Create train_glowtts.py

train_glowtts.py

@@ -0,0 +1,71 @@
+import os
+import inspect
+from trainer import Trainer, TrainerArgs
+from TTS.tts.configs.glow_tts_config import GlowTTSConfig
+from TTS.tts.models.glow_tts import GlowTTS
+from TTS.tts.configs.shared_configs import BaseDatasetConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.utils.text.tokenizer import TTSTokenizer
+from TTS.utils.audio import AudioProcessor
+
+def main():
+    output_path = os.path.dirname(os.path.abspath(__file__))
+
+    dataset_config = BaseDatasetConfig(
+        formatter="ljspeech",
+        meta_file_train="metadata.csv",
+        path=os.path.join(output_path, "LJSpeech-1.1/")
+    )
+
+    config = GlowTTSConfig(
+        batch_size=256,
+        eval_batch_size=128,
+        num_loader_workers=4,
+        num_eval_loader_workers=2,
+        run_eval=True,
+        test_delay_epochs=-1,
+        epochs=600,
+        text_cleaner="phoneme_cleaners",
+        use_phonemes=True,
+        phoneme_language="en-us",
+        phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+        print_step=25,
+        print_eval=False,
+        mixed_precision=True,
+        output_path=output_path,
+        datasets=[dataset_config],
+	    max_audio_len=22050 * 10,
+        min_audio_len=22050 * 1,
+    )
+
+    ap = AudioProcessor(config=config.audio)
+
+    tokenizer, config = TTSTokenizer.init_from_config(config)
+
+    train_samples, eval_samples = load_tts_samples(
+        config,
+        eval_split=True,
+        eval_split_max_size=20,
+    )
+
+    model = GlowTTS(config, ap, tokenizer=tokenizer, speaker_manager=None)
+
+    trainer = Trainer(
+        TrainerArgs(),
+        config,
+        output_path,
+        model=model,
+        train_samples=train_samples,
+        eval_samples=eval_samples,
+        training_assets={'audio_processor': ap},
+    )
+
+    if getattr(trainer, "best_loss", None) is None:
+        trainer.best_loss = {"train_loss": float("inf")}
+    elif isinstance(trainer.best_loss, dict) and trainer.best_loss.get("train_loss") is None:
+        trainer.best_loss["train_loss"] = float("inf")
+
+    trainer.fit()
+
+if __name__ == "__main__":
+    main()