def _test_forward_with_speaker_id(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
speaker_ids = torch.randint(0, 24, (batch_size, )).long().to(device)
# create model
config = GlowTTSConfig(
num_chars=32,
use_speaker_embedding=True,
num_speakers=24,
)
model = GlowTTS.init_from_config(config, verbose=False).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,
{"speaker_ids": speaker_ids})
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, ))
def _test_forward_with_d_vector(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
d_vector = torch.rand(batch_size, 256).to(device)
# create model
config = GlowTTSConfig(
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 = GlowTTS.init_from_config(config, verbose=False).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,
{"d_vectors": d_vector})
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, ))
def test_test_run(self):
config = GlowTTSConfig(num_chars=32)
model = GlowTTS.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_load_checkpoint(self):
chkp_path = os.path.join(get_tests_output_path(),
"dummy_glow_tts_checkpoint.pth")
config = GlowTTSConfig(num_chars=32)
model = GlowTTS.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_init_from_config(self):
config = GlowTTSConfig(num_chars=32)
model = GlowTTS.init_from_config(config, verbose=False).to(device)
config = GlowTTSConfig(num_chars=32, num_speakers=2)
model = GlowTTS.init_from_config(config, verbose=False).to(device)
self.assertTrue(model.num_speakers == 2)
self.assertTrue(not hasattr(model, "emb_g"))
config = GlowTTSConfig(num_chars=32,
num_speakers=2,
use_speaker_embedding=True)
model = GlowTTS.init_from_config(config, verbose=False).to(device)
self.assertTrue(model.num_speakers == 2)
self.assertTrue(hasattr(model, "emb_g"))
config = GlowTTSConfig(
num_chars=32,
num_speakers=2,
use_speaker_embedding=True,
speakers_file=os.path.join(get_tests_data_path(), "ljspeech",
"speakers.json"),
)
model = GlowTTS.init_from_config(config, verbose=False).to(device)
self.assertTrue(model.num_speakers == 10)
self.assertTrue(hasattr(model, "emb_g"))
config = GlowTTSConfig(
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 = GlowTTS.init_from_config(config, verbose=False).to(device)
self.assertTrue(model.num_speakers == 1)
self.assertTrue(not hasattr(model, "emb_g"))
self.assertTrue(model.c_in_channels == config.d_vector_dim)
def _test_inference_with_speaker_ids(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
speaker_ids = torch.randint(0, 24, (batch_size, )).long().to(device)
# create model
config = GlowTTSConfig(
num_chars=32,
use_speaker_embedding=True,
num_speakers=24,
)
model = GlowTTS.init_from_config(config, verbose=False).to(device)
outputs = model.inference(input_dummy, {
"x_lengths": input_lengths,
"speaker_ids": speaker_ids
})
self._assert_inference_outputs(outputs, input_dummy, mel_spec)
def _test_inference_with_d_vector(self, batch_size):
input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids = self._create_inputs(
batch_size)
d_vector = torch.rand(batch_size, 256).to(device)
config = GlowTTSConfig(
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 = GlowTTS.init_from_config(config, verbose=False).to(device)
model.eval()
outputs = model.inference(input_dummy, {
"x_lengths": input_lengths,
"d_vectors": d_vector
})
self._assert_inference_outputs(outputs, input_dummy, mel_spec)
def test_train_eval_log(self):
batch_size = BATCH_SIZE
input_dummy, input_lengths, mel_spec, mel_lengths, _ = self._create_inputs(
batch_size)
batch = {}
batch["text_input"] = input_dummy
batch["text_lengths"] = input_lengths
batch["mel_lengths"] = mel_lengths
batch["mel_input"] = mel_spec
batch["d_vectors"] = None
batch["speaker_ids"] = None
config = GlowTTSConfig(num_chars=32)
model = GlowTTS.init_from_config(config, verbose=False).to(device)
model.run_data_dep_init = False
model.train()
logger = TensorboardLogger(log_dir=os.path.join(
get_tests_output_path(), "dummy_glow_tts_logs"),
model_name="glow_tts_test_train_log")
criterion = model.get_criterion()
outputs, _ = model.train_step(batch, criterion)
model.train_log(batch, outputs, logger, None, 1)
model.eval_log(batch, outputs, logger, None, 1)
logger.finish()
def test_get_criterion(self):
config = GlowTTSConfig(num_chars=32)
model = GlowTTS.init_from_config(config, verbose=False).to(device)
criterion = model.get_criterion()
self.assertTrue(criterion is not None)