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spec_utils.py
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spec_utils.py
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import audioread
import librosa
import numpy as np
import soundfile as sf
import math
import platform
import traceback
from . import pyrb
from scipy.signal import correlate, hilbert
import io
OPERATING_SYSTEM = platform.system()
SYSTEM_ARCH = platform.platform()
SYSTEM_PROC = platform.processor()
ARM = 'arm'
AUTO_PHASE = "Automatic"
POSITIVE_PHASE = "Positive Phase"
NEGATIVE_PHASE = "Negative Phase"
NONE_P = "None",
LOW_P = "Shifts: Low",
MED_P = "Shifts: Medium",
HIGH_P = "Shifts: High",
VHIGH_P = "Shifts: Very High"
MAXIMUM_P = "Shifts: Maximum"
progress_value = 0
last_update_time = 0
is_macos = False
if OPERATING_SYSTEM == 'Windows':
from pyrubberband import pyrb
else:
from . import pyrb
if OPERATING_SYSTEM == 'Darwin':
wav_resolution = "polyphase" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else "sinc_fastest"
wav_resolution_float_resampling = "kaiser_best" if SYSTEM_PROC == ARM or ARM in SYSTEM_ARCH else wav_resolution
is_macos = True
else:
wav_resolution = "sinc_fastest"
wav_resolution_float_resampling = wav_resolution
MAX_SPEC = 'Max Spec'
MIN_SPEC = 'Min Spec'
LIN_ENSE = 'Linear Ensemble'
MAX_WAV = MAX_SPEC
MIN_WAV = MIN_SPEC
AVERAGE = 'Average'
def crop_center(h1, h2):
h1_shape = h1.size()
h2_shape = h2.size()
if h1_shape[3] == h2_shape[3]:
return h1
elif h1_shape[3] < h2_shape[3]:
raise ValueError('h1_shape[3] must be greater than h2_shape[3]')
s_time = (h1_shape[3] - h2_shape[3]) // 2
e_time = s_time + h2_shape[3]
h1 = h1[:, :, :, s_time:e_time]
return h1
def preprocess(X_spec):
X_mag = np.abs(X_spec)
X_phase = np.angle(X_spec)
return X_mag, X_phase
def make_padding(width, cropsize, offset):
left = offset
roi_size = cropsize - offset * 2
if roi_size == 0:
roi_size = cropsize
right = roi_size - (width % roi_size) + left
return left, right, roi_size
def normalize(wave, is_normalize=False):
"""Normalize audio"""
maxv = np.abs(wave).max()
if maxv > 1.0:
if is_normalize:
print("Above clipping threshold.")
wave /= maxv
return wave
def auto_transpose(audio_array:np.ndarray):
"""
Ensure that the audio array is in the (channels, samples) format.
Parameters:
audio_array (ndarray): Input audio array.
Returns:
ndarray: Transposed audio array if necessary.
"""
# If the second dimension is 2 (indicating stereo channels), transpose the array
if audio_array.shape[1] == 2:
return audio_array.T
return audio_array
def write_array_to_mem(audio_data, subtype):
if isinstance(audio_data, np.ndarray):
audio_buffer = io.BytesIO()
sf.write(audio_buffer, audio_data, 44100, subtype=subtype, format='WAV')
audio_buffer.seek(0)
return audio_buffer
else:
return audio_data
def spectrogram_to_image(spec, mode='magnitude'):
if mode == 'magnitude':
if np.iscomplexobj(spec):
y = np.abs(spec)
else:
y = spec
y = np.log10(y ** 2 + 1e-8)
elif mode == 'phase':
if np.iscomplexobj(spec):
y = np.angle(spec)
else:
y = spec
y -= y.min()
y *= 255 / y.max()
img = np.uint8(y)
if y.ndim == 3:
img = img.transpose(1, 2, 0)
img = np.concatenate([
np.max(img, axis=2, keepdims=True), img
], axis=2)
return img
def reduce_vocal_aggressively(X, y, softmask):
v = X - y
y_mag_tmp = np.abs(y)
v_mag_tmp = np.abs(v)
v_mask = v_mag_tmp > y_mag_tmp
y_mag = np.clip(y_mag_tmp - v_mag_tmp * v_mask * softmask, 0, np.inf)
return y_mag * np.exp(1.j * np.angle(y))
def merge_artifacts(y_mask, thres=0.01, min_range=64, fade_size=32):
mask = y_mask
try:
if min_range < fade_size * 2:
raise ValueError('min_range must be >= fade_size * 2')
idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0]
start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0])
end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1])
artifact_idx = np.where(end_idx - start_idx > min_range)[0]
weight = np.zeros_like(y_mask)
if len(artifact_idx) > 0:
start_idx = start_idx[artifact_idx]
end_idx = end_idx[artifact_idx]
old_e = None
for s, e in zip(start_idx, end_idx):
if old_e is not None and s - old_e < fade_size:
s = old_e - fade_size * 2
if s != 0:
weight[:, :, s:s + fade_size] = np.linspace(0, 1, fade_size)
else:
s -= fade_size
if e != y_mask.shape[2]:
weight[:, :, e - fade_size:e] = np.linspace(1, 0, fade_size)
else:
e += fade_size
weight[:, :, s + fade_size:e - fade_size] = 1
old_e = e
v_mask = 1 - y_mask
y_mask += weight * v_mask
mask = y_mask
except Exception as e:
error_name = f'{type(e).__name__}'
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
message = f'{error_name}: "{e}"\n{traceback_text}"'
print('Post Process Failed: ', message)
return mask
def align_wave_head_and_tail(a, b):
l = min([a[0].size, b[0].size])
return a[:l,:l], b[:l,:l]
def convert_channels(spec, mp, band):
cc = mp.param['band'][band].get('convert_channels')
if 'mid_side_c' == cc:
spec_left = np.add(spec[0], spec[1] * .25)
spec_right = np.subtract(spec[1], spec[0] * .25)
elif 'mid_side' == cc:
spec_left = np.add(spec[0], spec[1]) / 2
spec_right = np.subtract(spec[0], spec[1])
elif 'stereo_n' == cc:
spec_left = np.add(spec[0], spec[1] * .25) / 0.9375
spec_right = np.add(spec[1], spec[0] * .25) / 0.9375
else:
return spec
return np.asfortranarray([spec_left, spec_right])
def combine_spectrograms(specs, mp, is_v51_model=False):
l = min([specs[i].shape[2] for i in specs])
spec_c = np.zeros(shape=(2, mp.param['bins'] + 1, l), dtype=np.complex64)
offset = 0
bands_n = len(mp.param['band'])
for d in range(1, bands_n + 1):
h = mp.param['band'][d]['crop_stop'] - mp.param['band'][d]['crop_start']
spec_c[:, offset:offset+h, :l] = specs[d][:, mp.param['band'][d]['crop_start']:mp.param['band'][d]['crop_stop'], :l]
offset += h
if offset > mp.param['bins']:
raise ValueError('Too much bins')
# lowpass fiter
if mp.param['pre_filter_start'] > 0:
if is_v51_model:
spec_c *= get_lp_filter_mask(spec_c.shape[1], mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
else:
if bands_n == 1:
spec_c = fft_lp_filter(spec_c, mp.param['pre_filter_start'], mp.param['pre_filter_stop'])
else:
gp = 1
for b in range(mp.param['pre_filter_start'] + 1, mp.param['pre_filter_stop']):
g = math.pow(10, -(b - mp.param['pre_filter_start']) * (3.5 - gp) / 20.0)
gp = g
spec_c[:, b, :] *= g
return np.asfortranarray(spec_c)
def wave_to_spectrogram(wave, hop_length, n_fft, mp, band, is_v51_model=False):
if wave.ndim == 1:
wave = np.asfortranarray([wave,wave])
if not is_v51_model:
if mp.param['reverse']:
wave_left = np.flip(np.asfortranarray(wave[0]))
wave_right = np.flip(np.asfortranarray(wave[1]))
elif mp.param['mid_side']:
wave_left = np.asfortranarray(np.add(wave[0], wave[1]) / 2)
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1]))
elif mp.param['mid_side_b2']:
wave_left = np.asfortranarray(np.add(wave[1], wave[0] * .5))
wave_right = np.asfortranarray(np.subtract(wave[0], wave[1] * .5))
else:
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
else:
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
spec = np.asfortranarray([spec_left, spec_right])
if is_v51_model:
spec = convert_channels(spec, mp, band)
return spec
def spectrogram_to_wave(spec, hop_length=1024, mp={}, band=0, is_v51_model=True):
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)
if is_v51_model:
cc = mp.param['band'][band].get('convert_channels')
if 'mid_side_c' == cc:
return np.asfortranarray([np.subtract(wave_left / 1.0625, wave_right / 4.25), np.add(wave_right / 1.0625, wave_left / 4.25)])
elif 'mid_side' == cc:
return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
elif 'stereo_n' == cc:
return np.asfortranarray([np.subtract(wave_left, wave_right * .25), np.subtract(wave_right, wave_left * .25)])
else:
if mp.param['reverse']:
return np.asfortranarray([np.flip(wave_left), np.flip(wave_right)])
elif mp.param['mid_side']:
return np.asfortranarray([np.add(wave_left, wave_right / 2), np.subtract(wave_left, wave_right / 2)])
elif mp.param['mid_side_b2']:
return np.asfortranarray([np.add(wave_right / 1.25, .4 * wave_left), np.subtract(wave_left / 1.25, .4 * wave_right)])
return np.asfortranarray([wave_left, wave_right])
def cmb_spectrogram_to_wave(spec_m, mp, extra_bins_h=None, extra_bins=None, is_v51_model=False):
bands_n = len(mp.param['band'])
offset = 0
for d in range(1, bands_n + 1):
bp = mp.param['band'][d]
spec_s = np.ndarray(shape=(2, bp['n_fft'] // 2 + 1, spec_m.shape[2]), dtype=complex)
h = bp['crop_stop'] - bp['crop_start']
spec_s[:, bp['crop_start']:bp['crop_stop'], :] = spec_m[:, offset:offset+h, :]
offset += h
if d == bands_n: # higher
if extra_bins_h: # if --high_end_process bypass
max_bin = bp['n_fft'] // 2
spec_s[:, max_bin-extra_bins_h:max_bin, :] = extra_bins[:, :extra_bins_h, :]
if bp['hpf_start'] > 0:
if is_v51_model:
spec_s *= get_hp_filter_mask(spec_s.shape[1], bp['hpf_start'], bp['hpf_stop'] - 1)
else:
spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
if bands_n == 1:
wave = spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model)
else:
wave = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model))
else:
sr = mp.param['band'][d+1]['sr']
if d == 1: # lower
if is_v51_model:
spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop'])
else:
spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
wave = librosa.resample(spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model), bp['sr'], sr, res_type=wav_resolution)
else: # mid
if is_v51_model:
spec_s *= get_hp_filter_mask(spec_s.shape[1], bp['hpf_start'], bp['hpf_stop'] - 1)
spec_s *= get_lp_filter_mask(spec_s.shape[1], bp['lpf_start'], bp['lpf_stop'])
else:
spec_s = fft_hp_filter(spec_s, bp['hpf_start'], bp['hpf_stop'] - 1)
spec_s = fft_lp_filter(spec_s, bp['lpf_start'], bp['lpf_stop'])
wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp, d, is_v51_model))
wave = librosa.resample(wave2, bp['sr'], sr, res_type=wav_resolution)
return wave
def get_lp_filter_mask(n_bins, bin_start, bin_stop):
mask = np.concatenate([
np.ones((bin_start - 1, 1)),
np.linspace(1, 0, bin_stop - bin_start + 1)[:, None],
np.zeros((n_bins - bin_stop, 1))
], axis=0)
return mask
def get_hp_filter_mask(n_bins, bin_start, bin_stop):
mask = np.concatenate([
np.zeros((bin_stop + 1, 1)),
np.linspace(0, 1, 1 + bin_start - bin_stop)[:, None],
np.ones((n_bins - bin_start - 2, 1))
], axis=0)
return mask
def fft_lp_filter(spec, bin_start, bin_stop):
g = 1.0
for b in range(bin_start, bin_stop):
g -= 1 / (bin_stop - bin_start)
spec[:, b, :] = g * spec[:, b, :]
spec[:, bin_stop:, :] *= 0
return spec
def fft_hp_filter(spec, bin_start, bin_stop):
g = 1.0
for b in range(bin_start, bin_stop, -1):
g -= 1 / (bin_start - bin_stop)
spec[:, b, :] = g * spec[:, b, :]
spec[:, 0:bin_stop+1, :] *= 0
return spec
def spectrogram_to_wave_old(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_old(wave, hop_length, n_fft):
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length)
spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length)
spec = np.asfortranarray([spec_left, spec_right])
return spec
def mirroring(a, spec_m, input_high_end, mp):
if 'mirroring' == a:
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)
mirror = mirror * np.exp(1.j * np.angle(input_high_end))
return np.where(np.abs(input_high_end) <= np.abs(mirror), input_high_end, mirror)
if 'mirroring2' == a:
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)
mi = np.multiply(mirror, input_high_end * 1.7)
return np.where(np.abs(input_high_end) <= np.abs(mi), input_high_end, mi)
def adjust_aggr(mask, is_non_accom_stem, aggressiveness):
aggr = aggressiveness['value'] * 2
if aggr != 0:
if is_non_accom_stem:
aggr = 1 - aggr
aggr = [aggr, aggr]
if aggressiveness['aggr_correction'] is not None:
aggr[0] += aggressiveness['aggr_correction']['left']
aggr[1] += aggressiveness['aggr_correction']['right']
for ch in range(2):
mask[ch, :aggressiveness['split_bin']] = np.power(mask[ch, :aggressiveness['split_bin']], 1 + aggr[ch] / 3)
mask[ch, aggressiveness['split_bin']:] = np.power(mask[ch, aggressiveness['split_bin']:], 1 + aggr[ch])
return mask
def stft(wave, nfft, hl):
wave_left = np.asfortranarray(wave[0])
wave_right = np.asfortranarray(wave[1])
spec_left = librosa.stft(wave_left, nfft, hop_length=hl)
spec_right = librosa.stft(wave_right, nfft, hop_length=hl)
spec = np.asfortranarray([spec_left, spec_right])
return spec
def istft(spec, hl):
spec_left = np.asfortranarray(spec[0])
spec_right = np.asfortranarray(spec[1])
wave_left = librosa.istft(spec_left, hop_length=hl)
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_no_mp(spec, n_fft=2048, hop_length=1024):
wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)
if wave.ndim == 1:
wave = np.asfortranarray([wave,wave])
return wave
def wave_to_spectrogram_no_mp(wave):
spec = librosa.stft(wave, n_fft=2048, hop_length=1024)
if spec.ndim == 1:
spec = np.asfortranarray([spec,spec])
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, inputs, is_wavs=False):
for i in range(1, len(inputs)):
if i == 1:
input = inputs[0]
if is_wavs:
ln = min([input.shape[1], inputs[i].shape[1]])
input = input[:,:ln]
inputs[i] = inputs[i][:,:ln]
else:
ln = min([input.shape[2], inputs[i].shape[2]])
input = input[:,:,:ln]
inputs[i] = inputs[i][:,:,:ln]
if MIN_SPEC == a:
input = np.where(np.abs(inputs[i]) <= np.abs(input), inputs[i], input)
if MAX_SPEC == a:
input = np.where(np.abs(inputs[i]) >= np.abs(input), inputs[i], input)
#linear_ensemble
#input = ensemble_wav(inputs, split_size=1)
return input
def ensemble_for_align(waves):
specs = []
for wav in waves:
spec = wave_to_spectrogram_no_mp(wav.T)
specs.append(spec)
wav_aligned = spectrogram_to_wave_no_mp(ensembling(MIN_SPEC, specs)).T
wav_aligned = match_array_shapes(wav_aligned, waves[1], is_swap=True)
return wav_aligned
def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path, is_wave=False, is_array=False):
wavs_ = []
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)
wavs_.append(wave)
spec = wave if is_wave else wave_to_spectrogram_no_mp(wave)
specs.append(spec)
wave_shapes = [w.shape[1] for w in wavs_]
target_shape = wavs_[wave_shapes.index(max(wave_shapes))]
if is_wave:
output = ensembling(algorithm, specs, is_wavs=True)
else:
output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
output = to_shape(output, target_shape.shape)
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 detect_leading_silence(audio, sr, silence_threshold=0.007, frame_length=1024):
"""
Detect silence at the beginning of an audio signal.
:param audio: np.array, audio signal
:param sr: int, sample rate
:param silence_threshold: float, magnitude threshold below which is considered silence
:param frame_length: int, the number of samples to consider for each check
:return: float, duration of the leading silence in milliseconds
"""
if len(audio.shape) == 2:
# If stereo, pick the channel with more energy to determine the silence
channel = np.argmax(np.sum(np.abs(audio), axis=1))
audio = audio[channel]
for i in range(0, len(audio), frame_length):
if np.max(np.abs(audio[i:i+frame_length])) > silence_threshold:
return (i / sr) * 1000
return (len(audio) / sr) * 1000
def adjust_leading_silence(target_audio, reference_audio, silence_threshold=0.01, frame_length=1024):
"""
Adjust the leading silence of the target_audio to match the leading silence of the reference_audio.
:param target_audio: np.array, audio signal that will have its silence adjusted
:param reference_audio: np.array, audio signal used as a reference
:param sr: int, sample rate
:param silence_threshold: float, magnitude threshold below which is considered silence
:param frame_length: int, the number of samples to consider for each check
:return: np.array, target_audio adjusted to have the same leading silence as reference_audio
"""
def find_silence_end(audio):
if len(audio.shape) == 2:
# If stereo, pick the channel with more energy to determine the silence
channel = np.argmax(np.sum(np.abs(audio), axis=1))
audio_mono = audio[channel]
else:
audio_mono = audio
for i in range(0, len(audio_mono), frame_length):
if np.max(np.abs(audio_mono[i:i+frame_length])) > silence_threshold:
return i
return len(audio_mono)
ref_silence_end = find_silence_end(reference_audio)
target_silence_end = find_silence_end(target_audio)
silence_difference = ref_silence_end - target_silence_end
try:
ref_silence_end_p = (ref_silence_end / 44100) * 1000
target_silence_end_p = (target_silence_end / 44100) * 1000
silence_difference_p = ref_silence_end_p - target_silence_end_p
print("silence_difference: ", silence_difference_p)
except Exception as e:
pass
if silence_difference > 0: # Add silence to target_audio
if len(target_audio.shape) == 2: # stereo
silence_to_add = np.zeros((target_audio.shape[0], silence_difference))
else: # mono
silence_to_add = np.zeros(silence_difference)
return np.hstack((silence_to_add, target_audio))
elif silence_difference < 0: # Remove silence from target_audio
if len(target_audio.shape) == 2: # stereo
return target_audio[:, -silence_difference:]
else: # mono
return target_audio[-silence_difference:]
else: # No adjustment needed
return target_audio
def match_array_shapes(array_1:np.ndarray, array_2:np.ndarray, is_swap=False):
if is_swap:
array_1, array_2 = array_1.T, array_2.T
#print("before", array_1.shape, array_2.shape)
if array_1.shape[1] > array_2.shape[1]:
array_1 = array_1[:,:array_2.shape[1]]
elif array_1.shape[1] < array_2.shape[1]:
padding = array_2.shape[1] - array_1.shape[1]
array_1 = np.pad(array_1, ((0,0), (0,padding)), 'constant', constant_values=0)
#print("after", array_1.shape, array_2.shape)
if is_swap:
array_1, array_2 = array_1.T, array_2.T
return array_1
def match_mono_array_shapes(array_1: np.ndarray, array_2: np.ndarray):
if len(array_1) > len(array_2):
array_1 = array_1[:len(array_2)]
elif len(array_1) < len(array_2):
padding = len(array_2) - len(array_1)
array_1 = np.pad(array_1, (0, padding), 'constant', constant_values=0)
return array_1
def change_pitch_semitones(y, sr, semitone_shift):
factor = 2 ** (semitone_shift / 12) # Convert semitone shift to factor for resampling
y_pitch_tuned = []
for y_channel in y:
y_pitch_tuned.append(librosa.resample(y_channel, sr, sr*factor, res_type=wav_resolution_float_resampling))
y_pitch_tuned = np.array(y_pitch_tuned)
new_sr = sr * factor
return y_pitch_tuned, new_sr
def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False, is_time_correction=True):
wav, sr = librosa.load(audio_file, sr=44100, mono=False)
if wav.ndim == 1:
wav = np.asfortranarray([wav,wav])
if not is_time_correction:
wav_mix = change_pitch_semitones(wav, 44100, semitone_shift=-rate)[0]
else:
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 reshape_sources_ref(wav_1_shape, wav_2: np.ndarray):
if wav_1_shape > wav_2.shape:
wav_2 = to_shape(wav_2, wav_1_shape)
return wav_2
def combine_arrarys(audio_sources, is_swap=False):
source = np.zeros_like(max(audio_sources, key=np.size))
for v in audio_sources:
v = match_array_shapes(v, source, is_swap=is_swap)
source += v
return source
def combine_audio(paths: list, audio_file_base=None, wav_type_set='FLOAT', save_format=None):
source = combine_arrarys([load_audio(i) for i in paths])
save_path = f"{audio_file_base}_combined.wav"
sf.write(save_path, source.T, 44100, subtype=wav_type_set)
save_format(save_path)
def reduce_mix_bv(inst_source, voc_source, reduction_rate=0.9):
# Reduce the volume
inst_source = inst_source * (1 - reduction_rate)
mix_reduced = combine_arrarys([inst_source, voc_source], is_swap=True)
return mix_reduced
def organize_inputs(inputs):
input_list = {
"target":None,
"reference":None,
"reverb":None,
"inst":None
}
for i in inputs:
if i.endswith("_(Vocals).wav"):
input_list["reference"] = i
elif "_RVC_" in i:
input_list["target"] = i
elif i.endswith("reverbed_stem.wav"):
input_list["reverb"] = i
elif i.endswith("_(Instrumental).wav"):
input_list["inst"] = i
return input_list
def check_if_phase_inverted(wav1, wav2, is_mono=False):
# Load the audio files
if not is_mono:
wav1 = np.mean(wav1, axis=0)
wav2 = np.mean(wav2, axis=0)
# Compute the correlation
correlation = np.corrcoef(wav1[:1000], wav2[:1000])
return correlation[0,1] < 0
def align_audio(file1,
file2,
file2_aligned,
file_subtracted,
wav_type_set,
is_save_aligned,
command_Text,
save_format,
align_window:list,
align_intro_val:list,
db_analysis:tuple,
set_progress_bar,
phase_option,
phase_shifts,
is_match_silence,
is_spec_match):
global progress_value
progress_value = 0
is_mono = False
def get_diff(a, b):
corr = np.correlate(a, b, "full")
diff = corr.argmax() - (b.shape[0] - 1)
return diff
def progress_bar(length):
global progress_value
progress_value += 1
if (0.90/length*progress_value) >= 0.9:
length = progress_value + 1
set_progress_bar(0.1, (0.9/length*progress_value))
# read tracks
if file1.endswith(".mp3") and is_macos:
length1 = rerun_mp3(file1)
wav1, sr1 = librosa.load(file1, duration=length1, sr=44100, mono=False)
else:
wav1, sr1 = librosa.load(file1, sr=44100, mono=False)
if file2.endswith(".mp3") and is_macos:
length2 = rerun_mp3(file2)
wav2, sr2 = librosa.load(file2, duration=length2, sr=44100, mono=False)
else:
wav2, sr2 = librosa.load(file2, sr=44100, mono=False)
if wav1.ndim == 1 and wav2.ndim == 1:
is_mono = True
elif wav1.ndim == 1:
wav1 = np.asfortranarray([wav1,wav1])
elif wav2.ndim == 1:
wav2 = np.asfortranarray([wav2,wav2])
# Check if phase is inverted
if phase_option == AUTO_PHASE:
if check_if_phase_inverted(wav1, wav2, is_mono=is_mono):
wav2 = -wav2
elif phase_option == POSITIVE_PHASE:
wav2 = +wav2
elif phase_option == NEGATIVE_PHASE:
wav2 = -wav2
if is_match_silence:
wav2 = adjust_leading_silence(wav2, wav1)
wav1_length = int(librosa.get_duration(y=wav1, sr=44100))
wav2_length = int(librosa.get_duration(y=wav2, sr=44100))
if not is_mono:
wav1 = wav1.transpose()
wav2 = wav2.transpose()
wav2_org = wav2.copy()
command_Text("Processing files... \n")
seconds_length = min(wav1_length, wav2_length)
wav2_aligned_sources = []
for sec_len in align_intro_val:
# pick a position at 1 second in and get diff
sec_seg = 1 if sec_len == 1 else int(seconds_length // sec_len)
index = sr1*sec_seg # 1 second in, assuming sr1 = sr2 = 44100
if is_mono:
samp1, samp2 = wav1[index : index + sr1], wav2[index : index + sr1]
diff = get_diff(samp1, samp2)
#print(f"Estimated difference: {diff}\n")
else:
index = sr1*sec_seg # 1 second in, assuming sr1 = sr2 = 44100
samp1, samp2 = wav1[index : index + sr1, 0], wav2[index : index + sr1, 0]
samp1_r, samp2_r = wav1[index : index + sr1, 1], wav2[index : index + sr1, 1]
diff, diff_r = get_diff(samp1, samp2), get_diff(samp1_r, samp2_r)
#print(f"Estimated difference Left Channel: {diff}\nEstimated difference Right Channel: {diff_r}\n")
# make aligned track 2
if diff > 0:
zeros_to_append = np.zeros(diff) if is_mono else np.zeros((diff, 2))
wav2_aligned = np.append(zeros_to_append, wav2_org, axis=0)
elif diff < 0:
wav2_aligned = wav2_org[-diff:]
else:
wav2_aligned = wav2_org
#command_Text(f"Audio files already aligned.\n")
if not any(np.array_equal(wav2_aligned, source) for source in wav2_aligned_sources):
wav2_aligned_sources.append(wav2_aligned)
#print("Unique Sources: ", len(wav2_aligned_sources))
unique_sources = len(wav2_aligned_sources)
sub_mapper_big_mapper = {}
for s in wav2_aligned_sources:
wav2_aligned = match_mono_array_shapes(s, wav1) if is_mono else match_array_shapes(s, wav1, is_swap=True)
if align_window:
wav_sub = time_correction(wav1, wav2_aligned, seconds_length, align_window=align_window, db_analysis=db_analysis, progress_bar=progress_bar, unique_sources=unique_sources, phase_shifts=phase_shifts)
wav_sub_size = np.abs(wav_sub).mean()
sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size:wav_sub}}
else:
wav2_aligned = wav2_aligned * np.power(10, db_analysis[0] / 20)
db_range = db_analysis[1]
for db_adjustment in db_range:
# Adjust the dB of track2
s_adjusted = wav2_aligned * (10 ** (db_adjustment / 20))
wav_sub = wav1 - s_adjusted
wav_sub_size = np.abs(wav_sub).mean()
sub_mapper_big_mapper = {**sub_mapper_big_mapper, **{wav_sub_size:wav_sub}}
#print(sub_mapper_big_mapper.keys(), min(sub_mapper_big_mapper.keys()))