Added Windows TTS training recipe

Added modified vits recipe for Thorsten-Voice model training using Windows

Youtube/train_vits_win.py

@@ -0,0 +1,94 @@
+import os
+
+from trainer import Trainer, TrainerArgs
+
+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, VitsAudioConfig
+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__))
+	#output_path = "c:\\temp\tts"
+	dataset_config = BaseDatasetConfig(
+		formatter="ljspeech", meta_file_train="metadata_small.csv", path="C:\\Users\\ThorstenVoice\\TTS-Training\\ThorstenVoice-Dataset_2022.10"
+	)
+	audio_config = VitsAudioConfig(
+		sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None
+	)
+
+	config = VitsConfig(
+		audio=audio_config,
+		run_name="vits_thorsten-voice",
+		batch_size=4,
+		eval_batch_size=4,
+		batch_group_size=5,
+		num_loader_workers=1,
+		num_eval_loader_workers=1,
+		run_eval=True,
+		test_delay_epochs=-1,
+		epochs=1000,
+		text_cleaner="phoneme_cleaners",
+		use_phonemes=True,
+		phoneme_language="de",
+		phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+		compute_input_seq_cache=True,
+		print_step=25,
+		print_eval=True,
+		mixed_precision=False,
+		output_path=output_path,
+		datasets=[dataset_config],
+		cudnn_benchmark=False,
+		test_sentences=[
+		  "Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
+		  "Sei eine Stimme, kein Echo.",
+		  "Es tut mir Leid David. Das kann ich leider nicht machen.",
+		  "Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
+		  "Vor dem 22. November 1963.",
+		],
+	)
+
+	# INITIALIZE THE AUDIO PROCESSOR
+	# 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)
+
+	# INITIALIZE THE TOKENIZER
+	# Tokenizer is used to convert text to sequences of token IDs.
+	# config is updated with the default characters if not defined in the config.
+	tokenizer, config = 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,
+		eval_split_max_size=config.eval_split_max_size,
+		eval_split_size=config.eval_split_size,
+	)
+
+	# init model
+	model = Vits(config, ap, tokenizer, speaker_manager=None)
+
+	# init the trainer and 🚀
+	trainer = Trainer(
+		TrainerArgs(),
+		config,
+		output_path,
+		model=model,
+		train_samples=train_samples,
+		eval_samples=eval_samples,
+	)
+	trainer.fit()
+	print("Fertig!")
+
+from multiprocessing import Process, freeze_support
+if __name__ == '__main__':
+    freeze_support()  # needed for Windows
+    main()