""" /* 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 from torch.utils.data import Dataset import numpy as np from utils.silk_features import silk_feature_factory from utils.pitch import hangover, calculate_acorr_window class SilkEnhancementSet(Dataset): def __init__(self, path, frames_per_sample=100, no_pitch_value=256, preemph=0.85, skip=91, 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, ): assert frames_per_sample % 4 == 0 self.frame_size = 80 self.frames_per_sample = frames_per_sample self.no_pitch_value = no_pitch_value self.preemph = preemph self.skip = skip self.acorr_radius = acorr_radius self.pitch_hangover = pitch_hangover self.num_bands_clean_spec = num_bands_clean_spec self.num_bands_noisy_spec = num_bands_noisy_spec self.noisy_spec_scale = noisy_spec_scale self.add_double_lag_acorr = add_double_lag_acorr self.lpcs = np.fromfile(os.path.join(path, 'features_lpc.f32'), dtype=np.float32).reshape(-1, 16) self.ltps = np.fromfile(os.path.join(path, 'features_ltp.f32'), dtype=np.float32).reshape(-1, 5) self.periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16) self.gains = np.fromfile(os.path.join(path, 'features_gain.f32'), dtype=np.float32) self.num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32) self.num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32) self.clean_signal_hp = np.fromfile(os.path.join(path, 'clean_hp.s16'), dtype=np.int16) self.clean_signal = np.fromfile(os.path.join(path, 'clean.s16'), dtype=np.int16) self.coded_signal = np.fromfile(os.path.join(path, 'coded.s16'), dtype=np.int16) self.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) self.history_len = 700 if add_double_lag_acorr else 350 # discard some frames to have enough signal history self.skip_frames = 4 * ((skip + self.history_len + 319) // 320 + 2) num_frames = self.clean_signal_hp.shape[0] // 80 - self.skip_frames self.len = num_frames // frames_per_sample def __len__(self): return self.len def __getitem__(self, index): frame_start = self.frames_per_sample * index + self.skip_frames frame_stop = frame_start + self.frames_per_sample signal_start = frame_start * self.frame_size - self.skip signal_stop = frame_stop * self.frame_size - self.skip clean_signal_hp = self.clean_signal_hp[signal_start : signal_stop].astype(np.float32) / 2**15 clean_signal = self.clean_signal[signal_start : signal_stop].astype(np.float32) / 2**15 coded_signal = self.coded_signal[signal_start : signal_stop].astype(np.float32) / 2**15 coded_signal_history = self.coded_signal[signal_start - self.history_len : signal_start].astype(np.float32) / 2**15 features, periods = self.create_features( coded_signal, coded_signal_history, self.lpcs[frame_start : frame_stop], self.gains[frame_start : frame_stop], self.ltps[frame_start : frame_stop], self.periods[frame_start : frame_stop] ) if self.preemph > 0: clean_signal[1:] -= self.preemph * clean_signal[: -1] clean_signal_hp[1:] -= self.preemph * clean_signal_hp[: -1] coded_signal[1:] -= self.preemph * coded_signal[: -1] num_bits = np.repeat(self.num_bits[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) num_bits_smooth = np.repeat(self.num_bits_smooth[frame_start // 4 : frame_stop // 4], 4).astype(np.float32).reshape(-1, 1) numbits = np.concatenate((num_bits, num_bits_smooth), axis=-1) return { 'features' : features, 'periods' : periods.astype(np.int64), 'target_orig' : clean_signal.astype(np.float32), 'target' : clean_signal_hp.astype(np.float32), 'signals' : coded_signal.reshape(-1, 1).astype(np.float32), 'numbits' : numbits.astype(np.float32) }