Source code for torchaudio.functional.filtering
import math
import warnings
from typing import Optional
import torch
from torch import Tensor
def _dB2Linear(x: float) -> float:
return math.exp(x * math.log(10) / 20.0)
def _generate_wave_table(
wave_type: str,
data_type: str,
table_size: int,
min: float,
max: float,
phase: float,
device: torch.device,
) -> Tensor:
r"""A helper function for phaser. Generates a table with given parameters.
Args:
wave_type (str): SINE or TRIANGULAR
data_type (str): desired data_type ( `INT` or `FLOAT` )
table_size (int): desired table size
min (float): desired min value
max (float): desired max value
phase (float): desired phase
device (torch.device): Torch device on which table must be generated
Returns:
Tensor: A 1D tensor with wave table values
"""
phase_offset = int(phase / math.pi / 2 * table_size + 0.5)
t = torch.arange(table_size, device=device, dtype=torch.int32)
point = (t + phase_offset) % table_size
d = torch.zeros_like(point, device=device, dtype=torch.float64)
if wave_type == "SINE":
d = (torch.sin(point.to(torch.float64) / table_size * 2 * math.pi) + 1) / 2
elif wave_type == "TRIANGLE":
d = point.to(torch.float64) * 2 / table_size
value = torch.div(4 * point, table_size, rounding_mode="floor")
d[value == 0] = d[value == 0] + 0.5
d[value == 1] = 1.5 - d[value == 1]
d[value == 2] = 1.5 - d[value == 2]
d[value == 3] = d[value == 3] - 1.5
d = d * (max - min) + min
if data_type == "INT":
mask = d < 0
d[mask] = d[mask] - 0.5
d[~mask] = d[~mask] + 0.5
d = d.to(torch.int32)
elif data_type == "FLOAT":
d = d.to(torch.float32)
return d
[docs]def allpass_biquad(waveform: Tensor, sample_rate: int, central_freq: float, Q: float = 0.707) -> Tensor:
r"""Design two-pole all-pass filter. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform(torch.Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
central_freq (float or torch.Tensor): central frequency (in Hz)
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
"""
dtype = waveform.dtype
device = waveform.device
central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
w0 = 2 * math.pi * central_freq / sample_rate
alpha = torch.sin(w0) / 2 / Q
b0 = 1 - alpha
b1 = -2 * torch.cos(w0)
b2 = 1 + alpha
a0 = 1 + alpha
a1 = -2 * torch.cos(w0)
a2 = 1 - alpha
return biquad(waveform, b0, b1, b2, a0, a1, a2)
[docs]def band_biquad(
waveform: Tensor,
sample_rate: int,
central_freq: float,
Q: float = 0.707,
noise: bool = False,
) -> Tensor:
r"""Design two-pole band filter. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
central_freq (float or torch.Tensor): central frequency (in Hz)
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``).
noise (bool, optional) : If ``True``, uses the alternate mode for un-pitched audio (e.g. percussion).
If ``False``, uses mode oriented to pitched audio, i.e. voice, singing,
or instrumental music (Default: ``False``).
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
"""
dtype = waveform.dtype
device = waveform.device
central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
w0 = 2 * math.pi * central_freq / sample_rate
bw_Hz = central_freq / Q
a0 = 1.0
a2 = torch.exp(-2 * math.pi * bw_Hz / sample_rate)
a1 = -4 * a2 / (1 + a2) * torch.cos(w0)
b0 = torch.sqrt(1 - a1 * a1 / (4 * a2)) * (1 - a2)
if noise:
mult = torch.sqrt(((1 + a2) * (1 + a2) - a1 * a1) * (1 - a2) / (1 + a2)) / b0
b0 = mult * b0
b1 = 0.0
b2 = 0.0
return biquad(waveform, b0, b1, b2, a0, a1, a2)
[docs]def bandpass_biquad(
waveform: Tensor,
sample_rate: int,
central_freq: float,
Q: float = 0.707,
const_skirt_gain: bool = False,
) -> Tensor:
r"""Design two-pole band-pass filter. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
central_freq (float or torch.Tensor): central frequency (in Hz)
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
const_skirt_gain (bool, optional) : If ``True``, uses a constant skirt gain (peak gain = Q).
If ``False``, uses a constant 0dB peak gain. (Default: ``False``)
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
"""
dtype = waveform.dtype
device = waveform.device
central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
w0 = 2 * math.pi * central_freq / sample_rate
alpha = torch.sin(w0) / 2 / Q
temp = torch.sin(w0) / 2 if const_skirt_gain else alpha
b0 = temp
b1 = 0.0
b2 = -temp
a0 = 1 + alpha
a1 = -2 * torch.cos(w0)
a2 = 1 - alpha
return biquad(waveform, b0, b1, b2, a0, a1, a2)
[docs]def bandreject_biquad(waveform: Tensor, sample_rate: int, central_freq: float, Q: float = 0.707) -> Tensor:
r"""Design two-pole band-reject filter. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
central_freq (float or torch.Tensor): central frequency (in Hz)
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
"""
dtype = waveform.dtype
device = waveform.device
central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
w0 = 2 * math.pi * central_freq / sample_rate
alpha = torch.sin(w0) / 2 / Q
b0 = 1.0
b1 = -2 * torch.cos(w0)
b2 = 1.0
a0 = 1 + alpha
a1 = -2 * torch.cos(w0)
a2 = 1 - alpha
return biquad(waveform, b0, b1, b2, a0, a1, a2)
[docs]def bass_biquad(
waveform: Tensor,
sample_rate: int,
gain: float,
central_freq: float = 100,
Q: float = 0.707,
) -> Tensor:
r"""Design a bass tone-control effect. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB.
central_freq (float or torch.Tensor, optional): central frequency (in Hz). (Default: ``100``)
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``).
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
"""
dtype = waveform.dtype
device = waveform.device
central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
gain = torch.as_tensor(gain, dtype=dtype, device=device)
w0 = 2 * math.pi * central_freq / sample_rate
alpha = torch.sin(w0) / 2 / Q
A = torch.exp(gain / 40 * math.log(10))
temp1 = 2 * torch.sqrt(A) * alpha
temp2 = (A - 1) * torch.cos(w0)
temp3 = (A + 1) * torch.cos(w0)
b0 = A * ((A + 1) - temp2 + temp1)
b1 = 2 * A * ((A - 1) - temp3)
b2 = A * ((A + 1) - temp2 - temp1)
a0 = (A + 1) + temp2 + temp1
a1 = -2 * ((A - 1) + temp3)
a2 = (A + 1) + temp2 - temp1
return biquad(waveform, b0 / a0, b1 / a0, b2 / a0, a0 / a0, a1 / a0, a2 / a0)
[docs]def biquad(waveform: Tensor, b0: float, b1: float, b2: float, a0: float, a1: float, a2: float) -> Tensor:
r"""Perform a biquad filter of input tensor. Initial conditions set to 0.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
b0 (float or torch.Tensor): numerator coefficient of current input, x[n]
b1 (float or torch.Tensor): numerator coefficient of input one time step ago x[n-1]
b2 (float or torch.Tensor): numerator coefficient of input two time steps ago x[n-2]
a0 (float or torch.Tensor): denominator coefficient of current output y[n], typically 1
a1 (float or torch.Tensor): denominator coefficient of current output y[n-1]
a2 (float or torch.Tensor): denominator coefficient of current output y[n-2]
Returns:
Tensor: Waveform with dimension of `(..., time)`
Reference:
- https://en.wikipedia.org/wiki/Digital_biquad_filter
"""
device = waveform.device
dtype = waveform.dtype
b0 = torch.as_tensor(b0, dtype=dtype, device=device).view(1)
b1 = torch.as_tensor(b1, dtype=dtype, device=device).view(1)
b2 = torch.as_tensor(b2, dtype=dtype, device=device).view(1)
a0 = torch.as_tensor(a0, dtype=dtype, device=device).view(1)
a1 = torch.as_tensor(a1, dtype=dtype, device=device).view(1)
a2 = torch.as_tensor(a2, dtype=dtype, device=device).view(1)
output_waveform = lfilter(
waveform,
torch.cat([a0, a1, a2]),
torch.cat([b0, b1, b2]),
)
return output_waveform
[docs]def contrast(waveform: Tensor, enhancement_amount: float = 75.0) -> Tensor:
r"""Apply contrast effect. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Comparable with compression, this effect modifies an audio signal to make it sound louder
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
enhancement_amount (float, optional): controls the amount of the enhancement
Allowed range of values for enhancement_amount : 0-100
Note that enhancement_amount = 0 still gives a significant contrast enhancement
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
"""
if not 0 <= enhancement_amount <= 100:
raise ValueError("Allowed range of values for enhancement_amount : 0-100")
contrast = enhancement_amount / 750.0
temp1 = waveform * (math.pi / 2)
temp2 = contrast * torch.sin(temp1 * 4)
output_waveform = torch.sin(temp1 + temp2)
return output_waveform
[docs]def dcshift(waveform: Tensor, shift: float, limiter_gain: Optional[float] = None) -> Tensor:
r"""Apply a DC shift to the audio. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: TorchScript
This can be useful to remove a DC offset
(caused perhaps by a hardware problem in the recording chain) from the audio
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
shift (float): indicates the amount to shift the audio
Allowed range of values for shift : -2.0 to +2.0
limiter_gain (float of None, optional): It is used only on peaks to prevent clipping
It should have a value much less than 1 (e.g. 0.05 or 0.02)
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
"""
output_waveform = waveform
limiter_threshold = 0.0
if limiter_gain is not None:
limiter_threshold = 1.0 - (abs(shift) - limiter_gain)
# Note:
# the following index-based update breaks auto-grad support
if limiter_gain is not None and shift > 0:
mask = waveform > limiter_threshold
temp = (waveform[mask] - limiter_threshold) * limiter_gain / (1 - limiter_threshold)
output_waveform[mask] = (temp + limiter_threshold + shift).clamp(max=limiter_threshold)
output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1)
elif limiter_gain is not None and shift < 0:
mask = waveform < -limiter_threshold
temp = (waveform[mask] + limiter_threshold) * limiter_gain / (1 - limiter_threshold)
output_waveform[mask] = (temp - limiter_threshold + shift).clamp(min=-limiter_threshold)
output_waveform[~mask] = (waveform[~mask] + shift).clamp(min=-1, max=1)
else:
output_waveform = (waveform + shift).clamp(min=-1, max=1)
return output_waveform
[docs]def deemph_biquad(waveform: Tensor, sample_rate: int) -> Tensor:
r"""Apply ISO 908 CD de-emphasis (shelving) IIR filter. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, Allowed sample rate ``44100`` or ``48000``
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
"""
if sample_rate == 44100:
central_freq = 5283
width_slope = 0.4845
gain = -9.477
elif sample_rate == 48000:
central_freq = 5356
width_slope = 0.479
gain = -9.62
else:
raise ValueError("Sample rate must be 44100 (audio-CD) or 48000 (DAT)")
w0 = 2 * math.pi * central_freq / sample_rate
A = math.exp(gain / 40.0 * math.log(10))
alpha = math.sin(w0) / 2 * math.sqrt((A + 1 / A) * (1 / width_slope - 1) + 2)
temp1 = 2 * math.sqrt(A) * alpha
temp2 = (A - 1) * math.cos(w0)
temp3 = (A + 1) * math.cos(w0)
b0 = A * ((A + 1) + temp2 + temp1)
b1 = -2 * A * ((A - 1) + temp3)
b2 = A * ((A + 1) + temp2 - temp1)
a0 = (A + 1) - temp2 + temp1
a1 = 2 * ((A - 1) - temp3)
a2 = (A + 1) - temp2 - temp1
return biquad(waveform, b0, b1, b2, a0, a1, a2)
def _add_noise_shaping(dithered_waveform: Tensor, waveform: Tensor) -> Tensor:
r"""Noise shaping is calculated by error:
error[n] = dithered[n] - original[n]
noise_shaped_waveform[n] = dithered[n] + error[n-1]
"""
wf_shape = waveform.size()
waveform = waveform.reshape(-1, wf_shape[-1])
dithered_shape = dithered_waveform.size()
dithered_waveform = dithered_waveform.reshape(-1, dithered_shape[-1])
error = dithered_waveform - waveform
# add error[n-1] to dithered_waveform[n], so offset the error by 1 index
zeros = torch.zeros(1, dtype=error.dtype, device=error.device)
for index in range(error.size()[0]):
err = error[index]
error_offset = torch.cat((zeros, err))
error[index] = error_offset[: waveform.size()[1]]
noise_shaped = dithered_waveform + error
return noise_shaped.reshape(dithered_shape[:-1] + noise_shaped.shape[-1:])
def _apply_probability_distribution(waveform: Tensor, density_function: str = "TPDF") -> Tensor:
r"""Apply a probability distribution function on a waveform.
Triangular probability density function (TPDF) dither noise has a
triangular distribution; values in the center of the range have a higher
probability of occurring.
Rectangular probability density function (RPDF) dither noise has a
uniform distribution; any value in the specified range has the same
probability of occurring.
Gaussian probability density function (GPDF) has a normal distribution.
The relationship of probabilities of results follows a bell-shaped,
or Gaussian curve, typical of dither generated by analog sources.
Args:
waveform (Tensor): Tensor of audio of dimension (..., time)
density_function (str, optional): The density function of a
continuous random variable (Default: ``"TPDF"``)
Options: Triangular Probability Density Function - `TPDF`
Rectangular Probability Density Function - `RPDF`
Gaussian Probability Density Function - `GPDF`
Returns:
Tensor: waveform dithered with TPDF
"""
# pack batch
shape = waveform.size()
waveform = waveform.reshape(-1, shape[-1])
channel_size = waveform.size()[0] - 1
time_size = waveform.size()[-1] - 1
random_channel = (
int(
torch.randint(
channel_size,
[
1,
],
).item()
)
if channel_size > 0
else 0
)
random_time = (
int(
torch.randint(
time_size,
[
1,
],
).item()
)
if time_size > 0
else 0
)
number_of_bits = 16
up_scaling = 2 ** (number_of_bits - 1) - 2
signal_scaled = waveform * up_scaling
down_scaling = 2 ** (number_of_bits - 1)
signal_scaled_dis = waveform
if density_function == "RPDF":
RPDF = waveform[random_channel][random_time] - 0.5
signal_scaled_dis = signal_scaled + RPDF
elif density_function == "GPDF":
# TODO Replace by distribution code once
# https://github.com/pytorch/pytorch/issues/29843 is resolved
# gaussian = torch.distributions.normal.Normal(torch.mean(waveform, -1), 1).sample()
num_rand_variables = 6
gaussian = waveform[random_channel][random_time]
for ws in num_rand_variables * [time_size]:
rand_chan = int(
torch.randint(
channel_size,
[
1,
],
).item()
)
gaussian += waveform[rand_chan][
int(
torch.randint(
ws,
[
1,
],
).item()
)
]
signal_scaled_dis = signal_scaled + gaussian
else:
# dtype needed for https://github.com/pytorch/pytorch/issues/32358
TPDF = torch.bartlett_window(time_size + 1, dtype=signal_scaled.dtype, device=signal_scaled.device)
TPDF = TPDF.repeat((channel_size + 1), 1)
signal_scaled_dis = signal_scaled + TPDF
quantised_signal_scaled = torch.round(signal_scaled_dis)
quantised_signal = quantised_signal_scaled / down_scaling
# unpack batch
return quantised_signal.reshape(shape[:-1] + quantised_signal.shape[-1:])
[docs]def dither(waveform: Tensor, density_function: str = "TPDF", noise_shaping: bool = False) -> Tensor:
r"""Apply dither
.. devices:: CPU CUDA
.. properties:: TorchScript
Dither increases the perceived dynamic range of audio stored at a
particular bit-depth by eliminating nonlinear truncation distortion
(i.e. adding minimally perceived noise to mask distortion caused by quantization).
Args:
waveform (Tensor): Tensor of audio of dimension (..., time)
density_function (str, optional):
The density function of a continuous random variable. One of
``"TPDF"`` (Triangular Probability Density Function),
``"RPDF"`` (Rectangular Probability Density Function) or
``"GPDF"`` (Gaussian Probability Density Function) (Default: ``"TPDF"``).
noise_shaping (bool, optional): a filtering process that shapes the spectral
energy of quantisation error (Default: ``False``)
Returns:
Tensor: waveform dithered
"""
dithered = _apply_probability_distribution(waveform, density_function=density_function)
if noise_shaping:
return _add_noise_shaping(dithered, waveform)
else:
return dithered
[docs]def equalizer_biquad(
waveform: Tensor,
sample_rate: int,
center_freq: float,
gain: float,
Q: float = 0.707,
) -> Tensor:
r"""Design biquad peaking equalizer filter and perform filtering. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
center_freq (float): filter's central frequency
gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
Returns:
Tensor: Waveform of dimension of `(..., time)`
"""
dtype = waveform.dtype
device = waveform.device
center_freq = torch.as_tensor(center_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
gain = torch.as_tensor(gain, dtype=dtype, device=device)
w0 = 2 * math.pi * center_freq / sample_rate
A = torch.exp(gain / 40.0 * math.log(10))
alpha = torch.sin(w0) / 2 / Q
b0 = 1 + alpha * A
b1 = -2 * torch.cos(w0)
b2 = 1 - alpha * A
a0 = 1 + alpha / A
a1 = -2 * torch.cos(w0)
a2 = 1 - alpha / A
return biquad(waveform, b0, b1, b2, a0, a1, a2)
[docs]def filtfilt(
waveform: Tensor,
a_coeffs: Tensor,
b_coeffs: Tensor,
clamp: bool = True,
) -> Tensor:
r"""Apply an IIR filter forward and backward to a waveform.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
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.
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)`.
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)`.
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.
"""
forward_filtered = lfilter(waveform, a_coeffs, b_coeffs, clamp=False, batching=True)
backward_filtered = lfilter(
forward_filtered.flip(-1),
a_coeffs,
b_coeffs,
clamp=clamp,
batching=True,
).flip(-1)
return backward_filtered
[docs]def flanger(
waveform: Tensor,
sample_rate: int,
delay: float = 0.0,
depth: float = 2.0,
regen: float = 0.0,
width: float = 71.0,
speed: float = 0.5,
phase: float = 25.0,
modulation: str = "sinusoidal",
interpolation: str = "linear",
) -> Tensor:
r"""Apply a flanger effect to the audio. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., channel, time)` .
Max 4 channels allowed
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
delay (float, optional): desired delay in milliseconds(ms)
Allowed range of values are 0 to 30
depth (float, optional): desired delay depth in milliseconds(ms)
Allowed range of values are 0 to 10
regen (float, optional): desired regen(feedback gain) in dB
Allowed range of values are -95 to 95
width (float, optional): desired width(delay gain) in dB
Allowed range of values are 0 to 100
speed (float, optional): modulation speed in Hz
Allowed range of values are 0.1 to 10
phase (float, optional): percentage phase-shift for multi-channel
Allowed range of values are 0 to 100
modulation (str, optional): Use either "sinusoidal" or "triangular" modulation. (Default: ``sinusoidal``)
interpolation (str, optional): Use either "linear" or "quadratic" for delay-line interpolation.
(Default: ``linear``)
Returns:
Tensor: Waveform of dimension of `(..., channel, time)`
Reference:
- http://sox.sourceforge.net/sox.html
- Scott Lehman, `Effects Explained`_,
.. _Effects Explained:
https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html
"""
if modulation not in ("sinusoidal", "triangular"):
raise ValueError("Only 'sinusoidal' or 'triangular' modulation allowed")
if interpolation not in ("linear", "quadratic"):
raise ValueError("Only 'linear' or 'quadratic' interpolation allowed")
actual_shape = waveform.shape
device, dtype = waveform.device, waveform.dtype
if actual_shape[-2] > 4:
raise ValueError("Max 4 channels allowed")
# convert to 3D (batch, channels, time)
waveform = waveform.view(-1, actual_shape[-2], actual_shape[-1])
# Scaling
feedback_gain = regen / 100
delay_gain = width / 100
channel_phase = phase / 100
delay_min = delay / 1000
delay_depth = depth / 1000
n_channels = waveform.shape[-2]
if modulation == "sinusoidal":
wave_type = "SINE"
else:
wave_type = "TRIANGLE"
# Balance output:
in_gain = 1.0 / (1 + delay_gain)
delay_gain = delay_gain / (1 + delay_gain)
# Balance feedback loop:
delay_gain = delay_gain * (1 - abs(feedback_gain))
delay_buf_length = int((delay_min + delay_depth) * sample_rate + 0.5)
delay_buf_length = delay_buf_length + 2
delay_bufs = torch.zeros(waveform.shape[0], n_channels, delay_buf_length, dtype=dtype, device=device)
delay_last = torch.zeros(waveform.shape[0], n_channels, dtype=dtype, device=device)
lfo_length = int(sample_rate / speed)
table_min = math.floor(delay_min * sample_rate + 0.5)
table_max = delay_buf_length - 2.0
lfo = _generate_wave_table(
wave_type=wave_type,
data_type="FLOAT",
table_size=lfo_length,
min=float(table_min),
max=float(table_max),
phase=3 * math.pi / 2,
device=device,
)
output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device)
delay_buf_pos = 0
lfo_pos = 0
channel_idxs = torch.arange(0, n_channels, device=device)
for i in range(waveform.shape[-1]):
delay_buf_pos = (delay_buf_pos + delay_buf_length - 1) % delay_buf_length
cur_channel_phase = (channel_idxs * lfo_length * channel_phase + 0.5).to(torch.int64)
delay_tensor = lfo[(lfo_pos + cur_channel_phase) % lfo_length]
frac_delay = torch.frac(delay_tensor)
delay_tensor = torch.floor(delay_tensor)
int_delay = delay_tensor.to(torch.int64)
temp = waveform[:, :, i]
delay_bufs[:, :, delay_buf_pos] = temp + delay_last * feedback_gain
delayed_0 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length]
int_delay = int_delay + 1
delayed_1 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length]
int_delay = int_delay + 1
if interpolation == "linear":
delayed = delayed_0 + (delayed_1 - delayed_0) * frac_delay
else:
delayed_2 = delay_bufs[:, channel_idxs, (delay_buf_pos + int_delay) % delay_buf_length]
int_delay = int_delay + 1
delayed_2 = delayed_2 - delayed_0
delayed_1 = delayed_1 - delayed_0
a = delayed_2 * 0.5 - delayed_1
b = delayed_1 * 2 - delayed_2 * 0.5
delayed = delayed_0 + (a * frac_delay + b) * frac_delay
delay_last = delayed
output_waveform[:, :, i] = waveform[:, :, i] * in_gain + delayed * delay_gain
lfo_pos = (lfo_pos + 1) % lfo_length
return output_waveform.clamp(min=-1, max=1).view(actual_shape)
[docs]def gain(waveform: Tensor, gain_db: float = 1.0) -> Tensor:
r"""Apply amplification or attenuation to the whole waveform.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): Tensor of audio of dimension (..., time).
gain_db (float, optional) Gain adjustment in decibels (dB) (Default: ``1.0``).
Returns:
Tensor: the whole waveform amplified by gain_db.
"""
if gain_db == 0:
return waveform
ratio = 10 ** (gain_db / 20)
return waveform * ratio
[docs]def highpass_biquad(waveform: Tensor, sample_rate: int, cutoff_freq: float, Q: float = 0.707) -> Tensor:
r"""Design biquad highpass filter and perform filtering. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
cutoff_freq (float or torch.Tensor): filter cutoff frequency
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
Returns:
Tensor: Waveform dimension of `(..., time)`
"""
dtype = waveform.dtype
device = waveform.device
cutoff_freq = torch.as_tensor(cutoff_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
w0 = 2 * math.pi * cutoff_freq / sample_rate
alpha = torch.sin(w0) / 2.0 / Q
b0 = (1 + torch.cos(w0)) / 2
b1 = -1 - torch.cos(w0)
b2 = b0
a0 = 1 + alpha
a1 = -2 * torch.cos(w0)
a2 = 1 - alpha
return biquad(waveform, b0, b1, b2, a0, a1, a2)
def _lfilter_core_generic_loop(input_signal_windows: Tensor, a_coeffs_flipped: Tensor, padded_output_waveform: Tensor):
n_order = a_coeffs_flipped.size(1)
a_coeffs_flipped = a_coeffs_flipped.unsqueeze(2)
for i_sample, o0 in enumerate(input_signal_windows.permute(2, 0, 1)):
windowed_output_signal = padded_output_waveform[:, :, i_sample : i_sample + n_order]
o0 -= (windowed_output_signal.transpose(0, 1) @ a_coeffs_flipped)[..., 0].t()
padded_output_waveform[:, :, i_sample + n_order - 1] = o0
try:
_lfilter_core_cpu_loop = torch.ops.torchaudio._lfilter_core_loop
except RuntimeError as err:
assert str(err) == "No such operator torchaudio::_lfilter_core_loop"
_lfilter_core_cpu_loop = _lfilter_core_generic_loop
def _lfilter_core(
waveform: Tensor,
a_coeffs: Tensor,
b_coeffs: Tensor,
) -> Tensor:
assert a_coeffs.size() == b_coeffs.size()
assert len(waveform.size()) == 3
assert waveform.device == a_coeffs.device
assert b_coeffs.device == a_coeffs.device
n_batch, n_channel, n_sample = waveform.size()
n_order = a_coeffs.size(1)
assert n_order > 0
# Pad the input and create output
padded_waveform = torch.nn.functional.pad(waveform, [n_order - 1, 0])
padded_output_waveform = torch.zeros_like(padded_waveform)
# Set up the coefficients matrix
# Flip coefficients' order
a_coeffs_flipped = a_coeffs.flip(1)
b_coeffs_flipped = b_coeffs.flip(1)
# calculate windowed_input_signal in parallel using convolution
input_signal_windows = torch.nn.functional.conv1d(padded_waveform, b_coeffs_flipped.unsqueeze(1), groups=n_channel)
input_signal_windows.div_(a_coeffs[:, :1])
a_coeffs_flipped.div_(a_coeffs[:, :1])
if (
input_signal_windows.device == torch.device("cpu")
and a_coeffs_flipped.device == torch.device("cpu")
and padded_output_waveform.device == torch.device("cpu")
):
_lfilter_core_cpu_loop(input_signal_windows, a_coeffs_flipped, padded_output_waveform)
else:
_lfilter_core_generic_loop(input_signal_windows, a_coeffs_flipped, padded_output_waveform)
output = padded_output_waveform[:, :, n_order - 1 :]
return output
try:
_lfilter = torch.ops.torchaudio._lfilter
except RuntimeError as err:
assert str(err) == "No such operator torchaudio::_lfilter"
_lfilter = _lfilter_core
[docs]def lfilter(waveform: Tensor, a_coeffs: Tensor, b_coeffs: Tensor, clamp: bool = True, batching: bool = True) -> Tensor:
r"""Perform an IIR filter by evaluating difference equation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Note:
To avoid numerical problems, small filter order is preferred.
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.
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)`.
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)`.
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``)
batching (bool, optional): Effective only when coefficients are 2D. If ``True``, then waveform should be at
least 2D, and the size of second axis from last should equals to ``num_filters``.
The output can be expressed as ``output[..., i, :] = lfilter(waveform[..., i, :],
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.
"""
assert a_coeffs.size() == b_coeffs.size()
assert a_coeffs.ndim <= 2
if a_coeffs.ndim > 1:
if batching:
assert waveform.ndim > 1
assert waveform.shape[-2] == a_coeffs.shape[0]
else:
waveform = torch.stack([waveform] * a_coeffs.shape[0], -2)
else:
a_coeffs = a_coeffs.unsqueeze(0)
b_coeffs = b_coeffs.unsqueeze(0)
# pack batch
shape = waveform.size()
waveform = waveform.reshape(-1, a_coeffs.shape[0], shape[-1])
output = _lfilter(waveform, a_coeffs, b_coeffs)
if clamp:
output = torch.clamp(output, min=-1.0, max=1.0)
# unpack batch
output = output.reshape(shape[:-1] + output.shape[-1:])
return output
[docs]def lowpass_biquad(waveform: Tensor, sample_rate: int, cutoff_freq: float, Q: float = 0.707) -> Tensor:
r"""Design biquad lowpass filter and perform filtering. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (torch.Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
cutoff_freq (float or torch.Tensor): filter cutoff frequency
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``)
Returns:
Tensor: Waveform of dimension of `(..., time)`
"""
dtype = waveform.dtype
device = waveform.device
cutoff_freq = torch.as_tensor(cutoff_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
w0 = 2 * math.pi * cutoff_freq / sample_rate
alpha = torch.sin(w0) / 2 / Q
b0 = (1 - torch.cos(w0)) / 2
b1 = 1 - torch.cos(w0)
b2 = b0
a0 = 1 + alpha
a1 = -2 * torch.cos(w0)
a2 = 1 - alpha
return biquad(waveform, b0, b1, b2, a0, a1, a2)
def _overdrive_core_loop_generic(
waveform: Tensor, temp: Tensor, last_in: Tensor, last_out: Tensor, output_waveform: Tensor
):
for i in range(waveform.shape[-1]):
last_out = temp[:, i] - last_in + 0.995 * last_out
last_in = temp[:, i]
output_waveform[:, i] = waveform[:, i] * 0.5 + last_out * 0.75
try:
_overdrive_core_loop_cpu = torch.ops.torchaudio._overdrive_core_loop
except RuntimeError as err:
assert str(err) == "No such operator torchaudio::_overdrive_core_loop"
_overdrive_core_loop_cpu = _overdrive_core_loop_generic
[docs]def overdrive(waveform: Tensor, gain: float = 20, colour: float = 20) -> Tensor:
r"""Apply a overdrive effect to the audio. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
This effect applies a non linear distortion to the audio signal.
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
gain (float, optional): desired gain at the boost (or attenuation) in dB
Allowed range of values are 0 to 100
colour (float, optional): controls the amount of even harmonic content in the over-driven output
Allowed range of values are 0 to 100
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
"""
actual_shape = waveform.shape
device, dtype = waveform.device, waveform.dtype
# convert to 2D (..,time)
waveform = waveform.view(-1, actual_shape[-1])
gain = _dB2Linear(gain)
colour = colour / 200
last_in = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device)
last_out = torch.zeros(waveform.shape[:-1], dtype=dtype, device=device)
temp = waveform * gain + colour
mask1 = temp < -1
temp[mask1] = torch.tensor(-2.0 / 3.0, dtype=dtype, device=device)
# Wrapping the constant with Tensor is required for Torchscript
mask2 = temp > 1
temp[mask2] = torch.tensor(2.0 / 3.0, dtype=dtype, device=device)
mask3 = ~mask1 & ~mask2
temp[mask3] = temp[mask3] - (temp[mask3] ** 3) * (1.0 / 3)
output_waveform = torch.zeros_like(waveform, dtype=dtype, device=device)
# Uses CPU optimized loop function if available for CPU device
if device == torch.device("cpu"):
_overdrive_core_loop_cpu(waveform, temp, last_in, last_out, output_waveform)
else:
_overdrive_core_loop_generic(waveform, temp, last_in, last_out, output_waveform)
return output_waveform.clamp(min=-1, max=1).view(actual_shape)
[docs]def phaser(
waveform: Tensor,
sample_rate: int,
gain_in: float = 0.4,
gain_out: float = 0.74,
delay_ms: float = 3.0,
decay: float = 0.4,
mod_speed: float = 0.5,
sinusoidal: bool = True,
) -> Tensor:
r"""Apply a phasing effect to the audio. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
gain_in (float, optional): desired input gain at the boost (or attenuation) in dB
Allowed range of values are 0 to 1
gain_out (float, optional): desired output gain at the boost (or attenuation) in dB
Allowed range of values are 0 to 1e9
delay_ms (float, optional): desired delay in milliseconds
Allowed range of values are 0 to 5.0
decay (float, optional): desired decay relative to gain-in
Allowed range of values are 0 to 0.99
mod_speed (float, optional): modulation speed in Hz
Allowed range of values are 0.1 to 2
sinusoidal (bool, optional): If ``True``, uses sinusoidal modulation (preferable for multiple instruments)
If ``False``, uses triangular modulation (gives single instruments a sharper phasing effect)
(Default: ``True``)
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- Scott Lehman, `Effects Explained`_.
.. _Effects Explained:
https://web.archive.org/web/20051125072557/http://www.harmony-central.com/Effects/effects-explained.html
"""
actual_shape = waveform.shape
device, dtype = waveform.device, waveform.dtype
# convert to 2D (channels,time)
waveform = waveform.view(-1, actual_shape[-1])
delay_buf_len = int((delay_ms * 0.001 * sample_rate) + 0.5)
delay_buf = torch.zeros(waveform.shape[0], delay_buf_len, dtype=dtype, device=device)
mod_buf_len = int(sample_rate / mod_speed + 0.5)
if sinusoidal:
wave_type = "SINE"
else:
wave_type = "TRIANGLE"
mod_buf = _generate_wave_table(
wave_type=wave_type,
data_type="INT",
table_size=mod_buf_len,
min=1.0,
max=float(delay_buf_len),
phase=math.pi / 2,
device=device,
)
delay_pos = 0
mod_pos = 0
output_waveform_pre_gain_list = []
waveform = waveform * gain_in
delay_buf = delay_buf * decay
waveform_list = [waveform[:, i] for i in range(waveform.size(1))]
delay_buf_list = [delay_buf[:, i] for i in range(delay_buf.size(1))]
mod_buf_list = [mod_buf[i] for i in range(mod_buf.size(0))]
for i in range(waveform.shape[-1]):
idx = int((delay_pos + mod_buf_list[mod_pos]) % delay_buf_len)
mod_pos = (mod_pos + 1) % mod_buf_len
delay_pos = (delay_pos + 1) % delay_buf_len
temp = (waveform_list[i]) + (delay_buf_list[idx])
delay_buf_list[delay_pos] = temp * decay
output_waveform_pre_gain_list.append(temp)
output_waveform = torch.stack(output_waveform_pre_gain_list, dim=1).to(dtype=dtype, device=device)
output_waveform.mul_(gain_out)
return output_waveform.clamp(min=-1, max=1).view(actual_shape)
[docs]def riaa_biquad(waveform: Tensor, sample_rate: int) -> Tensor:
r"""Apply RIAA vinyl playback equalization. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz).
Allowed sample rates in Hz : ``44100``,``48000``,``88200``,``96000``
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
"""
if sample_rate == 44100:
zeros = [-0.2014898, 0.9233820]
poles = [0.7083149, 0.9924091]
elif sample_rate == 48000:
zeros = [-0.1766069, 0.9321590]
poles = [0.7396325, 0.9931330]
elif sample_rate == 88200:
zeros = [-0.1168735, 0.9648312]
poles = [0.8590646, 0.9964002]
elif sample_rate == 96000:
zeros = [-0.1141486, 0.9676817]
poles = [0.8699137, 0.9966946]
else:
raise ValueError("Sample rate must be 44.1k, 48k, 88.2k, or 96k")
# polynomial coefficients with roots zeros[0] and zeros[1]
b0 = 1.0
b1 = -(zeros[0] + zeros[1])
b2 = zeros[0] * zeros[1]
# polynomial coefficients with roots poles[0] and poles[1]
a0 = 1.0
a1 = -(poles[0] + poles[1])
a2 = poles[0] * poles[1]
# Normalize to 0dB at 1kHz
y = 2 * math.pi * 1000 / sample_rate
b_re = b0 + b1 * math.cos(-y) + b2 * math.cos(-2 * y)
a_re = a0 + a1 * math.cos(-y) + a2 * math.cos(-2 * y)
b_im = b1 * math.sin(-y) + b2 * math.sin(-2 * y)
a_im = a1 * math.sin(-y) + a2 * math.sin(-2 * y)
g = 1 / math.sqrt((b_re**2 + b_im**2) / (a_re**2 + a_im**2))
b0 *= g
b1 *= g
b2 *= g
return biquad(waveform, b0, b1, b2, a0, a1, a2)
[docs]def treble_biquad(
waveform: Tensor,
sample_rate: int,
gain: float,
central_freq: float = 3000,
Q: float = 0.707,
) -> Tensor:
r"""Design a treble tone-control effect. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: Autograd TorchScript
Args:
waveform (Tensor): audio waveform of dimension of `(..., time)`
sample_rate (int): sampling rate of the waveform, e.g. 44100 (Hz)
gain (float or torch.Tensor): desired gain at the boost (or attenuation) in dB.
central_freq (float or torch.Tensor, optional): central frequency (in Hz). (Default: ``3000``)
Q (float or torch.Tensor, optional): https://en.wikipedia.org/wiki/Q_factor (Default: ``0.707``).
Returns:
Tensor: Waveform of dimension of `(..., time)`
Reference:
- http://sox.sourceforge.net/sox.html
- https://www.w3.org/2011/audio/audio-eq-cookbook.html#APF
"""
dtype = waveform.dtype
device = waveform.device
central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
gain = torch.as_tensor(gain, dtype=dtype, device=device)
w0 = 2 * math.pi * central_freq / sample_rate
alpha = torch.sin(w0) / 2 / Q
A = torch.exp(gain / 40 * math.log(10))
temp1 = 2 * torch.sqrt(A) * alpha
temp2 = (A - 1) * torch.cos(w0)
temp3 = (A + 1) * torch.cos(w0)
b0 = A * ((A + 1) + temp2 + temp1)
b1 = -2 * A * ((A - 1) + temp3)
b2 = A * ((A + 1) + temp2 - temp1)
a0 = (A + 1) - temp2 + temp1
a1 = 2 * ((A - 1) - temp3)
a2 = (A + 1) - temp2 - temp1
return biquad(waveform, b0, b1, b2, a0, a1, a2)
def _measure(
measure_len_ws: int,
samples: Tensor,
spectrum: Tensor,
noise_spectrum: Tensor,
spectrum_window: Tensor,
spectrum_start: int,
spectrum_end: int,
cepstrum_window: Tensor,
cepstrum_start: int,
cepstrum_end: int,
noise_reduction_amount: float,
measure_smooth_time_mult: float,
noise_up_time_mult: float,
noise_down_time_mult: float,
index_ns: int,
boot_count: int,
) -> float:
assert spectrum.size()[-1] == noise_spectrum.size()[-1]
samplesLen_ns = samples.size()[-1]
dft_len_ws = spectrum.size()[-1]
dftBuf = torch.zeros(dft_len_ws)
_index_ns = torch.tensor([index_ns] + [(index_ns + i) % samplesLen_ns for i in range(1, measure_len_ws)])
dftBuf[:measure_len_ws] = samples[_index_ns] * spectrum_window[:measure_len_ws]
# memset(c->dftBuf + i, 0, (p->dft_len_ws - i) * sizeof(*c->dftBuf));
dftBuf[measure_len_ws:dft_len_ws].zero_()
# lsx_safe_rdft((int)p->dft_len_ws, 1, c->dftBuf);
_dftBuf = torch.fft.rfft(dftBuf)
# memset(c->dftBuf, 0, p->spectrum_start * sizeof(*c->dftBuf));
_dftBuf[:spectrum_start].zero_()
mult: float = boot_count / (1.0 + boot_count) if boot_count >= 0 else measure_smooth_time_mult
_d = _dftBuf[spectrum_start:spectrum_end].abs()
spectrum[spectrum_start:spectrum_end].mul_(mult).add_(_d * (1 - mult))
_d = spectrum[spectrum_start:spectrum_end] ** 2
_zeros = torch.zeros(spectrum_end - spectrum_start)
_mult = (
_zeros
if boot_count >= 0
else torch.where(
_d > noise_spectrum[spectrum_start:spectrum_end],
torch.tensor(noise_up_time_mult), # if
torch.tensor(noise_down_time_mult), # else
)
)
noise_spectrum[spectrum_start:spectrum_end].mul_(_mult).add_(_d * (1 - _mult))
_d = torch.sqrt(
torch.max(
_zeros,
_d - noise_reduction_amount * noise_spectrum[spectrum_start:spectrum_end],
)
)
_cepstrum_Buf: Tensor = torch.zeros(dft_len_ws >> 1)
_cepstrum_Buf[spectrum_start:spectrum_end] = _d * cepstrum_window
_cepstrum_Buf[spectrum_end : dft_len_ws >> 1].zero_()
# lsx_safe_rdft((int)p->dft_len_ws >> 1, 1, c->dftBuf);
_cepstrum_Buf = torch.fft.rfft(_cepstrum_Buf)
result: float = float(torch.sum(_cepstrum_Buf[cepstrum_start:cepstrum_end].abs().pow(2)))
result = math.log(result / (cepstrum_end - cepstrum_start)) if result > 0 else -math.inf
return max(0, 21 + result)
[docs]def vad(
waveform: Tensor,
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,
# Fine-tuning parameters
boot_time: float = 0.35,
noise_up_time: float = 0.1,
noise_down_time: float = 0.01,
noise_reduction_amount: float = 1.35,
measure_freq: float = 20.0,
measure_duration: Optional[float] = None,
measure_smooth_time: float = 0.4,
hp_filter_freq: float = 50.0,
lp_filter_freq: float = 6000.0,
hp_lifter_freq: float = 150.0,
lp_lifter_freq: float = 2000.0,
) -> Tensor:
r"""Voice Activity Detector. Similar to SoX implementation.
.. devices:: CPU CUDA
.. properties:: TorchScript
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:
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.
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
quieter/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, 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)
Returns:
Tensor: Tensor of audio of dimension `(..., time)`.
Reference:
- http://sox.sourceforge.net/sox.html
"""
if waveform.ndim > 2:
warnings.warn(
"Expected input tensor dimension of 1 for single channel"
f" or 2 for multi-channel. Got {waveform.ndim} instead. "
"Batch semantics is not supported. "
"Please refer to https://github.com/pytorch/audio/issues/1348"
" and https://github.com/pytorch/audio/issues/1468."
)
measure_duration: float = 2.0 / measure_freq if measure_duration is None else measure_duration
measure_len_ws = int(sample_rate * measure_duration + 0.5)
measure_len_ns = measure_len_ws
# for (dft_len_ws = 16; dft_len_ws < measure_len_ws; dft_len_ws <<= 1);
dft_len_ws = 16
while dft_len_ws < measure_len_ws:
dft_len_ws *= 2
measure_period_ns = int(sample_rate / measure_freq + 0.5)
measures_len = math.ceil(search_time * measure_freq)
search_pre_trigger_len_ns = measures_len * measure_period_ns
gap_len = int(allowed_gap * measure_freq + 0.5)
fixed_pre_trigger_len_ns = int(pre_trigger_time * sample_rate + 0.5)
samplesLen_ns = fixed_pre_trigger_len_ns + search_pre_trigger_len_ns + measure_len_ns
spectrum_window = torch.zeros(measure_len_ws)
for i in range(measure_len_ws):
# sox.h:741 define SOX_SAMPLE_MIN (sox_sample_t)SOX_INT_MIN(32)
spectrum_window[i] = 2.0 / math.sqrt(float(measure_len_ws))
# lsx_apply_hann(spectrum_window, (int)measure_len_ws);
spectrum_window *= torch.hann_window(measure_len_ws, dtype=torch.float)
spectrum_start: int = int(hp_filter_freq / sample_rate * dft_len_ws + 0.5)
spectrum_start: int = max(spectrum_start, 1)
spectrum_end: int = int(lp_filter_freq / sample_rate * dft_len_ws + 0.5)
spectrum_end: int = min(spectrum_end, dft_len_ws // 2)
cepstrum_window = torch.zeros(spectrum_end - spectrum_start)
for i in range(spectrum_end - spectrum_start):
cepstrum_window[i] = 2.0 / math.sqrt(float(spectrum_end) - spectrum_start)
# lsx_apply_hann(cepstrum_window,(int)(spectrum_end - spectrum_start));
cepstrum_window *= torch.hann_window(spectrum_end - spectrum_start, dtype=torch.float)
cepstrum_start = math.ceil(sample_rate * 0.5 / lp_lifter_freq)
cepstrum_end = math.floor(sample_rate * 0.5 / hp_lifter_freq)
cepstrum_end = min(cepstrum_end, dft_len_ws // 4)
assert cepstrum_end > cepstrum_start
noise_up_time_mult = math.exp(-1.0 / (noise_up_time * measure_freq))
noise_down_time_mult = math.exp(-1.0 / (noise_down_time * measure_freq))
measure_smooth_time_mult = math.exp(-1.0 / (measure_smooth_time * measure_freq))
trigger_meas_time_mult = math.exp(-1.0 / (trigger_time * measure_freq))
boot_count_max = int(boot_time * measure_freq - 0.5)
measure_timer_ns = measure_len_ns
boot_count = measures_index = flushedLen_ns = samplesIndex_ns = 0
# pack batch
shape = waveform.size()
waveform = waveform.view(-1, shape[-1])
n_channels, ilen = waveform.size()
mean_meas = torch.zeros(n_channels)
samples = torch.zeros(n_channels, samplesLen_ns)
spectrum = torch.zeros(n_channels, dft_len_ws)
noise_spectrum = torch.zeros(n_channels, dft_len_ws)
measures = torch.zeros(n_channels, measures_len)
has_triggered: bool = False
num_measures_to_flush: int = 0
pos: int = 0
while pos < ilen and not has_triggered:
measure_timer_ns -= 1
for i in range(n_channels):
samples[i, samplesIndex_ns] = waveform[i, pos]
# if (!p->measure_timer_ns) {
if measure_timer_ns == 0:
index_ns: int = (samplesIndex_ns + samplesLen_ns - measure_len_ns) % samplesLen_ns
meas: float = _measure(
measure_len_ws=measure_len_ws,
samples=samples[i],
spectrum=spectrum[i],
noise_spectrum=noise_spectrum[i],
spectrum_window=spectrum_window,
spectrum_start=spectrum_start,
spectrum_end=spectrum_end,
cepstrum_window=cepstrum_window,
cepstrum_start=cepstrum_start,
cepstrum_end=cepstrum_end,
noise_reduction_amount=noise_reduction_amount,
measure_smooth_time_mult=measure_smooth_time_mult,
noise_up_time_mult=noise_up_time_mult,
noise_down_time_mult=noise_down_time_mult,
index_ns=index_ns,
boot_count=boot_count,
)
measures[i, measures_index] = meas
mean_meas[i] = mean_meas[i] * trigger_meas_time_mult + meas * (1.0 - trigger_meas_time_mult)
has_triggered = has_triggered or (mean_meas[i] >= trigger_level)
if has_triggered:
n: int = measures_len
k: int = measures_index
jTrigger: int = n
jZero: int = n
j: int = 0
for j in range(n):
if (measures[i, k] >= trigger_level) and (j <= jTrigger + gap_len):
jZero = jTrigger = j
elif (measures[i, k] == 0) and (jTrigger >= jZero):
jZero = j
k = (k + n - 1) % n
j = min(j, jZero)
# num_measures_to_flush = range_limit(j, num_measures_to_flush, n);
num_measures_to_flush = min(max(num_measures_to_flush, j), n)
# end if has_triggered
# end if (measure_timer_ns == 0):
# end for
samplesIndex_ns += 1
pos += 1
# end while
if samplesIndex_ns == samplesLen_ns:
samplesIndex_ns = 0
if measure_timer_ns == 0:
measure_timer_ns = measure_period_ns
measures_index += 1
measures_index = measures_index % measures_len
if boot_count >= 0:
boot_count = -1 if boot_count == boot_count_max else boot_count + 1
if has_triggered:
flushedLen_ns = (measures_len - num_measures_to_flush) * measure_period_ns
samplesIndex_ns = (samplesIndex_ns + flushedLen_ns) % samplesLen_ns
res = waveform[:, pos - samplesLen_ns + flushedLen_ns :]
# unpack batch
return res.view(shape[:-1] + res.shape[-1:])
