| | |
| | | return waveform |
| | | speed = random.choice(self.speed_perturb) |
| | | if speed != 1.0: |
| | | waveform, _ = torchaudio.sox_effects.apply_effects_tensor( |
| | | torch.tensor(waveform).view(1, -1), fs, [['speed', str(speed)], ['rate', str(fs)]]) |
| | | waveform = waveform.view(-1) |
| | | with torch.no_grad(): |
| | | waveform, _ = torchaudio.sox_effects.apply_effects_tensor( |
| | | torch.tensor(waveform).view(1, -1), fs, [['speed', str(speed)], ['rate', str(fs)]]) |
| | | waveform = waveform.view(-1) |
| | | |
| | | return waveform |
| | | |
| | |
| | | speed_stats["total_time"] = total_time |
| | | |
| | | |
| | | pbar.update(1) |
| | | |
| | | if self.local_rank == 0: |
| | | pbar.update(1) |
| | | gpu_info = "GPU, memory: {:.3f} GB, " \ |
| | | "{:.3f} GB, "\ |
| | | "{:.3f} GB, "\ |
| | |
| | | f"(loss: {loss.detach().cpu().item():.3f}), " |
| | | f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}" |
| | | f"{gpu_info}" |
| | | f"rank: {self.local_rank}" |
| | | ) |
| | | pbar.set_description(description) |
| | | if self.writer: |
| | |
| | | loss = loss |
| | | time4 = time.perf_counter() |
| | | |
| | | pbar.update(1) |
| | | |
| | | if self.local_rank == 0: |
| | | pbar.update(1) |
| | | description = ( |
| | | f"validation epoch: {epoch}/{self.max_epoch}, " |
| | | f"step {batch_idx}/{len(self.dataloader_train)}, " |
| | | f"{speed_stats}, " |
| | | f"(loss: {loss.detach().cpu().item():.3f}), " |
| | | f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}" |
| | | f"rank: {self.local_rank}" |
| | | ) |
| | | pbar.set_description(description) |
| | | if self.writer: |