def test_multispeaker_inference(self):
num_speakers = 10
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True)
model = Vits(config).to(device)
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(
config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
outputs = model.inference(input_dummy, {
"x_lengths": input_lengths,
"speaker_ids": speaker_ids
})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
def test_inference(self):
num_speakers = 0
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True)
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
model = Vits(config).to(device)
_ = model.inference(input_dummy)
def test_multilingual_forward(self):
num_speakers = 10
num_langs = 3
batch_size = 2
args = VitsArgs(language_ids_file=LANG_FILE,
use_language_embedding=True,
spec_segment_size=10)
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True,
model_args=args)
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(
config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
lang_ids = torch.randint(0, num_langs,
(batch_size, )).long().to(device)
model = Vits(config).to(device)
output_dict = model.forward(
input_dummy,
input_lengths,
spec,
spec_lengths,
waveform,
aux_input={
"speaker_ids": speaker_ids,
"language_ids": lang_ids
},
)
self._check_forward_outputs(config, output_dict)
def test_get_aux_input(self):
aux_input = {
"speaker_ids": None,
"style_wav": None,
"d_vectors": None,
"language_ids": None
}
args = VitsArgs()
model = Vits(args)
aux_out = model.get_aux_input(aux_input)
speaker_id = torch.randint(10, (1, ))
language_id = torch.randint(10, (1, ))
d_vector = torch.rand(1, 128)
aux_input = {
"speaker_ids": speaker_id,
"style_wav": None,
"d_vectors": d_vector,
"language_ids": language_id
}
aux_out = model.get_aux_input(aux_input)
self.assertEqual(aux_out["speaker_ids"].shape, speaker_id.shape)
self.assertEqual(aux_out["language_ids"].shape, language_id.shape)
self.assertEqual(aux_out["d_vectors"].shape,
d_vector.unsqueeze(0).transpose(2, 1).shape)
def test_init_multilingual(self):
args = VitsArgs(language_ids_file=None, use_language_embedding=False)
model = Vits(args)
self.assertEqual(model.language_manager, None)
self.assertEqual(model.embedded_language_dim, 0)
assertHasNotAttr(self, model, "emb_l")
args = VitsArgs(language_ids_file=LANG_FILE)
model = Vits(args)
self.assertNotEqual(model.language_manager, None)
self.assertEqual(model.embedded_language_dim, 0)
assertHasNotAttr(self, model, "emb_l")
args = VitsArgs(language_ids_file=LANG_FILE,
use_language_embedding=True)
model = Vits(args)
self.assertNotEqual(model.language_manager, None)
self.assertEqual(model.embedded_language_dim,
args.embedded_language_dim)
assertHasAttr(self, model, "emb_l")
args = VitsArgs(language_ids_file=LANG_FILE,
use_language_embedding=True,
embedded_language_dim=102)
model = Vits(args)
self.assertNotEqual(model.language_manager, None)
self.assertEqual(model.embedded_language_dim,
args.embedded_language_dim)
assertHasAttr(self, model, "emb_l")
def test_forward(self):
num_speakers = 0
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True)
config.model_args.spec_segment_size = 10
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(
config)
model = Vits(config).to(device)
output_dict = model.forward(input_dummy, input_lengths, spec,
spec_lengths, waveform)
self._check_forward_outputs(config, output_dict)
def test_multilingual_inference(self):
num_speakers = 10
num_langs = 3
args = VitsArgs(language_ids_file=LANG_FILE,
use_language_embedding=True,
spec_segment_size=10)
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True,
model_args=args)
model = Vits(config).to(device)
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
speaker_ids = torch.randint(0, num_speakers, (1, )).long().to(device)
lang_ids = torch.randint(0, num_langs, (1, )).long().to(device)
_ = model.inference(input_dummy, {
"speaker_ids": speaker_ids,
"language_ids": lang_ids
})
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
lang_ids = torch.randint(0, num_langs,
(batch_size, )).long().to(device)
outputs = model.inference(input_dummy, {
"speaker_ids": speaker_ids,
"language_ids": lang_ids
})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(
config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
lang_ids = torch.randint(0, num_langs,
(batch_size, )).long().to(device)
outputs = model.inference(
input_dummy, {
"x_lengths": input_lengths,
"speaker_ids": speaker_ids,
"language_ids": lang_ids
})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
def test_d_vector_inference(self):
args = VitsArgs(
spec_segment_size=10,
num_chars=32,
use_d_vector_file=True,
d_vector_dim=256,
d_vector_file=os.path.join(get_tests_data_path(),
"dummy_speakers.json"),
)
config = VitsConfig(model_args=args)
model = Vits.init_from_config(config, verbose=False).to(device)
model.eval()
# batch size = 1
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
d_vectors = torch.randn(1, 256).to(device)
outputs = model.inference(input_dummy,
aux_input={"d_vectors": d_vectors})
self._check_inference_outputs(config, outputs, input_dummy)
# batch size = 2
input_dummy, input_lengths, *_ = self._create_inputs(config)
d_vectors = torch.randn(2, 256).to(device)
outputs = model.inference(input_dummy,
aux_input={
"x_lengths": input_lengths,
"d_vectors": d_vectors
})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=2)
def test_init_multispeaker(self):
num_speakers = 10
args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
model = Vits(args)
assertHasAttr(self, model, "emb_g")
args = VitsArgs(num_speakers=0, use_speaker_embedding=True)
model = Vits(args)
assertHasNotAttr(self, model, "emb_g")
args = VitsArgs(num_speakers=10, use_speaker_embedding=False)
model = Vits(args)
assertHasNotAttr(self, model, "emb_g")
args = VitsArgs(d_vector_dim=101, use_d_vector_file=True)
model = Vits(args)
self.assertEqual(model.embedded_speaker_dim, 101)
def test_test_run(self):
config = VitsConfig(model_args=VitsArgs(num_chars=32))
model = Vits.init_from_config(config, verbose=False).to(device)
model.run_data_dep_init = False
model.eval()
test_figures, test_audios = model.test_run(None)
self.assertTrue(test_figures is not None)
self.assertTrue(test_audios is not None)
def test_multilingual_inference(self):
num_speakers = 10
num_langs = 3
args = VitsArgs(language_ids_file=LANG_FILE,
use_language_embedding=True,
spec_segment_size=10)
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True,
model_args=args)
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
speaker_ids = torch.randint(0, num_speakers, (1, )).long().to(device)
lang_ids = torch.randint(0, num_langs, (1, )).long().to(device)
model = Vits(config).to(device)
_ = model.inference(input_dummy, {
"speaker_ids": speaker_ids,
"language_ids": lang_ids
})
def test_secl_forward(self):
num_speakers = 10
num_langs = 3
batch_size = 2
speaker_encoder_config = load_config(SPEAKER_ENCODER_CONFIG)
speaker_encoder_config.model_params["use_torch_spec"] = True
speaker_encoder = setup_encoder_model(speaker_encoder_config).to(
device)
speaker_manager = SpeakerManager()
speaker_manager.encoder = speaker_encoder
args = VitsArgs(
language_ids_file=LANG_FILE,
use_language_embedding=True,
spec_segment_size=10,
use_speaker_encoder_as_loss=True,
)
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True,
model_args=args)
config.audio.sample_rate = 16000
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(
config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers,
(batch_size, )).long().to(device)
lang_ids = torch.randint(0, num_langs,
(batch_size, )).long().to(device)
model = Vits(config, speaker_manager=speaker_manager).to(device)
output_dict = model.forward(
input_dummy,
input_lengths,
spec,
spec_lengths,
waveform,
aux_input={
"speaker_ids": speaker_ids,
"language_ids": lang_ids
},
)
self._check_forward_outputs(config, output_dict,
speaker_encoder_config)
def test_multispeaker_forward(self):
num_speakers = 10
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True)
config.model_args.spec_segment_size = 10
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(
config)
speaker_ids = torch.randint(0, num_speakers, (8, )).long().to(device)
model = Vits(config).to(device)
output_dict = model.forward(input_dummy,
input_lengths,
spec,
spec_lengths,
waveform,
aux_input={"speaker_ids": speaker_ids})
self._check_forward_outputs(config, output_dict)
def test_load_checkpoint(self):
chkp_path = os.path.join(get_tests_output_path(),
"dummy_glow_tts_checkpoint.pth")
config = VitsConfig(VitsArgs(num_chars=32))
model = Vits.init_from_config(config, verbose=False).to(device)
chkp = {}
chkp["model"] = model.state_dict()
torch.save(chkp, chkp_path)
model.load_checkpoint(config, chkp_path)
self.assertTrue(model.training)
model.load_checkpoint(config, chkp_path, eval=True)
self.assertFalse(model.training)
def test_voice_conversion(self):
num_speakers = 10
spec_len = 101
spec_effective_len = 50
args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
model = Vits(args)
ref_inp = torch.randn(1, 513, spec_len)
ref_inp_len = torch.randint(1, spec_effective_len, (1, ))
ref_spk_id = torch.randint(1, num_speakers, (1, ))
tgt_spk_id = torch.randint(1, num_speakers, (1, ))
o_hat, y_mask, (z, z_p, z_hat) = model.voice_conversion(
ref_inp, ref_inp_len, ref_spk_id, tgt_spk_id)
self.assertEqual(o_hat.shape, (1, 1, spec_len * 256))
self.assertEqual(y_mask.shape, (1, 1, spec_len))
self.assertEqual(y_mask.sum(), ref_inp_len[0])
self.assertEqual(z.shape, (1, args.hidden_channels, spec_len))
self.assertEqual(z_p.shape, (1, args.hidden_channels, spec_len))
self.assertEqual(z_hat.shape, (1, args.hidden_channels, spec_len))
def test_inference(self):
num_speakers = 0
config = VitsConfig(num_speakers=num_speakers,
use_speaker_embedding=True)
model = Vits(config).to(device)
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
outputs = model.inference(input_dummy)
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(
config, batch_size=batch_size)
outputs = model.inference(input_dummy,
aux_input={"x_lengths": input_lengths})
self._check_inference_outputs(config,
outputs,
input_dummy,
batch_size=batch_size)
def test_init_from_config(self):
config = VitsConfig(model_args=VitsArgs(num_chars=32))
model = Vits.init_from_config(config, verbose=False).to(device)
config = VitsConfig(model_args=VitsArgs(num_chars=32, num_speakers=2))
model = Vits.init_from_config(config, verbose=False).to(device)
self.assertTrue(not hasattr(model, "emb_g"))
config = VitsConfig(model_args=VitsArgs(
num_chars=32, num_speakers=2, use_speaker_embedding=True))
model = Vits.init_from_config(config, verbose=False).to(device)
self.assertEqual(model.num_speakers, 2)
self.assertTrue(hasattr(model, "emb_g"))
config = VitsConfig(model_args=VitsArgs(
num_chars=32,
num_speakers=2,
use_speaker_embedding=True,
speakers_file=os.path.join(get_tests_data_path(), "ljspeech",
"speakers.json"),
))
model = Vits.init_from_config(config, verbose=False).to(device)
self.assertEqual(model.num_speakers, 10)
self.assertTrue(hasattr(model, "emb_g"))
config = VitsConfig(model_args=VitsArgs(
num_chars=32,
use_d_vector_file=True,
d_vector_dim=256,
d_vector_file=os.path.join(get_tests_data_path(),
"dummy_speakers.json"),
))
model = Vits.init_from_config(config, verbose=False).to(device)
self.assertTrue(model.num_speakers == 1)
self.assertTrue(not hasattr(model, "emb_g"))
self.assertTrue(model.embedded_speaker_dim == config.d_vector_dim)
def test_train_eval_log(self):
batch_size = 2
config = VitsConfig(
model_args=VitsArgs(num_chars=32, spec_segment_size=10))
model = Vits.init_from_config(config, verbose=False).to(device)
model.run_data_dep_init = False
model.train()
batch = self._create_batch(config, batch_size)
logger = TensorboardLogger(log_dir=os.path.join(
get_tests_output_path(), "dummy_vits_logs"),
model_name="vits_test_train_log")
criterion = model.get_criterion()
criterion = [criterion[0].to(device), criterion[1].to(device)]
outputs = [None] * 2
outputs[0], _ = model.train_step(batch, criterion, 0)
outputs[1], _ = model.train_step(batch, criterion, 1)
model.train_log(batch, outputs, logger, None, 1)
model.eval_log(batch, outputs, logger, None, 1)
logger.finish()
def test_d_vector_forward(self):
batch_size = 2
args = VitsArgs(
spec_segment_size=10,
num_chars=32,
use_d_vector_file=True,
d_vector_dim=256,
d_vector_file=os.path.join(get_tests_data_path(),
"dummy_speakers.json"),
)
config = VitsConfig(model_args=args)
model = Vits.init_from_config(config, verbose=False).to(device)
model.train()
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(
config, batch_size=batch_size)
d_vectors = torch.randn(batch_size, 256).to(device)
output_dict = model.forward(input_dummy,
input_lengths,
spec,
spec_lengths,
waveform,
aux_input={"d_vectors": d_vectors})
self._check_forward_outputs(config, output_dict)
output_path=output_path,
datasets=[dataset_config],
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# load training samples
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# 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_speaker_ids_from_data(train_samples + eval_samples)
config.model_args.num_speakers = speaker_manager.num_speakers
# init model
model = Vits(config, speaker_manager)
# init the trainer and 🚀
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
)
trainer.fit()
def test_get_criterion(self):
config = VitsConfig(VitsArgs(num_chars=32))
model = Vits.init_from_config(config, verbose=False).to(device)
criterion = model.get_criterion()
self.assertTrue(criterion is not None)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# load training samples
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# 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_speaker_ids_from_data(train_samples + eval_samples)
config.model_args.num_speakers = speaker_manager.num_speakers
language_manager = LanguageManager(config=config)
config.model_args.num_languages = language_manager.num_languages
# init model
model = Vits(config, speaker_manager, language_manager)
# init the trainer and 🚀
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
)
trainer.fit()
# 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,
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.model_args.num_speakers = speaker_manager.num_speakers
# init model
model = Vits(config, ap, tokenizer, speaker_manager)
# init the trainer and 🚀
trainer = Trainer(
TrainerArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
)
trainer.fit()
def test_train_step_upsampling_interpolation(self):
# setup the model
with torch.autograd.set_detect_anomaly(True):
model_args = VitsArgs(num_chars=32,
spec_segment_size=10,
encoder_sample_rate=11025,
interpolate_z=True)
config = VitsConfig(model_args=model_args)
model = Vits(config).to(device)
model.train()
# model to train
optimizers = model.get_optimizer()
criterions = model.get_criterion()
criterions = [criterions[0].to(device), criterions[1].to(device)]
# reference model to compare model weights
model_ref = Vits(config).to(device)
# # 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 = count + 1
for _ in range(5):
batch = self._create_batch(config, 2)
for idx in [0, 1]:
outputs, loss_dict = model.train_step(
batch, criterions, idx)
self.assertFalse(not outputs)
self.assertFalse(not loss_dict)
loss_dict["loss"].backward()
optimizers[idx].step()
optimizers[idx].zero_grad()
# check parameter changes
self._check_parameter_changes(model, model_ref)
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
compute_input_seq_cache=True,
print_step=25,
print_eval=True,
mixed_precision=True,
max_seq_len=500000,
output_path=output_path,
datasets=[dataset_config],
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# load training samples
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init model
model = Vits(config)
# init the trainer and 🚀
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
)
trainer.fit()
