black reformat
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parent
c256b5478e
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@ -28,7 +28,7 @@ class Inference:
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if isinstance(audio, (np.ndarray, torch.Tensor)):
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if isinstance(audio, (np.ndarray, torch.Tensor)):
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assert sr is not None, "Invalid sampling rate!"
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assert sr is not None, "Invalid sampling rate!"
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if len(audio.shape) == 1:
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if len(audio.shape) == 1:
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audio = audio.reshape(1,-1)
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audio = audio.reshape(1, -1)
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if isinstance(audio, str):
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if isinstance(audio, str):
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audio = Path(audio)
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audio = Path(audio)
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@ -105,8 +105,7 @@ class Inference:
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window = get_window(window=window, Nx=data.shape[-1])
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window = get_window(window=window, Nx=data.shape[-1])
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window = torch.from_numpy(window).to(data.device)
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window = torch.from_numpy(window).to(data.device)
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data *= window
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data *= window
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step_size = window_size//2 if step_size is None else step_size
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step_size = window_size // 2 if step_size is None else step_size
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data = data.permute(1, 2, 0)
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data = data.permute(1, 2, 0)
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data = F.fold(
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data = F.fold(
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@ -134,8 +133,8 @@ class Inference:
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if isinstance(filename, str):
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if isinstance(filename, str):
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filename = Path(filename)
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filename = Path(filename)
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parent, name = filename.parent, "cleaned_"+filename.name
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parent, name = filename.parent, "cleaned_" + filename.name
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filename = parent/Path(name)
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filename = parent / Path(name)
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if filename.is_file():
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if filename.is_file():
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raise FileExistsError(f"file {filename} already exists")
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raise FileExistsError(f"file {filename} already exists")
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else:
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else:
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