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| # Copyright 2024 LY Corporation | |
| # LY Corporation licenses this file to you under the Apache License, | |
| # version 2.0 (the "License"); you may not use this file except in compliance | |
| # with the License. You may obtain a copy of the License at: | |
| # https://www.apache.org/licenses/LICENSE-2.0 | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT | |
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the | |
| # License for the specific language governing permissions and limitations | |
| # under the License. | |
| from pathlib import Path | |
| import hydra | |
| import numpy as np | |
| import pandas as pd | |
| import torch | |
| import torchaudio | |
| from hydra.utils import instantiate | |
| from joblib import Parallel, delayed | |
| from omegaconf import DictConfig, OmegaConf | |
| from promptttspp.utils.joblib import tqdm_joblib | |
| def main(cfg: DictConfig): | |
| data_root = Path(cfg.path.data_root) | |
| mel_dir = Path(cfg.path.mel_dir) | |
| if (mel_dir / "finish").exists(): | |
| print("Already finished") | |
| return | |
| df = pd.read_csv(cfg.path.data_file) | |
| # NOTE: use cpu for multi-processing | |
| device = torch.device("cpu") | |
| to_mel = instantiate(cfg.transforms).to(device) | |
| def process(row): | |
| spk_id, utt_id = row["spk_id"], row["item_name"] | |
| wav_path = data_root / f"{spk_id}/wav24k/{utt_id}.wav" | |
| wav, _ = torchaudio.load(wav_path) | |
| wav = wav.to(device) | |
| mel = to_mel(wav).squeeze().cpu() | |
| spk_dir = mel_dir / f"{spk_id}" | |
| spk_dir.mkdir(parents=True, exist_ok=True) | |
| np.save(spk_dir / f"{utt_id}.npy", mel.numpy()) | |
| return mel | |
| with tqdm_joblib(len(df)): | |
| mels = Parallel(n_jobs=cfg.n_jobs)( | |
| delayed(process)(df.iloc[idx]) for idx in range(len(df)) | |
| ) | |
| mels = torch.cat(mels, dim=1) | |
| stats = { | |
| "min": float(mels.min()), | |
| "max": float(mels.max()), | |
| "mean": float(mels.mean()), | |
| "std": float(mels.std()), | |
| "var": float(mels.var()), | |
| } | |
| conf = OmegaConf.create(stats) | |
| OmegaConf.save(conf, mel_dir / "stats.yaml") | |
| with open(mel_dir / "finish", "w") as f: | |
| f.write("finish") | |
| if __name__ == "__main__": | |
| main() | |