Update VITS

TTS/tts/models/vits.py

@@ -217,7 +217,7 @@ class Vits(BaseTTS):
 
     def __init__(self, config: Coqpit):
 
-        super().__init__()
+        super().__init__(config)
 
         self.END2END = True
 
@@ -576,22 +576,7 @@ class Vits(BaseTTS):
                 )
         return outputs, loss_dict
 
-    def train_log(
+    def _log(self, ap, batch, outputs, name_prefix="train"):
-        self, ap: AudioProcessor, batch: Dict, outputs: List, name_prefix="train"
-    ):  # pylint: disable=no-self-use
-        """Create visualizations and waveform examples.
-
-        For example, here you can plot spectrograms and generate sample sample waveforms from these spectrograms to
-        be projected onto Tensorboard.
-
-        Args:
-            ap (AudioProcessor): audio processor used at training.
-            batch (Dict): Model inputs used at the previous training step.
-            outputs (Dict): Model outputs generated at the previoud training step.
-
-        Returns:
-            Tuple[Dict, np.ndarray]: training plots and output waveform.
-        """
         y_hat = outputs[0]["model_outputs"]
         y = outputs[0]["waveform_seg"]
         figures = plot_results(y_hat, y, ap, name_prefix)
@@ -609,12 +594,32 @@ class Vits(BaseTTS):
 
         return figures, audios
 
+    def train_log(
+        self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
+    ):  # pylint: disable=no-self-use
+        """Create visualizations and waveform examples.
+
+        For example, here you can plot spectrograms and generate sample sample waveforms from these spectrograms to
+        be projected onto Tensorboard.
+
+        Args:
+            ap (AudioProcessor): audio processor used at training.
+            batch (Dict): Model inputs used at the previous training step.
+            outputs (Dict): Model outputs generated at the previoud training step.
+
+        Returns:
+            Tuple[Dict, np.ndarray]: training plots and output waveform.
+        """
+        ap = assets["audio_processor"]
+        self._log(ap, batch, outputs, "train")
+
     @torch.no_grad()
     def eval_step(self, batch: dict, criterion: nn.Module, optimizer_idx: int):
         return self.train_step(batch, criterion, optimizer_idx)
 
-    def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict):
+    def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None:
-        return self.train_log(ap, batch, outputs, "eval")
+        ap = assets["audio_processor"]
+        return self._log(ap, batch, outputs, "eval")
 
     @torch.no_grad()
     def test_run(self, ap) -> Tuple[Dict, Dict]:

recipes/ljspeech/vits_tts/train_vits.py

@@ -1,8 +1,12 @@
 import os
 
 from TTS.config.shared_configs import BaseAudioConfig
-from TTS.trainer import Trainer, TrainingArgs, init_training
+from TTS.trainer import Trainer, TrainingArgs
 from TTS.tts.configs import BaseDatasetConfig, VitsConfig
+from TTS.tts.models.vits import Vits
+from TTS.utils.audio import AudioProcessor
+from TTS.tts.datasets import load_tts_samples
+
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 dataset_config = BaseDatasetConfig(
@@ -24,6 +28,7 @@ audio_config = BaseAudioConfig(
     signal_norm=False,
     do_amp_to_db_linear=False,
 )
+
 config = VitsConfig(
     audio=audio_config,
     run_name="vits_ljspeech",
@@ -47,6 +52,24 @@ config = VitsConfig(
     output_path=output_path,
     datasets=[dataset_config],
 )
-args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config)
+
-trainer = Trainer(args, config, output_path, c_logger, tb_logger, cudnn_benchmark=True)
+# init audio processor
+ap = AudioProcessor(**config.audio.to_dict())
+
+# load training samples
+train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
+
+# init model
+model = Vits(config)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainingArgs(),
+    config,
+    output_path,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+    training_assets={"audio_processor": ap},
+)
 trainer.fit()