Update Logger API, recipes

TTS/bin/train_encoder.py

@@ -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(

TTS/trainer.py

@@ -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
 

TTS/utils/logging/tensorboard_logger.py

@@ -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):
+    def log_artifact(self, file_or_dir, name, artifact_type, aliases=None): # pylint: disable=W0613, R0201
-        return
+        yield
 
     def flush(self):
-        return
+        self.writer.flush()
 
     def finish(self):
-        return
+        self.writer.close()

TTS/utils/logging/wandb_logger.py

@@ -1,5 +1,7 @@
-from pathlib import Path
+# pylint: disable=W0613
+
 import traceback
+from pathlib import Path
 
 try:
     import wandb
@@ -29,10 +31,8 @@ class WandbLogger:
                 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.log_dict["weights/layer{}-{}/param".format(layer_num, name)] = wandb.Histogram(param)
-                self.writer.add_histogram("layer{}-{}/param".format(layer_num, name), param, step)
+                self.log_dict["weights/layer{}-{}/grad".format(layer_num, name)] = wandb.Histogram(param.grad)
-                self.writer.add_histogram("layer{}-{}/grad".format(layer_num, name), param.grad, step)
-                '''
             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

recipes/ljspeech/align_tts/train_aligntts.py

@@ -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)
+args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
+trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
 trainer.fit()

recipes/ljspeech/glow_tts/train_glowtts.py

@@ -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)
+args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
+trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
 trainer.fit()

recipes/ljspeech/hifigan/train_hifigan.py

@@ -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)
+args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
+trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
 trainer.fit()

recipes/ljspeech/multiband_melgan/train_multiband_melgan.py

@@ -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)
+args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
+trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
 trainer.fit()

recipes/ljspeech/univnet/train.py

@@ -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)
+args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
+trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
 trainer.fit()

recipes/ljspeech/wavegrad/train_wavegrad.py

@@ -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)
+args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger)
+trainer = Trainer(args, config, output_path, c_logger, dashboard_logger)
 trainer.fit()

recipes/ljspeech/wavernn/train_wavernn.py

@@ -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)
+args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config)
-trainer = Trainer(args, config, output_path, c_logger, tb_logger, wandb_logger, cudnn_benchmark=True)
+trainer = Trainer(args, config, output_path, c_logger, dashboard_logger, cudnn_benchmark=True)
 trainer.fit()