Nick CaoandClaude
2bc183f6f0
Signed-off-by: Nick Cao <ncao@redhat.com> Co-authored-by: Claude <noreply@anthropic.com>
46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Audio utility functions shared across models and entrypoints."""
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import torch
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from torchaudio.functional import melscale_fbanks
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def mel_filter_bank(
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sr: int,
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n_fft: int,
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n_mels: int,
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fmin: float = 0.0,
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fmax: float | None = None,
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) -> torch.Tensor:
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"""Compute a mel filterbank matrix.
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Drop-in replacement for ``librosa.filters.mel`` using
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``torchaudio.functional.melscale_fbanks``.
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Args:
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sr: Sample rate of the audio.
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n_fft: FFT window size.
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n_mels: Number of mel bands.
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fmin: Minimum frequency (Hz).
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fmax: Maximum frequency (Hz). Defaults to ``sr / 2``.
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Returns:
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Tensor of shape ``(n_mels, n_fft // 2 + 1)``.
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"""
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if fmax is None:
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fmax = float(sr) / 2.0
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# Use mel_scale='slaney' and norm='slaney' to match librosa's
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# default behaviour (Slaney 1998 frequency mapping with area
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# normalization).
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return melscale_fbanks(
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n_freqs=n_fft // 2 + 1,
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f_min=float(fmin),
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f_max=float(fmax),
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n_mels=n_mels,
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sample_rate=sr,
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mel_scale="slaney",
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norm="slaney",
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).T
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