def generate_plots(self, model: ForwardTacotron,
session: ForwardSession) -> None:
model.eval()
device = next(model.parameters()).device
x, m, ids, x_lens, mel_lens, dur = session.val_sample
x, m, dur, mel_lens = x.to(device), m.to(device), dur.to(
device), mel_lens.to(device)
m1_hat, m2_hat, dur_hat = model(x, m, dur, mel_lens)
m1_hat = np_now(m1_hat)[0, :600, :]
m2_hat = np_now(m2_hat)[0, :600, :]
m = np_now(m)[0, :600, :]
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
m_fig = plot_mel(m)
# pitch_fig = plot_pitch(np_now(pitch[0]))
# pitch_gta_fig = plot_pitch(np_now(pitch_hat.squeeze()[0]))
# self.writer.add_figure('Pitch/target', pitch_fig, model.step)
# self.writer.add_figure('Pitch/ground_truth_aligned', pitch_gta_fig, model.step)
self.writer.add_figure('Ground_Truth_Aligned/target', m_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/linear', m1_hat_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/postnet', m2_hat_fig,
model.step)
m2_hat_wav = reconstruct_waveform(m2_hat)
target_wav = reconstruct_waveform(m)
self.writer.add_audio(tag='Ground_Truth_Aligned/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Ground_Truth_Aligned/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
m1_hat, m2_hat, dur_hat = model.generate(x[0, :x_lens[0]].tolist())
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
# pitch_gen_fig = plot_pitch(np_now(pitch_hat.squeeze()))
# self.writer.add_figure('Pitch/generated', pitch_gen_fig, model.step)
self.writer.add_figure('Generated/target', m_fig, model.step)
self.writer.add_figure('Generated/linear', m1_hat_fig, model.step)
self.writer.add_figure('Generated/postnet', m2_hat_fig, model.step)
m2_hat_wav = reconstruct_waveform(m2_hat)
self.writer.add_audio(tag='Generated/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Generated/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
def generate_plots(self, model: Tacotron, session: TTSSession) -> None:
model.eval()
device = next(model.parameters()).device
x, m, ids, x_lens, m_lens = session.val_sample
x, m = x.to(device), m.to(device)
m1_hat, m2_hat, att = model(x, m)
att = np_now(att)[0]
m1_hat = np_now(m1_hat)[0, :600, :]
m2_hat = np_now(m2_hat)[0, :600, :]
m = np_now(m)[0, :600, :]
att_fig = plot_attention(att)
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
m_fig = plot_mel(m)
self.writer.add_figure('Ground_Truth_Aligned/attention', att_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/target', m_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/linear', m1_hat_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/postnet', m2_hat_fig,
model.step)
m2_hat_wav = reconstruct_waveform(m2_hat)
target_wav = reconstruct_waveform(m)
self.writer.add_audio(tag='Ground_Truth_Aligned/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Ground_Truth_Aligned/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
m1_hat, m2_hat, att = model.generate(x[0].tolist(),
steps=m_lens[0] + 20)
att_fig = plot_attention(att)
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
self.writer.add_figure('Generated/attention', att_fig, model.step)
self.writer.add_figure('Generated/target', m_fig, model.step)
self.writer.add_figure('Generated/linear', m1_hat_fig, model.step)
self.writer.add_figure('Generated/postnet', m2_hat_fig, model.step)
m2_hat_wav = reconstruct_waveform(m2_hat)
self.writer.add_audio(tag='Generated/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Generated/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
def generate_plots(self, model: ForwardTacotron,
session: TTSSession) -> None:
model.eval()
device = next(model.parameters()).device
x, m, ids, lens, dur = session.val_sample
x, m, dur = x.to(device), m.to(device), dur.to(device)
m1_hat, m2_hat, dur_hat = model(x, m, dur)
m1_hat = np_now(m1_hat)[0, :600, :]
m2_hat = np_now(m2_hat)[0, :600, :]
m = np_now(m)[0, :600, :]
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
m_fig = plot_mel(m)
self.writer.add_figure('Ground_Truth_Aligned/target', m_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/linear', m1_hat_fig,
model.step)
self.writer.add_figure('Ground_Truth_Aligned/postnet', m2_hat_fig,
model.step)
m1_hat, m2_hat, m = rescale_mel(m1_hat), rescale_mel(
m2_hat), rescale_mel(m)
m2_hat_wav = reconstruct_waveform(m2_hat)
target_wav = reconstruct_waveform(m)
self.writer.add_audio(tag='Ground_Truth_Aligned/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Ground_Truth_Aligned/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
m1_hat, m2_hat, dur_hat = model.generate(x[0].tolist())
m1_hat, m2_hat = rescale_mel(m1_hat), rescale_mel(m2_hat)
m1_hat_fig = plot_mel(m1_hat)
m2_hat_fig = plot_mel(m2_hat)
self.writer.add_figure('Generated/target', m_fig, model.step)
self.writer.add_figure('Generated/linear', m1_hat_fig, model.step)
self.writer.add_figure('Generated/postnet', m2_hat_fig, model.step)
m2_hat_wav = reconstruct_waveform(m2_hat)
self.writer.add_audio(tag='Generated/target_wav',
snd_tensor=target_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
self.writer.add_audio(tag='Generated/postnet_wav',
snd_tensor=m2_hat_wav,
global_step=model.step,
sample_rate=hp.sample_rate)
def create_align_features(model: Tacotron, train_set: DataLoader,
val_set: DataLoader, save_path: Path):
assert model.r == 1, f'Reduction factor of tacotron must be 1 for creating alignment features! ' \
f'Reduction factor was: {model.r}'
model.eval()
device = next(
model.parameters()).device # use same device as model parameters
iters = len(val_set) + len(train_set)
dataset = itertools.chain(train_set, val_set)
for i, (x, mels, ids, mel_lens) in enumerate(dataset, 1):
x, mels = x.to(device), mels.to(device)
with torch.no_grad():
_, _, attn = model(x, mels)
attn = np_now(attn)
bs, chars = attn.shape[0], attn.shape[2]
argmax = np.argmax(attn[:, :, :], axis=2)
mel_counts = np.zeros(shape=(bs, chars), dtype=np.int32)
for b in range(attn.shape[0]):
# fix random jumps in attention
for j in range(1, argmax.shape[1]):
if abs(argmax[b, j] - argmax[b, j - 1]) > 10:
argmax[b, j] = argmax[b, j - 1]
count = np.bincount(argmax[b, :mel_lens[b]])
mel_counts[b, :len(count)] = count[:len(count)]
for j, item_id in enumerate(ids):
np.save(str(save_path / f'{item_id}.npy'),
mel_counts[j, :],
allow_pickle=False)
bar = progbar(i, iters)
msg = f'{bar} {i}/{iters} Batches '
stream(msg)
def create_align_features(
model: Tacotron,
train_set: DataLoader,
val_set: DataLoader,
save_path_alg: Path,
# save_path_pitch: Path
):
assert model.r == 1, f'Reduction factor of tacotron must be 1 for creating alignment features! ' \
f'Reduction factor was: {model.r}'
model.eval()
device = next(
model.parameters()).device # use same device as model parameters
if val_set is not None:
iters = len(val_set) + len(train_set)
dataset = itertools.chain(train_set, val_set)
else:
# print('here')
iters = len(train_set)
# print(iters)
dataset = itertools.chain(train_set)
att_score_dict = {}
if hp.extract_durations_with_dijkstra:
print('Extracting durations using dijkstra...')
dur_extraction_func = extract_durations_with_dijkstra
else:
print('Extracting durations using attention peak counts...')
dur_extraction_func = extract_durations_per_count
# for i in dataset:
# print(i)
for i, (x, mels, ids, x_lens, mel_lens) in enumerate(dataset, 1):
x, mels = x.to(device), mels.to(device)
# print(x)
# print(mels)
with torch.no_grad():
_, _, att_batch = model(x, mels)
align_score, sharp_score = attention_score(att_batch, mel_lens, r=1)
att_batch = np_now(att_batch)
seq, att, mel_len, item_id = x[0], att_batch[0], mel_lens[0], ids[0]
align_score, sharp_score = float(align_score[0]), float(sharp_score[0])
att_score_dict[item_id] = (align_score, sharp_score)
durs = dur_extraction_func(seq, att, mel_len)
if np.sum(durs) != mel_len:
print(
f'WARNINNG: Sum of durations did not match mel length for item {item_id}!'
)
np.save(str(save_path_alg / f'{item_id}.npy'),
durs,
allow_pickle=False)
bar = progbar(i, iters)
msg = f'{bar} {i}/{iters} Batches '
stream(msg)
pickle_binary(att_score_dict, paths.data / 'att_score_dict.pkl')
