From 7d5c8eb943f92a94b420568778d61e0bd6f7df43 Mon Sep 17 00:00:00 2001
From: claude-bot <claude@ancap.in.ua>
Date: Mon, 13 Jul 2026 12:40:01 +0000
Subject: Import xiph/opus @ 034c1b61a250457649d788bbf983b3f0fb63f02e

Snapshot for re3/reVC vendoring, per @lzcnt.
Source: https://github.com/xiph/opus (034c1b61a250457649d788bbf983b3f0fb63f02e).
---
 scripts/dump_rnn.py | 57 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)
 create mode 100755 scripts/dump_rnn.py

(limited to 'scripts/dump_rnn.py')

diff --git a/scripts/dump_rnn.py b/scripts/dump_rnn.py
new file mode 100755
index 0000000..dd66403
--- /dev/null
+++ b/scripts/dump_rnn.py
@@ -0,0 +1,57 @@
+#!/usr/bin/python
+
+from __future__ import print_function
+
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM
+from keras.layers import GRU
+from keras.models import load_model
+from keras import backend as K
+
+import numpy as np
+
+def printVector(f, vector, name):
+    v = np.reshape(vector, (-1));
+    #print('static const float ', name, '[', len(v), '] = \n', file=f)
+    f.write('static const opus_int16 {}[{}] = {{\n   '.format(name, len(v)))
+    for i in range(0, len(v)):
+        f.write('{}'.format(int(round(8192*v[i]))))
+        if (i!=len(v)-1):
+            f.write(',')
+        else:
+            break;
+        if (i%8==7):
+            f.write("\n   ")
+        else:
+            f.write(" ")
+    #print(v, file=f)
+    f.write('\n};\n\n')
+    return;
+
+def binary_crossentrop2(y_true, y_pred):
+        return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
+
+
+model = load_model("weights.hdf5", custom_objects={'binary_crossentrop2': binary_crossentrop2})
+
+weights = model.get_weights()
+
+f = open('rnn_weights.c', 'w')
+
+f.write('/*This file is automatically generated from a Keras model*/\n\n')
+f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "mlp.h"\n\n')
+
+printVector(f, weights[0], 'layer0_weights')
+printVector(f, weights[1], 'layer0_bias')
+printVector(f, weights[2], 'layer1_weights')
+printVector(f, weights[3], 'layer1_recur_weights')
+printVector(f, weights[4], 'layer1_bias')
+printVector(f, weights[5], 'layer2_weights')
+printVector(f, weights[6], 'layer2_bias')
+
+f.write('const DenseLayer layer0 = {\n   layer0_bias,\n   layer0_weights,\n   25, 16, 0\n};\n\n')
+f.write('const GRULayer layer1 = {\n   layer1_bias,\n   layer1_weights,\n   layer1_recur_weights,\n   16, 12\n};\n\n')
+f.write('const DenseLayer layer2 = {\n   layer2_bias,\n   layer2_weights,\n   12, 2, 1\n};\n\n')
+
+f.close()
-- 
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