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@ -5,8 +5,9 @@ import math
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import random
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import math
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import platform
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import traceback
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from . import pyrb
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#cur
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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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@ -18,7 +19,7 @@ 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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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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@ -35,8 +36,6 @@ def crop_center(h1, h2):
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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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@ -116,6 +115,8 @@ def normalize(wave, is_normalize=False):
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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"The result was not normalized.")
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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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@ -128,11 +129,14 @@ def normalize_two_stem(wave, mix, is_normalize=False):
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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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print(f"\nNormalization Set {is_normalize}: Primary source above threshold for clipping. Max:{maxv}")
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print(f"\nNormalization Set {is_normalize}: Mixture above threshold for clipping. Max:{max_mix}")
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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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mix /= maxv
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else:
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print(f"The result was not normalized.")
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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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@ -205,75 +209,51 @@ def reduce_vocal_aggressively(X, y, softmask):
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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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mask = y_mask
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try:
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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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mask = y_mask
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except Exception as e:
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error_name = f'{type(e).__name__}'
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traceback_text = ''.join(traceback.format_tb(e.__traceback__))
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message = f'{error_name}: "{e}"\n{traceback_text}"'
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print('Post Process Failed: ', message)
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return mask
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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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@ -386,11 +366,11 @@ def mirroring(a, spec_m, input_high_end, mp):
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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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def adjust_aggr(mask, is_non_accom_stem, aggressiveness):
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aggr = aggressiveness['value']
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if aggr != 0:
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if is_vocal_model:
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if is_non_accom_stem:
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aggr = 1 - aggr
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aggr = [aggr, aggr]
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@ -403,6 +383,9 @@ def adjust_aggr(mask, is_vocal_model, aggressiveness):
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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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# if is_non_accom_stem:
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# mask = (1.0 - mask)
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return mask
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def stft(wave, nfft, hl):
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@ -442,36 +425,20 @@ def spec_effects(wave, algorithm='Default', value=None):
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return wave
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def spectrogram_to_wave_bare(spec, hop_length=1024):
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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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wave = np.asfortranarray([wave_left, wave_right])
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return wave
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def spectrogram_to_wave_no_mp(spec, hop_length=1024):
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if spec.ndim == 2:
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wave = librosa.istft(spec, hop_length=hop_length)
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elif spec.ndim == 3:
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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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wave = np.asfortranarray([wave_left, wave_right])
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def spectrogram_to_wave_no_mp(spec, n_fft=2048, hop_length=1024):
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wave = librosa.istft(spec, n_fft=n_fft, hop_length=hop_length)
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if wave.ndim == 1:
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wave = np.asfortranarray([wave,wave])
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return wave
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def wave_to_spectrogram_no_mp(wave):
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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=2048, hop_length=1024)
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spec_right = librosa.stft(wave_right, n_fft=2048, hop_length=1024)
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spec = np.asfortranarray([spec_left, spec_right])
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spec = librosa.stft(wave, n_fft=2048, hop_length=1024)
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if spec.ndim == 1:
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spec = np.asfortranarray([spec,spec])
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return spec
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@ -519,6 +486,8 @@ def ensembling(a, specs):
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return spec
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def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save_path):
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wavs_ = []
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if algorithm == AVERAGE:
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output = average_audio(audio_input)
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@ -528,10 +497,15 @@ def ensemble_inputs(audio_input, algorithm, is_normalization, wav_type_set, save
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for i in range(len(audio_input)):
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wave, samplerate = librosa.load(audio_input[i], mono=False, sr=44100)
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wavs_.append(wave)
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spec = wave_to_spectrogram_no_mp(wave)
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specs.append(spec)
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wave_shapes = [w.shape[1] for w in wavs_]
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target_shape = wavs_[wave_shapes.index(max(wave_shapes))]
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output = spectrogram_to_wave_no_mp(ensembling(algorithm, specs))
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output = to_shape(output, target_shape.shape)
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sf.write(save_path, normalize(output.T, is_normalization), samplerate, subtype=wav_type_set)
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@ -555,7 +529,7 @@ def to_shape_minimize(x: np.ndarray, target_shape):
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return np.pad(x, tuple(padding_list), mode='constant')
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def augment_audio(export_path, audio_file, rate, is_normalization, wav_type_set, save_format=None, is_pitch=False):
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print('Rate: ', rate)
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wav, sr = librosa.load(audio_file, sr=44100, mono=False)
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if wav.ndim == 1:
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