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Source code for torchaudio.transforms

# -*- coding: utf-8 -*-

import math
import warnings
from typing import Callable, Optional

import torch
from torch import Tensor
from torchaudio import functional as F

from .functional.functional import (
    _get_sinc_resample_kernel,
    _apply_sinc_resample_kernel,
)

__all__ = [
    'Spectrogram',
    'InverseSpectrogram',
    'GriffinLim',
    'AmplitudeToDB',
    'MelScale',
    'InverseMelScale',
    'MelSpectrogram',
    'MFCC',
    'LFCC',
    'MuLawEncoding',
    'MuLawDecoding',
    'Resample',
    'ComplexNorm',
    'TimeStretch',
    'Fade',
    'FrequencyMasking',
    'TimeMasking',
    'SlidingWindowCmn',
    'Vad',
    'SpectralCentroid',
    'Vol',
    'ComputeDeltas',
    'PitchShift',
    'RNNTLoss',
    'PSD',
    'MVDR',
]


[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``) center (bool, optional): whether to pad :attr:`waveform` on both sides so that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. (Default: ``True``) pad_mode (string, optional): controls the padding method used when :attr:`center` is ``True``. (Default: ``"reflect"``) onesided (bool, optional): controls whether to return half of results to avoid redundancy (Default: ``True``) return_complex (bool, optional): Indicates whether the resulting complex-valued Tensor should be represented with native complex dtype, such as `torch.cfloat` and `torch.cdouble`, or real dtype mimicking complex value with an extra dimension for real and imaginary parts. (See also ``torch.view_as_real``.) This argument is only effective when ``power=None``. It is ignored for cases where ``power`` is a number as in those cases, the returned tensor is power spectrogram, which is a real-valued tensor. Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True) >>> transform = torchaudio.transforms.Spectrogram(n_fft=800) >>> spectrogram = transform(waveform) """ __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, center: bool = True, pad_mode: str = "reflect", onesided: bool = True, return_complex: bool = True) -> 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 frequencies 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 self.center = center self.pad_mode = pad_mode self.onesided = onesided self.return_complex = return_complex
[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, self.center, self.pad_mode, self.onesided, self.return_complex, )
[docs]class InverseSpectrogram(torch.nn.Module): r"""Create an inverse spectrogram to recover an audio signal from a spectrogram. 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``) normalized (bool, optional): Whether the spectrogram was normalized by magnitude after stft. (Default: ``False``) wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``) center (bool, optional): whether the signal in spectrogram was padded on both sides so that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. (Default: ``True``) pad_mode (string, optional): controls the padding method used when :attr:`center` is ``True``. (Default: ``"reflect"``) onesided (bool, optional): controls whether spectrogram was used to return half of results to avoid redundancy (Default: ``True``) Example >>> batch, freq, time = 2, 257, 100 >>> length = 25344 >>> spectrogram = torch.randn(batch, freq, time, dtype=torch.cdouble) >>> transform = transforms.InverseSpectrogram(n_fft=512) >>> waveform = transform(spectrogram, length) """ __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, normalized: bool = False, wkwargs: Optional[dict] = None, center: bool = True, pad_mode: str = "reflect", onesided: bool = True) -> None: super(InverseSpectrogram, self).__init__() self.n_fft = n_fft # number of FFT bins. the returned STFT result will have n_fft // 2 + 1 # number of frequencies 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.normalized = normalized self.center = center self.pad_mode = pad_mode self.onesided = onesided
[docs] def forward(self, spectrogram: Tensor, length: Optional[int] = None) -> Tensor: r""" Args: spectrogram (Tensor): Complex tensor of audio of dimension (..., freq, time). length (int or None, optional): The output length of the waveform. Returns: Tensor: Dimension (..., time), Least squares estimation of the original signal. """ return F.inverse_spectrogram( spectrogram, length, self.pad, self.window, self.n_fft, self.hop_length, self.win_length, self.normalized, self.center, self.pad_mode, self.onesided, )
[docs]class GriffinLim(torch.nn.Module): r"""Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation. Implementation ported from *librosa* [:footcite:`brian_mcfee-proc-scipy-2015`], *A fast Griffin-Lim algorithm* [:footcite:`6701851`] and *Signal estimation from modified short-time Fourier transform* [:footcite:`1172092`]. 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``) 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``) Example >>> batch, freq, time = 2, 257, 100 >>> spectrogram = torch.randn(batch, freq, time) >>> transform = transforms.GriffinLim(n_fft=512) >>> waveform = transform(spectrogram) """ __constants__ = ['n_fft', 'n_iter', 'win_length', 'hop_length', 'power', '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., 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={} > 1 can be unstable'.format(momentum) assert momentum >= 0, 'momentum={} < 0'.format(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.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.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 or None, 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.org/doc/latest/generated/librosa.amplitude_to_db.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. See ``n_fft`` in :class:`Spectrogram`. (Default: ``201``) norm (str or None, optional): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). (Default: ``None``) mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) See also: :py:func:`torchaudio.functional.melscale_fbanks` - The function used to generate the filter banks. """ __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: int = 201, norm: Optional[str] = None, mel_scale: str = "htk") -> 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 self.norm = norm self.mel_scale = mel_scale assert f_min <= self.f_max, 'Require f_min: {} < f_max: {}'.format(f_min, self.f_max) fb = F.melscale_fbanks( n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, self.norm, self.mel_scale) 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). """ # (..., time, freq) dot (freq, n_mels) -> (..., n_mels, time) mel_specgram = torch.matmul(specgram.transpose(-1, -2), self.fb).transpose(-1, -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``) norm (str or None, optional): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). (Default: ``None``) mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) """ __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, norm: Optional[str] = None, mel_scale: str = "htk") -> 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_max: {}'.format(f_min, self.f_max) fb = F.melscale_fbanks(n_stft, self.f_min, self.f_max, self.n_mels, self.sample_rate, norm, mel_scale) 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 :py:func:`torchaudio.transforms.Spectrogram` and and :py:func:`torchaudio.transforms.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``) 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``) 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``) 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``) center (bool, optional): whether to pad :attr:`waveform` on both sides so that the :math:`t`-th frame is centered at time :math:`t \times \text{hop\_length}`. (Default: ``True``) pad_mode (string, optional): controls the padding method used when :attr:`center` is ``True``. (Default: ``"reflect"``) onesided (bool, optional): controls whether to return half of results to avoid redundancy. (Default: ``True``) norm (str or None, optional): If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). (Default: ``None``) mel_scale (str, optional): Scale to use: ``htk`` or ``slaney``. (Default: ``htk``) Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True) >>> transform = transforms.MelSpectrogram(sample_rate) >>> mel_specgram = transform(waveform) # (channel, n_mels, time) See also: :py:func:`torchaudio.functional.melscale_fbanks` - The function used to generate the filter banks. """ __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, power: float = 2., normalized: bool = False, wkwargs: Optional[dict] = None, center: bool = True, pad_mode: str = "reflect", onesided: bool = True, norm: Optional[str] = None, mel_scale: str = "htk") -> 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.power = power self.normalized = normalized 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=self.power, normalized=self.normalized, wkwargs=wkwargs, center=center, pad_mode=pad_mode, onesided=onesided) self.mel_scale = MelScale( self.n_mels, self.sample_rate, self.f_min, self.f_max, self.n_fft // 2 + 1, norm, mel_scale )
[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``) See also: :py:func:`torchaudio.functional.melscale_fbanks` - The function used to generate the filter banks. """ __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) melkwargs = melkwargs or {} self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate, **melkwargs) 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). """ 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) # (..., time, n_mels) dot (n_mels, n_mfcc) -> (..., n_nfcc, time) mfcc = torch.matmul(mel_specgram.transpose(-1, -2), self.dct_mat).transpose(-1, -2) return mfcc
[docs]class LFCC(torch.nn.Module): r"""Create the linear-frequency cepstrum coefficients from an audio signal. By default, this calculates the LFCC on the DB-scaled linear filtered 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_filter (int, optional): Number of linear filters to apply. (Default: ``128``) n_lfcc (int, optional): Number of lfc coefficients to retain. (Default: ``40``) f_min (float, optional): Minimum frequency. (Default: ``0.``) f_max (float or None, optional): Maximum frequency. (Default: ``None``) dct_type (int, optional): type of DCT (discrete cosine transform) to use. (Default: ``2``) norm (str, optional): norm to use. (Default: ``'ortho'``) log_lf (bool, optional): whether to use log-lf spectrograms instead of db-scaled. (Default: ``False``) speckwargs (dict or None, optional): arguments for Spectrogram. (Default: ``None``) See also: :py:func:`torchaudio.functional.linear_fbanks` - The function used to generate the filter banks. """ __constants__ = ['sample_rate', 'n_filter', 'n_lfcc', 'dct_type', 'top_db', 'log_lf'] def __init__(self, sample_rate: int = 16000, n_filter: int = 128, f_min: float = 0., f_max: Optional[float] = None, n_lfcc: int = 40, dct_type: int = 2, norm: str = 'ortho', log_lf: bool = False, speckwargs: Optional[dict] = None) -> None: super(LFCC, 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.f_min = f_min self.f_max = f_max if f_max is not None else float(sample_rate // 2) self.n_filter = n_filter self.n_lfcc = n_lfcc self.dct_type = dct_type self.norm = norm self.top_db = 80.0 self.amplitude_to_DB = AmplitudeToDB('power', self.top_db) speckwargs = speckwargs or {} self.Spectrogram = Spectrogram(**speckwargs) if self.n_lfcc > self.Spectrogram.n_fft: raise ValueError('Cannot select more LFCC coefficients than # fft bins') filter_mat = F.linear_fbanks( n_freqs=self.Spectrogram.n_fft // 2 + 1, f_min=self.f_min, f_max=self.f_max, n_filter=self.n_filter, sample_rate=self.sample_rate, ) self.register_buffer("filter_mat", filter_mat) dct_mat = F.create_dct(self.n_lfcc, self.n_filter, self.norm) self.register_buffer('dct_mat', dct_mat) self.log_lf = log_lf
[docs] def forward(self, waveform: Tensor) -> Tensor: r""" Args: waveform (Tensor): Tensor of audio of dimension (..., time). Returns: Tensor: Linear Frequency Cepstral Coefficients of size (..., ``n_lfcc``, time). """ specgram = self.Spectrogram(waveform) # (..., time, freq) dot (freq, n_filter) -> (..., n_filter, time) specgram = torch.matmul(specgram.transpose(-1, -2), self.filter_mat).transpose(-1, -2) if self.log_lf: log_offset = 1e-6 specgram = torch.log(specgram + log_offset) else: specgram = self.amplitude_to_DB(specgram) # (..., time, n_filter) dot (n_filter, n_lfcc) -> (..., n_lfcc, time) lfcc = torch.matmul(specgram.transpose(-1, -2), self.dct_mat).transpose(-1, -2) return lfcc
[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``) Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True) >>> transform = torchaudio.transforms.MuLawEncoding(quantization_channels=512) >>> mulawtrans = transform(waveform) """ __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: 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``) Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True) >>> transform = torchaudio.transforms.MuLawDecoding(quantization_channels=512) >>> mulawtrans = transform(waveform) """ __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. Note: If resampling on waveforms of higher precision than float32, there may be a small loss of precision because the kernel is cached once as float32. If high precision resampling is important for your application, the functional form will retain higher precision, but run slower because it does not cache the kernel. Alternatively, you could rewrite a transform that caches a higher precision kernel. Args: orig_freq (int, optional): The original frequency of the signal. (Default: ``16000``) new_freq (int, optional): The desired frequency. (Default: ``16000``) resampling_method (str, optional): The resampling method to use. Options: [``sinc_interpolation``, ``kaiser_window``] (Default: ``'sinc_interpolation'``) lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper but less efficient. (Default: ``6``) rolloff (float, optional): The roll-off frequency of the filter, as a fraction of the Nyquist. Lower values reduce anti-aliasing, but also reduce some of the highest frequencies. (Default: ``0.99``) beta (float or None, optional): The shape parameter used for kaiser window. dtype (torch.device, optional): Determnines the precision that resampling kernel is pre-computed and cached. If not provided, kernel is computed with ``torch.float64`` then cached as ``torch.float32``. If you need higher precision, provide ``torch.float64``, and the pre-computed kernel is computed and cached as ``torch.float64``. If you use resample with lower precision, then instead of providing this providing this argument, please use ``Resample.to(dtype)``, so that the kernel generation is still carried out on ``torch.float64``. Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True) >>> transform = transforms.Resample(sample_rate, sample_rate/10) >>> waveform = transform(waveform) """ def __init__( self, orig_freq: int = 16000, new_freq: int = 16000, resampling_method: str = 'sinc_interpolation', lowpass_filter_width: int = 6, rolloff: float = 0.99, beta: Optional[float] = None, *, dtype: Optional[torch.dtype] = None, ) -> None: super().__init__() self.orig_freq = orig_freq self.new_freq = new_freq self.gcd = math.gcd(int(self.orig_freq), int(self.new_freq)) self.resampling_method = resampling_method self.lowpass_filter_width = lowpass_filter_width self.rolloff = rolloff self.beta = beta if self.orig_freq != self.new_freq: kernel, self.width = _get_sinc_resample_kernel( self.orig_freq, self.new_freq, self.gcd, self.lowpass_filter_width, self.rolloff, self.resampling_method, beta, dtype=dtype) self.register_buffer('kernel', kernel)
[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.orig_freq == self.new_freq: return waveform return _apply_sinc_resample_kernel( waveform, self.orig_freq, self.new_freq, self.gcd, self.kernel, self.width)
[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``) Example >>> complex_tensor = ... # Tensor shape of (…, complex=2) >>> transform = transforms.ComplexNorm(power=2) >>> complex_norm = transform(complex_tensor) """ __constants__ = ['power'] def __init__(self, power: float = 1.0) -> None: warnings.warn( 'torchaudio.transforms.ComplexNorm has been deprecated ' 'and will be removed from future release.' 'Please convert the input Tensor to complex type with `torch.view_as_complex` then ' 'use `torch.abs` and `torch.angle`. ' 'Please refer to https://github.com/pytorch/audio/issues/1337 ' "for more details about torchaudio's plan to migrate to native complex type." ) 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, optional): The window length used for computing delta. (Default: ``5``) mode (str, optional): 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. Proposed in *SpecAugment* [:footcite:`specaugment`]. 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``) Example >>> spectrogram = torchaudio.transforms.Spectrogram() >>> stretch = torchaudio.transforms.TimeStretch() >>> >>> original = spectrogram(waveform) >>> streched_1_2 = stretch(original, 1.2) >>> streched_0_9 = stretch(original, 0.9) .. image:: https://download.pytorch.org/torchaudio/doc-assets/specaugment_time_stretch_1.png :width: 600 :alt: Spectrogram streched by 1.2 .. image:: https://download.pytorch.org/torchaudio/doc-assets/specaugment_time_stretch_2.png :width: 600 :alt: The original spectrogram .. image:: https://download.pytorch.org/torchaudio/doc-assets/specaugment_time_stretch_3.png :width: 600 :alt: Spectrogram streched by 0.9 """ __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): Either a real tensor of dimension of `(..., freq, num_frame, complex=2)` or a tensor of dimension `(..., freq, num_frame)` with complex dtype. 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 spectrogram. The resulting tensor is of the same dtype as the input spectrogram, but the number of frames is changed to ``ceil(num_frame / rate)``. """ if overriding_rate is None: if self.fixed_rate is None: raise ValueError( "If no fixed_rate is specified, must pass a valid rate to the forward method.") rate = self.fixed_rate else: rate = overriding_rate 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"``) Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True) >>> transform = transforms.Fade(fade_in_len=sample_rate, fade_out_len=2 * sample_rate, fade_shape='linear') >>> faded_waveform = transform(waveform) """ 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, device) * self._fade_out(waveform_length, device) * waveform )
def _fade_in(self, waveform_length: int, device: torch.device) -> Tensor: fade = torch.linspace(0, 1, self.fade_in_len, device=device) ones = torch.ones(waveform_length - self.fade_in_len, device=device) 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, device: torch.device) -> Tensor: fade = torch.linspace(0, 1, self.fade_out_len, device=device) ones = torch.ones(waveform_length - self.fade_out_len, device=device) 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. This option is applicable only when the input tensor is 4D. """ __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. Proposed in *SpecAugment* [:footcite:`specaugment`]. 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 different masks to each example/channel in the batch. (Default: ``False``) This option is applicable only when the input tensor is 4D. Example >>> spectrogram = torchaudio.transforms.Spectrogram() >>> masking = torchaudio.transforms.FrequencyMasking(freq_mask_param=80) >>> >>> original = spectrogram(waveform) >>> masked = masking(original) .. image:: https://download.pytorch.org/torchaudio/doc-assets/specaugment_freq_masking1.png :alt: The original spectrogram .. image:: https://download.pytorch.org/torchaudio/doc-assets/specaugment_freq_masking2.png :alt: The spectrogram masked along frequency axis """ 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. Proposed in *SpecAugment* [:footcite:`specaugment`]. 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 different masks to each example/channel in the batch. (Default: ``False``) This option is applicable only when the input tensor is 4D. Example >>> spectrogram = torchaudio.transforms.Spectrogram() >>> masking = torchaudio.transforms.TimeMasking(time_mask_param=80) >>> >>> original = spectrogram(waveform) >>> masked = masking(original) .. image:: https://download.pytorch.org/torchaudio/doc-assets/specaugment_time_masking1.png :alt: The original spectrogram .. image:: https://download.pytorch.org/torchaudio/doc-assets/specaugment_time_masking2.png :alt: The spectrogram masked along time axis """ 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)
[docs]class SlidingWindowCmn(torch.nn.Module): r""" Apply sliding-window cepstral mean (and optionally variance) normalization per utterance. Args: cmn_window (int, optional): Window in frames for running average CMN computation (int, default = 600) min_cmn_window (int, optional): Minimum CMN window used at start of decoding (adds latency only at start). Only applicable if center == false, ignored if center==true (int, default = 100) center (bool, optional): If true, use a window centered on the current frame (to the extent possible, modulo end effects). If false, window is to the left. (bool, default = false) norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false) """ def __init__(self, cmn_window: int = 600, min_cmn_window: int = 100, center: bool = False, norm_vars: bool = False) -> None: super().__init__() self.cmn_window = cmn_window self.min_cmn_window = min_cmn_window self.center = center self.norm_vars = norm_vars
[docs] def forward(self, specgram: Tensor) -> Tensor: r""" Args: specgram (Tensor): Tensor of spectrogram of dimension `(..., time, freq)`. Returns: Tensor: Tensor of spectrogram of dimension `(..., time, freq)`. """ cmn_specgram = F.sliding_window_cmn( specgram, self.cmn_window, self.min_cmn_window, self.center, self.norm_vars) return cmn_specgram
[docs]class Vad(torch.nn.Module): r"""Voice Activity Detector. Similar to SoX implementation. Attempts to trim silence and quiet background sounds from the ends of recordings of speech. The algorithm currently uses a simple cepstral power measurement to detect voice, so may be fooled by other things, especially music. The effect can trim only from the front of the audio, so in order to trim from the back, the reverse effect must also be used. Args: sample_rate (int): Sample rate of audio signal. trigger_level (float, optional): The measurement level used to trigger activity detection. This may need to be cahnged depending on the noise level, signal level, and other characteristics of the input audio. (Default: 7.0) trigger_time (float, optional): The time constant (in seconds) used to help ignore short bursts of sound. (Default: 0.25) search_time (float, optional): The amount of audio (in seconds) to search for quieter/shorter bursts of audio to include prior to the detected trigger point. (Default: 1.0) allowed_gap (float, optional): The allowed gap (in seconds) between quiteter/shorter bursts of audio to include prior to the detected trigger point. (Default: 0.25) pre_trigger_time (float, optional): The amount of audio (in seconds) to preserve before the trigger point and any found quieter/shorter bursts. (Default: 0.0) boot_time (float, optional) The algorithm (internally) uses adaptive noise estimation/reduction in order to detect the start of the wanted audio. This option sets the time for the initial noise estimate. (Default: 0.35) noise_up_time (float, optional) Time constant used by the adaptive noise estimator for when the noise level is increasing. (Default: 0.1) noise_down_time (float, optional) Time constant used by the adaptive noise estimator for when the noise level is decreasing. (Default: 0.01) noise_reduction_amount (float, optional) Amount of noise reduction to use in the detection algorithm (e.g. 0, 0.5, ...). (Default: 1.35) measure_freq (float, optional) Frequency of the algorithm’s processing/measurements. (Default: 20.0) measure_duration: (float or None, optional) Measurement duration. (Default: Twice the measurement period; i.e. with overlap.) measure_smooth_time (float, optional) Time constant used to smooth spectral measurements. (Default: 0.4) hp_filter_freq (float, optional) "Brick-wall" frequency of high-pass filter applied at the input to the detector algorithm. (Default: 50.0) lp_filter_freq (float, optional) "Brick-wall" frequency of low-pass filter applied at the input to the detector algorithm. (Default: 6000.0) hp_lifter_freq (float, optional) "Brick-wall" frequency of high-pass lifter used in the detector algorithm. (Default: 150.0) lp_lifter_freq (float, optional) "Brick-wall" frequency of low-pass lifter used in the detector algorithm. (Default: 2000.0) Reference: - http://sox.sourceforge.net/sox.html """ def __init__(self, sample_rate: int, trigger_level: float = 7.0, trigger_time: float = 0.25, search_time: float = 1.0, allowed_gap: float = 0.25, pre_trigger_time: float = 0.0, boot_time: float = .35, noise_up_time: float = .1, noise_down_time: float = .01, noise_reduction_amount: float = 1.35, measure_freq: float = 20.0, measure_duration: Optional[float] = None, measure_smooth_time: float = .4, hp_filter_freq: float = 50., lp_filter_freq: float = 6000., hp_lifter_freq: float = 150., lp_lifter_freq: float = 2000.) -> None: super().__init__() self.sample_rate = sample_rate self.trigger_level = trigger_level self.trigger_time = trigger_time self.search_time = search_time self.allowed_gap = allowed_gap self.pre_trigger_time = pre_trigger_time self.boot_time = boot_time self.noise_up_time = noise_up_time self.noise_down_time = noise_down_time self.noise_reduction_amount = noise_reduction_amount self.measure_freq = measure_freq self.measure_duration = measure_duration self.measure_smooth_time = measure_smooth_time self.hp_filter_freq = hp_filter_freq self.lp_filter_freq = lp_filter_freq self.hp_lifter_freq = hp_lifter_freq self.lp_lifter_freq = lp_lifter_freq
[docs] def forward(self, waveform: Tensor) -> Tensor: r""" Args: waveform (Tensor): Tensor of audio of dimension `(channels, time)` or `(time)` Tensor of shape `(channels, time)` is treated as a multi-channel recording of the same event and the resulting output will be trimmed to the earliest voice activity in any channel. """ return F.vad( waveform=waveform, sample_rate=self.sample_rate, trigger_level=self.trigger_level, trigger_time=self.trigger_time, search_time=self.search_time, allowed_gap=self.allowed_gap, pre_trigger_time=self.pre_trigger_time, boot_time=self.boot_time, noise_up_time=self.noise_up_time, noise_down_time=self.noise_down_time, noise_reduction_amount=self.noise_reduction_amount, measure_freq=self.measure_freq, measure_duration=self.measure_duration, measure_smooth_time=self.measure_smooth_time, hp_filter_freq=self.hp_filter_freq, lp_filter_freq=self.lp_filter_freq, hp_lifter_freq=self.hp_lifter_freq, lp_lifter_freq=self.lp_lifter_freq, )
[docs]class SpectralCentroid(torch.nn.Module): r"""Compute the spectral centroid for each channel along the time axis. The spectral centroid is defined as the weighted average of the frequency values, weighted by their magnitude. Args: sample_rate (int): Sample rate of audio signal. 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``) wkwargs (dict or None, optional): Arguments for window function. (Default: ``None``) Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True) >>> transform = transforms.SpectralCentroid(sample_rate) >>> spectral_centroid = transform(waveform) # (channel, time) """ __constants__ = ['sample_rate', 'n_fft', 'win_length', 'hop_length', 'pad'] def __init__(self, sample_rate: int, n_fft: int = 400, win_length: Optional[int] = None, hop_length: Optional[int] = None, pad: int = 0, window_fn: Callable[..., Tensor] = torch.hann_window, wkwargs: Optional[dict] = None) -> None: super(SpectralCentroid, 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 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
[docs] def forward(self, waveform: Tensor) -> Tensor: r""" Args: waveform (Tensor): Tensor of audio of dimension `(..., time)`. Returns: Tensor: Spectral Centroid of size `(..., time)`. """ return F.spectral_centroid(waveform, self.sample_rate, self.pad, self.window, self.n_fft, self.hop_length, self.win_length)
[docs]class PitchShift(torch.nn.Module): r"""Shift the pitch of a waveform by ``n_steps`` steps. Args: waveform (Tensor): The input waveform of shape `(..., time)`. sample_rate (int): Sample rate of `waveform`. n_steps (int): The (fractional) steps to shift `waveform`. bins_per_octave (int, optional): The number of steps per octave (Default : ``12``). n_fft (int, optional): Size of FFT, creates ``n_fft // 2 + 1`` bins (Default: ``512``). win_length (int or None, optional): Window size. If None, then ``n_fft`` is used. (Default: ``None``). hop_length (int or None, optional): Length of hop between STFT windows. If None, then ``win_length // 4`` is used (Default: ``None``). window (Tensor or None, optional): Window tensor that is applied/multiplied to each frame/window. If None, then ``torch.hann_window(win_length)`` is used (Default: ``None``). Example >>> waveform, sample_rate = torchaudio.load('test.wav', normalize=True) >>> transform = transforms.PitchShift(sample_rate, 4) >>> waveform_shift = transform(waveform) # (channel, time) """ __constants__ = ['sample_rate', 'n_steps', 'bins_per_octave', 'n_fft', 'win_length', 'hop_length'] def __init__(self, sample_rate: int, n_steps: int, bins_per_octave: int = 12, n_fft: int = 512, win_length: Optional[int] = None, hop_length: Optional[int] = None, window_fn: Callable[..., Tensor] = torch.hann_window, wkwargs: Optional[dict] = None) -> None: super(PitchShift, self).__init__() self.n_steps = n_steps self.bins_per_octave = bins_per_octave 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 // 4 window = window_fn(self.win_length) if wkwargs is None else window_fn(self.win_length, **wkwargs) self.register_buffer('window', window)
[docs] def forward(self, waveform: Tensor) -> Tensor: r""" Args: waveform (Tensor): Tensor of audio of dimension `(..., time)`. Returns: Tensor: The pitch-shifted audio of shape `(..., time)`. """ return F.pitch_shift(waveform, self.sample_rate, self.n_steps, self.bins_per_octave, self.n_fft, self.win_length, self.hop_length, self.window)
[docs]class RNNTLoss(torch.nn.Module): """Compute the RNN Transducer loss from *Sequence Transduction with Recurrent Neural Networks* [:footcite:`graves2012sequence`]. The RNN Transducer loss extends the CTC loss by defining a distribution over output sequences of all lengths, and by jointly modelling both input-output and output-output dependencies. Args: blank (int, optional): blank label (Default: ``-1``) clamp (float, optional): clamp for gradients (Default: ``-1``) reduction (string, optional): Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. (Default: ``'mean'``) Example >>> # Hypothetical values >>> logits = torch.tensor([[[[0.1, 0.6, 0.1, 0.1, 0.1], >>> [0.1, 0.1, 0.6, 0.1, 0.1], >>> [0.1, 0.1, 0.2, 0.8, 0.1]], >>> [[0.1, 0.6, 0.1, 0.1, 0.1], >>> [0.1, 0.1, 0.2, 0.1, 0.1], >>> [0.7, 0.1, 0.2, 0.1, 0.1]]]], >>> dtype=torch.float32, >>> requires_grad=True) >>> targets = torch.tensor([[1, 2]], dtype=torch.int) >>> logit_lengths = torch.tensor([2], dtype=torch.int) >>> target_lengths = torch.tensor([2], dtype=torch.int) >>> transform = transforms.RNNTLoss(blank=0) >>> loss = transform(logits, targets, logit_lengths, target_lengths) >>> loss.backward() """ def __init__( self, blank: int = -1, clamp: float = -1., reduction: str = "mean", ): super().__init__() self.blank = blank self.clamp = clamp self.reduction = reduction
[docs] def forward( self, logits: Tensor, targets: Tensor, logit_lengths: Tensor, target_lengths: Tensor, ): """ Args: logits (Tensor): Tensor of dimension `(batch, max seq length, max target length + 1, class)` containing output from joiner targets (Tensor): Tensor of dimension `(batch, max target length)` containing targets with zero padded logit_lengths (Tensor): Tensor of dimension `(batch)` containing lengths of each sequence from encoder target_lengths (Tensor): Tensor of dimension `(batch)` containing lengths of targets for each sequence Returns: Tensor: Loss with the reduction option applied. If ``reduction`` is ``'none'``, then size (batch), otherwise scalar. """ return F.rnnt_loss( logits, targets, logit_lengths, target_lengths, self.blank, self.clamp, self.reduction )
def _get_mat_trace(input: torch.Tensor, dim1: int = -1, dim2: int = -2) -> torch.Tensor: r"""Compute the trace of a Tensor along ``dim1`` and ``dim2`` dimensions. Args: input (torch.Tensor): Tensor of dimension `(..., channel, channel)` dim1 (int, optional): the first dimension of the diagonal matrix (Default: -1) dim2 (int, optional): the second dimension of the diagonal matrix (Default: -2) Returns: torch.Tensor: trace of the input Tensor """ assert input.ndim >= 2, "The dimension of the tensor must be at least 2." assert input.shape[dim1] == input.shape[dim2],\ "The size of ``dim1`` and ``dim2`` must be the same." input = torch.diagonal(input, 0, dim1=dim1, dim2=dim2) return input.sum(dim=-1)
[docs]class PSD(torch.nn.Module): r"""Compute cross-channel power spectral density (PSD) matrix. Args: multi_mask (bool, optional): whether to use multi-channel Time-Frequency masks. (Default: ``False``) normalize (bool, optional): whether normalize the mask along the time dimension. eps (float, optional): a value added to the denominator in mask normalization. (Default: 1e-15) """ def __init__(self, multi_mask: bool = False, normalize: bool = True, eps: float = 1e-15): super().__init__() self.multi_mask = multi_mask self.normalize = normalize self.eps = eps
[docs] def forward(self, specgram: torch.Tensor, mask: Optional[torch.Tensor] = None): """ Args: specgram (torch.Tensor): multi-channel complex-valued STFT matrix. Tensor of dimension `(..., channel, freq, time)` mask (torch.Tensor or None, optional): Time-Frequency mask for normalization. Tensor of dimension `(..., freq, time)` if multi_mask is ``False`` or of dimension `(..., channel, freq, time)` if multi_mask is ``True`` Returns: Tensor: PSD matrix of the input STFT matrix. Tensor of dimension `(..., freq, channel, channel)` """ # outer product: # (..., ch_1, freq, time) x (..., ch_2, freq, time) -> (..., time, ch_1, ch_2) psd = torch.einsum("...cft,...eft->...ftce", [specgram, specgram.conj()]) if mask is not None: if self.multi_mask: # Averaging mask along channel dimension mask = mask.mean(dim=-3) # (..., freq, time) # Normalized mask along time dimension: if self.normalize: mask = mask / (mask.sum(dim=-1, keepdim=True) + self.eps) psd = psd * mask.unsqueeze(-1).unsqueeze(-1) psd = psd.sum(dim=-3) return psd
[docs]class MVDR(torch.nn.Module): """Minimum Variance Distortionless Response (MVDR) module that performs MVDR beamforming with Time-Frequency masks. Based on https://github.com/espnet/espnet/blob/master/espnet2/enh/layers/beamformer.py We provide three solutions of MVDR beamforming. One is based on *reference channel selection* [:footcite:`souden2009optimal`] (``solution=ref_channel``). .. math:: \\textbf{w}_{\\text{MVDR}}(f) =\ \\frac{{{\\bf{\\Phi}_{\\textbf{NN}}^{-1}}(f){\\bf{\\Phi}_{\\textbf{SS}}}}(f)}\ {\\text{Trace}({{{\\bf{\\Phi}_{\\textbf{NN}}^{-1}}(f) \\bf{\\Phi}_{\\textbf{SS}}}(f))}}\\bm{u} where :math:`\\bf{\\Phi}_{\\textbf{SS}}` and :math:`\\bf{\\Phi}_{\\textbf{NN}}` are the covariance\ matrices of speech and noise, respectively. :math:`\\bf{u}` is an one-hot vector to determine the\ reference channel. The other two solutions are based on the steering vector (``solution=stv_evd`` or ``solution=stv_power``). .. math:: \\textbf{w}_{\\text{MVDR}}(f) =\ \\frac{{{\\bf{\\Phi}_{\\textbf{NN}}^{-1}}(f){\\bm{v}}(f)}}\ {{\\bm{v}^{\\mathsf{H}}}(f){\\bf{\\Phi}_{\\textbf{NN}}^{-1}}(f){\\bm{v}}(f)} where :math:`\\bm{v}` is the acoustic transfer function or the steering vector.\ :math:`.^{\\mathsf{H}}` denotes the Hermitian Conjugate operation. We apply either *eigenvalue decomposition* [:footcite:`higuchi2016robust`] or the *power method* [:footcite:`mises1929praktische`] to get the steering vector from the PSD matrix of speech. After estimating the beamforming weight, the enhanced Short-time Fourier Transform (STFT) is obtained by .. math:: \\hat{\\bf{S}} = {\\bf{w}^\\mathsf{H}}{\\bf{Y}}, {\\bf{w}} \\in \\mathbb{C}^{M \\times F} where :math:`\\bf{Y}` and :math:`\\hat{\\bf{S}}` are the STFT of the multi-channel noisy speech and\ the single-channel enhanced speech, respectively. For online streaming audio, we provide a *recursive method* [:footcite:`higuchi2017online`] to update the PSD matrices of speech and noise, respectively. Args: ref_channel (int, optional): the reference channel for beamforming. (Default: ``0``) solution (str, optional): the solution to get MVDR weight. Options: [``ref_channel``, ``stv_evd``, ``stv_power``]. (Default: ``ref_channel``) multi_mask (bool, optional): whether to use multi-channel Time-Frequency masks. (Default: ``False``) diag_loading (bool, optional): whether apply diagonal loading on the psd matrix of noise. (Default: ``True``) diag_eps (float, optional): the coefficient multipied to the identity matrix for diagonal loading. (Default: 1e-7) online (bool, optional): whether to update the mvdr vector based on the previous psd matrices. (Default: ``False``) Note: The MVDR Module requires the input STFT to be double precision (``torch.complex128`` or ``torch.cdouble``), to improve the numerical stability. You can downgrade the precision to ``torch.float`` after generating the enhanced waveform for ASR joint training. Note: If you use ``stv_evd`` solution, the gradient of the same input may not be identical if the eigenvalues of the PSD matrix are not distinct (i.e. some eigenvalues are close or identical). """ def __init__( self, ref_channel: int = 0, solution: str = "ref_channel", multi_mask: bool = False, diag_loading: bool = True, diag_eps: float = 1e-7, online: bool = False, ): super().__init__() assert solution in ["ref_channel", "stv_evd", "stv_power"],\ "Unknown solution provided. Must be one of [``ref_channel``, ``stv_evd``, ``stv_power``]." self.ref_channel = ref_channel self.solution = solution self.multi_mask = multi_mask self.diag_loading = diag_loading self.diag_eps = diag_eps self.online = online self.psd = PSD(multi_mask) psd_s: torch.Tensor = torch.zeros(1) psd_n: torch.Tensor = torch.zeros(1) mask_sum_s: torch.Tensor = torch.zeros(1) mask_sum_n: torch.Tensor = torch.zeros(1) self.register_buffer('psd_s', psd_s) self.register_buffer('psd_n', psd_n) self.register_buffer('mask_sum_s', mask_sum_s) self.register_buffer('mask_sum_n', mask_sum_n) def _get_updated_mvdr_vector( self, psd_s: torch.Tensor, psd_n: torch.Tensor, mask_s: torch.Tensor, mask_n: torch.Tensor, reference_vector: torch.Tensor, solution: str = 'ref_channel', diagonal_loading: bool = True, diag_eps: float = 1e-7, eps: float = 1e-8, ) -> torch.Tensor: r"""Recursively update the MVDR beamforming vector. Args: psd_s (torch.Tensor): psd matrix of target speech psd_n (torch.Tensor): psd matrix of noise mask_s (torch.Tensor): T-F mask of target speech mask_n (torch.Tensor): T-F mask of noise reference_vector (torch.Tensor): one-hot reference channel matrix solution (str, optional): the solution to estimate the beamforming weight (Default: ``ref_channel``) diagonal_loading (bool, optional): whether to apply diagonal loading to psd_n (Default: ``True``) diag_eps (float, optional): The coefficient multipied to the identity matrix for diagonal loading (Default: 1e-7) eps (float, optional): a value added to the denominator in mask normalization. (Default: 1e-8) Returns: Tensor: the mvdr beamforming weight matrix """ if self.multi_mask: # Averaging mask along channel dimension mask_s = mask_s.mean(dim=-3) # (..., freq, time) mask_n = mask_n.mean(dim=-3) # (..., freq, time) if self.psd_s.ndim == 1: self.psd_s = psd_s self.psd_n = psd_n self.mask_sum_s = mask_s.sum(dim=-1) self.mask_sum_n = mask_n.sum(dim=-1) return self._get_mvdr_vector(psd_s, psd_n, reference_vector, solution, diagonal_loading, diag_eps, eps) else: psd_s = self._get_updated_psd_speech(psd_s, mask_s) psd_n = self._get_updated_psd_noise(psd_n, mask_n) self.psd_s = psd_s self.psd_n = psd_n self.mask_sum_s = self.mask_sum_s + mask_s.sum(dim=-1) self.mask_sum_n = self.mask_sum_n + mask_n.sum(dim=-1) return self._get_mvdr_vector(psd_s, psd_n, reference_vector, solution, diagonal_loading, diag_eps, eps) def _get_updated_psd_speech(self, psd_s: torch.Tensor, mask_s: torch.Tensor) -> torch.Tensor: r"""Update psd of speech recursively. Args: psd_s (torch.Tensor): psd matrix of target speech mask_s (torch.Tensor): T-F mask of target speech Returns: torch.Tensor: the updated psd of speech """ numerator = self.mask_sum_s / (self.mask_sum_s + mask_s.sum(dim=-1)) denominator = 1 / (self.mask_sum_s + mask_s.sum(dim=-1)) psd_s = self.psd_s * numerator[..., None, None] + psd_s * denominator[..., None, None] return psd_s def _get_updated_psd_noise(self, psd_n: torch.Tensor, mask_n: torch.Tensor) -> torch.Tensor: r"""Update psd of noise recursively. Args: psd_n (torch.Tensor): psd matrix of target noise mask_n (torch.Tensor): T-F mask of target noise Returns: torch.Tensor: the updated psd of noise """ numerator = self.mask_sum_n / (self.mask_sum_n + mask_n.sum(dim=-1)) denominator = 1 / (self.mask_sum_n + mask_n.sum(dim=-1)) psd_n = self.psd_n * numerator[..., None, None] + psd_n * denominator[..., None, None] return psd_n def _get_mvdr_vector( self, psd_s: torch.Tensor, psd_n: torch.Tensor, reference_vector: torch.Tensor, solution: str = 'ref_channel', diagonal_loading: bool = True, diag_eps: float = 1e-7, eps: float = 1e-8, ) -> torch.Tensor: r"""Compute beamforming vector by the reference channel selection method. Args: psd_s (torch.Tensor): psd matrix of target speech psd_n (torch.Tensor): psd matrix of noise reference_vector (torch.Tensor): one-hot reference channel matrix solution (str, optional): the solution to estimate the beamforming weight (Default: ``ref_channel``) diagonal_loading (bool, optional): whether to apply diagonal loading to psd_n (Default: ``True``) diag_eps (float, optional): The coefficient multipied to the identity matrix for diagonal loading (Default: 1e-7) eps (float, optional): a value added to the denominator in mask normalization. Default: 1e-8 Returns: torch.Tensor: the mvdr beamforming weight matrix """ if diagonal_loading: psd_n = self._tik_reg(psd_n, reg=diag_eps, eps=eps) if solution == "ref_channel": numerator = torch.linalg.solve(psd_n, psd_s) # psd_n.inv() @ psd_s # ws: (..., C, C) / (...,) -> (..., C, C) ws = numerator / (_get_mat_trace(numerator)[..., None, None] + eps) # h: (..., F, C_1, C_2) x (..., C_2) -> (..., F, C_1) beamform_vector = torch.einsum("...fec,...c->...fe", [ws, reference_vector]) else: if solution == "stv_evd": stv = self._get_steering_vector_evd(psd_s) else: stv = self._get_steering_vector_power(psd_s, psd_n, reference_vector) # numerator = psd_n.inv() @ stv numerator = torch.linalg.solve(psd_n, stv).squeeze(-1) # (..., freq, channel) # denominator = stv^H @ psd_n.inv() @ stv denominator = torch.einsum("...d,...d->...", [stv.conj().squeeze(-1), numerator]) # normalzie the numerator scale = stv.squeeze(-1)[..., self.ref_channel, None].conj() beamform_vector = numerator * scale / (denominator.real.unsqueeze(-1) + eps) return beamform_vector def _get_steering_vector_evd(self, psd_s: torch.Tensor) -> torch.Tensor: r"""Estimate the steering vector by eigenvalue decomposition. Args: psd_s (torch.tensor): covariance matrix of speech Tensor of dimension `(..., freq, channel, channel)` Returns: torch.Tensor: the enhanced STFT Tensor of dimension `(..., freq, channel, 1)` """ w, v = torch.linalg.eig(psd_s) # (..., freq, channel, channel) _, indices = torch.max(w.abs(), dim=-1, keepdim=True) indices = indices.unsqueeze(-1) stv = v.gather(-1, indices.expand(psd_s.shape[:-1] + (1,))) # (..., freq, channel, 1) return stv def _get_steering_vector_power( self, psd_s: torch.Tensor, psd_n: torch.Tensor, reference_vector: torch.Tensor ) -> torch.Tensor: r"""Estimate the steering vector by the power method. Args: psd_s (torch.tensor): covariance matrix of speech Tensor of dimension `(..., freq, channel, channel)` psd_n (torch.Tensor): covariance matrix of noise Tensor of dimension `(..., freq, channel, channel)` reference_vector (torch.Tensor): one-hot reference channel matrix Returns: torch.Tensor: the enhanced STFT Tensor of dimension `(..., freq, channel, 1)` """ phi = torch.linalg.solve(psd_n, psd_s) # psd_n.inv() @ psd_s stv = torch.einsum("...fec,...c->...fe", [phi, reference_vector]) stv = stv.unsqueeze(-1) stv = torch.matmul(phi, stv) stv = torch.matmul(psd_s, stv) return stv def _apply_beamforming_vector( self, specgram: torch.Tensor, beamform_vector: torch.Tensor ) -> torch.Tensor: r"""Apply the beamforming weight to the noisy STFT Args: specgram (torch.tensor): multi-channel noisy STFT Tensor of dimension `(..., channel, freq, time)` beamform_vector (torch.Tensor): beamforming weight matrix Tensor of dimension `(..., freq, channel)` Returns: torch.Tensor: the enhanced STFT Tensor of dimension `(..., freq, time)` """ # (..., channel) x (..., channel, freq, time) -> (..., freq, time) specgram_enhanced = torch.einsum("...fc,...cft->...ft", [beamform_vector.conj(), specgram]) return specgram_enhanced def _tik_reg( self, mat: torch.Tensor, reg: float = 1e-7, eps: float = 1e-8 ) -> torch.Tensor: """Perform Tikhonov regularization (only modifying real part). Args: mat (torch.Tensor): input matrix (..., channel, channel) reg (float, optional): regularization factor (Default: 1e-8) eps (float, optional): a value to avoid the correlation matrix is all-zero (Default: 1e-8) Returns: torch.Tensor: regularized matrix (..., channel, channel) """ # Add eps C = mat.size(-1) eye = torch.eye(C, dtype=mat.dtype, device=mat.device) with torch.no_grad(): epsilon = _get_mat_trace(mat).real[..., None, None] * reg # in case that correlation_matrix is all-zero epsilon = epsilon + eps mat = mat + epsilon * eye[..., :, :] return mat
[docs] def forward( self, specgram: torch.Tensor, mask_s: torch.Tensor, mask_n: Optional[torch.Tensor] = None ) -> torch.Tensor: """Perform MVDR beamforming. Args: specgram (torch.Tensor): the multi-channel STF of the noisy speech. Tensor of dimension `(..., channel, freq, time)` mask_s (torch.Tensor): Time-Frequency mask of target speech. Tensor of dimension `(..., freq, time)` if multi_mask is ``False`` or or dimension `(..., channel, freq, time)` if multi_mask is ``True`` mask_n (torch.Tensor or None, optional): Time-Frequency mask of noise. Tensor of dimension `(..., freq, time)` if multi_mask is ``False`` or or dimension `(..., channel, freq, time)` if multi_mask is ``True`` (Default: None) Returns: torch.Tensor: The single-channel STFT of the enhanced speech. Tensor of dimension `(..., freq, time)` """ if specgram.ndim < 3: raise ValueError( f"Expected at least 3D tensor (..., channel, freq, time). Found: {specgram.shape}" ) if specgram.dtype != torch.cdouble: raise ValueError( f"The type of ``specgram`` tensor must be ``torch.cdouble``. Found: {specgram.dtype}" ) if mask_n is None: warnings.warn( "``mask_n`` is not provided, use ``1 - mask_s`` as ``mask_n``." ) mask_n = 1 - mask_s shape = specgram.size() # pack batch specgram = specgram.reshape(-1, shape[-3], shape[-2], shape[-1]) if self.multi_mask: mask_s = mask_s.reshape(-1, shape[-3], shape[-2], shape[-1]) mask_n = mask_n.reshape(-1, shape[-3], shape[-2], shape[-1]) else: mask_s = mask_s.reshape(-1, shape[-2], shape[-1]) mask_n = mask_n.reshape(-1, shape[-2], shape[-1]) psd_s = self.psd(specgram, mask_s) # (..., freq, time, channel, channel) psd_n = self.psd(specgram, mask_n) # (..., freq, time, channel, channel) u = torch.zeros( specgram.size()[:-2], device=specgram.device, dtype=torch.cdouble ) # (..., channel) u[..., self.ref_channel].fill_(1) if self.online: w_mvdr = self._get_updated_mvdr_vector( psd_s, psd_n, mask_s, mask_n, u, self.solution, self.diag_loading, self.diag_eps ) else: w_mvdr = self._get_mvdr_vector( psd_s, psd_n, u, self.solution, self.diag_loading, self.diag_eps ) specgram_enhanced = self._apply_beamforming_vector(specgram, w_mvdr) # unpack batch specgram_enhanced = specgram_enhanced.reshape(shape[:-3] + shape[-2:]) return specgram_enhanced

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