def sample(self, condition):
x = None
seq = torch.from_numpy(process_blizzard(condition)).long().unsqueeze(0)
input_lengths = torch.LongTensor([seq[0].shape[0]]).cuda()
audio_lengths = torch.LongTensor([0]).cuda()
## Tier 1 ##
tqdm.write('Tier 1')
for t in tqdm(range(self.args.timestep // self.t_div)):
audio_lengths += 1
if x is None:
x = torch.zeros((1, self.n_mels // self.f_div, 1)).cuda()
else:
x = torch.cat(
[x,
torch.zeros((1, self.n_mels // self.f_div, 1)).cuda()],
dim=-1)
for m in tqdm(range(self.n_mels // self.f_div)):
torch.cuda.synchronize()
if self.infer_hp.conditional:
mu, std, pi, _ = self.tiers[1](x, seq, input_lengths,
audio_lengths)
else:
mu, std, pi = self.tiers[1](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
x[:, m, t] = temp[:, m, t]
## Tier 2~N ##
for tier in tqdm(range(2, self.hp.model.tier + 1)):
tqdm.write('Tier %d' % tier)
mu, std, pi = self.tiers[tier](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
x = self.tierutil.interleave(x, temp, tier + 1)
return x
def sample_model_with_breakdown(model, condition):
x = None
seq = torch.from_numpy(process_blizzard(condition)).long().unsqueeze(0)
input_lengths = torch.LongTensor([seq[0].shape[0]]).cuda()
audio_lengths = torch.LongTensor([0]).cuda()
breakdown = {}
## Tier 1 ##
tqdm.write('Tier 1')
for t in tqdm(range(model.args.timestep // model.t_div)):
audio_lengths += 1
if x is None:
x = torch.zeros((1, model.n_mels // model.f_div, 1)).cuda()
else:
x = torch.cat([x, torch.zeros((1, model.n_mels // model.f_div, 1)).cuda()], dim=-1)
for m in tqdm(range(model.n_mels // model.f_div)):
torch.cuda.synchronize()
if model.infer_hp.conditional:
mu, std, pi, _ = model.tiers[1](x, seq, input_lengths, audio_lengths)
else:
mu, std, pi = model.tiers[1](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
x[:, m, t] = temp[:, m, t]
breakdown[1] = (x.clone()[0].cpu().detach().numpy(), x.clone()[0].cpu().detach().numpy())
## Tier 2~N ##
for tier in tqdm(range(2, model.hp.model.tier + 1)):
tqdm.write('Tier %d' % tier)
mu, std, pi = model.tiers[tier](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
breakdown[tier] = (x.clone()[0].cpu().detach().numpy(), temp.clone()[0].cpu().detach().numpy())
x = model.tierutil.interleave(x, temp, tier + 1)
return breakdown, x
def run_inference_on_tier(source, tier, text, timestep):
# Returns a tuple, (inference, next_tier)
# inference is the conditional inference on the current tier
# next_tier interleaves the inference with the input to generate the next tier
args = parse_inference_args(['-c', 'config/blizzard_compressed_experiments.yaml', '-p', 'config/inference.yaml', '-t', str(timestep), '-n', 'hw_blizzard_compressed', '-i', text])
hp = HParam('./config/blizzard_compressed_experiments.yaml')
infererence_hp = HParam(args.infer_config)
assert timestep % t_div[hp.model.tier] == 0, \
"timestep should be divisible by %d, got %d" % (t_div[hp.model.tier], timestep)
model = MelNet(hp, args, infererence_hp).cuda()
model.load_tiers()
model.eval()
audio_lengths = torch.LongTensor([0]).cuda()
if tier > 1:
for t in tqdm(range(model.args.timestep // model.t_div)):
audio_lengths += 1
# source = breakdown[tier][0]
x = torch.unsqueeze(torch.from_numpy(source), 0)
mu, std, pi = model.tiers[tier](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
next_tier = model.tierutil.interleave(x, temp, tier + 1)
return next_tier[0].cpu().detach().numpy(), temp[0].cpu().detach().numpy()
def sample(self, condition):
x = None
if condition is not None:
# seq = torch.from_numpy(text_to_sequence(condition)).long().unsqueeze(0)
x = condition
else:
seq = torch.LongTensor([[0]])
# input_lengths = torch.LongTensor([seq[0].shape[0]]).cuda()
if x is not None:
audio_lengths = torch.LongTensor([x.size()[-1]]).cuda()
else:
audio_lengths = torch.LongTensor([0]).cuda()
## Tier 1 ##
tqdm.write('Tier 1')
if self.args.timestep == 0:
mu, std, pi = self.tiers[1](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
return temp
for t in tqdm(range(self.args.timestep // self.t_div)):
audio_lengths += 1
if x is None:
x = torch.zeros((1, self.n_mels // self.f_div, 1)).cuda()
else:
x = torch.cat(
[x,
torch.zeros((1, self.n_mels // self.f_div, 1)).cuda()],
dim=-1)
for m in tqdm(range(self.n_mels // self.f_div)):
torch.cuda.synchronize()
if self.infer_hp.conditional:
# mu, std, pi, _ = self.tiers[1](x, seq, input_lengths, audio_lengths)
break
else:
mu, std, pi = self.tiers[1](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
new_idx = audio_lengths.item() - 1
x[:, m, new_idx] = temp[:, m, new_idx]
## Tier 2~N ##
for tier in tqdm(range(2, self.hp.model.tier + 1)):
tqdm.write('Tier %d' % tier)
mu, std, pi = self.tiers[tier](x)
temp = sample_gmm(mu, std, pi)
x = self.tierutil.interleave(x, temp, tier + 1)
return x
def sample_dependence(self, condition, label, dependence_length):
x = None
if condition is not None:
# seq = torch.from_numpy(text_to_sequence(condition)).long().unsqueeze(0)
x = condition
else:
seq = torch.LongTensor([[0]])
if x is not None:
audio_lengths = torch.LongTensor([x.size()[-1]]).cuda()
else:
audio_lengths = torch.LongTensor([0]).cuda()
for t in tqdm(range(self.args.timestep // self.t_div)):
# audio_lengths += 1
if x is None:
x = torch.zeros((1, self.n_mels // self.f_div, 1)).cuda()
else:
x = torch.cat(
[x,
torch.zeros((1, self.n_mels // self.f_div, 1)).cuda()],
dim=-1)
for m in tqdm(range(self.n_mels // self.f_div)):
torch.cuda.synchronize()
if self.infer_hp.conditional:
# mu, std, pi, _ = self.tiers[1](x, seq, input_lengths, audio_lengths)
break
else:
class_label = torch.tensor(
label, dtype=torch.long) if isinstance(
label, int) else torch.LongTensor(label)
if m == 0:
mu, std, pi, h_t, h_c = self.tiers[1](
x[:, :, -dependence_length:],
audio_lengths,
class_label.cuda(non_blocking=True).unsqueeze(0),
save_hidden=True,
hidden_t=None,
hidden_c=None)
else:
mu, std, pi = self.tiers[1](
x[:, :, -dependence_length:],
audio_lengths,
class_label.cuda(non_blocking=True).unsqueeze(0),
save_hidden=False,
hidden_t=h_t,
hidden_c=h_c)
temp = sample_gmm(mu, std, pi)
new_idx = audio_lengths.item() - 1
x[:, m, -1] = temp[:, m, new_idx]
return x
def run_inference(source, timestep, tier_to_breakdown):
# First load in the model
hp = HParam('./config/blizzard_alldata_v5.yaml')
infer_hp = HParam('./config/inference.yaml')
args = parse_inference_args([
'-c', 'config/blizzard_alldata_v5.yaml', '-p', 'config/inference.yaml',
'-t',
str(timestep), '-n', 'test_tiers', '-i', SENTENCE
])
model = MelNet(hp, args, infer_hp).cuda()
model.load_tiers()
model.eval()
audio_lengths = torch.LongTensor([0]).cuda()
for t in tqdm(range(model.args.timestep // model.t_div)):
audio_lengths += 1
## Tier 2~N ##
x = torch.unsqueeze(torch.from_numpy(source), 0)
for tier in tqdm(
range(model.hp.model.tier + 1 - TESTING_TIERS,
model.hp.model.tier + 1)):
tqdm.write('Tier %d' % tier)
# Save original source and inference source
actual_source = tier_to_breakdown[tier][0]
actual_target = tier_to_breakdown[tier][1]
actual_interleaved = tier_to_breakdown[tier + 1][0]
current_source = x
save_image(x.detach().numpy()[0], 'tier_%d_inference_source' % tier)
save_image(actual_source, 'tier_%d_actual_source' % tier)
save_image(actual_target, 'tier_%d_actual_target' % tier)
mu, std, pi = model.tiers[tier](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
save_image(temp[0].cpu().detach().numpy(),
'tier_%d_inference_target' % tier)
x = model.tierutil.interleave(x, temp, tier + 1)
save_image(x.detach().numpy()[0],
'tier_%d_inference_interleaved' % tier)
save_image(actual_interleaved, 'tier_%d_actual_interleaved' % tier)
reconstructed_mel_tensor = x.detach().numpy()
return reconstructed_mel_tensor[0]
def run_inference(sentence, timestep, tier_to_breakdown):
# First load in the model
hp = HParam('./config/blizzard_alldata_v5.yaml')
infer_hp = HParam('./config/inference.yaml')
args = parse_inference_args([
'-c', 'config/blizzard_alldata_v5.yaml', '-p', 'config/inference.yaml',
'-t',
str(timestep), '-n', 'test_tiers', '-i', SENTENCE
])
model = MelNet(hp, args, infer_hp).cuda()
model.load_tiers()
model.eval()
x = None
seq = torch.from_numpy(process_blizzard(sentence)).long().unsqueeze(0)
input_lengths = torch.LongTensor([seq[0].shape[0]]).cuda()
audio_lengths = torch.LongTensor([0]).cuda()
actual_target = tier_to_breakdown[1][1]
# save_image(seq.detach().numpy(), 'tier_1_seq_source')
## Tier 1 ##
tqdm.write('Tier 1')
for t in tqdm(range(model.args.timestep // model.t_div)):
audio_lengths += 1
if x is None:
x = torch.zeros((1, model.n_mels // model.f_div, 1)).cuda()
else:
x = torch.cat(
[x, torch.zeros((1, model.n_mels // model.f_div, 1)).cuda()],
dim=-1)
for m in tqdm(range(model.n_mels // model.f_div)):
torch.cuda.synchronize()
if model.infer_hp.conditional:
mu, std, pi, _ = model.tiers[1](x, seq, input_lengths,
audio_lengths)
else:
mu, std, pi = model.tiers[1](x, audio_lengths)
temp = sample_gmm(mu, std, pi)
x[:, m, t] = temp[:, m, t]
save_image(x[0].cpu().detach().numpy(), 'tier_1_inference_target')
save_image(actual_target, 'tier_1_actual_target')
# for t in tqdm(range(model.args.timestep // model.t_div)):
# audio_lengths += 1
# ## Tier 2~N ##
# x = torch.unsqueeze(torch.from_numpy(source), 0)
# for tier in tqdm(range(model.hp.model.tier + 1 - TESTING_TIERS, model.hp.model.tier + 1)):
# tqdm.write('Tier %d' % tier)
# # Save original source and inference source
# actual_source = tier_to_breakdown[tier][0]
# actual_target = tier_to_breakdown[tier][1]
# actual_interleaved = tier_to_breakdown[tier+1][0]
# current_source = x
# save_image(x.detach().numpy()[0], 'tier_%d_inference_source' % tier)
# save_image(actual_source, 'tier_%d_actual_source' % tier)
# save_image(actual_target, 'tier_%d_actual_target' % tier)
# mu, std, pi = model.tiers[tier](x, audio_lengths)
# temp = sample_gmm(mu, std, pi)
# save_image(temp[0].cpu().detach().numpy(), 'tier_%d_inference_target' % tier)
# x = model.tierutil.interleave(x, temp, tier + 1)
# save_image(x.detach().numpy()[0], 'tier_%d_inference_interleaved' % tier)
# save_image(actual_interleaved, 'tier_%d_actual_interleaved' % tier)
reconstructed_mel_tensor = x.detach().numpy()
return reconstructed_mel_tensor[0]