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Commit 8f270d09 authored by Caroline Chen's avatar Caroline Chen
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Standardize tensor shapes format in docs (#1838)

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torchaudio/functional/filtering.py
View file @ 8f270d09
......@@ -650,20 +650,20 @@ def filtfilt(
Inspired by https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.filtfilt.html
Args:
waveform (Tensor): audio waveform of dimension of ``(..., time)``. Must be normalized to -1 to 1.
waveform (Tensor): audio waveform of dimension of `(..., time)`. Must be normalized to -1 to 1.
a_coeffs (Tensor): denominator coefficients of difference equation of dimension of either
1D with shape ``(num_order + 1)`` or 2D with shape ``(num_filters, num_order + 1)``.
1D with shape `(num_order + 1)` or 2D with shape `(num_filters, num_order + 1)`.
Lower delay coefficients are first, e.g. ``[a0, a1, a2, ...]``.
Must be same size as b_coeffs (pad with 0's as necessary).
b_coeffs (Tensor): numerator coefficients of difference equation of dimension of either
1D with shape ``(num_order + 1)`` or 2D with shape ``(num_filters, num_order + 1)``.
1D with shape `(num_order + 1)` or 2D with shape `(num_filters, num_order + 1)`.
Lower delay coefficients are first, e.g. ``[b0, b1, b2, ...]``.
Must be same size as a_coeffs (pad with 0's as necessary).
clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``)
Returns:
Tensor: Waveform with dimension of either ``(..., num_filters, time)`` if ``a_coeffs`` and ``b_coeffs``
are 2D Tensors, or ``(..., time)`` otherwise.
Tensor: Waveform with dimension of either `(..., num_filters, time)` if ``a_coeffs`` and ``b_coeffs``
are 2D Tensors, or `(..., time)` otherwise.
"""
forward_filtered = lfilter(waveform, a_coeffs, b_coeffs, clamp=False, batching=True)
backward_filtered = lfilter(
......@@ -970,13 +970,13 @@ def lfilter(
Using double precision could also minimize numerical precision errors.
Args:
waveform (Tensor): audio waveform of dimension of ``(..., time)``. Must be normalized to -1 to 1.
waveform (Tensor): audio waveform of dimension of `(..., time)`. Must be normalized to -1 to 1.
a_coeffs (Tensor): denominator coefficients of difference equation of dimension of either
1D with shape ``(num_order + 1)`` or 2D with shape ``(num_filters, num_order + 1)``.
1D with shape `(num_order + 1)` or 2D with shape `(num_filters, num_order + 1)`.
Lower delays coefficients are first, e.g. ``[a0, a1, a2, ...]``.
Must be same size as b_coeffs (pad with 0's as necessary).
b_coeffs (Tensor): numerator coefficients of difference equation of dimension of either
1D with shape ``(num_order + 1)`` or 2D with shape ``(num_filters, num_order + 1)``.
1D with shape `(num_order + 1)` or 2D with shape `(num_filters, num_order + 1)`.
Lower delays coefficients are first, e.g. ``[b0, b1, b2, ...]``.
Must be same size as a_coeffs (pad with 0's as necessary).
clamp (bool, optional): If ``True``, clamp the output signal to be in the range [-1, 1] (Default: ``True``)
......@@ -986,8 +986,8 @@ def lfilter(
a_coeffs[i], b_coeffs[i], clamp=clamp, batching=False)``. (Default: ``True``)
Returns:
Tensor: Waveform with dimension of either ``(..., num_filters, time)`` if ``a_coeffs`` and ``b_coeffs``
are 2D Tensors, or ``(..., time)`` otherwise.
Tensor: Waveform with dimension of either `(..., num_filters, time)` if ``a_coeffs`` and ``b_coeffs``
are 2D Tensors, or `(..., time)` otherwise.
"""
assert a_coeffs.size() == b_coeffs.size()
assert a_coeffs.ndim <= 2
......
torchaudio/functional/functional.py
View file @ 8f270d09
......@@ -62,7 +62,7 @@ def spectrogram(
The spectrogram can be either magnitude-only or complex.
Args:
waveform (Tensor): Tensor of audio of dimension (..., time)
waveform (Tensor): Tensor of audio of dimension `(..., time)`
pad (int): Two sided padding of signal
window (Tensor): Window tensor that is applied/multiplied to each frame/window
n_fft (int): Size of FFT
......@@ -89,7 +89,7 @@ def spectrogram(
power spectrogram, which is a real-valued tensor.
Returns:
Tensor: Dimension (..., freq, time), freq is
Tensor: Dimension `(..., freq, time)`, freq is
``n_fft // 2 + 1`` and ``n_fft`` is the number of
Fourier bins, and time is the number of window hops (n_frame).
"""
......@@ -172,7 +172,7 @@ def inverse_spectrogram(
Default: ``True``
Returns:
Tensor: Dimension (..., time). Least squares estimation of the original signal.
Tensor: Dimension `(..., time)`. Least squares estimation of the original signal.
"""
if spectrogram.dtype == torch.float32 or spectrogram.dtype == torch.float64:
......@@ -246,7 +246,7 @@ def griffinlim(
and *Signal estimation from modified short-time Fourier transform* [:footcite:`1172092`].
Args:
specgram (Tensor): A magnitude-only STFT spectrogram of dimension (..., freq, frames)
specgram (Tensor): A magnitude-only STFT spectrogram of dimension `(..., freq, frames)`
where freq is ``n_fft // 2 + 1``.
window (Tensor): Window tensor that is applied/multiplied to each frame/window
n_fft (int): Size of FFT, creates ``n_fft // 2 + 1`` bins
......@@ -263,7 +263,7 @@ def griffinlim(
rand_init (bool): Initializes phase randomly if True, to zero otherwise.
Returns:
torch.Tensor: waveform of (..., time), where time equals the ``length`` parameter if given.
torch.Tensor: waveform of `(..., time)`, where time equals the ``length`` parameter if given.
"""
assert momentum < 1, 'momentum={} > 1 can be unstable'.format(momentum)
assert momentum >= 0, 'momentum={} < 0'.format(momentum)
......@@ -791,10 +791,10 @@ def phase_vocoder(
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.
Either a real tensor of dimension of `(..., freq, num_frame, complex=2)`
or a tensor of dimension `(..., freq, num_frame)` with complex dtype.
rate (float): Speed-up factor
phase_advance (Tensor): Expected phase advance in each bin. Dimension of (freq, 1)
phase_advance (Tensor): Expected phase advance in each bin. Dimension of `(freq, 1)`
Returns:
Tensor:
......@@ -907,13 +907,13 @@ def mask_along_axis_iid(
``v`` is sampled from ``uniform(0, mask_param)``, and ``v_0`` from ``uniform(0, max_v - v)``.
Args:
specgrams (Tensor): Real spectrograms (batch, channel, freq, time)
specgrams (Tensor): Real spectrograms `(batch, channel, freq, time)`
mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param]
mask_value (float): Value to assign to the masked columns
axis (int): Axis to apply masking on (2 -> frequency, 3 -> time)
Returns:
Tensor: Masked spectrograms of dimensions (batch, channel, freq, time)
Tensor: Masked spectrograms of dimensions `(batch, channel, freq, time)`
"""
if axis not in [2, 3]:
......@@ -950,13 +950,13 @@ def mask_along_axis(
All examples will have the same mask interval.
Args:
specgram (Tensor): Real spectrogram (channel, freq, time)
specgram (Tensor): Real spectrogram `(channel, freq, time)`
mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param]
mask_value (float): Value to assign to the masked columns
axis (int): Axis to apply masking on (1 -> frequency, 2 -> time)
Returns:
Tensor: Masked spectrogram of dimensions (channel, freq, time)
Tensor: Masked spectrogram of dimensions `(channel, freq, time)`
"""
if axis not in [1, 2]:
raise ValueError('Only Frequency and Time masking are supported')
......@@ -999,12 +999,12 @@ def compute_deltas(
:math:`N` is ``(win_length-1)//2``.
Args:
specgram (Tensor): Tensor of audio of dimension (..., freq, time)
specgram (Tensor): Tensor of audio of dimension `(..., freq, time)`
win_length (int, optional): The window length used for computing delta (Default: ``5``)
mode (str, optional): Mode parameter passed to padding (Default: ``"replicate"``)
Returns:
Tensor: Tensor of deltas of dimension (..., freq, time)
Tensor: Tensor of deltas of dimension `(..., freq, time)`
Example
>>> specgram = torch.randn(1, 40, 1000)
......@@ -1172,7 +1172,7 @@ def detect_pitch_frequency(
It is implemented using normalized cross-correlation function and median smoothing.
Args:
waveform (Tensor): Tensor of audio of dimension (..., freq, time)
waveform (Tensor): Tensor of audio of dimension `(..., freq, time)`
sample_rate (int): The sample rate of the waveform (Hz)
frame_time (float, optional): Duration of a frame (Default: ``10 ** (-2)``).
win_length (int, optional): The window length for median smoothing (in number of frames) (Default: ``30``).
......@@ -1180,7 +1180,7 @@ def detect_pitch_frequency(
freq_high (int, optional): Highest frequency that can be detected (Hz) (Default: ``3400``).
Returns:
Tensor: Tensor of freq of dimension (..., frame)
Tensor: Tensor of freq of dimension `(..., frame)`
"""
# pack batch
shape = list(waveform.size())
......@@ -1211,7 +1211,7 @@ def sliding_window_cmn(
Apply sliding-window cepstral mean (and optionally variance) normalization per utterance.
Args:
specgram (Tensor): Tensor of audio of dimension (..., time, freq)
specgram (Tensor): Tensor of audio of dimension `(..., time, freq)`
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)
......@@ -1220,7 +1220,7 @@ def sliding_window_cmn(
norm_vars (bool, optional): If true, normalize variance to one. (bool, default = false)
Returns:
Tensor: Tensor matching input shape (..., freq, time)
Tensor: Tensor matching input shape `(..., freq, time)`
"""
input_shape = specgram.shape
num_frames, num_feats = input_shape[-2:]
......@@ -1307,7 +1307,7 @@ def spectral_centroid(
frequency values, weighted by their magnitude.
Args:
waveform (Tensor): Tensor of audio of dimension (..., time)
waveform (Tensor): Tensor of audio of dimension `(..., time)`
sample_rate (int): Sample rate of the audio waveform
pad (int): Two sided padding of signal
window (Tensor): Window tensor that is applied/multiplied to each frame/window
......@@ -1316,7 +1316,7 @@ def spectral_centroid(
win_length (int): Window size
Returns:
Tensor: Dimension (..., time)
Tensor: Dimension `(..., time)`
"""
specgram = spectrogram(waveform, pad=pad, window=window, n_fft=n_fft, hop_length=hop_length,
win_length=win_length, power=1., normalized=False)
......@@ -1344,8 +1344,8 @@ def apply_codec(
sample_rate (int): Sample rate of the audio waveform.
format (str): File format.
channels_first (bool, optional):
When True, both the input and output Tensor have dimension ``[channel, time]``.
Otherwise, they have dimension ``[time, channel]``.
When True, both the input and output Tensor have dimension `(channel, time)`.
Otherwise, they have dimension `(time, channel)`.
compression (float or None, optional): Used for formats other than WAV.
For more details see :py:func:`torchaudio.backend.sox_io_backend.save`.
encoding (str or None, optional): Changes the encoding for the supported formats.
......@@ -1355,7 +1355,7 @@ def apply_codec(
Returns:
torch.Tensor: Resulting Tensor.
If ``channels_first=True``, it has ``[channel, time]`` else ``[time, channel]``.
If ``channels_first=True``, it has `(channel, time)` else `(time, channel)`.
"""
bytes = io.BytesIO()
torchaudio.backend.sox_io_backend.save(bytes,
......@@ -1453,7 +1453,7 @@ def compute_kaldi_pitch(
This makes different types of features give the same number of frames. (default: True)
Returns:
Tensor: Pitch feature. Shape: ``(batch, frames 2)`` where the last dimension
Tensor: Pitch feature. Shape: `(batch, frames 2)` where the last dimension
corresponds to pitch and NCCF.
"""
shape = waveform.shape
......@@ -1605,7 +1605,7 @@ def resample(
more efficient computation if resampling multiple waveforms with the same resampling parameters.
Args:
waveform (Tensor): The input signal of dimension (..., time)
waveform (Tensor): The input signal of dimension `(..., time)`
orig_freq (float): The original frequency of the signal
new_freq (float): The desired frequency
lowpass_filter_width (int, optional): Controls the sharpness of the filter, more == sharper
......@@ -1617,7 +1617,7 @@ def resample(
beta (float or None, optional): The shape parameter used for kaiser window.
Returns:
Tensor: The waveform at the new frequency of dimension (..., time).
Tensor: The waveform at the new frequency of dimension `(..., time).`
"""
assert orig_freq > 0.0 and new_freq > 0.0
......
torchaudio/models/wav2vec2/components.py
View file @ 8f270d09
......@@ -301,9 +301,9 @@ class FeedForward(Module):
def forward(self, x):
"""
Args:
x (Tensor): shape: ``(batch, sequence_length, io_features)``
x (Tensor): shape: `(batch, sequence_length, io_features)`
Returns:
x (Tensor): shape: ``(batch, sequence_length, io_features)``
x (Tensor): shape: `(batch, sequence_length, io_features)`
"""
x = self.intermediate_dense(x)
x = torch.nn.functional.gelu(x)
......@@ -339,9 +339,9 @@ class EncoderLayer(Module):
):
"""
Args:
x (Tensor): shape: ``(batch, sequence_length, embed_dim)``
x (Tensor): shape: `(batch, sequence_length, embed_dim)`
attention_mask (Tensor or None, optional):
shape: ``(batch, 1, sequence_length, sequence_length)``
shape: `(batch, 1, sequence_length, sequence_length)`
"""
residual = x
......
torchaudio/models/wav2vec2/model.py
View file @ 8f270d09
......@@ -48,10 +48,10 @@ class Wav2Vec2Model(Module):
transformer block in encoder.
Args:
waveforms (Tensor): Audio tensor of shape ``(batch, frames)``.
waveforms (Tensor): Audio tensor of shape `(batch, frames)`.
lengths (Tensor or None, optional):
Indicates the valid length of each audio sample in the batch.
Shape: ``(batch, )``.
Shape: `(batch, )`.
num_layers (int or None, optional):
If given, limit the number of intermediate layers to go through.
Providing `1` will stop the computation after going through one
......@@ -62,9 +62,9 @@ class Wav2Vec2Model(Module):
List of Tensors and an optional Tensor:
List of Tensors
Features from requested layers.
Each Tensor is of shape: ``(batch, frames, feature dimention)``
Each Tensor is of shape: `(batch, frames, feature dimention)`
Tensor or None
If ``lengths`` argument was provided, a Tensor of shape ``(batch, )``
If ``lengths`` argument was provided, a Tensor of shape `(batch, )`
is retuned. It indicates the valid length of each feature in the batch.
"""
x, lengths = self.feature_extractor(waveforms, lengths)
......@@ -79,18 +79,18 @@ class Wav2Vec2Model(Module):
"""Compute the sequence of probability distribution over labels.
Args:
waveforms (Tensor): Audio tensor of shape ``(batch, frames)``.
waveforms (Tensor): Audio tensor of shape `(batch, frames)`.
lengths (Tensor or None, optional):
Indicates the valid length of each audio sample in the batch.
Shape: ``(batch, )``.
Shape: `(batch, )`.
Returns:
Tensor and an optional Tensor:
Tensor
The sequences of probability distribution (in logit) over labels.
Shape: ``(batch, frames, num labels)``.
Shape: `(batch, frames, num labels)`.
Tensor or None
If ``lengths`` argument was provided, a Tensor of shape ``(batch, )``
If ``lengths`` argument was provided, a Tensor of shape `(batch, )`
is retuned. It indicates the valid length of each feature in the batch.
"""
x, lengths = self.feature_extractor(waveforms, lengths)
......
torchaudio/transforms.py
View file @ 8f270d09
......@@ -971,8 +971,8 @@ class TimeStretch(torch.nn.Module):
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.
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``)
......@@ -1018,10 +1018,10 @@ class Fade(torch.nn.Module):
def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
waveform (Tensor): Tensor of audio of dimension `(..., time)`.
Returns:
Tensor: Tensor of audio of dimension (..., time).
Tensor: Tensor of audio of dimension `(..., time)`.
"""
waveform_length = waveform.size()[-1]
device = waveform.device
......@@ -1092,11 +1092,11 @@ class _AxisMasking(torch.nn.Module):
def forward(self, specgram: Tensor, mask_value: float = 0.) -> Tensor:
r"""
Args:
specgram (Tensor): Tensor of dimension (..., freq, time).
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).
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:
......@@ -1157,10 +1157,10 @@ class Vol(torch.nn.Module):
def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
waveform (Tensor): Tensor of audio of dimension `(..., time)`.
Returns:
Tensor: Tensor of audio of dimension (..., time).
Tensor: Tensor of audio of dimension `(..., time)`.
"""
if self.gain_type == "amplitude":
waveform = waveform * self.gain
......@@ -1201,10 +1201,10 @@ class SlidingWindowCmn(torch.nn.Module):
def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
waveform (Tensor): Tensor of audio of dimension `(..., time)`.
Returns:
Tensor: Tensor of audio of dimension (..., time).
Tensor: Tensor of audio of dimension `(..., time)`.
"""
cmn_waveform = F.sliding_window_cmn(
waveform, self.cmn_window, self.min_cmn_window, self.center, self.norm_vars)
......@@ -1374,10 +1374,10 @@ class SpectralCentroid(torch.nn.Module):
def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
waveform (Tensor): Tensor of audio of dimension `(..., time)`.
Returns:
Tensor: Spectral Centroid of size (..., time).
Tensor: Spectral Centroid of size `(..., time)`.
"""
return F.spectral_centroid(waveform, self.sample_rate, self.pad, self.window, self.n_fft, self.hop_length,
......@@ -1428,7 +1428,7 @@ class PitchShift(torch.nn.Module):
def forward(self, waveform: Tensor) -> Tensor:
r"""
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
waveform (Tensor): Tensor of audio of dimension `(..., time)`.
Returns:
Tensor: The pitch-shifted audio of shape `(..., time)`.
......@@ -1513,7 +1513,7 @@ def _get_mat_trace(input: torch.Tensor, dim1: int = -1, dim2: int = -2) -> torch
r"""Compute the trace of a Tensor along ``dim1`` and ``dim2`` dimensions.
Args:
input (torch.Tensor): Tensor of dimension (..., channel, channel)
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
......@@ -1548,14 +1548,14 @@ class PSD(torch.nn.Module):
"""
Args:
specgram (torch.Tensor): multi-channel complex-valued STFT matrix.
Tensor of dimension (..., channel, freq, time)
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``
Tensor of dimension `(..., freq, time)` if multi_mask is ``False`` or
of dimension `(..., channel, freq, time)` if multi_mask is ``True``
Returns:
torch.Tensor: PSD matrix of the input STFT matrix.
Tensor of dimension (..., freq, channel, channel)
Tensor of dimension `(..., freq, channel, channel)`
"""
# outer product:
# (..., ch_1, freq, time) x (..., ch_2, freq, time) -> (..., time, ch_1, ch_2)
......@@ -1804,11 +1804,11 @@ class MVDR(torch.nn.Module):
Args:
psd_s (torch.tensor): covariance matrix of speech
Tensor of dimension (..., freq, channel, channel)
Tensor of dimension `(..., freq, channel, channel)`
Returns:
torch.Tensor: the enhanced STFT
Tensor of dimension (..., freq, channel, 1)
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)
......@@ -1826,14 +1826,14 @@ class MVDR(torch.nn.Module):
Args:
psd_s (torch.tensor): covariance matrix of speech
Tensor of dimension (..., freq, channel, channel)
Tensor of dimension `(..., freq, channel, channel)`
psd_n (torch.Tensor): covariance matrix of noise
Tensor of dimension (..., freq, channel, channel)
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)
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])
......@@ -1850,13 +1850,13 @@ class MVDR(torch.nn.Module):
r"""Apply the beamforming weight to the noisy STFT
Args:
specgram (torch.tensor): multi-channel noisy STFT
Tensor of dimension (..., channel, freq, time)
Tensor of dimension `(..., channel, freq, time)`
beamform_vector (torch.Tensor): beamforming weight matrix
Tensor of dimension (..., freq, channel)
Tensor of dimension `(..., freq, channel)`
Returns:
torch.Tensor: the enhanced STFT
Tensor of dimension (..., freq, time)
Tensor of dimension `(..., freq, time)`
"""
# (..., channel) x (..., channel, freq, time) -> (..., freq, time)
specgram_enhanced = torch.einsum("...fc,...cft->...ft", [beamform_vector.conj(), specgram])
......@@ -1897,18 +1897,18 @@ class MVDR(torch.nn.Module):
Args:
specgram (torch.Tensor): the multi-channel STF of the noisy speech.
Tensor of dimension (..., channel, freq, time)
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``
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``
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)
Tensor of dimension `(..., freq, time)`
"""
if specgram.ndim < 3:
raise ValueError(
......
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