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import copy
import os
import unittest
import torch
from torch import optim
from trainer.logging.tensorboard_logger import TensorboardLogger
from tests import get_tests_data_path, get_tests_input_path, get_tests_output_path
from TTS.tts.configs.glow_tts_config import GlowTTSConfig
from TTS.tts.layers.losses import GlowTTSLoss
from TTS.tts.models.glow_tts import GlowTTS
from TTS.tts.utils.speakers import SpeakerManager
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")
c = GlowTTSConfig()
ap = AudioProcessor(**c.audio)
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
BATCH_SIZE = 3
def count_parameters(model):
r"""Count number of trainable parameters in a network"""
return sum(p.numel() for p in model.parameters() if p.requires_grad)
class TestGlowTTS(unittest.TestCase):
@staticmethod
def _create_inputs(batch_size=8):
input_dummy = torch.randint(0, 24, (batch_size, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (batch_size,)).long().to(device)
input_lengths[-1] = 128
mel_spec = torch.rand(batch_size, 30, c.audio["num_mels"]).to(device)
mel_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
speaker_ids = torch.randint(0, 5, (batch_size,)).long().to(device)
return input_dummy, input_lengths, mel_spec, mel_lengths, speaker_ids
@staticmethod
def _check_parameter_changes(model, model_ref):
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_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_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_lock_act_norm_layers(self):
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config).to(device)
model.lock_act_norm_layers()
for f in model.decoder.flows:
if getattr(f, "set_ddi", False):
self.assertTrue(f.initialized)
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,))
def test_forward(self):
self._test_forward(1)
self._test_forward(3)
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_forward_with_d_vector(self):
self._test_forward_with_d_vector(1)
self._test_forward_with_d_vector(3)
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_speaker_id(self):
self._test_forward_with_speaker_id(1)
self._test_forward_with_speaker_id(3)
def _assert_inference_outputs(self, outputs, input_dummy, mel_spec):
output_shape = outputs["model_outputs"].shape
self.assertEqual(outputs["model_outputs"].shape[::2], mel_spec.shape[::2])
self.assertEqual(outputs["logdet"], None)
self.assertEqual(outputs["y_mean"].shape, output_shape)
self.assertEqual(outputs["y_log_scale"].shape, output_shape)
self.assertEqual(outputs["alignments"].shape, output_shape[:2] + (input_dummy.shape[1],))
self.assertEqual(outputs["durations_log"].shape, input_dummy.shape + (1,))
self.assertEqual(outputs["total_durations_log"].shape, input_dummy.shape + (1,))
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_inference(self):
self._test_inference(1)
self._test_inference(3)
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_inference_with_d_vector(self):
self._test_inference_with_d_vector(1)
self._test_inference_with_d_vector(3)
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_speaker_ids(self):
self._test_inference_with_speaker_ids(1)
self._test_inference_with_speaker_ids(3)
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_inference_with_MAS(self):
self._test_inference_with_MAS(1)
self._test_inference_with_MAS(3)
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_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_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_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)
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)
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