Create train_vocoder.py

train_vocoder.py

@@ -0,0 +1,102 @@
+# train_vocoder.py
+import os
+from trainer import Trainer, TrainerArgs
+from TTS.utils.audio import AudioProcessor
+from TTS.config.shared_configs import BaseAudioConfig
+from TTS.vocoder.configs import HifiganConfig
+from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.vocoder.models.gan import GAN
+
+
+def main():
+    output_path = os.path.dirname(os.path.abspath(__file__))
+    data_path = os.path.join(output_path, "LJSpeech-1.1/wavs/")
+
+    audio_config = BaseAudioConfig(
+        sample_rate=22050,
+        resample=False,
+        do_trim_silence=True,
+        trim_db=45,
+
+        fft_size=1024,
+        win_length=1024,
+        hop_length=256,
+        frame_shift_ms=None,
+        frame_length_ms=None,
+
+        num_mels=80,
+        mel_fmin=0.0,
+        mel_fmax=None,
+
+        signal_norm=True,
+        symmetric_norm=True,
+        max_norm=4.0,
+        clip_norm=True,
+        ref_level_db=20,
+        min_level_db=-100,
+        spec_gain=20.0,
+        log_func="np.log10",
+        preemphasis=0.0,
+
+        stats_path=None,
+    )
+
+    config = HifiganConfig(
+        run_name="hifigan_ljspeech",
+        run_description="HiFi-GAN v1 from scratch, GlowTTS-compatible mels",
+
+        data_path=data_path,
+        output_path=output_path,
+        eval_split_size=10,
+
+        audio=audio_config,
+
+        epochs=2000,
+        batch_size=64,
+        eval_batch_size=16,
+        num_loader_workers=4,
+        num_eval_loader_workers=2,
+        run_eval=True,
+        test_delay_epochs=5,
+        mixed_precision=True,
+
+        seq_len=8192,
+        pad_short=2000,
+        use_noise_augment=True,
+
+        lr_gen=2e-4,
+        lr_disc=2e-4,
+
+        print_step=50,
+        print_eval=False,
+        save_step=5000,
+        save_n_checkpoints=5,
+        save_checkpoints=True,
+        log_model_step=10000,
+        plot_step=500,
+    )
+
+    ap = AudioProcessor(config=config.audio)
+
+    eval_samples, train_samples = load_wav_data(
+        config.data_path,
+        config.eval_split_size,
+    )
+
+    model = GAN(config)
+
+    trainer = Trainer(
+        TrainerArgs(),
+        config,
+        output_path,
+        model=model,
+        train_samples=train_samples,
+        eval_samples=eval_samples,
+        training_assets={"audio_processor": ap},
+    )
+
+    trainer.fit()
+
+
+if __name__ == "__main__":
+    main()