def get_and_plot_alignments(hp, epoch, attention_graph, sess, attention_inputs, attention_mels, alignment_dir):
return_values = sess.run([attention_graph.alignments], # use attention_graph to obtain attention maps for a few given inputs and mels
{attention_graph.L: attention_inputs,
attention_graph.mels: attention_mels})
alignments = return_values[0] # sess run returns a list, so unpack this list
for i in range(hp.num_sentences_to_plot_attention):
plot_alignment(hp, alignments[i], i+1, epoch, dir=alignment_dir)
def synthesis(test_lines, model, device, log_dir):
global global_epoch
global global_step
synthesis_dir = os.path.join(log_dir, "synthesis_mels")
os.makedirs(synthesis_dir, exist_ok=True)
model.eval()
with torch.no_grad():
for idx, line in enumerate(test_lines):
txt = text_to_seq(line)
if device > -1:
txt = txt.cuda(device)
frames, _, _, alignment = model(txt)
dst_alignment_path = join(
synthesis_dir, "{}_alignment_{}.png".format(global_step, idx))
dst_mels_path = join(synthesis_dir,
"{}_mels_{}.npy".format(global_step, idx))
plot_alignment(alignment.T,
dst_alignment_path,
info="{}, {}".format(hparams.builder, global_step))
np.save(dst_mels_path, frames)
def synthesize(hp, speaker_id='', num_sentences=0, ncores=1, topoutdir='', t2m_epoch=-1, ssrn_epoch=-1):
'''
topoutdir: store samples under here; defaults to hp.sampledir
t2m_epoch and ssrn_epoch: default -1 means use latest. Otherwise go to archived models.
'''
assert hp.vocoder in ['griffin_lim', 'world'], 'Other vocoders than griffin_lim/world not yet supported'
dataset = load_data(hp, mode="synthesis") #since mode != 'train' or 'validation', will load test_transcript rather than transcript
fpaths, L = dataset['fpaths'], dataset['texts']
position_in_phone_data = duration_data = labels = None # default
if hp.use_external_durations:
duration_data = dataset['durations']
if num_sentences > 0:
duration_data = duration_data[:num_sentences, :, :]
if 'position_in_phone' in hp.history_type:
## TODO: combine + deduplicate with relevant code in train.py for making validation set
def duration2position(duration, fractional=False):
### very roundabout -- need to deflate A matrix back to integers:
duration = duration.sum(axis=0)
#print(duration)
# sys.exit('evs')
positions = durations_to_position(duration, fractional=fractional)
###positions = end_pad_for_reduction_shape_sync(positions, hp)
positions = positions[0::hp.r, :]
#print(positions)
return positions
position_in_phone_data = [duration2position(dur, fractional=('fractional' in hp.history_type)) \
for dur in duration_data]
position_in_phone_data = list2batch(position_in_phone_data, hp.max_T)
# Ensure we aren't trying to generate more utterances than are actually in our test_transcript
if num_sentences > 0:
assert num_sentences < len(fpaths)
L = L[:num_sentences, :]
fpaths = fpaths[:num_sentences]
bases = [basename(fpath) for fpath in fpaths]
if hp.merlin_label_dir:
labels = [np.load("{}/{}".format(hp.merlin_label_dir, basename(fpath)+".npy")) \
for fpath in fpaths ]
labels = list2batch(labels, hp.max_N)
if speaker_id:
speaker2ix = dict(zip(hp.speaker_list, range(len(hp.speaker_list))))
speaker_ix = speaker2ix[speaker_id]
## Speaker codes are held in (batch, 1) matrix -- tiling is done inside the graph:
speaker_data = np.ones((len(L), 1)) * speaker_ix
else:
speaker_data = None
# Load graph
## TODO: generalise to combine other types of models into a synthesis pipeline?
g1 = Text2MelGraph(hp, mode="synthesize"); print("Graph 1 (t2m) loaded")
g2 = SSRNGraph(hp, mode="synthesize"); print("Graph 2 (ssrn) loaded")
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
### TODO: specify epoch from comm line?
### TODO: t2m and ssrn from separate configs?
if t2m_epoch > -1:
restore_archived_model_parameters(sess, hp, 't2m', t2m_epoch)
else:
t2m_epoch = restore_latest_model_parameters(sess, hp, 't2m')
if ssrn_epoch > -1:
restore_archived_model_parameters(sess, hp, 'ssrn', ssrn_epoch)
else:
ssrn_epoch = restore_latest_model_parameters(sess, hp, 'ssrn')
# Pass input L through Text2Mel Graph
t = start_clock('Text2Mel generating...')
### TODO: after futher efficiency testing, remove this fork
if 1: ### efficient route -- only make K&V once ## 3.86, 3.70, 3.80 seconds (2 sentences)
text_lengths = get_text_lengths(L)
K, V = encode_text(hp, L, g1, sess, speaker_data=speaker_data, labels=labels)
Y, lengths, alignments = synth_codedtext2mel(hp, K, V, text_lengths, g1, sess, \
speaker_data=speaker_data, duration_data=duration_data, \
position_in_phone_data=position_in_phone_data,\
labels=labels)
else: ## 5.68, 5.43, 5.38 seconds (2 sentences)
Y, lengths = synth_text2mel(hp, L, g1, sess, speaker_data=speaker_data, \
duration_data=duration_data, \
position_in_phone_data=position_in_phone_data, \
labels=labels)
stop_clock(t)
### TODO: useful to test this?
# print(Y[0,:,:])
# print (np.isnan(Y).any())
# print('nan1')
# Then pass output Y of Text2Mel Graph through SSRN graph to get high res spectrogram Z.
t = start_clock('Mel2Mag generating...')
Z = synth_mel2mag(hp, Y, g2, sess)
stop_clock(t)
if (np.isnan(Z).any()): ### TODO: keep?
Z = np.nan_to_num(Z)
# Generate wav files
if not topoutdir:
topoutdir = hp.sampledir
outdir = os.path.join(topoutdir, 't2m%s_ssrn%s'%(t2m_epoch, ssrn_epoch))
if speaker_id:
outdir += '_speaker-%s'%(speaker_id)
safe_makedir(outdir)
print("Generating wav files, will save to following dir: %s"%(outdir))
assert hp.vocoder in ['griffin_lim', 'world'], 'Other vocoders than griffin_lim/world not yet supported'
if ncores==1:
for i, mag in tqdm(enumerate(Z)):
outfile = os.path.join(outdir, bases[i] + '.wav')
mag = mag[:lengths[i]*hp.r,:] ### trim to generated length
synth_wave(hp, mag, outfile)
else:
executor = ProcessPoolExecutor(max_workers=ncores)
futures = []
for i, mag in tqdm(enumerate(Z)):
outfile = os.path.join(outdir, bases[i] + '.wav')
mag = mag[:lengths[i]*hp.r,:] ### trim to generated length
futures.append(executor.submit(synth_wave, hp, mag, outfile))
proc_list = [future.result() for future in tqdm(futures)]
# for i, mag in enumerate(Z):
# print("Working on %s"%(bases[i]))
# mag = mag[:lengths[i]*hp.r,:] ### trim to generated length
# if hp.vocoder=='magphase_compressed':
# mag = denorm(mag, s, hp.normtype)
# streams = split_streams(mag, ['mag', 'lf0', 'vuv', 'real', 'imag'], [60,1,1,45,45])
# wav = magphase_synth_from_compressed(streams, samplerate=hp.sr)
# elif hp.vocoder=='griffin_lim':
# wav = spectrogram2wav(hp, mag)
# else:
# sys.exit('Unsupported vocoder type: %s'%(hp.vocoder))
# #write(outdir + "/{}.wav".format(bases[i]), hp.sr, wav)
# soundfile.write(outdir + "/{}.wav".format(bases[i]), wav, hp.sr)
# Plot attention alignments
for i in range(num_sentences):
plot_alignment(hp, alignments[i], utt_idx=i+1, t2m_epoch=t2m_epoch, dir=outdir)
def main():
# DataSet Loader
if args.dataset == "ljspeech":
from datasets.ljspeech import LJSpeech
# LJSpeech-1.1 dataset loader
ljs = LJSpeech(
path=cfg.dataset_path,
save_to='npy',
load_from=None
if not os.path.exists(cfg.dataset_path + "/npy") else "npy",
verbose=cfg.verbose)
else:
raise NotImplementedError("[-] Not Implemented Yet...")
# Train/Test split
tr_size = int(len(ljs) * (1. - cfg.test_size))
tr_text_data, va_text_data = \
ljs.text_data[:tr_size], ljs.text_data[tr_size:]
tr_text_len_data, va_text_len_data = \
ljs.text_len_data[:tr_size], ljs.text_len_data[tr_size:]
tr_mels, va_mels = ljs.mels[:tr_size], ljs.mels[tr_size:]
tr_mags, va_mags = ljs.mags[:tr_size], ljs.mags[tr_size:]
del ljs # memory release
# Data Iterator
di = DataIterator(text=tr_text_data,
text_len=tr_text_len_data,
mel=tr_mels,
mag=tr_mags,
batch_size=cfg.batch_size)
if cfg.verbose:
print("[*] Train/Test split : %d/%d (%.2f/%.2f)" %
(tr_text_data.shape[0], va_text_data.shape[0],
1. - cfg.test_size, cfg.test_size))
print(" Train")
print("\ttext : ", tr_text_data.shape)
print("\ttext_len : ", tr_text_len_data.shape)
print("\tmels : ", tr_mels.shape)
print("\tmags : ", tr_mags.shape)
print(" Test")
print("\ttext : ", va_text_data.shape)
print("\ttext_len : ", va_text_len_data.shape)
print("\tmels : ", va_mels.shape)
print("\tmags : ", va_mags.shape)
# Model Loading
gpu_config = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
gpu_options=gpu_config)
with tf.Session(config=config) as sess:
if cfg.model == "Tacotron":
model = Tacotron(sess=sess,
mode=args.mode,
sample_rate=cfg.sample_rate,
vocab_size=cfg.vocab_size,
embed_size=cfg.embed_size,
n_mels=cfg.n_mels,
n_fft=cfg.n_fft,
reduction_factor=cfg.reduction_factor,
n_encoder_banks=cfg.n_encoder_banks,
n_decoder_banks=cfg.n_decoder_banks,
n_highway_blocks=cfg.n_highway_blocks,
lr=cfg.lr,
lr_decay=cfg.lr_decay,
optimizer=cfg.optimizer,
grad_clip=cfg.grad_clip,
model_path=cfg.model_path)
else:
raise NotImplementedError("[-] Not Implemented Yet...")
if cfg.verbose:
print("[*] %s model is loaded!" % cfg.model)
# Initializing
sess.run(tf.global_variables_initializer())
# Load model & Graph & Weights
global_step = 0
ckpt = tf.train.get_checkpoint_state(cfg.model_path)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
model.saver.restore(sess, ckpt.model_checkpoint_path)
global_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
print("[+] global step : %d" % global_step, " successfully loaded")
else:
print('[-] No checkpoint file found')
start_time = time.time()
best_loss = np.inf
batch_size = cfg.batch_size
model.global_step.assign(tf.constant(global_step))
restored_epochs = global_step // (di.text.shape[0] // batch_size)
for epoch in range(restored_epochs, cfg.epochs):
for text, text_len, mel, mag in di.iterate():
batch_start = time.time()
_, y_loss, z_loss = sess.run(
[model.train_op, model.y_loss, model.z_loss],
feed_dict={
model.x: text,
model.x_len: text_len,
model.y: mel,
model.z: mag,
})
batch_end = time.time()
if global_step and global_step % cfg.logging_step == 0:
va_y_loss, va_z_loss = 0., 0.
va_batch = 20
va_iter = len(va_text_data)
for idx in range(0, va_iter, va_batch):
va_y, va_z = sess.run(
[model.y_loss, model.z_loss],
feed_dict={
model.x:
va_text_data[va_batch * idx:va_batch *
(idx + 1)],
model.x_len:
va_text_len_data[va_batch * idx:va_batch *
(idx + 1)],
model.y:
va_mels[va_batch * idx:va_batch * (idx + 1)],
model.z:
va_mags[va_batch * idx:va_batch * (idx + 1)],
})
va_y_loss += va_y
va_z_loss += va_z
va_y_loss /= (va_iter // va_batch)
va_z_loss /= (va_iter // va_batch)
print(
"[*] epoch %03d global step %07d [%.03f sec/step]" %
(epoch, global_step, (batch_end - batch_start)),
" Train \n"
" y_loss : {:.6f} z_loss : {:.6f}".format(
y_loss, z_loss), " Valid \n"
" y_loss : {:.6f} z_loss : {:.6f}".format(
va_y_loss, va_z_loss))
# summary
summary = sess.run(model.merged,
feed_dict={
model.x:
va_text_data[:batch_size],
model.x_len:
va_text_len_data[:batch_size],
model.y:
va_mels[:batch_size],
model.z:
va_mags[:batch_size],
})
# getting/plotting alignment (important)
alignment = sess.run(model.alignments,
feed_dict={
model.x:
va_text_data[:batch_size],
model.x_len:
va_text_len_data[:batch_size],
model.y:
va_mels[:batch_size],
})
plot_alignment(alignments=alignment,
gs=global_step,
path=os.path.join(cfg.model_path,
"alignments"))
# Summary saver
model.writer.add_summary(summary, global_step)
# Model save
model.saver.save(sess,
cfg.model_path + '%s.ckpt' % cfg.model,
global_step=global_step)
if va_y_loss + va_z_loss < best_loss:
model.best_saver.save(sess,
cfg.model_path +
'%s-best_loss.ckpt' % cfg.model,
global_step=global_step)
best_loss = va_y_loss + va_z_loss
model.global_step.assign_add(tf.constant(1))
global_step += 1
end_time = time.time()
print("[+] Training Done! Elapsed {:.8f}s".format(end_time -
start_time))
def synthesize(hp, speaker_id='', num_sentences=0, ncores=1, topoutdir='', t2m_epoch=-1, ssrn_epoch=-1):
'''
topoutdir: store samples under here; defaults to hp.sampledir
t2m_epoch and ssrn_epoch: default -1 means use latest. Otherwise go to archived models.
'''
assert hp.vocoder in ['griffin_lim', 'world'], 'Other vocoders than griffin_lim/world not yet supported'
dataset = load_data(hp, mode="synthesis") #since mode != 'train' or 'validation', will load test_transcript rather than transcript
fpaths, L = dataset['fpaths'], dataset['texts']
position_in_phone_data = duration_data = labels = None # default
if hp.use_external_durations:
duration_data = dataset['durations']
if num_sentences > 0:
duration_data = duration_data[:num_sentences, :, :]
if 'position_in_phone' in hp.history_type:
## TODO: combine + deduplicate with relevant code in train.py for making validation set
def duration2position(duration, fractional=False):
### very roundabout -- need to deflate A matrix back to integers:
duration = duration.sum(axis=0)
#print(duration)
# sys.exit('evs')
positions = durations_to_position(duration, fractional=fractional)
###positions = end_pad_for_reduction_shape_sync(positions, hp)
positions = positions[0::hp.r, :]
#print(positions)
return positions
position_in_phone_data = [duration2position(dur, fractional=('fractional' in hp.history_type)) \
for dur in duration_data]
position_in_phone_data = list2batch(position_in_phone_data, hp.max_T)
# Ensure we aren't trying to generate more utterances than are actually in our test_transcript
if num_sentences > 0:
assert num_sentences <= len(fpaths)
L = L[:num_sentences, :]
fpaths = fpaths[:num_sentences]
bases = [basename(fpath) for fpath in fpaths]
if hp.merlin_label_dir:
labels = []
for fpath in fpaths:
label = np.load("{}/{}".format(hp.merlin_label_dir, basename(fpath)+".npy"))
if hp.select_central:
central_ind = get_labels_indices(hp.merlin_lab_dim)
label = label[:,central_ind==1]
labels.append(label)
labels = list2batch(labels, hp.max_N)
if speaker_id:
speaker2ix = dict(zip(hp.speaker_list, range(len(hp.speaker_list))))
speaker_ix = speaker2ix[speaker_id]
## Speaker codes are held in (batch, 1) matrix -- tiling is done inside the graph:
speaker_data = np.ones((len(L), 1)) * speaker_ix
else:
speaker_data = None
if hp.turn_off_monotonic_for_synthesis: # if FIA mechanism is turn off
text_lengths = get_text_lengths(L)
hp.text_lengths = text_lengths + 1
# Load graph
## TODO: generalise to combine other types of models into a synthesis pipeline?
g1 = Text2MelGraph(hp, mode="synthesize"); print("Graph 1 (t2m) loaded")
if hp.norm == None :
t2m_layer_norm = False
hp.norm = 'layer'
hp.lr = 0.001
hp.beta1 = 0.9
hp.beta2 = 0.999
hp.epsilon = 0.00000001
hp.decay_lr = True
hp.batchsize = {'t2m': 32, 'ssrn': 8}
else:
t2m_layer_norm = True
g2 = SSRNGraph(hp, mode="synthesize"); print("Graph 2 (ssrn) loaded")
if t2m_layer_norm == False:
hp.norm = None
hp.lr = 0.0002
hp.beta1 = 0.5
hp.beta2 = 0.9
hp.epsilon = 0.000001
hp.decay_lr = False
hp.batchsize = {'t2m': 16, 'ssrn': 8}
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
### TODO: specify epoch from comm line?
### TODO: t2m and ssrn from separate configs?
if t2m_epoch > -1:
restore_archived_model_parameters(sess, hp, 't2m', t2m_epoch)
else:
t2m_epoch = restore_latest_model_parameters(sess, hp, 't2m')
if ssrn_epoch > -1:
restore_archived_model_parameters(sess, hp, 'ssrn', ssrn_epoch)
else:
ssrn_epoch = restore_latest_model_parameters(sess, hp, 'ssrn')
# Pass input L through Text2Mel Graph
t = start_clock('Text2Mel generating...')
### TODO: after futher efficiency testing, remove this fork
if 1: ### efficient route -- only make K&V once ## 3.86, 3.70, 3.80 seconds (2 sentences)
text_lengths = get_text_lengths(L)
K, V = encode_text(hp, L, g1, sess, speaker_data=speaker_data, labels=labels)
Y, lengths, alignments = synth_codedtext2mel(hp, K, V, text_lengths, g1, sess, \
speaker_data=speaker_data, duration_data=duration_data, \
position_in_phone_data=position_in_phone_data,\
labels=labels)
else: ## 5.68, 5.43, 5.38 seconds (2 sentences)
Y, lengths = synth_text2mel(hp, L, g1, sess, speaker_data=speaker_data, \
duration_data=duration_data, \
position_in_phone_data=position_in_phone_data, \
labels=labels)
stop_clock(t)
### TODO: useful to test this?
# print(Y[0,:,:])
# print (np.isnan(Y).any())
# print('nan1')
# Then pass output Y of Text2Mel Graph through SSRN graph to get high res spectrogram Z.
t = start_clock('Mel2Mag generating...')
Z = synth_mel2mag(hp, Y, g2, sess)
stop_clock(t)
if (np.isnan(Z).any()): ### TODO: keep?
Z = np.nan_to_num(Z)
# Generate wav files
if not topoutdir:
topoutdir = hp.sampledir
outdir = os.path.join(topoutdir, 't2m%s_ssrn%s'%(t2m_epoch, ssrn_epoch))
if speaker_id:
outdir += '_speaker-%s'%(speaker_id)
safe_makedir(outdir)
# Plot trimmed attention alignment with filename
print("Plot attention, will save to following dir: %s"%(outdir))
print("File | CDP | Ain")
for i, mag in enumerate(Z):
outfile = os.path.join(outdir, bases[i])
trimmed_alignment = alignments[i,:text_lengths[i],:lengths[i]]
plot_alignment(hp, trimmed_alignment, utt_idx=i+1, t2m_epoch=t2m_epoch, dir=outdir, outfile=outfile)
CDP = getCDP(trimmed_alignment)
APin, APout = getAP(trimmed_alignment)
print("%s | %.2f | %.2f"%( bases[i], CDP, APin))
print("Generating wav files, will save to following dir: %s"%(outdir))
assert hp.vocoder in ['griffin_lim', 'world'], 'Other vocoders than griffin_lim/world not yet supported'
if ncores==1:
for i, mag in tqdm(enumerate(Z)):
outfile = os.path.join(outdir, bases[i] + '.wav')
mag = mag[:lengths[i]*hp.r,:] ### trim to generated length
synth_wave(hp, mag, outfile)
else:
executor = ProcessPoolExecutor(max_workers=ncores)
futures = []
for i, mag in tqdm(enumerate(Z)):
outfile = os.path.join(outdir, bases[i] + '.wav')
mag = mag[:lengths[i]*hp.r,:] ### trim to generated length
futures.append(executor.submit(synth_wave, hp, mag, outfile))
proc_list = [future.result() for future in tqdm(futures)]
def synthesize(self,
text=None,
emo_code=None,
mels=None,
speaker_id='',
num_sentences=0,
ncores=1,
topoutdir=''):
'''
topoutdir: store samples under here; defaults to hp.sampledir
t2m_epoch and ssrn_epoch: default -1 means use latest. Otherwise go to archived models.
'''
assert self.hp.vocoder in [
'griffin_lim', 'world'
], 'Other vocoders than griffin_lim/world not yet supported'
if text is not None:
text_to_phonetic(text=text)
dataset = load_data(self.hp, mode='demo')
else:
dataset = load_data(
self.hp, mode="synthesis"
) #since mode != 'train' or 'validation', will load test_transcript rather than transcript
fpaths, L = dataset['fpaths'], dataset['texts']
position_in_phone_data = duration_data = labels = None # default
if self.hp.use_external_durations:
duration_data = dataset['durations']
if num_sentences > 0:
duration_data = duration_data[:num_sentences, :, :]
if 'position_in_phone' in self.hp.history_type:
## TODO: combine + deduplicate with relevant code in train.py for making validation set
def duration2position(duration, fractional=False):
### very roundabout -- need to deflate A matrix back to integers:
duration = duration.sum(axis=0)
#print(duration)
# sys.exit('evs')
positions = durations_to_position(duration,
fractional=fractional)
###positions = end_pad_for_reduction_shape_sync(positions, hp)
positions = positions[0::hp.r, :]
#print(positions)
return positions
position_in_phone_data = [duration2position(dur, fractional=('fractional' in hp.history_type)) \
for dur in duration_data]
position_in_phone_data = list2batch(position_in_phone_data,
hp.max_T)
# Ensure we aren't trying to generate more utterances than are actually in our test_transcript
if num_sentences > 0:
assert num_sentences < len(fpaths)
L = L[:num_sentences, :]
fpaths = fpaths[:num_sentences]
bases = [basename(fpath) for fpath in fpaths]
if self.hp.merlin_label_dir:
labels = [np.load("{}/{}".format(hp.merlin_label_dir, basename(fpath)+".npy")) \
for fpath in fpaths ]
labels = list2batch(labels, hp.max_N)
if speaker_id:
speaker2ix = dict(zip(hp.speaker_list,
range(len(hp.speaker_list))))
speaker_ix = speaker2ix[speaker_id]
## Speaker codes are held in (batch, 1) matrix -- tiling is done inside the graph:
speaker_data = np.ones((len(L), 1)) * speaker_ix
else:
speaker_data = None
# Pass input L through Text2Mel Graph
t = start_clock('Text2Mel generating...')
### TODO: after futher efficiency testing, remove this fork
if 1: ### efficient route -- only make K&V once ## 3.86, 3.70, 3.80 seconds (2 sentences)
text_lengths = get_text_lengths(L)
if mels is not None:
emo_code = encode_audio2emo(self.hp, mels, self.g1, self.sess)
K, V = encode_text(self.hp,
L,
self.g1,
self.sess,
emo_mean=emo_code,
speaker_data=speaker_data,
labels=labels)
Y, lengths, alignments = synth_codedtext2mel(self.hp, K, V, text_lengths, self.g1, self.sess, \
speaker_data=speaker_data, duration_data=duration_data, \
position_in_phone_data=position_in_phone_data,\
labels=labels)
else: ## 5.68, 5.43, 5.38 seconds (2 sentences)
Y, lengths = synth_text2mel(self.hp, L, self.g1, self.sess, speaker_data=speaker_data, \
duration_data=duration_data, \
position_in_phone_data=position_in_phone_data, \
labels=labels)
stop_clock(t)
### TODO: useful to test this?
# print(Y[0,:,:])
# print (np.isnan(Y).any())
# print('nan1')
# Then pass output Y of Text2Mel Graph through SSRN graph to get high res spectrogram Z.
t = start_clock('Mel2Mag generating...')
Z = synth_mel2mag(self.hp, Y, self.g2, self.sess)
stop_clock(t)
if (np.isnan(Z).any()): ### TODO: keep?
Z = np.nan_to_num(Z)
# Generate wav files
if not topoutdir:
topoutdir = self.hp.sampledir
outdir = os.path.join(
topoutdir, 't2m%s_ssrn%s' % (self.t2m_epoch, self.ssrn_epoch))
if speaker_id:
outdir += '_speaker-%s' % (speaker_id)
safe_makedir(outdir)
print("Generating wav files, will save to following dir: %s" %
(outdir))
assert self.hp.vocoder in [
'griffin_lim', 'world'
], 'Other vocoders than griffin_lim/world not yet supported'
if ncores == 1:
for i, mag in tqdm(enumerate(Z)):
outfile = os.path.join(outdir, bases[i] + '.wav')
mag = mag[:lengths[i] *
self.hp.r, :] ### trim to generated length
synth_wave(self.hp, mag, outfile)
else:
executor = ProcessPoolExecutor(max_workers=ncores)
futures = []
for i, mag in tqdm(enumerate(Z)):
outfile = os.path.join(outdir, bases[i] + '.wav')
mag = mag[:lengths[i] *
self.hp.r, :] ### trim to generated length
futures.append(
executor.submit(synth_wave, self.hp, mag, outfile))
proc_list = [future.result() for future in tqdm(futures)]
# Plot attention alignments
for i in range(num_sentences):
plot_alignment(self.hp,
alignments[i],
utt_idx=i + 1,
t2m_epoch=self.t2m_epoch,
dir=outdir)
self.outdir = outdir
]
for idx, sent in enumerate(sentences):
wav, attn = eval_model(
dv3, sent, replace_pronounciation_prob, min_level_db,
ref_level_db, power, n_iter, win_length, hop_length,
preemphasis)
wav_path = os.path.join(
state_dir, "waveform",
"eval_sample_{:09d}.wav".format(global_step))
sf.write(wav_path, wav, sample_rate)
writer.add_audio(
"eval_sample_{}".format(idx),
wav,
global_step,
sample_rate=sample_rate)
attn_path = os.path.join(
state_dir, "alignments",
"eval_sample_attn_{:09d}.png".format(global_step))
plot_alignment(attn, attn_path)
writer.add_image(
"eval_sample_attn{}".format(idx),
cm.viridis(attn),
global_step,
dataformats="HWC")
# save checkpoint
if global_step % save_interval == 0:
io.save_parameters(ckpt_dir, global_step, dv3, optim)
global_step += 1
def main(args):
torch.manual_seed(0)
# Get device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Get dataset
dataset = Dataset("train.txt")
loader = DataLoader(dataset,
batch_size=hp.batch_size**2,
shuffle=True,
collate_fn=dataset.collate_fn,
drop_last=True,
num_workers=0)
# Define model
model = nn.DataParallel(STYLER()).to(device)
print("Model Has Been Defined")
# Parameters
num_param = utils.get_param_num(model)
text_encoder = utils.get_param_num(
model.module.style_modeling.style_encoder.text_encoder)
audio_encoder = utils.get_param_num(
model.module.style_modeling.style_encoder.audio_encoder)
predictors = utils.get_param_num(model.module.style_modeling.duration_predictor)\
+ utils.get_param_num(model.module.style_modeling.pitch_predictor)\
+ utils.get_param_num(model.module.style_modeling.energy_predictor)
decoder = utils.get_param_num(model.module.decoder)
print('Number of Model Parameters :', num_param)
print('Number of Text Encoder Parameters :', text_encoder)
print('Number of Audio Encoder Parameters :', audio_encoder)
print('Number of Predictor Parameters :', predictors)
print('Number of Decoder Parameters :', decoder)
# Optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
betas=hp.betas,
eps=hp.eps,
weight_decay=hp.weight_decay)
scheduled_optim = ScheduledOptim(optimizer, hp.decoder_hidden,
hp.n_warm_up_step, args.restore_step)
Loss = STYLERLoss().to(device)
DATLoss = DomainAdversarialTrainingLoss().to(device)
print("Optimizer and Loss Function Defined.")
# Load checkpoint if exists
checkpoint_path = os.path.join(hp.checkpoint_path())
try:
checkpoint = torch.load(
os.path.join(checkpoint_path,
'checkpoint_{}.pth.tar'.format(args.restore_step)))
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("\n---Model Restored at Step {}---\n".format(args.restore_step))
except:
print("\n---Start New Training---\n")
if not os.path.exists(checkpoint_path):
os.makedirs(checkpoint_path)
# Load vocoder
vocoder = utils.get_vocoder()
# Init logger
log_path = hp.log_path()
if not os.path.exists(log_path):
os.makedirs(log_path)
os.makedirs(os.path.join(log_path, 'train'))
os.makedirs(os.path.join(log_path, 'validation'))
train_logger = SummaryWriter(os.path.join(log_path, 'train'))
val_logger = SummaryWriter(os.path.join(log_path, 'validation'))
# Init synthesis directory
synth_path = hp.synth_path()
if not os.path.exists(synth_path):
os.makedirs(synth_path)
# Define Some Information
Time = np.array([])
Start = time.perf_counter()
# Training
model = model.train()
for epoch in range(hp.epochs):
# Get Training Loader
total_step = hp.epochs * len(loader) * hp.batch_size
for i, batchs in enumerate(loader):
for j, data_of_batch in enumerate(batchs):
start_time = time.perf_counter()
current_step = i*hp.batch_size + j + args.restore_step + \
epoch*len(loader)*hp.batch_size + 1
# Get Data
text = torch.from_numpy(
data_of_batch["text"]).long().to(device)
mel_target = torch.from_numpy(
data_of_batch["mel_target"]).float().to(device)
mel_aug = torch.from_numpy(
data_of_batch["mel_aug"]).float().to(device)
D = torch.from_numpy(data_of_batch["D"]).long().to(device)
log_D = torch.from_numpy(
data_of_batch["log_D"]).float().to(device)
f0 = torch.from_numpy(data_of_batch["f0"]).float().to(device)
f0_norm = torch.from_numpy(
data_of_batch["f0_norm"]).float().to(device)
f0_norm_aug = torch.from_numpy(
data_of_batch["f0_norm_aug"]).float().to(device)
energy = torch.from_numpy(
data_of_batch["energy"]).float().to(device)
energy_input = torch.from_numpy(
data_of_batch["energy_input"]).float().to(device)
energy_input_aug = torch.from_numpy(
data_of_batch["energy_input_aug"]).float().to(device)
speaker_embed = torch.from_numpy(
data_of_batch["speaker_embed"]).float().to(device)
src_len = torch.from_numpy(
data_of_batch["src_len"]).long().to(device)
mel_len = torch.from_numpy(
data_of_batch["mel_len"]).long().to(device)
max_src_len = np.max(data_of_batch["src_len"]).astype(np.int32)
max_mel_len = np.max(data_of_batch["mel_len"]).astype(np.int32)
# Forward
mel_outputs, mel_postnet_outputs, log_duration_output, f0_output, energy_output, src_mask, mel_mask, _, aug_posteriors = model(
text,
mel_target,
mel_aug,
f0_norm,
energy_input,
src_len,
mel_len,
D,
f0,
energy,
max_src_len,
max_mel_len,
speaker_embed=speaker_embed)
# Cal Loss Clean
mel_output, mel_postnet_output = mel_outputs[
0], mel_postnet_outputs[0]
mel_loss, mel_postnet_loss, d_loss, f_loss, e_loss, classifier_loss_a = Loss(
log_duration_output, log_D, f0_output, f0, energy_output, energy, mel_output, mel_postnet_output, mel_target, ~src_mask, ~mel_mask, src_len, mel_len,\
aug_posteriors, torch.zeros(mel_target.size(0)).long().to(device))
# Cal Loss Noisy
mel_output_noisy, mel_postnet_output_noisy = mel_outputs[
1], mel_postnet_outputs[1]
mel_noisy_loss, mel_postnet_noisy_loss = Loss.cal_mel_loss(
mel_output_noisy, mel_postnet_output_noisy, mel_aug,
~mel_mask)
# Forward DAT
enc_cat = model.module.style_modeling.style_encoder.encoder_input_cat(
mel_aug, f0_norm_aug, energy_input_aug, mel_aug)
duration_encoding, pitch_encoding, energy_encoding, _ = model.module.style_modeling.style_encoder.audio_encoder(
enc_cat, mel_len, src_len, mask=None)
aug_posterior_d = model.module.style_modeling.augmentation_classifier_d(
duration_encoding)
aug_posterior_p = model.module.style_modeling.augmentation_classifier_p(
pitch_encoding)
aug_posterior_e = model.module.style_modeling.augmentation_classifier_e(
energy_encoding)
# Cal Loss DAT
classifier_loss_a_dat = DATLoss(
(aug_posterior_d, aug_posterior_p, aug_posterior_e),
torch.ones(mel_target.size(0)).long().to(device))
# Total loss
total_loss = mel_loss + mel_postnet_loss + mel_noisy_loss + mel_postnet_noisy_loss + d_loss + f_loss + e_loss\
+ hp.dat_weight*(classifier_loss_a + classifier_loss_a_dat)
# Logger
t_l = total_loss.item()
m_l = mel_loss.item()
m_p_l = mel_postnet_loss.item()
m_n_l = mel_noisy_loss.item()
m_p_n_l = mel_postnet_noisy_loss.item()
d_l = d_loss.item()
f_l = f_loss.item()
e_l = e_loss.item()
cl_a = classifier_loss_a.item()
cl_a_dat = classifier_loss_a_dat.item()
# Backward
total_loss = total_loss / hp.acc_steps
total_loss.backward()
if current_step % hp.acc_steps != 0:
continue
# Clipping gradients to avoid gradient explosion
nn.utils.clip_grad_norm_(model.parameters(),
hp.grad_clip_thresh)
# Update weights
scheduled_optim.step_and_update_lr()
scheduled_optim.zero_grad()
# Print
if current_step == 1 or current_step % hp.log_step == 0:
Now = time.perf_counter()
str1 = "Epoch [{}/{}], Step [{}/{}]:".format(
epoch + 1, hp.epochs, current_step, total_step)
str2 = "Total Loss: {:.4f}, Mel Loss: {:.4f}, Mel PostNet Loss: {:.4f}, Duration Loss: {:.4f}, F0 Loss: {:.4f}, Energy Loss: {:.4f};".format(
t_l, m_l, m_p_l, d_l, f_l, e_l)
str3 = "Time Used: {:.3f}s, Estimated Time Remaining: {:.3f}s.".format(
(Now - Start),
(total_step - current_step) * np.mean(Time))
print("\n" + str1)
print(str2)
print(str3)
train_logger.add_scalar('Loss/total_loss', t_l,
current_step)
train_logger.add_scalar('Loss/mel_loss', m_l, current_step)
train_logger.add_scalar('Loss/mel_postnet_loss', m_p_l,
current_step)
train_logger.add_scalar('Loss/mel_noisy_loss', m_n_l,
current_step)
train_logger.add_scalar('Loss/mel_postnet_noisy_loss',
m_p_n_l, current_step)
train_logger.add_scalar('Loss/duration_loss', d_l,
current_step)
train_logger.add_scalar('Loss/F0_loss', f_l, current_step)
train_logger.add_scalar('Loss/energy_loss', e_l,
current_step)
train_logger.add_scalar('Loss/dat_clean_loss', cl_a,
current_step)
train_logger.add_scalar('Loss/dat_noisy_loss', cl_a_dat,
current_step)
if current_step % hp.save_step == 0:
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict()
},
os.path.join(
checkpoint_path,
'checkpoint_{}.pth.tar'.format(current_step)))
print("save model at step {} ...".format(current_step))
if current_step == 1 or current_step % hp.synth_step == 0:
length = mel_len[0].item()
mel_target_torch = mel_target[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_aug_torch = mel_aug[0, :length].detach().unsqueeze(
0).transpose(1, 2)
mel_target = mel_target[
0, :length].detach().cpu().transpose(0, 1)
mel_aug = mel_aug[0, :length].detach().cpu().transpose(
0, 1)
mel_torch = mel_output[0, :length].detach().unsqueeze(
0).transpose(1, 2)
mel_noisy_torch = mel_output_noisy[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel = mel_output[0, :length].detach().cpu().transpose(0, 1)
mel_noisy = mel_output_noisy[
0, :length].detach().cpu().transpose(0, 1)
mel_postnet_torch = mel_postnet_output[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_postnet_noisy_torch = mel_postnet_output_noisy[
0, :length].detach().unsqueeze(0).transpose(1, 2)
mel_postnet = mel_postnet_output[
0, :length].detach().cpu().transpose(0, 1)
mel_postnet_noisy = mel_postnet_output_noisy[
0, :length].detach().cpu().transpose(0, 1)
# Audio.tools.inv_mel_spec(mel, os.path.join(
# synth_path, "step_{}_{}_griffin_lim.wav".format(current_step, "c")))
# Audio.tools.inv_mel_spec(mel_postnet, os.path.join(
# synth_path, "step_{}_{}_postnet_griffin_lim.wav".format(current_step, "c")))
# Audio.tools.inv_mel_spec(mel_noisy, os.path.join(
# synth_path, "step_{}_{}_griffin_lim.wav".format(current_step, "n")))
# Audio.tools.inv_mel_spec(mel_postnet_noisy, os.path.join(
# synth_path, "step_{}_{}_postnet_griffin_lim.wav".format(current_step, "n")))
wav_mel = utils.vocoder_infer(
mel_torch, vocoder,
os.path.join(
hp.synth_path(),
'step_{}_{}_{}.wav'.format(current_step, "c",
hp.vocoder)))
wav_mel_postnet = utils.vocoder_infer(
mel_postnet_torch, vocoder,
os.path.join(
hp.synth_path(),
'step_{}_{}_postnet_{}.wav'.format(
current_step, "c", hp.vocoder)))
wav_ground_truth = utils.vocoder_infer(
mel_target_torch, vocoder,
os.path.join(
hp.synth_path(),
'step_{}_{}_ground-truth_{}.wav'.format(
current_step, "c", hp.vocoder)))
wav_mel_noisy = utils.vocoder_infer(
mel_noisy_torch, vocoder,
os.path.join(
hp.synth_path(),
'step_{}_{}_{}.wav'.format(current_step, "n",
hp.vocoder)))
wav_mel_postnet_noisy = utils.vocoder_infer(
mel_postnet_noisy_torch, vocoder,
os.path.join(
hp.synth_path(),
'step_{}_{}_postnet_{}.wav'.format(
current_step, "n", hp.vocoder)))
wav_aug = utils.vocoder_infer(
mel_aug_torch, vocoder,
os.path.join(
hp.synth_path(),
'step_{}_{}_ground-truth_{}.wav'.format(
current_step, "n", hp.vocoder)))
# Model duration prediction
log_duration_output = log_duration_output[
0, :src_len[0].item()].detach().cpu() # [seg_len]
log_duration_output = torch.clamp(torch.round(
torch.exp(log_duration_output) - hp.log_offset),
min=0).int()
model_duration = utils.get_alignment_2D(
log_duration_output).T # [seg_len, mel_len]
model_duration = utils.plot_alignment([model_duration])
# Model mel prediction
f0 = f0[0, :length].detach().cpu().numpy()
energy = energy[0, :length].detach().cpu().numpy()
f0_output = f0_output[0, :length].detach().cpu().numpy()
energy_output = energy_output[
0, :length].detach().cpu().numpy()
mel_predicted = utils.plot_data(
[(mel_postnet.numpy(), f0_output, energy_output),
(mel_target.numpy(), f0, energy)], [
'Synthetized Spectrogram Clean',
'Ground-Truth Spectrogram'
],
filename=os.path.join(
synth_path,
'step_{}_{}.png'.format(current_step, "c")))
mel_noisy_predicted = utils.plot_data(
[(mel_postnet_noisy.numpy(), f0_output, energy_output),
(mel_aug.numpy(), f0, energy)],
['Synthetized Spectrogram Noisy', 'Aug Spectrogram'],
filename=os.path.join(
synth_path,
'step_{}_{}.png'.format(current_step, "n")))
# Normalize audio for tensorboard logger. See https://github.com/lanpa/tensorboardX/issues/511#issuecomment-537600045
wav_ground_truth = wav_ground_truth / max(wav_ground_truth)
wav_mel = wav_mel / max(wav_mel)
wav_mel_postnet = wav_mel_postnet / max(wav_mel_postnet)
wav_aug = wav_aug / max(wav_aug)
wav_mel_noisy = wav_mel_noisy / max(wav_mel_noisy)
wav_mel_postnet_noisy = wav_mel_postnet_noisy / max(
wav_mel_postnet_noisy)
train_logger.add_image("model_duration",
model_duration,
current_step,
dataformats='HWC')
train_logger.add_image("mel_predicted/Clean",
mel_predicted,
current_step,
dataformats='HWC')
train_logger.add_image("mel_predicted/Noisy",
mel_noisy_predicted,
current_step,
dataformats='HWC')
train_logger.add_audio("Clean/wav_ground_truth",
wav_ground_truth,
current_step,
sample_rate=hp.sampling_rate)
train_logger.add_audio("Clean/wav_mel",
wav_mel,
current_step,
sample_rate=hp.sampling_rate)
train_logger.add_audio("Clean/wav_mel_postnet",
wav_mel_postnet,
current_step,
sample_rate=hp.sampling_rate)
train_logger.add_audio("Noisy/wav_aug",
wav_aug,
current_step,
sample_rate=hp.sampling_rate)
train_logger.add_audio("Noisy/wav_mel_noisy",
wav_mel_noisy,
current_step,
sample_rate=hp.sampling_rate)
train_logger.add_audio("Noisy/wav_mel_postnet_noisy",
wav_mel_postnet_noisy,
current_step,
sample_rate=hp.sampling_rate)
if current_step == 1 or current_step % hp.eval_step == 0:
model.eval()
with torch.no_grad():
d_l, f_l, e_l, cl_a, cl_a_dat, m_l, m_p_l, m_n_l, m_p_n_l = evaluate(
model, current_step)
t_l = d_l + f_l + e_l + m_l + m_p_l + m_n_l + m_p_n_l\
+ hp.dat_weight*(cl_a + cl_a_dat)
val_logger.add_scalar('Loss/total_loss', t_l,
current_step)
val_logger.add_scalar('Loss/mel_loss', m_l,
current_step)
val_logger.add_scalar('Loss/mel_postnet_loss', m_p_l,
current_step)
val_logger.add_scalar('Loss/mel_noisy_loss', m_n_l,
current_step)
val_logger.add_scalar('Loss/mel_postnet_noisy_loss',
m_p_n_l, current_step)
val_logger.add_scalar('Loss/duration_loss', d_l,
current_step)
val_logger.add_scalar('Loss/F0_loss', f_l,
current_step)
val_logger.add_scalar('Loss/energy_loss', e_l,
current_step)
val_logger.add_scalar('Loss/dat_clean_loss', cl_a,
current_step)
val_logger.add_scalar('Loss/dat_noisy_loss', cl_a_dat,
current_step)
model.train()
end_time = time.perf_counter()
Time = np.append(Time, end_time - start_time)
if len(Time) == hp.clear_Time:
temp_value = np.mean(Time)
Time = np.delete(Time, [i for i in range(len(Time))],
axis=None)
Time = np.append(Time, temp_value)
def train(train_loader, model, device, mels_criterion, stop_criterion,
optimizer, scheduler, writer, train_dir):
batch_time = ValueWindow()
data_time = ValueWindow()
losses = ValueWindow()
# switch to train mode
model.train()
end = time.time()
global global_epoch
global global_step
for i, (txts, mels, stop_tokens, txt_lengths,
mels_lengths) in enumerate(train_loader):
scheduler.adjust_learning_rate(optimizer, global_step)
# measure data loading time
data_time.update(time.time() - end)
if device > -1:
txts = txts.cuda(device)
mels = mels.cuda(device)
stop_tokens = stop_tokens.cuda(device)
txt_lengths = txt_lengths.cuda(device)
mels_lengths = mels_lengths.cuda(device)
# compute output
frames, decoder_frames, stop_tokens_predict, alignment = model(
txts, txt_lengths, mels)
decoder_frames_loss = mels_criterion(decoder_frames,
mels,
lengths=mels_lengths)
frames_loss = mels_criterion(frames, mels, lengths=mels_lengths)
stop_token_loss = stop_criterion(stop_tokens_predict,
stop_tokens,
lengths=mels_lengths)
loss = decoder_frames_loss + frames_loss + stop_token_loss
#print(frames_loss, decoder_frames_loss)
losses.update(loss.item())
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
if hparams.clip_thresh > 0:
grad_norm = torch.nn.utils.clip_grad_norm_(
model.get_trainable_parameters(), hparams.clip_thresh)
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % hparams.print_freq == 0:
log('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})'.format(
global_epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses))
# Logs
writer.add_scalar("loss", float(loss.item()), global_step)
writer.add_scalar(
"avg_loss in {} window".format(losses.get_dinwow_size),
float(losses.avg), global_step)
writer.add_scalar("stop_token_loss", float(stop_token_loss.item()),
global_step)
writer.add_scalar("decoder_frames_loss",
float(decoder_frames_loss.item()), global_step)
writer.add_scalar("output_frames_loss", float(frames_loss.item()),
global_step)
if hparams.clip_thresh > 0:
writer.add_scalar("gradient norm", grad_norm, global_step)
writer.add_scalar("learning rate", optimizer.param_groups[0]['lr'],
global_step)
global_step += 1
dst_alignment_path = join(train_dir,
"{}_alignment.png".format(global_step))
alignment = alignment.cpu().detach().numpy()
plot_alignment(alignment[0, :txt_lengths[0], :mels_lengths[0]],
dst_alignment_path,
info="{}, {}".format(hparams.builder, global_step))
def validate(val_loader, model, device, mels_criterion, stop_criterion, writer,
val_dir):
batch_time = ValueWindow()
losses = ValueWindow()
# switch to evaluate mode
model.eval()
global global_epoch
global global_step
with torch.no_grad():
end = time.time()
for i, (txts, mels, stop_tokens, txt_lengths,
mels_lengths) in enumerate(val_loader):
# measure data loading time
batch_time.update(time.time() - end)
if device > -1:
txts = txts.cuda(device)
mels = mels.cuda(device)
stop_tokens = stop_tokens.cuda(device)
txt_lengths = txt_lengths.cuda(device)
mels_lengths = mels_lengths.cuda(device)
# compute output
frames, decoder_frames, stop_tokens_predict, alignment = model(
txts, txt_lengths, mels)
decoder_frames_loss = mels_criterion(decoder_frames,
mels,
lengths=mels_lengths)
frames_loss = mels_criterion(frames, mels, lengths=mels_lengths)
stop_token_loss = stop_criterion(stop_tokens_predict,
stop_tokens,
lengths=mels_lengths)
loss = decoder_frames_loss + frames_loss + stop_token_loss
losses.update(loss.item())
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % hparams.print_freq == 0:
log('Epoch: [{0}]\t'
'Test: [{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})'.format(
global_epoch,
i,
len(val_loader),
batch_time=batch_time,
loss=losses))
# Logs
writer.add_scalar("loss", float(loss.item()), global_step)
writer.add_scalar(
"avg_loss in {} window".format(losses.get_dinwow_size),
float(losses.avg), global_step)
writer.add_scalar("stop_token_loss", float(stop_token_loss.item()),
global_step)
writer.add_scalar("decoder_frames_loss",
float(decoder_frames_loss.item()), global_step)
writer.add_scalar("output_frames_loss", float(frames_loss.item()),
global_step)
dst_alignment_path = join(val_dir,
"{}_alignment.png".format(global_step))
alignment = alignment.cpu().detach().numpy()
plot_alignment(alignment[0, :txt_lengths[0], :mels_lengths[0]],
dst_alignment_path,
info="{}, {}".format(hparams.builder, global_step))
return losses.avg
ref_level_db = transform_config["ref_level_db"]
preemphasis = transform_config["preemphasis"]
win_length = transform_config["win_length"]
hop_length = transform_config["hop_length"]
synthesis_config = config["synthesis"]
power = synthesis_config["power"]
n_iter = synthesis_config["n_iter"]
synthesis_dir = os.path.join(args.output, "synthesis")
if not os.path.exists(synthesis_dir):
os.makedirs(synthesis_dir)
with open(args.text, "rt", encoding="utf-8") as f:
lines = f.readlines()
for idx, line in enumerate(lines):
text = line[:-1]
dv3.eval()
wav, attn = eval_model(dv3, text, replace_pronounciation_prob,
min_level_db, ref_level_db, power,
n_iter, win_length, hop_length,
preemphasis)
plot_alignment(
attn,
os.path.join(synthesis_dir,
"test_{}_step_{}.png".format(idx, iteration)))
sf.write(
os.path.join(synthesis_dir,
"test_{}_step{}.wav".format(idx, iteration)),
wav, sample_rate)
