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import argparse |
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import os |
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import librosa |
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import numpy as np |
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import soundfile as sf |
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import torch |
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from lib import dataset |
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from lib import nets |
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from lib import spec_utils |
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import inference |
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def main(): |
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p = argparse.ArgumentParser() |
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p.add_argument('--gpu', '-g', type=int, default=-1) |
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p.add_argument('--pretrained_model', '-P', type=str, default='models/baseline.pth') |
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p.add_argument('--mixtures', '-m', required=True) |
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p.add_argument('--instruments', '-i', required=True) |
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p.add_argument('--sr', '-r', type=int, default=44100) |
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p.add_argument('--n_fft', '-f', type=int, default=2048) |
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p.add_argument('--hop_length', '-H', type=int, default=1024) |
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p.add_argument('--batchsize', '-B', type=int, default=4) |
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p.add_argument('--cropsize', '-c', type=int, default=256) |
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p.add_argument('--postprocess', '-p', action='store_true') |
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args = p.parse_args() |
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print('loading model...', end=' ') |
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device = torch.device('cpu') |
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model = nets.CascadedNet(args.n_fft, args.hop_length) |
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model.load_state_dict(torch.load(args.pretrained_model, map_location=device)) |
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if torch.cuda.is_available() and args.gpu >= 0: |
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device = torch.device('cuda:{}'.format(args.gpu)) |
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model.to(device) |
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print('done') |
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filelist = dataset.make_pair(args.mixtures, args.instruments) |
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for mix_path, inst_path in filelist: |
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basename = os.path.splitext(os.path.basename(mix_path))[0] |
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print(basename) |
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print('loading wave source...', end=' ') |
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X, sr = librosa.load( |
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mix_path, sr=args.sr, mono=False, dtype=np.float32, res_type='kaiser_fast') |
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y, sr = librosa.load( |
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inst_path, sr=args.sr, mono=False, dtype=np.float32, res_type='kaiser_fast') |
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print('done') |
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if X.ndim == 1: |
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X = np.asarray([X, X]) |
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print('stft of wave source...', end=' ') |
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X, y = spec_utils.align_wave_head_and_tail(X, y, sr) |
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X = spec_utils.wave_to_spectrogram(X, args.hop_length, args.n_fft) |
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y = spec_utils.wave_to_spectrogram(y, args.hop_length, args.n_fft) |
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print('done') |
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sp = inference.Separator(model, device, args.batchsize, args.cropsize, args.postprocess) |
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a_spec, _ = sp.separate_tta(X - y) |
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print('inverse stft of pseudo instruments...', end=' ') |
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pseudo_inst = y + a_spec |
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print('done') |
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sf.write('pseudo/{}_PseudoInstruments.wav'.format(basename), [0], sr) |
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np.save('pseudo/{}_PseudoInstruments.npy'.format(basename), pseudo_inst) |
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if __name__ == '__main__': |
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main() |
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