""" /* Copyright (c) 2023 Amazon Written by Jan Buethe */ /* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ """ import math as m import numpy as np import scipy import scipy.fftpack import torch def erb(f): return 24.7 * (4.37 * f + 1) def inv_erb(e): return (e / 24.7 - 1) / 4.37 def bark(f): return 6 * m.asinh(f/600) def inv_bark(b): return 600 * m.sinh(b / 6) scale_dict = { 'bark': [bark, inv_bark], 'erb': [erb, inv_erb] } def gen_filterbank(N, Fs=16000, keep_size=False): in_freq = (np.arange(N+1, dtype='float32')/N*Fs/2)[None,:] M = N + 1 if keep_size else N out_freq = (np.arange(M, dtype='float32')/N*Fs/2)[:,None] #ERB from B.C.J Moore, An Introduction to the Psychology of Hearing, 5th Ed., page 73. ERB_N = 24.7 + .108*in_freq delta = np.abs(in_freq-out_freq)/ERB_N center = (delta<.5).astype('float32') R = -12*center*delta**2 + (1-center)*(3-12*delta) RE = 10.**(R/10.) norm = np.sum(RE, axis=1) RE = RE/norm[:, np.newaxis] return torch.from_numpy(RE) def create_filter_bank(num_bands, n_fft=320, fs=16000, scale='bark', round_center_bins=False, return_upper=False, normalize=False): f0 = 0 num_bins = n_fft // 2 + 1 f1 = fs / n_fft * (num_bins - 1) fstep = fs / n_fft if scale == 'opus': bins_5ms = [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 34, 40] fac = 1000 * n_fft / fs / 5 if num_bands != 18: print("warning: requested Opus filter bank with num_bands != 18. Adjusting num_bands.") num_bands = 18 center_bins = np.array([fac * bin for bin in bins_5ms]) else: to_scale, from_scale = scale_dict[scale] s0 = to_scale(f0) s1 = to_scale(f1) center_freqs = np.array([f0] + [from_scale(s0 + i * (s1 - s0) / (num_bands)) for i in range(1, num_bands - 1)] + [f1]) center_bins = (center_freqs - f0) / fstep if round_center_bins: center_bins = np.round(center_bins) filter_bank = np.zeros((num_bands, num_bins)) band = 0 for bin in range(num_bins): # update band index if bin > center_bins[band + 1]: band += 1 # calculate filter coefficients frac = (center_bins[band + 1] - bin) / (center_bins[band + 1] - center_bins[band]) filter_bank[band][bin] = frac filter_bank[band + 1][bin] = 1 - frac if return_upper: extend = n_fft - num_bins filter_bank = np.concatenate((filter_bank, np.fliplr(filter_bank[:, 1:extend+1])), axis=1) if normalize: filter_bank = filter_bank / np.sum(filter_bank, axis=1).reshape(-1, 1) return filter_bank def compressed_log_spec(pspec): lpspec = np.zeros_like(pspec) num_bands = pspec.shape[-1] log_max = -2 follow = -2 for i in range(num_bands): tmp = np.log10(pspec[i] + 1e-9) tmp = max(log_max, max(follow - 2.5, tmp)) lpspec[i] = tmp log_max = max(log_max, tmp) follow = max(follow - 2.5, tmp) return lpspec def log_spectrum_from_lpc(a, fb=None, n_fft=320, eps=1e-9, gamma=1, compress=False, power=1): """ calculates cepstrum from SILK lpcs """ order = a.shape[-1] assert order + 1 < n_fft a = a * (gamma ** (1 + np.arange(order))).astype(np.float32) x = np.zeros((*a.shape[:-1], n_fft )) x[..., 0] = 1 x[..., 1:1 + order] = -a X = np.fft.fft(x, axis=-1) X = np.abs(X[..., :n_fft//2 + 1]) ** power S = 1 / (X + eps) if fb is None: Sf = S else: Sf = np.matmul(S, fb.T) if compress: Sf = np.apply_along_axis(compressed_log_spec, -1, Sf) else: Sf = np.log(Sf + eps) return Sf def cepstrum_from_lpc(a, fb=None, n_fft=320, eps=1e-9, gamma=1, compress=False): """ calculates cepstrum from SILK lpcs """ Sf = log_spectrum_from_lpc(a, fb, n_fft, eps, gamma, compress) cepstrum = scipy.fftpack.dct(Sf, 2, norm='ortho') return cepstrum def log_spectrum(x, frame_size, fb=None, window=None, power=1): """ calculate cepstrum on 50% overlapping frames """ assert(2*len(x)) % frame_size == 0 assert frame_size % 2 == 0 n = len(x) num_even = n // frame_size num_odd = (n - frame_size // 2) // frame_size num_bins = frame_size // 2 + 1 x_even = x[:num_even * frame_size].reshape(-1, frame_size) x_odd = x[frame_size//2 : frame_size//2 + frame_size * num_odd].reshape(-1, frame_size) x_unfold = np.empty((x_even.size + x_odd.size), dtype=x.dtype).reshape((-1, frame_size)) x_unfold[::2, :] = x_even x_unfold[1::2, :] = x_odd if window is not None: x_unfold *= window.reshape(1, -1) X = np.abs(np.fft.fft(x_unfold, n=frame_size, axis=-1))[:, :num_bins] ** power if fb is not None: X = np.matmul(X, fb.T) return np.log(X + 1e-9) def cepstrum(x, frame_size, fb=None, window=None): """ calculate cepstrum on 50% overlapping frames """ X = log_spectrum(x, frame_size, fb, window) cepstrum = scipy.fftpack.dct(X, 2, norm='ortho') return cepstrum