added dataset for SILK to LPCNet feature conversion

1 files changed, 132 insertions, 0 deletions

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 00000000..8f646756
--- /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
+            }