def forward(self, x, return_maxima=False):
if not self.training or self.warping_fn is None:
fbanks = self.fbanks
x = x.matmul(fbanks)
else:
independent_axis = [ax if ax >= 0 else x.ndim+ax for ax in self.independent_axis]
assert all([ax < x.ndim-1 for ax in independent_axis])
size = [
x.shape[i] if i in independent_axis else 1
for i in range(x.ndim-1)
]
fbanks = get_fbanks(
sample_rate=self.sample_rate,
stft_size=self.stft_size,
number_of_filters=self.number_of_filters,
lowest_frequency=self.lowest_frequency,
highest_frequency=self.highest_frequency,
htk_mel=self.htk_mel,
warping_fn=self.warping_fn,
size=size,
).astype(np.float32)
fbanks = fbanks / (fbanks.sum(axis=-1, keepdims=True) + 1e-6)
fbanks = torch.from_numpy(fbanks).transpose(-2, -1).to(x.device)
if fbanks.shape[-3] == 1:
x = x.matmul(fbanks.squeeze(-3))
else:
x = x[..., None, :].matmul(fbanks).squeeze(-2)
if self.log:
x = torch.log(x + self.eps)
if return_maxima:
maxima = (fbanks.argmax(-2) + 1) * (fbanks.sum(-2) > 0) - 1
return x, maxima
return x
def __init__(
self,
n_mels: int,
sample_rate: int,
fft_length: int,
fmin: Optional[float] = 50.,
fmax: Optional[float] = None,
log: bool = True,
eps=1e-12,
*,
warping_fn=None,
):
"""
Transforms linear spectrogram to (log) mel spectrogram.
Args:
sample_rate: sample rate of audio signal
fft_length: fft_length used in stft
n_mels: number of filters to be applied
fmin: lowest frequency (onset of first filter)
fmax: highest frequency (offset of last filter)
log: apply log to mel spectrogram
eps:
>>> mel_transform = MelTransform(40, 16000, 512)
>>> spec = torch.zeros((10, 1, 100, 257))
>>> logmelspec = mel_transform(spec)
>>> logmelspec.shape
torch.Size([10, 1, 100, 40])
>>> rec = mel_transform.inverse(logmelspec)
>>> rec.shape
torch.Size([10, 1, 100, 257])
"""
super().__init__()
self.sample_rate = sample_rate
self.fft_length = fft_length
self.n_mels = n_mels
self.fmin = fmin
self.fmax = fmax
self.log = log
self.eps = eps
self.warping_fn = warping_fn
fbanks = get_fbanks(
n_mels=self.n_mels,
fft_length=self.fft_length,
sample_rate=self.sample_rate,
fmin=self.fmin,
fmax=self.fmax,
).astype(np.float32)
fbanks = fbanks / (fbanks.sum(axis=-1, keepdims=True) + 1e-6)
self._fbanks = nn.Parameter(torch.from_numpy(fbanks.T),
requires_grad=False)
def get_fbanks(self, x):
if not self.training or self.warping_fn is None:
fbanks = self._fbanks
else:
fbanks = get_fbanks(n_mels=self.n_mels,
fft_length=self.fft_length,
sample_rate=self.sample_rate,
fmin=self.fmin,
fmax=self.fmax,
warping_fn=partial(self.warping_fn,
n=x.shape[0])).astype(
np.float32)
fbanks = fbanks / (fbanks.sum(axis=-1, keepdims=True) + 1e-6)
fbanks = torch.from_numpy(fbanks).transpose(-2, -1).to(x.device)
while x.dim() > fbanks.dim():
fbanks = fbanks[:, None]
return nn.ReLU()(fbanks)
def __init__(
self,
sample_rate: int,
stft_size: int,
number_of_filters: int,
lowest_frequency: Optional[float] = 50.,
highest_frequency: Optional[float] = None,
htk_mel=True,
log: bool = True,
eps=1e-12,
*,
warping_fn=None,
independent_axis=0,
):
"""
Transforms linear spectrogram to (log) mel spectrogram.
Args:
sample_rate: sample rate of audio signal
stft_size: fft_length used in stft
number_of_filters: number of filters to be applied
lowest_frequency: lowest frequency (onset of first filter)
highest_frequency: highest frequency (offset of last filter)
log: apply log to mel spectrogram
eps:
>>> sample_rate = 16000
>>> highest_frequency = sample_rate/2
>>> mel_transform = MelTransform(sample_rate, 512, 40)
>>> spec = torch.rand((3, 1, 100, 257))
>>> logmelspec = mel_transform(spec)
>>> logmelspec.shape
torch.Size([3, 1, 100, 40])
>>> rec = mel_transform.inverse(logmelspec)
>>> rec.shape
torch.Size([3, 1, 100, 257])
>>> from paderbox.transform.module_fbank import HzWarping
>>> from paderbox.utils.random_utils import Uniform
>>> warping_fn = HzWarping(\
warp_factor_sampling_fn=Uniform(low=.9, high=1.1),\
boundary_frequency_ratio_sampling_fn=Uniform(low=.6, high=.7),\
highest_frequency=highest_frequency,\
)
>>> mel_transform = MelTransform(sample_rate, 512, 40, warping_fn=warping_fn)
>>> mel_transform(spec).shape
torch.Size([3, 1, 100, 40])
>>> mel_transform = MelTransform(sample_rate, 512, 40, warping_fn=warping_fn, independent_axis=(0,1,2))
>>> np.random.seed(0)
>>> x = mel_transform(spec)
>>> x.shape
torch.Size([3, 1, 100, 40])
>>> from paderbox.transform.module_fbank import MelTransform as MelTransformNumpy
>>> mel_transform_np = MelTransformNumpy(sample_rate, 512, 40, warping_fn=warping_fn, independent_axis=(0,1,2))
>>> np.random.seed(0)
>>> x_ref = mel_transform_np(spec.numpy())
>>> assert (x.numpy()-x_ref).max() < 1e-6
"""
super().__init__()
self.sample_rate = sample_rate
self.stft_size = stft_size
self.number_of_filters = number_of_filters
self.lowest_frequency = lowest_frequency
self.highest_frequency = highest_frequency
self.htk_mel = htk_mel
self.log = log
self.eps = eps
self.warping_fn = warping_fn
self.independent_axis = [independent_axis] if np.isscalar(independent_axis) else independent_axis
fbanks = get_fbanks(
sample_rate=self.sample_rate,
stft_size=self.stft_size,
number_of_filters=self.number_of_filters,
lowest_frequency=self.lowest_frequency,
highest_frequency=self.highest_frequency,
htk_mel=htk_mel,
).astype(np.float32)
fbanks = fbanks / (fbanks.sum(axis=-1, keepdims=True) + 1e-6)
self.fbanks = nn.Parameter(
torch.from_numpy(fbanks.T), requires_grad=False
)