 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/')
checkpoint_path = os.path.join(save_dir, 'tacotron_model.ckpt')
input_path = os.path.join(args.base_dir, args.tacotron_input)
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')
os.makedirs(eval_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_plot_dir, exist_ok=True)
os.makedirs(eval_wav_dir, exist_ok=True)
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)
#Book keeping
step = 0
time_window = ValueWindow(100)
loss_window = ValueWindow(100)
saver = tf.train.Saver(max_to_keep=5)
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
#Train
with tf.Session(config=config) as sess:
try:
summary_writer = tf.summary.FileWriter(log_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)
except tf.errors.OutOfRangeError as e:
log('Cannot restore checkpoint: {}'.format(e))
if (checkpoint_state and checkpoint_state.model_checkpoint_path):
log('Loading checkpoint {}'.format(checkpoint_state.model_checkpoint_path))
saver.restore(sess, checkpoint_state.model_checkpoint_path)
else:
if not args.restore:
log('Starting new training!')
else:
log('No model to load at {}'.format(save_dir))
#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')
if loss > 1000 or 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 = sess.run(
[eval_model.loss, eval_model.before_loss, eval_model.after_loss,
eval_model.stop_token_loss, eval_model.linear_loss, eval_model.mel_outputs[0],
eval_model.mel_targets[0], eval_model.targets_lengths[0],
eval_model.alignments[0], eval_model.linear_outputs[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)
wav = audio.inv_linear_spectrogram(lin_p.T, hparams)
audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-waveform-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.loss, eval_model.before_loss, eval_model.after_loss,
eval_model.stop_token_loss, eval_model.mel_outputs[0], eval_model.mel_targets[0],
eval_model.targets_lengths[0], eval_model.alignments[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
wav = audio.inv_mel_spectrogram(mel_p.T, hparams)
audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-waveform-mel.wav'.format(step)), sr=hparams.sample_rate)
plot.plot_alignment(align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)),
info='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, eloss),
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)),
info='{}, {}, step={}, loss={:.5}'.format(args.model, time_string(), step, eloss), target_spectrogram=mel_t,
max_len=t_len)
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:
#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 = sess.run([
model.inputs[0],
model.mel_outputs[0],
model.linear_outputs[0],
model.alignments[0],
model.mel_targets[0],
model.targets_lengths[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)
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)
else:
input_seq, mel_prediction, alignment, target, target_length = sess.run([model.inputs[0],
model.mel_outputs[0],
model.alignments[0],
model.mel_targets[0],
model.targets_lengths[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)
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)),
info='{}, {}, 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)),
info='{}, {}, step={}, loss={:.5}'.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)))
log('Tacotron training complete after {} global steps!'.format(args.tacotron_train_steps))
return save_dir
except Exception as e:
log('Exiting due to exception: {}'.format(e))
traceback.print_exc()
coord.request_stop(e)
def synthesize(self, texts, basenames, out_dir, log_dir, mel_filenames):
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.synthesis_batch_size != 0:
texts.append(texts[-1])
basenames.append(basenames[-1])
if mel_filenames is not None:
mel_filenames.append(mel_filenames[-1])
seqs = [np.asarray(text_to_sequence(text)) for text in texts]
input_lengths = [len(seq) for seq in seqs]
input_seqs, max_seq_len = self._prepare_inputs(seqs)
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]
target_seqs, max_target_len = self._prepare_targets(np_targets, self._hparams.outputs_per_step)
feed_dict[self.targets] = target_seqs
assert len(np_targets) == len(texts)
linears = None
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)
# 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)
# 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
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 "synthesizer.py, line-165" and feel free to make a Pull Request ;) Thanks!')
return
saved_mels_paths = []
for i, mel in enumerate(mels):
# 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 linears:
# save wav (linear -> wav)
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
