mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-12-01 02:27:21 +01:00
77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
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import argparse
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import os
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import subprocess
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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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from tqdm import tqdm
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from lib import spec_utils
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if __name__ == '__main__':
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p = argparse.ArgumentParser()
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p.add_argument('--sr', '-r', type=int, default=44100)
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p.add_argument('--hop_length', '-l', type=int, default=1024)
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p.add_argument('--pitch', '-p', type=int, default=-2)
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p.add_argument('--mixture_dataset', '-m', required=True)
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p.add_argument('--instrumental_dataset', '-i', required=True)
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args = p.parse_args()
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input_exts = ['.wav', '.m4a', '.3gp', '.oma', '.mp3', '.mp4']
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X_list = sorted([
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os.path.join(args.mixture_dataset, fname)
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for fname in os.listdir(args.mixture_dataset)
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if os.path.splitext(fname)[1] in input_exts])
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y_list = sorted([
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os.path.join(args.instrumental_dataset, fname)
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for fname in os.listdir(args.instrumental_dataset)
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if os.path.splitext(fname)[1] in input_exts])
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input_i = 'input_i_{}.wav'.format(args.pitch)
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input_v = 'input_v_{}.wav'.format(args.pitch)
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output_i = 'output_i_{}.wav'.format(args.pitch)
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output_v = 'output_v_{}.wav'.format(args.pitch)
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cmd_i = 'soundstretch {} {} -pitch={}'.format(input_i, output_i, args.pitch)
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cmd_v = 'soundstretch {} {} -pitch={}'.format(input_v, output_v, args.pitch)
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suffix = '_pitch{}.npy'.format(args.pitch)
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filelist = list(zip(X_list, y_list))
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for mix_path, inst_path in tqdm(filelist):
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X, _ = librosa.load(
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mix_path, args.sr, False, dtype=np.float32, res_type='kaiser_fast')
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y, _ = librosa.load(
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inst_path, args.sr, False, dtype=np.float32, res_type='kaiser_fast')
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X, _ = librosa.effects.trim(X)
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y, _ = librosa.effects.trim(y)
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X, y = spec_utils.align_wave_head_and_tail(X, y, args.sr)
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v = X - y
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sf.write(input_i, y.T, args.sr)
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sf.write(input_v, v.T, args.sr)
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subprocess.call(cmd_i, stderr=subprocess.DEVNULL)
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subprocess.call(cmd_v, stderr=subprocess.DEVNULL)
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y, _ = librosa.load(
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output_i, args.sr, False, dtype=np.float32, res_type='kaiser_fast')
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v, _ = librosa.load(
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output_v, args.sr, False, dtype=np.float32, res_type='kaiser_fast')
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X = y + v
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spec = spec_utils.calc_spec(X, args.hop_length)
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basename, _ = os.path.splitext(os.path.basename(mix_path))
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outpath = os.path.join(args.mixture_dataset, basename + suffix)
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np.save(outpath, np.abs(spec))
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spec = spec_utils.calc_spec(y, args.hop_length)
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basename, _ = os.path.splitext(os.path.basename(inst_path))
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outpath = os.path.join(args.instrumental_dataset, basename + suffix)
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np.save(outpath, np.abs(spec))
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os.remove(input_i)
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os.remove(input_v)
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os.remove(output_i)
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os.remove(output_v)
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