0683dde5d3
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>
67 lines
2.2 KiB
Python
Executable File
67 lines
2.2 KiB
Python
Executable File
#!/usr/bin/python
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from __future__ import print_function
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from keras.models import Sequential
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from keras.models import Model
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from keras.layers import Input
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from keras.layers import Dense
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from keras.layers import LSTM
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from keras.layers import GRU
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from keras.models import load_model
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from keras import backend as K
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import sys
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import numpy as np
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def printVector(f, vector, name):
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v = np.reshape(vector, (-1));
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#print('static const float ', name, '[', len(v), '] = \n', file=f)
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f.write('static const opus_int8 {}[{}] = {{\n '.format(name, len(v)))
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for i in range(0, len(v)):
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f.write('{}'.format(max(-128,min(127,int(round(128*v[i]))))))
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if (i!=len(v)-1):
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f.write(',')
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else:
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break;
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if (i%8==7):
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f.write("\n ")
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else:
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f.write(" ")
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#print(v, file=f)
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f.write('\n};\n\n')
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return;
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def binary_crossentrop2(y_true, y_pred):
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return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
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#model = load_model(sys.argv[1], custom_objects={'binary_crossentrop2': binary_crossentrop2})
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main_input = Input(shape=(None, 25), name='main_input')
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x = Dense(32, activation='tanh')(main_input)
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x = GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x)
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x = Dense(2, activation='sigmoid')(x)
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model = Model(inputs=main_input, outputs=x)
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model.load_weights(sys.argv[1])
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weights = model.get_weights()
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f = open(sys.argv[2], 'w')
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f.write('/*This file is automatically generated from a Keras model*/\n\n')
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f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "mlp.h"\n\n')
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printVector(f, weights[0], 'layer0_weights')
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printVector(f, weights[1], 'layer0_bias')
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printVector(f, weights[2], 'layer1_weights')
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printVector(f, weights[3], 'layer1_recur_weights')
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printVector(f, weights[4], 'layer1_bias')
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printVector(f, weights[5], 'layer2_weights')
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printVector(f, weights[6], 'layer2_bias')
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f.write('const DenseLayer layer0 = {\n layer0_bias,\n layer0_weights,\n 25, 32, 0\n};\n\n')
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f.write('const GRULayer layer1 = {\n layer1_bias,\n layer1_weights,\n layer1_recur_weights,\n 32, 24\n};\n\n')
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f.write('const DenseLayer layer2 = {\n layer2_bias,\n layer2_weights,\n 24, 2, 1\n};\n\n')
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f.close()
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