Update imports for trainer

TTS/bin/distribute.py

@@ -8,14 +8,14 @@ import time
 
 import torch
 
-from TTS.trainer import TrainingArgs
+from trainer import TrainerArgs
 
 
 def main():
     """
     Call train.py as a new process and pass command arguments
     """
-    parser = TrainingArgs().init_argparse(arg_prefix="")
+    parser = TrainerArgs().init_argparse(arg_prefix="")
     parser.add_argument("--script", type=str, help="Target training script to distibute.")
     args, unargs = parser.parse_known_args()
 

TTS/bin/train_encoder.py

@@ -9,6 +9,8 @@ import traceback
 import torch
 from torch.utils.data import DataLoader
 
+from trainer.torch import NoamLR
+
 from TTS.speaker_encoder.dataset import SpeakerEncoderDataset
 from TTS.speaker_encoder.losses import AngleProtoLoss, GE2ELoss, SoftmaxAngleProtoLoss
 from TTS.speaker_encoder.utils.generic_utils import save_best_model, setup_speaker_encoder_model
@@ -19,7 +21,7 @@ from TTS.utils.audio import AudioProcessor
 from TTS.utils.generic_utils import count_parameters, remove_experiment_folder, set_init_dict
 from TTS.utils.io import load_fsspec
 from TTS.utils.radam import RAdam
-from TTS.utils.training import NoamLR, check_update
+from TTS.utils.training import check_update
 
 torch.backends.cudnn.enabled = True
 torch.backends.cudnn.benchmark = True

TTS/bin/train_tts.py

@@ -1,3 +1,4 @@
+from dataclasses import dataclass, field
 import os
 
 from trainer import Trainer, TrainerArgs
@@ -7,10 +8,15 @@ from TTS.tts.datasets import load_tts_samples
 from TTS.tts.models import setup_model
 
 
+@dataclass
+class TrainTTSArgs(TrainerArgs):
+    config_path: str = field(default=None, metadata={"help": "Path to the config file."})
+
+
 def main():
     """Run `tts` model training directly by a `config.json` file."""
     # init trainer args
-    train_args = TrainerArgs()
+    train_args = TrainTTSArgs()
     parser = train_args.init_argparse(arg_prefix="")
 
     # override trainer args from comman-line args

TTS/bin/train_vocoder.py

@@ -1,3 +1,4 @@
+from dataclasses import dataclass, field
 import os
 
 from trainer import Trainer, TrainerArgs
@@ -8,10 +9,15 @@ from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
 from TTS.vocoder.models import setup_model
 
 
+@dataclass
+class TrainVocoderArgs(TrainerArgs):
+    config_path: str = field(default=None, metadata={"help": "Path to the config file."})
+
+
 def main():
     """Run `tts` model training directly by a `config.json` file."""
     # init trainer args
-    train_args = TrainerArgs()
+    train_args = TrainVocoderArgs()
     parser = train_args.init_argparse(arg_prefix="")
 
     # override trainer args from comman-line args

TTS/server/server.py

@@ -88,7 +88,7 @@ if args.model_name is not None and not args.model_path:
 if args.vocoder_name is not None and not args.vocoder_path:
     vocoder_path, vocoder_config_path, _ = manager.download_model(args.vocoder_name)
 
-# CASE3: set custome model paths
+# CASE3: set custom model paths
 if args.model_path is not None:
     model_path = args.model_path
     config_path = args.config_path
@@ -170,9 +170,9 @@ def tts():
     text = request.args.get("text")
     speaker_idx = request.args.get("speaker_id", "")
     style_wav = request.args.get("style_wav", "")
-
     style_wav = style_wav_uri_to_dict(style_wav)
     print(" > Model input: {}".format(text))
+    print(" > Speaker Idx: {}".format(speaker_idx))
     wavs = synthesizer.tts(text, speaker_name=speaker_idx, style_wav=style_wav)
     out = io.BytesIO()
     synthesizer.save_wav(wavs, out)

TTS/speaker_encoder/utils/training.py

@@ -1,19 +1,26 @@
+from asyncio.log import logger
+from dataclasses import dataclass, field
 import os
 
 from coqpit import Coqpit
 
 from TTS.config import load_config, register_config
-from TTS.trainer import TrainingArgs
+from trainer import TrainerArgs
 from TTS.tts.utils.text.characters import parse_symbols
 from TTS.utils.generic_utils import get_experiment_folder_path, get_git_branch
 from TTS.utils.io import copy_model_files
-from TTS.utils.logging import init_dashboard_logger
-from TTS.utils.logging.console_logger import ConsoleLogger
+from trainer.logging import logger_factory
+from trainer.logging.console_logger import ConsoleLogger
 from TTS.utils.trainer_utils import get_last_checkpoint
 
 
+@dataclass
+class TrainArgs(TrainerArgs):
+    config_path: str = field(default=None, metadata={"help": "Path to the config file."})
+
+
 def getarguments():
-    train_config = TrainingArgs()
+    train_config = TrainArgs()
     parser = train_config.init_argparse(arg_prefix="")
     return parser
 
@@ -75,13 +82,13 @@ def process_args(args, config=None):
             used_characters = parse_symbols()
             new_fields["characters"] = used_characters
         copy_model_files(config, experiment_path, new_fields)
-        dashboard_logger = init_dashboard_logger(config)
+        dashboard_logger = logger_factory(config, experiment_path)
     c_logger = ConsoleLogger()
     return config, experiment_path, audio_path, c_logger, dashboard_logger
 
 
 def init_arguments():
-    train_config = TrainingArgs()
+    train_config = TrainArgs()
     parser = train_config.init_argparse(arg_prefix="")
     return parser