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TTS
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Update Logger API, recipes

pull/719/head
Ayush Chaurasia 2021-07-09 17:43:15 +05:30 committed by Eren Gölge
parent f63cf46c55
commit 936a47504d
12 changed files with 29 additions and 34 deletions
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4
TTS/bin/train_encoder.py
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@ -116,12 +116,12 @@ def train(model, optimizer, scheduler, criterion, data_loader, global_step):
"step_time": step_time,
"avg_loader_time": avg_loader_time,
}
tb_logger.tb_train_epoch_stats(global_step, train_stats)
dashboard_logger.train_epoch_stats(global_step, train_stats)
figures = {
# FIXME: not constant
"UMAP Plot": plot_embeddings(outputs.detach().cpu().numpy(), 10),
}
tb_logger.tb_train_figures(global_step, figures)
dashboard_logger.train_figures(global_step, figures)
if global_step % c.print_step == 0:
print(

4
TTS/trainer.py
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@ -184,7 +184,6 @@ class Trainer:
if not self.config.log_model_step:
self.config.log_model_step = self.config.save_step
log_file = os.path.join(self.output_path, f"trainer_{args.rank}_log.txt")
self._setup_logger_config(log_file)
@ -1147,7 +1146,7 @@ def process_args(args, config=None):
os.chmod(experiment_path, 0o775)
if config.dashboard_logger == "tensorboard":
dashboard_logger = TensorboardLogger(output_path, model_name=config.model)
dashboard_logger = TensorboardLogger(config.output_path, model_name=config.model)
dashboard_logger.add_text("model-config", f"<pre>{config.to_json()}</pre>", 0)
elif config.dashboard_logger == "wandb":
@ -1162,7 +1161,6 @@ def process_args(args, config=None):
entity=config.wandb_entity,
)
c_logger = ConsoleLogger()
return config, experiment_path, audio_path, c_logger, dashboard_logger

0
TTS/utils/logging/logger_base.py Normal file
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14
TTS/utils/logging/tensorboard_logger.py
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@ -7,8 +7,6 @@ class TensorboardLogger(object):
def __init__(self, log_dir, model_name):
self.model_name = model_name
self.writer = SummaryWriter(log_dir)
self.train_stats = {}
self.eval_stats = {}
def model_weights(self, model, step):
layer_num = 1
@ -71,11 +69,11 @@ class TensorboardLogger(object):
def add_text(self, title, text, step):
self.writer.add_text(title, text, step)
def log_artifact(self, file_or_dir, name, artifact_type, aliases=None):
return
def log_artifact(self, file_or_dir, name, artifact_type, aliases=None): # pylint: disable=W0613, R0201
yield
def flush(self):
return
self.writer.flush()
def finish(self):
return
self.writer.close()

13
TTS/utils/logging/wandb_logger.py
View File

@ -1,5 +1,7 @@
from pathlib import Path
# pylint: disable=W0613
import traceback
from pathlib import Path
try:
import wandb
@ -23,16 +25,14 @@ class WandbLogger:
layer_num = 1
for name, param in model.named_parameters():
if param.numel() == 1:
self.dict_to_scalar("weights",{"layer{}-{}/value".format(layer_num, name): param.max()})
self.dict_to_scalar("weights", {"layer{}-{}/value".format(layer_num, name): param.max()})
else:
self.dict_to_scalar("weights", {"layer{}-{}/max".format(layer_num, name): param.max()})
self.dict_to_scalar("weights", {"layer{}-{}/min".format(layer_num, name): param.min()})
self.dict_to_scalar("weights", {"layer{}-{}/mean".format(layer_num, name): param.mean()})
self.dict_to_scalar("weights", {"layer{}-{}/std".format(layer_num, name): param.std()})
'''
self.writer.add_histogram("layer{}-{}/param".format(layer_num, name), param, step)
self.writer.add_histogram("layer{}-{}/grad".format(layer_num, name), param.grad, step)
'''
self.log_dict["weights/layer{}-{}/param".format(layer_num, name)] = wandb.Histogram(param)
self.log_dict["weights/layer{}-{}/grad".format(layer_num, name)] = wandb.Histogram(param.grad)
layer_num += 1
def dict_to_scalar(self, scope_name, stats):
@ -52,7 +52,6 @@ class WandbLogger:
except RuntimeError:
traceback.print_exc()
def log(self, log_dict, prefix="", flush=False):
for key, value in log_dict.items():
self.log_dict[prefix + key] = value

4
recipes/ljspeech/align_tts/train_aligntts.py
View File

@ -25,6 +25,6 @@ config = AlignTTSConfig(
output_path=output_path,
datasets=[dataset_config],
)
args, config, output_path, _, c_logger, tb_logger, wandb_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
trainer.fit()

4
recipes/ljspeech/glow_tts/train_glowtts.py
View File

@ -25,6 +25,6 @@ config = GlowTTSConfig(
output_path=output_path,
datasets=[dataset_config],
)
args, config, output_path, _, c_logger, tb_logger, wandb_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
trainer.fit()

4
recipes/ljspeech/hifigan/train_hifigan.py
View File

@ -24,6 +24,6 @@ config = HifiganConfig(
data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
output_path=output_path,
)
args, config, output_path, _, c_logger, tb_logger, wandb_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
trainer.fit()

4
recipes/ljspeech/multiband_melgan/train_multiband_melgan.py
View File

@ -24,6 +24,6 @@ config = MultibandMelganConfig(
data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
output_path=output_path,
)
args, config, output_path, _, c_logger, tb_logger, wandb_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
trainer.fit()

4
recipes/ljspeech/univnet/train.py
View File

@ -24,6 +24,6 @@ config = UnivnetConfig(
data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
output_path=output_path,
)
args, config, output_path, _, c_logger, tb_logger, wandb_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
trainer.fit()

4
recipes/ljspeech/wavegrad/train_wavegrad.py
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@ -22,6 +22,6 @@ config = WavegradConfig(
data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
output_path=output_path,
)
args, config, output_path, _, c_logger, tb_logger, wandb_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
trainer.fit()

4
recipes/ljspeech/wavernn/train_wavernn.py
View File

@ -24,6 +24,6 @@ config = WavernnConfig(
data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
output_path=output_path,
)
args, config, output_path, _, c_logger, tb_logger, wandb_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger, cudnn_benchmark=True)
args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
trainer = Trainer(args, config, output_path, c_logger, dashboard_logger, cudnn_benchmark=True)
trainer.fit()