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