Create train_vits-2.py

train_vits-2.py

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+import os
+
+from trainer import Trainer, TrainerArgs
+
+from TTS.tts.configs.shared_configs import BaseDatasetConfig , CharactersConfig
+from TTS.config.shared_configs import BaseAudioConfig
+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
+from TTS.tts.utils.speakers import SpeakerManager
+
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+
+
+dataset_names={
+    "persian-tts-dataset-famale":"dilara",
+    "persian-tts-dataset":"changiz",
+    "persian-tts-dataset-male":"farid"
+}
+def mozilla_with_speaker(root_path, meta_file, **kwargs):  # pylint: disable=unused-argument
+    """Normalizes Mozilla meta data files to TTS format"""
+    txt_file = os.path.join(root_path, meta_file)
+    items = []
+    speaker_name = dataset_names[os.path.basename(root_path)]
+    print(speaker_name)
+    with open(txt_file, "r", encoding="utf-8") as ttf:
+        for line in ttf:
+            cols = line.split("|")
+            wav_file = cols[1].strip()
+            text = cols[0].strip()
+            wav_file = os.path.join(root_path, "wavs", wav_file)
+            items.append({"text": text, "audio_file": wav_file, "speaker_name": speaker_name, "root_path": root_path})
+    return items
+
+
+dataset_config1 = BaseDatasetConfig(
+    formatter="mozilla" ,meta_file_train="metadata.csv", path="/kaggle/input/persian-tts-dataset" 
+)
+
+dataset_config2 = BaseDatasetConfig(
+    formatter="mozilla" ,meta_file_train="metadata.csv", path="/kaggle/input/persian-tts-dataset-famale" 
+)
+
+dataset_config3 = BaseDatasetConfig(
+    formatter="mozilla" ,meta_file_train="metadata.csv", path="/kaggle/input/persian-tts-dataset-male" 
+)
+
+
+
+audio_config = BaseAudioConfig(
+    sample_rate=22050,
+    do_trim_silence=False,
+    resample=False,
+    mel_fmin=0,
+    mel_fmax=None 
+)
+character_config=CharactersConfig(
+  characters='ءابتثجحخدذرزسشصضطظعغفقلمنهويِپچژکگیآأؤإئًَُّ',
+  punctuations='!(),-.:;? ̠،؛؟‌<>',
+  phonemes='ˈˌːˑpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟaegiouwyɪʊ̩æɑɔəɚɛɝɨ̃ʉʌʍ0123456789"#$%*+/=ABCDEFGHIJKLMNOPRSTUVWXYZ[]^_{}',
+  pad="<PAD>",
+  eos="<EOS>",
+  bos="<BOS>",
+  blank="<BLNK>",
+  characters_class="TTS.tts.utils.text.characters.IPAPhonemes",
+  )
+config = VitsConfig(
+    audio=audio_config,
+    run_name="vits_fa_female",
+    batch_size=8,
+    eval_batch_size=4,
+    batch_group_size=5,
+    num_loader_workers=0,
+    num_eval_loader_workers=2,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1000,
+    save_step=1000,
+    text_cleaner="basic_cleaners",
+    use_phonemes=True,
+    phoneme_language="fa",
+    characters=character_config,
+    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+    compute_input_seq_cache=True,
+    print_step=25,
+    print_eval=True,
+    mixed_precision=False,
+    test_sentences=[
+        ["سلطان محمود در زمستانی سخت به طلخک گفت که","dilara",null,"fa"],
+        [" با این جامه ی یک لا در این سرما چه می کنی ","farid",null,"fa"],
+        ["مردی نزد بقالی آمد و گفت پیاز هم ده تا دهان بدان خو شبوی سازم.","farid",null,"fa"],
+        ["از مال خود پاره ای گوشت بستان و زیره بایی معطّر بساز","dilara",null,"fa"],
+        ["یک بار هم از جهنم بگویید.","changiz",null,"fa"],
+        ["یکی اسبی به عاریت خواست","changiz",null,"fa"]
+    ],
+    output_path=output_path,
+    datasets=[dataset_config1,dataset_config2,dataset_config3],
+)
+
+# 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(
+    config.datasets,
+    formatter=mozilla_with_speaker,
+    eval_split=True,
+    eval_split_max_size=config.eval_split_max_size,
+    eval_split_size=config.eval_split_size,
+)
+
+
+
+speaker_manager = SpeakerManager()
+speaker_manager.set_ids_from_data(train_samples + eval_samples, parse_key="speaker_name")
+config.num_speakers = speaker_manager.num_speakers
+print("\n"*10)
+print("#>"*10)
+print(speaker_manager.speaker_names)
+print("\n"*10)
+
+# init model
+model = Vits(config, ap, tokenizer, speaker_manager=speaker_manager)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainerArgs(),
+    config,
+    output_path,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+)
+trainer.fit()