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243 lines
8.1 KiB
243 lines
8.1 KiB
"""
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Low level signal processing utilities
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Authors
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* Peter Plantinga 2020
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* Francois Grondin 2020
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* William Aris 2020
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* Samuele Cornell 2020
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* Sarthak Yadav 2022
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"""
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import paddle
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import math
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from packaging import version
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import numpy as np
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def blackman_window(window_length, periodic=True):
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"""Blackman window function.
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Arguments
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---------
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window_length : int
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Controlling the returned window size.
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periodic : bool
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Determines whether the returned window trims off the
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last duplicate value from the symmetric window
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Returns
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-------
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A 1-D tensor of size (window_length) containing the window
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"""
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if window_length == 0:
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return []
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if window_length == 1:
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return paddle.ones([1])
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if periodic:
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window_length += 1
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window = paddle.arange(window_length) * (np.pi / (window_length - 1))
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window = 0.08 * paddle.cos(window * 4) - 0.5 * paddle.cos(window * 2) + 0.42
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return window[:-1] if periodic else window
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def compute_amplitude(waveforms, lengths=None, amp_type="avg", scale="linear"):
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"""Compute amplitude of a batch of waveforms.
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Arguments
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---------
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waveform : tensor
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The waveforms used for computing amplitude.
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Shape should be `[time]` or `[batch, time]` or
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`[batch, time, channels]`.
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lengths : tensor
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The lengths of the waveforms excluding the padding.
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Shape should be a single dimension, `[batch]`.
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amp_type : str
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Whether to compute "avg" average or "peak" amplitude.
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Choose between ["avg", "peak"].
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scale : str
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Whether to compute amplitude in "dB" or "linear" scale.
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Choose between ["linear", "dB"].
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Returns
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-------
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The average amplitude of the waveforms.
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Example
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-------
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>>> signal = paddle.sin(paddle.arange(16000.0)).unsqueeze(0)
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>>> compute_amplitude(signal, signal.size(1))
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tensor([[0.6366]])
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"""
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if len(waveforms.shape) == 1:
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waveforms = waveforms.unsqueeze(0)
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assert amp_type in ["avg", "peak"]
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assert scale in ["linear", "dB"]
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if amp_type == "avg":
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if lengths is None:
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out = paddle.mean(paddle.abs(waveforms), axis=1, keepdim=True)
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else:
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wav_sum = paddle.sum(paddle.abs(waveforms), axis=1, keepdim=True)
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out = wav_sum / lengths
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elif amp_type == "peak":
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out = paddle.max(paddle.abs(waveforms), axis=1, keepdim=True)[0]
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else:
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raise NotImplementedError
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if scale == "linear":
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return out
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elif scale == "dB":
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return paddle.clip(20 * paddle.log10(out), min=-80) # clamp zeros
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else:
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raise NotImplementedError
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def convolve1d(
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waveform,
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kernel,
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padding=0,
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pad_type="constant",
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stride=1,
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groups=1,
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use_fft=False,
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rotation_index=0,
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):
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"""Use paddle.nn.functional to perform 1d padding and conv.
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Arguments
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---------
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waveform : tensor
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The tensor to perform operations on.
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kernel : tensor
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The filter to apply during convolution.
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padding : int or tuple
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The padding (pad_left, pad_right) to apply.
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If an integer is passed instead, this is passed
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to the conv1d function and pad_type is ignored.
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pad_type : str
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The type of padding to use. Passed directly to
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`paddle.nn.functional.pad`, see Paddle documentation
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for available options.
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stride : int
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The number of units to move each time convolution is applied.
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Passed to conv1d. Has no effect if `use_fft` is True.
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groups : int
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This option is passed to `conv1d` to split the input into groups for
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convolution. Input channels should be divisible by the number of groups.
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use_fft : bool
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When `use_fft` is passed `True`, then compute the convolution in the
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spectral domain using complex multiply. This is more efficient on CPU
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when the size of the kernel is large (e.g. reverberation). WARNING:
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Without padding, circular convolution occurs. This makes little
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difference in the case of reverberation, but may make more difference
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with different kernels.
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rotation_index : int
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This option only applies if `use_fft` is true. If so, the kernel is
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rolled by this amount before convolution to shift the output location.
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Returns
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-------
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The convolved waveform.
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Example
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-------
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>>> from speechbrain.dataio.dataio import read_audio
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>>> signal = read_audio('tests/samples/single-mic/example1.wav')
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>>> signal = signal.unsqueeze(0).unsqueeze(2)
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>>> kernel = paddle.rand([1, 10, 1])
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>>> signal = convolve1d(signal, kernel, padding=(9, 0))
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"""
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if len(waveform.shape) != 3:
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raise ValueError("Convolve1D expects a 3-dimensional tensor")
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# Move time dimension last, which pad and fft and conv expect.
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waveform = waveform.transpose([0, 2, 1])
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kernel = kernel.transpose([0, 2, 1])
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# Padding can be a tuple (left_pad, right_pad) or an int
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if isinstance(padding, tuple):
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waveform = paddle.nn.functional.pad(
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x=waveform, pad=padding, mode=pad_type, data_format='NCL'
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)
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# This approach uses FFT, which is more efficient if the kernel is large
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if use_fft:
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# Pad kernel to same length as signal, ensuring correct alignment
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zero_length = waveform.shape[-1] - kernel.shape[-1]
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# Handle case where signal is shorter
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if zero_length < 0:
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kernel = kernel[..., :zero_length]
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zero_length = 0
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# Perform rotation to ensure alignment
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zeros = paddle.zeros(
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[kernel.shape[0], kernel.shape[1], zero_length],
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dtype=kernel.dtype
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)
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after_index = kernel[..., rotation_index:]
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before_index = kernel[..., :rotation_index]
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kernel = paddle.concat((after_index, zeros, before_index), axis=-1)
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# Multiply in frequency domain to convolve in time domain
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import paddle.fft as fft
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result = fft.rfft(waveform) * fft.rfft(kernel)
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convolved = fft.irfft(result, n=waveform.shape[-1])
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# Use the implementation given by paddle, which should be efficient on GPU
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else:
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convolved = paddle.nn.functional.conv1d(
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x=waveform,
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weight=kernel,
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stride=stride,
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groups=groups,
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padding=padding if not isinstance(padding, tuple) else 0,
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)
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# Return time dimension to the second dimension.
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return convolved.transpose([0, 2, 1])
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def notch_filter(notch_freq, filter_width=101, notch_width=0.05):
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"""Returns a notch filter constructed from a high-pass and low-pass filter.
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(from https://tomroelandts.com/articles/
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how-to-create-simple-band-pass-and-band-reject-filters)
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Arguments
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---------
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notch_freq : float
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frequency to put notch as a fraction of the
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sampling rate / 2. The range of possible inputs is 0 to 1.
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filter_width : int
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Filter width in samples. Longer filters have
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smaller transition bands, but are more inefficient.
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notch_width : float
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Width of the notch, as a fraction of the sampling_rate / 2.
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"""
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# Check inputs
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assert 0 < notch_freq <= 1
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assert filter_width % 2 != 0
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pad = filter_width // 2
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inputs = paddle.arange(filter_width) - pad
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# Avoid frequencies that are too low
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notch_freq += notch_width
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# Define sinc function, avoiding division by zero
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def sinc(x):
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"Computes the sinc function."
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def _sinc(x):
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return paddle.sin(x) / x
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# The zero is at the middle index
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return paddle.concat([_sinc(x[:pad]), paddle.ones([1]), _sinc(x[pad + 1 :])])
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# Compute a low-pass filter with cutoff frequency notch_freq.
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hlpf = sinc(3 * (notch_freq - notch_width) * inputs)
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hlpf *= blackman_window(filter_width)
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hlpf /= paddle.sum(hlpf)
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# Compute a high-pass filter with cutoff frequency notch_freq.
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hhpf = sinc(3 * (notch_freq + notch_width) * inputs)
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hhpf *= blackman_window(filter_width)
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hhpf /= -paddle.sum(hhpf)
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hhpf[pad] += 1
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# Adding filters creates notch filter
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return (hlpf + hhpf).view(1, -1, 1)
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