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| import argparse | |
| import pathlib | |
| import tqdm | |
| from torch.utils.data import Dataset, DataLoader | |
| import torchaudio | |
| from score import Score | |
| import torch | |
| def get_arg(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--bs", required=False, default=None, type=int) | |
| parser.add_argument("--mode", required=True, choices=["predict_file", "predict_dir"], type=str) | |
| parser.add_argument("--ckpt_path", required=False, default="epoch=3-step=7459.ckpt", type=pathlib.Path) | |
| parser.add_argument("--inp_dir", required=False, default=None, type=pathlib.Path) | |
| parser.add_argument("--inp_path", required=False, default=None, type=pathlib.Path) | |
| parser.add_argument("--out_path", required=True, type=pathlib.Path) | |
| parser.add_argument("--num_workers", required=False, default=0, type=int) | |
| return parser.parse_args() | |
| class Dataset(Dataset): | |
| def __init__(self, dir_path: pathlib.Path): | |
| self.wavlist = list(dir_path.glob("*.wav")) | |
| _, self.sr = torchaudio.load(self.wavlist[0]) | |
| def __len__(self): | |
| return len(self.wavlist) | |
| def __getitem__(self, idx): | |
| fname = self.wavlist[idx] | |
| wav, _ = torchaudio.load(fname) | |
| sample = { | |
| "wav": wav} | |
| return sample | |
| def collate_fn(self, batch): | |
| max_len = max([x["wav"].shape[1] for x in batch]) | |
| out = [] | |
| # Performing repeat padding | |
| for t in batch: | |
| wav = t["wav"] | |
| amount_to_pad = max_len - wav.shape[1] | |
| padding_tensor = wav.repeat(1,1+amount_to_pad//wav.size(1)) | |
| out.append(torch.cat((wav,padding_tensor[:,:amount_to_pad]),dim=1)) | |
| return torch.stack(out, dim=0) | |
| def main(): | |
| args = get_arg() | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if args.mode == "predict_file": | |
| assert args.inp_path is not None, "inp_path is required when mode is predict_file." | |
| assert args.inp_dir is None, "inp_dir should be None." | |
| assert args.inp_path.exists() | |
| assert args.inp_path.is_file() | |
| wav, sr = torchaudio.load(args.inp_path) | |
| scorer = Score(ckpt_path=args.ckpt_path, input_sample_rate=sr, device=device) | |
| score = scorer.score(wav.to(device)) | |
| with open(args.out_path, "w") as fw: | |
| fw.write(str(score[0])) | |
| else: | |
| assert args.inp_dir is not None, "inp_dir is required when mode is predict_dir." | |
| assert args.bs is not None, "bs is required when mode is predict_dir." | |
| assert args.inp_path is None, "inp_path should be None." | |
| assert args.inp_dir.exists() | |
| assert args.inp_dir.is_dir() | |
| dataset = Dataset(dir_path=args.inp_dir) | |
| loader = DataLoader( | |
| dataset, | |
| batch_size=args.bs, | |
| collate_fn=dataset.collate_fn, | |
| shuffle=True, | |
| num_workers=args.num_workers) | |
| sr = dataset.sr | |
| scorer = Score(ckpt_path=args.ckpt_path, input_sample_rate=sr, device=device) | |
| with open(args.out_path, 'w'): | |
| pass | |
| for batch in tqdm.tqdm(loader): | |
| scores = scorer.score(batch.to(device)) | |
| with open(args.out_path, 'a') as fw: | |
| for s in scores: | |
| fw.write(str(s) + "\n") | |
| if __name__ == '__main__': | |
| main() |