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import librosa
import numpy as np
import torch
from typing import Tuple
from .nets_utils import make_pad_mask
class LogMel(torch.nn.Module):
"""Convert STFT to fbank feats
The arguments is same as librosa.filters.mel
Args:
fs: number > 0 [scalar] sampling rate of the incoming signal
n_fft: int > 0 [scalar] number of FFT components
n_mels: int > 0 [scalar] number of Mel bands to generate
fmin: float >= 0 [scalar] lowest frequency (in Hz)
fmax: float >= 0 [scalar] highest frequency (in Hz).
If `None`, use `fmax = fs / 2.0`
htk: use HTK formula instead of Slaney
norm: {None, 1, np.inf} [scalar]
if 1, divide the triangular mel weights by the width of the mel band
(area normalization). Otherwise, leave all the triangles aiming for
a peak value of 1.0
"""
def __init__(
self,
fs: int = 16000,
n_fft: int = 512,
n_mels: int = 80,
fmin: float = None,
fmax: float = None,
htk: bool = False,
norm=1,
):
super().__init__()
fmin = 0 if fmin is None else fmin
fmax = fs / 2 if fmax is None else fmax
_mel_options = dict(
sr=fs, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax, htk=htk, norm=norm
)
self.mel_options = _mel_options
# Note(kamo): The mel matrix of librosa is different from kaldi.
melmat = librosa.filters.mel(**_mel_options)
# melmat: (D2, D1) -> (D1, D2)
self.register_buffer("melmat", torch.from_numpy(melmat.T).float())
inv_mel = np.linalg.pinv(melmat)
self.register_buffer("inv_melmat", torch.from_numpy(inv_mel.T).float())
def extra_repr(self):
return ", ".join(f"{k}={v}" for k, v in self.mel_options.items())
def forward(
self, feat: torch.Tensor, ilens: torch.Tensor = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
# feat: (B, T, D1) x melmat: (D1, D2) -> mel_feat: (B, T, D2)
mel_feat = torch.matmul(feat, self.melmat)
logmel_feat = (mel_feat + 1e-20).log()
# Zero padding
if ilens is not None:
logmel_feat = logmel_feat.masked_fill(
make_pad_mask(ilens, logmel_feat, 1), 0.0
)
else:
ilens = feat.new_full(
[feat.size(0)], fill_value=feat.size(1), dtype=torch.long
)
return logmel_feat, ilens
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