"""Train the BigVGAN-style vocoder on FSD50K.""" from __fut...

Prompt

"""Train the BigVGAN-style vocoder on FSD50K.""" from __future__ import annotations import torch from torch import nn from app.ml.audio_codec.vocoder import BigVGANVocoder from training.config import TrainSettings, get_train_settings from training.data.fsd50k import iter_fsd50k from training.engine import fit_loop from training.torch_data import AudioClipDataset from training.train_vae import _split def stft_loss(predicted: torch.Tensor, target: torch.Tensor, n_fft: int, hop: int) -> torch.Tensor: """Multi-band STFT magnitude loss (parallel-wavegan style). Parameters: predicted: [B, 1, T] waveform. target: [B, 1, T] waveform. n_fft: FFT size. hop: Hop length. Returns: Scalar L1 magnitude loss. """ window = torch.hann_window(n_fft, device=predicted.device) pred_spec = torch.stft( predicted.squeeze(1), n_fft, hop_length=hop, window=window, return_complex=True ).abs() target_spec = torch.stft( target.squeeze(1), n_fft, hop_length=hop, window=window, return_complex=True ).abs() return torch.nn.functional.l1_loss(pred_spec, target_spec) def train(settings: TrainSettings) -> object: """Run vocoder training (GAN: generator + dual discriminators). Parameters: settings: Training settings. Returns: The checkpoint directory Path. Raises: RuntimeError: When no FSD50K clips are indexed. """ spec = settings.spec entries = list(iter_fsd50k(settings)) if not entries: raise RuntimeError( "no FSD50K clips with audio on disk; run `python -m training.data.fsd50k` " "to download the dev audio archive first" ) train_entries, val_entries = _split(entries, settings.val_fraction, settings.seed) train_set = AudioClipDataset(settings, spec, train_entries) val_set = AudioClipDataset(settings, spec, val_entries) generator = BigVGANVocoder(spec.vocoder, spec.n_mels) window_g = 2048 hop_g = spec.hop_length * 2 discriminator = _MultiPeriodDiscriminator() opt_g = torch.optim.AdamW( generator.parameters(), lr=settings.lr_vocoder, betas=(0.5, 0.999) ) opt_d = torch.optim.AdamW( discriminator.parameters(), lr=settings.lr_vocoder, betas=(0.5, 0.999) ) checkpoint_dir = settings.output_dir / settings.variant / "vocoder" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def loss_fn(model: nn.Module, batch: dict[str, object]) -> tuple[torch.Tensor, dict[str, float]]: """Generator GAN + STFT loss for one batch. Parameters: model: Generator (vocoder). batch: Batch with mel and waveform. Returns: (loss, parts). """ del model generator.train() discriminator.to(device).train() mel = batch["mel"] waveform = batch["waveform"].unsqueeze(1) generated = generator(mel)[..., : waveform.shape[-1]] target = waveform[..., : generated.shape[-1]] d_real = discriminator(target) d_fake = discriminator(generated.detach()) loss_d = _discriminator_loss(d_real, d_fake) opt_d.zero_grad(set_to_none=True) loss_d.backward() opt_d.step() d_fake_g = discriminator(generated) adv = _generator_adv_loss(d_fake_g) spec_loss = stft_loss(generated, target, window_g, hop_g) + stft_loss( generated, target, 512, 128 ) loss_g = spec_loss + 2.5 * adv parts = {"gen_total": float(loss_g), "adv": float(adv), "spec": float(spec_loss)} return loss_g, parts def evaluate_fn(model: nn.Module, batch: dict[str, object]) -> dict[str, float]: """Validation: STFT reconstruction quality of the (EMA) generator. Parameters: model: Generator candidate. batch: Batch with mel and waveform. Returns: Metrics with `loss`. """ model.eval() mel = batch["mel"] waveform = batch["waveform"].unsqueeze(1) with torch.no_grad(): generated = model(mel)[..., : waveform.shape[-1]] target = waveform[..., : generated.shape[-1]] loss = stft_loss(generated, target, window_g, hop_g) return {"loss": float(loss)} fit_loop( stage="vocoder", model=generator, optimizer=opt_g, train_dataset=train_set, val_dataset=val_set, epochs=settings.effective_epochs(settings.epochs_vocoder), batch_size=settings.batch_size_vocoder, device=device, num_workers=settings.num_workers, checkpoint_dir=checkpoint_dir, ema_decay=settings.ema_decay, accumulation_steps=settings.accumulation_steps, loss_fn=loss_fn, evaluate_fn=evaluate_fn, ) torch.save(generator.state_dict(), checkpoint_dir / "vocoder-final.pt") return checkpoint_dir class _MultiPeriodDiscriminator(nn.Module): """Stack of period discriminators (periods 2,3,5,7,11).""" def __init__(self) -> None: """Build five period branches.""" super().__init__() self.periods = (2, 3, 5, 7, 11) self.branches = nn.ModuleList( [nn.Conv1d(1, 1, kernel_size=period * 4, stride=period, groups=1) for period in self.periods] ) def forward(self, waveform: torch.Tensor) -> list[torch.Tensor]: """Score a waveform per period. Parameters: waveform: [B, 1, T]. Returns: List of branch logits. """ outputs: list[torch.Tensor] = [] for branch in self.branches: outputs.append(branch(waveform)) return outputs def _discriminator_loss(d_real: list[torch.Tensor], d_fake: list[torch.Tensor]) -> torch.Tensor: """Least-squares discriminator objective. Parameters: d_real: Real logits per branch. d_fake: Fake logits per branch. Returns: Scalar loss. """ loss = 0.0 for real, fake in zip(d_real, d_fake, strict=True): loss = loss + torch.mean((real - 1.0) ** 2) + torch.mean(fake**2) return loss / len(d_real) def _generator_adv_loss(d_fake: list[torch.Tensor]) -> torch.Tensor: """Least-squares generator adversarial objective. Parameters: d_fake: Fake logits per branch. Returns: Scalar loss. """ loss = 0.0 for fake in d_fake: loss = loss + torch.mean((fake - 1.0) ** 2) return loss / len(d_fake) def main() -> int: """CLI entry for `python -m training.train_vocoder`. Returns: 0 on success. """ train(get_train_settings()) return 0 if __name__ == "__main__": raise SystemExit(main()) i said Read every single character from the first to the last, and list all the errors with complete thoroughness. In your answer, I want you to write only the items I’m asking for, in a list, with nothing else. Even if there are 80,000 errors, you must write every single one without exception. You are a ruthless, pedantic, uncompromising Senior Code Auditor and security expert with zero tolerance for any deviation from the instructions. Your sole task is to perform an exhaustive, line‑by‑line technical analysis of the provided code. Write a maximum of 500 errors per message. When you are done and have given me every single error, tell me “that’s all.” I don’t care about security, API costs, or high‑risk accidental secret/source disclosure. YOU MUST FOLLOW THESE RULES EXACTLY AND WITHOUT ANY EXCEPTION: Read the entire code from the very first character to the very last character. Identify and list EVERY SINGLE error. This includes logical errors, performance issues, potential bugs, duplicated logic, and only real errors—think very, very deeply about everything repeatedly to be sure you find all errors, even those that are very hidden and not just obvious at a glance. Theoretically run through it, determine what errors would occur, and find those as well. de ne talalj ki nem letezo hulye hibakat hanem ha nincs 500 akkor egyszeruen leirod mindet amjt talalsz es amikor leirtad szolsz hogy ennyi volt I DO NOT CARE ANY SECURITY ERROR AND I DONT AGREE TO MENTION ANY OF THEM what a hell are you dont understand on that: list until 500 but if there is no 500 error list them all and said thats all Send back the complete code with all the fixes. Fix each of the listed errors one by one, making sure to actually correct them so that there are 0 errors remaining. Keep the original imports, since the files exist. Write out every single character; do not abbreviate anything. Fix every error. There must be exactly one file. Do not write anything else; just output the complete code, and it must not contain any comments. Never, under any circumstances, use simplified, substitute, dummy, simulated, or fake code. Write the entire file as complete, unabridged, production-ready code in a single code block. It must be 100% error-free, a complete, error-free file, and must be submitted as a downloadable file. These requirements are mandatory and must be strictly adhered to. If no list of errors is provided, you must find all the errors and fix them. If there were comments in the original code, delete them. And most importantly: YOU MUST NEVER SIMPLIFY!