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)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
criterion = GlowTTSLoss()
# model to train
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
# reference model to compare model weights
model_ref = GlowTTS(config).to(device)
model.train()
print(" > Num parameters for GlowTTS model:%s" %
(count_parameters(model)))
# pass the state to ref model
model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
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=0.001)
for _ in range(5):
optimizer.zero_grad()
outputs = model.forward(input_dummy, input_lengths, mel_spec,
mel_lengths, None)
loss_dict = criterion(
outputs["z"],
outputs["y_mean"],
outputs["y_log_scale"],
outputs["logdet"],
mel_lengths,
outputs["durations_log"],
outputs["total_durations_log"],
input_lengths,
)
loss = loss_dict["loss"]
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1
def test_inference():
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)
mel_lengths = torch.randint(20, 30, (8,)).long().to(device)
speaker_ids = torch.randint(0, 5, (8,)).long().to(device)
# create model
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.eval()
print(" > Num parameters for GlowTTS model:%s" % (count_parameters(model)))
# inference encoder and decoder with MAS
y = model.inference_with_MAS(input_dummy, input_lengths, mel_spec, mel_lengths)
y2 = model.decoder_inference(mel_spec, mel_lengths)
assert (
y2["model_outputs"].shape == y["model_outputs"].shape
), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
y["model_outputs"].shape, y2["model_outputs"].shape
)
def test_unlock_act_norm_layers(self):
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.unlock_act_norm_layers()
for f in model.decoder.flows:
if getattr(f, "set_ddi", False):
self.assertFalse(f.initialized)
def _test_inference(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.eval()
outputs = model.inference(input_dummy, {"x_lengths": input_lengths})
self._assert_inference_outputs(outputs, input_dummy, mel_spec)
def test_train_step(self):
batch_size = BATCH_SIZE
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
criterion = GlowTTSLoss()
# model to train
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
# reference model to compare model weights
model_ref = GlowTTS(config).to(device)
model.train()
print(" > Num parameters for GlowTTS model:%s" %
(count_parameters(model)))
# pass the state to ref model
model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
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=0.001)
for _ in range(5):
optimizer.zero_grad()
outputs = model.forward(input_dummy, input_lengths, mel_spec,
mel_lengths, None)
loss_dict = criterion(
outputs["z"],
outputs["y_mean"],
outputs["y_log_scale"],
outputs["logdet"],
mel_lengths,
outputs["durations_log"],
outputs["total_durations_log"],
input_lengths,
)
loss = loss_dict["loss"]
loss.backward()
optimizer.step()
# check parameter changes
self._check_parameter_changes(model, model_ref)
def test_inference():
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, c.audio["num_mels"], 30).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
# create model
model = GlowTTS(
num_chars=32,
hidden_channels_enc=48,
hidden_channels_dec=48,
hidden_channels_dp=32,
out_channels=80,
encoder_type="rel_pos_transformer",
encoder_params={
"kernel_size": 3,
"dropout_p": 0.1,
"num_layers": 6,
"num_heads": 2,
"hidden_channels_ffn": 16, # 4 times the hidden_channels
"input_length": None,
},
use_encoder_prenet=True,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=1,
num_block_layers=4,
dropout_p_dec=0.0,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_squeeze=1,
sigmoid_scale=False,
mean_only=False,
).to(device)
model.eval()
print(" > Num parameters for GlowTTS model:%s" %
(count_parameters(model)))
# inference encoder and decoder with MAS
y, *_ = model.inference_with_MAS(input_dummy, input_lengths, mel_spec,
mel_lengths, None)
y_dec, _ = model.decoder_inference(mel_spec, mel_lengths)
assert (
y_dec.shape == y.shape
), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
y.shape, y_dec.shape)
def test_init_multispeaker(self):
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config)
# speaker embedding with default speaker_embedding_dim
config.use_speaker_embedding = True
config.num_speakers = 5
config.d_vector_dim = None
model.init_multispeaker(config)
self.assertEqual(model.c_in_channels, model.hidden_channels_enc)
# use external speaker embeddings with speaker_embedding_dim = 301
config = GlowTTSConfig(num_chars=32)
config.use_d_vector_file = True
config.d_vector_dim = 301
model = GlowTTS(config)
model.init_multispeaker(config)
self.assertEqual(model.c_in_channels, 301)
# use speaker embedddings by the provided speaker_manager
config = GlowTTSConfig(num_chars=32)
config.use_speaker_embedding = True
config.speakers_file = os.path.join(get_tests_data_path(), "ljspeech",
"speakers.json")
speaker_manager = SpeakerManager.init_from_config(config)
model = GlowTTS(config)
model.speaker_manager = speaker_manager
model.init_multispeaker(config)
self.assertEqual(model.c_in_channels, model.hidden_channels_enc)
self.assertEqual(model.num_speakers, speaker_manager.num_speakers)
# use external speaker embeddings by the provided speaker_manager
config = GlowTTSConfig(num_chars=32)
config.use_d_vector_file = True
config.d_vector_dim = 256
config.d_vector_file = os.path.join(get_tests_data_path(),
"dummy_speakers.json")
speaker_manager = SpeakerManager.init_from_config(config)
model = GlowTTS(config)
model.speaker_manager = speaker_manager
model.init_multispeaker(config)
self.assertEqual(model.c_in_channels, speaker_manager.embedding_dim)
self.assertEqual(model.num_speakers, speaker_manager.num_speakers)
def _test_inference_with_MAS(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
# create model
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.eval()
# inference encoder and decoder with MAS
y = model.inference_with_MAS(input_dummy, input_lengths, mel_spec,
mel_lengths)
y2 = model.decoder_inference(mel_spec, mel_lengths)
assert (
y2["model_outputs"].shape == y["model_outputs"].shape
), "Difference between the shapes of the glowTTS inference with MAS ({}) and the inference using only the decoder ({}) !!".format(
y["model_outputs"].shape, y2["model_outputs"].shape)
def _test_forward(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
# create model
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.train()
print(" > Num parameters for GlowTTS model:%s" %
(count_parameters(model)))
# inference encoder and decoder with MAS
y = model.forward(input_dummy, input_lengths, mel_spec, mel_lengths)
self.assertEqual(y["z"].shape, mel_spec.shape)
self.assertEqual(y["logdet"].shape, torch.Size([batch_size]))
self.assertEqual(y["y_mean"].shape, mel_spec.shape)
self.assertEqual(y["y_log_scale"].shape, mel_spec.shape)
self.assertEqual(y["alignments"].shape,
mel_spec.shape[:2] + (input_dummy.shape[1], ))
self.assertEqual(y["durations_log"].shape, input_dummy.shape + (1, ))
self.assertEqual(y["total_durations_log"].shape,
input_dummy.shape + (1, ))
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(
dataset_config,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
# init speaker manager for multi-speaker training
# it maps speaker-id to speaker-name in the model and data-loader
speaker_manager = SpeakerManager()
speaker_manager.set_ids_from_data(train_samples + eval_samples,
parse_key="speaker_name")
config.num_speakers = speaker_manager.num_speakers
# init model
model = GlowTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(TrainerArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples)
# AND... 3,2,1... 🚀
trainer.fit()
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor(**config.audio.to_dict())
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# INITIALIZE THE MODEL
# Models take a config object and a speaker manager as input
# Config defines the details of the model like the number of layers, the size of the embedding, etc.
# Speaker manager is used by multi-speaker models.
model = GlowTTS(config, speaker_manager=None)
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap}, # assets are objetcs used by the models but not class members.
)
# AND... 3,2,1... 🚀
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, c.audio["num_mels"], 30).to(device)
mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
speaker_ids = torch.randint(0, 5, (8, )).long().to(device)
criterion = GlowTTSLoss()
# model to train
model = GlowTTS(
num_chars=32,
hidden_channels_enc=48,
hidden_channels_dec=48,
hidden_channels_dp=32,
out_channels=80,
encoder_type="rel_pos_transformer",
encoder_params={
"kernel_size": 3,
"dropout_p": 0.1,
"num_layers": 6,
"num_heads": 2,
"hidden_channels_ffn": 16, # 4 times the hidden_channels
"input_length": None,
},
use_encoder_prenet=True,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=1,
num_block_layers=4,
dropout_p_dec=0.0,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_squeeze=1,
sigmoid_scale=False,
mean_only=False,
).to(device)
# reference model to compare model weights
model_ref = GlowTTS(
num_chars=32,
hidden_channels_enc=48,
hidden_channels_dec=48,
hidden_channels_dp=32,
out_channels=80,
encoder_type="rel_pos_transformer",
encoder_params={
"kernel_size": 3,
"dropout_p": 0.1,
"num_layers": 6,
"num_heads": 2,
"hidden_channels_ffn": 16, # 4 times the hidden_channels
"input_length": None,
},
use_encoder_prenet=True,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=1,
num_block_layers=4,
dropout_p_dec=0.0,
num_speakers=0,
c_in_channels=0,
num_splits=4,
num_squeeze=1,
sigmoid_scale=False,
mean_only=False,
).to(device)
model.train()
print(" > Num parameters for GlowTTS model:%s" %
(count_parameters(model)))
# pass the state to ref model
model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
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=0.001)
for _ in range(5):
optimizer.zero_grad()
z, logdet, y_mean, y_log_scale, alignments, o_dur_log, o_total_dur = model.forward(
input_dummy, input_lengths, mel_spec, mel_lengths, None)
loss_dict = criterion(z, y_mean, y_log_scale, logdet, mel_lengths,
o_dur_log, o_total_dur, input_lengths)
loss = loss_dict["loss"]
loss.backward()
optimizer.step()
# check parameter changes
count = 0
for param, param_ref in zip(model.parameters(),
model_ref.parameters()):
assert (param != param_ref).any(
), "param {} with shape {} not updated!! \n{}\n{}".format(
count, param.shape, param, param_ref)
count += 1