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_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
import shutil
from tests import get_device_id, get_tests_output_path, run_cli
from TTS.tts.configs import GlowTTSConfig
config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")
config = GlowTTSConfig(
batch_size=8,
eval_batch_size=8,
num_loader_workers=0,
num_val_loader_workers=0,
text_cleaner="english_cleaners",
use_phonemes=True,
phoneme_language="zh-CN",
phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
run_eval=True,
test_delay_epochs=-1,
epochs=1,
print_step=1,
print_eval=True,
)
config.audio.do_trim_silence = True
config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = (
f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_glow_tts.py --config_path {config_path} "
f"--coqpit.output_path {output_path} "
import torch
from torch import optim
from tests import get_tests_input_path
from TTS.tts.configs import GlowTTSConfig
from TTS.tts.layers.losses import GlowTTSLoss
from TTS.tts.models.glow_tts import GlowTTS
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")
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 GlowTTSTrainTest(unittest.TestCase):
@staticmethod
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)
from TTS.tts.configs import GlowTTSConfig
config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")
config = GlowTTSConfig(
batch_size=8,
eval_batch_size=8,
num_loader_workers=0,
num_eval_loader_workers=0,
text_cleaner="english_cleaners",
use_phonemes=True,
use_espeak_phonemes=True,
phoneme_language="en-us",
phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
run_eval=True,
test_delay_epochs=-1,
epochs=1,
print_step=1,
print_eval=True,
test_sentences=[
"Be a voice, not an echo.",
],
)
config.audio.do_trim_silence = True
config.audio.trim_db = 60
config.save_json(config_path)
# train the model for one epoch
command_train = (
from TTS.trainer import Trainer, TrainingArgs, init_training
from TTS.tts.configs import BaseDatasetConfig, GlowTTSConfig
output_path = os.path.dirname(os.path.abspath(__file__))
dataset_config = BaseDatasetConfig(name="ljspeech",
meta_file_train="metadata.csv",
path=os.path.join(output_path,
"../LJSpeech-1.1/"))
config = GlowTTSConfig(
batch_size=32,
eval_batch_size=16,
num_loader_workers=4,
num_eval_loader_workers=4,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
text_cleaner="phoneme_cleaners",
use_phonemes=True,
phoneme_language="en-us",
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
print_step=25,
print_eval=False,
mixed_precision=True,
output_path=output_path,
datasets=[dataset_config],
)
args, config, output_path, _, c_logger, dashboard_logger = init_training(
TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
trainer.fit()