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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
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)
}
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