Update GlowTTS

TTS/tts/layers/glow_tts/glow.py

@@ -106,7 +106,6 @@ class InvConvNear(nn.Module):
             - x: :math:`[B, C, T]`
             - x_mask: :math:`[B, 1, T]`
         """
-
         b, c, t = x.size()
         assert c % self.num_splits == 0
         if x_mask is None:

TTS/tts/models/glow_tts.py

@@ -1,4 +1,5 @@
 import math
+from typing import Dict, Tuple
 
 import torch
 from torch import nn
@@ -47,7 +48,7 @@ class GlowTTS(BaseTTS):
 
     def __init__(self, config: GlowTTSConfig):
 
-        super().__init__()
+        super().__init__(config)
 
         # pass all config fields to `self`
         # for fewer code change
@@ -387,7 +388,7 @@ class GlowTTS(BaseTTS):
                 )
         return outputs, loss_dict
 
-    def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict):  # pylint: disable=no-self-use
+    def _create_logs(self, batch, outputs, ap):
         alignments = outputs["alignments"]
         text_input = batch["text_input"]
         text_lengths = batch["text_lengths"]
@@ -416,15 +417,26 @@ class GlowTTS(BaseTTS):
         train_audio = ap.inv_melspectrogram(pred_spec.T)
         return figures, {"audio": train_audio}
 
+    def train_log(
+        self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
+    ) -> None:  # pylint: disable=no-self-use
+        ap = assets["audio_processor"]
+        figures, audios = self._create_logs(batch, outputs, ap)
+        logger.train_figures(steps, figures)
+        logger.train_audios(steps, audios, ap.sample_rate)
+
     @torch.no_grad()
     def eval_step(self, batch: dict, criterion: nn.Module):
         return self.train_step(batch, criterion)
 
-    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)
+        ap = assets["audio_processor"]
+        figures, audios = self._create_logs(batch, outputs, ap)
+        logger.eval_figures(steps, figures)
+        logger.eval_audios(steps, audios, ap.sample_rate)
 
     @torch.no_grad()
-    def test_run(self, ap):
+    def test_run(self, assets: Dict) -> Tuple[Dict, Dict]:
         """Generic test run for `tts` models used by `Trainer`.
 
         You can override this for a different behaviour.
@@ -432,6 +444,7 @@ class GlowTTS(BaseTTS):
         Returns:
             Tuple[Dict, Dict]: Test figures and audios to be projected to Tensorboard.
         """
+        ap = assets["audio_processor"]
         print(" | > Synthesizing test sentences.")
         test_audios = {}
         test_figures = {}

recipes/ljspeech/glow_tts/train_glowtts.py

@@ -1,7 +1,10 @@
 import os
 
-from TTS.trainer import Trainer, TrainingArgs, init_training
+from TTS.trainer import Trainer, TrainingArgs
 from TTS.tts.configs import BaseDatasetConfig, GlowTTSConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.models.glow_tts import GlowTTS
+from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 dataset_config = BaseDatasetConfig(
@@ -25,6 +28,24 @@ config = GlowTTSConfig(
     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)
+# 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 = GlowTTS(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()