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"""
/* 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 os
import numpy as np
import torch
import scipy
import scipy.signal
from utils.pitch import hangover, calculate_acorr_window
from utils.spec import create_filter_bank, cepstrum, log_spectrum, log_spectrum_from_lpc
def spec_from_lpc(a, n_fft=128, eps=1e-9):
order = a.shape[-1]
assert order + 1 < n_fft
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]) ** 2
S = 1 / (X + eps)
return S
def silk_feature_factory(no_pitch_value=256,
acorr_radius=2,
pitch_hangover=8,
num_bands_clean_spec=64,
num_bands_noisy_spec=18,
noisy_spec_scale='opus',
noisy_apply_dct=True,
add_double_lag_acorr=False
):
w = scipy.signal.windows.cosine(320)
fb_clean_spec = create_filter_bank(num_bands_clean_spec, 320, scale='erb', round_center_bins=True, normalize=True)
fb_noisy_spec = create_filter_bank(num_bands_noisy_spec, 320, scale=noisy_spec_scale, round_center_bins=True, normalize=True)
def create_features(noisy, noisy_history, lpcs, gains, ltps, periods):
periods = periods.copy()
if pitch_hangover > 0:
periods = hangover(periods, num_frames=pitch_hangover)
periods[periods == 0] = no_pitch_value
clean_spectrum = 0.3 * log_spectrum_from_lpc(lpcs, fb=fb_clean_spec, n_fft=320)
if noisy_apply_dct:
noisy_cepstrum = np.repeat(
cepstrum(np.concatenate((noisy_history[-160:], noisy), dtype=np.float32), 320, fb_noisy_spec, w), 2, 0)
else:
noisy_cepstrum = np.repeat(
log_spectrum(np.concatenate((noisy_history[-160:], noisy), dtype=np.float32), 320, fb_noisy_spec, w), 2, 0)
log_gains = np.log(gains + 1e-9).reshape(-1, 1)
acorr, _ = calculate_acorr_window(noisy, 80, periods, noisy_history, radius=acorr_radius, add_double_lag_acorr=add_double_lag_acorr)
features = np.concatenate((clean_spectrum, noisy_cepstrum, acorr, ltps, log_gains), axis=-1, dtype=np.float32)
return features, periods.astype(np.int64)
return create_features
def load_inference_data(path,
no_pitch_value=256,
skip=92,
preemph=0.85,
acorr_radius=2,
pitch_hangover=8,
num_bands_clean_spec=64,
num_bands_noisy_spec=18,
noisy_spec_scale='opus',
noisy_apply_dct=True,
add_double_lag_acorr=False,
**kwargs):
print(f"[load_inference_data]: ignoring keyword arguments {kwargs.keys()}...")
lpcs = np.fromfile(os.path.join(path, 'features_lpc.f32'), dtype=np.float32).reshape(-1, 16)
ltps = np.fromfile(os.path.join(path, 'features_ltp.f32'), dtype=np.float32).reshape(-1, 5)
gains = np.fromfile(os.path.join(path, 'features_gain.f32'), dtype=np.float32)
periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16)
num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32).astype(np.float32).reshape(-1, 1)
num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32).reshape(-1, 1)
# load signal, add back delay and pre-emphasize
signal = np.fromfile(os.path.join(path, 'noisy.s16'), dtype=np.int16).astype(np.float32) / (2 ** 15)
signal = np.concatenate((np.zeros(skip, dtype=np.float32), signal), dtype=np.float32)
create_features = silk_feature_factory(no_pitch_value, acorr_radius, pitch_hangover, num_bands_clean_spec, num_bands_noisy_spec, noisy_spec_scale, noisy_apply_dct, add_double_lag_acorr)
num_frames = min((len(signal) // 320) * 4, len(lpcs))
signal = signal[: num_frames * 80]
lpcs = lpcs[: num_frames]
ltps = ltps[: num_frames]
gains = gains[: num_frames]
periods = periods[: num_frames]
num_bits = num_bits[: num_frames // 4]
num_bits_smooth = num_bits[: num_frames // 4]
numbits = np.repeat(np.concatenate((num_bits, num_bits_smooth), axis=-1, dtype=np.float32), 4, axis=0)
features, periods = create_features(signal, np.zeros(350, dtype=signal.dtype), lpcs, gains, ltps, periods)
if preemph > 0:
signal[1:] -= preemph * signal[:-1]
return torch.from_numpy(signal), torch.from_numpy(features), torch.from_numpy(periods), torch.from_numpy(numbits)
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