From 101fd2411a697eebbbcf1079006fb2af27e1ae86 Mon Sep 17 00:00:00 2001
From: Jan Buethe <jbuethe@amazon.de>
Date: Mon, 24 Jul 2023 14:02:35 -0700
Subject: [PATCH] added dataset for SILK to LPCNet feature conversion
---
dnn/torch/osce/data/silk_conversion_set.py | 132 +++++++++++++++++++++
1 file changed, 132 insertions(+)
create mode 100644 dnn/torch/osce/data/silk_conversion_set.py
diff --git a/dnn/torch/osce/data/silk_conversion_set.py b/dnn/torch/osce/data/silk_conversion_set.py
new file mode 100644
index 000000000..8f6467561
--- /dev/null
+++ b/dnn/torch/osce/data/silk_conversion_set.py
@@ -0,0 +1,132 @@
+"""
+/* 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=9,
+ 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_offset=False,
+ 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.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.offsets = np.fromfile(os.path.join(path, 'features_offset.f32'), dtype=np.float32)
+ self.lpcnet_features = np.from_file(os.path.join(path, 'features_lpcnet.f32'), dtype=np.float32).reshape(-1, 36)
+
+ 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_offset,
+ 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 * ((self.history_len + 319) // 320 + 2)
+
+ num_frames = self.clean_signal.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
+
+ 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],
+ self.offsets[frame_start : frame_stop]
+ )
+
+ lpcnet_features = self.lpcnet_features[frame_start // 2 : frame_stop // 2, :20]
+
+ 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 {
+ 'silk_features' : features,
+ 'periods' : periods.astype(np.int64),
+ 'numbits' : numbits.astype(np.float32),
+ 'lpcnet_features' : lpcnet_features
+ }
--
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