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| | | @lru_cache(maxsize=None) |
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| | | def mel_filters(device, n_mels: int) -> torch.Tensor: |
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| | | def mel_filters(device, n_mels: int, filters_path: str=None) -> torch.Tensor: |
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| | | """ |
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| | | load the mel filterbank matrix for projecting STFT into a Mel spectrogram. |
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| | | Allows decoupling librosa dependency; saved using: |
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| | |
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| | | ) |
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| | | """ |
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| | | assert n_mels in {80, 128}, f"Unsupported n_mels: {n_mels}" |
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| | | |
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| | | filters_path = os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") |
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| | | if filters_path is None: |
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| | | filters_path = os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") |
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| | | with np.load(filters_path, allow_pickle=False) as f: |
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| | | return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) |
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| | | |
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