mirror of
https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-06-05 01:10:22 +08:00
chore(format): run black on dev (#101)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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51c85fcc49
@@ -142,7 +142,7 @@ def load_audio(
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np.copyto(decoded_audio[..., offset:end_index], frame_data)
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offset += len(frame_data[0])
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container.close()
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# Truncate the array to the actual size
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@@ -188,7 +188,6 @@ def resample_audio(
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output_container.close()
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def get_audio_properties(input_path: str) -> Tuple[int, int]:
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container = av.open(input_path)
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audio_stream = next(s for s in container.streams if s.type == "audio")
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@@ -104,7 +104,13 @@ def summarize(
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def latest_checkpoint_path(dir_path, regex="G_*.pth"):
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f_list = glob.glob(os.path.join(dir_path, regex))
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f_list.sort(key=lambda f: 999999999999 if isinstance(f, str) and f == "latest" else int("0"+"".join(filter(str.isdigit, f))))
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f_list.sort(
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key=lambda f: (
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999999999999
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if isinstance(f, str) and f == "latest"
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else int("0" + "".join(filter(str.isdigit, f)))
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)
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)
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x = f_list[-1]
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logger.debug(x)
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return x
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@@ -24,9 +24,7 @@ def crop_center(h1, h2):
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return h1
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def split_lr_waves(
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wave, mid_side=False, mid_side_b2=False, reverse=False
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):
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def split_lr_waves(wave, 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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@@ -48,17 +46,23 @@ def run_librosa_stft(wv, n_fft, hop_length, reverse):
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return librosa.stft(wv, n_fft=n_fft, hop_length=hop_length)
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return librosa.stft(np.asfortranarray(wv), n_fft=n_fft, hop_length=hop_length)
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def wave_to_spectrogram_mt(
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wave, hop_length, n_fft, mid_side=False, mid_side_b2=False, reverse=False
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):
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with ThreadPoolExecutor(max_workers=2) as tp:
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spec = np.asfortranarray(
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[spec for spec in tp.map(
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run_librosa_stft,
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split_lr_waves(wave, mid_side, mid_side_b2, reverse),
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[n_fft, n_fft], [hop_length, hop_length], [reverse, reverse]
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)]
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[
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spec
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for spec in tp.map(
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run_librosa_stft,
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split_lr_waves(wave, mid_side, mid_side_b2, reverse),
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[n_fft, n_fft],
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[hop_length, hop_length],
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[reverse, reverse],
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)
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]
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)
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return spec
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@@ -144,10 +148,13 @@ def mask_silence(mag, ref, thres=0.2, min_range=64, fade_size=32):
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def run_librosa_istft(specx, hop_length):
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return librosa.istft(np.asfortranarray(specx), hop_length=hop_length)
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def spectrogram_to_wave(spec, hop_length, mid_side, mid_side_b2, reverse):
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with ThreadPoolExecutor(max_workers=2) as tp:
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wave_left, wave_right = tp.map(run_librosa_istft, spec, [hop_length, hop_length])
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wave_left, wave_right = tp.map(
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run_librosa_istft, spec, [hop_length, hop_length]
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)
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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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@@ -207,12 +207,21 @@ class Predictor:
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sources = self.demix(mix.T)
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opt = sources[0].T
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save_audio(
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"%s/vocal_%s.%s" % (vocal_root, basename, format), mix - opt, rate, True, format=format,
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"%s/vocal_%s.%s" % (vocal_root, basename, format),
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mix - opt,
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rate,
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True,
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format=format,
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)
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save_audio(
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"%s/instrument_%s.%s" % (others_root, basename, format), opt, rate, True, format=format,
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"%s/instrument_%s.%s" % (others_root, basename, format),
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opt,
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rate,
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True,
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format=format,
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)
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class MDXNetDereverb:
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def __init__(self, chunks, device):
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self.onnx = "assets/uvr5_weights/onnx_dereverb_By_FoxJoy"
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@@ -119,7 +119,10 @@ class AudioPre:
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if ins_root is not None:
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if self.data["high_end_process"].startswith("mirroring"):
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input_high_end_ = spec_utils.mirroring(
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self.data["high_end_process"], y_spec_m, input_high_end, self.mp.param["pre_filter_start"]
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self.data["high_end_process"],
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y_spec_m,
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input_high_end,
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self.mp.param["pre_filter_start"],
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)
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wav_instrument = spec_utils.cmb_spectrogram_to_wave(
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y_spec_m, self.mp, input_high_end_h, input_high_end_
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@@ -139,7 +142,7 @@ class AudioPre:
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wav_instrument,
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self.mp.param["sr"],
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f32=True,
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format=format
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format=format,
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)
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if vocal_root is not None:
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if self.is_reverse:
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@@ -148,7 +151,10 @@ class AudioPre:
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head = "vocal_"
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if self.data["high_end_process"].startswith("mirroring"):
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input_high_end_ = spec_utils.mirroring(
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self.data["high_end_process"], v_spec_m, input_high_end, self.mp.param["pre_filter_start"]
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self.data["high_end_process"],
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v_spec_m,
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input_high_end,
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self.mp.param["pre_filter_start"],
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)
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wav_vocals = spec_utils.cmb_spectrogram_to_wave(
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v_spec_m, self.mp, input_high_end_h, input_high_end_
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@@ -164,5 +170,5 @@ class AudioPre:
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wav_vocals,
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self.mp.param["sr"],
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f32=True,
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format=format
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format=format,
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)
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@@ -252,8 +252,7 @@ class VC:
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try:
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tgt_sr, audio_opt = opt
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save_audio(
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"%s/%s.%s"
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% (opt_root, os.path.basename(path), format1),
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"%s/%s.%s" % (opt_root, os.path.basename(path), format1),
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audio_opt,
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tgt_sr,
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f32=True,
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