mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-09-24 11:38:37 +02:00
718 lines
25 KiB
Python
718 lines
25 KiB
Python
import librosa
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import numpy as np
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import soundfile as sf
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import math
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import random
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import math
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import platform
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from . import pyrb
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OPERATING_SYSTEM = platform.system()
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SYSTEM_ARCH = platform.platform()
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SYSTEM_PROC = platform.processor()
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ARM = 'arm'
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if OPERATING_SYSTEM == 'Windows':
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from pyrubberband import pyrb
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else:
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from . import pyrb
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if OPERATING_SYSTEM == 'Darwin':
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wav_resolution = "polyphase" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else 'sinc_fastest'
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else:
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wav_resolution = "sinc_fastest"
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MAX_SPEC = 'Max Spec'
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MIN_SPEC = 'Min Spec'
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AVERAGE = 'Average'
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def crop_center(h1, h2):
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h1_shape = h1.size()
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h2_shape = h2.size()
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if h1_shape[3] == h2_shape[3]:
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return h1
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elif h1_shape[3] < h2_shape[3]:
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raise ValueError('h1_shape[3] must be greater than h2_shape[3]')
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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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s_time = (h1_shape[3] - h2_shape[3]) // 2
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e_time = s_time + h2_shape[3]
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h1 = h1[:, :, :, s_time:e_time]
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return h1
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def preprocess(X_spec):
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X_mag = np.abs(X_spec)
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X_phase = np.angle(X_spec)
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return X_mag, X_phase
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def make_padding(width, cropsize, offset):
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left = offset
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roi_size = cropsize - offset * 2
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if roi_size == 0:
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roi_size = cropsize
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right = roi_size - (width % roi_size) + left
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return left, right, roi_size
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def wave_to_spectrogram(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
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if reverse:
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wave_left = np.flip(np.asfortranarray(wave[0]))
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wave_right = np.flip(np.asfortranarray(wave[1]))
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elif mid_side:
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wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
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elif mid_side_b2:
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wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
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else:
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
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spec_right = librosa.stft(wave_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 wave_to_spectrogram_mt(wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False):
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import threading
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if reverse:
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wave_left = np.flip(np.asfortranarray(wave[0]))
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wave_right = np.flip(np.asfortranarray(wave[1]))
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elif mid_side:
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wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
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elif mid_side_b2:
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wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
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wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
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else:
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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def run_thread(**kwargs):
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global spec_left
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spec_left = librosa.stft(**kwargs)
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thread = threading.Thread(target=run_thread, kwargs={'y': wave_left, 'n_fft': n_fft, 'hop_length': hop_length})
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thread.start()
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spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
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thread.join()
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def normalize(wave, is_normalize=False):
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"""Save output music files"""
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maxv = np.abs(wave).max()
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if maxv > 1.0:
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print(f"\nNormalization Set {is_normalize}: Input above threshold for clipping. Max:{maxv}")
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if is_normalize:
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print(f"The result was normalized.")
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wave /= maxv
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else:
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print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
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return wave
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def normalize_two_stem(wave, mix, is_normalize=False):
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"""Save output music files"""
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maxv = np.abs(wave).max()
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max_mix = np.abs(mix).max()
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if maxv > 1.0:
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print(f"\nNormalization Set {is_normalize}: Primary source above threshold for clipping. The result was normalized. Max:{maxv}")
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print(f"\nNormalization Set {is_normalize}: Mixture above threshold for clipping. The result was normalized. Max:{max_mix}")
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if is_normalize:
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wave /= maxv
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mix /= maxv
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else:
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print(f"\nNormalization Set {is_normalize}: Input not above threshold for clipping. Max:{maxv}")
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print(f"\nNormalization Set {is_normalize}: Primary source - Max:{np.abs(wave).max()}")
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print(f"\nNormalization Set {is_normalize}: Mixture - Max:{np.abs(mix).max()}")
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return wave, mix
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def combine_spectrograms(specs, mp):
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l = min([specs[i].shape[2] for i in specs])
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spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
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offset = 0
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bands_n = len(mp.param['band'])
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for d in range(1, bands_n + 1):
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h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start']
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spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
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offset += h
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if offset > mp.param['bins']:
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raise ValueError('Too much bins')
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# lowpass fiter
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if mp.param['pre_filter_start'] > 0: # and mp.param['band'][bands_n]['res_type'] in ['scipy', 'polyphase']:
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if bands_n == 1:
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spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
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else:
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gp = 1
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for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']):
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g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
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gp = g
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spec_c[:, b, :] *= g
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return np.asfortranarray(spec_c)
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def spectrogram_to_image(spec, mode='magnitude'):
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if mode == 'magnitude':
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if np.iscomplexobj(spec):
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y = np.abs(spec)
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else:
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y = spec
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y = np.log10(y ** 2 + 1e-8)
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elif mode == 'phase':
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if np.iscomplexobj(spec):
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y = np.angle(spec)
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else:
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y = spec
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y -= y.min()
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y *= 255 / y.max()
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img = np.uint8(y)
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if y.ndim == 3:
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img = img.transpose(1, 2, 0)
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img = np.concatenate([
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np.max(img, axis=2, keepdims=True), img
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], axis=2)
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return img
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def reduce_vocal_aggressively(X, y, softmask):
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v = X - y
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y_mag_tmp = np.abs(y)
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v_mag_tmp = np.abs(v)
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v_mask = v_mag_tmp > y_mag_tmp
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y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
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return y_mag * np.exp(1.j * np.angle(y))
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def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
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if min_range < fade_size * 2:
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raise ValueError('min_range must be >= fade_size * 2')
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idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]
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start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
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end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
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artifact_idx = np.where(end_idx - start_idx > min_range)[0]
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weight = np.zeros_like(y_mask)
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if len(artifact_idx) > 0:
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start_idx = start_idx[artifact_idx]
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end_idx = end_idx[artifact_idx]
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old_e = None
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for s, e in zip(start_idx, end_idx):
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if old_e is not None and s - old_e < fade_size:
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s = old_e - fade_size * 2
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if s != 0:
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weight[:, :, s:s + fade_size] = np.linspace(0, 1, fade_size)
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else:
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s -= fade_size
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if e != y_mask.shape[2]:
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weight[:, :, e - fade_size:e] = np.linspace(1, 0, fade_size)
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else:
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e += fade_size
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weight[:, :, s + fade_size:e - fade_size] = 1
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old_e = e
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v_mask = 1 - y_mask
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y_mask += weight * v_mask
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return y_mask
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def mask_silence(mag, ref, thres=0.1, min_range=64, fade_size=32):
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if min_range < fade_size * 2:
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raise ValueError('min_range must be >= fade_area * 2')
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mag = mag.copy()
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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_size:
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s = old_e - fade_size * 2
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if s != 0:
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weight = np.linspace(0, 1, fade_size)
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mag[:, :, s:s + fade_size] += weight * ref[:, :, s:s + fade_size]
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else:
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s -= fade_size
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if e != mag.shape[2]:
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weight = np.linspace(1, 0, fade_size)
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mag[:, :, e - fade_size:e] += weight * ref[:, :, e - fade_size:e]
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else:
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e += fade_size
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mag[:, :, s + fade_size:e - fade_size] += ref[:, :, s + fade_size:e - fade_size]
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old_e = e
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return mag
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def align_wave_head_and_tail(a, b):
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l = min([a[0].size, b[0].size])
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return a[:l,:l], b[:l,:l]
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def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse, clamp=False):
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wave_left = librosa.istft(spec_left, hop_length=hop_length)
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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if reverse:
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return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
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elif mid_side:
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return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
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elif mid_side_b2:
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return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
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else:
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return np.asfortranarray([wave_left, wave_right])
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def spectrogram_to_wave_mt(spec, hop_length, mid_side, reverse, mid_side_b2):
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import threading
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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def run_thread(**kwargs):
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global wave_left
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wave_left = librosa.istft(**kwargs)
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thread = threading.Thread(target=run_thread, kwargs={'stft_matrix': spec_left, 'hop_length': hop_length})
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thread.start()
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wave_right = librosa.istft(spec_right, hop_length=hop_length)
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thread.join()
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if reverse:
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return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
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elif mid_side:
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return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
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elif mid_side_b2:
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return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
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else:
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return np.asfortranarray([wave_left, wave_right])
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def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None):
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bands_n = len(mp.param['band'])
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offset = 0
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for d in range(1, bands_n + 1):
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bp = mp.param['band'][d]
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spec_s = np.ndarray(shape=(2, bp['n_fft'] // 2 + 1, spec_m.shape[2]), dtype=complex)
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h = bp['crop_stop'] - bp['crop_start']
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spec_s[:, bp['crop_start']:bp['crop_stop'], :] = spec_m[:, offset:offset+h, :]
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offset += h
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if d == bands_n: # higher
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if extra_bins_h: # if --high_end_process bypass
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max_bin = bp['n_fft'] // 2
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spec_s[:, max_bin-extra_bins_h:max_bin, :] = extra_bins[:, :extra_bins_h, :]
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if bp['hpf_start'] > 0:
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spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
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if bands_n == 1:
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wave = spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse'])
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else:
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wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
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else:
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sr = mp.param['band'][d+1]['sr']
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if d == 1: # lower
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spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
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wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']), bp['sr'], sr, res_type=wav_resolution)
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else: # mid
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spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
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spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
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wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
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wave = librosa.resample(wave2, bp['sr'], sr, res_type=wav_resolution)
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return wave
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def fft_lp_filter(spec, bin_start, bin_stop):
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g = 1.0
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for b in range(bin_start, bin_stop):
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g -= 1 / (bin_stop - bin_start)
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spec[:, b, :] = g * spec[:, b, :]
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spec[:, bin_stop:, :] *= 0
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return spec
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def fft_hp_filter(spec, bin_start, bin_stop):
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g = 1.0
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for b in range(bin_start, bin_stop, -1):
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g -= 1 / (bin_start - bin_stop)
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spec[:, b, :] = g * spec[:, b, :]
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spec[:, 0:bin_stop+1, :] *= 0
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return spec
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def mirroring(a, spec_m, input_high_end, mp):
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if 'mirroring' == a:
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mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
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mirror = mirror * np.exp(1.j * np.angle(input_high_end))
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return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)
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if 'mirroring2' == a:
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mirror = np.flip(np.abs(spec_m[:, mp.param['pre_filter_start']-10-input_high_end.shape[1]:mp.param['pre_filter_start']-10, :]), 1)
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mi = np.multiply(mirror, input_high_end * 1.7)
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return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
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def adjust_aggr(mask, is_vocal_model, aggressiveness):
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aggr = aggressiveness.get('value', 0.0) * 4
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if aggr != 0:
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if is_vocal_model:
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aggr = 1 - aggr
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aggr = [aggr, aggr]
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if aggressiveness['aggr_correction'] is not None:
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aggr[0] += aggressiveness['aggr_correction']['left']
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aggr[1] += aggressiveness['aggr_correction']['right']
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for ch in range(2):
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mask[ch, :aggressiveness['split_bin']] = np.power(mask[ch, :aggressiveness['split_bin']], 1 + aggr[ch] / 3)
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mask[ch, aggressiveness['split_bin']:] = np.power(mask[ch, aggressiveness['split_bin']:], 1 + aggr[ch])
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return mask
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def stft(wave, nfft, hl):
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wave_left = np.asfortranarray(wave[0])
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wave_right = np.asfortranarray(wave[1])
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spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
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spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
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spec = np.asfortranarray([spec_left, spec_right])
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return spec
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def istft(spec, hl):
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spec_left = np.asfortranarray(spec[0])
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spec_right = np.asfortranarray(spec[1])
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wave_left = librosa.istft(spec_left, hop_length=hl)
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|
wave_right = librosa.istft(spec_right, hop_length=hl)
|
|
wave = np.asfortranarray([wave_left, wave_right])
|
|
|
|
return wave
|
|
|
|
def spec_effects(wave, algorithm='Default', value=None):
|
|
spec = [stft(wave[0],2048,1024), stft(wave[1],2048,1024)]
|
|
if algorithm == 'Min_Mag':
|
|
v_spec_m = np.where(np.abs(spec[1]) <= np.abs(spec[0]), spec[1], spec[0])
|
|
wave = istft(v_spec_m,1024)
|
|
elif algorithm == 'Max_Mag':
|
|
v_spec_m = np.where(np.abs(spec[1]) >= np.abs(spec[0]), spec[1], spec[0])
|
|
wave = istft(v_spec_m,1024)
|
|
elif algorithm == 'Default':
|
|
wave = (wave[1] * value) + (wave[0] * (1-value))
|
|
elif algorithm == 'Invert_p':
|
|
X_mag = np.abs(spec[0])
|
|
y_mag = np.abs(spec[1])
|
|
max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
|
|
v_spec = spec[1] - max_mag * np.exp(1.j * np.angle(spec[0]))
|
|
wave = istft(v_spec,1024)
|
|
|
|
return wave
|
|
|
|
def spectrogram_to_wave_bare(spec, hop_length=1024):
|
|
spec_left = np.asfortranarray(spec[0])
|
|
spec_right = np.asfortranarray(spec[1])
|
|
wave_left = librosa.istft(spec_left, hop_length=hop_length)
|
|
wave_right = librosa.istft(spec_right, hop_length=hop_length)
|
|
wave = np.asfortranarray([wave_left, wave_right])
|
|
|
|
return wave
|
|
|
|
def spectrogram_to_wave_no_mp(spec, hop_length=1024):
|
|
if spec.ndim == 2:
|
|
wave = librosa.istft(spec, hop_length=hop_length)
|
|
elif spec.ndim == 3:
|
|
spec_left = np.asfortranarray(spec[0])
|
|
spec_right = np.asfortranarray(spec[1])
|
|
|
|
wave_left = librosa.istft(spec_left, hop_length=hop_length)
|
|
wave_right = librosa.istft(spec_right, hop_length=hop_length)
|
|
wave = np.asfortranarray([wave_left, wave_right])
|
|
|
|
return wave
|
|
|
|
def wave_to_spectrogram_no_mp(wave):
|
|
|
|
wave_left = np.asfortranarray(wave[0])
|
|
wave_right = np.asfortranarray(wave[1])
|
|
|
|
spec_left = librosa.stft(wave_left, n_fft=2048, hop_length=1024)
|
|
spec_right = librosa.stft(wave_right, n_fft=2048, hop_length=1024)
|
|
spec = np.asfortranarray([spec_left, spec_right])
|
|
|
|
return spec
|
|
|
|
def invert_audio(specs, invert_p=True):
|
|
|
|
ln = min([specs[0].shape[2], specs[1].shape[2]])
|
|
specs[0] = specs[0][:,:,:ln]
|
|
specs[1] = specs[1][:,:,:ln]
|
|
|
|
if invert_p:
|
|
X_mag = np.abs(specs[0])
|
|
y_mag = np.abs(specs[1])
|
|
max_mag = np.where(X_mag >= y_mag, X_mag, y_mag)
|
|
v_spec = specs[1] - max_mag * np.exp(1.j * np.angle(specs[0]))
|
|
else:
|
|
specs[1] = reduce_vocal_aggressively(specs[0], specs[1], 0.2)
|
|
v_spec = specs[0] - specs[1]
|
|
|
|
return v_spec
|
|
|
|
def invert_stem(mixture, stem):
|
|
|
|
mixture = wave_to_spectrogram_no_mp(mixture)
|
|
stem = wave_to_spectrogram_no_mp(stem)
|
|
output = spectrogram_to_wave_no_mp(invert_audio([mixture, stem]))
|
|
|
|
return -output.T
|
|
|
|
def ensembling(a, specs):
|
|
for i in range(1, len(specs)):
|
|
if i == 1:
|
|
spec = specs[0]
|
|
|
|
ln = min([spec.shape[2], specs[i].shape[2]])
|
|
spec = spec[:,:,:ln]
|
|
specs[i] = specs[i][:,:,:ln]
|
|
|
|
if MIN_SPEC == a:
|
|
spec = np.where(np.abs(specs[i]) <= np.abs(spec), specs[i], spec)
|
|
if MAX_SPEC == a:
|
|
spec = np.where(np.abs(specs[i]) >= np.abs(spec), specs[i], spec)
|
|
if AVERAGE == a:
|
|
spec = np.where(np.abs(specs[i]) == np.abs(spec), specs[i], spec)
|
|
|
|
return spec
|
|
|
|
def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path):
|
|
|
|
if algorithm == AVERAGE:
|
|
output = average_audio(audio_input)
|
|
samplerate = 44100
|
|
else:
|
|
specs = []
|
|
|
|
for i in range(len(audio_input)):
|
|
wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)
|
|
spec = wave_to_spectrogram_no_mp(wave)
|
|
specs.append(spec)
|
|
|
|
output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
|
|
|
|
sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set)
|
|
|
|
def to_shape(x, target_shape):
|
|
padding_list = []
|
|
for x_dim, target_dim in zip(x.shape, target_shape):
|
|
pad_value = (target_dim - x_dim)
|
|
pad_tuple = ((0, pad_value))
|
|
padding_list.append(pad_tuple)
|
|
|
|
return np.pad(x, tuple(padding_list), mode='constant')
|
|
|
|
def to_shape_minimize(x: np.ndarray, target_shape):
|
|
|
|
padding_list = []
|
|
for x_dim, target_dim in zip(x.shape, target_shape):
|
|
pad_value = (target_dim - x_dim)
|
|
pad_tuple = ((0, pad_value))
|
|
padding_list.append(pad_tuple)
|
|
|
|
return np.pad(x, tuple(padding_list), mode='constant')
|
|
|
|
def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False):
|
|
print('Rate: ', rate)
|
|
wav, sr = librosa.load(audio_file, sr=44100, mono=False)
|
|
|
|
if wav.ndim == 1:
|
|
wav = np.asfortranarray([wav,wav])
|
|
|
|
if is_pitch:
|
|
wav_1 = pyrb.pitch_shift(wav[0], sr, rate, rbargs=None)
|
|
wav_2 = pyrb.pitch_shift(wav[1], sr, rate, rbargs=None)
|
|
else:
|
|
wav_1 = pyrb.time_stretch(wav[0], sr, rate, rbargs=None)
|
|
wav_2 = pyrb.time_stretch(wav[1], sr, rate, rbargs=None)
|
|
|
|
if wav_1.shape > wav_2.shape:
|
|
wav_2 = to_shape(wav_2, wav_1.shape)
|
|
if wav_1.shape < wav_2.shape:
|
|
wav_1 = to_shape(wav_1, wav_2.shape)
|
|
|
|
wav_mix = np.asfortranarray([wav_1, wav_2])
|
|
|
|
sf.write(export_path, normalize(wav_mix.T, is_normalization), sr, subtype=wav_type_set)
|
|
save_format(export_path)
|
|
|
|
def average_audio(audio):
|
|
|
|
waves = []
|
|
wave_shapes = []
|
|
final_waves = []
|
|
|
|
for i in range(len(audio)):
|
|
wave = librosa.load(audio[i], sr=44100, mono=False)
|
|
waves.append(wave[0])
|
|
wave_shapes.append(wave[0].shape[1])
|
|
|
|
wave_shapes_index = wave_shapes.index(max(wave_shapes))
|
|
target_shape = waves[wave_shapes_index]
|
|
waves.pop(wave_shapes_index)
|
|
final_waves.append(target_shape)
|
|
|
|
for n_array in waves:
|
|
wav_target = to_shape(n_array, target_shape.shape)
|
|
final_waves.append(wav_target)
|
|
|
|
waves = sum(final_waves)
|
|
waves = waves/len(audio)
|
|
|
|
return waves
|
|
|
|
def average_dual_sources(wav_1, wav_2, value):
|
|
|
|
if wav_1.shape > wav_2.shape:
|
|
wav_2 = to_shape(wav_2, wav_1.shape)
|
|
if wav_1.shape < wav_2.shape:
|
|
wav_1 = to_shape(wav_1, wav_2.shape)
|
|
|
|
wave = (wav_1 * value) + (wav_2 * (1-value))
|
|
|
|
return wave
|
|
|
|
def reshape_sources(wav_1: np.ndarray, wav_2: np.ndarray):
|
|
|
|
if wav_1.shape > wav_2.shape:
|
|
wav_2 = to_shape(wav_2, wav_1.shape)
|
|
if wav_1.shape < wav_2.shape:
|
|
ln = min([wav_1.shape[1], wav_2.shape[1]])
|
|
wav_2 = wav_2[:,:ln]
|
|
|
|
ln = min([wav_1.shape[1], wav_2.shape[1]])
|
|
wav_1 = wav_1[:,:ln]
|
|
wav_2 = wav_2[:,:ln]
|
|
|
|
return wav_2
|
|
|
|
def align_audio(file1, file2, file2_aligned, file_subtracted, wav_type_set, is_normalization, command_Text, progress_bar_main_var, save_format):
|
|
def get_diff(a, b):
|
|
corr = np.correlate(a, b, "full")
|
|
diff = corr.argmax() - (b.shape[0] - 1)
|
|
return diff
|
|
|
|
progress_bar_main_var.set(10)
|
|
|
|
# read tracks
|
|
wav1, sr1 = librosa.load(file1, sr=44100, mono=False)
|
|
wav2, sr2 = librosa.load(file2, sr=44100, mono=False)
|
|
wav1 = wav1.transpose()
|
|
wav2 = wav2.transpose()
|
|
|
|
command_Text(f"Audio file shapes: {wav1.shape} / {wav2.shape}\n")
|
|
|
|
wav2_org = wav2.copy()
|
|
progress_bar_main_var.set(20)
|
|
|
|
command_Text("Processing files... \n")
|
|
|
|
# pick random position and get diff
|
|
|
|
counts = {} # counting up for each diff value
|
|
progress = 20
|
|
|
|
check_range = 64
|
|
|
|
base = (64 / check_range)
|
|
|
|
for i in range(check_range):
|
|
index = int(random.uniform(44100 * 2, min(wav1.shape[0], wav2.shape[0]) - 44100 * 2))
|
|
shift = int(random.uniform(-22050,+22050))
|
|
samp1 = wav1[index :index +44100, 0] # currently use left channel
|
|
samp2 = wav2[index+shift:index+shift+44100, 0]
|
|
progress += 1 * base
|
|
progress_bar_main_var.set(progress)
|
|
diff = get_diff(samp1, samp2)
|
|
diff -= shift
|
|
|
|
if abs(diff) < 22050:
|
|
if not diff in counts:
|
|
counts[diff] = 0
|
|
counts[diff] += 1
|
|
|
|
# use max counted diff value
|
|
max_count = 0
|
|
est_diff = 0
|
|
for diff in counts.keys():
|
|
if counts[diff] > max_count:
|
|
max_count = counts[diff]
|
|
est_diff = diff
|
|
|
|
command_Text(f"Estimated difference is {est_diff} (count: {max_count})\n")
|
|
|
|
progress_bar_main_var.set(90)
|
|
|
|
audio_files = []
|
|
|
|
def save_aligned_audio(wav2_aligned):
|
|
command_Text(f"Aligned File 2 with File 1.\n")
|
|
command_Text(f"Saving files... ")
|
|
sf.write(file2_aligned, normalize(wav2_aligned, is_normalization), sr2, subtype=wav_type_set)
|
|
save_format(file2_aligned)
|
|
min_len = min(wav1.shape[0], wav2_aligned.shape[0])
|
|
wav_sub = wav1[:min_len] - wav2_aligned[:min_len]
|
|
audio_files.append(file2_aligned)
|
|
return min_len, wav_sub
|
|
|
|
# make aligned track 2
|
|
if est_diff > 0:
|
|
wav2_aligned = np.append(np.zeros((est_diff, 2)), wav2_org, axis=0)
|
|
min_len, wav_sub = save_aligned_audio(wav2_aligned)
|
|
elif est_diff < 0:
|
|
wav2_aligned = wav2_org[-est_diff:]
|
|
min_len, wav_sub = save_aligned_audio(wav2_aligned)
|
|
else:
|
|
command_Text(f"Audio files already aligned.\n")
|
|
command_Text(f"Saving inverted track... ")
|
|
min_len = min(wav1.shape[0], wav2.shape[0])
|
|
wav_sub = wav1[:min_len] - wav2[:min_len]
|
|
|
|
wav_sub = np.clip(wav_sub, -1, +1)
|
|
|
|
sf.write(file_subtracted, normalize(wav_sub, is_normalization), sr1, subtype=wav_type_set)
|
|
save_format(file_subtracted)
|
|
|
|
progress_bar_main_var.set(95) |