| from pathlib import Path | |
| import pandas as pd | |
| import torch | |
| import torchaudio | |
| from .superb_sid import SuperbSID | |
| torchaudio.set_audio_backend("sox_io") | |
| class CommonExample(SuperbSID): | |
| def default_config(self) -> dict: | |
| config = super().default_config() | |
| config["prepare_data"] = {} | |
| config["train"] = dict( | |
| total_steps=10, | |
| log_step=1, | |
| eval_step=5, | |
| save_step=5, | |
| gradient_clipping=1.0, | |
| gradient_accumulate=1, | |
| valid_metric="accuracy", | |
| valid_higher_better=True, | |
| auto_resume=True, | |
| ) | |
| return config | |
| def prepare_data( | |
| self, | |
| prepare_data: dict, | |
| target_dir: str, | |
| cache_dir: str, | |
| get_path_only: bool = False, | |
| ): | |
| target_dir: Path = Path(target_dir) | |
| wavs = [torch.randn(1, 16000 * 2) for i in range(5)] | |
| wav_paths = [] | |
| for idx, wav in enumerate(wavs): | |
| wav_path = str(Path(target_dir) / f"{idx}.wav") | |
| torchaudio.save(wav_path, wav, sample_rate=16000) | |
| wav_paths.append(wav_path) | |
| ids = [Path(path).stem for path in wav_paths] | |
| labels = ["a", "a", "b", "c", "d"] | |
| df = pd.DataFrame({"id": ids, "wav_path": wav_paths, "label": labels}) | |
| train_csv, valid_csv, test_csv = ( | |
| target_dir / "train.csv", | |
| target_dir / "valid.csv", | |
| target_dir / "test.csv", | |
| ) | |
| df.iloc[:3].to_csv(train_csv) | |
| df.iloc[3:4].to_csv(valid_csv) | |
| df.iloc[4:].to_csv(test_csv) | |
| return train_csv, valid_csv, [test_csv] | |