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pseudo.py
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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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# if '_mixture' in mix_path and '_inst' in inst_path:
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# continue
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# else:
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# pass
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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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# mono to stereo
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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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