Upload train_wavernn.py with huggingface_hub

train_wavernn.py

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+import os
+
+from trainer import Trainer, TrainerArgs
+
+from TTS.utils.audio import AudioProcessor
+from TTS.vocoder.configs import WavernnConfig
+from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.vocoder.models.wavernn import Wavernn
+from TTS.config.shared_configs import BaseAudioConfig
+from TTS.tts.configs.shared_configs import BaseDatasetConfig , CharactersConfig
+from TTS.tts.datasets import load_tts_samples
+
+
+
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+dataset_config = BaseDatasetConfig(
+    formatter="mozilla", meta_file_train="metadata.csv", path="/kaggle/input/persian-tts-dataset-famale" 
+)
+audio_config = BaseAudioConfig(
+    sample_rate=24000,
+    do_trim_silence=True,
+    resample=False,
+    mel_fmin=95,
+    mel_fmax=8000.0,
+    
+    
+)
+
+config = WavernnConfig(
+    batch_size=64,#
+    eval_batch_size=16,#
+    num_loader_workers=1,
+    num_eval_loader_workers=1,
+    run_eval=True,
+    test_delay_epochs=-1,
+    epochs=1000,
+    seq_len=1280,
+    pad_short=2000,
+    use_noise_augment=False,
+    save_step=1000,
+    eval_split_size=10,
+    print_step=25,
+    print_eval=True,
+    mixed_precision=False,
+    lr=1e-4,
+    data_path="/kaggle/input/persian-tts-dataset-famale/wavs/",
+    output_path=output_path,
+    audio=audio_config,
+    
+)
+
+# init audio processor
+ap = AudioProcessor(**config.audio.to_dict())
+
+# load training samples
+eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
+
+# init model
+model = Wavernn(config)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainerArgs(),
+    config,
+    output_path,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+    training_assets={"audio_processor": ap},
+)
+trainer.fit()