Update VITS LJspeech recipe

recipes/ljspeech/vits_tts/train_vits.py

@@ -6,6 +6,7 @@ from TTS.tts.configs.shared_configs import BaseDatasetConfig
 from TTS.tts.configs.vits_config import VitsConfig
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.models.vits import Vits
+from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
@@ -35,7 +36,7 @@ config = VitsConfig(
     batch_size=48,
     eval_batch_size=16,
     batch_group_size=5,
-    num_loader_workers=4,
+    num_loader_workers=0,
     num_eval_loader_workers=4,
     run_eval=True,
     test_delay_epochs=-1,
@@ -53,14 +54,24 @@ config = VitsConfig(
     datasets=[dataset_config],
 )
 
-# init audio processor
+# INITIALIZE THE AUDIO PROCESSOR
-ap = AudioProcessor(**config.audio.to_dict())
+# Audio processor is used for feature extraction and audio I/O.
+# It mainly serves to the dataloader and the training loggers.
+ap = AudioProcessor.init_from_config(config)
 
-# load training samples
+# INITIALIZE THE TOKENIZER
+# Tokenizer is used to convert text to sequences of token IDs.
+tokenizer = TTSTokenizer.init_from_config(config)
+
+# LOAD DATA SAMPLES
+# Each sample is a list of ```[text, audio_file_path, speaker_name]```
+# You can define your custom sample loader returning the list of samples.
+# Or define your custom formatter and pass it to the `load_tts_samples`.
+# Check `TTS.tts.datasets.load_tts_samples` for more details.
 train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
 
 # init model
-model = Vits(config)
+model = Vits(config, ap, tokenizer, speaker_manager=None)
 
 # init the trainer and 🚀
 trainer = Trainer(
@@ -70,6 +81,5 @@ trainer = Trainer(
     model=model,
     train_samples=train_samples,
     eval_samples=eval_samples,
-    training_assets={"audio_processor": ap},
 )
 trainer.fit()

tests/inputs/test_vocoder_multiband_melgan_config.json

@@ -86,7 +86,7 @@
         "mel_fmax": null
     },
 
-    "target_loss": "avg_G_loss",  // loss value to pick the best model to save after each epoch
+    "target_loss": "G_avg_loss",  // loss value to pick the best model to save after each epoch
 
     // DISCRIMINATOR
     "discriminator_model": "melgan_multiscale_discriminator",