fix(windows): vendor audiopus_sys + patch libopus for clang-cl SIMD

cargo-xwin drives the Windows MSVC cross-compile via clang-cl, under
which CMake sets MSVC=1 — causing libopus 1.3.1's `if(NOT MSVC)` guards
to skip the per-file `-msse4.1` / `-mssse3` COMPILE_FLAGS that its x86
SIMD source files need. Clang-cl (unlike real cl.exe) still honors
Clang's target-feature system, so those files then fail to compile
with "always_inline function '_mm_cvtepi16_epi32' requires target
feature 'sse4.1'" errors across silk/NSQ_sse4_1.c, NSQ_del_dec_sse4_1.c,
and VQ_WMat_EC_sse4_1.c.

Earlier attempts to fix this downstream (cargo-xwin toolchain file,
override.cmake CMAKE_C_COMPILE_OBJECT <FLAGS> replace, CFLAGS env vars)
all failed because cargo-xwin rewrites override.cmake from scratch on
every `cargo xwin build` invocation and cmake-rs's -DCMAKE_C_FLAGS=
assembly happens before toolchain FORCE sets propagate.

Fixing it upstream at the source: vendor audiopus_sys 0.2.2 into
vendor/audiopus_sys, patch its bundled opus/CMakeLists.txt to introduce
an MSVC_CL var (true only when CMAKE_C_COMPILER_ID == "MSVC", i.e. real
cl.exe), and flip the eight `if(NOT MSVC)` SIMD guards to
`if(NOT MSVC_CL)`. Clang-cl then gets the GCC-style per-file flags and
the SSE4.1 sources build cleanly. Also flip the `if(MSVC)` global /arch
block at line 445 to `if(MSVC_CL)` so only cl.exe applies /arch:AVX and
clang-cl relies purely on per-file flags (no global/per-file mixing).

Wire via [patch.crates-io] in the workspace root Cargo.toml; the patch
is resolved relative to the workspace root as `vendor/audiopus_sys`.

Upstream context: xiph/opus#256, xiph/opus PR #257 (both stale).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

vendor/audiopus_sys/opus/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()