mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-12-01 02:27:21 +01:00
137 lines
4.5 KiB
Python
137 lines
4.5 KiB
Python
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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def crop_center(h1, h2, concat=True):
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# s_freq = (h2.shape[2] - h1.shape[2]) // 2
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# e_freq = s_freq + h1.shape[2]
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h1_shape = h1.size()
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h2_shape = h2.size()
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if h2_shape[3] < h1_shape[3]:
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raise ValueError('h2_shape[3] must be greater than h1_shape[3]')
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s_time = (h2_shape[3] - h1_shape[3]) // 2
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e_time = s_time + h1_shape[3]
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h2 = h2[:, :, :, s_time:e_time]
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if concat:
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return torch.cat([h1, h2], dim=1)
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else:
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return h2
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def calc_spec(X, hop_length):
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n_fft = (hop_length - 1) * 2
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audio_left = np.asfortranarray(X[0])
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audio_right = np.asfortranarray(X[1])
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spec_left = librosa.stft(audio_left, n_fft, hop_length=hop_length)
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spec_right = librosa.stft(audio_right, n_fft, hop_length=hop_length)
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def mask_uninformative(mask, ref, thres=0.3, min_range=64, fade_area=32):
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if min_range < fade_area * 2:
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raise ValueError('min_range must be >= fade_area * 2')
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idx = np.where(ref.mean(axis=(0, 1)) < thres)[0]
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starts = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
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ends = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
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uninformative = np.where(ends - starts > min_range)[0]
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if len(uninformative) > 0:
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starts = starts[uninformative]
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ends = ends[uninformative]
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old_e = None
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for s, e in zip(starts, ends):
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if old_e is not None and s - old_e < fade_area:
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s = old_e - fade_area * 2
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elif s != 0:
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start_mask = mask[:, :, s:s + fade_area]
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np.clip(
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start_mask + np.linspace(0, 1, fade_area), 0, 1,
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out=start_mask)
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if e != mask.shape[2]:
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end_mask = mask[:, :, e - fade_area:e]
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np.clip(
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end_mask + np.linspace(1, 0, fade_area), 0, 1,
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out=end_mask)
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mask[:, :, s + fade_area:e - fade_area] = 1
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old_e = e
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return mask
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def align_wave_head_and_tail(a, b, sr):
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a_mono = a[:, :sr * 4].sum(axis=0)
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b_mono = b[:, :sr * 4].sum(axis=0)
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a_mono -= a_mono.mean()
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b_mono -= b_mono.mean()
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offset = len(a_mono) - 1
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delay = np.argmax(np.correlate(a_mono, b_mono, 'full')) - offset
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if delay > 0:
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a = a[:, delay:]
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else:
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b = b[:, np.abs(delay):]
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if a.shape[1] < b.shape[1]:
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b = b[:, :a.shape[1]]
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else:
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a = a[:, :b.shape[1]]
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return a, b
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def cache_or_load(mix_path, inst_path, sr, hop_length):
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_, mix_ext = os.path.splitext(mix_path)
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_, inst_ext = os.path.splitext(inst_path)
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spec_mix_path = mix_path.replace(mix_ext, '.npy')
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spec_inst_path = inst_path.replace(inst_ext, '.npy')
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if os.path.exists(spec_mix_path) and os.path.exists(spec_inst_path):
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X = np.load(spec_mix_path)
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y = np.load(spec_inst_path)
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else:
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X, _ = librosa.load(
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mix_path, sr, False, dtype=np.float32, res_type='kaiser_fast')
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y, _ = librosa.load(
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inst_path, 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 = align_wave_head_and_tail(X, y, sr)
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X = np.abs(calc_spec(X, hop_length))
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y = np.abs(calc_spec(y, hop_length))
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_, ext = os.path.splitext(mix_path)
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np.save(spec_mix_path, X)
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np.save(spec_inst_path, y)
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return X, y
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def spec_to_wav(mag, phase, hop_length):
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spec = mag * phase
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wav_left = librosa.istft(spec_left, hop_length=hop_length)
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wav_right = librosa.istft(spec_right, hop_length=hop_length)
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wav = np.asfortranarray([wav_left, wav_right])
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return wav
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if __name__ == "__main__":
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import sys
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X, _ = librosa.load(
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sys.argv[1], 44100, False, dtype=np.float32, res_type='kaiser_fast')
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y, _ = librosa.load(
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sys.argv[2], 44100, 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 = align_wave_head_and_tail(X, y, 44100)
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sf.write('test_i.wav', y.T, 44100)
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sf.write('test_m.wav', X.T, 44100)
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sf.write('test_v.wav', (X - y).T, 44100)
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