video-dubbing / TTS /tests /tts_tests /test_tacotron2_model.py
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import copy
import os
import unittest
import torch
from torch import nn, optim
from tests import get_tests_input_path
from TTS.tts.configs.shared_configs import CapacitronVAEConfig, GSTConfig
from TTS.tts.configs.tacotron2_config import Tacotron2Config
from TTS.tts.layers.losses import MSELossMasked
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.utils.audio import AudioProcessor
# pylint: disable=unused-variable
torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
config_global = Tacotron2Config(num_chars=32, num_speakers=5, out_channels=80, decoder_output_dim=80)
ap = AudioProcessor(**config_global.audio)
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
class TacotronTrainTest(unittest.TestCase):
"""Test vanilla Tacotron2 model."""
def test_train_step(self): # pylint: disable=no-self-use
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, 128, (8,)).long().to(device)
input_lengths = torch.sort(input_lengths, descending=True)[0]
mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
mel_lengths[0] = 30
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 = MSELossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron2(config).to(device)
model.train()
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 i in range(5):
outputs = model.forward(input_dummy, input_lengths, mel_spec, mel_lengths)
assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
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"], mel_postnet_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
class MultiSpeakerTacotronTrainTest(unittest.TestCase):
"""Test multi-speaker Tacotron2 with speaker embedding layer"""
@staticmethod
def test_train_step():
config = config_global.copy()
config.use_speaker_embedding = True
config.num_speakers = 5
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 128, (8,)).long().to(device)
input_lengths = torch.sort(input_lengths, descending=True)[0]
mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
mel_lengths[0] = 30
stop_targets = torch.zeros(8, 30, 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 = MSELossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
config.d_vector_dim = 55
model = Tacotron2(config).to(device)
model.train()
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, aux_input={"speaker_ids": speaker_ids}
)
assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
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"], mel_postnet_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
class TacotronGSTTrainTest(unittest.TestCase):
"""Test multi-speaker Tacotron2 with Global Style Token and Speaker Embedding"""
# pylint: disable=no-self-use
def test_train_step(self):
# with random gst mel style
config = config_global.copy()
config.use_speaker_embedding = True
config.num_speakers = 10
config.use_gst = True
config.gst = GSTConfig()
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 128, (8,)).long().to(device)
input_lengths = torch.sort(input_lengths, descending=True)[0]
mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
mel_lengths[0] = 30
stop_targets = torch.zeros(8, 30, 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 = MSELossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
config.use_gst = True
config.gst = GSTConfig()
model = Tacotron2(config).to(device)
model.train()
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 i in range(10):
outputs = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids}
)
assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
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"], mel_postnet_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":
# print(param.grad)
continue
assert (param != param_ref).any(), "param {} {} with shape {} not updated!! \n{}\n{}".format(
name, count, param.shape, param, param_ref
)
count += 1
# with file gst style
mel_spec = (
torch.FloatTensor(ap.melspectrogram(ap.load_wav(WAV_FILE)))[:, :30].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, 128, (8,)).long().to(device)
input_lengths = torch.sort(input_lengths, descending=True)[0]
mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
mel_lengths[0] = 30
stop_targets = torch.zeros(8, 30, 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 = MSELossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
model = Tacotron2(config).to(device)
model.train()
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 i in range(10):
outputs = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"speaker_ids": speaker_ids}
)
assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
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"], mel_postnet_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":
# print(param.grad)
continue
assert (param != param_ref).any(), "param {} {} with shape {} not updated!! \n{}\n{}".format(
name, count, param.shape, param, param_ref
)
count += 1
class TacotronCapacitronTrainTest(unittest.TestCase):
@staticmethod
def test_train_step():
config = Tacotron2Config(
num_chars=32,
num_speakers=10,
use_speaker_embedding=True,
out_channels=80,
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["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 = Tacotron2(config).to(device)
criterion = model.get_criterion().to(device)
optimizer = model.get_optimizer()
model.train()
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
class SCGSTMultiSpeakeTacotronTrainTest(unittest.TestCase):
"""Test multi-speaker Tacotron2 with Global Style Tokens and d-vector inputs."""
@staticmethod
def test_train_step():
config = config_global.copy()
config.use_d_vector_file = True
config.use_gst = True
config.gst = GSTConfig()
input_dummy = torch.randint(0, 24, (8, 128)).long().to(device)
input_lengths = torch.randint(100, 128, (8,)).long().to(device)
input_lengths = torch.sort(input_lengths, descending=True)[0]
mel_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_postnet_spec = torch.rand(8, 30, config.audio["num_mels"]).to(device)
mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
mel_lengths[0] = 30
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) // config.r, -1)
stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
criterion = MSELossMasked(seq_len_norm=False).to(device)
criterion_st = nn.BCEWithLogitsLoss().to(device)
config.d_vector_dim = 55
model = Tacotron2(config).to(device)
model.train()
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 i in range(5):
outputs = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, aux_input={"d_vectors": speaker_embeddings}
)
assert torch.sigmoid(outputs["stop_tokens"]).data.max() <= 1.0
assert torch.sigmoid(outputs["stop_tokens"]).data.min() >= 0.0
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"], mel_postnet_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