# -*- coding: utf-8 -*-
import math
from typing import Callable, Optional
from warnings import warn
import torch
from torch import Tensor
from torchaudio import functional as F
from torchaudio.compliance import kaldi
__all__ = [
'Spectrogram',
'GriffinLim',
'AmplitudeToDB',
'MelScale',
'InverseMelScale',
'MelSpectrogram',
'MFCC',
'MuLawEncoding',
'MuLawDecoding',
'Resample',
'ComplexNorm',
'TimeStretch',
'Fade',
'FrequencyMasking',
'TimeMasking',
]
[docs]class Spectrogram(torch.nn.Module):
r"""Create a spectrogram from a audio signal.
Args:
n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
win_length (int or None, optional): Window size. (Default: ``n_fft``)
hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
pad (int, optional): Two sided padding of signal. (Default: ``0``)
window_fn (Callable[..., Tensor], optional): A function to create a window tensor
that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
power (float or None, optional): Exponent for the magnitude spectrogram,
(must be > 0) e.g., 1 for energy, 2 for power, etc.
If None, then the complex spectrum is returned instead. (Default: ``2``)
normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: ``False``)
wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
"""
__constants__ = ['n_fft', 'win_length', 'hop_length', 'pad', 'power', 'normalized']
def __init__(self,
n_fft: int = 400,
win_length: Optional[int] = None,
hop_length: Optional[int] = None,
pad: int = 0,
window_fn: Callable[..., Tensor] = torch.hann_window,
power: Optional[float] = 2.,
normalized: bool = False,
wkwargs: Optional[dict] = None) -> None:
super(Spectrogram, self).__init__()
self.n_fft = n_fft
# number of FFT bins. the returned STFT result will have n_fft // 2 + 1
# number of frequecies due to onesided=True in torch.stft
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 2
window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
self.register_buffer('window', window)
self.pad = pad
self.power = power
self.normalized = normalized
[docs] def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
Returns:
Tensor: Dimension (..., freq, time), where freq is
``n_fft // 2 + 1`` where ``n_fft`` is the number of
Fourier bins, and time is the number of window hops (n_frame).
"""
return F.spectrogram(waveform, self.pad, self.window, self.n_fft, self.hop_length,
self.win_length, self.power, self.normalized)
[docs]class GriffinLim(torch.nn.Module):
r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation.
Implementation ported from `librosa`.
.. [1] McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto.
"librosa: Audio and music signal analysis in python."
In Proceedings of the 14th python in science conference, pp. 18-25. 2015.
.. [2] Perraudin, N., Balazs, P., & Søndergaard, P. L.
"A fast Griffin-Lim algorithm,"
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4),
Oct. 2013.
.. [3] D. W. Griffin and J. S. Lim,
"Signal estimation from modified short-time Fourier transform,"
IEEE Trans. ASSP, vol.32, no.2, pp.236–243, Apr. 1984.
Args:
n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
n_iter (int, optional): Number of iteration for phase recovery process. (Default: ``32``)
win_length (int or None, optional): Window size. (Default: ``n_fft``)
hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
window_fn (Callable[..., Tensor], optional): A function to create a window tensor
that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
power (float, optional): Exponent for the magnitude spectrogram,
(must be > 0) e.g., 1 for energy, 2 for power, etc. (Default: ``2``)
normalized (bool, optional): Whether to normalize by magnitude after stft. (Default: ``False``)
wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``)
momentum (float, optional): The momentum parameter for fast Griffin-Lim.
Setting this to 0 recovers the original Griffin-Lim method.
Values near 1 can lead to faster convergence, but above 1 may not converge. (Default: ``0.99``)
length (int, optional): Array length of the expected output. (Default: ``None``)
rand_init (bool, optional): Initializes phase randomly if True and to zero otherwise. (Default: ``True``)
"""
__constants__ = ['n_fft', 'n_iter', 'win_length', 'hop_length', 'power', 'normalized',
'length', 'momentum', 'rand_init']
def __init__(self,
n_fft: int = 400,
n_iter: int = 32,
win_length: Optional[int] = None,
hop_length: Optional[int] = None,
window_fn: Callable[..., Tensor] = torch.hann_window,
power: float = 2.,
normalized: bool = False,
wkwargs: Optional[dict] = None,
momentum: float = 0.99,
length: Optional[int] = None,
rand_init: bool = True) -> None:
super(GriffinLim, self).__init__()
assert momentum < 1, 'momentum=%s > 1 can be unstable' % momentum
assert momentum > 0, 'momentum=%s < 0' % momentum
self.n_fft = n_fft
self.n_iter = n_iter
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 2
window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs)
self.register_buffer('window', window)
self.normalized = normalized
self.length = length
self.power = power
self.momentum = momentum / (1 + momentum)
self.rand_init = rand_init
[docs] def forward(self, specgram: Tensor) -> Tensor:
r"""
Args:
specgram (Tensor): A magnitude-only STFT spectrogram of dimension (..., freq, frames)
where freq is ``n_fft // 2 + 1``.
Returns:
Tensor: waveform of (..., time), where time equals the ``length`` parameter if given.
"""
return F.griffinlim(specgram, self.window, self.n_fft, self.hop_length, self.win_length, self.power,
self.normalized, self.n_iter, self.momentum, self.length, self.rand_init)
[docs]class AmplitudeToDB(torch.nn.Module):
r"""Turn a tensor from the power/amplitude scale to the decibel scale.
This output depends on the maximum value in the input tensor, and so
may return different values for an audio clip split into snippets vs. a
a full clip.
Args:
stype (str, optional): scale of input tensor ('power' or 'magnitude'). The
power being the elementwise square of the magnitude. (Default: ``'power'``)
top_db (float, optional): minimum negative cut-off in decibels. A reasonable number
is 80. (Default: ``None``)
"""
__constants__ = ['multiplier', 'amin', 'ref_value', 'db_multiplier']
def __init__(self, stype: str = 'power', top_db: Optional[float] = None) -> None:
super(AmplitudeToDB, self).__init__()
self.stype = stype
if top_db is not None and top_db < 0:
raise ValueError('top_db must be positive value')
self.top_db = top_db
self.multiplier = 10.0 if stype == 'power' else 20.0
self.amin = 1e-10
self.ref_value = 1.0
self.db_multiplier = math.log10(max(self.amin, self.ref_value))
[docs] def forward(self, x: Tensor) -> Tensor:
r"""Numerically stable implementation from Librosa.
https://librosa.github.io/librosa/_modules/librosa/core/spectrum.html
Args:
x (Tensor): Input tensor before being converted to decibel scale.
Returns:
Tensor: Output tensor in decibel scale.
"""
return F.amplitude_to_DB(x, self.multiplier, self.amin, self.db_multiplier, self.top_db)
[docs]class MelScale(torch.nn.Module):
r"""Turn a normal STFT into a mel frequency STFT, using a conversion
matrix. This uses triangular filter banks.
User can control which device the filter bank (`fb`) is (e.g. fb.to(spec_f.device)).
Args:
n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
f_min (float, optional): Minimum frequency. (Default: ``0.``)
f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``)
n_stft (int, optional): Number of bins in STFT. Calculated from first input
if None is given. See ``n_fft`` in :class:`Spectrogram`. (Default: ``None``)
"""
__constants__ = ['n_mels', 'sample_rate', 'f_min', 'f_max']
def __init__(self,
n_mels: int = 128,
sample_rate: int = 16000,
f_min: float = 0.,
f_max: Optional[float] = None,
n_stft: Optional[int] = None) -> None:
super(MelScale, self).__init__()
self.n_mels = n_mels
self.sample_rate = sample_rate
self.f_max = f_max if f_max is not None else float(sample_rate // 2)
self.f_min = f_min
assert f_min <= self.f_max, 'Require f_min: %f < f_max: %f' % (f_min, self.f_max)
fb = torch.empty(0) if n_stft is None else F.create_fb_matrix(
n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate)
self.register_buffer('fb', fb)
[docs] def forward(self, specgram: Tensor) -> Tensor:
r"""
Args:
specgram (Tensor): A spectrogram STFT of dimension (..., freq, time).
Returns:
Tensor: Mel frequency spectrogram of size (..., ``n_mels``, time).
"""
# pack batch
shape = specgram.size()
specgram = specgram.view(-1, shape[-2], shape[-1])
if self.fb.numel() == 0:
tmp_fb = F.create_fb_matrix(specgram.size(1), self.f_min, self.f_max, self.n_mels, self.sample_rate)
# Attributes cannot be reassigned outside __init__ so workaround
self.fb.resize_(tmp_fb.size())
self.fb.copy_(tmp_fb)
# (channel, frequency, time).transpose(...) dot (frequency, n_mels)
# -> (channel, time, n_mels).transpose(...)
mel_specgram = torch.matmul(specgram.transpose(1, 2), self.fb).transpose(1, 2)
# unpack batch
mel_specgram = mel_specgram.view(shape[:-2] + mel_specgram.shape[-2:])
return mel_specgram
[docs]class InverseMelScale(torch.nn.Module):
r"""Solve for a normal STFT from a mel frequency STFT, using a conversion
matrix. This uses triangular filter banks.
It minimizes the euclidian norm between the input mel-spectrogram and the product between
the estimated spectrogram and the filter banks using SGD.
Args:
n_stft (int): Number of bins in STFT. See ``n_fft`` in :class:`Spectrogram`.
n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
f_min (float, optional): Minimum frequency. (Default: ``0.``)
f_max (float or None, optional): Maximum frequency. (Default: ``sample_rate // 2``)
max_iter (int, optional): Maximum number of optimization iterations. (Default: ``100000``)
tolerance_loss (float, optional): Value of loss to stop optimization at. (Default: ``1e-5``)
tolerance_change (float, optional): Difference in losses to stop optimization at. (Default: ``1e-8``)
sgdargs (dict or None, optional): Arguments for the SGD optimizer. (Default: ``None``)
"""
__constants__ = ['n_stft', 'n_mels', 'sample_rate', 'f_min', 'f_max', 'max_iter', 'tolerance_loss',
'tolerance_change', 'sgdargs']
def __init__(self,
n_stft: int,
n_mels: int = 128,
sample_rate: int = 16000,
f_min: float = 0.,
f_max: Optional[float] = None,
max_iter: int = 100000,
tolerance_loss: float = 1e-5,
tolerance_change: float = 1e-8,
sgdargs: Optional[dict] = None) -> None:
super(InverseMelScale, self).__init__()
self.n_mels = n_mels
self.sample_rate = sample_rate
self.f_max = f_max or float(sample_rate // 2)
self.f_min = f_min
self.max_iter = max_iter
self.tolerance_loss = tolerance_loss
self.tolerance_change = tolerance_change
self.sgdargs = sgdargs or {'lr': 0.1, 'momentum': 0.9}
assert f_min <= self.f_max, 'Require f_min: %f < f_max: %f' % (f_min, self.f_max)
fb = F.create_fb_matrix(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate)
self.register_buffer('fb', fb)
[docs] def forward(self, melspec: Tensor) -> Tensor:
r"""
Args:
melspec (Tensor): A Mel frequency spectrogram of dimension (..., ``n_mels``, time)
Returns:
Tensor: Linear scale spectrogram of size (..., freq, time)
"""
# pack batch
shape = melspec.size()
melspec = melspec.view(-1, shape[-2], shape[-1])
n_mels, time = shape[-2], shape[-1]
freq, _ = self.fb.size() # (freq, n_mels)
melspec = melspec.transpose(-1, -2)
assert self.n_mels == n_mels
specgram = torch.rand(melspec.size()[0], time, freq, requires_grad=True,
dtype=melspec.dtype, device=melspec.device)
optim = torch.optim.SGD([specgram], **self.sgdargs)
loss = float('inf')
for _ in range(self.max_iter):
optim.zero_grad()
diff = melspec - specgram.matmul(self.fb)
new_loss = diff.pow(2).sum(axis=-1).mean()
# take sum over mel-frequency then average over other dimensions
# so that loss threshold is applied par unit timeframe
new_loss.backward()
optim.step()
specgram.data = specgram.data.clamp(min=0)
new_loss = new_loss.item()
if new_loss < self.tolerance_loss or abs(loss - new_loss) < self.tolerance_change:
break
loss = new_loss
specgram.requires_grad_(False)
specgram = specgram.clamp(min=0).transpose(-1, -2)
# unpack batch
specgram = specgram.view(shape[:-2] + (freq, time))
return specgram
[docs]class MelSpectrogram(torch.nn.Module):
r"""Create MelSpectrogram for a raw audio signal. This is a composition of Spectrogram
and MelScale.
Sources
* https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe
* https://timsainb.github.io/spectrograms-mfccs-and-inversion-in-python.html
* http://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html
Args:
sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
win_length (int or None, optional): Window size. (Default: ``n_fft``)
hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins. (Default: ``400``)
f_min (float, optional): Minimum frequency. (Default: ``0.``)
f_max (float or None, optional): Maximum frequency. (Default: ``None``)
pad (int, optional): Two sided padding of signal. (Default: ``0``)
n_mels (int, optional): Number of mel filterbanks. (Default: ``128``)
window_fn (Callable[..., Tensor], optional): A function to create a window tensor
that is applied/multiplied to each frame/window. (Default: ``torch.hann_window``)
wkwargs (Dict[..., ...] or None, optional): Arguments for window function. (Default: ``None``)
Example
>>> waveform, sample_rate = torchaudio.load('test.wav', normalization=True)
>>> mel_specgram = transforms.MelSpectrogram(sample_rate)(waveform) # (channel, n_mels, time)
"""
__constants__ = ['sample_rate', 'n_fft', 'win_length', 'hop_length', 'pad', 'n_mels', 'f_min']
def __init__(self,
sample_rate: int = 16000,
n_fft: int = 400,
win_length: Optional[int] = None,
hop_length: Optional[int] = None,
f_min: float = 0.,
f_max: Optional[float] = None,
pad: int = 0,
n_mels: int = 128,
window_fn: Callable[..., Tensor] = torch.hann_window,
wkwargs: Optional[dict] = None) -> None:
super(MelSpectrogram, self).__init__()
self.sample_rate = sample_rate
self.n_fft = n_fft
self.win_length = win_length if win_length is not None else n_fft
self.hop_length = hop_length if hop_length is not None else self.win_length // 2
self.pad = pad
self.n_mels = n_mels # number of mel frequency bins
self.f_max = f_max
self.f_min = f_min
self.spectrogram = Spectrogram(n_fft=self.n_fft, win_length=self.win_length,
hop_length=self.hop_length,
pad=self.pad, window_fn=window_fn, power=2.,
normalized=False, wkwargs=wkwargs)
self.mel_scale = MelScale(self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1)
[docs] def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
Returns:
Tensor: Mel frequency spectrogram of size (..., ``n_mels``, time).
"""
specgram = self.spectrogram(waveform)
mel_specgram = self.mel_scale(specgram)
return mel_specgram
[docs]class MFCC(torch.nn.Module):
r"""Create the Mel-frequency cepstrum coefficients from an audio signal.
By default, this calculates the MFCC on the DB-scaled Mel spectrogram.
This is not the textbook implementation, but is implemented here to
give consistency with librosa.
This output depends on the maximum value in the input spectrogram, and so
may return different values for an audio clip split into snippets vs. a
a full clip.
Args:
sample_rate (int, optional): Sample rate of audio signal. (Default: ``16000``)
n_mfcc (int, optional): Number of mfc coefficients to retain. (Default: ``40``)
dct_type (int, optional): type of DCT (discrete cosine transform) to use. (Default: ``2``)
norm (str, optional): norm to use. (Default: ``'ortho'``)
log_mels (bool, optional): whether to use log-mel spectrograms instead of db-scaled. (Default: ``False``)
melkwargs (dict or None, optional): arguments for MelSpectrogram. (Default: ``None``)
"""
__constants__ = ['sample_rate', 'n_mfcc', 'dct_type', 'top_db', 'log_mels']
def __init__(self,
sample_rate: int = 16000,
n_mfcc: int = 40,
dct_type: int = 2,
norm: str = 'ortho',
log_mels: bool = False,
melkwargs: Optional[dict] = None) -> None:
super(MFCC, self).__init__()
supported_dct_types = [2]
if dct_type not in supported_dct_types:
raise ValueError('DCT type not supported'.format(dct_type))
self.sample_rate = sample_rate
self.n_mfcc = n_mfcc
self.dct_type = dct_type
self.norm = norm
self.top_db = 80.0
self.amplitude_to_DB = AmplitudeToDB('power', self.top_db)
if melkwargs is not None:
self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate, **melkwargs)
else:
self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate)
if self.n_mfcc > self.MelSpectrogram.n_mels:
raise ValueError('Cannot select more MFCC coefficients than # mel bins')
dct_mat = F.create_dct(self.n_mfcc, self.MelSpectrogram.n_mels, self.norm)
self.register_buffer('dct_mat', dct_mat)
self.log_mels = log_mels
[docs] def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
Returns:
Tensor: specgram_mel_db of size (..., ``n_mfcc``, time).
"""
# pack batch
shape = waveform.size()
waveform = waveform.view(-1, shape[-1])
mel_specgram = self.MelSpectrogram(waveform)
if self.log_mels:
log_offset = 1e-6
mel_specgram = torch.log(mel_specgram + log_offset)
else:
mel_specgram = self.amplitude_to_DB(mel_specgram)
# (channel, n_mels, time).tranpose(...) dot (n_mels, n_mfcc)
# -> (channel, time, n_mfcc).tranpose(...)
mfcc = torch.matmul(mel_specgram.transpose(1, 2), self.dct_mat).transpose(1, 2)
# unpack batch
mfcc = mfcc.view(shape[:-1] + mfcc.shape[-2:])
return mfcc
[docs]class MuLawEncoding(torch.nn.Module):
r"""Encode signal based on mu-law companding. For more info see the
`Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
This algorithm assumes the signal has been scaled to between -1 and 1 and
returns a signal encoded with values from 0 to quantization_channels - 1
Args:
quantization_channels (int, optional): Number of channels. (Default: ``256``)
"""
__constants__ = ['quantization_channels']
def __init__(self, quantization_channels: int = 256) -> None:
super(MuLawEncoding, self).__init__()
self.quantization_channels = quantization_channels
[docs] def forward(self, x: Tensor) -> Tensor:
r"""
Args:
x (Tensor): A signal to be encoded.
Returns:
x_mu (Tensor): An encoded signal.
"""
return F.mu_law_encoding(x, self.quantization_channels)
[docs]class MuLawDecoding(torch.nn.Module):
r"""Decode mu-law encoded signal. For more info see the
`Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
This expects an input with values between 0 and quantization_channels - 1
and returns a signal scaled between -1 and 1.
Args:
quantization_channels (int, optional): Number of channels. (Default: ``256``)
"""
__constants__ = ['quantization_channels']
def __init__(self, quantization_channels: int = 256) -> None:
super(MuLawDecoding, self).__init__()
self.quantization_channels = quantization_channels
[docs] def forward(self, x_mu: Tensor) -> Tensor:
r"""
Args:
x_mu (Tensor): A mu-law encoded signal which needs to be decoded.
Returns:
Tensor: The signal decoded.
"""
return F.mu_law_decoding(x_mu, self.quantization_channels)
[docs]class Resample(torch.nn.Module):
r"""Resample a signal from one frequency to another. A resampling method can be given.
Args:
orig_freq (float, optional): The original frequency of the signal. (Default: ``16000``)
new_freq (float, optional): The desired frequency. (Default: ``16000``)
resampling_method (str, optional): The resampling method. (Default: ``'sinc_interpolation'``)
"""
def __init__(self,
orig_freq: int = 16000,
new_freq: int = 16000,
resampling_method: str = 'sinc_interpolation') -> None:
super(Resample, self).__init__()
self.orig_freq = orig_freq
self.new_freq = new_freq
self.resampling_method = resampling_method
[docs] def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
Returns:
Tensor: Output signal of dimension (..., time).
"""
if self.resampling_method == 'sinc_interpolation':
# pack batch
shape = waveform.size()
waveform = waveform.view(-1, shape[-1])
waveform = kaldi.resample_waveform(waveform, self.orig_freq, self.new_freq)
# unpack batch
waveform = waveform.view(shape[:-1] + waveform.shape[-1:])
return waveform
raise ValueError('Invalid resampling method: %s' % (self.resampling_method))
[docs]class ComplexNorm(torch.nn.Module):
r"""Compute the norm of complex tensor input.
Args:
power (float, optional): Power of the norm. (Default: to ``1.0``)
"""
__constants__ = ['power']
def __init__(self, power: float = 1.0) -> None:
super(ComplexNorm, self).__init__()
self.power = power
[docs] def forward(self, complex_tensor: Tensor) -> Tensor:
r"""
Args:
complex_tensor (Tensor): Tensor shape of `(..., complex=2)`.
Returns:
Tensor: norm of the input tensor, shape of `(..., )`.
"""
return F.complex_norm(complex_tensor, self.power)
[docs]class ComputeDeltas(torch.nn.Module):
r"""Compute delta coefficients of a tensor, usually a spectrogram.
See `torchaudio.functional.compute_deltas` for more details.
Args:
win_length (int): The window length used for computing delta. (Default: ``5``)
mode (str): Mode parameter passed to padding. (Default: ``'replicate'``)
"""
__constants__ = ['win_length']
def __init__(self, win_length: int = 5, mode: str = "replicate") -> None:
super(ComputeDeltas, self).__init__()
self.win_length = win_length
self.mode = mode
[docs] def forward(self, specgram: Tensor) -> Tensor:
r"""
Args:
specgram (Tensor): Tensor of audio of dimension (..., freq, time).
Returns:
Tensor: Tensor of deltas of dimension (..., freq, time).
"""
return F.compute_deltas(specgram, win_length=self.win_length, mode=self.mode)
[docs]class TimeStretch(torch.nn.Module):
r"""Stretch stft in time without modifying pitch for a given rate.
Args:
hop_length (int or None, optional): Length of hop between STFT windows. (Default: ``win_length // 2``)
n_freq (int, optional): number of filter banks from stft. (Default: ``201``)
fixed_rate (float or None, optional): rate to speed up or slow down by.
If None is provided, rate must be passed to the forward method. (Default: ``None``)
"""
__constants__ = ['fixed_rate']
def __init__(self,
hop_length: Optional[int] = None,
n_freq: int = 201,
fixed_rate: Optional[float] = None) -> None:
super(TimeStretch, self).__init__()
self.fixed_rate = fixed_rate
n_fft = (n_freq - 1) * 2
hop_length = hop_length if hop_length is not None else n_fft // 2
self.register_buffer('phase_advance', torch.linspace(0, math.pi * hop_length, n_freq)[..., None])
[docs] def forward(self, complex_specgrams: Tensor, overriding_rate: Optional[float] = None) -> Tensor:
r"""
Args:
complex_specgrams (Tensor): complex spectrogram (..., freq, time, complex=2).
overriding_rate (float or None, optional): speed up to apply to this batch.
If no rate is passed, use ``self.fixed_rate``. (Default: ``None``)
Returns:
Tensor: Stretched complex spectrogram of dimension (..., freq, ceil(time/rate), complex=2).
"""
assert complex_specgrams.size(-1) == 2, "complex_specgrams should be a complex tensor, shape (..., complex=2)"
if overriding_rate is None:
rate = self.fixed_rate
if rate is None:
raise ValueError("If no fixed_rate is specified"
", must pass a valid rate to the forward method.")
else:
rate = overriding_rate
if rate == 1.0:
return complex_specgrams
return F.phase_vocoder(complex_specgrams, rate, self.phase_advance)
[docs]class Fade(torch.nn.Module):
r"""Add a fade in and/or fade out to an waveform.
Args:
fade_in_len (int, optional): Length of fade-in (time frames). (Default: ``0``)
fade_out_len (int, optional): Length of fade-out (time frames). (Default: ``0``)
fade_shape (str, optional): Shape of fade. Must be one of: "quarter_sine",
"half_sine", "linear", "logarithmic", "exponential". (Default: ``"linear"``)
"""
def __init__(self,
fade_in_len: int = 0,
fade_out_len: int = 0,
fade_shape: str = "linear") -> None:
super(Fade, self).__init__()
self.fade_in_len = fade_in_len
self.fade_out_len = fade_out_len
self.fade_shape = fade_shape
[docs] def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
Returns:
Tensor: Tensor of audio of dimension (..., time).
"""
waveform_length = waveform.size()[-1]
device = waveform.device
return self._fade_in(waveform_length).to(device) * \
self._fade_out(waveform_length).to(device) * waveform
def _fade_in(self, waveform_length: int) -> Tensor:
fade = torch.linspace(0, 1, self.fade_in_len)
ones = torch.ones(waveform_length - self.fade_in_len)
if self.fade_shape == "linear":
fade = fade
if self.fade_shape == "exponential":
fade = torch.pow(2, (fade - 1)) * fade
if self.fade_shape == "logarithmic":
fade = torch.log10(.1 + fade) + 1
if self.fade_shape == "quarter_sine":
fade = torch.sin(fade * math.pi / 2)
if self.fade_shape == "half_sine":
fade = torch.sin(fade * math.pi - math.pi / 2) / 2 + 0.5
return torch.cat((fade, ones)).clamp_(0, 1)
def _fade_out(self, waveform_length: int) -> Tensor:
fade = torch.linspace(0, 1, self.fade_out_len)
ones = torch.ones(waveform_length - self.fade_out_len)
if self.fade_shape == "linear":
fade = - fade + 1
if self.fade_shape == "exponential":
fade = torch.pow(2, - fade) * (1 - fade)
if self.fade_shape == "logarithmic":
fade = torch.log10(1.1 - fade) + 1
if self.fade_shape == "quarter_sine":
fade = torch.sin(fade * math.pi / 2 + math.pi / 2)
if self.fade_shape == "half_sine":
fade = torch.sin(fade * math.pi + math.pi / 2) / 2 + 0.5
return torch.cat((ones, fade)).clamp_(0, 1)
class _AxisMasking(torch.nn.Module):
r"""Apply masking to a spectrogram.
Args:
mask_param (int): Maximum possible length of the mask.
axis (int): What dimension the mask is applied on.
iid_masks (bool): Applies iid masks to each of the examples in the batch dimension.
"""
__constants__ = ['mask_param', 'axis', 'iid_masks']
def __init__(self, mask_param: int, axis: int, iid_masks: bool) -> None:
super(_AxisMasking, self).__init__()
self.mask_param = mask_param
self.axis = axis
self.iid_masks = iid_masks
def forward(self, specgram: Tensor, mask_value: float = 0.) -> Tensor:
r"""
Args:
specgram (Tensor): Tensor of dimension (..., freq, time).
mask_value (float): Value to assign to the masked columns.
Returns:
Tensor: Masked spectrogram of dimensions (..., freq, time).
"""
# if iid_masks flag marked and specgram has a batch dimension
if self.iid_masks and specgram.dim() == 4:
return F.mask_along_axis_iid(specgram, self.mask_param, mask_value, self.axis + 1)
else:
return F.mask_along_axis(specgram, self.mask_param, mask_value, self.axis)
[docs]class FrequencyMasking(_AxisMasking):
r"""Apply masking to a spectrogram in the frequency domain.
Args:
freq_mask_param (int): maximum possible length of the mask.
Indices uniformly sampled from [0, freq_mask_param).
iid_masks (bool, optional): whether to apply the same mask to all
the examples/channels in the batch. (Default: ``False``)
"""
def __init__(self, freq_mask_param: int, iid_masks: bool = False) -> None:
super(FrequencyMasking, self).__init__(freq_mask_param, 1, iid_masks)
[docs]class TimeMasking(_AxisMasking):
r"""Apply masking to a spectrogram in the time domain.
Args:
time_mask_param (int): maximum possible length of the mask.
Indices uniformly sampled from [0, time_mask_param).
iid_masks (bool, optional): whether to apply the same mask to all
the examples/channels in the batch. (Default: ``False``)
"""
def __init__(self, time_mask_param: int, iid_masks: bool = False) -> None:
super(TimeMasking, self).__init__(time_mask_param, 2, iid_masks)
[docs]class Vol(torch.nn.Module):
r"""Add a volume to an waveform.
Args:
gain (float): Interpreted according to the given gain_type:
If `gain_type’ = ‘amplitude’, `gain’ is a positive amplitude ratio.
If `gain_type’ = ‘power’, `gain’ is a power (voltage squared).
If `gain_type’ = ‘db’, `gain’ is in decibels.
gain_type (str, optional): Type of gain. One of: ‘amplitude’, ‘power’, ‘db’ (Default: ``"amplitude"``)
"""
def __init__(self, gain: float, gain_type: str = 'amplitude'):
super(Vol, self).__init__()
self.gain = gain
self.gain_type = gain_type
if gain_type in ['amplitude', 'power'] and gain < 0:
raise ValueError("If gain_type = amplitude or power, gain must be positive.")
[docs] def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
Returns:
Tensor: Tensor of audio of dimension (..., time).
"""
if self.gain_type == "amplitude":
waveform = waveform * self.gain
if self.gain_type == "db":
waveform = F.gain(waveform, self.gain)
if self.gain_type == "power":
waveform = F.gain(waveform, 10 * math.log10(self.gain))
return torch.clamp(waveform, -1, 1)
