def test_train_step():
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, 120, c.audio['num_mels']).to(device)
linear_spec = torch.rand(8, 120, c.audio['num_freq']).to(device)
mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
mel_lengths[-1] = 120
stop_targets = torch.zeros(8, 120, 1).float().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input_dummy.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron(
num_chars=32,
num_speakers=5,
gst=True,
postnet_output_dim=c.audio['num_freq'],
decoder_output_dim=c.audio['num_mels'],
r=c.r,
memory_size=c.memory_size
).to(device) #FIXME: missing num_speakers parameter to Tacotron ctor
model.train()
print(model)
print(" > Num parameters for Tacotron GST model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for _ in range(10):
mel_out, linear_out, align, stop_tokens = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(linear_out, linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_train_step():
config = config_global.copy()
config.use_speaker_embedding = False
config.num_speakers = 1
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
linear_spec = torch.rand(8, 30,
config.audio["fft_size"] // 2 + 1).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
mel_lengths[-1] = mel_spec.size(1)
stop_targets = torch.zeros(8, 30, 1).float().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input_dummy.shape[0],
stop_targets.size(1) // config.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron(config).to(
device) # FIXME: missing num_speakers parameter to Tacotron ctor
model.train()
print(" > Num parameters for Tacotron model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=config.lr)
for _ in range(5):
outputs = model.forward(input_dummy, input_lengths, mel_spec,
mel_lengths)
optimizer.zero_grad()
loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
loss = loss + criterion(outputs["model_outputs"], linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
# if count not in [145, 59]:
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_train_step():
config = config_global.copy()
config.use_speaker_embedding = True
config.num_speakers = 10
config.use_gst = True
config.gst = GSTConfig()
# with random gst mel style
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, 120, config.audio["num_mels"]).to(device)
linear_spec = torch.rand(8, 120,
config.audio["fft_size"] // 2 + 1).to(device)
mel_lengths = torch.randint(20, 120, (8, )).long().to(device)
mel_lengths[-1] = 120
stop_targets = torch.zeros(8, 120, 1).float().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input_dummy.shape[0],
stop_targets.size(1) // config.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
config.use_gst = True
config.gst = GSTConfig()
model = Tacotron(config).to(
device) # FIXME: missing num_speakers parameter to Tacotron ctor
model.train()
# print(model)
print(" > Num parameters for Tacotron GST model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=config.lr)
for _ in range(10):
outputs = model.forward(input_dummy,
input_lengths,
mel_spec,
mel_lengths,
aux_input={"speaker_ids": speaker_ids})
optimizer.zero_grad()
loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
loss = loss + criterion(outputs["model_outputs"], linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
# with file gst style
mel_spec = (torch.FloatTensor(ap.melspectrogram(
ap.load_wav(WAV_FILE)))[:, :120].unsqueeze(0).transpose(
1, 2).to(device))
mel_spec = mel_spec.repeat(8, 1, 1)
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
linear_spec = torch.rand(8, mel_spec.size(1),
config.audio["fft_size"] // 2 + 1).to(device)
mel_lengths = torch.randint(20, mel_spec.size(1),
(8, )).long().to(device)
mel_lengths[-1] = mel_spec.size(1)
stop_targets = torch.zeros(8, mel_spec.size(1), 1).float().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input_dummy.shape[0],
stop_targets.size(1) // config.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron(config).to(
device) # FIXME: missing num_speakers parameter to Tacotron ctor
model.train()
# print(model)
print(" > Num parameters for Tacotron GST model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=config.lr)
for _ in range(10):
outputs = model.forward(input_dummy,
input_lengths,
mel_spec,
mel_lengths,
aux_input={"speaker_ids": speaker_ids})
optimizer.zero_grad()
loss = criterion(outputs["decoder_outputs"], mel_spec, mel_lengths)
stop_loss = criterion_st(outputs["stop_tokens"], stop_targets)
loss = loss + criterion(outputs["model_outputs"], linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_train_step():
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (8, )).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(8, 30, c.audio["num_mels"]).to(device)
linear_spec = torch.rand(8, 30, c.audio["fft_size"]).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
mel_lengths[-1] = mel_spec.size(1)
stop_targets = torch.zeros(8, 30, 1).float().to(device)
speaker_embeddings = torch.rand(8, 55).to(device)
for idx in mel_lengths:
stop_targets[:, int(idx.item()):, 0] = 1.0
stop_targets = stop_targets.view(input_dummy.shape[0],
stop_targets.size(1) // c.r, -1)
stop_targets = (stop_targets.sum(2) >
0.0).unsqueeze(2).float().squeeze()
criterion = L1LossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron(
num_chars=32,
num_speakers=5,
postnet_output_dim=c.audio["fft_size"],
decoder_output_dim=c.audio["num_mels"],
gst=True,
gst_embedding_dim=c.gst["gst_embedding_dim"],
gst_num_heads=c.gst["gst_num_heads"],
gst_style_tokens=c.gst["gst_style_tokens"],
gst_use_speaker_embedding=c.gst["gst_use_speaker_embedding"],
r=c.r,
memory_size=c.memory_size,
speaker_embedding_dim=55,
).to(device) # FIXME: missing num_speakers parameter to Tacotron ctor
model.train()
print(" > Num parameters for Tacotron model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
optimizer = optim.Adam(model.parameters(), lr=c.lr)
for _ in range(5):
mel_out, linear_out, align, stop_tokens = model.forward(
input_dummy,
input_lengths,
mel_spec,
mel_lengths,
speaker_embeddings=speaker_embeddings)
optimizer.zero_grad()
loss = criterion(mel_out, mel_spec, mel_lengths)
stop_loss = criterion_st(stop_tokens, stop_targets)
loss = loss + criterion(linear_out, linear_spec,
mel_lengths) + stop_loss
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for name_param, param_ref in zip(model.named_parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
# if count not in [145, 59]:
name, param = name_param
if name == "gst_layer.encoder.recurrence.weight_hh_l0":
continue
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_train_step():
config = TacotronConfig(
num_chars=32,
num_speakers=10,
use_speaker_embedding=True,
out_channels=513,
decoder_output_dim=80,
use_capacitron_vae=True,
capacitron_vae=CapacitronVAEConfig(),
optimizer="CapacitronOptimizer",
optimizer_params={
"RAdam": {
"betas": [0.9, 0.998],
"weight_decay": 1e-6
},
"SGD": {
"lr": 1e-5,
"momentum": 0.9
},
},
)
batch = dict({})
batch["text_input"] = torch.randint(0, 24, (8, 128)).long().to(device)
batch["text_lengths"] = torch.randint(100, 129,
(8, )).long().to(device)
batch["text_lengths"] = torch.sort(batch["text_lengths"],
descending=True)[0]
batch["text_lengths"][0] = 128
batch["linear_input"] = torch.rand(
8, 120, config.audio["fft_size"] // 2 + 1).to(device)
batch["mel_input"] = torch.rand(8, 120,
config.audio["num_mels"]).to(device)
batch["mel_lengths"] = torch.randint(20, 120, (8, )).long().to(device)
batch["mel_lengths"] = torch.sort(batch["mel_lengths"],
descending=True)[0]
batch["mel_lengths"][0] = 120
batch["stop_targets"] = torch.zeros(8, 120, 1).float().to(device)
batch["stop_target_lengths"] = torch.randint(0, 120, (8, )).to(device)
batch["speaker_ids"] = torch.randint(0, 5, (8, )).long().to(device)
batch["d_vectors"] = None
for idx in batch["mel_lengths"]:
batch["stop_targets"][:, int(idx.item()):, 0] = 1.0
batch["stop_targets"] = batch["stop_targets"].view(
batch["text_input"].shape[0],
batch["stop_targets"].size(1) // config.r, -1)
batch["stop_targets"] = (batch["stop_targets"].sum(2) >
0.0).unsqueeze(2).float().squeeze()
model = Tacotron(config).to(device)
criterion = model.get_criterion()
optimizer = model.get_optimizer()
model.train()
print(" > Num parameters for Tacotron with Capacitron VAE model:%s" %
(count_parameters(model)))
model_ref = copy.deepcopy(model)
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count += 1
for _ in range(10):
_, loss_dict = model.train_step(batch, criterion)
optimizer.zero_grad()
loss_dict["capacitron_vae_beta_loss"].backward()
optimizer.first_step()
loss_dict["loss"].backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
# ignore pre-higway layer since it works conditional
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
