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synced 2025-03-11 09:58:49 +03:00
5.5 mv volume
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@ -175,7 +175,7 @@ class DDSP_SVC:
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f0 = self.f0_detector.extract(self.audio_buffer, uv_interp=True)
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f0 = torch.from_numpy(f0).float().unsqueeze(-1).unsqueeze(0)
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f0 = f0 * 2 ** (float(10) / 12)
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f0 = f0 * 2 ** (float(20) / 12)
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volume = self.volume_extractor.extract(self.audio_buffer)
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@ -259,19 +259,10 @@ class DDSP_SVC:
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# seg_output = seg_output.squeeze().cpu().numpy()
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# print("SEG:", seg_output)
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audio, sample_rate = librosa.load("tmp2.wav", sr=None)
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print("SR:", sample_rate)
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c = data[0]
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f0 = data[1]
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volume = data[2]
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if len(audio.shape) > 1:
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audio = librosa.to_mono(audio)
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hop_size = self.args.data.block_size * sample_rate / self.args.data.sampling_rate
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print("hop_size", hop_size)
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mask = (volume > 10 ** (float(-60) / 20)).astype('float')
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mask = np.pad(mask, (4, 4), constant_values=(mask[0], mask[-1]))
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mask = np.array([np.max(mask[n: n + 9]) for n in range(len(mask) - 8)])
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@ -319,25 +310,6 @@ class DDSP_SVC:
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del self.onnx_session
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def split(audio, sample_rate, hop_size, db_thresh=-40, min_len=5000):
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slicer = Slicer(
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sr=sample_rate,
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threshold=db_thresh,
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min_length=min_len)
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chunks = dict(slicer.slice(audio))
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result = []
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for k, v in chunks.items():
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tag = v["split_time"].split(",")
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if tag[0] != tag[1]:
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start_frame = int(int(tag[0]) // hop_size)
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end_frame = int(int(tag[1]) // hop_size)
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if end_frame > start_frame:
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result.append((
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start_frame,
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audio[int(start_frame * hop_size): int(end_frame * hop_size)]))
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return result
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def cross_fade(a: np.ndarray, b: np.ndarray, idx: int):
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result = np.zeros(idx + b.shape[0])
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fade_len = a.shape[0] - idx
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