NASE/enhancement.py at master · YUCHEN005/NASE · GitHub

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import glob
from argparse import ArgumentParser
from os.path import join

import torch
from soundfile import write
from torchaudio import load
from tqdm import tqdm

from sgmse.model import ScoreModel
from sgmse.util.other import ensure_dir, pad_spec

if __name__ == '__main__':
    parser = ArgumentParser()
    parser.add_argument("--test_dir", type=str, required=True, help='Directory containing the test data (must have subdirectory noisy/)')
    parser.add_argument("--test_set", type=str, default='noisy')
    parser.add_argument("--enhanced_dir", type=str, required=True, help='Directory containing the enhanced data')
    parser.add_argument("--ckpt", type=str,  help='Path to model checkpoint.')
    parser.add_argument("--pretrain_class_model", type=str, default="/home3/huyuchen/pytorch_workplace/sgmse/BEATs_iter3_plus_AS2M.pt")
    parser.add_argument("--corrector", type=str, choices=("ald", "langevin", "none"), default="ald", help="Corrector class for the PC sampler.")
    parser.add_argument("--corrector_steps", type=int, default=1, help="Number of corrector steps")
    parser.add_argument("--snr", type=float, default=0.5, help="SNR value for (annealed) Langevin dynmaics.")
    parser.add_argument("--N", type=int, default=30, help="Number of reverse steps")
    args = parser.parse_args()

    noisy_dir = join(args.test_dir, args.test_set)
    checkpoint_file = args.ckpt
    corrector_cls = args.corrector
    pretrain_class_model = args.pretrain_class_model

    target_dir = args.enhanced_dir
    ensure_dir(target_dir)

    # Settings
    sr = 16000
    snr = args.snr
    N = args.N
    corrector_steps = args.corrector_steps

    # Load score model, with your specified beats model path in <pretrain_class_model>
    model = ScoreModel.load_from_checkpoint(
        checkpoint_file,
        base_dir='',
        batch_size=16,
        num_workers=0,
        pretrain_class_model=pretrain_class_model,
        kwargs=dict(gpu=False))
    model.eval(no_ema=False)
    model.cuda()

    noisy_files = sorted(glob.glob('{}/*.wav'.format(noisy_dir)))

    print('start inference!')
    for noisy_file in tqdm(noisy_files):
        filename = noisy_file.split('/')[-1]

        # Load wav
        y, _ = load(noisy_file)
        T_orig = y.size(1)

        y_wav = y.clone()

        # Normalize
        norm_factor = y.abs().max()
        y = y / norm_factor

        # Prepare DNN input
        Y = torch.unsqueeze(model._forward_transform(model._stft(y.cuda())), 0)
        Y = pad_spec(Y)

        # Reverse sampling
        sampler = model.get_pc_sampler(
            'reverse_diffusion', corrector_cls, Y.cuda(), y_wav.cuda(), N=N,
            corrector_steps=corrector_steps, snr=snr)
        sample, _ = sampler()

        # Backward transform in time domain
        x_hat = model.to_audio(sample.squeeze(), T_orig)

        # Renormalize
        x_hat = x_hat * norm_factor

        # Write enhanced wav file
        write(join(target_dir, filename), x_hat.cpu().numpy(), 16000)