From 4787439e38162dfac8a70ccade68103fa0df6147 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Tue, 28 Aug 2018 09:48:23 -0400 Subject: [PATCH 01/23] Modularizing the RNN model --- Makefile.am | 30 ++++++++-------- configure.ac | 4 +-- examples/rnnoise_demo.c | 4 +-- include/{rnnoise.h => rnnoise-nu.h} | 5 +-- ...lled.pc.in => rnnoise-nu-uninstalled.pc.in | 6 ++-- rnnoise.pc.in => rnnoise-nu.pc.in | 6 ++-- src/denoise.c | 22 ++++++++---- src/rnn.c | 24 ++++++------- src/rnn.h | 1 + src/rnn_data.c | 32 +++++++++++++---- src/rnn_data.h | 35 ++++++++++--------- 11 files changed, 100 insertions(+), 69 deletions(-) rename include/{rnnoise.h => rnnoise-nu.h} (92%) rename rnnoise-uninstalled.pc.in => rnnoise-nu-uninstalled.pc.in (57%) rename rnnoise.pc.in => rnnoise-nu.pc.in (67%) diff --git a/Makefile.am b/Makefile.am index a004dc62..0134e61f 100644 --- a/Makefile.am +++ b/Makefile.am @@ -4,9 +4,9 @@ AM_CFLAGS = -I$(top_srcdir)/include $(DEPS_CFLAGS) dist_doc_DATA = COPYING AUTHORS README -include_HEADERS = include/rnnoise.h +include_HEADERS = include/rnnoise-nu.h -lib_LTLIBRARIES = librnnoise.la +lib_LTLIBRARIES = librnnoise-nu.la noinst_HEADERS = src/arch.h \ src/celt_lpc.h \ src/common.h \ @@ -18,7 +18,7 @@ noinst_HEADERS = src/arch.h \ src/rnn.h \ src/tansig_table.h -librnnoise_la_SOURCES = \ +librnnoise_nu_la_SOURCES = \ src/denoise.c \ src/rnn.c \ src/rnn_data.c \ @@ -26,34 +26,34 @@ librnnoise_la_SOURCES = \ src/kiss_fft.c \ src/celt_lpc.c -librnnoise_la_LIBADD = $(DEPS_LIBS) $(lrintf_lib) $(LIBM) -librnnoise_la_LDFLAGS = -no-undefined \ +librnnoise_nu_la_LIBADD = $(DEPS_LIBS) $(lrintf_lib) $(LIBM) +librnnoise_nu_la_LDFLAGS = -no-undefined \ -version-info @OP_LT_CURRENT@:@OP_LT_REVISION@:@OP_LT_AGE@ if OP_ENABLE_EXAMPLES -noinst_PROGRAMS = examples/rnnoise_demo +noinst_PROGRAMS = examples/rnnoise_nu_demo endif -examples_rnnoise_demo_SOURCES = examples/rnnoise_demo.c -examples_rnnoise_demo_LDADD = librnnoise.la +examples_rnnoise_nu_demo_SOURCES = examples/rnnoise_demo.c +examples_rnnoise_nu_demo_LDADD = librnnoise-nu.la pkgconfigdir = $(libdir)/pkgconfig -pkgconfig_DATA = rnnoise.pc +pkgconfig_DATA = rnnoise-nu.pc debug: $(MAKE) CFLAGS="${CFLAGS} -O0 -ggdb -DOP_ENABLE_ASSERTIONS" all EXTRA_DIST = \ - rnnoise.pc.in \ - rnnoise-uninstalled.pc.in \ + rnnoise-nu.pc.in \ + rnnoise-nu-uninstalled.pc.in \ doc/Doxyfile.in \ doc/Makefile # Targets to build and install just the library without the docs -librnnoise install-librnnoise: NO_DOXYGEN = 1 +librnnoise-nu install-librnnoise-nu: NO_DOXYGEN = 1 -rnnoise: all -install-rnnoise: install +rnnoise-nu: all +install-rnnoise-nu: install # Or just the docs docs: doc/doxygen-build.stamp @@ -126,4 +126,4 @@ dist-hook: echo 'PACKAGE_VERSION="$(PACKAGE_VERSION)"' > $(top_distdir)/package_version -.PHONY: rnnoise install-rnnoise docs install-docs +.PHONY: rnnoise-nu install-rnnoise-nu docs install-docs diff --git a/configure.ac b/configure.ac index 5ffc7c2d..e583302b 100644 --- a/configure.ac +++ b/configure.ac @@ -108,8 +108,8 @@ AM_CONDITIONAL([HAVE_DOXYGEN], [test "$HAVE_DOXYGEN" = "yes"]) AC_CONFIG_FILES([ Makefile - rnnoise.pc - rnnoise-uninstalled.pc + rnnoise-nu.pc + rnnoise-nu-uninstalled.pc doc/Doxyfile ]) AC_CONFIG_HEADERS([config.h]) diff --git a/examples/rnnoise_demo.c b/examples/rnnoise_demo.c index e1e239a2..b9ad0588 100644 --- a/examples/rnnoise_demo.c +++ b/examples/rnnoise_demo.c @@ -25,7 +25,7 @@ */ #include -#include "rnnoise.h" +#include "rnnoise-nu.h" #define FRAME_SIZE 480 @@ -35,7 +35,7 @@ int main(int argc, char **argv) { float x[FRAME_SIZE]; FILE *f1, *fout; DenoiseState *st; - st = rnnoise_create(); + st = rnnoise_create(NULL); if (argc!=3) { fprintf(stderr, "usage: %s \n", argv[0]); return 1; diff --git a/include/rnnoise.h b/include/rnnoise-nu.h similarity index 92% rename from include/rnnoise.h rename to include/rnnoise-nu.h index 562b34cb..f1804da2 100644 --- a/include/rnnoise.h +++ b/include/rnnoise-nu.h @@ -40,12 +40,13 @@ typedef struct DenoiseState DenoiseState; +typedef struct RNNModel RNNModel; RNNOISE_EXPORT int rnnoise_get_size(); -RNNOISE_EXPORT int rnnoise_init(DenoiseState *st); +RNNOISE_EXPORT int rnnoise_init(DenoiseState *st, RNNModel *model); -RNNOISE_EXPORT DenoiseState *rnnoise_create(); +RNNOISE_EXPORT DenoiseState *rnnoise_create(RNNModel *model); RNNOISE_EXPORT void rnnoise_destroy(DenoiseState *st); diff --git a/rnnoise-uninstalled.pc.in b/rnnoise-nu-uninstalled.pc.in similarity index 57% rename from rnnoise-uninstalled.pc.in rename to rnnoise-nu-uninstalled.pc.in index e2af62fe..d08ac80c 100644 --- a/rnnoise-uninstalled.pc.in +++ b/rnnoise-nu-uninstalled.pc.in @@ -5,9 +5,9 @@ exec_prefix= libdir=${pcfiledir}/.libs includedir=${pcfiledir}/@top_srcdir@/include -Name: rnnoise uninstalled -Description: RNN-based noise suppression (not installed) +Name: rnnoise-nu uninstalled +Description: RNN-based noise suppression, improved (not installed) Version: @PACKAGE_VERSION@ Conflicts: -Libs: ${libdir}/librnnoise.la @lrintf_lib@ +Libs: ${libdir}/librnnoise-nu.la @lrintf_lib@ Cflags: -I${includedir} diff --git a/rnnoise.pc.in b/rnnoise-nu.pc.in similarity index 67% rename from rnnoise.pc.in rename to rnnoise-nu.pc.in index 8c132500..d54460b4 100644 --- a/rnnoise.pc.in +++ b/rnnoise-nu.pc.in @@ -5,10 +5,10 @@ exec_prefix=@exec_prefix@ libdir=@libdir@ includedir=@includedir@ -Name: rnnoise -Description: RNN-based noise suppression +Name: rnnoise-nu +Description: RNN-based noise suppression, improved Version: @PACKAGE_VERSION@ Conflicts: -Libs: -L${libdir} -lrnnoise +Libs: -L${libdir} -lrnnoise-nu Libs.private: @lrintf_lib@ Cflags: -I${includedir}/ diff --git a/src/denoise.c b/src/denoise.c index 128cd999..75a947f0 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -34,7 +34,7 @@ #include "kiss_fft.h" #include "common.h" #include -#include "rnnoise.h" +#include "rnnoise-nu.h" #include "pitch.h" #include "arch.h" #include "rnn.h" @@ -70,6 +70,10 @@ #define TRAINING 0 #endif + +extern const struct RNNModel model_orig; + + static const opus_int16 eband5ms[] = { /*0 200 400 600 800 1k 1.2 1.4 1.6 2k 2.4 2.8 3.2 4k 4.8 5.6 6.8 8k 9.6 12k 15.6 20k*/ 0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 34, 40, 48, 60, 78, 100 @@ -284,15 +288,19 @@ int rnnoise_get_size() { return sizeof(DenoiseState); } -int rnnoise_init(DenoiseState *st) { +int rnnoise_init(DenoiseState *st, RNNModel *model) { memset(st, 0, sizeof(*st)); + if (model) + st->rnn.model = model; + else + st->rnn.model = &model_orig; return 0; } -DenoiseState *rnnoise_create() { +DenoiseState *rnnoise_create(RNNModel *model) { DenoiseState *st; st = malloc(rnnoise_get_size()); - rnnoise_init(st); + rnnoise_init(st, model); return st; } @@ -542,9 +550,9 @@ int main(int argc, char **argv) { DenoiseState *st; DenoiseState *noise_state; DenoiseState *noisy; - st = rnnoise_create(); - noise_state = rnnoise_create(); - noisy = rnnoise_create(); + st = rnnoise_create(NULL); + noise_state = rnnoise_create(NULL); + noisy = rnnoise_create(NULL); if (argc!=4) { fprintf(stderr, "usage: %s \n", argv[0]); return 1; diff --git a/src/rnn.c b/src/rnn.c index 1daa7b6c..c54958eb 100644 --- a/src/rnn.c +++ b/src/rnn.c @@ -162,17 +162,17 @@ void compute_rnn(RNNState *rnn, float *gains, float *vad, const float *input) { float dense_out[MAX_NEURONS]; float noise_input[MAX_NEURONS*3]; float denoise_input[MAX_NEURONS*3]; - compute_dense(&input_dense, dense_out, input); - compute_gru(&vad_gru, rnn->vad_gru_state, dense_out); - compute_dense(&vad_output, vad, rnn->vad_gru_state); - for (i=0;ivad_gru_state[i]; - for (i=0;inoise_gru_state, noise_input); + compute_dense(rnn->model->input_dense, dense_out, input); + compute_gru(rnn->model->vad_gru, rnn->vad_gru_state, dense_out); + compute_dense(rnn->model->vad_output, vad, rnn->vad_gru_state); + for (i=0;imodel->input_dense_size;i++) noise_input[i] = dense_out[i]; + for (i=0;imodel->vad_gru_size;i++) noise_input[i+rnn->model->input_dense_size] = rnn->vad_gru_state[i]; + for (i=0;imodel->input_dense_size+rnn->model->vad_gru_size] = input[i]; + compute_gru(rnn->model->noise_gru, rnn->noise_gru_state, noise_input); - for (i=0;ivad_gru_state[i]; - for (i=0;inoise_gru_state[i]; - for (i=0;idenoise_gru_state, denoise_input); - compute_dense(&denoise_output, gains, rnn->denoise_gru_state); + for (i=0;imodel->vad_gru_size;i++) denoise_input[i] = rnn->vad_gru_state[i]; + for (i=0;imodel->noise_gru_size;i++) denoise_input[i+rnn->model->vad_gru_size] = rnn->noise_gru_state[i]; + for (i=0;imodel->vad_gru_size+rnn->model->noise_gru_size] = input[i]; + compute_gru(rnn->model->denoise_gru, rnn->denoise_gru_state, denoise_input); + compute_dense(rnn->model->denoise_output, gains, rnn->denoise_gru_state); } diff --git a/src/rnn.h b/src/rnn.h index 9e08b44a..e7638957 100644 --- a/src/rnn.h +++ b/src/rnn.h @@ -56,6 +56,7 @@ typedef struct { int activation; } GRULayer; +typedef struct RNNModel RNNModel; typedef struct RNNState RNNState; void compute_dense(const DenseLayer *layer, float *output, const float *input); diff --git a/src/rnn_data.c b/src/rnn_data.c index 8f6c99bb..82a07f1d 100644 --- a/src/rnn_data.c +++ b/src/rnn_data.c @@ -5,6 +5,7 @@ #endif #include "rnn.h" +#include "rnn_data.h" static const rnn_weight input_dense_weights[1008] = { -10, 0, -3, 1, -8, -6, 3, -13, @@ -141,7 +142,7 @@ static const rnn_weight input_dense_bias[24] = { -126, 28, 127, 125, -30, 127, -89, -20 }; -const DenseLayer input_dense = { +static const DenseLayer input_dense = { input_dense_bias, input_dense_weights, 42, 24, ACTIVATION_TANH @@ -597,7 +598,7 @@ static const rnn_weight vad_gru_bias[72] = { -29, 127, 34, -66, 49, 53, 27, 62 }; -const GRULayer vad_gru = { +static const GRULayer vad_gru = { vad_gru_bias, vad_gru_weights, vad_gru_recurrent_weights, @@ -3115,7 +3116,7 @@ static const rnn_weight noise_gru_bias[144] = { -23, -64, 31, 86, -50, 2, -38, 7 }; -const GRULayer noise_gru = { +static const GRULayer noise_gru = { noise_gru_bias, noise_gru_weights, noise_gru_recurrent_weights, @@ -10727,7 +10728,7 @@ static const rnn_weight denoise_gru_bias[288] = { -21, 25, 18, -58, 25, 126, -84, 127 }; -const GRULayer denoise_gru = { +static const GRULayer denoise_gru = { denoise_gru_bias, denoise_gru_weights, denoise_gru_recurrent_weights, @@ -11007,7 +11008,7 @@ static const rnn_weight denoise_output_bias[22] = { -126, -105, -53, -49, -18, -9 }; -const DenseLayer denoise_output = { +static const DenseLayer denoise_output = { denoise_output_bias, denoise_output_weights, 96, 22, ACTIVATION_SIGMOID @@ -11023,9 +11024,28 @@ static const rnn_weight vad_output_bias[1] = { -50 }; -const DenseLayer vad_output = { +static const DenseLayer vad_output = { vad_output_bias, vad_output_weights, 24, 1, ACTIVATION_SIGMOID }; +const struct RNNModel model_orig = { + 24, + &input_dense, + + 24, + &vad_gru, + + 48, + &noise_gru, + + 96, + &denoise_gru, + + 22, + &denoise_output, + + 1, + &vad_output +}; diff --git a/src/rnn_data.h b/src/rnn_data.h index 56109804..2aa41f90 100644 --- a/src/rnn_data.h +++ b/src/rnn_data.h @@ -1,32 +1,33 @@ -/*This file is automatically generated from a Keras model*/ - #ifndef RNN_DATA_H #define RNN_DATA_H #include "rnn.h" -#define INPUT_DENSE_SIZE 24 -extern const DenseLayer input_dense; +struct RNNModel { + int input_dense_size; + DenseLayer *input_dense; -#define VAD_GRU_SIZE 24 -extern const GRULayer vad_gru; + int vad_gru_size; + GRULayer *vad_gru; -#define NOISE_GRU_SIZE 48 -extern const GRULayer noise_gru; + int noise_gru_size; + GRULayer *noise_gru; -#define DENOISE_GRU_SIZE 96 -extern const GRULayer denoise_gru; + int denoise_gru_size; + GRULayer *denoise_gru; -#define DENOISE_OUTPUT_SIZE 22 -extern const DenseLayer denoise_output; + int denoise_output_size; + DenseLayer *denoise_output; -#define VAD_OUTPUT_SIZE 1 -extern const DenseLayer vad_output; + int vad_output_size; + DenseLayer *vad_output; +}; struct RNNState { - float vad_gru_state[VAD_GRU_SIZE]; - float noise_gru_state[NOISE_GRU_SIZE]; - float denoise_gru_state[DENOISE_GRU_SIZE]; + const RNNModel *model; + float *vad_gru_state; + float *noise_gru_state; + float *denoise_gru_state; }; From 931e946b7b3a8de1d9a530667c57c48f8bdf3b94 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Tue, 28 Aug 2018 09:58:28 -0400 Subject: [PATCH 02/23] Fix some warnings --- doc/Doxyfile.in | 2 +- include/rnnoise-nu.h | 5 +++++ src/denoise.c | 33 ++++++--------------------------- src/rnn.h | 3 ++- src/rnn_data.h | 12 ++++++------ 5 files changed, 20 insertions(+), 35 deletions(-) diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in index b5438b31..6a52157f 100644 --- a/doc/Doxyfile.in +++ b/doc/Doxyfile.in @@ -3,7 +3,7 @@ PROJECT_NAME = @PACKAGE_NAME@ PROJECT_NUMBER = @PACKAGE_VERSION@ PROJECT_BRIEF = "RNN-based noise suppressor." -INPUT = @top_srcdir@/include/rnnoise.h +INPUT = @top_srcdir@/include/rnnoise-nu.h OPTIMIZE_OUTPUT_FOR_C = YES QUIET = YES diff --git a/include/rnnoise-nu.h b/include/rnnoise-nu.h index f1804da2..2b1bdecc 100644 --- a/include/rnnoise-nu.h +++ b/include/rnnoise-nu.h @@ -24,6 +24,9 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ +#ifndef RNNOISE_NU_H +#define RNNOISE_NU_H 1 + #ifndef RNNOISE_EXPORT # if defined(WIN32) # if defined(RNNOISE_BUILD) && defined(DLL_EXPORT) @@ -51,3 +54,5 @@ RNNOISE_EXPORT DenoiseState *rnnoise_create(RNNModel *model); RNNOISE_EXPORT void rnnoise_destroy(DenoiseState *st); RNNOISE_EXPORT float rnnoise_process_frame(DenoiseState *st, float *out, const float *in); + +#endif diff --git a/src/denoise.c b/src/denoise.c index 75a947f0..91e2c85d 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -546,7 +546,8 @@ int main(int argc, char **argv) { int vad_cnt=0; int gain_change_count=0; float speech_gain = 1, noise_gain = 1; - FILE *f1, *f2, *fout; + FILE *f1, *f2; + int maxCount; DenoiseState *st; DenoiseState *noise_state; DenoiseState *noisy; @@ -554,12 +555,12 @@ int main(int argc, char **argv) { noise_state = rnnoise_create(NULL); noisy = rnnoise_create(NULL); if (argc!=4) { - fprintf(stderr, "usage: %s \n", argv[0]); + fprintf(stderr, "usage: %s \n", argv[0]); return 1; } f1 = fopen(argv[1], "r"); f2 = fopen(argv[2], "r"); - fout = fopen(argv[3], "w"); + maxCount = atoi(argv[3]); for(i=0;i<150;i++) { short tmp[FRAME_SIZE]; fread(tmp, sizeof(short), FRAME_SIZE, f2); @@ -571,12 +572,11 @@ int main(int argc, char **argv) { float Ln[NB_BANDS]; float features[NB_FEATURES]; float g[NB_BANDS]; - float gf[FREQ_SIZE]={1}; short tmp[FRAME_SIZE]; float vad=0; - float vad_prob; float E=0; - if (count==50000000) break; + if (count==maxCount) break; + if ((count%1000)==0) fprintf(stderr, "%d\r", count); if (++gain_change_count > 2821) { speech_gain = pow(10., (-40+(rand()%60))/20.); noise_gain = pow(10., (-30+(rand()%50))/20.); @@ -651,37 +651,16 @@ int main(int argc, char **argv) { if (vad==0 && noise_gain==0) g[i] = -1; } count++; -#if 0 - for (i=0;irnn, g, &vad_prob, features); - interp_band_gain(gf, g); -#if 1 - for (i=0;i Date: Tue, 28 Aug 2018 10:40:28 -0400 Subject: [PATCH 03/23] Made dump_rnn output in nu format. --- training/dump_rnn.py | 42 +++++++++++++++++++++++------------------- 1 file changed, 23 insertions(+), 19 deletions(-) diff --git a/training/dump_rnn.py b/training/dump_rnn.py index 9f267a7e..c2b3acab 100755 --- a/training/dump_rnn.py +++ b/training/dump_rnn.py @@ -30,7 +30,7 @@ def printVector(f, vector, name): f.write('\n};\n\n') return; -def printLayer(f, hf, layer): +def printLayer(f, layer): weights = layer.get_weights() printVector(f, weights[0], layer.name + '_weights') if len(weights) > 2: @@ -39,19 +39,24 @@ def printLayer(f, hf, layer): name = layer.name activation = re.search('function (.*) at', str(layer.activation)).group(1).upper() if len(weights) > 2: - f.write('const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n' + f.write('static const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n' .format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]/3, activation)) - hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]/3)) - hf.write('extern const GRULayer {};\n\n'.format(name)); else: - f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n' + f.write('static const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n' .format(name, name, name, weights[0].shape[0], weights[0].shape[1], activation)) - hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1])) - hf.write('extern const DenseLayer {};\n\n'.format(name)); + +def structLayer(f, layer): + weights = layer.get_weights() + name = layer.name + if len(weights) > 2: + f.write(' {},\n'.format(weights[0].shape[1]/3)) + else: + f.write(' {},\n'.format(weights[0].shape[1])) + f.write(' &{},\n'.format(name)) def foo(c, name): - return 1 + return None def mean_squared_sqrt_error(y_true, y_pred): return K.mean(K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1) @@ -62,27 +67,26 @@ def mean_squared_sqrt_error(y_true, y_pred): weights = model.get_weights() f = open(sys.argv[2], 'w') -hf = open(sys.argv[3], '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 "rnn.h"\n\n') -hf.write('/*This file is automatically generated from a Keras model*/\n\n') -hf.write('#ifndef RNN_DATA_H\n#define RNN_DATA_H\n\n#include "rnn.h"\n\n') - layer_list = [] for i, layer in enumerate(model.layers): if len(layer.get_weights()) > 0: - printLayer(f, hf, layer) + printLayer(f, layer) if len(layer.get_weights()) > 2: layer_list.append(layer.name) -hf.write('struct RNNState {\n') -for i, name in enumerate(layer_list): - hf.write(' float {}_state[{}_SIZE];\n'.format(name, name.upper())) -hf.write('};\n') +f.write('const struct RNNModel model_{} = {{\n'.format(sys.argv[3])) +for i, layer in enumerate(model.layers): + if len(layer.get_weights()) > 0: + structLayer(f, layer) +f.write('};\n') -hf.write('\n\n#endif\n') +#hf.write('struct RNNState {\n') +#for i, name in enumerate(layer_list): +# hf.write(' float {}_state[{}_SIZE];\n'.format(name, name.upper())) +#hf.write('};\n') f.close() -hf.close() From b13c5334e9891c55d055c278872f154fea534fbf Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Tue, 28 Aug 2018 23:18:02 -0400 Subject: [PATCH 04/23] Neural network model files Extending the neural network dumper to dump to a simple text file format, and adding reader functions to read a neural network description from a FILE *. --- Makefile.am | 1 + include/rnnoise-nu.h | 8 ++- src/rnn_reader.c | 143 +++++++++++++++++++++++++++++++++++++++++++ training/dump_rnn.py | 23 ++++--- 4 files changed, 167 insertions(+), 8 deletions(-) create mode 100644 src/rnn_reader.c diff --git a/Makefile.am b/Makefile.am index 0134e61f..fcb79564 100644 --- a/Makefile.am +++ b/Makefile.am @@ -22,6 +22,7 @@ librnnoise_nu_la_SOURCES = \ src/denoise.c \ src/rnn.c \ src/rnn_data.c \ + src/rnn_reader.c \ src/pitch.c \ src/kiss_fft.c \ src/celt_lpc.c diff --git a/include/rnnoise-nu.h b/include/rnnoise-nu.h index 2b1bdecc..6de8a1aa 100644 --- a/include/rnnoise-nu.h +++ b/include/rnnoise-nu.h @@ -27,6 +27,9 @@ #ifndef RNNOISE_NU_H #define RNNOISE_NU_H 1 +#include + + #ifndef RNNOISE_EXPORT # if defined(WIN32) # if defined(RNNOISE_BUILD) && defined(DLL_EXPORT) @@ -41,7 +44,6 @@ # endif #endif - typedef struct DenoiseState DenoiseState; typedef struct RNNModel RNNModel; @@ -55,4 +57,8 @@ RNNOISE_EXPORT void rnnoise_destroy(DenoiseState *st); RNNOISE_EXPORT float rnnoise_process_frame(DenoiseState *st, float *out, const float *in); +RNNOISE_EXPORT RNNModel *rnnoise_model_from_file(FILE *f); + +RNNOISE_EXPORT void rnnoise_model_free(RNNModel *model); + #endif diff --git a/src/rnn_reader.c b/src/rnn_reader.c new file mode 100644 index 00000000..3d075ebb --- /dev/null +++ b/src/rnn_reader.c @@ -0,0 +1,143 @@ +/* Copyright (c) 2018 Gregor Richards */ +/* + 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 FOUNDATION 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. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include +#include +#include + +#include "rnn.h" +#include "rnn_data.h" +#include "rnnoise-nu.h" + +RNNModel *rnnoise_model_from_file(FILE *f) +{ + int i, in; + + if (fscanf(f, "rnnoise-nu model file version %d\n", &in) != 1 || in != 1) + return NULL; + + RNNModel *ret = calloc(1, sizeof(RNNModel)); + if (!ret) + return NULL; + +#define ALLOC_LAYER(type, name) \ + type *name; \ + name = calloc(1, sizeof(type)); \ + if (!name) { \ + rnnoise_model_free(ret); \ + return NULL; \ + } \ + ret->name = name + + ALLOC_LAYER(DenseLayer, input_dense); + ALLOC_LAYER(GRULayer, vad_gru); + ALLOC_LAYER(GRULayer, noise_gru); + ALLOC_LAYER(GRULayer, denoise_gru); + ALLOC_LAYER(DenseLayer, denoise_output); + ALLOC_LAYER(DenseLayer, vad_output); + +#define INPUT_VAL(name) do { \ + if (fscanf(f, "%d", &in) != 1 || in < 0 || in > 128) { \ + rnnoise_model_free(ret); \ + return NULL; \ + } \ + name = in; \ + } while (0) + +#define INPUT_ARRAY(name, len) do { \ + rnn_weight *values = malloc((len) * sizeof(rnn_weight)); \ + if (!values) { \ + rnnoise_model_free(ret); \ + return NULL; \ + } \ + name = values; \ + for (i = 0; i < (len); i++) { \ + if (fscanf(f, "%d", &in) != 1) { \ + rnnoise_model_free(ret); \ + return NULL; \ + } \ + values[i] = in; \ + } \ + } while (0) + +#define INPUT_DENSE(name) do { \ + INPUT_VAL(name->nb_inputs); \ + INPUT_VAL(name->nb_neurons); \ + INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \ + INPUT_ARRAY(name->bias, name->nb_neurons); \ + } while (0) + +#define INPUT_GRU(name) do { \ + INPUT_VAL(name->nb_inputs); \ + INPUT_VAL(name->nb_neurons); \ + INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons * 3); \ + INPUT_ARRAY(name->recurrent_weights, name->nb_neurons * name->nb_neurons * 3); \ + INPUT_ARRAY(name->bias, name->nb_neurons * 3); \ + } while (0) + + INPUT_DENSE(input_dense); + INPUT_GRU(vad_gru); + INPUT_GRU(noise_gru); + INPUT_GRU(denoise_gru); + INPUT_DENSE(denoise_output); + INPUT_DENSE(vad_output); + + return ret; +} + +void rnnoise_model_free(RNNModel *model) +{ +#define FREE_MAYBE(ptr) do { if (ptr) free(ptr); } while (0) +#define FREE_DENSE(name) do { \ + if (model->name) { \ + free((void *) model->name->input_weights); \ + free((void *) model->name->bias); \ + free((void *) model->name); \ + } \ + } while (0) +#define FREE_GRU(name) do { \ + if (model->name) { \ + free((void *) model->name->input_weights); \ + free((void *) model->name->recurrent_weights); \ + free((void *) model->name->bias); \ + free((void *) model->name); \ + } \ + } while (0) + + if (!model) + return; + FREE_DENSE(input_dense); + FREE_GRU(vad_gru); + FREE_GRU(noise_gru); + FREE_GRU(denoise_gru); + FREE_DENSE(denoise_output); + FREE_DENSE(vad_output); + free(model); +} diff --git a/training/dump_rnn.py b/training/dump_rnn.py index c2b3acab..797b185d 100755 --- a/training/dump_rnn.py +++ b/training/dump_rnn.py @@ -12,30 +12,37 @@ import re import numpy as np -def printVector(f, vector, name): +def printVector(f, ft, vector, name): v = np.reshape(vector, (-1)); #print('static const float ', name, '[', len(v), '] = \n', file=f) f.write('static const rnn_weight {}[{}] = {{\n '.format(name, len(v))) for i in range(0, len(v)): f.write('{}'.format(min(127, int(round(256*v[i]))))) + ft.write('{}'.format(min(127, int(round(256*v[i]))))) if (i!=len(v)-1): f.write(',') else: break; + ft.write(" ") if (i%8==7): f.write("\n ") else: f.write(" ") #print(v, file=f) f.write('\n};\n\n') + ft.write("\n") return; -def printLayer(f, layer): +def printLayer(f, ft, layer): weights = layer.get_weights() - printVector(f, weights[0], layer.name + '_weights') if len(weights) > 2: - printVector(f, weights[1], layer.name + '_recurrent_weights') - printVector(f, weights[-1], layer.name + '_bias') + ft.write('{} {}\n'.format(weights[0].shape[0], weights[0].shape[1]/3)) + else: + ft.write('{} {}\n'.format(weights[0].shape[0], weights[0].shape[1])) + printVector(f, ft, weights[0], layer.name + '_weights') + if len(weights) > 2: + printVector(f, ft, weights[1], layer.name + '_recurrent_weights') + printVector(f, ft, weights[-1], layer.name + '_bias') name = layer.name activation = re.search('function (.*) at', str(layer.activation)).group(1).upper() if len(weights) > 2: @@ -67,18 +74,20 @@ def mean_squared_sqrt_error(y_true, y_pred): weights = model.get_weights() f = open(sys.argv[2], 'w') +ft = open(sys.argv[3], '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 "rnn.h"\n\n') +ft.write('rnnoise-nu model file version 1\n') layer_list = [] for i, layer in enumerate(model.layers): if len(layer.get_weights()) > 0: - printLayer(f, layer) + printLayer(f, ft, layer) if len(layer.get_weights()) > 2: layer_list.append(layer.name) -f.write('const struct RNNModel model_{} = {{\n'.format(sys.argv[3])) +f.write('const struct RNNModel model_{} = {{\n'.format(sys.argv[4])) for i, layer in enumerate(model.layers): if len(layer.get_weights()) > 0: structLayer(f, layer) From 56701eae756ab046cf13610b9cf9117265962e9a Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Wed, 29 Aug 2018 16:52:44 -0400 Subject: [PATCH 05/23] Since the library itself is incompatible, reset the versioning. --- configure.ac | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/configure.ac b/configure.ac index e583302b..73b1d92f 100644 --- a/configure.ac +++ b/configure.ac @@ -31,16 +31,14 @@ m4_ifdef([AM_SILENT_RULES], [AM_SILENT_RULES([yes])]) AC_DEFINE([RNNOISE_BUILD], [], [This is a build of the library]) dnl Library versioning for libtool. -dnl Please update these for releases. -dnl CURRENT, REVISION, AGE -dnl - library source changed -> increment REVISION -dnl - interfaces added/removed/changed -> increment CURRENT, REVISION = 0 -dnl - interfaces added -> increment AGE -dnl - interfaces removed -> AGE = 0 - -OP_LT_CURRENT=4 -OP_LT_REVISION=1 -OP_LT_AGE=4 +dnl For some inane reason, in .so.major.minor.revision: +dnl - major = CURRENT-AGE +dnl - minor = AGE +dnl - revision = REVISION + +OP_LT_CURRENT=1 +OP_LT_REVISION=0 +OP_LT_AGE=0 AC_SUBST(OP_LT_CURRENT) AC_SUBST(OP_LT_REVISION) From 473e872651af6b92a8c173b006d378a500a15acf Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Wed, 29 Aug 2018 17:20:13 -0400 Subject: [PATCH 06/23] Trimming some fat. --- configure.ac | 36 ++------------- m4/lean.m4 | 119 +++++++++++++++++++++++++++++++++++++++++++++++++ scripts/EMPTY | 0 update_version | 65 --------------------------- 4 files changed, 123 insertions(+), 97 deletions(-) create mode 100644 m4/lean.m4 create mode 100644 scripts/EMPTY delete mode 100755 update_version diff --git a/configure.ac b/configure.ac index 73b1d92f..641f60e1 100644 --- a/configure.ac +++ b/configure.ac @@ -1,24 +1,15 @@ # autoconf source script for generating configure -dnl The package_version file will be automatically synced to the git revision -dnl by the update_version script when configured in the repository, but will -dnl remain constant in tarball releases unless it is manually edited. -m4_define([CURRENT_VERSION], - m4_esyscmd([ ./update_version 2>/dev/null || true - if test -e package_version; then - . ./package_version - printf "$PACKAGE_VERSION" - else - printf "unknown" - fi ])) - -AC_INIT([rnnoise],[CURRENT_VERSION],[jmvalin@jmvalin.ca]) +AC_INIT([rnnoise-nu],[1.0],[gregor.richards@uwaterloo.ca]) AC_CONFIG_SRCDIR([src/denoise.c]) +AC_CONFIG_AUX_DIR([scripts]) AC_CONFIG_MACRO_DIR([m4]) AC_USE_SYSTEM_EXTENSIONS AC_SYS_LARGEFILE +m4_include([m4/lean.m4]) + AM_INIT_AUTOMAKE([1.11 foreign no-define dist-zip subdir-objects]) AM_MAINTAINER_MODE([enable]) @@ -44,25 +35,6 @@ AC_SUBST(OP_LT_CURRENT) AC_SUBST(OP_LT_REVISION) AC_SUBST(OP_LT_AGE) -CC_CHECK_CFLAGS_APPEND( - [-pedantic -Wall -Wextra -Wno-sign-compare -Wno-parentheses -Wno-long-long]) - -# Platform-specific tweaks -case $host in - *-mingw*) - # -std=c89 causes some warnings under mingw. - CC_CHECK_CFLAGS_APPEND([-U__STRICT_ANSI__]) - # We need WINNT>=0x501 (WindowsXP) for getaddrinfo/freeaddrinfo. - # It's okay to define this even when HTTP support is disabled, as it only - # affects header declarations, not linking (unless we actually use some - # XP-only functions). - AC_DEFINE_UNQUOTED(_WIN32_WINNT,0x501, - [We need at least WindowsXP for getaddrinfo/freeaddrinfo]) - host_mingw=true - ;; -esac -AM_CONDITIONAL(OP_WIN32, test "$host_mingw" = "true") - AC_ARG_ENABLE([assertions], AS_HELP_STRING([--enable-assertions], [Enable assertions in code]),, enable_assertions=no) diff --git a/m4/lean.m4 b/m4/lean.m4 new file mode 100644 index 00000000..c7b956b9 --- /dev/null +++ b/m4/lean.m4 @@ -0,0 +1,119 @@ +# autotools lean macros +# hg 2012-09-01 05a8d3fa4611 + +# Copyright (c) 2012 Gregor Richards +# +# Permission to use, copy, modify, and/or distribute this software for any +# purpose with or without fee is hereby granted, provided that the above +# copyright notice and this permission notice appear in all copies. +# +# THE SOFTWARE IS PROVIDED "AS IS" AND ISC DISCLAIMS ALL WARRANTIES WITH REGARD +# TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND +# FITNESS. IN NO EVENT SHALL ISC BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, +# OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF +# USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER +# TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE +# OF THIS SOFTWARE. + +# These macros make auto* tests faster by removing some of autoconf's most +# absurd defaults. The basic principle is to not check for things that have no +# alternatives. That is, don't perform a test if it's either going to pass and +# affect nothing, or fail and just prevent you from building. These tests +# provide very little real value since modern systems that they fail on are few +# and far between. + + +# automake's sanity checks provide nothing useful, since all they can do is +# fail, sometimes spuriously, and prevent builds which may otherwise have +# succeeded. +AC_DEFUN([AM_SANITY_CHECK], [ ]) + + +# Checking for C89 compliance nowadays is just plain silly. +AC_DEFUN([_AC_PROG_CC_C89], [ ]) + + +# For the same reason, checking for C standard headers is usually stupid. +# However, we simply avoid checking for them in the most ridiculous cases. +m4_define([ACX_PRELEAN_AC_CHECK_HEADER], m4_defn([AC_CHECK_HEADER])) +AC_DEFUN([AC_CHECK_HEADER], [ + m4_case([$4], + [], [ACX_PRELEAN_AC_CHECK_HEADER([$1], [$2], [$3], [ ])], + [m4_indir([ACX_PRELEAN_AC_CHECK_HEADER], $@)]) +]) +m4_define([_AC_HEADERS_EXPANSION], [ + m4_divert_text([DEFAULTS], [ac_header_list=]) + AC_CHECK_HEADERS([$ac_header_list], [], [], [ ]) + m4_define([_AC_HEADERS_EXPANSION], []) +]) +m4_define([ACX_PRELEAN_AC_CHECK_SIZEOF], m4_defn([AC_CHECK_SIZEOF])) +AC_DEFUN([AC_CHECK_SIZEOF], [ + m4_case([$3], + [], [ACX_PRELEAN_AC_CHECK_SIZEOF([$1], [], [ ])], + [m4_indir([ACX_PRELEAN_AC_CHECK_SIZEOF])], $@)]) + + +# And add warnings for known-nasty builtin checks +m4_define([ACX_UNLEAN_AC_FUNC_MMAP], m4_defn([AC_FUNC_MMAP])) +AC_DEFUN([AC_FUNC_MMAP], [ + AC_DIAGNOSE([syntax], [$0: AC_FUNC_MMAP does not work in cross environments and incurs high costs. Check for mmap directly if you aren't concerned about enormously-broken implementations. Use ACX_LEAN_AC_FUNC_MMAP to silence this warning.]) + ACX_LEAN_AC_FUNC_MMAP +]) + + +# POSIX says that make sets $(MAKE). That's good enough for me. +AC_DEFUN([AC_PROG_MAKE_SET], [ + ac_cv_prog_make_make_set=yes + SET_MAKE= + AC_SUBST([SET_MAKE]) +]) + + +# configure will simply fail, often spuriously, if you don't tell it that +# you're cross compiling, so there's very little reason to explicitly check. +AC_DEFUN([_AC_COMPILER_EXEEXT_CROSS], [ + if test "$cross_compiling" = "maybe"; then + cross_compiling=yes + fi +]) + + +# Allow the default GCC-and-compatible CFLAGS to be changed +GCC_DEFAULT_CFLAGS="-g -O2" + + +# The builtin -g test is simplified by avoiding rechecks for GCC (of course GCC +# supports -g) +m4_define([ACX_PRELEAN__AC_PROG_CC_G], m4_defn([_AC_PROG_CC_G])) +m4_define([_AC_PROG_CC_G], [ + if test "$GCC" = "yes"; then + acx_lean_CFLAGS_set=${CFLAGS+set} + ac_cv_prog_cc_g=yes + if test "$acx_lean_CFLAGS_set" != "set"; then + CFLAGS="$GCC_DEFAULT_CFLAGS" + fi + else + ACX_PRELEAN__AC_PROG_CC_G + fi +]) + + +# Option to force caching +AC_DEFUN([ACX_LEAN_FORCE_CACHE], [ + m4_define([acx_lean_forced_cache], [yes]) + if test "$cache_file" = "/dev/null"; then + cache_file=config.cache + touch config.cache + fi +]) + + +# Force the use of a cache file if we use subdirectories, as otherwise we +# retest things in the subdirs. +m4_define([ACX_PRELEAN_AC_CONFIG_SUBDIRS], m4_defn([AC_CONFIG_SUBDIRS])) +AC_DEFUN([AC_CONFIG_SUBDIRS], [ + m4_ifdef([acx_lean_forced_cache], [], [ + AC_DIAGNOSE([syntax], [$0: Use ACX_LEAN_FORCE_CACHE after initialization to avoid extra costs with configure subdirs.]) + ]) + ACX_PRELEAN_AC_CONFIG_SUBDIRS($1) +]) diff --git a/scripts/EMPTY b/scripts/EMPTY new file mode 100644 index 00000000..e69de29b diff --git a/update_version b/update_version deleted file mode 100755 index a9999918..00000000 --- a/update_version +++ /dev/null @@ -1,65 +0,0 @@ -#!/bin/bash - -# Creates and updates the package_version information used by configure.ac -# (or other makefiles). When run inside a git repository it will use the -# version information that can be queried from it unless AUTO_UPDATE is set -# to 'no'. If no version is currently known it will be set to 'unknown'. -# -# If called with the argument 'release', the PACKAGE_VERSION will be updated -# even if AUTO_UPDATE=no, but the value of AUTO_UPDATE shall be preserved. -# This is used to force a version update whenever `make dist` is run. -# -# The exit status is 1 if package_version is not modified, else 0 is returned. -# -# This script should NOT be included in distributed tarballs, because if a -# parent directory contains a git repository we do not want to accidentally -# retrieve the version information from it instead. Tarballs should ship -# with only the package_version file. -# -# Ron , 2012. - -SRCDIR=$(dirname $0) - -if [ -e "$SRCDIR/package_version" ]; then - . "$SRCDIR/package_version" -fi - -if [ "$AUTO_UPDATE" = no ]; then - [ "$1" = release ] || exit 1 -else - AUTO_UPDATE=yes -fi - -# We run `git status` before describe here to ensure that we don't get a false -# -dirty from files that have been touched but are not actually altered in the -# working dir. -GIT_VERSION=$(cd "$SRCDIR" && git status > /dev/null 2>&1 \ - && git describe --tags --match 'v*' --dirty 2> /dev/null) -GIT_VERSION=${GIT_VERSION#v} - -if [ -n "$GIT_VERSION" ]; then - - [ "$GIT_VERSION" != "$PACKAGE_VERSION" ] || exit 1 - PACKAGE_VERSION="$GIT_VERSION" - -elif [ -z "$PACKAGE_VERSION" ]; then - # No current package_version and no git ... - # We really shouldn't ever get here, because this script should only be - # included in the git repository, and should usually be export-ignored. - PACKAGE_VERSION="unknown" -else - exit 1 -fi - -cat > "$SRCDIR/package_version" <<-EOF - # Automatically generated by update_version. - # This file may be sourced into a shell script or makefile. - - # Set this to 'no' if you do not wish the version information - # to be checked and updated for every build. Most people will - # never want to change this, it is an option for developers - # making frequent changes that they know will not be released. - AUTO_UPDATE=$AUTO_UPDATE - - PACKAGE_VERSION="$PACKAGE_VERSION" -EOF From bbeb92032e2f7c9ad9ea901a5134f8a3ce3a228f Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Wed, 29 Aug 2018 17:50:18 -0400 Subject: [PATCH 07/23] Adding the 'cb' model, and some fixes to make model files work. --- Makefile.am | 4 +- include/rnnoise-nu.h | 4 + src/models.c | 64 + src/models/cb.c | 11101 +++++++++++++++++++++++++++++++++++++++++ src/models/cb.rnnn | 22 + src/rnn_reader.c | 23 + training/dump_rnn.py | 14 +- 7 files changed, 11227 insertions(+), 5 deletions(-) create mode 100644 src/models.c create mode 100644 src/models/cb.c create mode 100644 src/models/cb.rnnn diff --git a/Makefile.am b/Makefile.am index fcb79564..8f50f7bb 100644 --- a/Makefile.am +++ b/Makefile.am @@ -1,6 +1,6 @@ ACLOCAL_AMFLAGS = -I m4 -AM_CFLAGS = -I$(top_srcdir)/include $(DEPS_CFLAGS) +AM_CFLAGS = -I$(top_srcdir)/include -I$(top_srcdir)/src $(DEPS_CFLAGS) dist_doc_DATA = COPYING AUTHORS README @@ -22,7 +22,9 @@ librnnoise_nu_la_SOURCES = \ src/denoise.c \ src/rnn.c \ src/rnn_data.c \ + src/models/cb.c \ src/rnn_reader.c \ + src/models.c \ src/pitch.c \ src/kiss_fft.c \ src/celt_lpc.c diff --git a/include/rnnoise-nu.h b/include/rnnoise-nu.h index 6de8a1aa..abd5a56d 100644 --- a/include/rnnoise-nu.h +++ b/include/rnnoise-nu.h @@ -61,4 +61,8 @@ RNNOISE_EXPORT RNNModel *rnnoise_model_from_file(FILE *f); RNNOISE_EXPORT void rnnoise_model_free(RNNModel *model); +RNNOISE_EXPORT const char **rnnoise_models(void); + +RNNOISE_EXPORT RNNModel *rnnoise_get_model(const char *name); + #endif diff --git a/src/models.c b/src/models.c new file mode 100644 index 00000000..d55c0d5c --- /dev/null +++ b/src/models.c @@ -0,0 +1,64 @@ +/* Copyright (c) Gregor Richards */ +/* + 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 FOUNDATION 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. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include + +#include "rnnoise-nu.h" + +/* This file is just a list of the built-in models and a way of fetching them. + * Nothing fancy. */ + +static const char *model_names[] = { + "orig", + "cb", + NULL +}; + +extern const struct RNNModel model_orig, model_cb; + +static const struct RNNModel *models[] = { + &model_orig, + &model_cb +}; + +const char **rnnoise_models() +{ + return model_names; +} + +RNNModel *rnnoise_get_model(const char *name) +{ + int i; + for (i = 0; model_names[i]; i++) { + if (!strcmp(name, model_names[i])) + return (RNNModel *) models[i]; + } + return NULL; +} diff --git a/src/models/cb.c b/src/models/cb.c new file mode 100644 index 00000000..aa246451 --- /dev/null +++ b/src/models/cb.c @@ -0,0 +1,11101 @@ +/* + * SUITE: conjoined-burgers-2018-08-28 + * + * INTENT: + * + * General use in a reasonable recording environment. Fans, AC, computers, etc. + * + * + * SIGNAL: + * + * voice + * hamlet-002-act-1-scene-1.1626 + * http://etc.usf.edu/lit2go/94/hamlet/1626/ + * human_rights_un_chi_nf.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * human_rights_un_cze_msed.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * huntingofthesnark_01_carrol_128kb + * https://librivox.org/the-hunting-of-the-snark-by-lewis-carroll-3/ + * teacupclub_01_armstrong_128kb + * https://librivox.org/the-teacup-club-by-eliza-armstrong/ + * warletters_01_allan_128kb.mp3 + * https://librivox.org/war-letters-from-a-young-queenslander-by-robert-marshall-allen/ + * TSPspeech + * http://www-mmsp.ece.mcgill.ca/Documents/Data/ + * + * sounds + * cough + * https://freesound.org/ sounds: 205012 222644 252240 323532 342587 343880 343885 343986 369295 391284 401334 407839 419781 425777 87412 + * laugh + * https://freesound.org/ sounds: 167578 189278 263775 319346 343934 344043 344064 367680 381275 383237 393353 401214 433921 73123 + * + * music + * Danse Macabre + * Gymnopedie No 1 + * Local Forecast + * Secret of Tiki Island + * Unpromised + * https://incompetech.com/music/royalty-free/ + * + * + * ./chopper voice/hamlet-002-act-1-scene-1.1626.raw voice/human_rights_un_chi_nf.raw sounds/cough.raw voice/human_rights_un_cze_msed.raw voice/huntingofthesnark_01_carrol_128kb.raw sounds/laugh.raw voice/teacupclub_01_armstrong_128kb.raw voice/warletters_01_allan_128kb.raw voice/TSPspeech.raw sounds/cough.raw music/Danse\ Macabre.raw music/Gymnopedie\ No\ 1.raw music/Local\ Forecast.raw sounds/laugh.raw music/Secret\ of\ Tiki\ Island.raw music/Unpromised.raw + * + * + * NOISE: + * + * rnnoise_contributions (only "other" and "none" samples) + * https://people.xiph.org/~jm/demo/rnnoise/rnnoise_contributions.tar.gz + * + * + * COUNT: 13212874 (x87) + * + * NOTE: All files processed into correct raws through ffmpeg: ffmpeg -i in -f s16le -ac 1 -ar 48000 out.raw + */ + +/*This file is automatically generated from a Keras model*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "rnn.h" +#include "rnn_data.h" + +static const rnn_weight input_dense_weights[1008] = { + -7, -20, -22, 13, -4, -54, 11, -9, + -3, 0, 6, 23, 15, -20, -5, 12, + 25, 3, -22, 18, -4, -18, -16, -14, + -35, -14, -17, 4, 36, -42, -30, -21, + 19, -10, 28, 9, -4, 6, -13, 1, + -6, -32, 1, 31, -49, 14, 19, 0, + -1, -35, -32, 29, -26, -29, -99, -40, + -39, 13, -25, 22, 29, -15, -19, 8, + -33, 52, 55, 44, -2, 35, 13, 9, + -55, 54, -16, 50, -57, 60, -31, -20, + -66, 16, 12, 50, 50, -8, -17, 5, + 26, -51, 29, -2, 10, 42, 24, 10, + 26, 50, 43, 67, 7, 34, 64, -12, + -6, 1, 26, -1, 28, 22, 50, 7, + 29, 55, 41, 37, 28, -16, -32, -4, + -69, -7, 40, 52, 57, -17, 58, -1, + 7, 31, 13, -48, 47, -4, -9, 5, + -66, -59, 57, -9, 2, 6, -12, 20, + -12, 106, 64, 65, 108, 7, -86, 65, + 123, 110, -67, -86, 107, 18, -89, -56, + 92, 94, 86, -13, 119, -63, -99, 3, + -1, 8, -14, 16, -106, 40, -35, 25, + 104, -24, -93, -28, 51, -119, -125, 104, + 127, -110, -35, -66, -38, 7, 39, -11, + -24, 127, 65, 15, 99, -20, 42, -6, + 127, -49, -116, -128, 20, -119, -118, -96, + 126, 121, -54, 73, 115, 64, -20, 118, + -23, 118, 121, 83, 85, 63, -120, 122, + 63, -58, 17, 64, 104, -58, -120, -1, + 61, -52, -54, -100, 123, -47, -29, -6, + 21, 30, 53, -37, -18, -30, -120, 36, + -4, -6, 9, -49, -19, 3, 27, -128, + 24, 106, -94, 4, 77, -27, -36, -23, + -28, -121, 38, 92, 74, -32, 19, 35, + -112, -37, 104, -46, -51, 120, -125, -81, + -106, 5, 47, -57, 121, 0, 83, -52, + -64, -8, 85, 23, 96, -50, 118, 71, + -111, -84, 58, -5, -35, -72, 107, -6, + 83, 127, -128, 97, -64, 48, -97, -13, + -52, 30, -46, 87, 73, 72, 56, 10, + -92, -39, -4, -128, -101, 40, -118, 10, + 32, -38, -6, 106, -119, 2, -34, -84, + -75, -118, -119, -19, -90, -72, -110, -48, + -37, -19, -98, -112, 55, -99, 76, -80, + 61, 105, -111, 0, -125, -87, -119, -122, + -100, -119, -110, 87, 63, -5, -117, -101, + -59, 24, -26, -67, 56, -112, 83, -1, + 126, 72, -127, 127, -107, 60, -119, -118, + -101, 23, 26, 49, 84, -18, -119, -98, + -78, 127, -128, -128, -37, -122, -17, -55, + 109, -16, -94, -6, -112, 80, -91, -13, + -51, 112, -78, 114, 10, 109, 38, -23, + -52, -72, 89, -63, -61, 34, 3, 122, + -93, 1, -62, 59, -114, -107, -128, -39, + -45, 73, 56, 76, 121, 74, -76, 122, + -33, 37, 31, 58, -93, 56, -13, -4, + 59, 101, -82, -13, -112, -53, -84, -34, + -118, -23, -18, -60, 68, 32, -112, -4, + -47, 105, -61, 59, -66, -34, -128, -62, + 24, -22, -45, -63, -88, -61, 20, 42, + -38, 55, 35, -128, 46, -4, -55, -83, + -127, -49, -76, -27, -78, 127, -51, 11, + -47, 27, 88, 119, 30, 122, -115, 10, + -54, 127, 32, -128, 78, -127, -32, -92, + 127, 88, -104, -125, 61, -113, -22, 5, + 84, -26, -54, 78, -97, -24, 113, -13, + 52, -49, -119, -13, -36, 17, -75, -88, + -48, -10, 39, 35, 127, 127, 120, 127, + -25, -3, 39, 127, -16, -128, -120, -78, + -13, -120, 10, -41, 2, -104, -31, -103, + -68, 94, -45, 106, 66, 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-57, -29, 3, + -36, 10, 63, 18, 71, 70, 11, -12, + -14, 117, -12, -93, 71, 0, 10, 47, + -94, 12, 1, -34, 0, 36, -4, 17, + 73, 11, -36, -22, 71, 88, -73, -22, + -82, -51, 57, 90, 119, -84, 6, 2, + 117, -120, -120, -110, 127, -128, 127, -126, + 19, -128, 127, 127, 121, 116, -30, 127, + -53, -10, 42, 121, 67, -32, 39, -127, + 121, -120, -120, -21, 127, -128, 121, -122, + -89, -128, 127, 60, 121, -65, 127, 127, + -5, 38, 11, 121, 57, -70, -125, -128, + 6, 49, -42, 121, -109, -19, 49, -65, + -120, 22, -102, 23, 16, -108, 88, -49, + 90, -44, -69, 74, -21, -98, -85, -6, + -14, -1, -86, 115, -20, -28, 127, 0, + 12, -15, 96, -26, -58, -4, 39, -51, + -25, -89, 8, -9, 8, -27, 92, 47, + 2, 87, -72, 98, -30, -35, 122, -80, + -9, 8, 24, 11, 12, -12, 13, 0, + -30, -22, 7, -33, -78, 67, 99, 53, + 15, 89, 65, -100, 34, 38, -8, 99, + 31, 39, -19, 78, -36, 68, -65, -1, + -67, 35, -64, -68, -117, -21, -8, 99, + 121, 50, -47, 106, -121, 30, -89, 61, + 65, -12, -49, -108, -57, -66, 12, 82, + 33, 39, -9, 28, 49, 49, 12, 19, + 76, 51, -90, 80, -61, -122, 50, 47, + 12, 4, 81, 36, 41, 8, 21, -26, + 55, 15, -51, -16, -79, -1, -118, -100 +}; + +static const rnn_weight input_dense_bias[24] = { + 122, -52, -26, 127, -107, 124, 81, 61, + -128, -5, -127, -118, 43, 54, -77, 61, + -128, -67, 98, 9, 122, 101, 21, -44 +}; + +static const DenseLayer input_dense = { + input_dense_bias, + input_dense_weights, + 42, 24, ACTIVATION_TANH +}; + +static const rnn_weight vad_gru_weights[1728] = { + 113, -120, 49, -37, 121, 83, 34, -73, + -124, 83, -114, -128, -81, -112, 18, -102, + -52, -71, 31, -45, -102, 67, -107, -116, + -15, -60, 30, -51, 121, -39, -56, -24, + -14, -15, -25, -121, -80, 68, -18, -33, + -53, -75, 84, -28, 25, 115, -43, -79, + -13, 31, 19, 26, -23, -23, 11, 108, + 18, 54, 61, 29, -56, -33, -12, -43, + -16, -11, -33, -5, -12, -46, 25, 5, + -39, -35, 0, 58, 16, 65, 61, 127, + -40, 63, 107, -18, -23, 99, -71, -31, + 77, 127, 81, 127, 82, 58, 108, 83, + -66, -20, 101, -35, -36, 80, 21, -17, + 126, -89, -122, 61, 36, -7, 29, 114, + -9, 9, 88, 11, 31, -71, 108, -14, + 54, 69, -21, -124, -31, -9, -82, 111, + -35, 13, 26, -74, -11, 90, -105, 63, + 90, 63, 11, -96, 121, 2, -93, -33, + 117, 49, -11, -15, 65, -25, 9, -21, + -11, -17, -46, -29, -67, -23, 105, -71, + 16, 118, 29, 73, 36, 47, -30, 120, + -76, 45, -10, -86, 81, 64, 18, -41, + 43, -80, -39, 14, 37, -124, -89, 107, + -87, 48, 56, -48, 58, -3, 121, -119, + -40, 52, 26, 13, -54, 33, -111, 34, + -97, 19, -13, -4, 13, 123, -18, 59, + 41, 40, 25, 4, 56, 34, -44, -82, + -36, -99, -4, -72, 114, 44, 115, -15, + -76, 127, -90, -53, -63, 27, -11, -42, + 3, -57, 26, 107, -13, 38, 122, -112, + 104, -71, 101, -34, 30, 111, 127, -50, + 35, -8, -120, 77, 40, 91, 120, 124, + 18, 42, 35, -5, 95, -100, -43, 46, + 59, 22, 2, 13, 65, -13, -31, -35, + 14, -4, -3, -2, -8, 31, 45, -23, + 32, 18, -18, 36, -67, 36, -52, 30, + 94, -3, -121, 99, -120, -33, 42, -100, + 28, -77, -128, 52, 36, 25, 53, 6, + 45, 75, -120, -119, -76, 63, -82, 65, + 60, -25, -30, 30, -5, 34, -31, 41, + -128, 27, 57, 51, -127, -118, -47, -31, + -29, -82, -128, -86, -126, 52, -38, -31, + -11, -77, 57, 0, -18, 19, 62, 20, + 95, -50, 35, 34, 20, 7, 13, -72, + 2, 54, -35, -1, -36, 17, 119, 11, + 110, 90, -80, -106, 127, 78, -85, 50, + 14, 51, 11, -113, -21, 4, -71, -2, + -18, -125, 41, 127, 30, -55, 54, 111, + -29, 54, 93, -97, 80, 10, -25, 33, + 10, 8, -45, 18, 61, -8, -38, -28, + -90, -31, 119, -79, 4, -37, 71, -26, + 87, 12, 80, -38, 23, -21, -33, -25, + -57, -22, 33, -43, -62, 11, 32, -7, + 31, 50, -19, -57, 6, 24, -12, -63, + 127, -127, 69, -119, 60, 81, -45, -124, + -100, 75, -52, -118, -74, -127, -47, 73, + 37, -33, 71, -122, -49, -104, -112, -53, + -52, -103, -98, 30, 41, 10, -38, 46, + 19, -87, -121, -81, -97, 35, -72, 101, + 47, 46, -44, 31, 66, 121, -34, -34, + 57, -13, 15, 37, 25, -68, 31, -44, + -48, 58, 17, 39, 9, 40, -6, 34, + 30, 41, 41, 93, -57, -38, 55, 86, + 99, -113, 108, -105, 127, 117, 58, 84, + -106, 123, -119, -122, -107, -112, -26, 23, 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-127, -17, -120, + -25, -45, 16, 19, -113, -99, 84, 120, + 127, -73, 50, -38, -55, 68, 7, 91, + 108, 121, -77, 110, 91, 119, 124, 64, + 18, -10, -61, 3, -128, -81, 127, 4, + 19, -90, 12, 127, 63, -61, 25, 59, + 75, -104, -51, 75, 87, -128, -40, 5, + 65, -9, -61, -85, 28, -5, -11, 0, + -29, -76, -3, -2, 18, -53, -1, -5, + 19, 7, 4, 30, -17, 51, 5, -21, + -87, 67, -70, 82, 8, -51, 43, -117, + -47, 85, -91, 104, -1, 62, -91, -21, + -76, -111, 12, -120, 16, -33, 30, 37, + -7, 35, -9, 17, 65, -1, -128, 38, + 3, 26, 35, 16, -10, 64, -13, -42, + -106, 31, 55, 4, 22, 100, -50, -36, + 54, 67, -2, -108, -82, 83, -14, 67, + 10, 10, -54, -35, -85, 67, 15, 24, + 16, 99, 3, -70, 65, -115, -15, 50, + 53, 123, -109, -13, 119, 9, 1, -118, + -116, 88, 54, 27, -8, -18, 68, -120, + -116, -117, -14, -95, -118, -119, 32, -25, + 10, 100, -11, -82, 19, -28, -53, -24, + 58, -39, -17, -37, -113, 43, 49, -89, + -111, 50, 67, -37, -36, 109, 58, -79, + 14, 61, 44, 14, -40, -23, 47, 13, + -38, 23, -66, -2, -61, 59, 28, 63, + 1, -44, 20, -10, 107, -102, 14, -41, + 8, 10, -19, 27, -73, -122, 20, -12, + 44, -47, -52, 104, -37, 107, -3, -58, + -62, -31, -13, -119, 97, 102, 54, -46, + 88, -66, 25, 26, -106, 21, 39, 47, + 89, 21, -12, -18, -30, 34, -20, -8, + 34, -52, 20, -80, -75, 9, -128, 32, + -1, 0, -8, 62, 24, 44, 64, -36, + 53, -6, -30, 53, -38, -123, 13, -57, + -25, -30, -47, 85, -81, 25, 36, -120, + -35, 47, 25, -80, 127, 102, -52, -64, + -101, 104, 47, -33, -17, 2, 94, -65, + 6, -2, 24, 40, -97, 5, 16, 34, + 82, 110, 18, -56, 84, 64, 63, 16, + 19, -46, -29, -68, 103, -9, 32, 46, + -13, 85, 19, -33, 77, 32, 73, -12, + 44, -34, -15, -78, -62, 79, 1, 43, + -43, 59, 31, 23, -36, 20, -63, -12, + 32, 61, 28, -3, 24, 50, 5, 4, + -106, 105, -31, -115, 19, -42, -10, -120, + 17, -57, -126, -12, 19, -104, 14, -42, + 35, -49, -85, -56, -107, -119, -122, -91, + 6, -2, 42, -74, 54, 72, -5, -33, + -34, -47, -20, -63, -75, 105, -25, 20, + -13, 96, 31, -125, 5, 34, -49, -13, + -37, -28, -5, -91, -51, 75, -14, 10, + -76, 19, -53, -82, -67, 107, -55, 45, + 15, 62, 68, -27, 12, -24, -90, 61, + 76, 25, 33, 67, 93, 44, -94, -120, + 115, 71, 12, 12, 3, -11, -20, -76, + 30, 108, -56, -35, 28, -12, -123, 35, + -96, -64, 1, -113, 62, 127, 106, -47, + 102, 90, -68, -31, -87, -49, 74, -7, + -47, -45, -90, -14, -47, -78, 69, -56, + 10, 72, -24, 36, 21, -52, -80, -46, + -67, 95, -27, -21, 22, 36, 13, 96, + -31, -48, -18, 7, 50, 67, -26, -21, + 55, 100, 52, 69, -61, -44, -31, 12, + 82, 121, 72, 115, -11, 127, 8, -6, + 6, 93, 52, 97, 126, -64, 78, -5, + 15, 94, 37, -18, -114, -4, 45, -128, + 12, -75, -50, 100, 37, 27, -52, 7, + -31, 123, 95, 73, -76, -110, 92, -26, + 127, 99, 42, -111, 51, -40, -120, 49, + -108, 26, -11, -101, 13, -54, -49, 92, + 47, -54, 23, 2, 26, 103, -65, 24 +}; + +static const rnn_weight vad_gru_recurrent_weights[1728] = { + -102, 43, -24, -86, 127, 105, 23, -26, + -17, 29, 60, 62, 55, -42, 24, 9, + -51, 36, 117, 33, -27, 58, 51, 40, + -43, 31, -36, -4, -28, 7, -68, 46, + 90, 44, 3, 29, 113, -14, 25, -44, + 45, 113, 92, 89, 3, 46, 27, 40, + -8, 19, -27, 10, -110, 8, 25, -16, + -13, 127, 64, -3, 43, 14, 22, -8, + -45, -81, -42, 25, -36, 28, -35, -2, + -41, 36, -72, -66, 73, -99, -75, -65, + -59, -120, 98, 21, 127, 29, -93, -122, + 75, -34, 41, -13, 2, 58, -36, 90, + -57, 56, -45, 5, -20, -11, 16, 40, + -60, -40, -44, -21, 1, 36, -31, 2, + 56, -24, 30, -20, -20, -46, 21, 12, + -29, 53, -113, -125, 110, -21, -48, 30, + -64, -81, 0, -59, -42, 58, 1, -33, + -1, 1, 127, -86, 97, 38, -37, 33, + -98, 41, -73, 99, 127, -112, 117, -29, + 111, -115, -122, 83, -87, 54, 101, 29, + -106, 50, 76, -51, 21, 106, 31, 56, + -46, 17, -41, -83, -7, -61, -91, 60, + 100, 35, -122, 53, -8, -43, -31, -46, + -14, 52, 74, 30, 72, 99, -31, -79, + 9, -14, 36, 112, -98, 23, 89, -57, + 6, -48, 85, 54, 38, -10, 83, -33, + -98, -54, -113, 101, -56, 8, 96, -21, + 107, -127, 24, 28, -6, 34, 20, -110, + 67, 105, -28, -94, -123, -36, 2, -9, + -103, -65, 4, -59, 2, 35, 7, -128, + 75, 9, 18, -7, 55, 102, 33, 54, + 77, -5, 51, -10, -24, 59, 24, 34, + -84, -66, 18, 61, 43, 3, -94, -40, + -36, 57, -6, 96, 21, -38, 97, -5, + 36, -23, -2, 51, 8, -29, 32, -65, + -113, -38, -71, 0, -17, -66, -2, 11, + -127, -3, 76, -128, 112, 127, -121, -21, + -94, 89, 127, -73, -15, -119, 127, -81, + 124, -84, -124, -68, -120, 78, -19, -4, + -93, -26, -91, 16, 58, -1, -120, 76, + -26, -123, 3, -61, -64, 107, -55, -7, + -100, -52, 80, -89, -67, 80, -9, 37, + 108, 61, -18, -114, 117, -22, 76, 111, + -112, 70, -26, -103, -107, 111, -72, 108, + -9, 109, 81, -102, 90, -114, -71, -10, + -66, -21, 117, -81, 127, -11, -19, -88, + 12, -128, 82, -106, 0, -34, 71, 59, + -128, -46, 94, 91, 98, 81, -128, 19, + 18, 28, -82, 55, -25, -28, -67, -40, + 99, 65, -41, 38, 97, -43, 9, -9, + -17, 52, 29, -10, 106, -47, -45, 44, + -23, -79, 21, 45, -63, 102, -12, -100, + 115, -13, 108, 27, 70, -49, 40, -84, + 25, -111, -75, 84, -38, -19, 93, -35, + 24, -72, 82, -128, 46, 88, 15, -58, + -6, 34, -66, -128, -46, -21, 123, 54, + -45, -49, -48, -128, -107, -60, -112, -20, + 19, 61, -17, 8, 67, -57, 17, 93, + 13, 13, -27, 94, 45, 16, 46, 34, + -64, 107, 29, 0, 3, 83, -52, 76, + -6, -77, -60, 109, 58, -56, 38, -57, + 127, -45, 46, 63, 123, -124, 85, -115, + -110, -104, -77, 105, -71, -118, 0, -17, + -128, 20, -82, -18, -6, -128, -35, -128, + -96, -127, -76, 80, -5, 2, -70, -18, + 99, 46, 6, -105, 6, 19, 90, 118, + -33, -117, -2, -17, 36, -31, 1, -41, + 16, -36, 24, -42, -24, -8, 9, 36, + 81, 31, -8, 8, -73, 23, 36, 28, + 7, -5, 29, -106, 13, -7, -21, 104, + -47, 7, -66, -33, 4, -8, -77, 13, + 27, 62, -14, -9, 26, 91, -79, 124, + 18, -11, 107, 15, -85, 14, 106, -12, + 127, 15, -43, -37, -75, 50, 66, 94, + -52, -50, -90, -53, 5, -22, -96, -127, + 94, 3, 120, 24, -40, -22, 76, 79, + -11, 122, -1, 41, -9, 37, 27, 7, + -11, -21, 46, 72, -27, -63, -34, 45, + -7, -123, -22, 86, 6, 75, -56, -85, + 116, 32, 0, 117, 78, -12, 9, 4, + -81, -79, -51, 50, -84, -88, 22, 8, + -105, 6, 57, -114, 127, 105, 8, -70, + -4, -40, 46, -101, 11, -35, 95, 61, + -128, -105, 123, 119, 2, 86, -71, -4, + -8, 49, 42, -120, 58, 76, 99, 11, + 66, 79, -7, -13, 50, 9, 80, 108, + -3, 23, 92, 61, 118, 44, 13, 25, + -51, -62, 30, 46, 20, 43, 24, -70, + 44, 46, 48, 59, 49, -29, 25, -39, + -86, -112, -64, 47, 6, -34, -13, -117, + 8, -123, 123, 76, 127, 117, 56, 120, + 60, 114, 105, 4, 22, -70, 119, 63, + -126, -86, 121, 113, -1, 18, 45, -62, + 13, -25, -74, -16, 3, 35, -83, -35, + 17, -17, 33, 11, 19, -41, -70, -9, + -84, -106, 32, 12, 49, 39, -14, -67, + -33, -74, 43, 116, 88, -63, -30, -128, + 62, -6, 127, 21, -7, -45, 29, -1, + 16, -48, -86, 127, -125, -35, 31, -109, + 34, -29, 87, 107, 55, 90, 127, 68, + 127, 64, -103, 49, -126, 54, 98, 106, + 22, 71, -111, -41, 56, 48, 87, -1, + 17, 38, 5, -10, 19, 33, 77, 23, + 74, 54, 41, 88, 28, 8, -15, 42, + -30, 16, 11, 77, 100, 25, -56, -15, + -2, -128, -5, 125, 29, -20, 50, -48, + 109, 42, 43, 78, 43, -63, -27, -87, + -126, -111, -62, 70, -114, -111, 87, -60, + 48, 45, 46, 127, -19, 45, 127, 127, + 127, -6, -9, 118, -33, 110, 125, 127, + -38, 127, -23, 105, 116, 95, 118, -53, + -7, 20, 58, 15, 10, 36, 36, 96, + 115, 28, 4, 95, 2, -14, 6, 15, + -84, -13, -12, 88, 16, -87, -23, 30, + -44, -116, -8, 127, -34, -58, -10, -6, + 64, -103, 23, 87, 127, -16, -65, 79, + -102, -127, -127, 0, -73, -110, 35, -91, + -27, -121, 1, -126, 24, 114, 69, 42, + 69, 19, 5, -127, -45, 41, 35, 93, + -69, 53, 126, 127, 7, -104, 80, -14, + 42, 11, 33, -37, 48, 23, -70, 2, + -54, 23, 119, 4, 67, -55, -24, 25, + 86, -8, -32, -17, -15, 2, 100, -74, + -10, 65, 10, 3, 11, -27, -51, 49, + -50, 0, -85, -92, -127, 89, -110, 121, + 79, 69, 59, -122, -11, 0, -102, 127, + 90, -13, 77, -128, 45, 72, -28, -75, + 66, 98, -5, -110, -3, -31, 127, 127, + -94, -107, 34, -30, -70, -79, -110, -25, + -23, 55, 13, -87, -1, 7, -8, 10, + 51, 9, -104, 91, 35, 23, 88, 17, + -19, 112, 96, 7, 20, 1, -120, -90, + -19, -73, -11, 104, 122, 10, 71, -73, + 123, -18, 47, 101, 115, -33, 84, -71, + -99, -120, -36, 125, -73, 1, 81, -68, + -17, -5, -71, 115, 25, 3, -39, 50, + -9, -53, -6, 24, 80, 59, -62, -125, + 71, 56, 86, 127, 48, 87, 127, -6, + -79, -54, -32, -98, -35, 47, -31, -26, + 28, -45, 12, -2, -71, 34, 8, -11, + -23, -45, 10, -34, -17, 11, 59, -35, + 23, 123, -55, -70, -87, 78, -96, 100, + -121, 61, 28, -19, -49, 89, -93, 94, + 64, 39, 46, -72, 86, 9, -41, 56, + -98, -89, -38, -118, -75, -85, -116, -128, + -116, -60, -27, -35, 59, -99, -74, -102, + -41, -5, 88, -24, -118, -94, -127, 63, + -15, -39, -84, 13, -11, -26, -23, -29, + -29, -20, -36, 11, -13, -7, 41, 91, + 72, -28, -8, -17, -28, 10, -70, 44, + 115, 12, 117, -35, -12, 113, 44, -91, + -25, 103, 101, -76, -58, -69, 95, -30, + 49, 60, 68, 16, 39, 68, -68, 85, + -119, -108, -22, -128, 66, 37, -115, -128, + -105, -53, -28, -109, -30, -101, -57, -104, + 56, -110, 53, 63, -94, -118, -78, 53, + 16, -28, -70, 76, 27, 11, -66, 0, + 6, -45, -110, -83, -58, -19, -34, 17, + 32, 6, 11, -93, -67, 49, -41, 33, + 72, 32, 37, -127, 66, -29, 11, 89, + -104, 33, -102, -72, -124, 2, 103, -21, + 127, 127, 49, 21, 56, 55, -81, 94, + -123, 124, -110, -91, -38, -23, -56, -57, + -117, -127, 122, 17, 88, 48, -84, -99, + 103, -26, -63, -112, -13, -48, -95, 99, + -52, 90, -1, -2, -37, -119, -12, 55, + 89, -103, 6, -15, 3, 107, -16, -116, + -70, 14, 22, 86, 12, -16, 67, 96, + -87, 127, -118, -121, 68, -127, -46, 116, + -90, -118, -124, -122, -48, 92, -66, -3, + 79, 127, 82, -110, 116, 44, -50, 52, + 120, -123, 61, -115, 77, 118, 68, 119, + 54, 126, 80, -101, -72, -76, 109, 76, + -103, -73, -4, 43, -58, 11, -50, -119, + 70, 26, -50, 111, 13, 71, -15, -16, + 28, 65, -5, 48, -31, 20, 37, 46, + 37, -9, -11, 41, 1, 10, -47, -20, + 13, -119, 18, 94, -16, 8, 103, -114, + 6, -28, 78, 118, 75, -6, 72, 14, + -38, -42, -126, 66, -85, -126, 110, -127, + -70, 104, -86, -34, -5, -116, -75, -52, + -128, -45, 64, 1, 86, 75, -128, -27, + 40, 59, 78, 30, -9, -46, 15, 118, + -26, 3, 29, 1, 0, -72, -79, 32, + -50, -88, -1, -77, 11, -42, -12, -1, + -11, 20, -66, 5, -83, 51, 117, -1, + 104, 93, 9, -126, -54, -67, -63, 49, + -81, -2, -89, -64, -110, 5, -32, -33, + 109, 91, 85, -128, 36, 111, -49, 122, + -33, 20, -99, 26, -4, -57, -72, -91, + -86, -127, -72, -3, 107, -18, -125, -17, + 95, 113, 31, 3, -71, -13, -105, 20, + -33, -23, -84, -13, -81, 22, 65, -90, + -62, -11, 70, -78, -71, -65, -61, -69, + 50, -30, -44, -64, -93, -42, 4, 8, + -87, 14, -64, 92, 46, -18, 75, -118, + 75, -92, 31, 11, 70, 6, 86, -61, + -31, -109, 64, 41, -106, 0, -1, -121, + 24, 94, -85, 127, -5, -8, 0, 76, + 77, -41, -128, 72, -72, 83, 4, 23, + -55, -30, -123, -120, 5, 92, 89, 19, + 26, 41, -51, -6, -39, 9, -53, -53, + -23, 52, -57, 70, 2, -58, -123, -45, + -76, -85, -52, 4, -41, 91, 30, 3, + -128, -84, -42, 127, -47, -25, 59, -51, + 38, -119, -30, 127, 84, -108, -1, -72, + -45, -85, -118, 86, -29, -75, 73, -88, + -63, -53, -54, -22, -59, -70, -107, -126, + -79, -12, -85, -88, 115, -26, -128, -107, + 105, 16, 45, -105, -121, -119, -93, 123, + -2, -47, 38, -1, 27, 8, -68, -10, + -43, 4, 24, -76, -51, 3, 48, -38, + 35, 26, -47, -55, -61, 50, 27, 65, + 65, 26, 29, -100, -36, 97, -28, 0, + -63, 109, -73, -127, -35, 39, -25, 31, + -11, 98, 66, -39, 55, -70, -91, 57 +}; + +static const rnn_weight vad_gru_bias[72] = { + -78, -70, 122, -124, 127, 119, -107, -72, + -88, 117, 127, -128, 12, -102, 35, -50, + -22, -112, 76, 119, -80, -99, -104, -92, + 10, 34, 53, -24, 72, 92, 57, 0, + -10, 13, 63, 28, 127, 28, 113, 83, + 37, 19, 127, 47, 79, -28, 55, 46, + 44, -11, -4, 59, 53, 4, 50, -53, + 19, 78, 62, 25, -48, -15, 42, -31, + -69, 8, 29, 13, -20, -43, 10, -36 +}; + +static const GRULayer vad_gru = { + vad_gru_bias, + vad_gru_weights, + vad_gru_recurrent_weights, + 24, 24, ACTIVATION_TANH +}; + +static const rnn_weight noise_gru_weights[12960] = { + 0, 48, -94, -16, -92, -34, 12, 5, + 1, -9, 37, 63, 18, 21, 49, -39, + 8, 59, -41, 15, 5, 47, 15, 26, + -2, 36, -16, 6, 55, -12, 45, -44, + -14, -74, 0, 6, 106, -4, -11, -19, + -59, -30, 12, -38, -36, 46, 27, -10, + -53, 5, 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57, -30, -82, 6, -28, 28, + 16, -69, -15, 12, -18, 45, 35, 52, + 10, -55, -122, -33, -29, 8, -54, -8, + -9, 60, -49, 55, -62, 26, 38, 8, + -22, 31, -17, -22, -48, -18, 22, 0, + 33, 68, 36, -32, -2, -24, 2, -27, + 120, -26, 2, 42, -28, -14, 25, -20, + -20, 7, -10, -12, 33, -67, -42, 7, + -128, -5, -7, 10, -15, 42, 8, 7, + 43, -1, -39, -39, -16, -45, -15, 1, + 31, 36, -7, -88, 1, -64, 21, -19, + 6, -40, -11, 2, -28, -16, -24, -25, + -11, -50, 46, -1, 61, -45, -15, 62 +}; + +static const rnn_weight noise_gru_recurrent_weights[6912] = { + -65, -18, 10, -5, -42, 31, -1, 77, + 22, -4, 82, 6, -64, -31, 6, -7, + 89, -78, -119, -45, 20, -93, 64, 17, + -17, 99, -39, -30, 22, 29, -55, 92, + 23, 33, 60, 43, 68, 115, -38, 49, + -8, 86, 61, -25, 5, -59, -35, -6, + -18, 8, -6, 28, 37, 89, -44, 51, + -22, -23, -20, -68, -38, 104, 12, 4, + 40, 13, 87, 71, -91, 99, -29, -23, + 48, 52, 38, -2, -76, -45, -39, -28, + -92, -2, -8, 103, -27, 24, 77, 68, + -27, 58, 2, -26, 62, 22, -27, 22, + -33, -14, 74, 48, 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-74, 47, 76, 59, + 82, 30, -39, -7, -19, 49, -41, 24, + 17, 59, 7, 43, 6, -18, -81, -82, + 66, 60, 51, 15, -107, -116, 0, 15, + 66, -23, -5, 57, -1, 2, -4, 50, + 59, -102, -21, 8, -38, -111, 62, 25, + 41, 48, -52, 119, -11, -34, 3, -52, + -56, 83, 1, -33, -45, 75, 13, 93, + 86, -2, -57, -57, 112, -13, 42, 24, + 23, 84, -67, -38, -9, -58, 121, 33, + 57, 5, -50, 70, 48, 19, -51, 81, + -12, 10, -27, 5, 15, -29, -64, -11, + -21, 11, -74, 16, 38, 14, -3, 0, + 2, 77, 1, 62, 37, 10, 24, -59, + 4, -95, -39, 45, -76, -3, 8, 21, + -27, -48, -9, 71, -100, -82, -34, 45, + -1, -19, 12, -77, 78, -73, 57, -73, + 35, -18, 9, -12, 17, -10, 103, 31, + 114, 28, 10, -12, -62, -22, -22, -6, + -8, 119, 0, 32, 58, -78, 13, -28, + 36, 98, -5, -43, -31, -8, -11, -40, + -38, -7, 50, -59, 57, -23, -36, -30 +}; + +static const rnn_weight noise_gru_bias[144] = { + 36, -16, 99, -2, 2, 36, 22, -5, + 11, -1, 49, -45, 19, 1, -11, 29, + 109, -2, 63, -31, 14, 44, 16, 48, + 19, 58, -22, 24, 62, 49, -78, 45, + 73, 32, 8, 91, 20, 18, -28, 62, + -8, 39, 61, -64, 40, -50, 80, 78, + 24, -27, -24, 11, -4, -14, -30, 23, + 5, -23, 27, 0, -41, -13, -32, 21, + 19, -9, 1, -32, 29, -16, -16, -17, + 10, 46, 23, -19, 18, 1, -13, 2, + -22, -5, 38, 48, -16, -4, -26, 8, + 24, 39, 16, 14, 15, 10, -18, 18, + 28, -44, 12, 18, -4, -33, -35, 25, + 14, 18, 37, 9, -11, -84, -33, -31, + -8, -18, 10, 21, -38, -19, -11, 1, + -66, -12, 26, 10, -6, -8, 28, 19, + -17, -17, -9, -27, 31, 18, 9, 10, + -12, -3, -10, 51, -8, -11, -43, -17 +}; + +static const GRULayer noise_gru = { + noise_gru_bias, + noise_gru_weights, + noise_gru_recurrent_weights, + 90, 48, ACTIVATION_RELU +}; + +static const rnn_weight denoise_gru_weights[32832] = { + 13, 9, -28, -63, 23, -8, 37, 1, + -40, 14, -14, -2, -47, 7, 32, -33, + -9, 19, 26, 81, -15, 26, 36, 47, + 22, -26, 27, -3, 9, -22, 34, -57, + -18, 71, -3, 38, -42, -34, -10, 47, + -15, 9, 6, 5, -77, 12, -37, 51, + -20, 2, 28, 18, 27, -33, -38, 15, + -29, -8, 43, 18, 4, -72, 22, -18, + 44, -22, 27, 22, 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12, -21, -7, 127, + 15, 3, -25, 89, -26, 61, 43, 7, + -37, 13, 5, -15, 34, 23, -34, 39, + 9, 2, 16, 29, -13, -51, 5, 18, + 25, 18, 14, 54, 40, -24, 15, 64, + -56, -25, -10, 2, 3, -29, 25, 14, + -8, -13, 18, -3, 8, -26, -24, 38, + -71, -27, 53, 119, 11, -8, -46, -10, + -32, -29, -27, 54, 27, -11, -61, 9, + -7, -16, -66, -15, 48, -3, 35, -3, + 52, -19, 1, 24, 57, -9, -72, -12, + -38, 19, -1, 5, 74, -33, -47, -2, + -10, -37, -18, -59, 49, -25, 56, -59, + 4, -4, 4, -50, -27, -29, -43, -59, + 121, 74, -31, 127, 12, -46, -2, -33, + 1, -8, 11, -2, 18, -9, -9, 13, + 52, 39, -3, 81, 50, -11, -8, -26, + -13, 31, -27, -18, 59, 24, -36, 0, + 8, -47, -33, -47, -4, -57, -15, -21, + -7, -39, 43, 17, 7, -6, 25, -16, + 5, -9, 75, -4, 1, -12, -39, -1, + 29, -33, -15, -14, 24, -21, -45, -22, + 9, 19, -8, 4, 0, -33, 26, 4, + 50, -7, 8, -51, -110, -3, 53, 16, + -23, 29, -28, -1, 10, 6, -6, 117, + -6, -1, -13, -20, -29, -12, 66, 18, + -9, 21, -7, -4, 5, 2, -50, 39, + -10, -18, -9, 8, -16, 30, 52, 81, + -80, 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-36, -5, -35, -37, 96, + 89, -23, -63, 97, 0, -32, 8, -4, + 3, 13, 13, 7, -25, -47, 28, -5, + 14, -10, 10, 13, -3, 9, -77, -2, + 7, -20, -1, -16, -10, 0, 83, -7, + 46, 1, -12, -10, 12, -10, -34, 26, + 119, 7, 35, 13, -9, 65, -21, 25, + 102, -11, -34, -9, 8, -13, -93, 10, + -4, -3, -1, 1, -1, -14, -12, -11, + 18, 3, -19, 11, -1, -12, -56, 0, + 2, 5, 41, -33, -4, 5, -8, 14, + 2, -6, 3, -26, -6, 2, -10, 14, + -9, -6, 74, -56, 5, -12, 50, 4 +}; + +static const rnn_weight denoise_gru_recurrent_weights[27648] = { + 127, 36, 100, 58, -32, -52, -33, 116, + 12, -102, 57, 13, 48, 82, 23, -3, + 106, 13, 50, 27, 127, -14, 40, -85, + 14, 124, 97, -27, 68, -45, -17, 6, + 10, -65, 22, 39, 24, 57, -25, 113, + -68, 39, -39, 17, -120, -75, 6, 2, + -110, -92, 73, 53, -34, 2, 127, -28, + -6, 83, -57, 42, 84, 74, 108, -1, + 26, 6, 98, 115, -109, -1, -66, -69, + 79, 61, -14, -128, -128, -58, -3, -86, + 83, -84, -58, 115, 1, 72, 60, 65, + -2, -4, 6, -60, -128, -6, 20, -30, + 22, -16, 20, 105, 48, 81, -42, 16, + 49, 71, 110, -8, 16, -96, 6, 66, + -22, 7, -45, 127, 127, 84, 30, -73, + -41, 64, -92, -20, 38, 66, 7, -41, + 35, -124, -104, 34, 51, -83, -97, -60, + -40, 9, 45, -56, -53, 29, -56, 86, + -42, -39, 80, -26, -14, 16, -52, -1, + -115, 12, -27, 48, 23, 104, 58, 55, + 123, -39, 13, 75, -71, 108, -39, 13, + -22, -77, -36, 21, 56, -30, -13, 43, + -56, -20, -125, 79, -20, -27, 59, 103, + 57, 39, 6, 96, -124, -111, 38, 0, + 109, 57, -23, -65, -8, -44, 30, 66, + 91, -2, -120, -95, -52, -60, 17, -69, + -2, -62, -37, -23, 10, 21, -118, 17, + -65, -2, -92, -28, -68, -9, 31, -37, + 40, -127, 29, -15, -14, -8, -23, 37, + 47, 80, 0, -84, -107, 6, 29, 23, + -67, -2, -7, -40, 6, -26, -126, -25, + 17, -84, 0, 103, 36, -6, -47, -62, + -17, -101, 81, 3, -19, -80, -127, -69, + -38, -2, 55, 19, -12, -58, -8, 32, + -20, -35, -25, -35, -41, 22, -59, 4, + -79, 35, 73, -64, -46, -19, 28, 48, + 51, 74, -122, 6, 33, -6, 79, -41, + 45, -113, -124, 17, 22, -127, 35, -56, + 22, 120, -68, 121, -126, 34, -54, 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-75, -2, 118, -47, -24, 2, -50, 55, + -35, -48, -105, 75, 8, 89, 32, 19, + -7, 39, -78, 40, -7, -26, 6, -123, + -123, -5, -31, 120, 21, -76, -16, -77, + 11, 45, -3, 16, 34, 127, 36, -34, + 10, 1, -47, -9, -121, -13, -52, 84, + -17, -15, -70, -35, -124, -67, -8, -78, + -22, -118, -80, 72, -37, 49, -34, 3, + -124, -75, -59, -54, -58, -78, 35, -122, + 69, -52, 27, -39, 53, -62, -5, 80, + -20, -96, -116, 49, -31, -126, 25, 22, + 54, -18, 1, 19, -17, -97, -6, 53, + -80, 56, -117, 67, -40, 32, -50, 12, + 3, -43, 11, 2, 86, 38, 22, -90, + -22, 23, -65, 87, -9, -40, 118, 86, + -83, -18, -28, -47, 44, 2, -97, 9, + 14, -68, 69, 54, 99, 62, 8, 65, + 22, 55, 66, 13, -39, -30, -28, 2, + 127, -13, 51, -15, 4, -53, -121, 78, + -45, -43, -56, -29, -17, -11, -41, 50, + 24, 43, 15, -66, -39, -25, -55, -20, + 50, 86, 31, -10, 15, -40, -17, 47, + 64, -70, 12, 14, -22, -15, 20, -45, + -44, -26, -6, -2, -7, 3, 39, -25, + -90, 26, -54, 96, 33, 83, 11, -7, + -77, 24, 47, -3, -18, -11, -40, 68, + 1, 38, 33, -15, 61, -17, -91, 36, + 118, -61, 3, -64, -75, -37, 39, -41, + 6, -115, 22, -56, 2, 87, -54, -73, + -57, 23, -51, -88, -84, -48, -62, -52, + 10, -21, 24, 2, 10, -54, -10, 61, + -6, -6, -73, 126, 22, 78, -102, -38, + -57, 68, 119, -26, -69, 0, 55, 14 +}; + +static const rnn_weight denoise_gru_bias[288] = { + 62, 59, -30, -12, -24, -55, 36, -13, + 30, 46, -48, 102, -71, -12, 17, 54, + -18, 39, 25, -29, 25, 87, 68, -17, + 10, -45, -7, 86, 36, 21, 108, -69, + 15, -61, 50, 13, -5, 36, -25, 25, + 25, -37, 29, 0, -105, 74, 47, -11, + 90, 42, 54, 5, -28, -10, 4, 28, + -95, 98, 40, 80, -55, -19, 23, -38, + 58, 68, 12, 12, 118, -58, 46, -11, + 24, 64, 61, 44, 38, 58, -42, 58, + -4, 89, 53, 31, -19, 5, 47, 58, + -10, 43, -11, 47, 56, 49, -29, 48, + -16, 16, -6, -27, -1, 35, 13, -33, + 27, 13, 37, -38, 31, 44, -15, 13, + -46, 4, 32, -34, 8, -12, 22, -24, + 46, -23, 19, -2, 22, 20, 5, -32, + -46, 2, 7, 8, 3, -26, 52, 37, + 33, 33, 40, 22, 9, 6, -19, -45, + 4, 22, -12, -17, 33, 31, 46, 10, + 33, -6, -25, 20, -15, -35, 29, 30, + -1, -11, -52, 7, 17, -5, 15, 31, + -9, -4, -19, -8, -3, 33, 22, -3, + 18, -18, 17, -2, 15, -3, 28, -11, + 5, 24, -15, 4, -32, 12, -18, 8, + -1, 1, 6, -15, -83, -46, -44, 28, + -2, -7, -87, -14, -14, 23, 9, 22, + 19, 25, -42, 22, -24, -23, 10, -21, + -13, 4, -12, 8, -14, -51, 25, -5, + -21, -16, -5, 7, -14, 39, -37, 23, + -33, -39, -29, 23, -54, 13, 3, 8, + -7, 7, 37, 2, 56, 21, 10, -27, + 14, -19, -4, 25, -23, 0, -5, -3, + 29, 0, -3, -26, 33, 0, -40, -15, + 49, -9, 2, 2, -30, 2, -6, 32, + -8, -3, 6, -16, 8, 0, 15, 7, + 37, -1, 20, -5, 22, -1, 16, -59 +}; + +static const GRULayer denoise_gru = { + denoise_gru_bias, + denoise_gru_weights, + denoise_gru_recurrent_weights, + 114, 96, ACTIVATION_TANH +}; + +static const rnn_weight denoise_output_weights[2112] = { + 39, 16, 10, 11, 19, 15, 15, 10, + 7, 6, 5, 4, 2, 4, -3, -1, + 13, 34, 49, 44, 43, 84, -34, -24, + -15, -10, -22, -26, -37, -29, -7, 7, + 22, 15, 9, 17, 27, 29, 8, 0, + -5, 7, 4, -53, 21, 25, 37, 44, + 38, 46, 55, 63, 49, 41, 46, 28, + 30, 37, 43, 49, 46, 28, 3, 3, + 2, 3, 10, -11, -12, -8, 7, 20, + 17, 10, 5, 13, 17, 27, 38, 48, + 61, 67, 58, 46, 36, 40, 30, 28, + -23, -23, -20, -16, -7, -7, -13, -22, + -25, -33, -45, -46, -56, -67, -78, -101, + -123, -124, -107, -112, -99, -58, -61, -28, + 3, 20, 29, 30, 17, -18, -65, -86, + -77, -67, -39, -29, -30, -18, -18, -21, + -22, -41, -75, -90, 26, 8, -28, -62, + -81, -84, -70, -60, -57, -61, -54, -64, + -63, -60, -41, -20, -6, -5, -2, -2, + 19, 21, -14, 7, 25, 37, 43, 36, + 28, 22, 23, 18, 16, 14, 20, 13, + 2, -3, -9, -23, -4, 4, 15, 16, + 1, 13, 15, 5, 4, 9, 5, 0, + 4, 18, 19, 18, 20, 17, 9, 11, + 20, 45, 73, 89, 104, 68, 92, 53, + 31, 24, 16, 10, 14, 15, 22, 22, + 26, 19, 5, 3, 4, 15, 14, 6, + -22, -16, 7, 7, 7, 2, 21, 30, + 38, 51, 70, 81, 87, 94, 96, 99, + 102, 109, 108, 110, 116, 101, 62, 72, + 67, 22, 10, 11, 8, 2, 6, 11, + 11, 10, 12, 12, 10, 14, 18, 11, + 2, -35, -80, -128, -128, -128, -77, -7, + -17, -34, -46, -27, -33, -41, 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20, 25, + 22, 17, 12, 3, 0, -1, 4, 15, + 16, -1, 127, 105, 62, 44, 39, 37, + 26, 19, 32, 38, 27, 39, 38, 40, + 35, 31, 17, 11, 30, 59, 92, 56, + 1, 9, 16, 14, 19, 16, 18, 11, + 9, 3, -2, 1, 6, 10, 8, 8, + 16, 22, 19, 17, 19, 29, 39, 30, + -4, -2, 22, 30, 17, 2, 9, 24, + -1, 18, 24, 31, 12, 2, -5, -4, + 10, 15, 11, -12, -58, -75, -81, -92, + -62, -47, -44, -32, -24, -21, -19, -17, + -8, 0, 11, 18, 16, 5, -2, 0, + -16, 24, -61, -32, 1, 4, 4, 4, + 13, 13, 20, 15, 6, 4, -2, -7, + -11, -4, -2, -11, -14, -21, -29, -23, + -13, -13, 5, 7, 4, 10, 10, 10, + 15, 13, 4, 10, 0, 2, 3, 12, + 7, -30, -31, -28, -35, -66, -11, -2, + 22, 29, 40, 46, 39, 39, 46, 42, + 41, 52, 49, 39, 39, 39, 33, 31, + 16, 14, 11, 3, 37, 32, -9, -18, + -30, -43, -42, -40, -41, -42, -34, -40, + -42, -36, -33, -39, -30, 20, 24, -2, + -42, -68, 14, 5, -5, -26, -32, -37, + -40, -37, -42, -53, -63, -75, -80, -79, + -70, -67, -63, -62, -80, -69, -52, -27, + 32, 20, -2, -30, -39, -24, -20, -27, + -31, -18, -19, -12, -17, -23, -33, -26, + -14, -3, -3, -8, -4, 39, -8, -31, + -55, -41, -34, -30, -29, -28, -21, -4, + 7, 7, 17, 32, 30, 24, 28, 52, + 51, 42, 29, 21, 18, -1, -20, -16, + -10, -11, -19, -17, -13, -13, -13, -8, + -4, 2, 19, 20, -2, -89, -128, -108, + -42, -6, 127, 127, 75, 25, 13, 15, + 9, 0, 5, 17, 27, 32, 35, 39, + 45, 47, 37, 14, 29, 29, 15, -5, + -18, -55, -67, -47, -21, -12, -16, -24, + -21, -28, -33, -16, -19, -22, -14, -7, + -13, 2, 24, 32, 32, 13, -62, -79, + -80, -88, -95, -89, -85, -67, -52, -38, + -31, -28, -31, -23, -12, -6, -15, -26, + -31, -20, -7, -11, 15, -22, -29, -15, + -15, -23, -37, -39, -36, -33, -32, -23, + -28, -25, -20, -16, -16, -17, -3, 7, + 16, 20, 87, 70, 31, -5, -4, -7, + -33, -65, -73, -69, -72, -30, -19, -36, + -72, -82, -54, -5, 10, 7, 23, 5, + -18, -9, 18, 32, 42, 39, 36, 35, + 34, 38, 37, 41, 47, 51, 60, 59, + 57, 27, 3, 13, -8, -126, -64, -35, + -17, -32, -42, -31, -31, -20, -30, -39, + -37, -43, -42, -43, -48, -63, -62, -58, + -65, -46, -24, -44, 28, 18, 18, 23, + 29, 33, 35, 31, 21, 10, 2, 2, + 6, 12, 15, 15, 18, -20, -87, -79, + -56, -33, 30, 27, 22, 29, 39, 48, + 63, 60, 34, 17, 20, 20, 21, 21, + 24, 31, 26, 13, 1, 10, 14, 23, + 47, 62, 72, 97, 81, 73, 42, 48, + 85, 127, 127, 127, 127, 127, 118, 74, + 61, 43, 24, 38, 31, 16, 1, -34, + -55, -46, -44, -37, -28, -15, -8, -10, + -12, -15, -10, -7, -7, -3, 1, -3, + 6, 25, 47, 124, -31, -52, -56, -52, + -43, -41, -40, -35, -24, -27, -29, -29, + -16, -10, -8, 9, 33, 25, 10, 2, + -19, -115, 40, 48, 44, 53, 31, 17, + 38, 40, 33, 18, 16, 21, 33, 43, + 40, 36, 37, 24, 25, 24, 25, 20, + 3, 24, 38, 26, 16, 7, 3, 6, + 15, 15, 20, 19, 10, -1, 1, -1, + 1, 5, 9, 18, 9, 16, 44, 62, + 46, 41, 43, 58, 71, 72, 68, 73, + 81, 86, 100, 102, 87, 78, 66, 60, + 97, 120, 127, 118, -27, -40, -41, -43, + -53, -51, -53, -60, -53, -43, -45, -36, + -40, -50, -71, -93, -93, -78, -82, -65, + -53, -85, -27, 14, 52, 95, 117, 115, + 96, 67, 36, 12, -3, 2, 12, 8, + -4, -17, -17, -16, -21, -7, -1, -26, + -46, -60, -78, -96, -96, 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+static const rnn_weight vad_output_bias[1] = { + 127 +}; + +static const DenseLayer vad_output = { + vad_output_bias, + vad_output_weights, + 24, 1, ACTIVATION_SIGMOID +}; + +const struct RNNModel model_cb = { + 24, + &input_dense, + 24, + &vad_gru, + 48, + &noise_gru, + 96, + &denoise_gru, + 22, + &denoise_output, + 1, + &vad_output, +}; diff --git a/src/models/cb.rnnn b/src/models/cb.rnnn new file mode 100644 index 00000000..bcbf6b56 --- /dev/null +++ b/src/models/cb.rnnn @@ -0,0 +1,22 @@ +rnnoise-nu model file version 1 +42 24 0 +-7 -20 -22 13 -4 -54 11 -9 -3 0 6 23 15 -20 -5 12 25 3 -22 18 -4 -18 -16 -14 -35 -14 -17 4 36 -42 -30 -21 19 -10 28 9 -4 6 -13 1 -6 -32 1 31 -49 14 19 0 -1 -35 -32 29 -26 -29 -99 -40 -39 13 -25 22 29 -15 -19 8 -33 52 55 44 -2 35 13 9 -55 54 -16 50 -57 60 -31 -20 -66 16 12 50 50 -8 -17 5 26 -51 29 -2 10 42 24 10 26 50 43 67 7 34 64 -12 -6 1 26 -1 28 22 50 7 29 55 41 37 28 -16 -32 -4 -69 -7 40 52 57 -17 58 -1 7 31 13 -48 47 -4 -9 5 -66 -59 57 -9 2 6 -12 20 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16 13 12 14 19 22 36 33 24 11 -20 -108 -128 -127 -122 -100 -87 -79 -74 -83 -78 -65 -62 -57 -40 -25 -19 -18 -18 -15 -5 6 -8 +-3 22 24 47 33 34 8 3 -7 -8 4 4 18 20 11 -16 -26 -12 -34 -23 -19 -13 +24 1 1 +-128 -35 127 127 -99 -128 127 -128 -125 123 -103 127 127 -128 127 -128 -128 -128 93 -114 -128 -128 127 -128 +127 diff --git a/src/rnn_reader.c b/src/rnn_reader.c index 3d075ebb..be1d1bb8 100644 --- a/src/rnn_reader.c +++ b/src/rnn_reader.c @@ -36,6 +36,12 @@ #include "rnn_data.h" #include "rnnoise-nu.h" +/* Although these values are the same as in rnn.h, we make them separate to + * avoid accidentally burning internal values into a file format */ +#define F_ACTIVATION_TANH 0 +#define F_ACTIVATION_SIGMOID 1 +#define F_ACTIVATION_RELU 2 + RNNModel *rnnoise_model_from_file(FILE *f) { int i, in; @@ -71,6 +77,21 @@ RNNModel *rnnoise_model_from_file(FILE *f) name = in; \ } while (0) +#define INPUT_ACTIVATION(name) do { \ + int activation; \ + INPUT_VAL(activation); \ + switch (activation) { \ + case F_ACTIVATION_SIGMOID: \ + name = ACTIVATION_SIGMOID; \ + break; \ + case F_ACTIVATION_RELU: \ + name = ACTIVATION_RELU; \ + break; \ + default: \ + name = ACTIVATION_TANH; \ + } \ + } while (0) + #define INPUT_ARRAY(name, len) do { \ rnn_weight *values = malloc((len) * sizeof(rnn_weight)); \ if (!values) { \ @@ -90,6 +111,7 @@ RNNModel *rnnoise_model_from_file(FILE *f) #define INPUT_DENSE(name) do { \ INPUT_VAL(name->nb_inputs); \ INPUT_VAL(name->nb_neurons); \ + INPUT_ACTIVATION(name->activation); \ INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \ INPUT_ARRAY(name->bias, name->nb_neurons); \ } while (0) @@ -97,6 +119,7 @@ RNNModel *rnnoise_model_from_file(FILE *f) #define INPUT_GRU(name) do { \ INPUT_VAL(name->nb_inputs); \ INPUT_VAL(name->nb_neurons); \ + INPUT_ACTIVATION(name->activation); \ INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons * 3); \ INPUT_ARRAY(name->recurrent_weights, name->nb_neurons * name->nb_neurons * 3); \ INPUT_ARRAY(name->bias, name->nb_neurons * 3); \ diff --git a/training/dump_rnn.py b/training/dump_rnn.py index 797b185d..f56ecaa1 100755 --- a/training/dump_rnn.py +++ b/training/dump_rnn.py @@ -35,16 +35,22 @@ def printVector(f, ft, vector, name): def printLayer(f, ft, layer): weights = layer.get_weights() + activation = re.search('function (.*) at', str(layer.activation)).group(1).upper() if len(weights) > 2: - ft.write('{} {}\n'.format(weights[0].shape[0], weights[0].shape[1]/3)) + ft.write('{} {} '.format(weights[0].shape[0], weights[0].shape[1]/3)) + else: + ft.write('{} {} '.format(weights[0].shape[0], weights[0].shape[1])) + if activation == 'SIGMOID': + ft.write('1\n') + elif activation == 'RELU': + ft.write('2\n') else: - ft.write('{} {}\n'.format(weights[0].shape[0], weights[0].shape[1])) + ft.write('0\n') printVector(f, ft, weights[0], layer.name + '_weights') if len(weights) > 2: printVector(f, ft, weights[1], layer.name + '_recurrent_weights') printVector(f, ft, weights[-1], layer.name + '_bias') name = layer.name - activation = re.search('function (.*) at', str(layer.activation)).group(1).upper() if len(weights) > 2: f.write('static const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n' .format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]/3, activation)) @@ -77,7 +83,7 @@ def mean_squared_sqrt_error(y_true, y_pred): ft = open(sys.argv[3], '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 "rnn.h"\n\n') +f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "rnn.h"\n#include "rnn_data.h"\n\n') ft.write('rnnoise-nu model file version 1\n') layer_list = [] From 6f257df1f47a7bf735f706a0fb7cac8c5015c82f Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Wed, 29 Aug 2018 21:05:15 -0400 Subject: [PATCH 08/23] Corrected a buggy lack of allocation. --- src/denoise.c | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/denoise.c b/src/denoise.c index 91e2c85d..7f1e4e79 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -294,6 +294,9 @@ int rnnoise_init(DenoiseState *st, RNNModel *model) { st->rnn.model = model; else st->rnn.model = &model_orig; + st->rnn.vad_gru_state = calloc(sizeof(float), st->rnn.model->vad_gru_size); + st->rnn.noise_gru_state = calloc(sizeof(float), st->rnn.model->noise_gru_size); + st->rnn.denoise_gru_state = calloc(sizeof(float), st->rnn.model->denoise_gru_size); return 0; } @@ -305,6 +308,9 @@ DenoiseState *rnnoise_create(RNNModel *model) { } void rnnoise_destroy(DenoiseState *st) { + free(st->rnn.vad_gru_state); + free(st->rnn.noise_gru_state); + free(st->rnn.denoise_gru_state); free(st); } From 76d0821e54588ab39d5f26fbc14d3a75143f8f6d Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Wed, 29 Aug 2018 21:06:20 -0400 Subject: [PATCH 09/23] Added parameterizable maximum attenuation. --- Makefile.am | 2 +- examples/rnnoise_demo.c | 72 ++++++++++++++++++++++++++++++----------- include/rnnoise-nu.h | 5 +++ src/denoise.c | 37 +++++++++++++++++++++ 4 files changed, 97 insertions(+), 19 deletions(-) diff --git a/Makefile.am b/Makefile.am index 8f50f7bb..22e5ff06 100644 --- a/Makefile.am +++ b/Makefile.am @@ -38,7 +38,7 @@ noinst_PROGRAMS = examples/rnnoise_nu_demo endif examples_rnnoise_nu_demo_SOURCES = examples/rnnoise_demo.c -examples_rnnoise_nu_demo_LDADD = librnnoise-nu.la +examples_rnnoise_nu_demo_LDADD = librnnoise-nu.la $(LIBM) pkgconfigdir = $(libdir)/pkgconfig pkgconfig_DATA = rnnoise-nu.pc diff --git a/examples/rnnoise_demo.c b/examples/rnnoise_demo.c index b9ad0588..a99ee848 100644 --- a/examples/rnnoise_demo.c +++ b/examples/rnnoise_demo.c @@ -24,36 +24,72 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ +#include #include +#include +#include #include "rnnoise-nu.h" #define FRAME_SIZE 480 int main(int argc, char **argv) { - int i; + int i, ci; int first = 1; + int channels; float x[FRAME_SIZE]; - FILE *f1, *fout; - DenoiseState *st; - st = rnnoise_create(NULL); - if (argc!=3) { - fprintf(stderr, "usage: %s \n", argv[0]); + short *tmp; + RNNModel *model = NULL; + DenoiseState **sts; + float max_attenuation; + if (argc < 3) { + fprintf(stderr, "usage: %s [model]\n", argv[0]); return 1; } - f1 = fopen(argv[1], "r"); - fout = fopen(argv[2], "w"); + + channels = atoi(argv[1]); + if (channels < 1) channels = 1; + max_attenuation = pow(10, -atof(argv[2])/10); + + if (argc >= 4) { + model = rnnoise_get_model(argv[3]); + if (!model) { + fprintf(stderr, "Model not found!\n"); + return 1; + } + } + + sts = malloc(channels * sizeof(DenoiseState *)); + if (!sts) { + perror("malloc"); + return 1; + } + tmp = malloc(channels * FRAME_SIZE * sizeof(short)); + if (!tmp) { + perror("malloc"); + return 1; + } + for (i = 0; i < channels; i++) { + sts[i] = rnnoise_create(model); + rnnoise_set_param(sts[i], RNNOISE_PARAM_MAX_ATTENUATION, max_attenuation); + } + while (1) { - short tmp[FRAME_SIZE]; - fread(tmp, sizeof(short), FRAME_SIZE, f1); - if (feof(f1)) break; - for (i=0;ilastg[i]); st->lastg[i] = g[i]; } + + /* Apply maximum attenuation (minimum value) */ + if (st->max_attenuation) { + float min = 1, mult; + for (i=0;imax_attenuation) { + if (min < MIN_MAX_ATTENUATION) + min = MIN_MAX_ATTENUATION; + mult = st->max_attenuation / min; + for (i=0;i 1) g[i] = 1; + st->lastg[i] = g[i]; + } + } + } + interp_band_gain(gf, g); #if 1 for (i=0;i MIN_MAX_ATTENUATION && value <= 1) || value == 0) + st->max_attenuation = value; + else + st->max_attenuation = MIN_MAX_ATTENUATION; + break; + } +} + #if TRAINING static float uni_rand() { From 60a8fa783af86c242ae5aa8618a124f3f2ca2319 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Wed, 29 Aug 2018 23:16:23 -0400 Subject: [PATCH 10/23] Trimming more fat --- Makefile.am | 36 ------------------------------------ 1 file changed, 36 deletions(-) diff --git a/Makefile.am b/Makefile.am index 8f50f7bb..20de3dd5 100644 --- a/Makefile.am +++ b/Makefile.am @@ -93,40 +93,4 @@ uninstall-local: endif -# We check this every time make is run, with configure.ac being touched to -# trigger an update of the build system files if update_version changes the -# current PACKAGE_VERSION (or if package_version was modified manually by a -# user with either AUTO_UPDATE=no or no update_version script present - the -# latter being the normal case for tarball releases). -# -# We can't just add the package_version file to CONFIGURE_DEPENDENCIES since -# simply running autoconf will not actually regenerate configure for us when -# the content of that file changes (due to autoconf dependency checking not -# knowing about that without us creating yet another file for it to include). -# -# The MAKECMDGOALS check is a gnu-make'ism, but will degrade 'gracefully' for -# makes that don't support it. The only loss of functionality is not forcing -# an update of package_version for `make dist` if AUTO_UPDATE=no, but that is -# unlikely to be a real problem for any real user. -$(top_srcdir)/configure.ac: force - @case "$(MAKECMDGOALS)" in \ - dist-hook) exit 0 ;; \ - dist-* | dist | distcheck | distclean) _arg=release ;; \ - esac; \ - if ! $(top_srcdir)/update_version $$_arg 2> /dev/null; then \ - if [ ! -e $(top_srcdir)/package_version ]; then \ - echo 'PACKAGE_VERSION="unknown"' > $(top_srcdir)/package_version; \ - fi; \ - . $(top_srcdir)/package_version || exit 1; \ - [ "$(PACKAGE_VERSION)" != "$$PACKAGE_VERSION" ] || exit 0; \ - fi; \ - touch $@ - -force: - -# Create a minimal package_version file when make dist is run. -dist-hook: - echo 'PACKAGE_VERSION="$(PACKAGE_VERSION)"' > $(top_distdir)/package_version - - .PHONY: rnnoise-nu install-rnnoise-nu docs install-docs From 8a81924f02c864ca19d96e76b5025e7786549f21 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Thu, 30 Aug 2018 08:54:45 -0400 Subject: [PATCH 11/23] Adding marathon-prescription model. --- Makefile.am | 1 + src/models.c | 9 +- src/models/cb.rnnn | 22 - src/models/mp.c | 11104 +++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 11112 insertions(+), 24 deletions(-) delete mode 100644 src/models/cb.rnnn create mode 100644 src/models/mp.c diff --git a/Makefile.am b/Makefile.am index 9c879429..d0895406 100644 --- a/Makefile.am +++ b/Makefile.am @@ -23,6 +23,7 @@ librnnoise_nu_la_SOURCES = \ src/rnn.c \ src/rnn_data.c \ src/models/cb.c \ + src/models/mp.c \ src/rnn_reader.c \ src/models.c \ src/pitch.c \ diff --git a/src/models.c b/src/models.c index d55c0d5c..e2cc8c31 100644 --- a/src/models.c +++ b/src/models.c @@ -38,14 +38,19 @@ static const char *model_names[] = { "orig", "cb", + "mp", NULL }; -extern const struct RNNModel model_orig, model_cb; +extern const struct RNNModel + model_orig, + model_cb, + model_mp; static const struct RNNModel *models[] = { &model_orig, - &model_cb + 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-128 -125 123 -103 127 127 -128 127 -128 -128 -128 93 -114 -128 -128 127 -128 -127 diff --git a/src/models/mp.c b/src/models/mp.c new file mode 100644 index 00000000..d4b09da4 --- /dev/null +++ b/src/models/mp.c @@ -0,0 +1,11104 @@ +/* + * SUITE: marathon-prescription-2018-08-29 + * + * INTENT: + * + * General use in a noisy recording environment. + * + * + * SIGNAL: + * + * voice + * hamlet-002-act-1-scene-1.1626 + * http://etc.usf.edu/lit2go/94/hamlet/1626/ + * human_rights_un_chi_nf.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * human_rights_un_cze_msed.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * huntingofthesnark_01_carrol_128kb + * https://librivox.org/the-hunting-of-the-snark-by-lewis-carroll-3/ + * teacupclub_01_armstrong_128kb + * https://librivox.org/the-teacup-club-by-eliza-armstrong/ + * warletters_01_allan_128kb.mp3 + * https://librivox.org/war-letters-from-a-young-queenslander-by-robert-marshall-allen/ + * TSPspeech + * http://www-mmsp.ece.mcgill.ca/Documents/Data/ + * + * sounds + * cough + * https://freesound.org/ sounds: 205012 222644 252240 323532 342587 343880 343885 343986 369295 391284 401334 407839 419781 425777 87412 + * laugh + * https://freesound.org/ sounds: 167578 189278 263775 319346 343934 344043 344064 367680 381275 383237 393353 401214 433921 73123 + * + * music + * Danse Macabre + * Gymnopedie No 1 + * Local Forecast + * Secret of Tiki Island + * Unpromised + * https://incompetech.com/music/royalty-free/ + * + * + * ./chopper voice/hamlet-002-act-1-scene-1.1626.raw voice/human_rights_un_chi_nf.raw sounds/cough.raw voice/human_rights_un_cze_msed.raw voice/huntingofthesnark_01_carrol_128kb.raw sounds/laugh.raw voice/teacupclub_01_armstrong_128kb.raw voice/warletters_01_allan_128kb.raw voice/TSPspeech.raw sounds/cough.raw music/Danse\ Macabre.raw music/Gymnopedie\ No\ 1.raw music/Local\ Forecast.raw sounds/laugh.raw music/Secret\ of\ Tiki\ Island.raw music/Unpromised.raw + * + * + * NOISE: + * + * rnnoise_contributions + * https://people.xiph.org/~jm/demo/rnnoise/rnnoise_contributions.tar.gz + * + * + * ./noise-chopper *.raw + * + * + * COUNT: 10000000 (x87) + * + * NOTE: All files processed into correct raws through ffmpeg: ffmpeg -i in -f s16le -ac 1 -ar 48000 out.raw + */ + +/*This file is automatically generated from a Keras model*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "rnn.h" +#include "rnn_data.h" + +static const rnn_weight input_dense_weights[1008] = { + 15, 4, -1, 3, -7, 4, 1, 18, + 25, 21, 3, 8, -62, -1, -10, 7, + -5, -5, -2, -15, 2, -9, 5, 7, + 29, 19, 31, 9, -18, -25, 27, -30, + 20, -19, 50, -20, -16, -23, -30, -24, + -2, 16, -3, -21, 10, 7, 35, -3, + 16, 50, -9, -29, -16, -25, -32, -22, + -16, -18, 49, -25, -45, 24, -21, -25, + -30, 4, -6, -45, 29, 19, 43, 24, + 36, 38, -32, 16, -18, -21, 0, 46, + 36, 62, -9, -4, -28, -42, 28, -11, + -20, -34, 12, -11, 7, 27, -66, 30, + -75, 4, -7, -13, 12, 11, 18, -12, + 20, -43, 55, 7, 11, 23, -21, 19, + 29, -59, -2, -5, -35, 63, -83, -1, + -21, -11, 63, 15, -6, 23, -32, 17, + 2, 37, -4, -26, 40, -56, -21, -47, + -45, -6, -6, -18, -60, 23, -48, -50, + -13, -41, -101, -71, 9, 79, -121, 110, + 72, -44, -125, -28, 32, 18, 45, -77, + -30, -114, -93, -104, -74, 107, -55, -29, + -128, 34, -13, -51, 17, -7, 121, 126, + 55, 11, -85, 126, 127, -128, 127, -19, + 40, -80, 46, 22, 61, 52, 96, 96, + -67, -37, -24, -112, 6, 46, 10, -67, + -62, 63, -35, 67, -23, 64, 44, 3, + 48, -128, -11, 0, -7, 121, -126, -51, + -25, -34, -15, 27, -29, 35, -77, -47, + 45, -20, -106, -1, 19, -126, 13, -17, + -11, -127, 56, 5, -62, 42, -127, -75, + -71, -15, -116, -103, -24, -9, 83, -70, + -128, -38, -31, 105, 36, 28, -7, -37, + 40, -81, -19, -32, -44, 85, -62, 13, + -10, -32, 90, -57, 102, -77, 30, 62, + -45, -36, -21, -1, 55, -128, 116, 90, + 8, 11, 72, 36, 9, -127, 43, -116, + -95, 0, 48, -124, -124, -8, -59, 21, + -4, -35, 74, 5, -96, -10, -123, 8, + 42, 29, 123, -124, -125, 40, -18, 19, + 3, -2, 63, 91, -104, 69, -10, 114, + 38, 9, 62, -40, 50, -80, -77, -8, + 122, 127, 38, -87, -96, 123, 50, 91, + 5, 6, 127, -128, -46, -3, -43, 29, + -124, 71, 16, 70, 32, -63, 52, 88, + 22, 40, 58, 16, -120, 74, -94, 71, + 125, 126, 6, -47, -76, -96, 79, -11, + -38, 100, 26, 98, -36, -127, 10, 62, + 3, -101, -31, -121, -98, 66, -5, 127, + 26, 68, 76, -70, -100, -25, 76, -37, + 41, 94, -94, 92, -79, 97, -51, -93, + 113, -74, -64, -111, -8, 124, 127, 125, + -53, 40, 96, 120, 27, 87, -36, 126, + 127, -44, -42, -128, -15, 33, -81, -3, + -109, -115, 72, -117, -125, 127, 18, 126, + -60, 12, 99, -19, -20, 24, -53, -21, + 6, 57, -111, -69, -30, -22, -100, -23, + 21, 27, -44, -126, -122, 74, -60, 58, + 73, -24, -57, -20, 6, 18, -65, -50, + 113, 121, -26, 50, 72, 62, 94, -33, + 52, -128, -116, -36, -38, 28, 9, 85, + -50, 92, -2, 14, -84, 9, 124, 3, + -21, -26, 120, -77, -1, 0, -46, -47, + 79, 82, 41, -7, 111, 93, -46, -125, + -51, 104, 123, -10, -27, -16, 47, 37, + -19, -20, 42, 127, 49, -29, 26, -32, + 59, 40, -26, -17, 95, -72, -47, 100, + 25, 127, -103, 57, -55, 125, 86, 112, + 127, -82, 113, 19, -11, -112, 65, 127, + -58, -75, -11, 65, -128, 50, 27, -95, + 93, 119, -125, -106, 83, 97, 93, 27, + 97, 121, 12, 8, 52, -3, -123, -84, + 21, 77, -15, -37, 115, -72, 127, -40, + 33, 120, -112, 38, 30, -60, 102, 124, + -113, -127, 110, -36, -72, -54, -127, -128, + -126, -76, 42, 42, 17, 55, 15, -2, + 92, 27, -48, -21, 71, 67, -99, -40, + 49, 77, -95, 36, 19, 21, -54, 103, + -37, -113, -14, 7, -23, -3, -123, 63, + 123, -53, -28, 18, 16, -115, 115, -122, + -97, 33, 90, 52, -28, 1, -41, -87, + -78, -70, -22, 11, 34, 35, 50, 53, + 127, 10, 120, -31, 102, 81, -126, 66, + -103, -40, -76, -10, -10, 9, 21, 65, + -29, 63, -48, -88, -93, 97, 42, -17, + -14, 20, 100, 98, 51, 72, 95, 44, + 46, -51, 50, 49, -10, -83, -75, -100, + 45, -67, 26, 127, 79, -33, -50, -127, + -92, -12, 13, -57, -76, 62, 79, -124, + 19, 55, 34, -34, -90, -20, -46, -1, + -7, 45, -8, 85, -84, 81, 83, -90, + -128, 44, 127, 54, 71, -84, 66, 11, + -45, 25, -17, -5, -24, -79, -2, -120, + 35, -20, 27, 17, 30, 71, -21, 5, + 69, 114, -26, 52, -5, 12, -12, 110, + 123, -20, -57, 11, -20, 29, 34, -92, + 14, -5, -89, -30, -27, 104, 80, 101, + 11, 58, -3, -52, 43, 52, -8, 114, + -90, -69, 14, 32, 41, -6, 7, 12, + 26, -126, -20, 12, -35, 97, -101, -123, + -22, 0, -80, 0, -32, -31, 12, -64, + -2, -28, -10, 7, 94, -18, -44, -7, + -18, 23, -31, 100, -7, -29, -69, -69, + 125, 127, 121, 25, -125, -21, 124, 127, + 63, -128, 99, -58, 40, -101, -46, 99, + -126, -12, 127, 123, -126, 44, 98, -69, + 91, 123, 56, 40, -127, 94, 107, 127, + 63, -95, 121, 119, -60, 83, 35, 52, + -121, -28, 127, 127, -121, 120, 1, -40, + -106, -111, -109, 29, -3, 14, -63, 62, + -49, 37, 69, 63, -72, -33, 14, 27, + 76, -121, -83, 125, 20, -2, -117, 74, + 85, -54, -42, 47, 3, 27, 32, 70, + -56, -15, 95, -81, -46, -38, -61, 66, + -36, 45, -76, 88, 52, -14, 9, 82, + 120, 14, -17, -11, -121, -126, -24, 48, + -22, 32, 13, 17, -67, 59, -121, -52, + 3, 77, -88, -25, 124, -113, 46, 86, + 78, -97, -118, 11, 107, 12, 22, 8, + 63, 43, -14, -109, 24, 15, -21, 80, + 4, -75, -76, -18, -5, -103, -19, -95, + -13, -34, -23, 31, -41, 51, -39, 1, + -26, 34, -36, 36, 75, 10, 56, -22, + 16, 18, 33, -2, 11, 50, 2, -1, + 62, -19, 62, 28, -38, 92, -40, 13, + 3, 8, -24, -5, -51, 16, -37, -13, + 42, 14, 34, 50, -26, 11, -20, 126 +}; + +static const rnn_weight input_dense_bias[24] = { + -69, -7, -38, 74, 127, -1, 19, -5, + -39, 4, 27, -127, 115, -126, 125, 3, + -29, 120, 10, 94, 74, -35, -16, 3 +}; + +static const DenseLayer input_dense = { + input_dense_bias, + input_dense_weights, + 42, 24, ACTIVATION_TANH +}; + +static const rnn_weight vad_gru_weights[1728] = { + 25, -122, 41, -91, 6, -9, 2, 72, + -100, -46, -102, -101, 31, 54, -48, -47, + -80, -107, -5, -31, -121, -5, -26, -38, + -16, -11, 36, -40, -19, 89, -25, 105, + -5, -107, -3, -9, 40, 28, 2, 15, + -54, -105, 2, 24, -83, -86, -6, 59, + 1, -63, -2, -28, 36, -47, 21, 29, + -28, 4, -14, 36, 25, -30, -27, 6, + -50, 33, -4, -50, 3, -69, 58, 4, + -19, -69, -45, 2, -41, 110, 48, -42, + -12, -100, 15, 127, 61, 64, 65, 51, + -74, 24, 4, -8, -61, -127, 14, -126, + -113, -81, -81, 31, -18, -11, 110, -5, + 37, -126, -90, -44, 26, -57, -45, 127, + -22, 36, 0, -103, 5, 24, 62, -117, + -18, -126, 99, 127, 34, -44, 77, 62, + -42, 12, -101, -103, 103, -59, 66, -40, + 9, 127, 10, -1, -13, 74, 106, 117, + 104, 126, 17, -127, 88, -126, 55, -127, + -125, 44, 119, -9, -68, -122, 115, 108, + 123, -79, 19, 18, -32, -86, 13, 31, + 68, -3, 11, -16, 27, -61, -34, -69, + -44, -93, -43, -26, -128, -86, 59, -10, + 96, -36, 10, 68, -9, 48, 66, 85, + 69, -5, 28, -13, 39, 30, 58, 24, + 49, -9, -65, -42, -40, -21, 32, 29, + -24, 66, -14, -69, 10, -19, -8, 1, + -47, -57, 5, -106, 14, -32, -77, 126, + -41, -69, 16, -44, -43, 9, -125, -63, + -37, -128, 50, -1, 113, 78, 67, -29, + 98, 40, -14, -12, 7, -19, 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-119, 17, -77, -128, -126, -127, + -4, -127, -120, -123, 126, -65, -37, -14, + -11, 52, -7, 100, 79, -89, 13, -127, + -99, -40, -93, -60, -27, -127, 76, -73, + 126, 42, -128, 125, 55, 85, -42, -125, + -19, -19, -19, 30, 1, -82, 51, 14, + -13, 16, -2, 17, -91, -11, 54, -25, + 11, 74, 25, 42, -97, -22, 23, 82, + -128, -81, 38, 126, -34, 11, -28, 94, + 29, -93, -128, 20, -19, -66, -128, -112, + -119, 7, -13, -125, 126, 75, -110, 27, + -16, -38, 29, -28, 82, -39, 60, 3, + -24, 46, 23, -21, 74, 96, -11, -83, + 7, -44, -38, -34, 31, -17, 36, 5, + 66, -78, -31, -27, 5, 41, 60, 7, + -43, -6, -7, -76, -71, 41, 11, -30, + 0, 47, -60, 0, 6, -7, 70, 51, + 106, 21, 39, -70, -85, 127, 91, 73, + 61, 102, 46, -52, -88, 125, -44, 34, + 121, 36, 69, 5, 115, 66, 6, -55, + -23, 61, 76, 32, -11, 117, -25, -5, + 65, 78, 19, 12, -60, -20, -66, -4, + -128, 23, -6, -13, -3, -19, -10, 115, + 31, 24, -16, -37, 76, 71, 6, -57, + 104, -97, 60, 17, -49, -99, -29, -42, + -103, -8, -18, -36, -36, 17, -2, -40, + 59, 18, -75, -32, 39, 70, -127, 41, + 127, -65, 93, 56, 78, 69, -38, -126, + -116, 67, -2, 15, -79, 63, 52, 106, + -49, -125, 73, 124, 11, -71, 89, -124, + -4, -9, 31, 10, 118, -69, -51, 35, + 54, 95, 23, 6, -52, 48, 8, -102, + -32, 55, 18, 19, -67, -30, -46, 24, + 3, 39, 12, 53, 3, -20, -9, 9, + 71, -29, 20, 90, -17, 21, -61, -2, + 58, 126, 95, -78, -46, 17, 58, -84, + -29, 127, 86, 4, 93, 0, 118, 86, + -62, 62, 65, -31, -117, -75, -39, -81, + -41, 93, -55, -128, -66, 65, -69, 38, + 23, 6, -48, -8, -65, 4, 2, -39, + -7, 31, 31, -113, -101, 31, -19, 30, + 48, 6, 43, -6, -25, 1, -8, -22, + -60, -9, -82, -77, -20, -78, 67, -19, + 38, 41, 10, -67, 11, -20, 30, 50, + -57, -74, 74, 32, 126, 39, 80, -44, + -118, 109, 65, -63, 0, -86, -64, -49, + -128, 62, 9, -63, -118, 41, 48, 127, + -17, 76, 4, -41, -32, 56, -49, 21, + -25, -86, -91, -61, 11, -46, 87, 71, + 119, 55, 110, 18, -91, 72, 29, -68, + 35, 74, 41, -12, 9, 54, 75, -60, + -14, 42, 15, 50, 14, 11, -20, -87, + -2, -1, 41, -25, 123, -1, -37, -54 +}; + +static const rnn_weight vad_gru_recurrent_weights[1728] = { + 66, 64, 124, 41, -73, -78, 53, 38, + -54, 79, 106, -53, -24, 110, 65, 98, + 3, -92, -49, 11, -34, 80, 84, 76, + -7, -49, 47, -3, -9, 10, 21, 33, + -56, -15, 68, 29, 9, 9, 30, -8, + 35, 13, -34, 25, 34, 71, 65, 118, + 31, -127, 80, -3, 86, -45, 75, 97, + -87, 21, 28, -38, 56, 35, 124, -82, + -23, 122, 39, -84, 63, -24, 21, -66, + -39, -65, -117, -22, 62, -42, -49, 57, + 10, -56, -60, 17, -8, 30, -85, -118, + -33, 78, -10, -61, 29, -33, -121, -84, + -23, 49, -93, -87, -21, -20, 34, -18, + 44, 24, -69, -32, -88, -66, -50, -39, + -86, 9, -8, -47, -15, -78, -14, 1, + 55, 70, 15, -42, -7, 6, -38, 19, + -48, -20, -35, 44, 71, -40, 59, -40, + -46, -33, -73, 26, -14, 96, -3, 13, + 80, 77, 72, 43, -94, 65, 98, -26, + -27, 63, 106, 52, 56, 44, 127, 112, + 43, 89, 12, 57, -33, -56, 116, -38, + 5, -73, 82, 80, -97, 42, 105, 54, + -20, 50, 53, -15, 11, -14, -20, 7, + -128, -23, 12, -16, 11, 87, -13, 34, + 62, -74, 46, 55, 28, -104, 8, 66, + -48, 25, -5, -22, 42, 49, 91, 9, + -13, -15, -70, -52, -16, 46, 28, -13, + -23, -3, -21, -34, -109, 5, 0, -98, + 37, -109, 127, -3, -60, -110, 22, -24, + -80, 106, -91, -114, 44, -20, -93, -6, + -17, -63, 32, -30, -74, 83, 53, -91, + -64, 28, 86, -8, -9, -45, -76, 11, + 94, -57, -117, -37, 3, -8, 120, 49, + 103, -43, 58, 55, 81, -126, 46, 8, + -128, 74, -93, -34, 98, 68, 126, -56, + -98, 59, -78, -15, -73, 64, 126, -9, + 80, -2, 126, 61, -2, 17, 118, 11, + -124, 124, 37, 1, 48, -31, 97, 30, + 74, 1, 85, -3, 72, 28, 102, 9, + 5, 44, 127, 16, -36, 28, -22, -10, + 7, 96, 45, 69, 81, 16, -8, -54, + -9, -15, -66, 118, 18, 8, 47, -1, + -115, 6, -69, -124, 51, 29, -63, 24, + -14, -43, 97, 77, -25, 63, -49, 108, + 41, -44, -14, 81, 22, -127, -53, -57, + -42, -127, 32, 14, 62, 21, 127, -3, + -51, -42, -32, 22, 59, 24, -19, 127, + -22, 29, 117, 33, -45, -50, -22, -96, + -33, 68, 48, -6, 43, 14, 8, 94, + -10, -4, 27, 22, 15, 23, 30, -31, + -50, -111, -5, -127, -16, -58, -36, 100, + -23, 73, -128, -62, 18, 40, 12, -61, + 54, -47, 35, 112, -103, -91, -115, 81, + 101, 3, 126, 42, 37, -18, -69, -41, + 40, 110, 57, 14, -82, -94, 28, -105, + -15, 34, 89, 43, -5, -121, 126, 125, + 84, 126, -105, 69, -3, -67, 27, 3, + -27, 61, 22, 103, -22, 37, 80, -9, + -50, -7, 13, -12, 58, -106, 1, 28, + 22, 49, -26, 1, 32, 75, 51, -27, + 78, -125, 119, 57, 5, -89, 82, 38, + -17, 115, -88, -62, 58, 19, 127, -91, + 10, 115, -16, -16, -36, -19, 103, 26, + 70, 106, 47, -118, -62, -18, -128, 78, + 33, 33, -7, -82, -85, -122, 124, -94, + 25, -95, -128, 69, 102, 75, -38, 60, + -62, -39, 27, -127, -60, -48, -127, -40, + -38, -78, -31, -29, -43, -104, -6, -31, + -27, -65, -7, 120, -6, -22, -35, -102, + 42, -47, 90, 104, 35, -57, 75, 87, + -80, 83, -104, -84, 47, 127, 101, -115, + -127, 80, -127, -87, -28, 45, -11, 84, + -50, -122, -42, 41, 23, -12, 70, 42, + -41, 2, -23, 5, -7, 31, -126, 127, + 31, 29, 123, -13, 111, 59, 35, 22, + 28, 69, 33, 117, 42, 7, 72, 17, + 1, 49, -24, -67, 30, 31, 6, -53, + 67, -124, 57, -126, 34, 53, 36, -46, + -81, 118, -19, -61, -27, 44, -90, 26, + 69, -59, 78, 65, -8, -53, -128, 82, + 74, -68, -29, 90, 57, 47, -82, -25, + -2, -19, 89, 19, -44, 3, 93, -28, + -122, 127, 120, -128, -61, -53, 0, -28, + -98, -95, 17, -86, 120, 50, 26, 37, + 16, 36, -84, -36, -47, 35, 13, 5, + -66, 2, -20, 28, 69, 30, 7, -51, + 67, 39, -92, 56, 60, 32, -51, -50, + 21, -72, 110, 22, 21, -128, -5, 63, + -66, 23, -43, -125, 114, 58, 123, -69, + -28, 127, 24, -66, 3, 29, 52, 35, + -70, -41, -72, -10, 85, 14, -103, 43, + -12, 5, -127, -12, 6, 33, -84, -123, + -124, -37, 93, -86, -19, -80, -44, -48, + 47, 35, -89, 6, 62, 17, -34, 37, + -1, -79, -32, -87, -30, 45, -15, -12, + -28, -30, 25, 45, 13, -73, -9, 39, + -83, 126, -49, -56, -52, 51, -94, -126, + 9, -46, 41, 92, -50, -74, -63, 33, + 29, -126, 115, 86, -8, -3, -127, 39, + 6, -127, -70, -78, 17, 52, -45, 70, + -10, -25, 43, -66, -31, 1, -128, 24, + -57, 4, 122, -17, 35, -30, -107, 1, + 25, 29, 0, 79, 91, -15, 3, 29, + 8, -60, -34, 21, 23, -6, -14, -17, + 47, 33, 10, -96, 91, -35, 28, -8, + -43, 39, -126, -35, -59, 0, -6, -27, + 47, -57, 70, 37, -49, -59, -107, 92, + 23, -57, -31, -11, 44, -19, -27, 35, + 10, 0, 59, 16, -122, -25, 3, -117, + -17, 0, 99, 17, -26, 34, 125, -118, + 22, -16, -71, 80, 29, -44, -40, -41, + 1, -12, -50, -13, -29, -62, 51, 29, + -28, 6, -28, -4, 13, -23, -63, -33, + -53, -86, -96, 29, -25, -52, -13, 44, + 87, -124, 34, 9, 103, -74, 38, 71, + -106, 70, -49, -47, 13, 28, 127, -83, + -105, 61, -39, -46, -105, 59, 79, -19, + 16, 76, 43, 95, 20, -27, -77, -128, + -51, 46, -44, -63, -8, 88, 96, -32, + 24, -1, 37, 46, -74, 127, 117, 124, + -3, 55, 27, -47, -87, 16, -102, -13, + -71, -23, -47, -37, -75, -86, 38, -60, + -2, -1, -19, 99, 0, 51, -72, -7, + 60, -87, 84, 0, -7, -56, -11, 85, + -47, 39, -39, -50, 78, -2, 127, -49, + -22, 77, -62, -52, -60, -48, 84, 58, + 68, 63, 59, 13, -27, -87, 80, -46, + -75, 26, 60, -9, -12, -53, 109, 59, + 30, -23, -29, 30, -4, 79, 111, 98, + 45, -8, 89, 48, -16, -29, 37, -28, + -65, 12, -1, 62, 65, 11, 64, -7, + 62, -7, -66, 97, -36, 33, 10, 5, + 14, -22, -43, -26, 117, -60, -29, 47, + 56, -20, -24, -45, 18, 63, 31, -79, + 27, 24, -48, 16, 69, -59, -40, 15, + -25, 61, -120, 29, 73, 116, 35, 22, + 41, -46, -63, 34, 61, -4, -35, -110, + 33, 124, 5, 63, -70, -90, -43, -24, + -53, -7, 11, 17, 11, -49, -11, 63, + -19, 15, 16, 19, 61, 60, -78, -55, + -14, -33, 1, -16, -27, -57, 4, -48, + -50, 125, -60, 20, -40, 52, -99, 36, + -19, -41, -8, 41, -36, 17, -128, 56, + 98, -128, 115, 31, -48, 12, -47, 36, + -13, -22, -69, 33, 25, 95, 34, -61, + 61, -76, -101, 127, 126, -104, 14, 51, + -77, 125, 54, 5, -60, -112, -7, -89, + -34, 52, 19, -59, 99, -20, -18, -59, + 39, -27, 1, 16, 15, 13, 33, 16, + 21, -1, 75, -40, -12, -24, 30, -119, + -127, 127, -55, -18, -122, 100, -21, -18, + 66, -58, 44, 48, -48, -88, -127, 127, + 73, -128, 59, 44, -62, 100, -81, 33, + 63, 64, 109, 58, 27, 44, 109, -112, + 30, 123, 56, -35, 20, -14, 104, 119, + 24, 126, 81, 98, -41, -9, 119, 55, + -43, -15, 32, -27, -11, -47, -8, -71, + -23, -42, -55, -43, -10, -76, 7, -47, + 42, -7, -3, -16, -12, 27, -40, -11, + -48, 7, 119, 0, 4, -22, -3, 72, + 4, -24, -7, -6, -23, 114, 54, -27, + -2, 83, -81, 16, 6, -18, 26, -68, + 73, 127, -102, -17, 127, -40, 38, -86, + 79, -85, 33, 127, 38, -128, 126, 125, + 127, 125, -127, 116, -125, -101, 105, -55, + 62, 7, 20, -9, -5, 5, 92, -85, + -72, -35, 66, 11, -20, 13, 37, 100, + 38, -14, -41, 31, -36, -91, 42, -73, + -70, 73, -8, 0, -5, 36, -9, -60, + -7, 16, 16, 101, -54, -122, -101, 127, + 95, -128, 127, 39, -96, -31, -34, -1, + -127, -125, -61, -58, -52, 38, -21, 116, + 45, -98, -21, -56, -4, -26, -123, -117, + -58, -62, 3, -48, 52, -87, -125, -61, + -62, -25, 3, -54, 64, -3, 19, -39, + 1, -68, -97, -6, -9, 17, -92, -36, + -53, -54, -43, -101, 44, 1, -58, -61, + -66, 125, -44, -68, -112, -6, -76, 26, + 32, 38, 45, 5, -24, -120, -7, 127, + 32, -116, 127, 61, -112, -16, -24, 31, + -113, -127, 97, -27, 127, -42, -63, -77, + -126, 59, -98, -57, -23, 30, -127, -73, + -128, -59, 124, -119, 127, 70, -34, 54, + -80, -12, 27, -40, 51, -69, 58, -98, + -18, -26, 27, 73, 104, -65, 28, 123, + 14, 32, 6, 127, -50, -121, -49, -128, + -127, 127, -127, -118, 80, 71, -126, -14, + 127, -91, 127, 127, -50, -88, -128, 127, + 127, -127, 109, 127, 19, 58, -126, -19, + 37, -125, 66, 115, -75, 126, 102, 127, + 101, 93, 125, -91, 3, 127, -50, -98, + -85, 26, 77, -118, 121, -116, -82, -37, + 101, -25, 65, 27, 12, 119, 74, 76, + -52, 41, -15, 51, 27, 127, -72, 127, + 77, 56, 47, -64, 34, -58, 95, 27, + 127, -74, 79, 99, 29, -68, 88, 126, + -77, 37, -32, -77, 89, 70, 116, -71, + -103, 41, -127, -47, -2, 47, 13, -3, + 60, 32, 104, -14, -65, 25, 112, -50, + 3, 18, 99, 71, 65, -124, 93, 123, + 63, 15, -96, 13, -65, -29, 66, 9, + -70, -44, 57, 38, -46, -12, 19, 30, + -19, -27, 28, -8, 49, -15, -24, -11, + -58, -2, -47, 48, 76, 36, 42, -93, + -40, -119, -6, -62, -36, 30, 74, 69, + -56, 16, -34, -3, 50, -52, 2, -7, + 103, 17, -44, 85, 5, -29, 83, -51, + -116, -128, 76, -100, 29, -20, -18, 94, + -20, -4, 114, -6, -29, 77, -128, -90, + -85, -128, -19, -87, 127, 41, -20, 127, + 124, -2, 65, -49, -36, 6, 43, 49, + -53, -30, 36, 13, 48, 30, 70, 67, + 127, 55, -2, 49, 125, 44, 39, -11, + 50, -127, 92, 65, 51, -96, 34, 124, + -9, 40, -127, -39, 76, 34, 59, -47, + -87, 124, -70, -125, 89, -14, 126, 19 +}; + +static const rnn_weight vad_gru_bias[72] = { + -128, -127, 24, -84, 28, 81, -106, 121, + -53, -34, -73, -128, -59, 31, -127, -123, + -127, -125, 97, -127, 123, 126, -125, -11, + 36, -8, 32, 85, 37, 52, 24, 79, + 63, 66, 40, 22, 39, 8, 2, 15, + 75, -24, 54, -38, 17, 49, 22, 58, + 6, -59, 5, 5, 38, -26, -4, -36, + -10, 11, -12, -19, 8, 5, 78, -53, + -54, 42, -120, -45, 59, 33, -7, 5 +}; + +static const GRULayer vad_gru = { + vad_gru_bias, + vad_gru_weights, + vad_gru_recurrent_weights, + 24, 24, ACTIVATION_TANH +}; + +static const rnn_weight noise_gru_weights[12960] = { + 0, 27, 12, 8, -81, 27, -43, -13, + 21, 40, -6, 36, -10, -75, -50, -1, + 12, 20, 43, -54, -26, 12, -69, 18, + -69, 36, -11, 8, -31, -54, -46, -21, + -29, 5, 11, 84, 90, -4, 25, -18, + -91, 41, 15, -6, -36, -22, -68, -3, + 25, -13, -15, 14, 22, -14, 33, 10, + 1, -4, 72, -9, -25, -46, -37, -84, + 7, -49, 9, -45, 0, -16, 1, -28, + -36, -23, 28, 4, -62, 2, 32, -60, + 34, -17, -3, 23, -22, -16, -33, -31, + -57, 1, -20, 66, 46, 59, -68, 31, + 51, -64, -6, 16, 36, -13, -43, -15, + 27, 4, -36, 7, 17, 11, -11, -5, + -79, 1, 28, 9, -27, 35, -49, 23, + -12, -7, 31, -10, 15, -40, -9, -46, + 36, -3, 17, -7, 19, -73, -6, 24, + 8, -21, 52, -4, -14, -5, -14, 15, + 17, 53, -1, 36, -9, -7, 12, -69, + 23, -16, 2, -52, 17, -57, 38, -80, + 23, -30, 39, -46, 24, 42, 107, 5, + -125, -43, -122, 51, -7, -125, 67, -43, + 104, 35, -2, 25, -21, -71, -15, 1, + -20, -88, -93, 26, 12, 76, -96, -8, + 10, -108, 52, -16, 44, 18, 107, 6, + 16, 19, 56, 20, 30, -57, 79, -17, + -19, 4, -14, -85, 79, -11, 28, -26, + -40, 10, 0, 9, -40, -46, -36, -80, + 28, 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-24, 34, + 35, -41, 48, 30, -72, -118, -20, 25, + 15, -44, 55, 102, 22, 2, -55, 72, + -31, -80, -50, -10, 12, 14, 3, -11, + -47, -17, 26, -71, -35, -11, 65, -13, + 17, -67, 38, -61, -24, 12, -71, 65, + 7, -60, -44, -49, 19, -8, -51, -50, + -45, 12, 27, -24, 10, -35, 31, -17, + 10, 30, -52, 41, 4, 8, -2, 15, + -14, -30, 26, -15, -70, -10, -96, -66, + 21, -19, -17, 40, -125, 4, 30, -15, + 50, 8, -1, -60, -36, -18, 47, 9, + 24, 29, -68, -10, 10, -7, -42, 84, + 34, -10, 45, 32, 43, -26, -26, 0, + -124, 11, 97, -14, 59, -36, 5, -90 +}; + +static const rnn_weight noise_gru_recurrent_weights[6912] = { + -26, -18, -10, 70, 11, -2, -69, -34, + 93, -62, -3, 23, -12, -9, -16, 62, + -5, -66, 15, -76, 124, -18, -75, -25, + 56, -13, 70, -5, -25, -96, -29, -27, + -9, 20, 3, -56, 68, 101, 60, 4, + -33, -27, 85, -21, 26, 96, -51, 108, + -9, 26, -45, 96, 0, -20, -13, -41, + -37, 34, 7, 20, -45, -36, -41, 67, + 2, 83, 46, -43, -4, -77, 11, 19, + 38, -86, 12, 37, 5, 1, -32, -23, + -26, -64, 33, 126, 18, 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5, -45, 22, -83, -55, + -15, 2, -89, -23, -7, -18, 33, 33, + -30, -70, -41, 127, 58, 33, 74, -26, + -110, 5, -120, 73, 16, 6, 14, 30, + 59, 82, 13, 32, 60, -5, 1, 8, + 75, 3, -83, 29, -36, 23, -120, -9, + 13, -94, 48, 11, 19, -67, 100, 9, + 30, -5, -39, 9, -12, -38, -110, 126, + -51, -11, -76, 72, 57, 33, 61, -22, + -87, -7, 91, 78, 26, 9, 1, 42, + -84, 33, 22, 58, 28, 17, -2, 0, + -61, -40, -52, -72, -23, -22, -35, 120, + -24, 23, -41, -2, 17, 18, 105, 35, + 2, -9, 127, -29, 52, 57, 44, -22, + -15, 30, -14, -85, 65, -32, -43, -22 +}; + +static const rnn_weight noise_gru_bias[144] = { + 41, -1, -4, -19, -13, 6, -31, 45, + 38, -13, 6, 6, -20, -20, -2, 7, + 2, 18, 67, 4, 16, 12, -43, 9, + 36, -13, 9, -8, 26, 18, 4, 16, + 11, 19, 41, -42, 61, 58, 33, -28, + 20, -1, 57, -7, 26, 41, 41, -2, + -13, 18, -15, 8, 22, -3, -7, 52, + 15, -5, 4, 17, 0, -38, 3, 3, + 50, 7, 18, 3, -4, -14, -23, 15, + 13, -7, 14, 2, 15, 4, 22, -9, + -21, -23, -15, 13, -15, -6, -31, -9, + -16, 5, 6, 3, 32, -13, 46, -49, + 23, 34, -19, -31, 7, -13, 14, 20, + 3, -15, 7, -18, 7, -42, -14, 22, + 21, -6, -6, 8, -8, -22, 7, -41, + -3, -19, -23, 7, -16, -30, -11, -9, + -6, -6, 6, -31, -33, -6, -27, 17, + 18, -4, -19, -6, -4, 26, -22, 19 +}; + +static const GRULayer noise_gru = { + noise_gru_bias, + noise_gru_weights, + noise_gru_recurrent_weights, + 90, 48, ACTIVATION_RELU +}; + +static const rnn_weight denoise_gru_weights[32832] = { + -18, -3, 82, -51, 1, -42, 27, 8, + -35, 27, -8, -7, -16, -33, 24, -2, + 15, 8, -29, -9, -7, -9, 39, 10, + 2, -18, -19, -10, -50, -3, -38, -101, + -28, -16, 5, -27, 24, -66, -9, 0, + 41, 13, 4, -54, 3, 34, -48, 9, + -5, 12, -25, -40, 6, 7, -34, -47, + -22, -30, 47, 3, 12, -26, 2, 15, + 4, -12, -20, -16, -3, -9, 6, 39, + -15, -7, -48, -17, 5, -23, -26, -28, + -22, -20, 37, -49, 3, -4, -4, 27, + -21, -22, -15, -1, -35, -35, -33, -42, + 12, -25, -39, 52, 55, -11, -6, 6, + -10, 6, -9, -6, 1, -14, 23, 4, + -63, 44, -69, 18, 41, 6, -38, -42, + -17, -10, 8, -8, 4, -5, -28, -17, + 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-15, 124, 33, + 49, 53, -10, -19, 35, 14, 107, 40, + -41, 112, -13, -19, -23, -21, -6, 35, + 10, -27, 37, 92, -26, 18, 24, 61, + 29, 6, 43, 9, -7, -38, -11, 13, + 116, 39, 29, 39, -1, 17, 12, 56, + 36, 19, 0, 13, -4, -34, 125, -35, + -32, 48, -15, -1, 7, 42, 5, 42, + 0, 19, 5, -5, 21, -35, -1, 6, + 18, 39, -16, -17, -27, 12, -32, -1, + -20, -67, 8, 23, -25, 17, -5, 31, + 56, -35, 69, 39, -24, -106, -12, 62, + 39, -50, 19, 71, 2, 52, -75, 36, + -14, -37, 11, -27, -30, 2, -39, 7, + -39, -31, 32, 49, 19, 31, -1, -71, + 42, -27, 88, 20, 66, 51, 15, -2, + 21, 11, -1, 9, 21, 18, -32, 83, + 20, -27, -9, -42, 6, -17, -4, -22, + -54, 41, 29, -35, -19, 59, 3, -11, + -39, -37, -3, -38, -15, 89, -22, -36, + 2, 3, -31, 1, 4, 38, -7, 3, + 13, -7, -3, 41, 16, -36, 42, -15, + 26, 5, -1, -25, -27, -48, 24, 10, + 25, 35, -54, 41, 23, 37, 9, 7, + -6, 49, -33, 2, -4, -4, 44, 23, + -31, 41, 3, -31, 4, -6, 63, -36, + 50, -57, -11, 9, 21, 17, -3, 1, + -26, 7, 31, -16, -34, -34, 15, -14, + -11, 48, 12, 15, 12, 2, 2, 39, + -94, 20, 77, 59, -16, -51, 2, 52, + 21, -5, 9, 11, 49, -54, 15, -17, + 63, -23, 8, -44, -16, 0, -13, -16, + 12, -36, -22, 6, 7, -100, 4, -9, + -28, 34, -81, 20, -47, -16, 15, -74, + 69, 53, -60, 7, -20, -21, -1, -19, + 0, -19, 5, -9, 73, 23, -37, -19, + -3, -34, -22, -93, 7, -6, 24, -9, + 10, -35, 31, -33, -16, -51, 35, -58, + -8, -9, 7, 10, -1, 11, -89, -68, + -56, -8, -35, -27, 25, -17, 23, 29, + -12, -9, -35, -20, -16, 1, -14, 24, + 11, -13, -1, -62, -22, -27, 41, -20, + -31, -19, 23, -46, -56, -19, -27, 3, + -13, -55, 8, 17, -11, -15, 35, -16, + 12, 0, -3, 25, -13, -56, 21, -74, + -30, -18, 11, 5, 53, 58, -14, 30, + 24, 5, -67, 60, 20, 26, -15, -25, + -23, 27, -125, -31, -43, 127, -1, 9, + -1, 8, -36, -15, -32, -100, -6, 4, + 47, 21, 12, 28, -16, -46, -46, 12, + 43, -14, -22, -8, 9, 5, -36, 15, + -39, 9, -37, -54, 8, -30, -43, 31, + 38, 46, 35, 29, 65, -43, -22, -66, + 52, 67, 24, 8, 14, -5, -20, 30, + -44, 16, 109, 45, 37, -29, 38, 21, + 49, 15, -25, 21, -1, -40, -48, -5, + -36, -17, -13, -2, 8, 48, 49, -3, + 3, -2, 29, 27, 8, 4, 9, 16, + 2, -126, -51, -17, -30, -31, -15, -9, + -19, -16, 39, -6, 40, -25, -36, -28, + 9, -22, -8, -13, -9, -3, 5, 16, + 11, 18, -13, -6, 15, 1, -15, -32, + -43, 7, 5, -52, 26, -46, -4, 8, + 8, 10, 7, 19, -31, -18, -51, -7, + -44, 35, 1, -12, -4, 9, -18, -27, + 14, 11, -13, -12, 7, 9, -18, -2, + -35, -22, 16, 5, -47, -9, 29, 0, + -43, 35, -69, -38, -18, 29, -3, -6 +}; + +static const rnn_weight denoise_gru_recurrent_weights[27648] = { + -39, -35, -78, 44, -64, 27, -127, 2, + 31, -36, -54, 21, 24, -72, -32, -74, + 49, -79, 106, 53, -19, -35, 48, -27, + 34, 43, -63, -65, 38, -2, 50, -25, + -42, -15, -83, 109, -13, 126, 34, -24, + 5, -20, -31, -75, -71, 6, -46, 88, + 54, -128, -117, -31, 71, 3, -2, -10, + -9, -13, 22, 20, -1, 8, -55, -53, + -3, -11, -21, 90, -59, 7, -33, -86, + -23, 127, -110, -76, -33, -100, 64, -117, + -2, -1, 7, -38, -28, -102, -38, -66, + 10, -91, -31, -80, -47, -3, -97, 65, + -21, 40, 37, 76, 23, -35, 48, 5, + 8, -29, 9, -13, -65, 33, 24, -13, + 16, -20, 35, -79, 5, 64, 16, -50, + -26, 8, 42, -26, 62, -1, -1, -69, + 29, 31, 23, -33, 5, 1, -73, -33, + 10, -30, 103, -13, -20, 37, 58, 2, + 79, -62, -8, 37, -4, 20, -43, 47, + -40, -9, -36, -46, -12, 0, -45, -24, + 16, 53, -34, 14, -62, -8, 33, -54, + 6, -69, 24, -18, 17, -51, 37, 32, + 16, -3, -16, 45, 5, -51, -11, 6, + 60, 42, -25, 15, 34, -10, 51, 18, + 57, -44, -36, -42, 107, -75, 10, -19, + -15, -63, -20, -11, -53, 62, 56, 125, + -40, -12, -52, 29, -128, -81, -79, -20, + -25, -26, -39, 42, -20, -43, 66, -39, + -84, -125, -4, -20, 16, -44, -7, 3, + 32, 3, -8, 32, -1, -65, -6, -58, + -36, 26, 70, 4, 21, -21, 32, 18, + 0, 97, 127, -55, -96, -78, -90, -2, + 24, 94, 19, -61, -6, -54, 26, -128, + -12, -10, -33, -60, 25, -42, 17, -54, + 44, -4, -87, 13, 40, 127, -60, 12, + -18, -35, -60, -7, -1, -55, -29, 7, + -46, 50, 77, 8, 40, 72, -16, 13, + 83, -35, -37, 123, -2, 17, -52, 14, + 61, 63, 16, 47, -96, -14, 14, -46, + 39, -36, 36, 72, 66, -70, -15, 36, + -71, -26, 59, 31, 12, 58, -10, 40, + -21, 6, -59, 25, 57, -44, 65, -5, + -45, 85, -44, 17, 75, -24, -43, -106, + -1, -79, 42, -59, -22, 12, -32, 72, + 69, -72, 18, 6, 4, 73, -51, 86, + 60, 0, -28, 21, -50, 7, -15, 17, + 47, -31, -16, 6, -20, -3, 36, 127, + -49, 3, 3, 96, 124, 51, 118, 43, + -92, 2, -34, 42, 16, 104, -52, -13, + -59, 36, -70, 73, -23, -10, -3, 84, + 21, 26, -39, -15, -99, 22, 51, -82, + 35, 21, 22, 47, 71, 75, -50, -34, + 1, 35, -18, 15, 87, 38, 32, -40, + 47, -5, -25, 79, -63, 35, -38, 20, + -60, 3, -83, -28, 15, -9, -61, 30, + 48, -62, -17, 9, -45, -15, 4, 32, + 68, -17, -54, 47, 53, 11, 28, 26, + -67, -42, 35, -115, 52, -44, -27, 39, + -20, 6, 16, 9, 54, -35, 37, 0, + 11, -79, 54, 17, 9, -2, 36, 78, + -55, 19, 29, -52, 48, 32, 12, 59, + 18, 98, 49, -1, 4, -34, -61, -23, + -11, 10, -25, 42, 3, 63, 40, 60, + -89, -19, 26, -63, -14, 23, -16, -15, + 5, 14, 17, 11, 23, 75, -31, 35, + 29, -48, 0, 65, -1, 54, -42, -52, + 40, -32, -4, -74, 16, -35, -13, -40, + -26, -63, -62, -30, 54, 16, 12, 9, + 51, -53, -8, 17, 31, 63, -48, 16, + 16, -23, 95, 8, 0, -89, 13, 59, + 4, -4, 4, 0, 41, -62, -10, 12, + -35, 50, -31, -33, -59, 56, -64, 5, + -95, 17, -54, -38, 12, -67, 81, -60, + 34, -1, 21, -7, -34, -35, 15, 105, + -110, 10, -115, -19, 49, 15, 39, -25, + -128, -77, 55, 55, -16, 23, -50, 35, + -43, 52, 4, -34, -31, 5, 110, 85, + -51, 39, -64, -50, -18, -65, -100, -14, + -23, 39, -121, 64, 61, 2, -44, 91, + 8, -65, -53, -5, -26, -103, 33, -100, + 55, -19, -74, -52, -56, -78, -127, 102, + 34, 38, 11, -20, 52, -15, 2, -19, + 19, -66, -121, -31, -53, -25, 7, 20, + 32, 22, 34, 58, -11, -10, 58, -107, + -118, -12, -5, -26, 16, 44, -37, -8, + -72, 84, -10, 32, -45, 33, 16, 37, + 43, 48, 48, 49, -13, -4, 37, 31, + -37, 26, 1, -41, 48, -17, -64, -40, + -35, 27, -7, 26, 17, 44, 47, 21, + 117, -30, 10, 126, 32, -4, 15, 51, + -74, 25, 51, -19, 13, -47, 22, 42, + 71, -24, 22, -31, 16, -54, 40, -5, + 20, 17, -3, -50, -49, -21, -21, -11, + -49, 82, 39, 88, 84, 86, -65, -39, + 20, -6, 86, 49, 90, 18, -30, -78, + -28, 50, 19, 12, 28, 35, -23, 126, + -128, 51, 66, 61, 30, -25, -77, 56, + 52, 51, 41, -34, 56, 19, 17, 37, + -1, 10, 41, 2, 81, 54, -10, -127, + 40, -117, 3, 91, 55, 84, -39, 73, + 11, -12, -99, 35, -44, -30, -128, 12, + 37, -75, -45, -81, 0, 126, -31, 11, + 68, -10, 2, -61, 53, 13, -15, -52, + 48, 47, 10, 32, -4, 85, 49, -22, + 112, 0, -47, 98, 19, 23, 107, -35, + -38, -95, 56, -65, 38, 48, -95, 18, + -3, -42, 69, -125, 83, -3, -69, 41, + 37, -5, 53, 77, 41, -32, -19, 5, + -26, -4, -23, 49, -91, 65, 32, -16, + -26, 72, 8, 16, 60, -128, -103, 56, + -21, -7, 20, -88, 29, 10, 88, 53, + -101, -91, -27, 53, -75, 48, 12, -18, + 1, 15, 3, -100, -114, -126, -50, -14, + -54, 57, -52, 56, 22, -13, -107, -25, + 19, -48, -6, -94, 4, -7, 27, -36, + -119, 18, 13, -71, 58, 21, -35, 44, + 126, 114, 50, -101, 16, 79, 37, 9, + -121, -41, 108, -128, -66, -42, -15, -45, + -79, -24, 56, -74, -103, 47, 12, -1, + 78, -51, -19, -89, 21, -13, -93, -55, + -49, -34, -25, -66, -80, -9, -31, 15, + 36, 32, -40, 1, 38, 94, 15, 29, + 3, 10, -39, 19, -28, 75, -33, 13, + 54, 5, -23, 57, 43, 2, 17, 48, + -17, 46, -12, 40, 32, -28, -18, 9, + 118, -94, -56, -124, -10, 20, 30, 2, + -14, 55, 3, 115, -7, -48, -22, 21, + 22, -123, -19, 9, 14, -38, 19, -76, + -51, -5, 32, -75, -8, 22, 37, -23, + 37, 1, -44, 20, -12, 9, -34, -33, + 82, 20, 59, -29, -11, -44, -62, -15, + -28, 31, -56, 43, -42, 40, -39, 73, + -2, 20, -24, -17, -29, 39, -12, -39, + 0, -6, -1, -13, 30, 15, 25, -36, + -95, -16, 79, 29, 40, 27, 1, -7, + 31, 38, -26, 14, 37, 46, -3, 41, + -9, 21, 87, 18, -34, 48, -44, 44, + 86, -3, 33, -44, 4, 89, 3, 84, + -17, 12, -26, -51, 11, -12, -61, -101, + 55, -45, -81, -11, -9, 0, 18, 56, + 53, -19, -3, 15, 64, 64, 33, 21, + 30, -72, -6, 19, -11, -26, 27, 25, + -40, -3, 83, -75, 74, -72, 23, -55, + 23, -17, -49, 46, -88, 25, 99, -5, + -29, 69, -107, 14, -96, 8, 41, -97, + 127, -66, -126, 1, 13, 105, -10, 3, + -21, 3, -6, -126, 1, -27, 125, -7, + 34, 96, 25, -102, 67, 58, -51, 117, + 121, 12, 105, 63, -18, 78, 27, -7, + 42, 42, 127, 13, 81, 23, -33, -54, + -12, 75, -89, 20, 127, -39, -35, 127, + 21, 94, -74, 23, -38, -27, 49, -28, + 89, 109, 1, 116, 99, -62, 98, 92, + 88, 127, -40, -92, -55, -116, 12, -18, + -126, 70, 42, 45, 127, 40, -90, 7, + -56, -126, -83, 76, 50, -90, -127, -18, + -128, -113, 118, -71, -4, 2, -62, 74, + 3, 115, 54, 70, -30, 127, -83, 34, + 117, -12, 31, 107, 86, -82, 61, 82, + 112, -96, 86, 6, 15, -8, -9, 25, + -12, 14, 102, -27, 29, -35, -64, 33, + -84, 36, -101, 126, 51, -125, 121, 51, + -36, 69, 13, 101, -104, 67, 54, -119, + 35, -37, 94, 22, 17, 63, 74, 77, + 34, -25, 24, 8, 127, 3, 90, -18, + 34, 62, 101, 15, 124, -45, 16, -107, + 77, 16, 13, 5, 82, -47, 56, 49, + -11, 46, 41, -23, 80, 38, 90, 80, + -38, 11, 56, -47, 95, 3, -22, 17, + -29, 12, 18, 124, 65, 25, -67, 27, + 96, -44, 65, -20, 39, 12, 42, 58, + -101, -47, 25, -36, -109, -16, -6, -75, + -3, 116, -56, -48, -51, -101, 78, 36, + 41, -34, 40, -106, -24, 0, 74, -70, + 7, 4, -84, 33, -126, -48, 37, 127, + -63, 38, -4, -69, 2, 65, 61, -19, + 11, -36, 42, -4, 116, -28, -20, 56, + -21, 127, -28, 6, 78, 4, 59, 87, + -50, -71, 41, 72, 66, -80, 78, 36, + 43, -60, -31, 29, 81, -84, 11, 69, + 44, -44, -96, 23, 35, 93, -54, 67, + 25, 123, -38, -71, 37, -117, 11, 90, + 61, -29, -78, 49, -18, -53, 98, 80, + 6, -47, -33, 65, 25, -17, 107, 74, + -122, 40, -122, -44, -64, 25, 43, -10, + -33, -32, 16, 82, -50, 6, -97, 9, + -125, 1, -84, 90, -64, 59, 54, -18, + -14, -10, 83, -61, -79, -125, -17, -9, + 32, -55, -15, 35, -22, 69, -46, -40, + -18, -47, -57, -71, 124, -52, 36, -19, + 72, -62, -111, -46, -47, -34, -88, 126, + -72, -14, -6, -48, 126, -8, 58, -16, + -19, -45, -43, -41, -67, 34, -31, -5, + 118, 69, -29, -8, -32, 39, -53, -2, + 17, 18, -13, 33, 55, 47, -8, 69, + 30, -46, 30, -43, 119, -60, -65, 43, + 26, -122, -28, 38, 19, 50, -113, 38, + 126, -43, -31, 0, -105, 12, 73, 33, + -18, 16, -4, 73, -29, -41, -51, 30, + 96, -101, 116, 35, 40, 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23, -58, 7, 56, -123, 17, 9, + -23, -36, 14, 69, -35, 6, 35, 21, + -21, -21, 43, -103, 38, 49, -55, 17, + -48, -19, -5, -127, 17, -8, 25, -39, + -29, -8, -49, -38, -20, 32, 15, -1, + 85, -36, 67, -11, -7, -75, 20, -91, + -38, -123, -16, -19, -82, -18, 32, -3, + 1, 14, -63, 18, -15, -2, 28, -18, + -116, 2, 45, -5, -30, 9, -19, -21, + 61, -32, -41, 1, -88, -67, 5, -40, + -71, 30, -44, -4, -94, -60, 32, -48, + 10, 49, -2, 72, -1, -9, -16, 10, + 1, 53, -38, -9, -24, -42, 38, -80, + -82, -3, 1, -55, 26, -79, 127, -93, + -26, -32, -17, -32, 42, -39, 80, -2, + 39, 46, -33, 36, -95, 6, 64, 24, + -91, 55, -10, 6, -48, 43, 30, -11, + -25, -8, 101, -52, 56, -79, -10, 22, + -22, 103, 8, 34, -14, 23, 22, 17, + -45, 32, 29, 20, -62, 7, -1, -51, + 13, -7, 12, -21, 73, -48, 18, 69 +}; + +static const rnn_weight denoise_gru_bias[288] = { + 31, 7, 30, -1, 44, 30, 37, 16, + 46, 69, 40, 33, -6, -6, 7, 63, + 16, 31, 19, 3, 115, 79, 62, -20, + -19, 34, 40, 49, -30, 12, -52, 46, + 64, 52, 90, 23, 47, -58, -87, 63, + 33, 12, 1, 21, 29, 67, -17, 13, + 14, 15, 37, 10, 52, 42, 16, 42, + 19, -11, 59, 27, 32, 8, 75, 39, + -35, 20, -10, 78, 21, 12, -1, -6, + 22, 53, 34, -7, 67, 44, 52, 34, + 52, -1, 15, -39, 41, 35, 61, 53, + 71, 31, 47, -13, 67, 20, 47, -7, + 12, 27, -20, -1, 3, 1, 18, -1, + 19, 27, 15, -11, 0, -18, -8, -23, + -21, -1, 24, 13, -15, 2, -2, -3, + -10, 12, -3, 0, -26, -26, 7, -41, + -12, 7, -41, 29, 29, 22, 11, 20, + 4, 2, -12, -13, 8, 6, 6, 35, + 15, 14, -18, 4, 40, 24, 18, 17, + 0, 4, 9, -18, -52, -20, -8, -2, + -4, 6, 14, -21, 11, -5, 1, 7, + 8, -15, -12, -25, -36, 14, 8, -66, + -19, 23, 0, 14, 2, -18, -22, 0, + -4, 1, -26, 24, -27, -1, -4, 30, + -27, 7, 18, 28, 16, -1, 33, -15, + -5, 6, 18, -21, -16, -8, -19, -26, + -18, 5, -59, 11, 11, 48, -2, 19, + -1, 4, 20, 0, -35, 4, -26, 14, + -3, -1, -17, 32, -2, -49, -29, 23, + -5, 6, -6, 1, 10, 17, 0, 39, + 1, 6, -5, 12, -34, 7, -10, -3, + -19, 14, -44, 9, 10, 7, 11, 19, + 25, 1, -35, -36, 22, 5, -25, 21, + 6, 1, 18, -13, -16, 12, 7, -6, + -11, 21, 14, -22, -1, -11, 28, -30, + -6, 14, -35, -11, -9, 33, 8, -2 +}; + +static const GRULayer denoise_gru = { + denoise_gru_bias, + denoise_gru_weights, + denoise_gru_recurrent_weights, + 114, 96, ACTIVATION_TANH +}; + +static const rnn_weight denoise_output_weights[2112] = { + -53, -69, -38, 4, 25, 28, 34, 31, + 31, 17, 19, 26, 32, 43, 41, 48, + 41, 19, -11, 8, 41, 9, 19, 16, + 40, 46, 21, 19, 21, 26, 24, 26, + 42, 39, 36, 30, 25, 25, 27, 22, + 12, 8, 24, 6, 59, 92, 80, 63, + 34, 9, 17, 34, 55, 62, 76, 78, + 61, 49, 23, 14, 17, 29, 21, 15, + 27, -1, 75, 73, 70, 102, 63, 12, + -21, -26, 2, -15, -14, 12, 26, 21, + 14, 23, 32, 45, 82, 110, 124, 91, + 41, 36, 30, 22, 37, 40, 32, 25, + 25, 24, 4, -20, -28, -21, -12, -11, + -18, -14, -15, -11, -4, 23, 12, 23, + 10, -3, 7, 4, 13, 17, 16, 13, + 23, 25, 30, 30, 22, 13, 2, -6, + 7, 12, 15, 14, 11, 8, -9, -28, + -30, -24, -27, -40, -38, -20, -22, 1, + 9, 24, 39, 46, 42, 39, 49, 50, + 51, 45, 18, -7, -20, -13, 13, 22, + 34, 28, 7, -9, -32, -41, -33, -33, + -35, -38, -31, -44, -30, -7, 34, 73, + 9, 23, 47, 58, 53, 43, 44, 46, + 54, 61, 75, 55, 46, 40, 40, 40, + 38, 31, 16, 10, 1, -18, 33, 40, + 2, -45, -89, -91, -73, -53, -30, 4, + 14, 20, 29, 53, 47, 42, 37, 53, + 31, 18, -15, -16, 34, 8, 16, 30, + 33, 41, 47, 49, 42, 33, 35, 43, + 40, 16, -11, -23, -26, -32, -48, -35, + -6, 31, 60, 49, 52, 29, 10, 7, + 18, 32, 22, 19, 35, 27, 25, 10, + 4, 14, 7, -22, -41, -54, -93, -92, + 38, 50, 30, 23, 15, 8, 12, 7, + -2, -13, -16, -35, -42, -45, -40, -35, + -18, 65, 118, 77, 55, 58, -12, 14, + 57, 56, 38, 43, 35, 37, 46, 62, + 53, 70, 63, 67, 62, 47, 46, 40, + 26, 33, 17, -5, -20, 12, 26, 0, + -22, -31, -40, -46, -49, -57, -55, -51, + -44, -32, -31, -44, -49, -48, -31, -48, + -77, -78, 49, 58, 23, 16, 18, 31, + 37, 46, 64, 73, 71, 54, 38, 21, + 24, 33, 34, 37, 36, 2, -2, -5, + 50, 72, 89, 77, 52, 50, 46, 39, + 31, 17, 14, 2, -7, -27, -47, -54, + -48, -40, -25, -14, -3, -19, 0, 37, + 57, 35, 14, 10, 29, 41, 41, 43, + 38, 21, 6, 0, 8, 9, 6, 19, + -6, -20, -55, -24, -24, 18, 13, 27, + 30, 1, -8, -45, -74, -97, -107, -106, + -90, -104, -125, -111, -90, -70, -46, -58, + -58, -48, -44, -26, -37, -57, -66, -74, + -69, -58, -74, -92, -91, -95, -79, -49, + -21, -5, 13, 17, 41, 59, 39, -39, + 127, 72, 6, 1, 1, 12, 17, 27, + 26, 34, 32, 42, 48, 51, 46, 44, + 33, 22, 20, 15, 6, -70, 73, 61, + 49, 45, 46, 57, 59, 58, 51, 43, + 44, 35, 42, 43, 53, 51, 40, 24, + 20, 31, 88, 74, 36, 37, 6, 2, + 12, 20, 6, -10, -4, -1, -11, 5, + 9, 10, 13, 11, 4, 0, 10, 10, + 28, 57, -58, -40, -53, -61, -32, -22, + -60, -94, -105, -90, -66, -52, -53, -51, + -46, -38, -32, -17, 22, 24, 64, 96, + 25, 16, 1, -32, -45, -22, -15, -1, + -13, -25, -18, -29, -29, -29, -29, -26, + -20, -16, -22, -39, -43, -21, -58, -86, + -89, -81, -57, -42, -27, -27, -39, -49, + -48, -37, -29, -14, -5, 9, 7, 19, + 20, 28, 22, -1, 63, 32, 16, 13, + 17, 14, 12, 11, 0, -3, 3, 10, + 7, 11, 19, 30, 22, 14, 22, 34, + 27, 20, 1, 4, -6, -34, -23, -6, + -3, -14, -33, -46, -43, -49, -46, -31, + -18, -17, -14, -39, -87, -68, -65, -116, + 24, 12, 14, 32, 40, 41, 28, 31, + 56, 62, 55, 54, 46, 39, 32, 28, + 26, 24, 16, 13, 18, 56, 96, 85, + 27, 21, 17, 25, 22, 29, 24, 35, + 20, 13, 15, 31, 27, 19, -1, -3, + 21, 48, 65, 44, -11, -23, -49, -68, + -55, -46, -35, -47, -46, -53, -64, -65, + -92, -110, -89, -61, -35, 0, 2, -9, + -14, -24, 11, 30, 41, 58, 52, 31, + 35, 37, 50, 50, 48, 49, 44, 46, + 47, 52, 51, 39, 9, -8, -27, -28, + 3, 21, 48, 58, 68, 70, 69, 72, + 72, 62, 56, 42, 45, 39, 25, 28, + 29, 36, 13, 12, 21, 36, -26, -20, + -22, -35, -36, -27, -29, -33, -30, -19, + -7, -8, -12, -18, -5, -9, -15, -35, + -23, -17, -13, -6, -79, -63, -55, -59, + -53, -51, -30, -14, -16, -16, -12, -37, + -40, -34, -35, -33, -28, -16, 16, 16, + -10, -70, 26, 39, 26, 35, 77, 116, + 89, 81, 76, 64, 57, 54, 41, 37, + 24, 20, 15, 31, 70, 74, 70, 39, + -2, -17, -10, -35, -45, -45, -34, -27, + -21, -29, -39, -66, -78, -92, -116, -126, + -119, -91, -9, 2, 16, -43, 23, 24, + 38, 50, 41, 39, 40, 53, 69, 84, + 89, 93, 111, 122, 127, 127, 127, 126, + 114, 101, 61, 38, -128, -128, -35, -33, + -52, -67, -66, -45, -15, -3, -12, -20, + -26, -29, -31, -34, -38, -34, -16, -6, + -23, -30, 60, 66, 57, 40, 20, 4, + -3, -8, -3, 15, 23, 23, 27, 25, + 15, 8, 10, 37, 58, 32, -1, -10, + 107, 64, 21, 7, 21, 32, 36, 22, + 15, 17, -1, 27, 25, 28, 33, 22, + 24, 13, 34, 37, 62, 88, -27, -18, + -28, -37, -33, -34, -36, -42, -44, -34, + -33, -31, -33, -35, -44, -48, -40, -25, + -28, -23, -12, 0, -23, -49, -34, -17, + -15, -1, 3, 12, 32, 48, 60, 74, + 97, 112, 126, 127, 127, 117, 100, 104, + 90, 28, -65, -56, -21, 5, 4, -4, + 6, 19, 23, 17, 28, 1, -5, -16, + -34, -40, -14, 55, 95, 27, -59, -61, + -40, -37, -19, 15, 39, 28, 17, 17, + 40, 56, 57, 62, 55, 52, 42, 22, + 5, 25, 3, -1, 0, 7, 97, 127, + 109, 87, 75, 65, 62, 63, 78, 72, + 59, 50, 57, 51, 41, 48, 45, 25, + 9, 9, 20, 59, -39, -55, -66, -67, + -61, -55, -50, -44, -41, -49, -55, -63, + -67, -72, -80, -82, -76, -51, -21, -37, + -34, -29, 12, 20, 33, 25, 11, 0, + 5, 3, 10, 21, 25, 30, 35, 32, + 35, 41, 43, 48, 28, 13, 4, 12, + 30, 47, 49, 41, 43, 50, 51, 51, + 43, 45, 48, 38, 42, 45, 37, 32, + 29, 27, 9, 31, 28, 34, -16, 3, + 5, 16, 24, 39, 68, 94, 87, 77, + 87, 53, 48, 55, 54, 59, 59, 22, + 15, 31, 23, 5, -12, -14, -41, -44, + -29, -20, -36, -40, -17, -6, -6, 9, + -1, -12, -20, -30, -39, -46, -47, -53, + -61, -38, -54, -87, -74, -76, -82, -70, + -48, -22, -6, -19, -35, -36, -32, -31, + -19, -7, -8, -15, -31, -27, -43, -9, + 36, 35, 47, 56, 66, 75, 91, 70, + 37, 22, 10, 8, 13, 15, 8, 5, + 4, 4, 5, 15, 45, 28, -14, -18, + -37, -33, -33, -29, -23, -16, -5, 2, + -14, -13, -12, 3, -8, -16, -39, -58, + -65, -105, -83, -38, -50, -42, 10, 44, + 50, 32, 16, -6, -24, -29, -44, -52, + -71, -95, -114, -128, -128, -115, -60, -49, + -57, -23, -37, -36, -56, -44, -47, -49, + -45, -48, -53, -47, -45, -45, -46, -40, + -46, -56, -60, -84, -124, -115, -69, -14, + 11, 56, 66, 45, 24, 17, 11, 20, + 13, 13, 11, -6, -12, -10, -6, -14, + -16, -14, -2, -5, -11, -29, -15, -2, + -36, -53, -104, -110, -98, -71, -99, -124, + -128, -128, -128, -114, -98, -98, -112, -87, + -29, -48, -66, -33, 15, 1, -8, -26, + -22, -25, -22, -17, -19, -20, -20, -32, + -37, -38, -30, -26, -32, -55, -113, -106, + -81, -50, -11, 3, 0, -9, -2, 3, + 7, 10, 8, 7, 8, 10, 8, 2, + -1, -3, -4, -2, 25, 28, 26, 13, + -53, -13, -33, -42, -38, -36, -40, -54, + -65, -60, -58, -26, -36, -39, -34, -42, + -64, -70, -40, -47, -33, 23, 27, 17, + 9, 7, 16, 30, 34, 40, 40, 46, + 42, 32, 25, 26, 27, 26, 21, 13, + 21, 28, 48, 55, 7, -8, -41, -44, + -44, -46, -37, -36, -12, -3, -1, -13, + -10, -5, -5, -26, -40, -80, -64, -19, + 28, -81, 14, 32, 40, 21, 6, 7, + -2, 3, 2, 13, 18, 22, 25, 33, + 54, 66, 52, 28, 5, 1, 13, -18, + 3, 25, 80, 92, 76, 43, 24, 28, + 48, 76, 72, 63, 60, 51, 33, 21, + 15, 17, 8, 10, 15, 27, -13, -8, + -15, -26, -21, -18, -17, -15, -18, -29, + -37, -35, -32, -26, -9, 21, 47, 79, + 127, 107, 84, 68, -25, -40, -48, -35, + -28, -28, -26, -11, -12, -10, 10, -1, + 4, 2, 9, 0, -2, -5, -20, -16, + -8, 7, 50, 60, 80, 89, 97, 104, + 98, 98, 94, 81, 68, 74, 69, 51, + 23, 26, 29, 42, 60, 38, 21, 46, + 28, 32, 39, 42, 33, 27, 25, 20, + 21, 29, 23, 23, 17, 15, 12, 12, + 21, 24, 41, 30, 10, -23, 33, 35, + 35, 55, 69, 57, 36, 28, 33, 32, + 30, 35, 41, 54, 55, 59, 62, 83, + 69, 51, 33, 22, 3, 27, 34, 25, + 15, 9, 1, -5, -6, -4, -8, -27, + -36, -36, -48, -55, -53, -39, -32, -44, + -64, -60, 29, 17, 2, -28, -51, -54, + -40, -33, -50, -49, -52, -59, -58, -49, + -39, -27, -20, -12, -12, -46, -76, -64, + 14, -2, 18, 28, 27, 23, 29, 27, + 31, 31, 28, 29, 27, 23, 23, 28, + 34, 38, 39, 18, -12, 3, -79, -84, + -80, -71, -47, -29, -29, -33, -31, -28, + -36, -41, -52, -47, -29, -37, -44, -37, + -6, -3, 9, 2, -4, 14, 28, 25, + 32, 26, 8, 1, 11, 22, 19, 16, + 18, 15, 9, 8, 10, 2, 9, 19, + 42, 17, -61, -60, -78, -76, -71, -67, + -71, -92, -104, -96, -96, -98, -96, -83, + -67, -57, -63, -60, -39, -39, -38, -23, + 9, 7, 20, 27, 33, 35, 37, 33, + 29, 39, 38, 42, 41, 44, 30, 25, + 17, 34, 63, 89, 58, 32, 47, 63, + 40, 9, -7, -2, 15, 13, 30, 38, + 56, 35, 17, 20, 46, 43, 22, -25, + -17, 2, 50, 66, -31, -72, -92, -105, + -90, -75, -64, -60, -51, -42, -19, -9, + 7, 14, 24, 29, 28, -1, -58, -65, + -38, -6, -53, -42, -42, -24, -24, -47, + -64, -59, -53, -43, -34, -57, -61, -51, + -40, -34, -24, -30, -51, -47, -56, -96, + -1, 17, 42, 55, 55, 48, 37, 28, + 18, 18, 57, 66, 74, 85, 101, 98, + 89, 35, -7, -16, 20, 47, -110, -110, + -85, -45, -12, -7, -3, 5, 1, -2, + -8, -4, -3, -8, -11, -14, -13, 4, + 7, 3, -3, 10, -2, 43, 87, 92, + 67, 65, 70, 79, 48, 42, 43, 32, + 38, 64, 88, 93, 88, 72, 47, 27, + 37, 44, -19, -74, -128, -128, -128, -128, + -128, -123, -104, -87, -88, -91, -104, -102, + -87, -69, -65, -34, -30, -34, -25, -9, + 20, 51, 26, 42, 26, 32, 43, 59, + 67, 74, 62, 73, 77, 78, 65, 50, + 34, 35, 60, 49, 14, -9, -28, -3, + -1, -5, -20, -27, -52, -63, -67, -62, + -47, -52, -48, -36, -21, -21, -19, -27, + -30, -44, -63, -85, 25, 11, 26, 19, + 14, 15, 23, 10, -4, -19, -24, -16, + -15, -27, -40, -34, -23, 17, 31, 32, + 25, 41, 27, 18, 5, 13, 21, 21, + 24, 21, 18, 8, 5, 2, 13, 7, + -1, -6, -14, -26, -91, -128, -128, -128, + 33, 56, 51, 65, 66, 52, 45, 34, + 7, -15, -28, -35, -35, -36, -35, -28, + -12, 18, 68, 41, 14, 30, -2, 16, + 25, 31, 40, 39, 34, 31, 40, 44, + 39, 36, 40, 42, 38, 42, 35, 61, + 69, 41, 1, -14, -101, -67, -49, -50, + -46, -34, -29, -36, -14, 2, -4, -4, + -18, -45, -64, -62, -48, -60, -112, -127, + -126, -128, -13, 18, 68, 107, 127, 120, + 77, 39, 1, -33, -36, -25, -16, -14, + -9, -9, -5, -1, 1, 0, -9, -43, + -7, -5, -37, -54, -58, -68, -90, -99, + -84, -92, -100, -95, -85, -78, -68, -72, + -70, -91, -112, -117, -119, -95, 11, 26, + 28, 38, 35, 48, 60, 66, 57, 66, + 82, 87, 98, 103, 102, 98, 89, 79, + 90, 79, 35, 20, -4, 0, 16, 22, + 12, -13, -16, -18, -14, -20, -21, -25, + -40, -49, -45, -44, -57, -115, -127, -79, + 6, 71, -32, -28, -9, -10, -5, -7, + -8, -17, -26, -31, -17, -15, -10, -4, + 4, 7, 12, 2, -15, -22, -33, -37 +}; + +static const rnn_weight denoise_output_bias[22] = { + 14, 35, 37, 27, 16, 26, 33, 19, + 22, 34, 19, 6, 8, 10, 5, 7, + 43, 58, 29, 1, 14, 12 +}; + +static const DenseLayer denoise_output = { + denoise_output_bias, + denoise_output_weights, + 96, 22, ACTIVATION_SIGMOID +}; + +static const rnn_weight vad_output_weights[24] = { + 127, -128, 127, 127, 127, -128, 127, 100, + -128, 127, -128, -128, 127, 127, 127, -128, + -128, 127, -127, -128, 81, -128, 127, 127 +}; + +static const rnn_weight vad_output_bias[1] = { + 80 +}; + +static const DenseLayer vad_output = { + vad_output_bias, + vad_output_weights, + 24, 1, ACTIVATION_SIGMOID +}; + +const struct RNNModel model_mp = { + 24, + &input_dense, + 24, + &vad_gru, + 48, + &noise_gru, + 96, + &denoise_gru, + 22, + &denoise_output, + 1, + &vad_output, +}; From 81ce39de75d9ba9adf75bfa2781ed5d36610f2cf Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Thu, 30 Aug 2018 13:34:36 -0400 Subject: [PATCH 12/23] Updated README --- README | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/README b/README index 27b18fa9..2c25d10f 100644 --- a/README +++ b/README @@ -1,7 +1,9 @@ -RNNoise is a noise suppression library based on a recurrent neural network +RNNoise is a noise suppression library based on a recurrent neural network. +RNNoise-nu is a modified version of RNNoise aimed to be a bit more general +purpose. To compile, just type: -% ./autogen.sh +% autoreconf -i % ./configure % make @@ -12,6 +14,9 @@ While it is meant to be used as a library, a simple command-line tool is provided as an example. It operates on RAW 16-bit (machine endian) mono PCM files sampled at 48 kHz. It can be used as: -./examples/rnnoise_demo input.pcm output.pcm +./examples/rnnoise_demo [model to use] < input.raw > output.raw The output is also a 16-bit raw PCM file. + +For information on the neural network models available, see +https://github.com/GregorR/rnnoise-models From c38457778bb4a4ddd2b5c34ce8b635510f0d52c2 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Thu, 30 Aug 2018 17:21:52 -0400 Subject: [PATCH 13/23] Adding instructions on training. --- TRAINING | 11 +++++++++++ training/rnn_train.py | 4 ++-- 2 files changed, 13 insertions(+), 2 deletions(-) create mode 100644 TRAINING diff --git a/TRAINING b/TRAINING new file mode 100644 index 00000000..86c5a4eb --- /dev/null +++ b/TRAINING @@ -0,0 +1,11 @@ +(1) cd src ; ./compile.sh + +(2) ./denoise_training signal.raw noise.raw count > training.f32 + + (note the matrix size and replace 500000 87 below) + +(3) cd training ; ./bin2hdf5.py ../src/training.f32 500000 87 training.h5 + +(4) ./rnn_train.py + +(5) ./dump_rnn.py weights.hdf5 ../src/rnn_data.c ../src/rnn_data.h diff --git a/training/rnn_train.py b/training/rnn_train.py index bb53f89b..06d7e1a4 100755 --- a/training/rnn_train.py +++ b/training/rnn_train.py @@ -82,7 +82,7 @@ def get_config(self): batch_size = 32 print('Loading data...') -with h5py.File('denoise_data9.h5', 'r') as hf: +with h5py.File('training.h5', 'r') as hf: all_data = hf['data'][:] print('done.') @@ -113,4 +113,4 @@ def get_config(self): batch_size=batch_size, epochs=120, validation_split=0.1) -model.save("newweights9i.hdf5") +model.save("weights.hdf5") From 0093ba98186c94d066f304c85fa3b84c47e16849 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Fri, 31 Aug 2018 21:34:57 -0400 Subject: [PATCH 14/23] Adding my own copyright lines would be wise... --- examples/rnnoise_demo.c | 3 ++- include/rnnoise-nu.h | 3 ++- src/denoise.c | 3 ++- src/models.c | 2 +- 4 files changed, 7 insertions(+), 4 deletions(-) diff --git a/examples/rnnoise_demo.c b/examples/rnnoise_demo.c index a99ee848..20deaf12 100644 --- a/examples/rnnoise_demo.c +++ b/examples/rnnoise_demo.c @@ -1,4 +1,5 @@ -/* Copyright (c) 2017 Mozilla */ +/* Copyright (c) 2018 Gregor Richards + * Copyright (c) 2017 Mozilla */ /* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions diff --git a/include/rnnoise-nu.h b/include/rnnoise-nu.h index d3d6b28b..cf74b8ec 100644 --- a/include/rnnoise-nu.h +++ b/include/rnnoise-nu.h @@ -1,4 +1,5 @@ -/* Copyright (c) 2017 Mozilla */ +/* Copyright (c) 2018 Gregor Richards + * Copyright (c) 2017 Mozilla */ /* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions diff --git a/src/denoise.c b/src/denoise.c index 694e1f45..1332ff3e 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -1,4 +1,5 @@ -/* Copyright (c) 2017 Mozilla */ +/* Copyright (c) 2018 Gregor Richards + * Copyright (c) 2017 Mozilla */ /* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions diff --git a/src/models.c b/src/models.c index e2cc8c31..fffce43c 100644 --- a/src/models.c +++ b/src/models.c @@ -1,4 +1,4 @@ -/* Copyright (c) Gregor Richards */ +/* Copyright (c) 2018 Gregor Richards */ /* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions From c7e342c500cf8779c860a45ca0730dc22ca9ed48 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Sat, 1 Sep 2018 09:26:35 -0400 Subject: [PATCH 15/23] More neural network models --- Makefile.am | 2 + TRAINING | 2 +- src/models.c | 10 +- src/models/bd.c | 11046 ++++++++++++++++++++++++++++++++++++++++++++++ src/models/lq.c | 11046 ++++++++++++++++++++++++++++++++++++++++++++++ 5 files changed, 22103 insertions(+), 3 deletions(-) create mode 100644 src/models/bd.c create mode 100644 src/models/lq.c diff --git a/Makefile.am b/Makefile.am index d0895406..61ad10c3 100644 --- a/Makefile.am +++ b/Makefile.am @@ -24,6 +24,8 @@ librnnoise_nu_la_SOURCES = \ src/rnn_data.c \ src/models/cb.c \ src/models/mp.c \ + src/models/bd.c \ + src/models/lq.c \ src/rnn_reader.c \ src/models.c \ src/pitch.c \ diff --git a/TRAINING b/TRAINING index 86c5a4eb..a2224268 100644 --- a/TRAINING +++ b/TRAINING @@ -8,4 +8,4 @@ (4) ./rnn_train.py -(5) ./dump_rnn.py weights.hdf5 ../src/rnn_data.c ../src/rnn_data.h +(5) ./dump_rnn.py weights.hdf5 rnn_data.c rnn_data.rnnn name diff --git a/src/models.c b/src/models.c index e2cc8c31..5633aabc 100644 --- a/src/models.c +++ b/src/models.c @@ -39,18 +39,24 @@ static const char *model_names[] = { "orig", "cb", "mp", + "bd", + "lq", NULL }; extern const struct RNNModel model_orig, model_cb, - model_mp; + model_mp, + model_bd, + model_lq; static const struct RNNModel *models[] = { &model_orig, &model_cb, - &model_mp + &model_mp, + &model_bd, + &model_lq }; const char **rnnoise_models() diff --git a/src/models/bd.c b/src/models/bd.c new file mode 100644 index 00000000..2f2bf7f6 --- /dev/null +++ b/src/models/bd.c @@ -0,0 +1,11046 @@ +/*This file is automatically generated from a Keras model*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "rnn.h" +#include "rnn_data.h" + +static const rnn_weight input_dense_weights[1008] = { + 3, -3, 3, 4, 20, 1, 1, 4, + 4, 6, 2, -9, 2, 8, 2, -3, + 8, 0, -11, 4, 9, 12, -4, 3, + -31, -2, 17, -3, 21, -9, -4, -26, + 18, -6, 1, -16, -4, 34, -2, 15, + 14, -5, -18, -20, 0, 7, -63, -21, + 37, -58, 36, 6, 49, -19, 16, -31, + -10, -1, 1, -40, -43, 24, -3, 12, + -10, 6, -18, -47, -10, -21, 23, -19, + 9, -40, 6, 4, 27, 5, -24, -36, + 40, 65, 6, -52, -21, 4, -31, -71, + -63, 7, -11, 11, 7, 21, 50, 11, + 20, 121, 37, 12, -106, 17, 37, -12, + -16, 59, 17, 17, 49, -6, 16, -57, + -97, 40, 8, 14, 1, 11, -78, 9, + -68, 61, -4, -1, 59, -12, 1, 10, + 44, 7, 1, -7, 24, -16, -4, 49, + -30, 17, -8, 34, 33, -10, 19, 23, + -111, -98, -114, 40, -104, -81, 119, 88, + -54, -64, 71, -118, -30, -127, -34, 127, + -16, 83, -35, -90, 116, -14, -51, -8, + -106, 107, -116, -15, 101, 23, -128, 9, + 100, 40, 95, 2, 29, -126, 12, -76, + -43, -30, -14, 80, 80, 45, 14, -119, + -99, 2, -111, 4, -83, 41, 69, -21, + -107, 11, 127, 47, 11, -125, -27, -20, + -62, -75, -32, 6, 113, 51, 127, -126, + 29, -24, -122, 39, -55, 22, -26, 59, + 127, -20, 23, -33, -73, -128, -84, 12, + -46, 7, -8, -24, 32, 116, -83, 16, + -27, 88, -81, 83, 93, -8, 98, 61, + -40, 91, -54, 83, -124, -2, -39, -40, + 123, -9, 24, -3, 38, -25, -124, 42, + -24, -9, -37, -76, -122, -58, -11, 127, + 26, -13, -92, 87, 63, 127, -66, -50, + 57, -80, 5, -77, 65, 106, -11, -124, + 45, -43, 67, 127, -40, 89, 122, 78, + -122, 25, -60, 28, -23, 65, 0, 124, + 6, -50, 36, -30, -34, -21, 55, -70, + -15, 113, 88, -28, 38, -1, 113, 75, + 100, -20, 15, -22, -93, 78, 41, 68, + -59, -71, -18, 125, -36, -66, -5, -97, + -27, 55, 20, 81, 67, 80, -54, 36, + -69, -24, 126, 38, 26, 65, 63, 2, + 120, -35, 66, -62, 31, -125, 54, 111, + 31, -84, 61, 80, 9, 71, -109, 62, + 26, 73, 123, 41, -68, 43, -20, -99, + 116, -75, -19, 7, -30, -48, -59, 106, + 127, 85, 9, 91, 127, 23, 19, 38, + -28, -29, 127, 105, -102, -100, 127, 28, + -23, -26, -30, 50, -85, -127, -128, -32, + -26, -128, 97, 127, -115, 127, 32, 121, + 32, 114, -119, -126, -43, -23, 93, 54, + -120, 9, -109, 10, -128, -51, 38, 127, + 28, 77, -60, 117, -26, -24, 126, 11, + -45, 21, 114, 38, -119, -51, 68, 118, + -118, -45, -19, -69, 75, 103, 33, 23, + 88, -122, -45, -11, 127, -107, -126, -15, + -2, -20, 127, -6, -32, -61, -79, -101, + 0, -68, 84, -101, 118, 92, -41, -34, + -127, -124, 92, 107, -97, -117, 8, -110, + -46, -54, 8, 2, -6, 8, -31, 21, + -120, -111, 37, -44, 96, -98, -126, -66, + 124, 127, 78, -44, 127, -18, -40, 4, + 13, -128, 15, 20, -128, -107, 110, 118, + -51, 79, -112, 62, -32, -96, -128, -32, + -103, -100, 36, 4, -116, 33, -11, 117, + 24, 127, -31, -127, 85, 35, 127, -78, + -24, -119, -116, 127, -32, 47, 61, 127, + 23, 114, 81, -70, 34, -79, 16, -128, + 11, 89, 72, -127, -7, 75, 127, -65, + 85, -128, -60, 127, -126, -24, -54, 68, + 19, -82, 127, 45, -78, -120, -8, -38, + 0, -2, -94, -128, -94, 44, 101, -120, + -17, -20, -127, -47, 7, -18, -22, -52, + 74, -127, 70, 27, -121, -13, -88, 36, + 18, 63, -57, -123, 64, -51, -67, -116, + -128, 113, -24, -6, 11, 27, -45, 93, + -123, 120, 51, 64, -64, -51, 33, -72, + 1, 35, 127, -19, 74, -14, 125, -117, + -54, 26, -92, -119, -34, 105, -28, -22, + 106, -26, -35, -33, 7, 99, -15, 4, + 2, -34, 100, -41, 62, -83, -33, 120, + 75, 127, -21, 12, 31, -46, 52, 9, + 88, 26, 16, 44, 96, -127, 18, -117, + 1, -121, -77, 50, 112, 31, -29, -5, + 14, 68, -13, 49, 43, -8, -106, 107, + 11, -67, 43, -30, -5, -68, 34, 0, + -20, 7, 2, -89, 13, 44, 65, -44, + -10, -45, -38, 122, 22, 45, -80, -51, + -116, 96, 59, 10, 71, 83, 9, -104, + -15, 19, -127, -27, -11, 25, -45, -17, + -52, 8, 37, -10, -30, -50, -8, -5, + -17, -91, 4, -54, -42, 76, 60, -103, + 53, -41, -47, -33, 8, 27, -106, -18, + -25, 58, 16, 67, -66, 19, 30, 56, + 110, 4, 16, 73, -27, -55, 8, -95, + -105, 75, 108, -8, 48, -29, 26, 80, + 79, 34, 63, -23, 49, -77, -123, 37, + -128, -12, 32, -35, -106, 18, 50, -102, + 14, 7, -81, -98, 71, -38, -17, -21, + -62, -30, 1, -118, 31, 95, -33, 58, + -79, -102, 84, -4, -49, 127, -126, 59, + -65, 74, -20, -7, 127, 127, -90, 127, + 124, 80, -128, 125, -128, -45, 127, 26, + -100, -119, 107, 100, 8, 127, -113, 103, + -104, 57, -124, -128, 108, 110, -65, 127, + 122, -79, -128, 125, -128, -77, 17, 122, + -46, -119, 127, 17, -5, -1, 50, 92, + 53, 5, 53, -31, -81, -23, 73, -35, + -65, -78, -6, 42, -34, -100, -95, 120, + -55, 68, 94, -43, 98, -55, -7, -32, + -28, 21, 109, 22, 127, 7, 119, -128, + -116, 76, -62, -111, -23, -58, 33, -74, + 52, 102, 96, 58, -58, 27, -85, 47, + -36, -51, 3, -36, 127, 48, 37, -65, + -62, 64, -50, -38, -54, -34, -20, -9, + 77, -25, -4, 66, 10, -34, 81, -3, + 60, -64, 27, 10, 44, -120, 19, -122, + -44, -28, 97, -7, 111, 127, -101, -56, + 101, 85, -55, -48, 12, -25, 25, -21, + 25, -50, 27, -82, -47, -35, -10, -37, + -2, -22, 2, 51, 29, 10, 76, -39, + 94, -6, -47, 61, 77, 43, -19, 4, + -27, 70, 9, 71, 86, -16, 1, -33, + -62, -50, -23, -51, 14, -26, -15, 108 +}; + +static const rnn_weight input_dense_bias[24] = { + -56, 125, -87, -127, -85, -91, -20, -124, + 120, 68, -125, -68, 2, 21, -43, -27, + -80, -13, -3, 34, -33, -108, 46, 63 +}; + +static const DenseLayer input_dense = { + input_dense_bias, + input_dense_weights, + 42, 24, ACTIVATION_TANH +}; + +static const rnn_weight vad_gru_weights[1728] = { + -6, 96, -13, -69, 77, 84, 21, -48, + 21, 22, -4, -128, 35, -47, 39, 37, + -69, 79, 35, -35, -31, 42, 100, -108, + 20, 29, 46, -18, 2, 40, 42, -82, + -40, -42, 51, -30, -36, 72, -39, -25, + -28, -76, -18, -13, 51, 10, 1, -84, + 66, 55, 20, 11, 25, -9, -42, -3, + 49, -48, -18, -6, -28, -9, 38, -8, + 59, -4, -35, 57, 12, -39, 27, -25, + 105, -41, 59, -34, 35, 52, -22, 46, + 126, 119, -46, 77, 67, 48, 10, -95, + -87, -73, 66, -39, -73, -97, -60, -128, + 41, 78, 119, 109, 97, 10, -2, -50, + -9, 0, 34, -79, -27, 39, -30, 11, + 72, 40, 15, -3, 27, 20, 83, 42, + 8, -52, -81, 51, 32, 80, -20, -34, + 11, 72, -23, -51, 23, -18, 23, -8, + 32, -7, -1, -75, -14, 60, -39, 56, + -86, 72, -31, 41, 69, -8, -38, -104, + -69, 69, 63, 67, -101, -35, -69, -1, + 41, 38, 107, 127, -25, -39, -9, 27, + -19, -48, -5, -102, -125, 29, -67, -54, + 25, 102, -13, 55, 54, 62, -13, 37, + 8, -113, -10, -113, 45, -24, -41, -30, + 41, 75, 125, -69, 45, -82, -3, -38, + -40, -15, 108, -53, 66, -1, -24, 101, + 97, 28, -83, 62, 125, -116, -36, 10, + -123, 109, 37, -53, -12, 54, -12, -123, + -37, 5, 100, -106, -118, -62, 30, 92, + -83, 87, 73, 28, 42, -24, -108, -9, + -85, 65, -28, -2, -76, 32, 3, -84, + -97, 92, 57, -8, -66, 0, -72, -33, + -120, -2, 19, 88, 8, -40, 27, -101, + 6, -45, 116, -59, 107, 16, -89, 51, + -18, 7, 32, 54, 19, -54, -94, 73, + -10, 96, 19, 90, 48, 3, -29, -24, + -71, 114, -41, 115, 105, 96, 42, -84, + -37, 66, -57, 46, -13, -2, -56, 88, + -109, -14, 81, 30, 50, 5, 62, 29, + -4, -6, -25, -55, -33, 12, 96, -58, + -105, 57, 64, -54, -7, 57, 49, 75, + -24, 45, -28, -32, 32, -1, -58, -11, + 0, 65, -6, 27, -19, -38, -68, 12, + 17, 8, 10, -55, -33, -77, 18, 91, + -11, -2, -12, 26, 59, 49, -17, 28, + -128, 70, -64, -31, -83, 127, -76, -45, + -44, 112, -67, -116, -115, -19, -39, -69, + -111, -128, -118, -94, -3, 57, -112, -98, + -115, 68, 12, -26, 54, -26, 21, 82, + -67, 40, -67, -87, -84, -45, -65, -73, + -100, -43, -105, 93, 44, 28, 5, -95, + -8, 9, 9, -106, -45, 34, -59, 77, + 1, 74, -51, 25, 41, 28, -30, 32, + 56, 69, -17, 19, -36, -56, -17, 60, + 16, -13, 42, -7, -5, -54, -38, -30, + -26, 73, 49, -26, -60, -103, 78, 123, + 114, 122, 114, 105, 61, 66, -53, 62, + 93, -31, 9, 92, 32, -1, 12, -98, + 94, 51, 98, 109, -59, 55, -29, -46, + -5, 18, 76, 66, 99, -16, 19, 75, + -18, -37, -51, 58, 61, 41, -11, -25, + -57, 98, -105, -25, -13, 43, -7, -75, + -67, -87, 37, -110, -57, 19, 20, -87, + -66, 114, 77, -31, 28, -65, -15, 71, + -28, 118, 124, -81, -44, -90, 5, 15, + -52, -62, 25, 24, 37, -39, -34, 17, + 35, 76, -78, -33, 2, 19, 2, -53, + 37, -52, 82, 20, -29, 94, 92, -26, + -68, 31, 86, 73, 69, -88, 51, -11, + 20, 11, 1, 20, -14, -49, -22, 88, + -19, 7, 9, 83, 52, 15, -26, 54, + -14, 105, -79, 58, 65, -69, 7, -8, + 67, 118, 9, -47, 79, 82, 127, -109, + 102, -26, -64, -31, 127, 47, 39, -44, + -82, 80, 41, -94, -101, 7, 24, 41, + 40, 46, -27, -22, -74, 118, 112, -42, + -22, 127, 21, -3, 38, -60, 75, 99, + -65, 97, 41, 17, 26, 18, -50, -45, + 50, 30, 89, 0, -14, 12, 0, 38, + 26, -58, 54, 0, -31, 24, -20, 63, + -4, 81, -127, 120, 89, -85, -34, -5, + 89, -29, -125, 5, 21, 37, -23, 41, + -12, 9, -116, -65, -100, -55, -61, -127, + 88, -124, -24, -108, -87, -5, 14, 85, + -1, -72, -91, -78, -76, 0, -4, 47, + 71, 38, -128, -60, 24, -46, -60, -85, + 12, -123, -23, -95, -51, -29, 43, -67, + -15, 96, 51, -118, 9, -9, 52, 65, + 36, -43, 46, 16, -36, -47, -40, 50, + -24, -39, 8, 41, 122, -72, -66, 57, + -128, 101, 127, 82, -21, 25, 76, 47, + 40, 50, -65, 18, 98, 31, 112, 116, + 35, 117, 119, 86, 68, 127, -51, -32, + -17, 49, 10, 109, -15, 35, 58, -91, + -34, 59, 104, 76, 52, 106, -125, 54, + -45, -40, -19, 12, 8, 43, -10, -64, + 62, -21, -40, -43, -21, 97, 4, 12, + 30, 52, -22, 3, -69, -37, 57, -22, + 59, 7, -21, 0, -55, 20, 63, 51, + -23, -120, 47, -112, -108, 16, 14, -82, + -100, 68, -128, -101, 40, -9, 87, 33, + 64, 18, -128, 66, 2, 80, 60, 35, + -14, -128, -47, 35, 92, -120, -121, 15, + 9, -117, -39, -64, -103, 9, 54, -121, + -123, 19, -108, -28, 46, -75, 23, -63, + 10, -4, -73, -13, -27, 33, -8, -35, + 29, -27, -52, -22, -38, -18, -13, -26, + -67, 51, 81, -15, 13, 34, 7, 20, + -63, -119, 116, -48, 46, 17, -78, 0, + -36, -31, 87, 45, -127, -124, 2, 79, + -25, 54, -46, -71, 126, -72, -27, -68, + -23, -117, -77, 20, 13, -61, 11, 40, + 73, -28, -106, -72, -12, 45, 127, 66, + -35, 13, 11, -56, 44, 28, 81, 85, + -17, 127, -25, -86, -67, -70, 52, -49, + -12, -110, 89, -9, 28, 14, -118, 38, + 25, 71, 37, 19, 21, 0, -57, 82, + -44, -40, -5, 37, 95, -38, -17, -68, + -120, -82, 3, 42, -98, -58, -128, -67, + -69, -120, -64, -19, 50, 29, 7, -128, + -30, -96, -38, -128, -2, 127, -47, -18, + -68, 113, -85, -60, 24, -43, -60, -70, + -98, -101, -96, -121, -57, -103, -48, 66, + -5, 67, 109, -105, -17, -27, 15, -14, + -4, -77, 125, -54, 0, -56, -91, 73, + 107, 106, -55, 71, 88, -95, -28, 42, + -56, 96, 3, 81, 27, -81, 94, 116, + -6, -29, -78, 110, 127, -12, 80, 125, + 94, 0, 127, 6, 60, 65, 51, -26, + 7, 88, -124, 29, -63, 16, -57, -78, + 55, 13, -35, 103, 80, 8, 58, 106, + 68, 79, 67, -78, -18, -8, -33, -15, + 58, -71, 1, -10, 48, -61, 1, 73, + -7, -20, 27, 60, 35, -33, 29, -35, + 54, -20, -11, -8, 46, 11, 69, 63, + -116, 118, 99, 60, 101, 1, -74, -61, + -115, 102, 125, -88, -112, -36, -112, -30, + -125, -127, 19, 78, 57, -55, 117, -96, + 8, -50, -110, -38, 30, 43, -44, -40, + -128, 55, 21, 27, -42, -22, 43, -61, + -127, -31, 11, -74, 8, -33, -20, -4, + -15, -58, 32, -113, 16, -54, -14, -56, + 7, 66, 0, 9, 62, 16, -51, 61, + 52, 74, 13, 115, 45, -103, 29, 60, + -50, -55, 127, 120, 1, -117, -70, 88, + -100, -8, 42, -56, -56, 26, -117, -22, + 36, -5, -13, 77, 119, 36, 75, -46, + -70, -61, -36, -128, 70, -105, -35, -57, + -14, -65, 23, -44, -128, 4, -52, 7, + -55, -45, 2, -55, 5, -20, 7, -37, + 44, -16, -7, -78, -59, -5, -4, -45, + -23, -33, 124, 65, -27, -91, -65, 84, + 28, 86, -37, 23, 26, 0, 22, 54, + 46, 4, -126, -49, 44, 59, -8, -4, + -3, 117, 81, -46, -95, 9, 64, -49, + -27, 6, 127, 118, -54, -64, -23, -12, + 56, 105, 5, 126, -46, 32, 8, -74, + -24, 0, 42, 28, 87, -17, 39, 87, + -114, 46, -90, 21, -31, -32, -10, -92, + 29, -11, 14, -58, -87, -32, 37, -42, + -64, 53, 35, -1, 62, 51, 46, -29, + -18, 76, -10, 69, 27, -85, -44, -84, + 96, 119, 91, -44, -24, -13, 75, -13, + 47, 81, -56, -38, 108, 27, 48, -67, + 56, -18, -34, -36, -113, 102, 52, 17, + 76, 4, -76, 107, 106, -60, -118, 61, + -26, -117, 52, 85, -18, -38, -59, -30, + -95, -26, -64, 50, -53, -9, 66, 25, + -59, -124, -43, -63, -51, -42, 35, -112, + 21, 108, -47, 34, -57, 58, 75, -46, + -114, -66, -14, -1, 27, -18, -75, -54, + 13, 33, 33, -66, 6, 11, 24, 23, + 115, -21, -27, 29, -85, -95, -120, -8, + -24, -125, -11, -127, -106, 17, -98, -128, + 48, 94, -10, -56, 119, 28, -41, -60, + -54, 67, 17, -31, 14, -115, 110, 60, + -62, -47, 73, -2, 2, -26, -19, 8, + 0, 10, 41, -120, 14, -71, 1, -25, + -49, -18, 90, 91, 13, 79, -55, 26, + 31, 42, -86, 77, 38, -27, 48, 64, + 33, 0, 29, -13, -57, -28, 28, -127, + 37, 14, 91, -101, 93, -2, 85, 127, + 24, 127, 80, -26, 110, 22, -12, 119, + -18, 83, 20, 127, -117, -112, 59, -94, + -40, -124, 48, 111, 88, 97, 83, 11, + -115, 51, -29, 70, -35, 106, -39, -92, + 52, 39, -81, 30, 40, 48, -7, 51, + 41, -15, 0, 22, -76, -6, 37, 5, + -127, 30, 4, 11, -100, 92, -9, -67, + -92, 18, 96, -122, 36, 12, 127, -37, + 26, 53, 110, -85, 4, -99, -31, 97, + -17, -25, 18, 16, 118, 124, -75, -5, + 5, 127, -108, 99, 3, 15, 69, -98, + -3, -39, 66, 26, 125, -52, -3, 36, + -122, -38, -15, 88, 78, 6, 1, 46, + 48, 81, -10, -79, -42, -13, -29, 55, + -7, -31, 27, 62, 46, -26, -63, 20, + -74, -36, -119, 23, 25, 32, 24, -48, + 40, 113, -86, 48, 78, 87, 46, 79, + -11, 0, -75, -26, -60, 40, -10, -51, + -126, -67, 64, -88, -119, -94, 21, -42, + 68, 36, 49, -62, -19, -47, 5, -44, + 31, -70, -33, -29, 43, -52, 12, -15, + 37, 97, -1, 55, 39, -20, -60, 58, + -20, 1, -15, 37, -62, 37, -12, -53, + -34, 67, -29, 41, 24, 47, -45, -54, + -19, -25, -28, -93, -3, 5, 12, 56, + -46, -105, -87, -84, 70, -54, 43, 32, + 11, -97, -87, -36, 95, -20, 8, -79, + 63, -54, -111, -119, -86, -77, 89, 36, + -100, -112, -122, -125, -65, 0, -85, 127, + 106, -74, -127, 114, -76, 3, 19, -38, + -68, -106, 115, -127, -57, -73, 4, -79, + -29, 60, 118, -112, 23, -51, 13, 47, + -31, -47, 120, 67, -83, 8, 4, 118, + -22, 9, 96, 102, 115, -67, -11, 35 +}; + +static const rnn_weight vad_gru_recurrent_weights[1728] = { + 122, 19, -128, -30, 127, 33, 127, 39, + -112, 96, -21, -106, 7, 14, -28, -109, + -90, -57, 39, -128, -96, -125, 105, -29, + 75, -28, 57, 89, 63, -36, -113, 118, + -29, -18, -72, 37, -108, -99, -113, 79, + 115, 94, 119, 70, -25, -27, 111, -6, + 47, -127, -117, 127, -117, 24, -82, -126, + 115, 101, -82, -98, 10, -47, 58, -37, + -126, -110, 121, -128, -94, 105, -120, -121, + 42, -103, 61, 127, -26, 48, 67, -22, + -61, -115, 34, -15, 73, 104, 72, 73, + 61, 76, 114, 65, 87, 46, -22, 93, + 76, -37, -32, -36, -117, 65, 113, -105, + -33, 60, -5, -48, 45, 32, 43, -8, + 38, -21, -31, -12, -87, -1, -88, -36, + 107, 127, 25, -33, -77, -128, -82, 13, + 101, -128, 50, -77, -14, -65, 46, -36, + -25, -60, -36, 8, -40, -28, -13, -103, + -79, 47, -59, 31, -79, -12, -127, 21, + -103, -10, 2, -10, -127, 2, 20, -6, + 25, -8, -128, 15, -2, 10, -40, 32, + 36, -26, 68, -2, 7, 52, -68, 53, + -11, -10, 29, 4, 33, 13, -46, 11, + 62, 8, 4, -11, -18, 32, 96, 37, + -7, 81, 2, -100, -40, -84, 101, -20, + 2, -22, 35, 69, 111, -33, -20, -1, + 16, 107, -5, 60, 88, -53, -7, 31, + 122, -108, -127, -101, 72, 42, 125, 11, + 123, 9, -106, 99, 118, -20, -60, -65, + -103, -128, -26, -113, -121, -126, 60, -70, + 34, -43, -47, -31, -33, 0, 99, -17, + 75, -20, -80, -7, 118, -62, 15, 28, + 93, -25, -49, 29, 32, 77, -60, 20, + 31, -25, -128, 72, 57, 127, 6, 10, + 18, 56, -125, 6, 5, 58, -3, -118, + -24, -95, 38, -128, -87, 53, -63, 10, + -126, 51, -85, -44, -61, 124, -127, -77, + 17, 126, -33, -55, -127, -109, -115, -16, + 22, 17, -70, 30, -32, -128, 30, -59, + -7, -55, -68, -24, 37, 43, -14, 21, + 6, 34, -76, 35, -42, 63, 56, -67, + -35, -87, -98, -127, 51, 39, -21, 101, + -75, -27, 40, 0, 29, 14, -92, 50, + 50, 30, 0, -25, -66, 10, -79, -23, + 12, -6, -10, -1, 117, -128, 77, 88, + -55, -63, -115, -92, 88, 123, -127, -74, + -13, 36, -107, 62, -25, -92, -121, -128, + -89, -117, -88, -106, -116, -54, -44, -1, + 19, -14, 23, -54, 9, 51, 25, -100, + -25, 83, -26, -33, -16, -13, 63, -71, + 1, -120, -89, -59, -65, 33, 39, 41, + -41, -16, 66, -29, 102, -4, 30, 67, + 30, 53, -22, -80, 44, 112, -89, -8, + 65, -19, -39, 15, 102, -11, 15, 97, + 127, -103, -53, 5, -26, -100, 126, -115, + 62, -128, -128, 37, 124, 107, -30, -125, + 7, -20, -23, -125, -120, 51, 4, 53, + 41, -103, -5, 9, -37, 7, 84, -43, + 15, -67, -83, -26, 64, 5, -83, -25, + -6, -32, 91, 105, -63, -6, 35, 38, + 49, 24, 52, -105, -108, 37, 46, 29, + 22, 18, 91, 74, 77, 119, -21, 120, + 46, 86, -123, 103, 76, -58, -63, -3, + -128, 112, 77, 97, -89, 56, -78, -37, + -18, -34, -22, -92, 95, -60, -122, -31, + -41, 49, -60, -96, 5, 24, -110, -80, + -71, 70, 15, 12, -102, 19, 29, -36, + -121, 123, 10, -55, 3, -24, 14, -123, + -117, -119, -73, -16, -127, -36, -98, -7, + -18, 121, 119, -117, 15, 57, -80, 118, + -51, -106, 127, 117, -28, 27, -77, 120, + 101, 76, -119, 127, 108, -128, 99, 105, + -106, 14, -97, -127, 119, 113, 127, 99, + 64, 118, -114, 93, -42, -62, -28, -103, + 85, -36, 37, -91, -95, -74, 61, -23, + 3, -100, 27, 40, -81, 11, -30, 113, + 44, -12, -118, 16, 21, -1, 16, 37, + 112, 54, 117, -10, 46, 74, -15, 55, + 118, -2, -96, 80, -113, -37, -82, -81, + -2, -19, -91, -30, -75, -114, 76, -115, + -35, -18, 21, -88, -86, 127, -128, -39, + 19, -20, -126, -126, 51, 109, -42, -118, + 46, 22, -108, -8, -21, -101, -85, -124, + -119, -128, -110, -128, -127, -126, 16, -79, + 40, -16, 29, -82, 22, 5, 67, -38, + -68, 23, 23, 13, -7, -53, -3, -88, + -37, -73, -74, -21, 50, -2, 56, 83, + -127, 30, 97, -69, 68, -41, 52, 26, + -13, 48, 89, -23, -17, 15, -108, 88, + 29, 17, 5, 82, 45, -69, -84, 95, + 57, 49, -41, 31, 25, -114, -22, 7, + -102, -105, 40, -57, 42, 34, -41, 127, + 9, -6, -46, 39, -70, 16, -42, -47, + -8, 32, 60, 1, -17, 0, -78, -8, + -55, -11, 3, 39, 4, -16, -13, -18, + 66, 82, -4, 59, -51, 30, -18, 99, + 3, 63, 2, -52, 11, -32, 78, -33, + 45, -101, 36, -34, 28, -66, 24, 87, + 43, 38, 28, 46, 127, 44, -26, -5, + 81, -18, -55, -16, -124, -120, -27, -44, + 27, -128, -55, -38, 127, -37, 126, -41, + -127, -30, -103, -75, 4, 65, -118, 52, + -26, 52, 5, -62, 20, -27, 61, 6, + -57, 15, 42, 26, -23, -11, -99, -17, + -52, -90, 85, 33, -22, 10, -101, -94, + 7, 58, 127, -108, -54, -32, 14, 69, + -53, -68, 126, 40, 42, 13, -30, 125, + 24, 99, -122, 125, 125, -52, -67, 107, + 127, -70, -128, -91, -10, -125, 120, -121, + 67, -127, -128, -46, 127, 52, 112, -45, + -113, -123, -120, -127, -119, -90, -82, -39, + 125, 3, 88, -52, -9, 18, -34, 55, + 3, 47, 16, 23, 82, 50, 34, -5, + -42, 60, 127, 126, -8, 66, 20, 0, + -43, 20, 31, -3, -46, 17, 116, 13, + -50, 90, 31, 107, 28, 112, -71, 125, + 20, 42, -75, 51, 44, -67, -63, -28, + 127, -102, -116, 16, -87, -42, -35, 6, + 115, -114, -109, 64, 115, 84, 101, -72, + -99, -88, -64, -94, -118, 99, -53, 16, + 61, -128, 3, 37, -11, -11, 51, -75, + -6, 3, -113, 66, 77, -9, 19, 43, + -47, 6, -28, 88, -31, 82, -32, 31, + -28, 62, 29, -7, 30, -62, 78, 50, + -98, -1, 72, 71, 32, 62, 11, 58, + 58, 66, -116, 82, 65, -11, -34, 45, + -41, 12, -126, -100, 34, 50, 98, -74, + 61, 109, 11, 108, 102, 34, -71, 114, + -22, 107, 127, 102, -25, -113, 127, 14, + -98, -18, 33, 29, 21, 36, 9, -35, + -48, 85, 35, -5, 36, -64, 12, 13, + 55, -17, -106, -63, -70, -70, -27, -10, + -80, -80, -88, 94, -18, 127, -77, 17, + 80, 125, -105, -70, -83, -84, 103, -37, + -8, -87, 94, -101, -32, 124, 61, -64, + 20, -2, -27, 12, -58, -111, -1, -39, + -59, -114, 16, 47, 26, 71, 12, 123, + -35, -63, -14, -11, -31, -10, -77, -19, + 22, 75, 22, 20, 26, -25, -38, -27, + -62, 86, 21, -8, 82, 7, -34, -64, + 33, 11, 39, 33, -25, 33, 19, -12, + 25, 44, 97, 33, -27, 67, 28, 20, + -8, -52, 127, -19, 111, -26, -9, 63, + -44, -12, 3, 9, 75, -110, -54, 44, + -118, 62, 127, 89, -44, 69, -127, -29, + -124, 9, 85, 41, -126, -63, -96, 120, + 59, 121, -58, 127, 124, 122, -33, 31, + -26, -48, 99, -41, 32, -11, -17, -6, + -102, 44, 92, -23, -19, 19, -1, 77, + 42, 25, -51, -23, -31, -27, 4, 61, + 57, 75, 115, -122, -38, -66, 14, 47, + -124, -116, 127, 71, 48, -5, -83, 62, + 124, 123, -108, 127, 8, -72, -10, 110, + 51, 0, 126, 117, -114, -45, -69, -18, + -115, 19, 112, 1, -127, 46, 38, 117, + 124, 55, -22, 118, 120, 119, -37, 124, + -90, 9, -5, 32, -10, -93, -58, -9, + -14, -64, 77, -5, -51, -2, -14, -4, + -4, 121, 46, 61, -47, 6, -13, 10, + 42, 103, 127, -120, -80, -66, 102, 4, + -93, -94, 126, 48, 36, 20, 2, 120, + 47, 50, -76, 127, 101, -37, -6, 9, + -123, -119, -78, -118, -39, 57, -99, -23, + 4, 51, -94, 127, -104, -114, -77, -73, + -20, -25, -75, -66, -47, -31, 1, -98, + -38, -59, -40, -43, -23, 12, -50, 36, + -6, 1, -5, 24, -68, 21, -39, -91, + 37, -26, 3, 1, -59, -42, 32, 49, + 29, -116, -125, 111, 123, 91, -14, 47, + 30, 84, -118, -90, -90, 22, 15, -127, + -81, -43, 122, -74, -126, 14, 66, -74, + 40, 59, 83, 59, -59, -112, 51, -29, + -46, -11, 28, -22, -7, 105, -6, 4, + 17, 31, -69, 60, 77, 118, 5, 25, + 38, 3, 37, -7, -4, -43, 41, 32, + -74, 74, -9, -28, 23, 70, 75, 10, + 14, 61, 89, 32, -10, -31, -4, -47, + 11, 82, 70, -58, -20, -122, 49, 6, + -57, -84, 124, -2, 105, -53, 20, 127, + -4, 55, -45, 42, 127, -75, -15, 3, + -1, 66, 13, -27, -16, -26, -104, -16, + -26, -66, 38, 3, 8, -5, -34, -42, + 0, -28, -20, 10, -5, -32, -4, 32, + 37, 14, 24, -5, 70, 21, -123, 70, + -30, 84, 37, -43, -34, 6, -7, -23, + -33, 63, 64, -3, -32, 53, 72, 102, + -49, -22, 75, -25, 5, -17, -12, -43, + -30, 13, 43, -37, 48, 21, -103, 19, + 36, 37, 74, -24, 53, -9, -38, 45, + -9, -83, -42, 14, 65, -55, 126, 32, + 100, 41, -33, 58, 100, -23, -34, -55, + 8, 95, 94, -38, -67, -44, 48, -33, + -39, 17, -58, -6, 26, -8, 39, -27, + 19, -6, 14, 30, -6, -59, -19, 16, + -4, 30, -10, 40, -88, 46, 17, -125, + 19, -72, -63, 40, 18, 96, -36, -41, + 108, 13, -128, -39, -118, 50, 100, -9, + -13, -90, 25, -104, -126, 110, 19, -43, + -128, 103, 88, 118, 32, 102, -127, -40, + 13, 126, 69, 18, -123, -58, -26, 124, + 106, 122, 73, 127, 120, -11, -33, -23, + -85, 45, -6, 62, -36, 46, -83, -62, + 24, 101, 22, -90, -38, -20, -40, -95, + -24, -60, -126, -87, -14, -111, -38, -120, + -32, -27, 44, 34, 51, 30, -123, 103, + -13, -46, 25, -9, 3, -13, 37, 36, + 65, -37, 74, -52, 68, -26, 65, 40, + -75, -10, 58, -10, -37, -30, -127, 35, + -48, -29, -128, 115, -102, -64, -29, -9, + -9, -41, -118, 9, -64, 117, -80, 44, + -19, -68, 54, 12, 72, -48, 49, 6, + 7, 86, -27, -54, -92, 26, -29, -105, + 27, -12, -53, -24, 124, 56, -69, -2, + 17, 127, 106, -127, -46, -39, 71, 66, + -80, 13, 127, 88, 34, 53, -9, 45, + 112, 112, -113, 71, 40, -86, -117, 66 +}; + +static const rnn_weight vad_gru_bias[72] = { + 123, 19, -128, -127, 119, 3, 125, 67, + 125, -23, -128, 98, 127, 49, -25, -123, + -100, -121, -103, -127, -128, -123, 78, -97, + 121, 42, -13, -24, 61, 40, 95, 36, + 118, 27, -19, 102, 115, 51, 67, 22, + 6, 34, 78, 36, -14, 88, 75, 31, + 6, 40, 27, 5, -65, -59, 80, 13, + 39, -15, 34, 26, 21, 31, -20, 48, + -12, 44, -50, 32, 24, 3, -127, 25 +}; + +static const GRULayer vad_gru = { + vad_gru_bias, + vad_gru_weights, + vad_gru_recurrent_weights, + 24, 24, ACTIVATION_TANH +}; + +static const rnn_weight noise_gru_weights[12960] = { + 42, 35, 23, 3, 6, -38, 61, 47, + 45, 37, 31, -16, -31, 58, -75, 35, + -4, -13, 84, 47, 25, 20, 0, -67, + 32, -40, 48, -60, 14, 25, -10, -17, + -21, -2, -12, 17, -16, -78, 16, 31, + -70, -12, -2, -34, 42, 32, 102, -85, + 77, -61, -63, 91, -33, 35, 50, 25, + -66, -15, 16, 5, 1, 21, -38, 19, + -43, -14, 35, -18, 29, -70, 13, 45, + 3, -40, -12, -10, 5, 21, -2, -57, + 32, 12, -8, -17, 51, 51, 83, -16, + 29, 13, 25, -57, 38, 32, -49, -27, + 51, -33, -51, 23, -14, 19, 9, -15, + 15, 25, 11, -20, -11, 12, -66, 9, + -14, -22, -24, 30, -11, 4, -4, -23, + 52, -27, 21, 23, 2, 24, 24, 80, + 1, -7, -2, 12, 10, 33, 22, -31, + -18, 37, 47, 4, -19, 46, 61, -37, + -83, 61, -34, 29, 28, -123, -2, -25, + 96, 57, 83, -9, -27, -32, -4, -78, + -85, 26, -89, 106, 14, -61, 26, -22, + 38, 94, -41, -12, 0, -59, 13, 17, + 55, 47, -47, 114, 28, 87, 31, 84, + 123, -72, -1, 1, 14, -26, 6, -76, + 1, -54, 7, -60, -16, -87, -22, -73, + -41, 5, -92, 7, -15, -82, 65, -88, + 5, -48, 1, -24, 45, -42, -26, 33, + 9, 124, -103, -105, 121, 64, 18, -25, + 4, 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-114, -52, -109, 102, + -32, -29, -3, 57, -46, 67, -24, 65, + -28, -6, 28, -13, -68, 47, 15, -14, + -8, -60, 21, 12, -21, -78, 56, -25, + -87, 37, 44, 19, -47, 19, 4, -71 +}; + +static const rnn_weight noise_gru_bias[144] = { + -2, 86, -18, -32, -14, 2, 93, -53, + -75, 57, 65, 7, -1, 19, -124, -9, + -1, 0, 19, -25, 2, -67, -31, -21, + -5, 35, 34, 10, 82, -29, -30, -78, + 7, 52, -6, 63, 92, 58, 42, 32, + 43, -28, 25, -55, 71, -27, 15, -27, + 59, 43, -6, 41, -3, -34, -20, -15, + -14, 24, -46, -16, 12, 34, 8, 11, + -26, 46, -30, -18, 9, -24, -46, 0, + 50, 15, -9, -2, 14, 25, -26, -21, + 9, -68, 19, -22, -16, -9, -10, -1, + -14, 40, -52, 1, 5, -8, 49, 63, + 15, -37, -26, 38, 1, -11, -12, 78, + -1, 12, -23, -59, -68, 13, -15, -34, + -28, 64, 32, -8, -7, 20, -22, 19, + 31, -69, -4, 3, 19, 15, 20, 21, + -17, -27, -8, 12, 10, -11, 2, -2, + 6, 46, -10, 48, -37, 53, -1, 36 +}; + +static const GRULayer noise_gru = { + noise_gru_bias, + noise_gru_weights, + noise_gru_recurrent_weights, + 90, 48, ACTIVATION_RELU +}; + +static const rnn_weight denoise_gru_weights[32832] = { + 61, -72, -112, 20, -47, 54, 30, 45, + -13, -16, -5, 28, 4, -32, 30, -82, + 127, 16, -15, -7, -2, 76, -16, 99, + 22, 43, -34, 77, 40, 75, 3, 42, + 74, 2, 51, -45, -16, 37, -13, -58, + 52, 7, 24, 55, 87, -56, 20, 96, + 26, -59, 26, -9, 24, 18, -32, -6, + -21, -29, 47, -11, 31, 35, 90, 4, + -55, 21, 34, 7, -103, 37, 35, 93, + -59, 48, 28, 93, -7, 27, 3, 94, + -29, 86, -5, 3, -14, 1, 61, 58, + 70, 55, -20, 68, 23, -65, 40, 55, + 27, 7, -14, 22, -8, 7, -16, 5, + -39, -42, -71, 12, 20, -19, -19, -111, + -67, -65, 4, -11, -34, 32, -7, 2, + -19, 29, 15, 40, 75, -12, -49, 23, + -20, 32, 50, 2, 12, 66, 17, 16, + -30, 1, -96, 15, 68, 13, 29, -3, + 25, 42, -4, 38, -15, -47, 29, -26, + 34, -12, 6, 8, 60, -79, -51, 68, + 14, -12, 33, 49, -18, -3, -90, -39, + 40, -6, 56, 12, 15, 34, -8, -49, + -14, -36, 5, 16, -19, 2, 28, -44, + -4, -14, 11, -46, 10, 16, -7, -127, + 5, 30, 23, -61, -18, -7, -12, -19, + -48, 14, 55, 6, 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-128, + -10, 41, -79, 127, -31, 20, -57, -30, + -99, -31, 82, 77, 84, 119, 43, -14, + -43, -128, -9, -54, -21, -53, -31, -54, + 35, 53, 125, 58, 90, 40, 126, 26, + -2, 50, -2, 33, 45, -12, 29, 68, + -112, 37, 102, 24, 67, -127, 21, -41, + 16, 71, -37, 49, 16, -21, -112, 83, + 126, -127, 34, -93, -27, -1, 24, 32, + 46, 88, -50, -76, -10, -64, -16, 75, + -1, -34, -59, -6, 31, 121, -15, 88, + -63, -86, -11, -37, 107, 33, 116, -48, + -57, 21, -126, 87, 23, 18, -16, -82, + 11, -4, 37, 59, -73, -22, 60, -34, + 62, -49, 24, -43, 17, -18, -84, -21, + -19, -11, -98, -126, -2, 30, -43, 76, + -6, 49, 25, 67, 103, -20, 126, 58, + 113, 88, 11, 95, -95, 57, 101, 36, + 5, -5, -3, 5, 64, 39, 19, -27, + 47, 33, 124, 2, -25, -1, -49, -2, + -28, 5, -56, 17, -27, -50, 7, -21, + 13, 34, 29, 26, 24, 19, 14, -18, + -7, -29, 13, -16, 23, 13, 5, 38, + 5, 21, 1, -3, 74, -17, 125, 39, + 41, 1, -14, -11, 21, 26, 26, 9, + 37, -17, -15, -71, 73, 8, -22, -30, + 50, 30, -48, 4, 11, -4, 17, -10, + -14, 68, 5, 72, -16, 45, -83, 21, + -8, -11, -16, 10, 21, 15, 31, 13, + 44, -2, 25, 17, 89, -5, 22, 27, + -32, 5, 48, -7, 98, -11, -13, -42, + 40, -70, -6, 121, 32, 25, -53, -53, + 4, 41, 15, -12, -9, -17, 16, -3, + 28, 22, 46, -11, 52, -32, -4, 1, + -47, -16, -22, 9, 48, -23, -43, -18, + 17, -95, 0, -29, 17, -44, 60, -54, + 104, -32, 48, 22, 10, -16, 55, -74, + 33, -1, -37, 123, 3, -41, 12, -34, + -35, 11, -18, 18, -9, 5, 13, -14, + 6, 37, 15, 0, 69, 35, -9, 2, + -12, 7, 17, 50, -6, -8, -16, -22, + 7, -26, -30, 1, -7, 48, -58, 3, + 14, 2, -18, 8, 53, -18, 21, -12, + -16, 16, 9, 16, -22, -30, 13, -18, + -30, 4, -26, -17, 15, 26, 6, 25, + 15, -8, -33, 6, -59, 79, -17, 47, + -4, 1, 3, 1, -7, 15, 0, 6, + -8, -95, -9, -10, 60, 2, -19, -1, + 8, -17, -17, -77, -5, 1, 26, -62, + -10, -5, -10, 9, 4, 18, -1, -9, + 3, -14, 30, -4, 2, -20, -9, -37, + 1, 80, 27, 40, -6, -9, -38, -32, + 4, 24, 11, 3, 3, -15, 17, 54, + -81, 0, 6, 22, -44, -12, 16, 16, + 48, -22, -22, -25, -24, 59, 50, 55, + -27, 1, -14, -73, 1, 10, -12, 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-9, -47, -1, 29, -32, + 12, -3, -6, -24, 16, 7, 3, -96, + -78, -9, 3, 4, -9, -18, 6, 15, + -1, -77, 2, -3, 0, -2, -20, 21, + -1, 19, -30, -8, -2, -7, -57, 21, + 0, -21, 19, -23, -5, 27, 39, -15, + -28, -10, -4, 2, 21, -1, 4, 6 +}; + +static const rnn_weight denoise_gru_recurrent_weights[27648] = { + -10, 83, 36, 6, 48, -72, 76, 19, + -45, 82, 52, -64, 32, 1, -34, 109, + 55, -33, -54, -104, -30, -45, -127, 50, + 126, -50, 41, 62, 69, 125, -75, 22, + 41, -19, 126, 15, 5, 105, 65, -39, + 77, 127, 11, -7, 33, 74, -59, -24, + -18, 47, 70, 30, 17, 118, 20, 16, + 126, 35, 39, -3, 127, 122, -15, 37, + 15, -26, 76, 6, -97, 32, -44, -59, + 125, 42, -22, 124, 35, 32, -2, 67, + 45, -125, 19, 46, 4, 108, 47, -47, + -128, 65, 121, 48, 52, 120, 37, 41, + -28, -28, 123, 30, 6, 31, -11, -95, + -82, 35, -1, 79, 23, -33, 40, 65, + 24, -42, -58, 25, -74, 13, -31, -74, + 119, -94, 44, 23, -123, 50, 102, 38, + 65, -5, 36, -40, -17, -56, 14, -126, + -67, 83, 59, -23, 21, 15, -2, 46, + 8, -22, 39, 2, -1, 29, 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-90, -15, 74, + -33, 13, -28, 53, 55, -15, 22, 115, + 6, -53, 35, -8, 45, 25, 21, -38, + 72, 4, -107, -15, 32, -7, -24, 10, + -30, 46, -49, -49, -41, -18, 63, -25, + -7, -16, 5, 46, 79, -52, 10, 26, + -97, 9, 82, -4, -33, -31, 40, 22, + -36, -16, -22, 22, -19, -47, -25, -66, + 41, -71, 55, -44, 32, -43, 32, 28, + -20, 32, 13, 2, 38, -72, -57, 33, + -4, 81, 113, 26, -42, 31, -13, -14, + 28, 40, 19, -68, -34, 56, -64, 65, + 31, 0, 17, 8, -12, 6, -15, 13, + -42, -74, 4, -20, -19, 104, -6, 33, + 12, -92, 39, 86, -87, 27, -34, 16, + 77, 28, 44, 39, 126, -13, 45, 87, + -47, -5, 17, 101, -40, 35, -23, 127, + -101, 33, 5, -30, 61, 28, 33, 25, + -70, -127, 108, 63, 15, 126, 5, 15, + 70, -34, 14, -58, -3, 28, 53, -23, + -3, 25, 20, -15, 75, -6, -40, 100, + 23, -20, 18, 27, 79, -23, 13, -30, + -62, 36, 17, -96, -3, -51, 51, 60, + -75, -59, 118, -20, -11, -66, -10, 79, + -54, 125, 76, 69, 84, -19, 28, 33, + 45, 26, -88, 13, 36, -46, 25, 46, + -1, 35, 63, 7, 70, -78, 5, -20, + 11, 23, -42, 33, 29, 125, -88, -9, + -90, 31, 96, -6, 20, -30, 78, -16, + -12, 64, 23, -26, -37, 29, 34, -20, + -11, -16, -10, -39, 127, 43, -39, -14, + 3, -84, 3, -10, 38, -16, -9, -59, + 91, -4, -68, 36, 10, -109, -7, 18, + -24, 126, -44, 16, 68, -51, 59, 72, + -39, 26, -29, -31, 26, 124, -47, 60, + 62, -17, -11, -20, 84, 26, 12, -80, + -31, -38, 56, -82, 19, 13, -26, -66, + 42, -25, 60, 8, -60, -7, 23, 10, + 56, 11, 74, -100, 41, 24, -7, -93, + -50, 48, -112, 33, -29, -32, -67, 127, + -35, -13, -9, 14, 35, 89, 44, 75, + -62, -19, -15, -73, -46, 81, 11, -9, + 49, -44, -85, 39, -87, 127, 24, -24, + -91, 127, -47, 62, -8, -69, 97, -16, + -128, -14, 93, -39, -3, -21, 23, 20, + -53, -60, -69, 82, -105, -31, 34, -33, + -29, 56, -7, 5, 17, -83, 37, -48, + 113, 31, 103, -55, -21, 22, -33, 16, + 35, 26, -38, -68, 61, -7, 5, 36, + -47, -19, -65, -90, -42, 33, 37, -20, + -20, 30, -30, 59, -12, 61, -87, -2, + -122, 36, -78, 17, -43, 126, -90, -21, + 107, -74, 10, -17, -7, -103, -127, -96, + 15, 52, -115, 73, 1, 22, -2, 41, + -50, 16, 46, -93, 124, -22, -118, 54, + 88, -39, -27, 52, -3, 13, -120, 64, + 63, -102, 13, 51, 30, -23, 0, -73, + -58, 4, 32, -25, -41, 36, -55, 32, + -44, 40, 12, -66, -127, -24, -4, -124, + 77, -46, 12, -20, 100, -76, -53, 22, + 10, -57, 41, -112, -77, -85, -94, 51, + 126, -111, 90, -110, -126, -105, -29, -99, + -24, 30, -121, -39, -103, -16, -125, -88, + 37, -40, -81, -54, 19, -47, 2, -76, + -70, -85, -62, -22, -62, 69, -39, 80, + -37, 1, 50, -28, -51, -2, 29, 33, + 18, -58, 75, -38, 15, 127, 92, 4, + -72, -76, 0, -61, 31, 55, -117, -62, + 55, -70, -27, 51, -54, 28, 54, 75, + -15, -43, 31, -5, 33, -34, 39, -53, + -21, -45, -85, 76, -40, 13, 32, 4, + 25, -78, 13, 90, 28, 2, -40, 84, + 64, -23, 8, 28, 17, -78, -61, -42, + 120, -34, -26, 9, -51, -49, 20, -92, + 24, 7, -26, 46, -57, -30, -58, -18, + -126, -12, 60, 57, -62, -12, 53, 73, + -26, -71, 70, 71, 75, -55, -9, 37, + 33, -30, -24, -23, 15, 58, -17, -126, + 54, -63, -40, 66, -15, -40, -127, -12, + 8, -34, -105, 13, -4, -11, -43, -23, + -35, 77, 59, -8, -20, 71, -18, 63, + -7, 46, -114, 11, -60, 28, 31, -77, + 124, -62, 18, -86, -67, -14, -55, 50, + 21, -52, 28, -62, 10, -33, -10, 14, + -37, -78, -77, -23, -79, 28, 7, -30, + -12, 127, -66, -2, -50, 52, -11, 1 +}; + +static const rnn_weight denoise_gru_bias[288] = { + 54, -38, -3, -3, 93, 5, 69, -11, + 54, 96, 29, -38, 35, -46, 23, -11, + -2, 107, 28, -16, 111, 22, -57, -2, + 16, -4, -6, -25, 18, 66, 16, -10, + -13, 69, 26, -28, 71, -9, 56, -25, + 51, 86, 18, 21, 71, -10, 60, -4, + -20, 23, 30, 31, 3, 17, 27, -13, + -37, -23, -41, -92, 4, 76, 9, 52, + -41, -43, 55, 82, 54, -61, 54, 62, + -27, 113, -4, 35, -11, 10, 61, -15, + -13, 74, -100, 36, 5, 34, 19, 36, + 5, 97, 67, 15, 83, 38, -27, 70, + 20, -10, 3, 16, -27, -20, 25, 2, + 3, 13, 33, -22, 23, -1, -2, 17, + -26, 46, 5, 23, 8, 43, -2, -34, + 75, -37, 0, 71, 44, -29, 19, 13, + 3, 41, -5, 9, -14, -34, -9, 10, + -31, -40, 23, 11, -5, 18, 18, 1, + 31, 26, -12, 7, -5, 63, -20, -8, + 23, 4, 31, 21, -13, -27, -48, -13, + -14, 29, -2, 11, 18, 14, 4, 19, + -41, -21, -10, 6, -6, 1, 65, 17, + -63, -1, -55, -19, -7, 28, 2, 13, + 40, 6, -17, 1, 15, 18, 16, 47, + 11, -87, -10, 21, 9, -14, 12, 8, + 14, -22, -17, 10, 1, -23, 1, -20, + -22, -6, 44, -5, 6, 37, 44, -11, + 62, 13, 0, -35, -63, 20, -32, -27, + -30, 20, 35, -25, -35, -20, 40, -30, + 16, 11, 63, -25, -13, -55, -14, 45, + -13, -27, 25, -10, -10, 17, 1, 14, + -17, -30, -68, 41, -9, -5, -19, -33, + -61, -34, 24, 59, 28, -32, 43, -40, + 11, 0, -18, 14, 28, -3, 25, 6, + -37, 14, 10, 51, 43, 26, 4, 19, + 12, -35, 70, -10, 49, -33, -14, -22 +}; + +static const GRULayer denoise_gru = { + denoise_gru_bias, + denoise_gru_weights, + denoise_gru_recurrent_weights, + 114, 96, ACTIVATION_TANH +}; + +static const rnn_weight denoise_output_weights[2112] = { + 18, 8, 16, 3, 3, 7, 30, 28, + 20, 15, 0, 4, -4, -6, -8, 10, + 9, -12, -38, -34, -31, 2, 29, 14, + 12, 11, 5, 12, 22, 26, 48, 61, + 67, 81, 91, 99, 98, 91, 84, 39, + -3, 11, 16, -4, 127, 127, 115, 60, + 25, 13, 13, 15, 5, 2, 11, 2, + -1, -1, 14, 20, 14, 8, 8, 10, + -10, -36, 28, 29, 35, 32, 32, 36, + 31, 24, 24, 27, 12, 13, 14, 8, + 8, 25, 27, 0, -8, -3, -6, 32, + -18, -13, -22, -46, -63, -70, -72, -81, + -70, -41, -13, -17, -15, -7, -3, -6, + -6, 4, 1, -9, -35, -52, -48, -25, + -1, 18, 12, -2, -5, 5, 32, 49, + 65, 67, 50, 43, 37, 20, 13, 11, + -16, -11, -11, -68, -13, 24, 27, 28, + 34, 35, 26, 15, 8, 8, 10, 2, + -4, -12, -36, -68, -72, -21, 28, 47, + 54, 126, -10, -30, -24, -33, -22, -20, + -25, -28, -30, -34, -41, -44, -40, -37, + -32, -23, -15, -12, -6, -13, -28, -1, + -51, -70, -62, -27, -6, -8, -3, -26, + -47, -46, -39, -18, -7, -1, -7, -7, + 3, -18, -37, -18, -1, 2, -22, -23, + -38, -37, -42, -38, -38, -32, -8, 15, + 24, 22, 12, -12, -39, -53, -42, 23, + 113, 126, 121, 107, -12, -6, 6, 19, + 17, 9, 6, 9, 5, -9, -8, -7, + -2, 10, 36, 43, 26, -2, -2, 3, + -12, -45, -71, -128, -128, -110, -67, -62, + -44, -38, -50, -64, -60, -60, -55, -53, + -38, -18, -14, -21, -33, -36, 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+ 44, 37, 33, 45, 67, 62, 64, 67, + 73, 76, 78, 79, 88, 83, 70, 86, + 106, 106, 5, 16, 29, 42, 52, 56, + 58, 52, 49, 48, 39, 35, 39, 41, + 45, 51, 60, 60, 64, 68, 71, 42, + -91, -97, -81, -71, -80, -73, -58, -43, + -43, -33, -23, -23, -17, -6, 9, 4, + -8, -24, -40, -44, -57, -41, -94, -47, + -7, -7, -16, -26, -24, -24, -28, -31, + -29, -35, -33, -31, -21, -7, 10, 16, + 16, -10, -45, -82, -50, -61, -49, -34, + -18, -11, -14, -23, -32, -28, -32, -32, + -35, -38, -37, -31, -17, 1, 1, -2, + -39, -65, 42, 40, 43, 30, 31, 30, + 26, 15, 7, 6, 18, 11, 7, -4, + -7, 2, 22, 28, 28, 19, 20, -35, + -27, -25, -56, -83, -86, -73, -80, -76, + -67, -49, -43, -42, -36, -22, -11, -7, + -7, -14, -12, -6, -14, -27, 12, 26, + 14, 15, 23, 21, 10, 6, 6, 11, + 17, 12, 18, 24, 24, 28, 27, 34, + 55, 64, 66, 82, 8, -27, -50, -35, + -29, -22, -7, 4, 24, 36, 25, 2, + -1, -7, -30, -36, -18, 39, 70, 23, + -43, -37, 15, 30, 37, 42, 36, 28, + 28, 32, 34, 31, 24, 30, 23, 22, + 22, 25, 28, 43, 49, 72, 103, 121, + 26, 7, -6, -8, 7, 17, 26, 29, + 21, 26, 31, 32, 49, 68, 88, 97, + 101, 107, 94, 73, 32, -32, 26, 13, + -25, -35, -24, -18, -31, -35, -22, -11, + -17, -3, 9, 16, 11, 17, 29, 77, + 110, 120, 127, 110, -30, 7, 24, 39, + 54, 37, 9, 18, 33, 44, 56, 64, + 66, 72, 78, 66, 67, 72, 76, 41, + 11, -24, 22, 59, 92, 78, 72, 63, + 69, 50, 21, -1, -9, -18, -21, -23, + -34, -44, -54, -60, -65, -64, -59, -25, + -128, -126, -23, -22, -42, -50, -42, -34, + -33, -36, -18, -21, -14, -25, -31, -27, + -39, -35, -34, -51, -52, 0, -1, 14, + 27, 19, 13, 5, 3, -7, -5, 0, + 2, 16, 23, 28, 24, 42, 82, 127, + 127, 115, 2, -112, -25, -36, -34, -42, + -43, -39, -30, -21, -39, -54, -66, -76, + -67, -59, -46, -23, -3, 19, 17, 18, + 27, 27, 9, 2, -10, -8, -5, -11, + -16, -13, -12, -20, -18, -25, -15, -8, + -1, 6, 21, 35, 52, 37, 16, -3, + 69, 9, -24, -28, -18, -9, -10, -2, + 4, 8, 10, 15, 14, 5, 6, 7, + -6, -44, -47, -46, -28, 13, -7, 16, + 38, 36, 39, 44, 43, 32, 31, 33, + 29, 44, 62, 60, 34, 8, 4, 18, + 21, 14, 16, 71, 10, 37, 71, 59, + 64, 55, 56, 53, 67, 75, 70, 60, + 47, 44, 41, 47, 41, 38, 11, -1, + -12, 2, 12, -5, -25, -23, -33, -40, + -45, -33, -30, -36, -34, -43, -25, -11, + 13, 27, 19, -56, -118, -94, -55, -75, + -71, -51, -25, -20, -15, -6, 2, 11, + 13, 15, 16, 26, 35, 50, 51, 44, + 41, 32, 29, 45, 67, 46, 15, 4, + 14, 11, 20, 32, 33, 32, 31, 21, + 13, 16, 21, 20, 14, 20, 23, 18, + 24, 28, 54, 75, 26, 37, -7, -83, + -128, -128, -119, -69, -22, 14, 23, 3, + -23, -21, -1, 11, 17, 15, 19, 19, + 29, 40, -96, -80, -53, -36, -31, -36, + -34, -34, -33, -32, -21, -23, -17, -18, + -19, -12, -6, -4, 12, 14, 18, 66 +}; + +static const rnn_weight denoise_output_bias[22] = { + -14, 10, 25, 43, 1, -20, -26, -40, + -33, -23, 7, 18, 17, 2, 4, 0, + 13, -11, -42, -54, -3, 34 +}; + +static const DenseLayer denoise_output = { + denoise_output_bias, + denoise_output_weights, + 96, 22, ACTIVATION_SIGMOID +}; + +static const rnn_weight vad_output_weights[24] = { + -125, 127, 127, -128, -65, -128, 74, 103, + -128, -128, 127, 108, 89, -127, -128, 127, + 127, 127, -123, 127, 127, -128, -73, 127 +}; + +static const rnn_weight vad_output_bias[1] = { + -127 +}; + +static const DenseLayer vad_output = { + vad_output_bias, + vad_output_weights, + 24, 1, ACTIVATION_SIGMOID +}; + +const struct RNNModel model_bd = { + 24, + &input_dense, + 24, + &vad_gru, + 48, + &noise_gru, + 96, + &denoise_gru, + 22, + &denoise_output, + 1, + &vad_output, +}; diff --git a/src/models/lq.c b/src/models/lq.c new file mode 100644 index 00000000..d069f84e --- /dev/null +++ b/src/models/lq.c @@ -0,0 +1,11046 @@ +/*This file is automatically generated from a Keras model*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "rnn.h" +#include "rnn_data.h" + +static const rnn_weight input_dense_weights[1008] = { + 2, -7, 5, -3, -2, -18, 0, 6, + -3, 1, 5, 1, -18, -2, -2, 8, + -8, 12, 19, -10, -43, 8, 5, 3, + -13, 9, 23, -8, 16, 29, -9, -10, + 1, -13, -20, 39, 0, 18, -22, -15, + -46, -1, 1, 8, 10, -25, 18, 23, + -30, -1, 42, -8, 6, -18, -13, 31, + 3, 2, -32, 9, -2, -28, -44, -29, + -41, 23, 14, 7, -37, -35, -11, 11, + 1, -18, 19, 15, -7, 19, 3, 10, + -7, 34, 4, -20, 16, 53, -37, -62, + 27, 48, 2, 3, 29, -31, 19, -26, + 5, -37, 1, 14, 2, -68, -9, -63, + -8, -17, 24, 48, 22, -23, -13, -62, + -9, 9, -12, -22, -56, 9, -53, -25, + -9, -5, 18, 11, -9, 41, -16, -48, + 16, -48, -2, -5, -19, 73, -19, -36, + -18, -25, 8, -23, 80, -45, 38, 1, + -65, -40, -59, -27, -90, -122, 29, -66, + -2, -56, -28, -124, -10, 10, -17, 123, + 67, 97, 34, -86, -78, -20, -110, -110, + -1, -6, -72, 98, 89, -21, -37, 0, + -6, 15, 127, -17, 11, 121, -128, 127, + 65, 30, -102, -86, 50, -20, 15, -69, + -88, -72, -86, 69, -58, -100, -43, -2, + 57, -99, 106, -102, 120, -125, -101, 126, + 74, 100, -50, -73, -61, -99, -111, -126, + -53, -87, -18, 71, -125, -26, 31, -101, + -7, 4, 56, -95, 0, 76, -55, -52, + 79, 18, 51, -99, 3, -74, 2, -87, + -10, -26, -127, -10, 33, -90, 75, 126, + 1, 64, -22, -20, 28, 23, 19, 106, + 127, -76, 70, -41, 80, 9, -125, 89, + -63, 83, -48, 4, -41, 51, 126, -30, + -68, 72, -126, 65, 30, -18, -13, 126, + 114, -82, 19, -124, 107, 28, 32, 14, + -4, 35, 127, 18, -38, 55, -12, -122, + 119, -79, 127, -53, 59, 31, 120, -76, + -117, 17, 105, 13, -128, 127, -100, 80, + -49, 68, -12, 34, 59, -61, 2, 55, + 23, 11, 4, 118, 125, 86, 72, -127, + -60, -72, 33, -67, 19, -63, 92, 114, + 47, 12, -125, 32, 40, -82, -127, 93, + 54, 122, 112, -24, -112, 31, 120, 18, + -57, -54, -20, -122, 61, -11, -97, 94, + 40, -126, -22, -6, 40, -87, -126, 28, + 25, 127, 44, -46, -17, 122, -13, -33, + -40, 115, 15, -61, 127, -32, 49, 84, + 27, -68, -50, 12, 95, -93, -100, 126, + 75, 24, 65, -55, 32, -92, 126, 15, + -122, 126, -83, -67, -122, -128, -7, -12, + 75, 95, -41, 127, -55, 48, -126, 8, + 123, 80, -3, -31, -126, 95, -72, -123, + -48, 127, 58, -35, 91, -128, 36, 95, + 26, -79, -85, 127, 17, -102, -109, 23, + 86, -19, 85, -33, -112, 98, 114, -10, + -124, -61, 77, 85, 84, -121, -125, 48, + -25, -99, -126, -36, -69, -86, 113, 99, + -7, 127, -127, -12, 127, -82, -70, 79, + -128, 125, -87, -121, -125, -128, 46, -94, + 78, -47, 46, 28, 71, -127, 54, 83, + 127, 120, -127, 124, 126, 26, 103, -106, + -54, 89, -38, 105, -88, -75, -80, 4, + -71, -38, -63, 25, 52, 104, -10, 69, + 102, -50, 126, 127, -70, 72, 49, 127, + -123, 34, -76, 14, -93, -117, 81, -67, + 127, 5, 76, 100, -98, -68, -116, 127, + -45, -128, -77, 18, 12, 125, -87, -20, + 21, 102, -29, -53, 10, 48, -8, 101, + 22, -4, 22, -12, 127, -87, -126, 51, + -128, -119, -122, 66, 78, 3, 32, 60, + -28, 40, 30, 4, 56, 56, 18, 82, + -104, 58, -47, -56, -41, -47, -113, 127, + 12, -19, -95, 56, 16, 51, -127, -51, + -118, 29, 9, -16, 66, -33, 11, 121, + 56, 21, -70, 10, -20, -21, -11, 76, + -29, 6, 71, -35, -62, 55, -52, -2, + -108, 31, -34, -33, 73, 121, 41, -123, + -67, -80, -18, -44, -2, -35, -105, -59, + -69, 81, -86, 55, 74, 61, -57, -117, + 17, -1, 37, 20, -84, -7, -77, -10, + 35, 15, 3, 70, -39, 4, 26, -12, + 58, 23, 35, -80, -14, -7, -85, 32, + -127, -21, -113, -98, 69, 96, -47, -67, + 124, 9, -1, 120, 12, -71, -9, 44, + -31, 113, 6, 13, 9, -44, -13, 31, + -21, 65, 2, -45, 15, 51, 15, 92, + 26, 34, 10, 32, 92, 98, 7, 108, + -15, 56, -19, 51, -45, 79, -8, 11, + -28, -31, -11, -9, 40, 38, -57, 21, + 18, 34, -125, -46, 57, -7, -23, -50, + 45, 70, 38, 12, -28, -51, -4, 61, + -123, 49, -59, -12, -80, 59, 19, 78, + 45, -31, -52, 33, -38, 127, -115, 49, + -19, -32, 85, -10, 77, 26, -77, -69, + 104, -112, -91, 26, -78, 44, -65, -23, + 26, -15, -118, -127, 79, -126, -4, -37, + 33, -11, 14, 52, -15, 18, -69, -55, + -76, 93, -12, -69, -44, 15, -18, -63, + -15, 94, -99, 77, -46, 111, -13, -77, + 14, -43, 0, 68, -37, 93, 106, -16, + -91, 127, -37, 13, -27, -39, -50, 17, + 27, 115, 125, 111, -117, 22, -128, 16, + 127, 127, 47, -18, -54, -24, -115, -99, + -47, -15, 66, 127, 32, 91, 46, 125, + 86, 66, 127, 86, -101, -52, -127, 122, + 126, 93, 100, 17, -126, 98, -99, -81, + 62, -15, 69, 90, -17, 75, -6, 127, + 121, -61, -41, -24, 10, 115, 99, 44, + -73, -41, 121, 17, 38, 33, -40, -8, + 56, 50, -64, -60, 98, -10, -56, -62, + 80, 123, -13, 15, 23, 80, -127, 81, + 127, 69, -67, -16, -85, -14, -38, 59, + -61, -56, 32, 127, -40, 4, 40, 33, + 33, 6, 117, -35, -4, 71, -6, 92, + 126, 66, 83, 17, -87, -31, 7, 17, + -8, -103, 0, 121, -34, -54, 38, 50, + 66, -98, -25, -17, 49, -49, 121, -58, + -92, -87, -1, -29, 127, 63, 13, 91, + 0, 111, -59, 26, -28, 127, -19, -118, + -11, 36, 124, 29, 16, 26, 50, 26, + 9, -40, 82, -25, 39, -78, -23, 1, + -25, -26, -13, -113, 17, -10, -19, -18, + 67, -24, -28, 127, 4, 56, 17, 23, + 13, 83, -5, -27, 48, -15, 10, -65, + -46, -22, 47, 28, 127, 25, 12, 18 +}; + +static const rnn_weight input_dense_bias[24] = { + -11, 127, -60, 4, 127, 125, 20, -58, + -127, 22, -74, 41, 103, 41, 25, -125, + 93, -59, -127, -26, -1, -128, 41, -1 +}; + +static const DenseLayer input_dense = { + input_dense_bias, + input_dense_weights, + 42, 24, ACTIVATION_TANH +}; + +static const rnn_weight vad_gru_weights[1728] = { + -10, -117, 52, 91, -127, -32, -128, -96, + 50, -114, 24, 25, 54, 28, -122, -127, + -97, -125, -27, -20, -70, 21, -10, 17, + 123, 50, 126, 7, -121, 32, 88, 2, + 9, -116, 37, -84, 17, 32, -26, -120, + -31, 21, 33, -54, -50, -47, 127, -36, + -109, -100, -16, 47, -114, -120, -65, 86, + 26, 121, -77, -22, 43, 14, 4, 40, + -122, 122, 6, -43, -128, 50, -14, -5, + -126, -97, 23, -121, -58, 68, -10, -111, + -122, -39, 127, -25, -103, 34, -55, -50, + 37, 4, -126, 96, -37, 127, -26, 127, + 41, -57, -25, 127, 14, 98, 5, 22, + 6, -83, 71, -77, 96, -18, -126, -100, + 26, -11, 45, 12, 23, 127, -51, 126, + 3, 99, 49, 14, 26, -27, 16, -50, + 34, 34, 73, -82, -10, 7, 48, -90, + 41, -2, 19, 40, -16, 13, -19, 57, + -65, 60, -37, -126, 74, -56, -25, 96, + -115, -39, -79, -47, 73, 71, 125, 22, + 41, 51, -124, -123, -127, 50, 127, -39, + 23, 92, -69, 126, 108, -125, -128, -95, + -88, -86, -85, 47, -72, -45, -100, -127, + 126, -112, 127, 14, 127, 66, -127, 14, + -34, 32, 21, -90, -56, -43, -5, 123, + -85, 26, -13, -94, 15, -23, -110, 59, + -12, 41, 16, 40, -59, -20, -14, 27, + 65, -117, -126, 35, -42, -84, -23, -125, + -90, -118, -128, -127, 32, 126, -127, -66, + -126, -126, -82, -53, -110, -69, -128, -50, + -44, -35, -38, -110, 54, 93, 13, -16, + -71, -128, 124, -118, -13, 125, -84, -56, + 53, -16, -93, -43, -113, 10, 87, 68, + -123, -34, 43, 83, 35, -20, -39, 63, + -54, 122, -45, -114, 59, -120, 2, -35, + -78, 127, 97, 54, -78, -48, 48, 50, + -123, 40, 127, 124, 25, 127, 58, -63, + 94, -63, -46, -58, -37, 126, 96, 121, + -64, 1, 84, 106, 14, 127, 102, -57, + 31, 27, -21, 126, 51, -69, -127, -31, + 16, 20, -121, 57, -32, -50, 6, 41, + 101, -125, 79, -9, 42, 61, -60, 54, + 33, 47, -90, 35, 108, 35, 80, -49, + 50, 11, 77, 28, -9, 9, -34, -13, + 72, -98, -17, -51, -31, -15, 87, -12, + -71, -30, 22, 87, -81, -24, -68, 22, + -36, -69, -42, -123, -41, 127, -59, -19, + -24, 31, -52, 51, 31, 127, -97, 88, + 99, -21, 91, 39, 46, -53, 60, -4, + -87, -20, -27, 47, 4, 47, -40, -41, + 4, 127, -3, 125, 77, 124, 71, 98, + -12, -49, -44, -9, -46, 34, -20, 35, + 42, -36, 0, -55, 58, -12, 33, -6, + -49, 37, 13, 2, 5, 20, -59, 85, + -81, 92, 47, 127, 30, -59, -31, -66, + -3, -124, -112, -7, -23, -94, 126, 74, + 9, -120, 72, 47, 24, 67, -68, -6, + 19, 127, -76, 52, -17, -11, -116, -113, + -73, -35, 14, 75, -32, 17, 20, 117, + -9, 127, 113, 69, -6, -39, -16, 24, + 91, -54, -37, 100, 88, 25, -39, -50, + 73, -90, 67, 88, -39, -58, 118, -17, + 45, -71, -80, -35, 120, -20, -59, -45, + -11, -109, 74, -15, -17, 99, 43, 123, + 1, 13, 43, 19, -15, -1, -109, -46, + -13, -2, -53, 40, -37, -120, 31, -124, + 121, 4, 96, 53, -84, 20, 19, -127, + -6, -27, 58, 60, 9, 46, 127, -8, + -26, 56, -3, 12, -39, 66, 99, -128, + -53, -6, -20, -36, 31, -106, -21, -24, + -64, -1, -78, -37, -20, 10, -38, 22, + -31, 16, -43, -59, -51, -3, -20, 4, + 123, 127, -127, -127, 97, -53, -6, -80, + -128, 73, -37, -12, -14, -126, -18, 21, + 122, -8, 8, -125, -73, -128, -101, -22, + -26, 61, -126, 9, -21, -54, 32, -120, + 17, -93, -76, 17, -74, 59, -45, -49, + -40, 14, -127, -59, 53, -16, -79, -32, + 6, -22, 40, -37, 93, 90, 0, 6, + 92, -3, -10, -25, 33, 52, 10, 45, + 39, -5, 97, 66, -43, 50, 108, -45, + 25, 112, -127, 11, 127, 4, 72, -24, + -21, 127, -12, -82, 52, -88, -2, -56, + -33, -34, 28, -19, 113, -31, -125, 3, + -82, -12, -28, -98, -90, 26, 126, -98, + -46, -74, -89, -103, 73, 99, 41, -38, + -67, 53, -128, 72, -60, -69, -29, -7, + -72, -116, 15, 44, -14, 27, -65, 15, + -63, 74, 41, -82, 16, 110, -96, 95, + -15, -57, 51, 29, -29, 0, -52, 103, + 77, -49, -74, 82, -21, -47, 123, -126, + -57, -103, -114, 124, 4, -51, 59, -51, + 108, 51, 55, 10, 36, -27, -72, -88, + 127, 127, -51, -40, -80, -126, 94, 21, + 4, -35, 104, 111, -31, 61, 29, -4, + -95, 122, -125, 79, -10, 61, -20, -20, + -65, -82, 43, 45, -31, -34, -48, -16, + 36, 37, -18, -25, -15, 51, -53, 4, + 23, 61, -21, -80, 57, -81, -52, -34, + 127, 35, -7, 109, -66, -34, 102, 28, + 127, 43, -20, 110, 39, -53, 86, -42, + 111, -7, -88, -42, -125, 53, 91, -12, + -51, -126, -40, -46, 23, 97, -125, -123, + 48, -7, 50, -41, 23, 118, 52, -1, + 127, 102, -85, -14, 125, 68, -118, -72, + -127, -103, 46, -78, -118, 48, -125, 84, + -120, 82, -100, -128, 47, 36, -73, 13, + -125, -11, 70, -18, 48, -40, -32, 43, + 4, 37, 125, -16, 19, -43, -79, 46, + -39, -4, 13, -46, -71, -15, -127, 12, + 4, -21, 10, 125, -80, 31, -67, 123, + -30, 46, 36, 20, 31, -49, 37, 29, + -8, -50, -43, -104, 2, -66, -33, 39, + 32, -81, -2, 27, -11, 13, -82, 7, + 39, 48, -17, -23, 5, 24, 16, 26, + 44, -4, 8, -32, 58, 1, -60, 10, + 65, -4, -66, -9, -40, 47, 0, -28, + -125, -126, 11, -93, 37, 45, -115, 34, + -75, -22, 14, -51, 64, -4, -84, 25, + -116, 117, -126, -124, 9, 127, -69, 45, + -67, -118, 2, -21, 87, -127, -6, 62, + -116, -54, -57, -29, 100, 88, -34, -27, + -6, -8, -120, 72, 13, 9, -126, 34, + -114, 15, 39, -15, -99, -80, -93, 76, + -62, 103, -106, -111, -6, -37, -101, 35, + -82, 67, 13, 71, -4, -47, -52, 44, + -120, 85, 49, 38, 23, 105, -29, -120, + 7, -127, -7, 125, 33, -123, -52, -7, + -17, -117, 110, 31, 14, -67, -52, -100, + -46, -86, -61, -17, -4, 76, 64, -68, + 96, 64, 78, -4, -47, -9, 112, -26, + -30, 54, -79, 22, -80, 66, 126, 96, + 82, -15, -37, 127, 100, 15, 119, -30, + 80, -126, -40, 67, -71, 108, 48, -27, + 82, -36, 22, -5, 55, -25, 29, -23, + 127, -11, -33, 127, 48, 90, 127, 67, + 127, 86, -64, 123, 23, -120, 97, 2, + 127, -23, 27, -90, 10, -34, 70, -31, + 87, 11, -15, -20, 12, -125, -124, 123, + -78, -76, -126, 121, 7, -50, 95, 35, + -127, 124, 2, 30, -4, -127, -13, 87, + -107, -6, -34, -78, 62, -92, 31, 77, + -44, 19, -20, -122, -27, -6, -87, 61, + -9, -7, 22, 9, 43, -18, -73, 67, + -124, -68, -12, 67, 46, 117, -127, -14, + -16, -127, 0, -107, -8, 36, -57, -9, + -107, -93, -92, 53, -25, 127, 35, 127, + -14, 52, 77, -55, 58, -108, -51, 45, + -17, 37, 2, -65, 121, 21, 14, 5, + 4, -115, 120, 125, -58, 68, 7, 127, + 62, 47, 18, 43, 17, -42, 18, 0, + -5, 7, 17, 0, -75, 55, 42, -42, + 23, 28, -6, 49, 12, 47, 64, 5, + 43, 82, 64, -79, 36, 10, -30, 127, + -99, 55, 74, 91, -83, -57, 70, 20, + -51, 108, -7, -32, 29, -81, 82, -128, + 83, 73, 65, -46, -40, -29, 17, 5, + -30, 42, -121, 33, -44, 11, 116, -55, + 50, 64, 16, -2, 51, -70, 33, -126, + -85, -55, 4, -17, 12, -1, -55, -26, + -63, 14, 23, 48, -6, -64, -11, 27, + -16, -2, -37, -7, -86, -46, -49, 37, + 28, 59, -62, 22, 19, 17, 121, -105, + -17, -30, -79, -105, 93, 33, 126, -43, + -36, -27, -37, -92, 48, -128, -90, -127, + -115, -74, -69, -34, 8, 64, -70, -63, + -28, -11, 6, 99, -98, 5, 46, 49, + 9, 3, 1, -82, -13, -127, 37, 14, + -30, -21, -12, -51, -49, 32, -41, 75, + 40, 80, -46, -73, 26, -6, -2, 49, + -25, 39, 69, -55, -9, 39, 49, -58, + -16, 127, -66, -1, -87, 46, -29, -26, + -63, 24, 13, -64, -66, -111, -70, -15, + 10, 79, -43, -101, -2, -62, -119, 72, + -128, -128, 20, -29, 62, 57, -122, -126, + 86, -28, -127, -114, 3, -94, -128, 2, + 93, -110, 48, 12, 10, 82, -103, 25, + -51, -44, 82, -7, -40, 22, 73, 75, + 3, -20, 58, -127, -55, -11, -128, -82, + -27, -57, -5, 7, 10, -43, 120, -19, + -22, 13, 70, -35, -103, -2, -35, 15, + -74, -65, -37, 5, -62, 60, -128, -24, + -27, -29, -65, 83, -83, 127, -125, 115, + 18, 84, 122, -44, 60, -15, -1, 4, + -123, -123, -26, 1, -89, -5, -80, 20, + 44, 8, -20, 31, -78, 103, -55, -39, + -37, -26, 45, -13, 43, 0, 34, 42, + -32, -10, -34, 10, 54, 6, -100, -18, + -47, -33, 46, -25, -27, -12, -27, 15, + 109, -43, -36, 13, -76, -63, -59, -122, + 127, 127, -61, -14, 49, 67, -16, -125, + 5, -17, -65, 84, 81, -128, -127, -63, + -25, 11, -27, -94, -13, -124, -5, 75, + -29, -84, -29, -104, 44, 33, -62, 40, + -106, 7, -100, 30, -71, -128, 80, 63, + -44, -64, 47, -78, -12, 38, -55, 110, + 9, 61, 30, -76, 16, 15, -26, -26, + -124, 88, 61, 0, -58, 33, -8, 65, + -126, 39, -55, 43, 114, -15, 45, 43, + -113, -128, -109, 60, 76, 81, 99, 93, + 112, 29, 3, -63, 71, -22, -32, -83, + -125, -78, 18, 46, 103, 56, -100, -49, + -37, -20, 33, 65, -128, 5, 56, 49, + 52, 127, -8, -50, 71, -62, -8, -52, + -114, -60, 9, -102, -105, -2, -32, 37, + -69, 79, 32, -95, 42, 100, -30, -1, + -91, 31, 70, -24, -33, -53, -61, 45, + -74, -122, -124, -81, 12, 13, -51, -118, + 10, -125, 92, 76, 63, 57, -37, -124, + 89, -114, -127, -40, -98, -91, -108, 56, + 69, -127, -122, 54, -56, 47, -61, 117, + -1, -38, -47, 38, -3, 127, -11, -43, + -31, 16, -16, -13, 30, 2, 53, 66, + -64, -6, 64, -49, -22, 4, 0, 40, + -45, 69, -89, -63, 60, 88, -101, 53, + -54, 87, 110, 71, -72, 10, -29, 6 +}; + +static const rnn_weight vad_gru_recurrent_weights[1728] = { + -111, 64, -6, 5, 37, 9, -89, -68, + 22, -3, -40, -112, -95, 7, -26, -53, + -22, 23, -60, -9, -46, -10, 32, 37, + -50, 35, 9, -53, 15, -6, -22, -20, + 9, 27, -9, 19, -32, -14, -13, 9, + -50, -9, 19, 73, -59, -38, -9, 34, + 127, -81, -24, -23, 84, -50, 16, -74, + 80, -128, 48, 72, 29, -128, 107, -7, + 127, -91, -20, 8, 21, 32, -14, -16, + -126, -128, -34, -18, 91, 46, -128, -127, + -105, -128, -48, -127, -20, 112, -98, -31, + -79, -127, -64, -12, -114, 13, 70, 97, + 1, -45, 14, -61, 26, 6, -19, 61, + 25, 6, 28, -62, -7, 14, -5, -11, + -8, -110, 81, -59, 100, 56, -12, -45, + 127, 127, -49, 46, 98, -5, 127, -127, + 98, -128, -22, 127, -103, -50, 126, -73, + 127, -88, -33, -34, -79, 74, 122, -44, + 48, -79, 35, -126, -116, -119, -83, -126, + -74, 20, 23, 127, 78, -103, -57, 93, + -37, 60, 96, 127, 125, 9, -85, 114, + 16, -50, -36, 85, 69, 2, 20, -102, + 66, -103, -34, 68, -39, 57, 123, 0, + 20, -41, -28, -67, -47, -29, 83, -34, + -127, -128, 60, -9, -48, -47, -97, 95, + -60, 126, -61, 23, 89, 21, 117, -124, + -112, 82, 121, 124, -77, 91, -87, -29, + -128, -69, -20, -41, -19, -18, -127, -117, + -26, -127, 1, -69, 11, 17, 80, 127, + -124, -119, -122, -92, -41, 111, -2, 1, + 6, -83, -53, -16, -11, 103, 33, -74, + -76, -48, 58, -31, -58, 0, -37, -21, + 76, -127, -64, -3, -71, 61, 17, 108, + 127, 127, -14, 83, 121, -3, 127, -76, + 125, -127, 18, 119, -90, -120, 0, -67, + 126, -110, 7, 55, -26, 85, 126, -55, + 66, -59, 19, 6, 26, -14, 29, -116, + 36, 66, -11, -48, -110, -68, -89, -62, + -39, -80, -39, -74, -74, -5, 16, -65, + 114, 37, -51, 53, 59, -87, -66, 6, + -24, -15, -58, -27, -48, 67, -48, -50, + 22, -75, 4, -108, -14, -25, -45, -14, + -18, 52, 25, -34, 37, -18, -41, 15, + 13, -21, -120, 92, 15, 15, 21, 41, + 52, -72, 83, 81, -3, -69, -2, -29, + 127, 127, 27, -88, 123, -73, 78, 76, + 76, 127, 127, 16, 109, -102, -122, -77, + 126, 43, 24, 32, 41, -70, -127, -55, + 122, 81, 30, 111, 2, -67, 127, 105, + -116, -25, 60, -25, -65, -36, -56, 39, + 41, 92, -32, 98, -87, 48, 69, 107, + 111, 16, -64, 48, 64, 126, 53, -123, + 53, -111, 117, 26, -76, -128, 19, 67, + 119, -110, -107, -41, 79, -50, -19, -14, + -47, 57, -21, 54, 109, 35, 28, -33, + 12, 20, 49, -14, -11, -77, -23, -73, + -11, -32, 17, -79, -49, -17, 124, 43, + 117, 17, -14, 63, 37, 22, -41, 21, + -42, -7, -1, -54, -44, -13, -89, -26, + -26, -76, -22, 33, -98, 6, 84, -6, + 127, 82, -29, 21, 120, 61, 72, -91, + 56, -128, 95, 127, -88, 16, 60, -29, + 127, -80, 26, 34, -68, 23, -10, 58, + 36, -80, -126, -83, -125, -57, 54, 124, + 4, -58, -31, 124, -34, 53, -17, 16, + -19, -47, 32, -21, 41, 3, -47, 41, + -47, -89, -13, -38, -88, 7, -19, -9, + 18, -92, -26, 36, 18, 38, -34, 29, + -88, -13, 50, 103, 38, -18, -44, 61, + -128, 12, 125, -79, -105, -49, -125, 125, + -124, 127, 9, -104, 76, 29, -99, -24, + -127, 43, 51, 11, -28, -36, -45, -57, + -24, 87, 7, -119, 100, -13, -90, -123, + 3, 11, -42, -124, -15, 6, -24, -98, + -121, -20, -46, -108, 21, -36, 22, 57, + 9, 11, -45, -71, 13, -24, -16, 13, + -26, -9, -79, 25, -50, -18, -46, -65, + 59, -102, -25, 5, -88, -30, 48, 74, + 116, 126, -62, 34, 6, 32, 73, -63, + 67, -12, 34, 78, -37, 32, -13, 65, + 97, -40, -19, -8, -12, -3, 127, 67, + 102, 24, 60, -32, -72, 62, 118, -21, + 92, 120, 50, 96, 95, 43, 39, 9, + -74, 83, 50, 68, 52, 3, 8, -23, + 98, 2, -11, 30, -49, 4, 4, 2, + 72, -27, -33, 43, 4, -23, -17, -15, + -13, -77, -69, -54, -75, -6, -3, 87, + -53, -17, 127, -50, 47, -11, 41, 52, + -48, 79, 9, 12, -12, 2, -16, 59, + -36, 10, 21, 25, 36, 87, 21, 39, + 6, 73, 63, -114, 87, 60, -127, -126, + -124, -16, 55, -114, 9, 50, 64, -9, + -79, -128, 63, -61, -8, 45, -4, 12, + 54, 122, 40, 100, 56, 43, 42, 95, + -60, 35, -12, -5, 9, -108, -67, 66, + 127, -127, 113, 37, -64, -12, 3, -2, + 127, 95, -36, 127, 126, 52, 82, -96, + 126, -125, 96, 106, -128, -38, 123, -67, + 125, -115, -78, -35, 6, -28, 30, -6, + -126, 11, 116, -8, 103, -1, -24, -125, + 13, -34, -48, -91, -128, -34, -3, 5, + -125, 30, -127, -8, -5, 124, 30, 29, + -42, 4, 4, -43, 20, -78, -2, -3, + -10, -17, -15, -76, -31, -19, 86, 7, + -3, -110, -59, -118, -114, -115, 5, -40, + 81, -8, 53, 63, 127, -63, 127, -127, + 16, 52, -35, 17, 6, 1, -38, -10, + 127, 10, 82, 78, -10, 56, 111, -64, + 117, 26, -62, 42, -85, -5, 58, 118, + 55, -47, 47, 112, -10, 76, -77, 82, + 102, 38, 127, 18, 67, -57, 69, 70, + -27, 40, -6, -14, 9, -46, 16, 28, + 50, 24, 1, 27, -16, 6, -44, 27, + -9, -41, -21, 45, 3, 45, -41, -67, + -126, -20, 47, -12, -31, -63, -9, -48, + -61, 70, -56, -71, 27, -64, 2, 27, + -127, 124, -53, 29, -8, 123, -43, -47, + 125, 126, -28, -11, 64, -9, 126, -25, + 127, 127, 127, 89, 88, -109, 11, -3, + 127, 110, -54, 36, -128, -48, 23, 25, + 108, 15, -122, -51, -76, -50, -50, -8, + -32, -37, -20, -15, 0, -42, -26, -16, + -65, 16, 24, -63, -54, -43, -107, -14, + 13, 120, -32, 96, 84, 127, 60, -61, + 19, 28, 61, 44, -90, 49, 11, 37, + 35, -35, -87, -34, 65, 127, 95, -32, + -118, -107, 125, -123, -93, -2, -122, -125, + -6, -121, -40, -82, -28, 124, 118, 127, + -128, -104, -125, 95, 127, 127, 115, 88, + -70, 98, 5, 88, -2, 127, 53, -56, + -40, -10, 56, 23, 68, 26, 41, -11, + 3, 21, 79, 10, -4, 70, 61, 126, + -127, -25, 16, 68, -80, -94, -17, 127, + -42, 111, -60, 112, 70, -17, 126, 5, + -128, 36, 115, -3, -99, -34, -94, 14, + 90, 37, -126, 49, 103, -70, 3, 16, + -106, 44, -28, -69, -82, 9, 127, 27, + 73, -96, -68, -127, -58, -128, -41, -90, + -30, 78, -80, -78, -13, -81, -77, -35, + -35, -31, 1, 67, -85, 38, 75, -47, + 90, -96, -46, -16, -53, 0, -34, -13, + 127, 46, -56, 90, -19, -2, 80, 51, + 28, -61, 69, -30, 17, 95, -27, 10, + 31, -57, -94, 36, 10, -51, 111, 75, + -23, 33, 75, 67, 119, 15, 51, 2, + -20, -16, 8, 28, -46, -20, -50, -51, + -15, 34, 3, 48, 2, -1, 30, -67, + 4, -78, 15, -50, 68, -79, -23, 28, + -20, 32, 4, -49, 6, 46, 52, -21, + 9, -8, 56, -14, 28, 11, 35, -14, + -49, -66, 50, -72, -30, -19, -87, -4, + 7, 50, -33, -53, -111, 14, 33, 58, + 7, -47, -10, -36, -69, -42, 5, -59, + 126, 114, 46, 126, -41, -61, 88, 122, + 71, 126, -6, 123, 37, 9, 20, 10, + 13, 117, 63, 126, 44, -74, -40, 10, + 84, 59, -26, 69, -18, -27, 36, 0, + 18, -13, 4, 60, -25, 17, -20, 29, + -41, 34, -11, 49, -100, -39, -1, 61, + -127, -68, 7, -127, -127, 20, -21, 126, + -57, 127, -99, -82, 33, 47, 54, -38, + -128, 24, -17, -81, -39, -87, -120, -4, + 35, -32, -123, -63, -126, -31, 111, -74, + 90, 78, 28, 46, 127, -53, -104, -7, + -18, -20, 10, 19, 119, -3, -54, 13, + -36, -116, -108, 48, -41, 75, 46, 13, + 127, -60, -93, 32, 23, 103, -5, 23, + -39, 25, -20, -9, -39, 52, -7, 115, + -128, -79, 123, -51, -53, 30, -40, 45, + -116, 127, -34, -127, 126, 51, 48, -12, + -128, 127, 86, 59, -59, 41, -35, 11, + 117, 16, -96, 87, -126, -17, 124, 33, + 85, 106, -49, 80, 34, 98, -78, -33, + 50, 66, 108, -99, -3, -125, -42, 56, + -56, -125, -44, -21, 69, -23, 57, 126, + 113, -13, -23, -35, 23, 102, 70, 0, + 3, -72, 37, -61, 32, -57, -124, 76, + -128, -128, 124, -127, -90, -103, -127, 55, + -125, 124, -7, -79, 119, 24, -72, 29, + -96, 127, 87, 41, -26, 65, -43, 7, + 127, -12, 15, 117, 102, -3, 64, 111, + 22, 47, -11, 114, -100, -112, 43, -127, + 127, 112, 25, -30, -116, -97, -14, -122, + 89, 12, -41, -64, 99, -67, -122, -69, + -20, 53, -58, 72, -82, -90, 7, -23, + 108, 21, 49, -126, -49, -119, -98, 49, + 38, 127, -121, 71, 66, 75, 84, -57, + 127, -126, 26, 29, -50, -57, -2, 21, + -5, -86, -83, 19, 127, 38, 15, -75, + 127, 76, -111, 52, -8, -124, 119, 84, + 13, -39, -122, 40, -117, -117, -120, -127, + 127, 124, 10, -128, -78, 28, -113, -77, + -45, -126, -100, -101, 55, -86, -13, -106, + 18, -60, -127, -3, -126, 112, -9, 18, + -50, -92, -126, -36, 42, -11, -124, 33, + -126, -114, 3, -118, -114, -79, -127, 122, + -120, 101, -108, -105, 68, 112, -126, 20, + -127, 75, 70, 127, -6, 39, -83, -59, + -73, 85, -107, -30, 117, 35, -6, 66, + -60, 85, 30, -104, -70, -79, 38, -126, + -59, -100, 12, -94, -113, -97, -97, -15, + 74, -55, 3, 65, 94, -35, 37, 26, + -8, -34, -14, -20, 10, -21, -93, -35, + 12, -123, 6, -29, -63, 12, -35, 47, + 127, 16, 14, 24, 117, 127, 122, -126, + 50, -128, 70, 72, -67, -17, 3, 43, + 127, -85, -126, 47, 53, 34, 84, -77, + -41, 6, 6, 32, 21, -23, -16, -93, + -13, -102, 114, -12, 114, -37, 21, 21, + -126, -74, 5, 127, 38, 127, -127, 126, + 107, 127, 6, 118, 46, 19, 55, -84, + 33, -33, 15, -63, 61, -121, 31, -54, + 81, -81, -101, -4, -63, -47, 82, 70, + 123, 125, -14, 123, 36, 60, 127, -128, + 94, -45, 127, 94, -128, 47, 126, -97, + 97, -28, -97, -127, 112, -75, 72, 51 +}; + +static const rnn_weight vad_gru_bias[72] = { + -128, -128, 106, -108, -128, -56, -127, -124, + -123, -127, -78, -116, -17, 118, 71, 127, + -128, -128, -101, 127, 54, 127, -33, 116, + -85, -30, 123, 85, -39, 69, 19, -25, + -28, 18, 32, -4, 41, 20, 36, 49, + -20, 109, 95, 124, 56, 80, 90, 5, + -109, -28, 5, -35, -75, -41, -44, 58, + -24, 14, -21, -20, -1, -28, 36, 2, + -59, 22, 4, -17, -23, -51, -64, 68 +}; + +static const GRULayer vad_gru = { + vad_gru_bias, + vad_gru_weights, + vad_gru_recurrent_weights, + 24, 24, ACTIVATION_TANH +}; + +static const rnn_weight noise_gru_weights[12960] = { + -94, 20, -36, 46, 32, 38, 10, -14, + -6, 56, -60, 12, -64, 15, 33, -59, + -56, -25, -29, 42, -41, 35, -37, 29, + -10, 121, -44, 15, 0, 19, -6, 83, + 30, -28, 49, 19, 21, 36, -49, 19, + -41, -53, -21, -113, -128, -53, -11, -17, + 33, -35, -2, -39, -22, 65, 0, 55, + -1, -40, -75, 38, 23, 40, -49, -26, + 25, -49, -56, 13, 35, 34, 1, -82, + -32, 21, 35, -19, -12, 14, 1, 58, + -11, -82, -76, 77, -23, 36, -28, -17, + 90, -13, -36, -35, -48, -96, 42, -4, + 85, 82, 13, -17, -35, -58, -38, 92, + -44, 73, 33, -9, 24, -20, 72, -27, + -48, -6, -24, -7, -54, 39, -10, -33, + -3, -44, -6, -6, 96, 29, 65, -31, + -89, 11, -30, -10, 13, -17, 55, 51, + -78, 46, -12, -79, 126, 15, 7, -31, + -22, 46, 22, -20, 1, 0, 25, 52, + -10, -73, -2, 60, 77, -5, -17, -44, + -10, 34, -70, -6, 19, 28, -16, 56, + -29, -1, 13, -59, -22, -18, 2, 5, + -14, -27, 14, 40, -6, -15, 46, 40, + -21, 25, 12, -35, -39, 0, 10, 5, + 35, 24, 14, -8, -3, 47, 47, -11, + -40, -52, 124, -48, 38, 16, 10, 25, + 39, 2, 12, 10, -17, 55, 9, 13, + -20, -28, 8, -27, 22, 13, 44, 49, + -53, 2, -76, 42, -33, -13, -45, -34, + 40, 20, -71, 66, 21, -9, 16, -21, + 18, -23, -17, -26, -22, -46, 3, 12, + 51, -49, 17, -37, 42, 12, 41, -19, + -16, 11, -25, -3, -34, -23, -2, 29, + -27, -45, 55, -9, -24, -31, -31, 73, + 25, 3, 1, -62, -15, 36, -34, 30, + 27, -59, -38, 0, 65, 0, 111, -32, + 46, 8, 18, 27, 78, 47, -5, 20, + 12, -58, -27, 54, 18, -20, 49, 16, + -49, -47, -95, 10, 26, -42, -20, -16, + 31, -47, 24, -123, 4, -28, 21, 33, + 67, 10, -19, -45, -75, -32, -7, -53, + -14, -2, -25, -16, 6, 18, 21, -58, + 33, -9, 46, 39, 6, -23, -6, 91, + -5, 7, 17, -31, 67, -46, -33, 32, + 22, -69, -82, 1, 8, 6, -20, -13, + -55, -54, 8, -71, -23, -45, 40, -23, + -12, -9, 64, -8, -25, 35, -43, 8, + 19, -19, -51, -30, -17, 40, -7, 57, + -30, -30, -20, -20, 18, 14, -47, -29, + -32, -28, 26, -37, -48, -20, -35, 7, + 7, -6, -44, 8, -23, -4, -46, 21, + -86, -36, -31, -36, 68, -49, -24, 6, + -3, -65, 24, 52, -20, -6, -30, 32, + -3, -63, -41, -27, 66, -33, -21, 19, + -93, -1, -56, -37, -59, 33, -25, -34, + -4, 37, -114, 14, -4, 53, -31, 66, + 83, 5, -87, -12, -65, -48, 35, -30, + -11, 37, -53, 44, 14, -46, 63, 58, + 32, 20, -43, -16, 117, -5, -123, 11, + -47, 119, 31, -90, -51, -30, 14, -43, + 39, -30, 48, -47, -9, 22, -40, 7, + 71, -22, -36, 95, -120, 27, -7, -88, + 16, -47, -26, 47, 11, 124, -63, -31, + -47, 10, -110, -4, -36, 26, 77, -17, + -34, -114, 20, 35, -91, -54, 26, -13, + -12, 35, -2, -25, -38, -51, 0, -35, + 68, -38, 7, -26, 13, -65, 46, 79, + 14, -5, -127, 32, 25, 18, 115, 1, + -62, 69, 27, 47, 13, 79, -52, 34, + 97, -1, 46, 8, 62, -5, -9, -3, + -42, 40, 29, 14, -77, 57, -3, 25, + 25, -42, -13, -59, 78, -25, -54, -74, + 44, 58, 48, -77, -4, 6, 72, 41, + 49, 17, 85, -43, 44, 18, 80, 85, + -28, 51, 114, -29, 41, 87, 43, 124, + 45, -54, 59, 20, 59, -23, -12, -22, + 10, 3, -44, 39, 48, 82, 11, 30, + 65, -16, -6, 71, 9, 2, 0, -29, + -1, -17, -48, -63, -49, -48, 49, 52, + 67, -77, -37, -53, 44, 7, 17, -38, + 28, -99, 82, 27, 66, -29, 43, 60, + 13, -14, 125, 30, 5, -17, -115, 24, + -8, -11, -77, -27, 11, -89, 80, 32, + -124, -3, 63, -3, -73, 32, 19, -57, + -64, 52, -10, -14, -18, 2, 15, -75, + -33, 10, -2, 52, -10, -18, -20, -13, + 52, 51, 9, 20, 16, 20, 7, -74, + 36, 9, -109, -9, -42, -18, -30, 50, + 6, -8, -78, -5, -21, -72, 20, -18, + -21, -46, 68, 112, -17, 18, -58, 26, + 57, 28, 51, -43, 2, 0, -5, -3, + 18, -21, 34, -45, 40, 41, -46, -7, + 42, 4, 31, 12, -21, -24, 33, -49, + -50, -23, 20, -34, -55, 13, -20, -34, + -27, 33, 37, 49, 46, -7, 75, 14, + 2, 8, -25, 2, 48, 27, 29, 28, + 48, -71, -3, -61, -1, 43, 32, 11, + -32, -24, 18, -54, -12, -31, 19, -43, + -9, 4, -30, 12, 11, -40, -15, 12, + -100, 24, -73, -95, -73, 38, -16, 1, + 40, -18, -3, -31, -47, 49, 29, -69, + 4, -20, -29, -26, 27, 18, -60, -15, + 34, 36, 15, -13, -22, 18, -4, 67, + 15, -40, -6, -8, -2, 8, 8, -11, + 20, 55, -5, 1, -49, -46, 23, 5, + -44, -6, -28, -46, -29, -20, 34, 13, + -27, 0, 31, -21, -16, -8, -24, -16, + -13, 6, -37, 0, 22, -5, 3, 9, + 23, -11, -16, 56, -39, 65, -14, 0, + -79, -71, 14, 14, 0, -1, -32, 40, + 28, 30, 10, -39, 84, 15, -35, 82, + -33, 17, 43, 122, -11, 116, -62, -7, + 61, 10, 54, 34, 5, 59, 78, -80, + 35, 28, -11, 62, -97, 71, 34, 72, + 31, 13, 41, -30, -29, 62, -25, 15, + 13, 102, -84, -56, 38, 2, 33, 31, + -10, 62, 23, -12, -76, 41, 18, -53, + 67, 34, -15, -54, 24, 14, -34, 112, + 16, -61, 10, -37, -37, 3, -30, -4, + 13, 47, 81, 78, 61, -20, 33, -17, + -11, -30, 27, 32, 51, 5, -27, -10, + 12, 23, -23, -19, 41, 1, -11, -46, + 117, -4, -53, -24, 4, 41, -75, -7, + 6, 12, -3, 6, 15, -71, -24, -4, + -41, 25, -85, -36, 17, -95, 104, -56, + 16, -11, 28, -14, 22, -39, 33, -9, + 58, -37, 27, -52, -98, 59, 65, -48, + 56, 26, 12, 52, -33, -27, 43, -24, + -18, 25, -19, -48, -32, 44, -1, 36, + -37, -67, -45, -17, 51, 9, 12, 11, + 109, 0, 16, -22, -101, -27, -41, 54, + -26, -37, 50, -74, 45, -88, 57, -38, + 41, 63, -13, -11, 11, 18, -5, 30, + 12, 59, 4, 50, -31, 21, 6, -70, + -52, -44, -125, -14, -54, -3, -38, -66, + -27, 16, 27, 11, 29, 26, 52, -121, + -36, 5, 41, 27, -7, -35, -40, -41, + 14, 63, -14, -2, -27, 38, -55, 5, + 9, -48, -19, -13, 63, 9, -50, -22, + -25, 37, -5, -37, 10, 21, -23, -55, + 77, 23, -23, 16, -24, -8, 2, 22, + -72, 25, 15, 33, -34, -37, -26, -3, + 46, 18, 9, 25, 34, 37, 19, -12, + -44, 35, -20, 17, -2, -35, 48, 34, + 14, -11, -1, 13, -41, -26, -28, 71, + 38, -16, 45, -106, -38, 17, -13, 4, + -38, 11, 37, -47, -18, -16, 12, -30, + 26, -10, -41, -76, -4, -55, -25, -41, + 15, -24, 10, -46, 8, 1, -78, -79, + 39, 23, -73, 12, -53, 46, 20, -38, + -78, 16, -31, 19, -21, -32, 46, -44, + -40, 79, 65, 22, -48, -8, -47, -34, + 8, 28, -49, -8, -61, -21, 8, 23, + -42, -12, -36, -48, 28, -32, 33, -39, + -3, 20, 33, -1, -35, 57, -67, 5, + 0, 17, 28, 2, 15, 45, 41, 28, + -7, 33, 14, -15, 54, -24, 48, -23, + -46, 36, -18, 55, -2, 13, 26, 26, + -3, 12, -1, 51, -44, -23, 38, -14, + 16, 17, 40, 9, 33, 7, 10, -47, + 2, -19, 117, 47, 72, 10, -30, -9, + 21, 33, 13, -31, -3, 23, -13, 14, + -25, -58, -62, 10, -24, 10, -42, -12, + 22, 7, 11, 20, -12, -15, 5, 35, + -66, -7, 45, 49, -5, -11, -60, 21, + -90, 36, -58, -5, -1, -30, -54, -47, + -90, -27, -59, -9, -5, -39, 37, -60, + 53, 43, 33, 37, -13, -65, -80, -45, + 0, -3, 30, -16, -56, -4, 0, -13, + 32, -24, 6, 29, 2, 36, -53, -63, + -14, 34, -78, -9, -61, 2, -34, -77, + -28, -12, -72, -55, 114, 30, 10, -15, + -12, 30, 31, 7, 46, -50, -1, 16, + 81, 40, -39, 67, 21, 118, 39, -53, + -47, -23, -28, -51, 12, -58, -35, -92, + 24, 57, 29, 16, -2, -61, 17, 7, + 19, 52, -31, 18, -27, 54, 43, 7, + 6, 52, -16, 30, -26, 65, -90, 67, + 21, 28, 85, 12, 27, 12, 12, -41, + 17, 54, -14, -1, -13, -20, 55, 30, + -38, -12, -71, 93, -31, -43, -20, 70, + 30, 42, -14, -15, -65, -12, 9, 11, + 34, 54, 102, 12, 22, -58, -54, 18, + -96, 72, -27, -51, 38, -29, -9, -32, + -22, -10, -39, -18, -36, -44, -40, -107, + -18, 7, -26, -59, -31, 85, -10, -54, + -2, -53, -2, 67, -40, 40, 54, -113, + 27, 124, -74, -48, -32, 4, -68, 73, + 13, -37, -4, 10, 6, 60, -100, -74, + 22, -5, -62, 4, 46, 13, 54, 24, + 17, -2, 33, -16, -22, 50, -48, 46, + 12, 9, -66, -14, -2, 23, 24, 106, + -16, 50, 4, -14, 0, 35, -30, -73, + 20, 37, 90, 5, 1, 32, -19, -33, + 1, 22, -31, 28, 13, 69, -48, 20, + 56, 66, 21, -33, 2, 46, -1, -72, + 1, 9, 94, -11, -22, 6, 4, -39, + -4, -3, -45, 19, -54, -47, -7, 37, + 69, 3, 43, -58, -14, 47, -34, 86, + 34, -48, 23, -53, 36, 6, 0, 32, + -63, -13, 43, -23, -46, 48, -66, -15, + 8, -88, -33, -81, 2, -38, 75, 61, + -2, 20, -33, 49, -75, 9, -17, 95, + -83, -31, -25, 62, -38, -53, 37, -19, + 100, 9, -78, -91, -15, -86, -3, 62, + -33, 22, 127, 11, -27, 3, -7, -96, + -1, -43, -50, 28, 51, -32, -26, 124, + -38, 53, 18, 1, -2, -62, 9, 5, + -6, 43, -1, 40, 54, -38, -14, 16, + -47, -35, 5, -63, -41, -10, -23, -37, + 4, -67, -17, 42, 18, -38, 8, 72, + 27, -27, -16, 10, -66, -39, 3, 24, + -36, -87, 11, 12, 4, 3, -52, 36, + -90, -25, -51, 20, -17, 16, -16, 44, + 12, -12, 14, 31, -59, 31, 30, 24, + 2, 72, -53, -38, -25, 25, 30, 12, + 4, 13, -1, -22, -34, -48, -62, -54, + -64, -17, 8, 85, 54, 58, 45, 30, + 80, 88, 6, -31, -4, -25, 50, -10, + -11, -22, 55, 72, -18, 28, -10, 15, + 63, 10, 65, 23, 44, 48, -13, 91, + 41, 6, -23, -17, 33, -38, 18, 17, + 19, 20, -37, -5, 24, 36, -21, 29, + 32, -47, -12, -11, -47, 23, -42, -11, + -7, -33, -16, 22, 11, -56, -29, 44, + 10, 34, 17, 64, -98, -15, 19, -17, + 28, -30, -37, 22, -48, -38, 0, -11, + -45, -80, 10, 9, 1, -9, 39, -44, + 24, 22, -25, 24, -50, -17, -1, 12, + 52, 4, -10, 25, 31, -48, -13, 25, + 26, -30, -34, 2, 33, -21, 33, 26, + -40, 18, -15, -23, 0, 7, 29, 8, + 54, 6, -48, -24, -4, 31, -13, -15, + -9, -15, 20, -16, -7, -61, -41, -6, + 45, 82, -107, -2, -22, -29, 52, -55, + 36, -9, 15, 98, 18, -45, 42, -34, + 14, -73, -25, -12, 70, 91, -22, -37, + -87, -32, 67, 16, 19, 24, 31, -54, + 6, -16, -20, -45, -12, 2, -10, 78, + -48, 31, -29, -9, -104, -61, 60, -27, + -7, -53, 35, -2, 32, 122, 44, 27, + 113, -19, 83, -10, 39, -4, -33, -47, + -18, -27, -42, -1, 51, 25, -4, -32, + 23, -13, 32, -14, -62, 37, -7, 38, + 2, -4, -2, 37, 53, 41, 9, -13, + 69, -62, 57, -45, 46, -65, 17, 0, + 59, -17, -48, -29, 46, -21, -43, -34, + 10, -54, 56, 1, 14, -55, 23, -38, + 27, 41, -44, -7, 66, 21, -13, 45, + -10, 3, 30, -5, 41, -43, -5, -30, + -25, -86, -26, 19, -16, 23, 50, -28, + -53, -22, -33, -14, 40, 33, -21, -51, + -27, -20, 40, 121, 34, -61, 31, -1, + -22, 127, 3, -85, -56, 33, -62, -21, + 6, 12, 74, 65, 75, -12, 8, 11, + -35, 48, 33, 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31, -8, + 19, 24, 69, 27, -41, 40, 12, 13, + -64, 36, 12, -34, 53, -10, -20, -20, + -20, 60, -47, -31, 17, 4, -33, 0, + 15, 39, -6, -5, 2, -77, -15, -72 +}; + +static const rnn_weight noise_gru_recurrent_weights[6912] = { + 12, 15, -79, 27, 0, -24, -53, 4, + 65, -30, 44, -28, 113, 53, 7, -2, + 12, -22, 15, -35, -66, 91, 14, -14, + 22, -13, 95, -4, 45, -3, 16, -47, + 61, 17, -38, 124, 11, -38, -78, -59, + -26, -29, -38, -25, 2, 16, 49, 123, + -23, -77, 53, -15, 0, -35, -15, 48, + 126, -7, -2, -23, -25, -31, -2, 53, + -43, -37, 12, -5, 39, -28, -6, -8, + 97, 36, -9, 30, -52, -11, -18, 58, + 55, 44, -27, 10, 110, -51, -64, -7, + 30, -53, 12, -23, 25, -35, 31, 22, + -12, -19, -43, 19, -16, -1, 14, 34, + 16, 26, -19, -29, 6, -6, -50, 18, + -57, 54, 13, 35, -21, -27, -4, 45, + 70, 30, 15, 31, 65, -10, 39, 55, + 11, 51, 56, 42, -97, 60, 19, -2, + -5, -23, -29, 10, 77, -8, -76, -113, + -14, 40, -41, 15, 16, -37, 24, -35, + 45, -26, -106, -39, 34, 51, 48, 103, + -27, -30, 80, 117, -117, -17, 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-80, -11 +}; + +static const rnn_weight noise_gru_bias[144] = { + 22, 57, 36, -20, 0, -19, 40, -68, + 49, 12, 8, 36, 47, 36, 18, -13, + -27, 44, 13, 77, 9, 35, 33, -10, + 19, 29, 45, 23, 44, 32, 12, 32, + 42, 66, -44, 68, 63, 5, 42, 28, + 10, -2, 9, -26, 25, -35, -73, 56, + 13, 6, -7, 20, -5, 0, 4, 13, + 49, -31, -4, 18, -7, 7, -13, -9, + -10, 9, -10, -2, 9, -18, -12, 25, + 14, 5, 14, 10, -8, 27, 14, 31, + -3, 0, -15, 15, -23, -12, 6, -37, + 20, 0, -13, -13, -8, 7, 10, 4, + -18, 23, 2, -13, 1, -5, 4, 10, + 4, -22, 47, 20, 15, -39, -9, -14, + -28, -6, -35, -19, 4, -13, -2, 13, + -18, -35, -20, -16, -20, -13, -13, 39, + 26, 0, 4, -46, -47, 3, 0, -26, + 12, -13, -1, -8, 2, 23, 11, -23 +}; + +static const GRULayer noise_gru = { + noise_gru_bias, + noise_gru_weights, + noise_gru_recurrent_weights, + 90, 48, ACTIVATION_RELU +}; + +static const rnn_weight denoise_gru_weights[32832] = { + -38, 5, 22, 0, -22, 54, -17, 20, + 20, 56, -13, 41, 30, -37, 21, 2, + -20, -27, -31, 17, 3, -12, -23, 25, 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23, 100, -10, 28, -2, 46, 18, + -73, 68, 61, -60, -52, 38, -76, 70, + -69, -73, 21, -35, -9, 57, 41, 127, + -119, 11, -36, -10, 16, -51, -121, -13, + 76, -126, -14, 20, 1, -34, -44, 42, + 14, -110, -33, -123, -3, -77, -19, 64, + -21, 53, -12, 19, -53, 127, -14, 86, + -24, 121, 63, 34, -19, 56, 120, 49, + 6, 126, -106, 6, 96, -25, -120, 34, + 38, -45, 25, -16, 45, -74, 70, -59, + -54, -44, -3, -10, -50, -22, -62, 61, + -39, -125, 79, -120, -30, 37, -1, 52, + 33, -113, 20, -13, 127, -30, -13, 14, + 125, 59, -124, -27, -3, -56, -25, -78, + 126, 84, -86, 77, -41, -17, -22, 37, + -5, 46, 37, -128, 54, -49, -35, -54, + -43, 24, 40, 10, -77, 9, 12, -5, + -6, -79, 1, -31, -33, -85, -32, 54, + -53, -5, 43, 69, 74, -34, -109, 3, + -10, -96, -80, -100, 39, 45, -64, 41, + -11, 13, -33, -47, 47, -56, 1, 14, + -1, -9, 123, 5, 14, 81, 42, -49, + -20, 5, -51, 18, -16, -35, -23, 59, + -127, -29, -127, -11, 125, 63, -18, 18, + 13, 48, 12, 85, -27, 28, -99, 28, + 8, -69, -29, -7, 8, 101, -60, -62, + 109, 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-32, -1, 5, -10, -22, -11, + -26, -8, -30, 4, -36, -27, 8, 0, + -24, -8, 17, 16, -42, 16, -4, 18, + -30, 13, 1, 62, 8, 22, 50, 26, + -36, 4, -32, -17, 5, -64, -10, -34, + -21, 10, -94, -15, 8, 11, -39, 5, + 29, 0, 1, 40, -2, 11, -14, -36, + -23, -23, -28, -44, -3, -13, 73, 11, + -9, 28, 50, -9, 0, -45, 15, -17, + -48, -10, 13, -74, 11, -11, 37, -8, + 5, -28, -7, -58, 25, -33, 49, -7, + -53, -9, -4, -8, 0, 49, -19, 106, + -11, -16, 25, -26, -3, 49, 0, -66, + -26, -24, 27, -3, -23, 12, 6, 2, + 13, 27, -40, 32, -10, 26, 27, 27, + 66, 6, -22, 7, 0, -44, -1, -22, + 2, -1, -15, -33, -8, -4, -8, -2, + -33, -89, 21, 4, 14, -40, 1, 30, + -25, -16, 10, -8, 10, 14, 8, -16, + -39, -31, -61, -9, -36, 22, -15, 16, + -16, -69, -32, -15, -24, -13, -16, 29, + 4, 4, -26, -51, -16, 0, -14, 1, + 3, 4, 12, -33, -11, -13, 5, 29, + 17, -16, 3, 88, 34, -42, -18, -21, + 50, 36, 16, 43, -51, -40, 64, 19, + -35, 44, -123, -14, -59, -46, 30, 50, + 21, -64, -47, -88, 126, 15, -33, -8, + -5, 50, 4, -68, 14, -29, -36, -46, + 6, -36, 16, 3, -16, -11, 25, -6, + -8, -14, -2, -7, -12, -84, -50, -14, + 35, 22, -9, 16, 25, -1, -1, -9, + -15, -59, 10, 13, -20, -38, 39, 13, + 26, 20, -6, 71, 0, -1, 12, 0, + -60, -2, 5, -26, 28, -16, 17, -6, + -16, 28, -5, -21, 15, 19, 54, 25, + 11, 21, 4, -5, 18, -35, -16, 68, + -10, 14, 41, -7, 23, -3, -33, 1, + -14, -1, 36, -1, -7, 38, 15, 15, + -27, -70, -23, 34, 13, 1, -12, 26, + 35, -31, -13, -26, 33, -26, 5, 8, + 55, -14, 0, -25, 14, 0, -12, 10, + -4, -7, 45, -3, -28, 32, 1, -14, + -96, 8, 8, -9, 12, 8, -23, 1, + 10, 66, -13, 9, -12, 20, 11, 22 +}; + +static const rnn_weight denoise_gru_recurrent_weights[27648] = { + -62, 32, -27, 24, -55, 82, -19, 59, + 127, -36, -23, 23, 35, 12, -11, 15, + -21, 15, -65, -39, -23, -9, 48, 28, + 33, -120, -7, 5, 41, -126, 99, 37, + -120, -33, -34, -31, 27, -66, -124, -20, + -78, -68, -36, 9, 50, 41, -48, 33, + -53, 48, 31, -21, -24, -34, -115, 23, + 26, -13, -125, 39, 66, -43, 82, -48, + 12, -81, -28, -117, 68, -42, 46, -23, + 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73, -111, 21, -3, + 64, 26, -28, -55, -95, 15, -20, -117, + -8, -88, -73, -93, 20, 70, -14, -36, + 70, 16, -57, 24, -78, 26, 8, 38, + 3, 32, -67, 52, -54, -56, 127, -30, + 5, 13, -91, 94, -7, -11, 105, -78, + 52, 46, -42, 33, -46, -104, -7, 18, + 19, -4, 39, 15, -57, 11, 70, 29, + 73, -10, 2, 127, 84, -16, -50, -82, + 15, -36, -126, -21, 33, -76, -56, 82, + -22, -10, 6, 48, -12, -33, 7, -55, + -8, 34, 81, -52, 98, 55, 6, -91, + 98, 33, 0, -12, -11, -5, -74, -29, + 30, -7, -43, -5, -68, 20, 51, 33, + 44, -37, 41, -59, -99, 73, 53, 30, + 16, 16, -34, -3, -74, -123, -78, 77, + -127, -32, 48, -59, 14, -51, 17, -37, + 39, 40, 124, -19, 0, 3, -79, -13, + 21, -1, 33, -125, -9, 20, -24, 26, + 115, -33, -37, 12, 14, -5, -73, 57, + 1, -47, 32, 39, -62, 44, -104, 42, + 23, 15, -16, -1, 8, 93, 36, -34, + 2, -6, 24, 127, -21, -11, 1, 32, + -12, 25, -94, -25, -32, 36, 16, 29, + -15, 37, 10, -53, -10, -26, 25, -26, + -55, -95, -51, 110, -19, -28, 9, -57, + 25, 46, 14, 64, -40, 35, -25, 117, + -1, -127, -61, 66, -48, -51, -1, 44, + 1, -61, -17, -3, 37, -100, 21, 51, + 72, 12, -23, -30, -25, -125, 22, 8 +}; + +static const rnn_weight denoise_gru_bias[288] = { + 59, -2, 0, 31, 15, 33, 41, 43, + 22, -5, 34, 32, 10, -6, 31, -11, + 11, 13, 4, 22, -3, 60, 1, 42, + 14, 31, -28, -7, 11, 49, 22, -14, + -20, 62, -8, 44, 50, 4, 29, 1, + 1, -14, 20, 43, 19, 32, 30, 61, + 19, 3, 22, 16, -5, -5, -23, -6, + 17, 95, 82, 14, 4, -17, 13, 41, + 51, -51, 51, 25, 25, 27, 48, -18, + 16, 33, 55, 9, 17, -22, 40, 32, + -21, 1, 8, 22, -18, 51, -11, -1, + 31, -5, -6, 19, -4, 28, 35, -22, + 10, 15, 20, -15, -4, -4, -13, -47, + 27, 34, -22, 50, -7, -12, -10, 10, + 5, -6, -6, 13, 32, -9, -16, 23, + -4, 33, -5, -17, 2, -13, 25, 47, + -28, -5, -18, 1, 33, -11, -2, -21, + 37, -11, 14, -8, 19, 14, -15, -12, + 51, 8, 36, 2, 18, -17, 17, -15, + 1, 1, -54, 32, 35, 3, -9, -23, + -9, -4, -6, 8, -3, -31, -10, 44, + -48, -7, 12, 13, -13, -31, 3, 30, + 46, 11, 2, 1, 6, -4, -16, 14, + 0, 12, 18, 5, 14, 3, -40, -38, + 11, 0, -6, -6, 19, -2, 3, -82, + -4, -44, 33, 17, -4, -15, 3, 6, + -3, -8, 8, 8, -1, 45, -34, -16, + -2, 36, -4, 9, -1, -11, 4, 5, + 13, -10, 5, 19, 2, 1, 22, 8, + 13, 27, 2, 11, -23, -1, 4, 2, + -35, -7, 35, 32, 16, 13, 15, 12, + -1, -22, 27, -33, -6, -11, 24, -4, + 10, -36, 1, 40, -1, 8, 51, -36, + 11, -19, 20, 28, -23, -12, 0, -20, + -1, -18, 18, -14, 9, 13, 3, 17, + -33, 7, -14, -37, 6, -11, 13, -10 +}; + +static const GRULayer denoise_gru = { + denoise_gru_bias, + denoise_gru_weights, + denoise_gru_recurrent_weights, + 114, 96, ACTIVATION_TANH +}; + +static const rnn_weight denoise_output_weights[2112] = { + 49, 63, 47, 40, 25, 26, 23, 8, + 2, 11, 17, 29, 19, 6, -7, 3, + 19, 28, 28, 33, 13, 41, 5, -23, + -33, -45, -36, -24, -25, -20, -18, -19, + -10, -18, -18, -24, -23, -21, -22, -16, + -7, -18, 0, 7, 3, 8, -3, -8, + -12, -16, -16, -10, -9, -14, -19, -13, + -18, -18, -12, -20, -34, -69, -63, -42, + -43, -67, 11, 12, 20, 14, 16, 28, + 29, 25, 26, 29, 28, 36, 35, 31, + 28, 29, 14, -7, -46, -72, -70, -51, + -62, -89, -59, -39, -7, 9, 9, 0, + -6, 10, 12, 14, 16, 27, 47, 45, + 43, 44, 15, 11, 11, 35, 63, 64, + 83, 62, 53, 56, 40, 41, 54, 64, + 65, 65, 54, 40, 24, -3, -33, -69, + -73, -63, -16, -44, -17, 25, 33, 2, + -14, -5, 1, -3, -1, 7, 3, 11, + -7, -21, -36, -43, -47, -31, 30, 37, + -1, -20, -7, -48, -40, -7, 30, 51, + 64, 38, -6, -42, -50, -38, -31, -50, + -88, -121, -126, -125, -99, -123, -98, -75, + -68, -106, -97, -76, -57, -59, -68, -56, + -38, -21, -5, -5, 5, 17, 24, 30, + 34, 30, 5, 0, -16, -60, -28, -53, + -87, -97, -119, -81, -51, -54, -45, -37, + -23, -43, -56, -51, -25, -18, -19, -14, + -7, 25, 31, -6, 37, 33, 66, 79, + 73, 72, 63, 74, 118, 125, 125, 123, + 114, 102, 104, 94, 79, 65, 80, 60, + 47, 42, -4, 26, 35, 43, 29, 14, + 4, -4, -2, -3, 5, 2, 12, 17, + 14, 9, 3, 10, 13, 12, 5, -13, + 36, 19, 21, 5, 6, 14, 15, 8, + 2, -4, -5, -19, -23, -49, -68, -59, + -41, -14, 14, 3, -17, -16, 20, -29, + -72, -28, 1, -7, -27, -30, -21, -13, + -21, -26, -39, -47, 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+ -20, -34, -44, -31, -24, -17, -20, -12, + -10, 4, 18, 16, 34, 41, -3, 11, + 5, 12, 2, 0, -13, -8, -1, -1, + 9, 3, 7, 4, 7, 5, 6, 6, + -2, -24, -53, -53, 88, 34, -12, -43, + -31, -31, -35, -31, -24, -21, -24, -24, + -17, -6, 14, 22, 36, 65, 82, 101, + 71, 127, -7, -13, -19, -21, -32, -21, + -10, -3, -26, -43, -45, -54, -61, -74, + -77, -74, -71, -79, -95, -101, -95, -33, + 47, 52, 44, 39, 39, 30, 19, 9, + 21, 19, 1, -7, -1, 13, 17, 10, + 2, 12, 16, 4, -15, 39, 73, 55, + 49, 55, 48, 45, 48, 52, 44, 50, + 59, 56, 47, 52, 55, 63, 57, 44, + 67, 79, 74, 58, -44, -66, -63, -53, + -54, -44, -40, -42, -38, -50, -69, -68, + -61, -60, -54, -58, -62, -67, -64, -39, + -30, -76, -40, -41, -55, -48, -40, -44, + -43, -22, -19, -28, -17, -19, -21, -18, + -9, -4, 5, -7, -45, -51, -81, -93, + -27, -27, -36, -47, -51, -57, -54, -59, + -58, -54, -49, -44, -52, -59, -50, -47, + -44, -31, -29, -29, -19, 61, -1, 9, + 51, 71, 69, 87, 114, 117, 90, 77, + 75, 60, 63, 59, 43, 32, 25, 16, + 11, -1, 2, 9, -11, -26, -27, -29, + -28, -29, -26, -23, -29, -36, -47, -72, + -81, -72, -45, -27, -12, -1, -8, 4, + 4, -16, -9, -62, -66, -56, -51, -53, + -54, -53, -66, -77, -75, -79, -81, -81, + -66, -55, -54, -41, -62, -82, -89, -83, + -32, -65, -68, -65, -71, -70, -66, -40, + -35, -48, -38, -43, -46, -43, -17, -4, + -3, -6, -23, -30, -28, -17, 53, 28, + 19, 4, -16, -8, -17, -20, 0, 12, + 14, 30, 28, 25, 9, 15, 32, 98, + 111, 95, 80, 54, -23, -30, -28, -31, + -35, -36, -40, -39, -52, -52, -43, -54, + -52, -51, -49, -51, -47, -32, 4, -21, + -40, -37, -49, -54, -31, -29, -22, -12, + -5, -14, -28, -31, -34, -26, -19, -23, + -31, -19, -13, -20, -43, -30, -8, 5, + 59, 101, 113, 85, 51, 46, 43, 43, + 34, 20, 25, 30, 33, 32, 23, 16, + 12, 9, 1, 8, 18, 1, 46, 63, + 43, 32, 24, 25, 22, 27, 28, 44, + 61, 68, 56, 60, 69, 71, 51, 45, + 57, 80, 125, 127, 48, 47, 43, 66, + 54, 33, 3, 21, 29, 43, 48, 48, + 49, 59, 55, 46, 37, 19, 22, 36, + 59, 51, -119, -91, -57, -52, -48, -52, + -37, -22, -14, -19, -22, -34, -45, -47, + -48, -49, -47, -39, -10, -8, -24, -24, + 23, 37, 30, 38, 27, 30, 24, 27, + 30, 30, 33, 35, 30, 24, 31, 31, + 28, 32, 27, 25, 0, -5, 75, 31, + 11, 32, 58, 63, 58, 33, 34, 41, + 22, 37, 22, 6, 2, 15, 20, 5, + 0, 8, 36, 54, -5, 1, 4, -4, + 5, 23, 34, 24, 19, 29, 30, 31, + 27, 21, 3, 3, 3, 0, -35, -37, + -23, 10, -24, -7, 14, 29, 39, 19, + 11, 9, 3, 6, 9, -9, -19, -18, + -5, -5, -5, -3, -2, -7, -9, 6 +}; + +static const rnn_weight denoise_output_bias[22] = { + 4, 52, 43, 17, -14, -8, -8, 11, + 21, 19, 20, 16, 34, 43, 8, -11, + 2, 26, 33, 16, 24, 5 +}; + +static const DenseLayer denoise_output = { + denoise_output_bias, + denoise_output_weights, + 96, 22, ACTIVATION_SIGMOID +}; + +static const rnn_weight vad_output_weights[24] = { + -128, -128, 127, -128, -128, -128, -128, 127, + -128, 127, -128, -128, 127, 127, -128, 127, + -128, 127, 127, 127, -128, 35, -128, 127 +}; + +static const rnn_weight vad_output_bias[1] = { + 104 +}; + +static const DenseLayer vad_output = { + vad_output_bias, + vad_output_weights, + 24, 1, ACTIVATION_SIGMOID +}; + +const struct RNNModel model_lq = { + 24, + &input_dense, + 24, + &vad_gru, + 48, + &noise_gru, + 96, + &denoise_gru, + 22, + &denoise_output, + 1, + &vad_output, +}; From 53f34de7d95af80c0c9101c791db47a05ec36196 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Sat, 1 Sep 2018 20:03:54 -0400 Subject: [PATCH 16/23] Made sample rate a changeable parameter. --- examples/rnnoise_demo.c | 16 +++-- include/rnnoise-nu.h | 9 +++ src/denoise.c | 154 +++++++++++++++++++++++----------------- 3 files changed, 109 insertions(+), 70 deletions(-) diff --git a/examples/rnnoise_demo.c b/examples/rnnoise_demo.c index 20deaf12..29ed1b18 100644 --- a/examples/rnnoise_demo.c +++ b/examples/rnnoise_demo.c @@ -39,20 +39,23 @@ int main(int argc, char **argv) { int channels; float x[FRAME_SIZE]; short *tmp; + int sample_rate; RNNModel *model = NULL; DenoiseState **sts; float max_attenuation; - if (argc < 3) { - fprintf(stderr, "usage: %s [model]\n", argv[0]); + if (argc < 4) { + fprintf(stderr, "usage: %s [model]\n", argv[0]); return 1; } - channels = atoi(argv[1]); + sample_rate = atoi(argv[1]); + if (sample_rate <= 0) sample_rate = 48000; + channels = atoi(argv[2]); if (channels < 1) channels = 1; - max_attenuation = pow(10, -atof(argv[2])/10); + max_attenuation = pow(10, -atof(argv[3])/10); - if (argc >= 4) { - model = rnnoise_get_model(argv[3]); + if (argc >= 5) { + model = rnnoise_get_model(argv[4]); if (!model) { fprintf(stderr, "Model not found!\n"); return 1; @@ -72,6 +75,7 @@ int main(int argc, char **argv) { for (i = 0; i < channels; i++) { sts[i] = rnnoise_create(model); rnnoise_set_param(sts[i], RNNOISE_PARAM_MAX_ATTENUATION, max_attenuation); + rnnoise_set_param(sts[i], RNNOISE_PARAM_SAMPLE_RATE, sample_rate); } while (1) { diff --git a/include/rnnoise-nu.h b/include/rnnoise-nu.h index cf74b8ec..7a8f7d81 100644 --- a/include/rnnoise-nu.h +++ b/include/rnnoise-nu.h @@ -67,8 +67,17 @@ RNNOISE_EXPORT const char **rnnoise_models(void); RNNOISE_EXPORT RNNModel *rnnoise_get_model(const char *name); /* Parameters to a denoise state */ + +/* RNNOISE_PARAM_MAX_ATTENUATION: The maximum attenuation to perform. Note that + * this is described in terms of *minimum* gain, so for a 20dB maximum + * attenuation, the correct value is 0.01. */ #define RNNOISE_PARAM_MAX_ATTENUATION 1 +/* RNNOISE_PARAM_SAMPLE_RATE: Sets the sample rate. This is just used to decide + * how to bin the audio so that it matches the bins in the neural network. This + * does not affect the frame size, which is always 480 samples. */ +#define RNNOISE_PARAM_SAMPLE_RATE 2 + RNNOISE_EXPORT void rnnoise_set_param(DenoiseState *st, int param, float value); #endif diff --git a/src/denoise.c b/src/denoise.c index 1332ff3e..21ebcc17 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -41,6 +41,8 @@ #include "rnn.h" #include "rnn_data.h" +#define DEFAULT_SAMPLE_RATE 48000 + #define FRAME_SIZE_SHIFT 2 #define FRAME_SIZE (120<band_bins[i+1] - st->band_bins[i]; for (j=0;jband_bins[i] + j].r); + tmp += SQUARE(X[st->band_bins[i] + j].i); sum[i] += (1-frac)*tmp; sum[i+1] += frac*tmp; } @@ -133,19 +139,19 @@ void compute_band_energy(float *bandE, const kiss_fft_cpx *X) { } } -void compute_band_corr(float *bandE, const kiss_fft_cpx *X, const kiss_fft_cpx *P) { +void compute_band_corr(DenoiseState *st, float *bandE, const kiss_fft_cpx *X, const kiss_fft_cpx *P) { int i; float sum[NB_BANDS] = {0}; for (i=0;iband_bins[i+1] - st->band_bins[i]; for (j=0;jband_bins[i] + j].r * P[st->band_bins[i] + j].r; + tmp += X[st->band_bins[i] + j].i * P[st->band_bins[i] + j].i; sum[i] += (1-frac)*tmp; sum[i+1] += frac*tmp; } @@ -158,114 +164,125 @@ void compute_band_corr(float *bandE, const kiss_fft_cpx *X, const kiss_fft_cpx * } } -void interp_band_gain(float *g, const float *bandE) { +void interp_band_gain(DenoiseState *st, float *g, const float *bandE) { int i; memset(g, 0, FREQ_SIZE); for (i=0;iband_bins[i+1] - st->band_bins[i]; for (j=0;jband_bins[i] + j] = (1-frac)*bandE[i] + frac*bandE[i+1]; } } } #else -void compute_band_energy(float *bandE, const kiss_fft_cpx *X) { +void compute_band_energy(DenoiseState *st, float *bandE, const kiss_fft_cpx *X) { int i; for (i=0;iband_bins[i+1] - st->band_bins[i]);j++) { + sum += SQUARE(X[st->band_bins[i] + j].r); + sum += SQUARE(X[st->band_bins[i] + j].i); } bandE[i] = sum; } } -void interp_band_gain(float *g, const float *bandE) { +void interp_band_gain(DenoiseState *st, float *g, const float *bandE) { int i; memset(g, 0, FREQ_SIZE); for (i=0;iband_bins[i+1] - st->band_bins[i]);j++) + g[st->band_bins[i] + j] = bandE[i]; } } #endif -CommonState common; - -static void check_init() { +static void check_init(DenoiseState *st) { int i; - if (common.init) return; - common.kfft = opus_fft_alloc_twiddles(2*FRAME_SIZE, NULL, NULL, NULL, 0); + if (st->init) return; + /* FIXME: Deallocate this! */ + st->kfft = opus_fft_alloc_twiddles(2*FRAME_SIZE, NULL, NULL, NULL, 0); + + /* Get the sample rate set up */ + if (st->sample_rate <= 0) st->sample_rate = DEFAULT_SAMPLE_RATE; + + /* Adjust the bins for the sample rate */ + for (i = 0; i < NB_BANDS; i++) + st->band_bins[i] = (((long) eband5ms[i]) << FRAME_SIZE_SHIFT) * DEFAULT_SAMPLE_RATE / st->sample_rate; + + /* Make sure nothing's above the Nyquist frequency */ + for (i = 0; i < NB_BANDS; i++) + if (st->band_bins[i] >= FRAME_SIZE) st->band_bins[i] = FRAME_SIZE - 1; + for (i=0;ihalf_window[i] = sin(.5*M_PI*sin(.5*M_PI*(i+.5)/FRAME_SIZE) * sin(.5*M_PI*(i+.5)/FRAME_SIZE)); for (i=0;idct_table[i*NB_BANDS + j] = cos((i+.5)*j*M_PI/NB_BANDS); + if (j==0) st->dct_table[i*NB_BANDS + j] *= sqrt(.5); } } - common.init = 1; + st->init = 1; } -static void dct(float *out, const float *in) { +static void dct(DenoiseState *st, float *out, const float *in) { int i; - check_init(); + check_init(st); for (i=0;idct_table[j*NB_BANDS + i]; } out[i] = sum*sqrt(2./22); } } #if 0 -static void idct(float *out, const float *in) { +static void idct(DenoiseState *st, float *out, const float *in) { int i; - check_init(); + check_init(st); for (i=0;idct_table[i*NB_BANDS + j]; } out[i] = sum*sqrt(2./22); } } #endif -static void forward_transform(kiss_fft_cpx *out, const float *in) { +static void forward_transform(DenoiseState *st, kiss_fft_cpx *out, const float *in) { int i; kiss_fft_cpx x[WINDOW_SIZE]; kiss_fft_cpx y[WINDOW_SIZE]; - check_init(); + check_init(st); for (i=0;ikfft, x, y, 0); for (i=0;ikfft, x, y, 0); /* output in reverse order for IFFT. */ out[0] = WINDOW_SIZE*y[0].r; for (i=1;ihalf_window[i]; + x[WINDOW_SIZE - 1 - i] *= st->half_window[i]; } } @@ -314,6 +331,8 @@ DenoiseState *rnnoise_create(RNNModel *model) { } void rnnoise_destroy(DenoiseState *st) { + if (st->init) + free(st->kfft); free(st->rnn.vad_gru_state); free(st->rnn.noise_gru_state); free(st->rnn.denoise_gru_state); @@ -331,13 +350,13 @@ static void frame_analysis(DenoiseState *st, kiss_fft_cpx *X, float *Ex, const f RNN_COPY(x, st->analysis_mem, FRAME_SIZE); for (i=0;ianalysis_mem, in, FRAME_SIZE); - apply_window(x); - forward_transform(X, x); + apply_window(st, x); + forward_transform(st, X, x); #if TRAINING for (i=lowpass;ilast_gain = gain; for (i=0;ipitch_buf[PITCH_BUF_SIZE-WINDOW_SIZE-pitch_index+i]; - apply_window(p); - forward_transform(P, p); - compute_band_energy(Ep, P); - compute_band_corr(Exp, X, P); + apply_window(st, p); + forward_transform(st, P, p); + compute_band_energy(st, Ep, P); + compute_band_corr(st, Exp, X, P); for (i=0;icepstral_mem[st->memid]; @@ -434,8 +453,8 @@ static int compute_frame_features(DenoiseState *st, kiss_fft_cpx *X, kiss_fft_cp static void frame_synthesis(DenoiseState *st, float *out, const kiss_fft_cpx *y) { float x[WINDOW_SIZE]; int i; - inverse_transform(x, y); - apply_window(x); + inverse_transform(st, x, y); + apply_window(st, x); for (i=0;isynthesis_mem[i]; RNN_COPY(st->synthesis_mem, &x[FRAME_SIZE], FRAME_SIZE); } @@ -452,7 +471,7 @@ static void biquad(float *y, float mem[2], const float *x, const float *b, const } } -void pitch_filter(kiss_fft_cpx *X, const kiss_fft_cpx *P, const float *Ex, const float *Ep, +void pitch_filter(DenoiseState *st, kiss_fft_cpx *X, const kiss_fft_cpx *P, const float *Ex, const float *Ep, const float *Exp, const float *g) { int i; float r[NB_BANDS]; @@ -469,19 +488,19 @@ void pitch_filter(kiss_fft_cpx *X, const kiss_fft_cpx *P, const float *Ex, const #endif r[i] *= sqrt(Ex[i]/(1e-8+Ep[i])); } - interp_band_gain(rf, r); + interp_band_gain(st, rf, r); for (i=0;irnn, g, &vad_prob, features); - pitch_filter(X, P, Ex, Ep, Exp, g); + pitch_filter(st, X, P, Ex, Ep, Exp, g); for (i=0;ilastg[i]); @@ -533,7 +552,7 @@ float rnnoise_process_frame(DenoiseState *st, float *out, const float *in) { } } - interp_band_gain(gf, g); + interp_band_gain(st, gf, g); #if 1 for (i=0;imax_attenuation = MIN_MAX_ATTENUATION; break; + + case RNNOISE_PARAM_SAMPLE_RATE: + if (value <= 0) + st->sample_rate = 0; + else + st->sample_rate = value; + break; } } @@ -685,7 +711,7 @@ int main(int argc, char **argv) { frame_analysis(noise_state, N, En, n); for (i=0;ilast_gain, noisy->last_period); for (i=0;i Date: Sun, 2 Sep 2018 12:00:01 -0400 Subject: [PATCH 17/23] Adding somnolent-hogwash model --- src/models.c | 7 +- src/models/bd.c | 47 + src/models/lq.c | 50 + src/models/sh.c | 11089 ++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 11191 insertions(+), 2 deletions(-) create mode 100644 src/models/sh.c diff --git a/src/models.c b/src/models.c index 6af5ce6d..f7047bae 100644 --- a/src/models.c +++ b/src/models.c @@ -41,6 +41,7 @@ static const char *model_names[] = { "mp", "bd", "lq", + "sh", NULL }; @@ -49,14 +50,16 @@ extern const struct RNNModel model_cb, model_mp, model_bd, - model_lq; + model_lq, + model_sh; static const struct RNNModel *models[] = { &model_orig, &model_cb, &model_mp, &model_bd, - &model_lq + &model_lq, + &model_sh }; const char **rnnoise_models() diff --git a/src/models/bd.c b/src/models/bd.c index 2f2bf7f6..b767e697 100644 --- a/src/models/bd.c +++ b/src/models/bd.c @@ -1,3 +1,50 @@ +/* + * SUITE: beguiling-drafter-2018-08-30 + * + * INTENT: + * + * Voice in a reasonable recording environment. Fans, AC, computers, etc. + * + * + * SIGNAL: + * + * voice + * hamlet-002-act-1-scene-1.1626 + * http://etc.usf.edu/lit2go/94/hamlet/1626/ + * human_rights_un_chi_nf.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * human_rights_un_cze_msed.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * huntingofthesnark_01_carrol_128kb + * https://librivox.org/the-hunting-of-the-snark-by-lewis-carroll-3/ + * teacupclub_01_armstrong_128kb + * https://librivox.org/the-teacup-club-by-eliza-armstrong/ + * warletters_01_allan_128kb.mp3 + * https://librivox.org/war-letters-from-a-young-queenslander-by-robert-marshall-allen/ + * TSPspeech + * http://www-mmsp.ece.mcgill.ca/Documents/Data/ + * + * sounds + * cough + * https://freesound.org/ sounds: 205012 222644 252240 323532 342587 343880 343885 343986 369295 391284 401334 407839 419781 425777 87412 + * laugh + * https://freesound.org/ sounds: 167578 189278 263775 319346 343934 344043 344064 367680 381275 383237 393353 401214 433921 73123 + * + * + * ./chopper voice/hamlet-002-act-1-scene-1.1626.raw voice/human_rights_un_chi_nf.raw sounds/cough.raw voice/human_rights_un_cze_msed.raw voice/huntingofthesnark_01_carrol_128kb.raw sounds/laugh.raw voice/teacupclub_01_armstrong_128kb.raw voice/warletters_01_allan_128kb.raw voice/TSPspeech.raw + * + * + * NOISE: + * + * rnnoise_contributions (only "other" and "none" samples) + * https://people.xiph.org/~jm/demo/rnnoise/rnnoise_contributions.tar.gz + * + * + * COUNT: 13212874 (x87) + * + * NOTE: All files processed into correct raws through ffmpeg: ffmpeg -i in -f s16le -ac 1 -ar 48000 out.raw + */ + /*This file is automatically generated from a Keras model*/ #ifdef HAVE_CONFIG_H diff --git a/src/models/lq.c b/src/models/lq.c index d069f84e..72f80365 100644 --- a/src/models/lq.c +++ b/src/models/lq.c @@ -1,3 +1,53 @@ +/* + * SUITE: leavened-quisling-2018-08-31 + * + * INTENT: + * + * Voice in a noisy recording environment. + * + * + * SIGNAL: + * + * voice + * hamlet-002-act-1-scene-1.1626 + * http://etc.usf.edu/lit2go/94/hamlet/1626/ + * human_rights_un_chi_nf.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * human_rights_un_cze_msed.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * huntingofthesnark_01_carrol_128kb + * https://librivox.org/the-hunting-of-the-snark-by-lewis-carroll-3/ + * teacupclub_01_armstrong_128kb + * https://librivox.org/the-teacup-club-by-eliza-armstrong/ + * warletters_01_allan_128kb.mp3 + * https://librivox.org/war-letters-from-a-young-queenslander-by-robert-marshall-allen/ + * TSPspeech + * http://www-mmsp.ece.mcgill.ca/Documents/Data/ + * + * sounds + * cough + * https://freesound.org/ sounds: 205012 222644 252240 323532 342587 343880 343885 343986 369295 391284 401334 407839 419781 425777 87412 + * laugh + * https://freesound.org/ sounds: 167578 189278 263775 319346 343934 344043 344064 367680 381275 383237 393353 401214 433921 73123 + * + * + * ./chopper voice/hamlet-002-act-1-scene-1.1626.raw voice/human_rights_un_chi_nf.raw sounds/cough.raw voice/human_rights_un_cze_msed.raw voice/huntingofthesnark_01_carrol_128kb.raw sounds/laugh.raw voice/teacupclub_01_armstrong_128kb.raw voice/warletters_01_allan_128kb.raw voice/TSPspeech.raw + * + * + * NOISE: + * + * rnnoise_contributions + * https://people.xiph.org/~jm/demo/rnnoise/rnnoise_contributions.tar.gz + * + * + * ./noise-chopper *.raw + * + * + * COUNT: 10000000 (x87) + * + * NOTE: All files processed into correct raws through ffmpeg: ffmpeg -i in -f s16le -ac 1 -ar 48000 out.raw + */ + /*This file is automatically generated from a Keras model*/ #ifdef HAVE_CONFIG_H diff --git a/src/models/sh.c b/src/models/sh.c new file mode 100644 index 00000000..e5cb584e --- /dev/null +++ b/src/models/sh.c @@ -0,0 +1,11089 @@ +/* + * SUITE: somnolent-hogwash-2018-09-01 + * + * INTENT: + * + * Speech in a reasonable recording environment. Fans, AC, computers, etc. Note + * that "speech" means speech, not other human sounds; laughter, coughing, etc are + * not included. + * + * + * SIGNAL: + * + * voice + * hamlet-002-act-1-scene-1.1626 + * http://etc.usf.edu/lit2go/94/hamlet/1626/ + * human_rights_un_chi_nf.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * human_rights_un_cze_msed.mp3 + * https://librivox.org/universal-declaration-of-human-rights-by-united-nations-volume-02/ + * huntingofthesnark_01_carrol_128kb + * https://librivox.org/the-hunting-of-the-snark-by-lewis-carroll-3/ + * teacupclub_01_armstrong_128kb + * https://librivox.org/the-teacup-club-by-eliza-armstrong/ + * warletters_01_allan_128kb.mp3 + * https://librivox.org/war-letters-from-a-young-queenslander-by-robert-marshall-allen/ + * TSPspeech + * http://www-mmsp.ece.mcgill.ca/Documents/Data/ + * + * + * ./chopper voice/hamlet-002-act-1-scene-1.1626.raw voice/human_rights_un_chi_nf.raw voice/human_rights_un_cze_msed.raw voice/huntingofthesnark_01_carrol_128kb.raw voice/teacupclub_01_armstrong_128kb.raw voice/warletters_01_allan_128kb.raw voice/TSPspeech.raw + * + * + * NOISE: + * + * rnnoise_contributions (only "other" and "none" samples) + * https://people.xiph.org/~jm/demo/rnnoise/rnnoise_contributions.tar.gz + * + * + * COUNT: 13212874 (x87) + * + * NOTE: All files processed into correct raws through ffmpeg: ffmpeg -i in -f s16le -ac 1 -ar 48000 out.raw + */ + +/*This file is automatically generated from a Keras model*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include "rnn.h" +#include "rnn_data.h" + +static const rnn_weight input_dense_weights[1008] = { + -16, 13, 24, 36, -17, 5, 8, 55, + 19, 3, 36, -72, 41, 11, 7, 1, + 57, -16, 0, -16, 7, 9, 7, -13, + -28, 17, 9, -39, -10, -4, 26, 23, + 11, -16, -13, -58, 58, -17, -7, 3, + 28, -32, 60, 35, -26, 6, 13, -11, + -15, 2, 68, -19, -40, 25, -44, -5, + 26, 10, 22, -54, 22, -18, 0, 7, + 40, 31, 18, 49, -32, -53, 22, -45, + -50, 8, 50, -68, -10, -52, 54, -37, + -30, 61, 40, 12, -33, -27, 5, 47, + -28, -40, -17, 13, -17, -71, -44, 25, + 61, 20, -8, 29, -8, -55, -54, 38, + -6, 0, 66, -23, -9, -6, 21, 34, + 0, -46, 22, -48, 25, -26, -81, -32, + -13, 38, -15, 10, 8, 28, 59, 66, + 6, -65, -2, -20, -68, -27, -2, 16, + 8, 27, 26, -10, 33, 31, -30, 9, + 114, -87, 1, -12, 80, -19, -66, 86, + 1, 6, -91, 81, -51, -85, 72, 4, + 117, -121, -125, -125, -17, 121, 56, 99, + 65, -3, -4, 99, 123, 54, 127, 64, + -32, -1, -53, -86, 52, -65, 72, 115, + 33, -53, 11, -40, -3, 94, 19, 126, + 127, -122, 9, 52, 127, 100, -60, 51, + 36, 2, -128, 73, -68, -69, 29, -4, + 106, -115, -126, -52, 29, 127, 29, 52, + -64, 96, -115, -13, 126, -47, -29, 40, + -59, -35, -59, 0, -114, 63, 18, 48, + 123, -121, -48, -87, -4, 30, -125, 66, + 30, 73, -117, 37, 70, -95, -121, -23, + 110, 110, 13, -97, -18, 58, -31, -30, + 53, -53, -69, -6, -5, 17, 46, 8, + 63, -119, -44, -107, 122, 94, 40, 14, + -112, -77, -66, 85, -128, -126, 48, 32, + 121, -48, 50, 7, -27, 13, 9, 3, + -112, 127, -86, 62, -51, 121, -127, -114, + 127, 88, -8, -53, 50, -10, -63, -39, + 127, 61, -20, -29, -69, 69, 83, 43, + -124, 87, -7, 104, 51, 62, 8, 81, + 127, 21, 126, 125, 106, -41, -37, -124, + 58, 39, 1, 126, -10, -47, 53, -24, + 24, -23, -13, -23, -1, 51, -23, 9, + 127, 89, -125, -125, 127, 127, -79, 12, + -20, 83, 1, 19, 51, 27, -51, 19, + 51, -23, -3, -39, -27, 126, 3, -30, + 99, 35, 122, -9, -109, 71, 9, 40, + 116, 90, -20, -118, 109, 32, 71, 48, + -128, 56, 125, 57, 29, -107, -120, 23, + 127, 65, -121, 80, 94, 89, -23, -124, + 81, 36, 8, 113, 80, 124, -78, 25, + -110, -54, 66, -8, -30, 127, 25, 122, + -11, 55, 127, -8, 111, 99, 75, 13, + 92, 22, 73, -24, 34, -122, -123, -57, + -47, -118, 17, 61, -6, 48, -55, -1, + 127, 108, 78, 6, -28, -32, 60, -111, + 107, -75, 1, 113, -45, 123, -11, -39, + 33, 27, 90, 77, 13, 67, -6, 90, + -20, -15, 100, 114, 120, 114, 124, -97, + -126, -72, 1, -128, -126, -35, -89, 117, + 6, -103, -24, 30, -21, -5, -56, 76, + 104, -11, -34, 46, -98, 52, 43, -88, + 39, 41, 114, -69, -9, 17, -32, -88, + -119, 127, 127, 114, 98, 111, -16, 8, + 117, -9, 122, 4, 126, -97, -34, -128, + 50, -97, 41, 61, -71, 127, -54, 5, + -71, 31, 21, -54, 15, 96, 39, 9, + -27, 121, 8, -89, 80, 127, 125, 115, + -128, -58, 39, 18, 127, -56, 47, -78, + -26, 26, 119, 95, -120, -128, -6, -128, + 122, -20, -125, -80, -37, -86, -6, 43, + -97, -101, 124, 89, 87, 102, 125, -128, + -118, -26, 85, 114, -101, 127, -31, -48, + -5, 91, -66, -67, 7, -86, 96, 9, + -76, 8, 82, -47, -108, 18, 126, -128, + -117, 117, -77, -20, -23, 81, 41, -9, + -28, 127, 127, -115, 127, 101, 86, 117, + 2, -106, -32, 12, 58, -52, -79, 3, + 31, 46, 119, 96, -45, -101, -70, -54, + 108, 4, -45, -49, 57, -128, 86, 55, + -105, -126, 120, 71, -57, 73, -20, -118, + -109, -21, -74, -34, -18, 127, -2, -70, + -13, 27, -21, 45, -70, -96, 17, 97, + 37, 32, 3, 102, -124, 3, -25, -21, + 39, 65, -2, -109, -19, 58, -60, -97, + 2, 127, 100, -52, 119, 38, 46, 32, + 3, -91, 12, 12, 87, -47, 25, -58, + -64, 4, 80, 9, -4, -36, -20, -12, + 40, -11, 4, 31, 51, 124, 61, 57, + -36, 54, 54, 78, -46, -46, 24, -49, + -10, -1, 3, -65, 1, 9, -59, 110, + -50, 14, 73, -33, -113, 15, 17, -47, + -64, 3, 27, -37, 7, 57, 17, -78, + -14, -71, 94, 50, 13, -50, 82, 21, + -84, 77, 39, 110, 78, 20, 46, 36, + -71, -18, 19, 75, -20, 47, 9, 0, + 120, 86, -48, -69, 29, -6, -69, -5, + -5, 23, 32, -81, -116, 34, 3, 121, + -48, 64, 31, 35, 25, -63, 11, -37, + -6, -58, 118, -70, 59, -6, -75, 11, + -3, -29, -37, 55, 99, 8, -20, 81, + -31, 68, -31, 43, -74, 60, -27, 1, + 58, 118, -127, -114, -116, 123, -13, 7, + 21, 127, 27, -121, -14, -54, -128, 98, + -53, 127, 126, 30, 118, -57, 124, -116, + 42, 127, -128, -114, -117, 123, 23, 58, + 70, 127, 3, -119, -119, 25, -128, 127, + -82, 127, 64, -122, 120, -31, 126, -116, + -64, 124, -111, -102, -112, -33, 26, 77, + 31, 42, -42, -82, -50, 114, 90, 117, + -44, 127, 71, -128, 50, -71, 3, 82, + -63, 9, 46, 118, 64, -59, -82, 39, + 106, -113, 11, 107, 59, 84, -17, -18, + -36, 61, 37, 97, -51, 22, -127, -50, + -77, 83, -6, -35, -77, 124, 73, 75, + 89, 74, -49, -29, 54, 99, -10, -39, + -39, 9, 34, 58, -23, -55, -30, -81, + -99, -25, 43, -123, 127, 0, 124, 123, + 78, -81, 43, 19, 46, 11, 119, 43, + 119, -16, 7, 49, -93, 8, -125, 127, + -70, -15, 27, 73, -1, -31, -18, -9, + -35, -46, -52, -34, 1, -33, 32, 20, + -22, -46, -33, 91, 44, -42, -38, 56, + -64, 61, -38, 28, 80, -19, -14, -55, + 39, -10, 65, 58, -25, 89, 4, 48, + 6, 58, -15, -61, 67, -128, -26, 49 +}; + +static const rnn_weight input_dense_bias[24] = { + -29, -110, -15, 27, -55, -124, 82, -100, + -127, -1, -121, -32, -64, -2, -55, 104, + -111, 27, -33, 42, 82, -34, 1, -82 +}; + +static const DenseLayer input_dense = { + input_dense_bias, + input_dense_weights, + 42, 24, ACTIVATION_TANH +}; + +static const rnn_weight vad_gru_weights[1728] = { + 6, 91, -48, -46, 101, -56, -50, -13, + -33, -73, -123, 79, -128, -75, 28, -6, + 19, 68, 116, -16, -30, -108, -80, 65, + -42, -41, -58, 66, -36, 51, 48, 60, + 35, -99, -44, 6, -86, -65, -61, 14, + -44, 58, 16, -37, -14, 18, 35, 0, + -41, 18, -5, 11, -35, 11, 8, 29, + -29, -63, 6, 87, 75, -38, -13, -18, + -40, -48, 60, 11, -26, -14, -30, -13, + -89, -111, -56, 34, -42, 16, -85, 17, + 40, -60, 114, -121, -61, 68, 73, -5, + 106, -13, 24, -56, 71, 7, 50, -57, + -13, -54, -59, -44, 42, 61, -119, -93, + -28, -6, 16, -32, 34, -44, 49, 32, + -50, 17, -62, 6, -52, -5, -13, 41, + -16, 5, 18, -44, 89, 0, -50, 14, + 1, 62, -27, -68, -33, 14, -7, -22, + 8, -29, -54, -18, -27, 69, -49, -80, + 63, 30, 127, 26, -28, 63, 48, 127, + -2, 28, 125, 29, 124, 119, -7, 106, + -26, -123, 63, 37, 95, 48, 113, -6, + 7, 74, 127, 74, 51, 48, -78, -1, + -33, -11, 125, -103, -10, -26, 78, -6, + 35, -60, -87, 105, -4, -38, -14, 64, + -17, 33, -27, -21, 5, -53, 7, 14, + 37, 45, 13, -80, 2, -45, 8, -60, + -9, -47, 67, -35, 69, 14, 29, 6, + -75, -68, -92, 99, 4, -120, -63, -45, + 0, -5, -102, -30, 44, 0, -66, -13, + -86, 12, 15, 78, 19, 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+ 53, 16, -74, 39, -72, -32, -28, -70, + 101, -6, -11, 49, 46, 60, -122, 78, + -5, 32, -128, -50, -67, -42, 8, 82, + 78, 127, 64, -24, -48, 115, -5, -34, + -105, -33, -22, 19, 14, 49, -4, -86, + -5, -10, -8, -18, 126, -53, 113, -19, + 62, 0, -50, -18, -55, -96, -17, -62, + 46, -9, -34, 8, 82, -126, -63, -3, + 81, 37, 94, -22, 26, -33, 78, -5, + 77, 104, -37, -16, -125, 5, -9, -30, + 18, -56, 48, 33, 123, 77, -33, 127, + -65, 1, -85, -59, 77, -5, 39, -17, + 73, -32, 7, -10, -12, -39, -68, 92, + -45, -45, -3, 20, -83, -12, 49, 55, + 1, -36, -71, 127, -9, 62, -22, -25, + 55, 35, -53, 97, 115, 117, 56, 74, + -35, -26, 38, 85, -81, -10, 27, -36, + -57, -36, -23, -13, -79, 20, -63, 61, + 22, -70, -59, 17, 34, -29, 84, 4, + 77, -7, 34, -11, -40, -117, -37, -122, + 48, -12, -29, -1, 68, 72, -15, 46, + -94, 53, 94, -128, -105, -53, -50, -51, + 53, 16, -9, 126, 93, -15, 114, 45, + -48, -97, -121, 28, -41, -47, -42, 32, + 68, 26, 121, 19, -84, -18, -84, 55, + -20, -25, 38, -54, 70, 54, -78, 62, + 11, 33, -45, -13, 24, 10, 115, -74, + -62, 68, 15, -106, -8, -10, -126, -111, + 30, 33, -31, 123, 47, 81, 56, 10, + 94, -59, 127, -9, -58, 118, 126, 124, + 74, 127, 38, 80, 96, 123, 102, 124, + 29, 22, -40, 65, 86, 1, -91, 18, + 98, 56, 35, 39, 81, 73, 16, 23, + 11, 60, 17, -9, 100, 13, 52, -13, + -59, -6, 60, 77, -85, 10, 46, -44, + -4, 32, -123, 26, 47, 43, 8, 64, + -71, -125, 43, -14, -35, -52, 63, -40, + 106, 9, -109, 62, 37, -14, -58, -57, + 29, -54, -122, -68, -126, -101, 66, 120, + -28, 117, 47, -42, -59, 112, -99, 98, + -91, -25, 52, -67, 51, -27, 73, 15, + -29, 47, -117, 71, -39, 55, 25, 25, + -124, -19, -32, 106, -127, -105, 102, -8, + 12, -87, 9, -66, 22, 17, -113, 113, + 49, 19, 2, -52, -21, -7, 116, 21, + 68, 126, -75, -66, -109, 34, -64, 26, + -67, -113, 102, 10, -37, -56, 122, -115, + 7, 19, 101, 61, 106, 127, 18, -6, + 90, 32, 68, 84, 73, -38, 50, -60, + 105, 117, -14, 28, 42, -114, -53, 56, + 37, -31, 29, 9, 23, -89, -13, -123, + 25, -56, 119, -124, 124, 125, -62, 10, + -15, 127, 14, 108, -32, -17, 54, -30, + -47, 26, -52, 85, -49, -45, -126, 70, + -121, -69, 87, 62, -30, -72, 32, -2 +}; + +static const rnn_weight vad_gru_recurrent_weights[1728] = { + 94, 99, 3, 47, 75, 15, 26, 17, + 43, 21, -51, -43, 52, 50, 77, 116, + 83, -33, 5, -64, 4, 127, 51, -27, + 80, 30, -35, -21, 13, 27, -121, 66, + 3, -36, -30, -96, -13, -24, -49, 32, + 52, -14, 7, -91, 31, -72, -54, -34, + 72, -111, 21, -101, 106, 1, -97, 127, + 127, 86, 21, -88, -4, 21, 127, -85, + 115, 90, -72, -36, -73, 127, -104, -101, + -25, -76, 38, -75, -32, -123, -75, -96, + -103, -63, -64, 50, 87, -123, -47, -53, + -108, -112, -5, -64, -57, -120, -126, -25, + 58, -50, 43, -56, -31, -59, 109, 10, + 18, -75, -25, -57, 50, -33, 11, -43, + -31, 44, 49, 28, -42, -92, 48, -13, + -17, 66, 20, 3, -56, -59, 17, 27, + -15, 31, 45, 82, -8, 17, -1, -54, + 44, 10, 54, -74, 31, 0, 63, -38, + -115, 127, 44, -83, -128, -123, -21, -87, + -109, 127, -128, 110, 114, 61, -128, -91, + -127, 37, 49, 1, -25, 106, 45, -125, + 36, 78, 21, 68, -39, 43, 17, 2, + -27, -74, 10, 75, 35, -48, -4, 1, + 62, 26, -31, 85, -114, -119, -25, -21, + 4, 48, 5, -97, 53, -51, -93, -16, + 17, -109, -100, 66, 97, 73, 115, 45, + 91, 33, -18, -125, 22, 80, -121, -121, + -49, -115, 68, -97, -27, -5, -83, -69, + -89, -55, -20, 92, 67, -103, -84, -127, + -98, -109, -72, -52, -107, -126, -22, -66, + 25, -2, 27, 53, 10, -22, -16, 24, + 40, -10, -117, 76, -47, -123, 0, -76, + -30, 19, -3, -77, 52, -96, 47, -48, + -92, 19, 118, 13, -116, -125, 18, -49, + -70, 42, 22, 0, 20, 17, 15, 9, + -11, -58, 4, -9, 51, -77, 75, 3, + 26, 125, -5, 61, -75, 52, 52, 51, + 16, 45, 71, 28, 26, 114, -20, 65, + -37, -10, 36, -19, -39, 77, 35, -10, + 61, 93, -82, 42, -57, -14, -30, -8, + 13, 9, 13, -26, -9, -18, 35, -25, + 88, -1, 23, -39, 43, 37, 0, 28, + 18, -61, 26, -44, 83, 53, 15, 90, + 90, 118, 80, 44, -4, 26, 82, 26, + 47, 47, -30, -35, -2, 79, -55, -61, + 88, -13, -50, 94, 127, -25, -19, 76, + -1, -53, -48, -128, -40, -107, 121, 96, + 103, 70, 42, -24, 65, 77, 58, 14, + 10, 21, -12, -27, 71, -26, -79, -51, + -26, -49, 0, -5, -2, -52, 13, -53, + -14, 63, -59, -89, -7, 13, 68, -41, + -42, 75, -103, 71, -76, 76, 84, -20, + -39, 15, -74, 120, -35, -10, -127, -38, + -127, -74, -38, -8, 106, -46, 112, 64, + 127, -112, 126, -21, 127, -62, -126, 0, + 16, -113, 43, 29, 44, -125, 125, 12, + -72, -32, 22, -22, 26, -94, -117, 25, + 120, -47, -4, 14, 10, -34, -53, 14, + 16, -104, 57, -11, -12, 23, 14, 112, + -4, -53, -17, 67, 47, 104, -22, -10, + -27, 99, -56, 51, -127, 114, -13, -82, + -125, 76, 0, 29, -88, -117, -127, 90, + -101, 8, -36, -18, 53, -99, -7, 52, + 22, 111, -86, 112, 38, -59, 103, 40, + 126, 14, -77, -21, -42, 61, 102, 81, + -24, 95, 69, 124, 125, 118, 71, 18, + -8, 40, -87, 83, 1, 5, 42, 64, + -4, 63, -3, -128, -35, 17, 23, -45, + 15, 61, 36, -20, -30, 36, -16, 21, + 91, -127, -15, -102, 97, -50, 11, 86, + 119, -1, 90, -12, 38, -37, 122, -56, + 60, 20, -61, -60, -64, 55, -34, -82, + -4, 127, -40, 116, 114, 99, 113, 122, + 54, 115, -51, -6, 29, 123, 116, 103, + 96, 76, 100, 82, 113, 127, 127, 85, + 34, 2, 31, -1, 14, 35, -9, 38, + 22, 88, 119, -15, 26, -37, 18, -50, + 75, -42, -96, -54, 14, -30, 17, -37, + -4, -80, 112, -43, 79, 25, -16, 18, + 51, 50, 24, -13, 82, -101, 88, -18, + 29, -34, 16, 74, 13, 11, 7, 33, + 68, -8, -74, -101, -32, -93, -116, -87, + 6, -43, 127, -118, -124, 31, -7, -38, + -28, -40, -104, -114, -125, -51, -66, -63, + 70, 6, 6, -12, 61, -78, -122, -93, + -105, 26, 124, 31, -104, -87, -127, -20, + 42, -67, -20, -63, -24, -11, -44, -88, + -30, -58, -20, 6, -42, 65, 8, -40, + 31, 86, -29, -62, -96, -35, -36, 46, + 83, 14, 17, 34, -25, 15, -26, 27, + -122, -67, 7, 40, -96, -102, 74, -66, + -128, 65, 84, 102, -21, 72, -126, -107, + -77, -101, 32, 70, 84, 34, 116, -123, + -61, 117, -65, -84, -15, 105, -1, 66, + -19, 123, 73, -108, 69, 119, -19, -117, + 7, 44, 104, -60, 120, -93, 114, -3, + -124, 17, -95, 42, -5, -24, -45, 29, + -85, -22, 121, -76, -102, -111, -14, 126, + 16, 20, 122, 124, 78, -99, -19, 112, + 116, 58, 105, -31, 126, 106, -10, 77, + 74, -100, -103, -128, -28, 29, 124, 50, + 97, 27, -54, 49, 114, 58, 80, -52, + -128, 40, 116, -91, -69, -15, 71, -114, + 98, -120, -1, -34, 122, 123, -10, -51, + 19, -56, -41, -29, -66, -62, 15, -46, + 127, -128, 22, -128, 127, -88, -126, 125, + 126, -36, 6, 114, 40, -128, 127, -53, + 117, 127, -126, -97, -126, 125, -48, -126, + -128, 120, 105, -95, -128, -128, -16, -93, + -120, 119, -123, 94, 108, 40, -128, -115, + -127, 95, -27, 29, -27, 116, 106, -116, + -33, 117, 16, -36, -111, 112, -69, -102, + -28, 127, -27, 32, 16, -48, -93, -57, + 46, -76, -29, -39, -108, -37, -40, 58, + 57, 18, 111, -68, 127, -82, -108, 83, + 99, -87, 10, -26, 51, 87, 127, -51, + 127, 33, -84, -126, 24, 109, -102, -125, + -127, 12, -51, 6, -126, -117, 98, 13, + -34, -63, 113, 122, -64, 79, -116, -121, + -63, -25, -13, 37, -2, -53, 87, 66, + -20, -92, -36, 125, -38, 37, 96, 34, + 31, 45, -83, 23, -101, 99, 60, -8, + 48, -26, 22, -19, 49, -18, 127, 74, + 10, -2, 117, -7, 127, -92, -60, 8, + 95, 29, 29, -15, 120, 66, 115, -73, + 117, -9, -10, -27, -88, 37, -86, 25, + 85, 127, -66, 113, 11, 42, -19, 34, + 20, 37, 34, 36, -49, 30, 86, 86, + -11, 9, 62, 60, 77, 101, 71, 20, + 3, 7, -2, 59, 32, -1, -18, 25, + -74, 115, -14, -15, -3, 17, 12, 83, + 48, 58, 61, 83, 29, 2, -27, 41, + -17, -3, 50, -107, -8, 67, -22, 75, + 43, 73, -79, -59, 67, -91, 7, 47, + 27, -96, -18, 8, -99, -31, 37, 43, + -65, -92, 25, 63, -2, 123, 104, 16, + -38, -4, -84, -16, 92, 54, 31, 62, + 121, 124, 42, -64, 58, 113, 126, 46, + -65, 28, -49, -79, -65, -49, -7, -66, + 41, -18, -98, 52, 20, 41, 30, 23, + -13, 14, -34, 100, -52, -78, 5, -36, + -81, -85, -93, 67, -127, 44, 80, -94, + -40, -24, -68, -118, -77, 12, 64, 94, + 126, -92, 38, 110, 90, -105, 114, 92, + -83, 20, -36, 0, -108, 68, 65, -3, + -34, 39, 86, 100, 18, 85, -113, -53, + 99, -17, 23, 1, -29, -36, -6, -8, + 30, 20, 11, 64, -27, -29, 14, 89, + 48, 18, -77, 47, 74, 107, -13, 81, + 35, 35, 32, 127, 89, 13, 63, 88, + 14, -16, 123, 21, 80, -49, -55, -36, + 59, -65, -86, -70, -24, -46, 44, -57, + 56, 11, -43, 30, -19, -23, -6, -8, + 100, 22, -83, 22, -56, 42, -126, -100, + 109, -19, -66, 15, -122, -31, 2, 97, + 38, 104, -61, -37, 41, 127, -82, -127, + -61, 12, -110, 5, -8, -81, -9, 56, + -51, 25, -65, -56, -32, 87, 16, 33, + -8, 84, 26, 13, -3, -64, -37, -6, + 72, -119, 13, -49, 13, -127, -61, 79, + 86, 45, 115, -34, 34, 47, 113, 37, + 33, 48, -68, -7, -103, 112, -102, -9, + 60, -18, 125, -48, -46, -76, 112, -17, + -11, 127, 122, 95, 122, -35, -108, -127, + -100, -87, -76, -83, -69, -118, -23, 29, + 9, -43, 40, 83, -34, -59, 63, 52, + -27, -59, -10, -60, 97, 31, -14, -45, + 7, -6, 43, 33, -5, 55, -93, -33, + -117, 30, -24, 69, -48, -34, 83, -42, + -68, 19, -70, 5, -32, -7, -62, 66, + -21, -79, 49, 49, 120, -26, -20, 8, + -16, -109, 23, -3, 88, 49, 122, 21, + -84, -41, 59, -38, -27, 78, 127, 54, + 47, -37, 68, -81, -13, -80, -45, 82, + 120, 35, -46, -103, 10, -82, 88, 19, + 5, 26, -54, 75, -107, -82, -52, 68, + -68, -4, 10, 9, 83, 116, 7, 31, + -120, 114, -76, 124, -125, 73, 63, -123, + -127, -39, -22, 35, -121, -120, -127, 127, + -110, -118, 77, 110, 106, -118, 114, 44, + -105, -114, 126, -122, 12, -116, -88, -108, + -108, 21, -28, 108, 122, -99, -120, -59, + -112, -125, -127, -100, -85, -127, -120, -101, + -25, -30, 119, -36, -8, -44, 18, -23, + -13, -115, 79, -35, 90, -20, -27, -40, + -41, -46, 25, 32, -21, -7, -54, -21, + -38, 127, -67, 75, -78, 64, 121, -57, + -127, -44, -2, 28, -96, -4, -128, -45, + -124, -42, -8, -51, 90, -115, 109, -7, + 26, 94, -51, 69, -87, -2, -110, 22, + 29, 28, 71, -102, -51, 27, -85, -29, + 17, 12, -26, 56, 49, 77, -32, -12, + -19, 29, 39, 79, -37, 7, 18, 32, + -36, 46, 78, 4, -4, 24, 24, -50, + 35, -44, 23, -71, -15, 93, -36, -27, + -22, -100, 13, -75, 41, 15, 7, -30, + 122, 115, -48, -62, 11, 99, 116, -46, + 89, 37, 21, -10, -1, 68, -115, -3, + 1, 59, 114, 35, 127, 71, -40, 27, + -10, -24, -26, -28, -30, -123, 108, 18, + 117, 1, -27, 0, 93, 3, -127, 5, + 78, 33, 117, -103, 87, 22, -72, 2, + -49, -68, 0, -32, -4, -90, 17, 20, + -113, -46, -88, 91, -7, 5, -9, 35, + 102, 38, -122, 9, -45, 127, 47, 11, + -48, 14, -62, -52, -43, -28, -79, -58, + -33, -9, -37, 40, 14, 9, 75, 4, + 17, -33, 37, 86, 127, 127, 102, 127, + -56, -49, -9, -43, -26, -21, 126, 94, + 127, 119, 107, 19, 108, 36, 121, 124, + -1, -22, -21, -77, 98, 2, -21, -1, + 25, 96, -7, 5, 39, 7, 11, -10, + -30, 45, 48, 17, 69, -28, 31, -6, + -70, 116, -56, 127, -52, 68, 57, -62, + -75, -37, -41, -25, -39, -77, -84, 94, + -30, -52, 14, 121, 7, -105, 114, 29 +}; + +static const rnn_weight vad_gru_bias[72] = { + -76, -101, 94, -127, -128, -80, -124, -119, + -112, 127, 127, 127, 126, -20, -119, -118, + -110, -123, -121, -124, -123, -121, -121, -96, + -15, 1, 78, -28, -55, 16, 22, 23, + 74, 8, 4, -12, 26, 32, 15, 43, + 40, 44, 32, 52, 61, 0, 11, 58, + 11, -13, 16, -39, 41, -100, -32, 9, + 25, -77, 19, -90, 7, 27, 44, -30, + 81, 21, -23, -33, -25, 13, -64, -41 +}; + +static const GRULayer vad_gru = { + vad_gru_bias, + vad_gru_weights, + vad_gru_recurrent_weights, + 24, 24, ACTIVATION_TANH +}; + +static const rnn_weight noise_gru_weights[12960] = { + -6, -26, 117, -37, 37, 10, 81, 20, + -35, -23, 51, -28, -20, -51, 12, 53, + 27, -29, -19, 75, 23, 5, -37, 59, + -35, 5, 35, -6, -39, -9, -18, 26, + 32, -34, -16, -51, 37, -64, -43, 60, + -14, 87, 31, 31, 14, -4, -44, 48, + 20, 13, -40, 12, 20, 22, -59, -11, + 7, -81, -95, -6, -21, 60, 13, 0, + 32, 91, -17, 6, -72, -29, -4, 10, + -30, -41, 51, 8, 11, 4, -20, -23, + 4, -6, -35, -40, -1, -85, 48, -13, + -4, 88, -53, -81, 32, 47, 50, 49, + -4, 10, -47, 37, -11, 26, -34, -8, + 19, -5, 6, 50, 50, -17, -24, 9, + 3, 19, 17, 4, -72, 3, 8, -18, + -27, 8, 16, 32, -95, 14, -33, -28, + 30, -13, 1, 19, 3, -16, 19, 21, + 30, -56, 51, -33, 62, 6, 1, -13, + -43, 20, -48, -33, -33, -14, 28, 70, + 16, 15, 8, -49, -73, 3, -21, 25, + -26, 19, 28, 32, 50, -78, 12, -26, + 78, 108, 78, -5, 49, -7, -60, -10, + 14, 56, -46, -40, 50, -4, -4, 36, + -29, 28, -13, -11, 27, 3, 31, -25, + -25, 9, 47, -47, 35, -13, -5, -22, + 27, 25, -25, 20, -29, -80, -6, 47, + -30, -99, -43, 10, -28, 36, 50, 20, + 24, 0, 46, 12, -52, -53, -51, 15, + -13, -19, 64, 45, -30, -66, -30, 3, + -28, 48, 56, 31, 4, -10, -12, 1, + -1, -12, -4, 11, 23, -29, 29, -1, + -8, 26, -17, 8, 36, 0, -11, 8, + 19, 7, 45, 33, 56, -32, 22, -17, + 32, 49, -67, -18, 40, 8, 3, -20, + -21, -2, 26, -46, 25, -13, 3, 13, + 22, 33, -39, -23, 55, -35, -11, -23, + -3, 92, 60, 45, 44, 59, -28, -29, + -7, -29, -42, 5, -24, -19, -37, 47, + 38, 29, -93, 29, -25, -39, 48, 7, + 41, -10, -24, 96, -16, 30, 33, -46, + -6, 11, 42, 38, 29, -44, -46, -91, + -37, 39, -63, 28, -22, 32, 75, 26, + 14, 17, 35, -61, -52, 77, -45, 13, + -20, 65, 34, 21, -34, -56, -14, -26, + -59, -42, 56, 19, 20, 59, -36, 15, + 57, 85, -92, 18, 53, -3, -37, -30, + 41, 39, 73, -17, -98, 18, 23, 0, + -36, -59, 0, 93, -45, 39, -31, -19, + 31, -40, -23, 7, -3, -63, -8, 17, + -37, 3, -23, 5, 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8, -126, -41, -16, + -12, -38, -121, 27, -47, -123, 26, -55, + -6, 21, 53, -31, -13, -45, 2, -8, + 21, -102, 38, -95, -64, -1, 15, -58, + -53, -35, -19, 8, -49, 7, 63, 92, + -15, -18, -58, -20, 48, -15, 24, -4, + 17, -73, -2, 43, -21, 38, -43, -109, + -40, 57, -63, 99, 5, -10, -81, -8, + 49, 13, 85, 106, -15, -128, -72, 42, + 30, -90, 86, -4, -44, -108, 17, -20, + -43, -16, -13, 8, 103, 18, 20, -94, + 24, -47, -5, -27, -23, 0, -8, -3, + -5, -17, -8, -6, 12, 37, -29, 6, + -40, -77, -95, -10, 5, -126, 71, -29, + 36, 41, 24, -61, -18, -16, 39, 15, + -62, 11, -72, -5, 31, 33, 45, -28, + 34, -45, 25, 8, -27, 15, 1, -22 +}; + +static const rnn_weight noise_gru_recurrent_weights[6912] = { + -36, -43, 8, -25, -42, -31, 127, -51, + -25, 26, 23, 31, 73, -36, -37, 72, + 15, -2, -103, -56, -58, 0, -15, -38, + -45, -46, 30, 32, 20, -1, -33, 36, + -78, 44, 4, 12, -63, -95, 40, -18, + 21, 48, -26, -5, 24, -29, 82, -17, + -21, 44, 41, -12, 7, 19, 47, -38, + -26, 22, -60, 57, -51, -36, 83, 47, + -30, 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-2, -43, -6, + -66, -11, 10, 16, 64, 2, 10, 49, + -39, 20, 18, 34, -25, -24, 2, 4, + 76, 32, 39, 32, -24, -46, -18, -11, + 13, 95, 40, 18, 45, -51, 31, -62, + -34, 20, 42, -29, -97, -9, 13, -4, + -77, -109, -11, -19, -2, -22, 13, 27, + 39, -8, -23, -6, -15, 30, -66, -10, + 7, 13, 84, 40, -88, -8, 71, 47, + -21, -75, 6, 11, 46, -65, 62, 11, + -43, -92, 37, -9, 43, 100, -28, 70, + -23, -60, -44, -58, 89, 30, 20, -89, + 12, 18, 127, 62, -20, -1, 76, -56, + -18, -9, -75, -49, -59, -9, 13, -90, + -46, 14, 114, 33, -78, -31, 92, -17, + 50, -74, 65, 22, -29, 31, -2, 20, + 20, -41, -78, -18, -28, 9, -113, 4, + 25, -17, -24, 80, -7, -92, -14, -51, + -44, 50, 11, -77, -14, 8, 4, -6, + 41, -121, -76, -11, 47, 49, 68, 77, + 3, -12, 4, 44, -18, -56, 20, -13, + 65, -34, 34, -16, -39, -35, -31, 39, + 33, -82, 26, 45, -10, 63, 57, 89, + 11, -17, 39, 47, 11, 59, -101, 75 +}; + +static const rnn_weight noise_gru_bias[144] = { + 41, -15, -66, 44, -25, -98, 39, -27, + -87, -36, -41, -62, 6, -27, 34, 92, + -12, -10, -42, -4, -45, -27, 59, 51, + 23, 38, 101, 41, 61, 30, 47, 7, + -82, -12, -31, 83, -54, 78, 94, 108, + 127, 50, -56, 23, -5, 45, 5, -44, + 3, 46, 7, -17, -30, 6, 50, 13, + 22, 50, 32, -13, -26, -6, -8, 10, + 22, -5, 54, 25, -16, 66, 20, -22, + 0, 22, -2, -7, 13, -10, -37, -8, + 2, 28, 10, 22, -27, -12, -61, -11, + 1, 11, -77, 1, 4, -3, 53, 25, + -1, 37, 24, -19, 2, 61, -29, 21, + 74, 8, -23, -34, 18, 10, 9, -1, + 21, -17, 42, 34, -73, -2, -22, 39, + -42, -15, 7, -9, -19, -19, -8, 20, + 65, 7, 26, 61, 16, -6, -42, -6, + -63, -11, 51, -1, -48, -15, 47, 44 +}; + +static const GRULayer noise_gru = { + noise_gru_bias, + noise_gru_weights, + noise_gru_recurrent_weights, + 90, 48, ACTIVATION_RELU +}; + +static const rnn_weight denoise_gru_weights[32832] = { + -22, 7, -53, -8, -46, -41, 27, 53, + 49, -36, 25, -42, 36, 33, 20, -8, + 32, -16, 49, 2, 26, -91, -37, -23, + -26, -28, -22, -7, 20, 25, -15, -41, + 3, 21, 6, 17, 19, -9, -1, -25, + 37, -43, 15, -74, 6, -29, 2, -8, + 2, 0, -87, 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-8, -111, -79, 114, -72, -119, + -111, 3, 110, -23, 86, -26, 99, -11, + -52, 127, -92, -106, -80, 22, -22, -14, + -74, 8, 84, -54, -40, 61, -90, 31, + -61, -127, -26, 8, 22, -91, -9, 81, + -57, 112, -86, -36, 76, 31, 53, 42, + 55, 7, -61, -11, 34, -99, 63, -46, + -119, 127, 37, -22, -85, -107, 55, 61, + -31, 45, 1, -109, -65, -52, -37, -52, + 10, -96, -31, 10, -124, -45, 44, 26, + 55, -31, 79, -32, -32, 42, -91, 34, + 46, 123, 83, -127, 18, 36, 49, -69, + 11, -1, -67, 99, -64, 2, -45, 14, + 18, -84, 125, -101, -101, 67, -126, -72, + -47, 54, 113, -10, -56, 51, 127, 7 +}; + +static const rnn_weight denoise_gru_bias[288] = { + 84, 77, 1, 12, 35, 67, -54, -6, + 22, 52, 45, 34, -39, 25, -34, 44, + -87, -53, -33, 45, 96, -32, 99, 23, + 29, 74, -82, 97, 4, -35, -30, 11, + 7, 22, -69, 54, 20, 0, 36, -10, + 15, 66, 12, -97, 44, 75, 40, 37, + -110, 105, -1, 20, -59, 75, 68, 125, + -127, 37, 16, 44, 65, -7, 105, -6, + 26, -126, 42, -51, -54, -6, 21, 48, + 0, 30, 10, 29, 21, 11, -2, -40, + 119, 48, 29, 63, 103, -56, 35, -15, + 30, 54, 55, -70, 56, 8, 25, 105, + 10, 4, -16, -26, -1, 31, 59, 34, + 25, 37, -45, 13, -20, -25, -7, -12, + 11, 56, -10, 46, 42, -7, -62, 32, + -21, 58, 34, 36, 3, -9, -51, 26, + -26, -6, 20, 41, 15, 40, 22, 23, + -33, -1, -20, 18, -19, -29, 55, 28, + -52, -36, -6, 50, 11, -14, -42, 34, + 22, 5, -5, -35, -9, -27, -4, 29, + -14, 10, 25, 16, 11, -62, 26, -21, + -26, -10, 34, 17, -21, -46, 4, -33, + 41, 16, -33, -25, -8, -13, -22, 29, + -6, 15, 31, 19, 14, -49, 11, 2, + -45, 9, -22, 6, 12, 87, 10, 54, + 7, 31, -37, 17, 85, -10, -16, -9, + 1, -13, 18, 30, 25, 2, -70, 9, + 11, -7, 36, 30, 4, 58, -24, -6, + -34, 13, 52, -32, 22, 127, -2, 29, + 25, -14, 24, 68, -23, 2, -1, -71, + -29, 27, 26, 43, 58, -70, 20, -25, + 78, -32, -12, 23, -33, 3, 11, 46, + 6, -92, 23, -24, -76, -10, -40, 14, + 7, 5, 47, 47, -26, -1, 20, -2, + -4, -6, -29, -30, 29, 61, -27, 49, + -5, 51, 27, -115, -7, 31, -14, 16 +}; + +static const GRULayer denoise_gru = { + denoise_gru_bias, + denoise_gru_weights, + denoise_gru_recurrent_weights, + 114, 96, ACTIVATION_TANH +}; + +static const rnn_weight denoise_output_weights[2112] = { + -9, 4, -9, -1, 78, 86, 70, 58, + 52, 37, 21, 15, 11, 14, 16, 7, + -6, -5, 2, 8, 23, 43, 85, 124, + 126, 123, 121, 106, 103, 88, 62, 35, + 29, 18, 11, -4, -5, 2, -2, -31, + 1, 14, 2, 41, 110, 82, 44, 28, + 19, 12, 6, 3, 9, 7, 0, -7, + -1, 2, 4, 42, 58, 59, 73, 68, + 49, -25, 20, 48, 61, 56, 41, 40, + 28, 16, 12, 8, -1, 3, 6, 10, + 8, 7, 4, 8, 10, 9, 24, 3, + 74, 111, 114, 69, 16, 5, -19, -34, + -29, -31, -25, -29, -30, -36, -36, -48, + -45, -45, -40, -29, -27, 12, -32, -25, + -24, -50, -64, -52, -50, -67, -62, -61, + -55, -47, -48, -54, -58, -77, -73, -26, + -18, -42, -67, -62, 12, 19, 36, 10, + -2, 1, -31, -11, -6, -1, -4, -6, + -15, -22, -39, -39, -37, -36, -40, -37, + -28, -9, 34, 36, 43, 40, 42, 31, + 31, 45, 32, 25, 15, 17, 12, 3, + -18, -31, -33, -29, -24, -39, -45, 3, + 46, 16, 27, 20, 11, -1, -8, -17, + -30, -32, -19, -13, -10, -16, -17, -34, + -39, -16, -5, -32, -122, -128, -22, -19, + -9, 19, 52, 50, 21, 23, 14, 10, + 3, 4, 6, 10, 10, 12, 22, 29, + 8, -6, -30, -71, 58, 48, 35, 21, + 10, 6, -1, -3, 12, 19, 15, 7, + -7, 4, 24, 11, -3, 17, 26, 25, + 33, 44, 6, -15, -22, -12, 6, 11, + -15, -32, -31, -31, -35, -19, -12, 0, + 15, 5, 14, 89, 124, 116, 125, 123, + 19, 78, 73, 67, 78, 3, 3, 20, + 30, 48, 58, 46, 52, 63, 59, 27, + 16, 11, -18, -29, -30, -23, -3, -6, + -12, 4, 10, 6, 4, -3, -14, -26, + -40, -57, -61, -57, -52, -46, -35, -21, + -4, 15, 53, 53, 10, -28, -43, -43, + -15, -37, -32, -30, -16, -33, -42, -28, + -26, -33, -29, -29, -33, -40, -25, -22, + -35, -38, -13, -36, -22, -25, -16, -21, + -10, -6, -15, -21, -23, -24, -7, -1, + -13, 9, 28, 26, -1, 0, 2, 9, + 127, 110, -16, 1, 7, 40, 36, 44, + 45, 40, 35, 27, 27, 33, 27, 35, + 47, 31, 10, 10, 14, 1, -90, -90, + -79, -63, -68, -79, -72, -83, -70, -76, + -78, -76, -55, -38, -29, -10, 0, -8, + -2, 9, 13, -11, 44, 1, 0, 74, + 40, -5, 16, -24, 1, 17, 25, 16, + 1, -6, -10, -1, 1, -2, 0, 4, + 7, 5, 57, 96, 53, -45, -81, -11, + -9, -80, -73, -76, -79, -75, -80, -88, + -90, -82, -71, -64, -61, -70, -93, -84, + -128, -128, -77, -21, 13, 37, 41, 20, + 10, 14, 15, 19, 33, 38, 18, 7, + 6, -4, 0, -4, -16, -39, -64, -125, + -122, -79, -66, -54, -31, -21, -25, -26, + -24, -18, -12, -10, -9, -5, -1, 7, + -1, 8, 21, 33, 34, 53, 57, 59, + 67, 63, 56, 42, 39, 46, 47, 44, + 50, 48, 28, 1, 6, 44, 28, -3, + -49, -20, -5, -7, 0, 0, -5, -9, + -15, -1, 3, 15, 21, 8, 6, 3, + 7, 18, 40, 27, 19, 11, 25, -3, + 31, -4, 6, 18, 37, 35, 39, 33, + 24, 31, 34, 27, 28, 30, 24, 24, + 20, 35, 44, 41, -15, -5, 75, 22, + 25, 34, 26, 10, 18, 18, 0, -1, + 9, -4, 6, 7, -8, 19, 37, 19, + -4, -12, 18, -29, -27, -1, 5, 7, + -11, 15, -24, -34, -12, -7, -11, -26, + -42, -38, -48, -58, -66, -72, -70, -66, + -51, -27, 37, 3, -41, -57, -49, -47, + -49, -50, -40, -32, -32, -32, -38, -54, + -67, -55, -39, -66, -78, -81, -72, -67, + 5, 11, -18, -37, -104, -123, -85, -49, + -36, -18, 1, 9, 0, -10, -24, -27, + -18, -8, 1, -1, 10, 23, -18, -64, + -94, -114, -127, -120, -121, -127, -125, -111, + -117, -115, -108, -90, -36, 9, 12, -1, + 6, 14, 26, 37, -34, -52, -47, -49, + -48, -57, -51, -36, -37, -37, -33, -33, + -34, -36, -33, -27, -27, -38, -26, -6, + 2, -21, -39, -8, 34, 85, 127, 127, + 103, 67, 8, -28, -22, 0, 30, 33, + 15, 11, -1, -8, -2, 16, 24, -21, + 20, 3, 11, 20, 17, 7, 3, 30, + 43, 34, 24, 18, 8, 8, 6, 10, + 11, 22, 16, 2, -13, 2, -31, -51, + -47, -49, -49, -60, -48, -38, -42, -23, + -18, -31, -34, -40, -36, -23, -12, -13, + -25, -12, -16, -38, -29, -38, -82, -92, + -113, -125, -125, -126, -126, -127, -126, -110, + -95, -75, -56, -43, -33, -1, 2, -12, + -20, -26, 10, 36, 39, 6, 23, 54, + 46, 0, -33, -37, 0, 32, 49, 50, + 48, 38, 25, 21, 25, 26, 28, 9, + 36, 61, 62, 40, 28, 36, 32, 19, + 22, 19, 17, 29, 32, 29, 25, 17, + 6, -12, -25, -19, 9, 9, 50, 40, + 44, 33, 38, 21, 9, 1, -7, 2, + 25, 32, 16, -20, -56, -98, -128, -128, + -127, -127, -59, 28, 1, 17, 17, 27, + 15, 0, -2, 19, 21, 18, 17, 24, + 34, 40, 47, 45, 25, 32, 65, 80, + 113, 127, 78, 44, 40, 44, 29, 32, + 37, 46, 64, 66, 54, 31, 41, 29, + 10, 20, 36, 26, 13, 5, 32, 45, + -36, -14, -13, -17, -25, -21, -35, -39, + -22, -11, -8, 0, -1, 4, 7, -9, + -22, -33, -36, -30, -35, -119, -103, -86, + -68, -56, -43, -47, -55, -42, -22, -23, + -18, -25, -13, -8, 11, -9, -18, -9, + -11, 2, -19, -124, 88, 34, 59, 19, + 36, 37, 50, 45, 61, 46, 36, 39, + 41, 35, 46, 58, 62, 80, 105, 127, + 127, 126, -25, -27, -49, -70, -66, -56, + -68, -85, -94, -89, -97, -89, -85, -79, + -64, -37, -26, -29, -35, -31, -32, -22, + -93, -46, -8, -3, -15, -14, -17, -38, + -54, -57, -48, -46, -46, -54, -71, -73, + -65, -65, -74, -76, -59, 43, 10, 23, + 47, 63, 70, 62, 43, 65, 77, 58, + 47, 57, 57, 45, 39, 31, 29, 35, + 31, 30, 34, 17, -31, -12, -17, -26, + -33, -40, -53, -77, -95, -83, -63, -50, + -48, -34, -24, -29, -45, -33, -18, -10, + -5, -53, -27, -25, -28, -20, -19, -15, + -17, -16, -23, -28, -30, -31, -38, -37, + -39, -33, -44, -70, -94, -107, -120, -49, + 127, 127, 89, 22, -7, -11, -21, -13, + 10, 60, 113, 119, 104, 107, 73, 62, + 44, 51, 50, 39, 34, 11, -20, -24, + -38, -47, -42, -36, -25, -14, -11, -12, + -12, -17, -10, -7, -10, -9, -15, -29, + -32, -40, -84, -128, -8, -12, 4, 16, + 7, 29, 90, 82, 61, 55, 54, 51, + 40, 31, 33, 31, 20, 15, 9, 19, + 33, 32, 19, 18, 10, 11, 9, 4, + -9, -5, 3, 3, -5, 1, 6, 5, + -7, -30, -41, -35, -34, -33, 12, 1, + -8, 22, 16, 13, -14, -30, -40, -54, + -70, -69, -61, -68, -81, -103, -126, -127, + -128, -127, -110, -94, -38, -6, 8, -1, + -22, -17, -19, -43, -40, -39, -49, -75, + -104, -126, -128, -128, -70, -58, -41, 21, + 36, 17, 20, 72, 13, 15, 31, 27, + 18, 7, 0, -9, -15, -25, -31, -21, + -9, 2, 16, 2, -47, -128, -128, -120, + -79, -36, -81, -78, -42, -7, -14, -13, + -9, -13, -18, -22, -15, -10, -5, -6, + -21, -37, -52, -41, -9, -21, -49, -51, + -19, -121, -128, -128, -128, -122, -89, -50, + -20, -13, -22, -41, -61, -61, -54, -26, + -32, -42, -52, -56, -38, -33, -6, -14, + -30, -38, -44, -47, -51, -47, -41, -42, + -45, -52, -57, -53, -39, -39, -48, -49, + -24, -9, 26, 12, 7, 10, 2, 9, + 14, 13, 13, 11, 3, 8, 6, 3, + 5, 17, 24, 49, 88, 127, 127, 127, + 111, 70, -25, -37, -45, -62, -57, -55, + -56, -47, -49, -47, -38, -28, -24, -22, + -15, -11, 0, 2, -9, -10, -8, 15, + -11, -34, -35, -37, -37, -41, -35, -39, + -54, -89, -104, -79, -71, -55, -15, -3, + 1, -43, -47, -14, -9, -4, 66, 54, + 30, 17, 33, 26, 23, 50, 53, 39, + 29, 25, 36, 44, 47, 46, 46, 52, + 38, 24, 16, 11, 37, 15, -11, -27, + -32, -24, -22, -17, -18, -11, -6, -12, + -8, -12, -14, -18, -15, -51, -54, -21, + 11, 50, 58, 21, 11, 11, 5, 11, + 7, -11, 2, 17, 14, 14, 10, 17, + 30, 25, 14, 7, 27, 31, 33, 32, + 62, 37, 18, 14, 15, 29, 26, 19, + 4, -17, -24, 1, 17, 22, 20, 11, + -28, -4, 63, 120, 127, 117, 50, 86, + 121, 127, 125, 127, 77, 33, 45, 66, + 81, 111, 100, 79, 77, 93, 88, 60, + 52, 47, 34, 26, 12, -7, -8, -13, + -14, -5, 2, 10, 18, 1, -36, -53, + -76, -73, -61, -56, -51, -35, -48, -79, + -101, -19, 26, 12, 36, 57, 65, 92, + 122, 127, 127, 90, 36, 28, 32, 30, + 18, 7, -5, 5, 15, 6, 3, 8, + 25, 22, 16, 10, 13, 22, 30, 37, + 57, 62, 70, 82, 81, 90, 105, 117, + 125, 111, 126, 127, 127, 127, -5, -31, + -31, -31, -28, -30, -20, -6, -13, -16, + -10, -17, -29, -32, -26, -15, -20, -26, + -28, -17, -20, -24, 21, 30, 37, 45, + 40, 34, 37, 50, 50, 54, 51, 54, + 54, 61, 57, 51, 51, 71, 112, 127, + 127, 127, -5, 47, 58, 40, 22, 10, + 2, 2, 18, 35, 52, 62, 60, 51, + 43, 32, 21, 25, 34, 43, 11, -53, + 16, 32, 41, 21, 13, 16, 23, 14, + 20, 28, 35, 37, 34, 39, 34, 20, + 10, 1, 10, 20, 18, 7, -38, -12, + -30, -31, -12, -18, -5, 2, -10, -10, + -18, -1, -9, -2, 4, 20, 13, 11, + 9, 0, 5, 66, 20, 34, 70, 67, + 9, 7, 46, 93, 90, 68, 44, 38, + 19, 17, 19, 34, 35, 12, -4, -3, + -4, 0, 104, 118, 109, 84, 67, 61, + 78, 68, 48, 46, 52, 46, 37, 36, + 41, 61, 60, 44, 43, 33, 13, -8, + 28, 4, -24, -29, -30, -35, -52, -48, + -15, 4, 7, -16, -16, -10, -8, -17, + -16, -1, 13, -7, -25, -47, 60, 67, + 83, 59, 40, 31, 30, 53, 65, 59, + 51, 55, 45, 51, 63, 49, 36, 37, + 32, 41, 44, -1, -15, -23, -13, -12, + -14, -11, -6, -8, -21, -49, -69, -71, + -71, -70, -49, -26, -2, 10, 18, 16, + 23, 39, -12, -7, 11, 8, 6, 22, + 13, -2, 14, 35, 42, 40, 58, 82, + 104, 123, 124, 107, 75, 66, 39, 79, + 41, 41, 64, 103, 117, 127, 127, 117, + 61, 28, 24, 35, 58, 74, 95, 104, + 118, 110, 53, 27, 12, -2, 95, 63, + 54, 38, 26, 25, 30, 30, 28, 19, + 21, 26, 33, 42, 21, -2, -21, -12, + 12, 19, 19, -64, -15, -29, -30, -20, + -36, -39, -52, -51, -30, 2, 33, 60, + 85, 97, 107, 103, 103, 80, 68, 51, + 26, -8, 8, 17, 20, -3, 32, 50, + 39, 37, 26, 14, 6, 2, 4, 8, + 13, 24, 21, 10, 22, 26, 15, 2, + -38, -26, -21, -32, -27, -23, -21, -33, + -25, -33, -28, -24, -25, -20, -17, 9, + 2, -60, -71, -42, -37, -26, -128, -61, + -69, -77, -78, -61, -60, -58, -33, -12, + 8, 42, 16, 24, 44, 24, 3, -2, + 6, 5, -8, -2, 26, 5, -25, -2, + 7, -43, -37, -13, -80, -104, -120, -114, + -100, -93, -87, -66, -50, -44, -52, -52, + -46, -30, 1, 15, 34, 23, 7, 4, + -11, -14, -10, 4, 7, -18, 12, 5, + -64, -122, -128, 25, 127, 53, -20, 27, + -7, -7, -6, 17, 31, 26, 31, 36, + 25, 14, 12, 17, 17, 18, 24, 15, + 8, -1, 2, 14, 28, 56, -31, -14, + -7, -7, -4, 1, 1, -2, 7, -4, + -3, -9, -11, -9, -10, 21, 48, 94, + 125, 76, -22, -125, 13, 9, 4, 1, + 7, 22, 9, 7, 12, 22, 20, 18, + 10, 10, 6, -1, -5, 34, 76, 58, + 13, -57, -41, -16, 4, 25, 45, 57, + 94, 100, 118, 126, 117, 105, 71, 37, + 1, -10, -13, -31, -30, -14, 13, 36, + -36, -14, -32, -38, -37, -35, -31, -40, + -48, -54, -62, -53, -55, -60, -62, -47, + -55, -69, -41, -25, -21, -27, -37, -50, + -47, -48, -55, -59, -66, -46, -31, -31, + -25, -29, -32, -37, -34, -39, -14, 0, + -1, 12, 45, 127, 43, 13, -2, 2, + 2, 0, -22, -74, -111, -110, -72, -37, + -9, 7, 33, 34, 21, 15, 11, 8, + 19, 31, 31, 4, -21, -18, -16, -13, + -17, 13, 21, 11, -3, 11, 27, 59, + 76, 70, 76, 102, 51, -50, -102, -59 +}; + +static const rnn_weight denoise_output_bias[22] = { + -30, 48, 50, 38, 8, -6, -39, -35, + 32, 45, 35, 31, 17, 13, 1, 14, + 40, -7, -38, -9, -14, -28 +}; + +static const DenseLayer denoise_output = { + denoise_output_bias, + denoise_output_weights, + 96, 22, ACTIVATION_SIGMOID +}; + +static const rnn_weight vad_output_weights[24] = { + 127, -128, 98, -128, 127, -128, -128, 127, + 127, 83, 127, 46, 96, 127, 127, -128, + 127, 127, -128, -128, -128, 127, -128, -128 +}; + +static const rnn_weight vad_output_bias[1] = { + 76 +}; + +static const DenseLayer vad_output = { + vad_output_bias, + vad_output_weights, + 24, 1, ACTIVATION_SIGMOID +}; + +const struct RNNModel model_sh = { + 24, + &input_dense, + 24, + &vad_gru, + 48, + &noise_gru, + 96, + &denoise_gru, + 22, + &denoise_output, + 1, + &vad_output, +}; From 6fc41992c057c1bfe957a154ec3b08c898da4613 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Sun, 2 Sep 2018 12:12:03 -0400 Subject: [PATCH 18/23] Whoops, forgot to add sh to the Makefile! --- Makefile.am | 1 + 1 file changed, 1 insertion(+) diff --git a/Makefile.am b/Makefile.am index 61ad10c3..419b4987 100644 --- a/Makefile.am +++ b/Makefile.am @@ -26,6 +26,7 @@ librnnoise_nu_la_SOURCES = \ src/models/mp.c \ src/models/bd.c \ src/models/lq.c \ + src/models/sh.c \ src/rnn_reader.c \ src/models.c \ src/pitch.c \ From da555289e0c22b5dd5175da78bb994dc9dbadcbc Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Mon, 3 Sep 2018 09:47:55 -0400 Subject: [PATCH 19/23] Fixing !SMOOTH_BANDS mode --- src/denoise.c | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/denoise.c b/src/denoise.c index 21ebcc17..d6a1e0fc 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -57,6 +57,8 @@ #define SMOOTH_BANDS 1 +#define NB_BAND_BOUNDARIES 22 + #if SMOOTH_BANDS #define NB_BANDS 22 #else @@ -102,7 +104,7 @@ struct DenoiseState { float pitch_enh_buf[PITCH_BUF_SIZE]; /* Bands adjusted for the sample rate */ - opus_int16 band_bins[NB_BANDS]; + opus_int16 band_bins[NB_BAND_BOUNDARIES]; float last_gain; int last_period; @@ -216,7 +218,7 @@ static void check_init(DenoiseState *st) { if (st->sample_rate <= 0) st->sample_rate = DEFAULT_SAMPLE_RATE; /* Adjust the bins for the sample rate */ - for (i = 0; i < NB_BANDS; i++) + for (i = 0; i < NB_BAND_BOUNDARIES; i++) st->band_bins[i] = (((long) eband5ms[i]) << FRAME_SIZE_SHIFT) * DEFAULT_SAMPLE_RATE / st->sample_rate; /* Make sure nothing's above the Nyquist frequency */ @@ -391,7 +393,9 @@ static int compute_frame_features(DenoiseState *st, kiss_fft_cpx *X, kiss_fft_cp apply_window(st, p); forward_transform(st, P, p); compute_band_energy(st, Ep, P); +#if SMOOTH_BANDS compute_band_corr(st, Exp, X, P); +#endif for (i=0;i Date: Mon, 3 Sep 2018 16:32:26 -0400 Subject: [PATCH 20/23] Let's scale our maximum attenuation correctly... --- src/denoise.c | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/denoise.c b/src/denoise.c index d6a1e0fc..d34800df 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -546,11 +546,10 @@ float rnnoise_process_frame(DenoiseState *st, float *out, const float *in) { if (min < st->max_attenuation) { if (min < MIN_MAX_ATTENUATION) min = MIN_MAX_ATTENUATION; - mult = st->max_attenuation / min; + mult = (1.0f-st->max_attenuation) / (1.0f-min); for (i=0;i 1) g[i] = 1; + g[i] = 1.0f-((1.0f-g[i]) * mult); st->lastg[i] = g[i]; } } From 925c2ba55ebd295ca3d9a47c8cb2d091dcf5fa36 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Mon, 3 Sep 2018 21:02:03 -0400 Subject: [PATCH 21/23] Less ridiculous inter-band interpolation. --- src/denoise.c | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/src/denoise.c b/src/denoise.c index d34800df..9e5acca1 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -168,15 +168,23 @@ void compute_band_corr(DenoiseState *st, float *bandE, const kiss_fft_cpx *X, co void interp_band_gain(DenoiseState *st, float *g, const float *bandE) { int i; + float prev, cur, next; memset(g, 0, FREQ_SIZE); + prev = cur = next = bandE[0]/2; for (i=0;iband_bins[i+1] - st->band_bins[i]; for (j=0;jband_bins[i] + j] = (1-frac)*bandE[i] + frac*bandE[i+1]; + g[st->band_bins[i] + j] = (1-frac)*prev + frac*next + cur; } } } From 877bb9504730a55ba4fb51e2f3cac312769a3a5a Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Mon, 10 Sep 2018 12:58:12 -0400 Subject: [PATCH 22/23] Switching the default model to 'cb' 'cb' was modeled on a wide variety of signals in noise like a recording environment, making it likely a better match to what people would expect from this library than the default, which is very speech-specific for a wide variety of noise (suited to VoIP) --- src/denoise.c | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/denoise.c b/src/denoise.c index 9e5acca1..d2021efc 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -79,7 +79,8 @@ #endif -extern const struct RNNModel model_orig; +/* cb is the default model */ +extern const struct RNNModel model_cb; static const opus_int16 eband5ms[] = { @@ -326,7 +327,7 @@ int rnnoise_init(DenoiseState *st, RNNModel *model) { if (model) st->rnn.model = model; else - st->rnn.model = &model_orig; + st->rnn.model = &model_cb; st->rnn.vad_gru_state = calloc(sizeof(float), st->rnn.model->vad_gru_size); st->rnn.noise_gru_state = calloc(sizeof(float), st->rnn.model->noise_gru_size); st->rnn.denoise_gru_state = calloc(sizeof(float), st->rnn.model->denoise_gru_size); From 26269304e120499485438cd93acf5127c6908c68 Mon Sep 17 00:00:00 2001 From: Gregor Richards Date: Sun, 16 Sep 2018 11:19:00 -0400 Subject: [PATCH 23/23] Backing out bad interpolation change. --- src/denoise.c | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/src/denoise.c b/src/denoise.c index d2021efc..5ca664e3 100644 --- a/src/denoise.c +++ b/src/denoise.c @@ -169,23 +169,15 @@ void compute_band_corr(DenoiseState *st, float *bandE, const kiss_fft_cpx *X, co void interp_band_gain(DenoiseState *st, float *g, const float *bandE) { int i; - float prev, cur, next; memset(g, 0, FREQ_SIZE); - prev = cur = next = bandE[0]/2; for (i=0;iband_bins[i+1] - st->band_bins[i]; for (j=0;jband_bins[i] + j] = (1-frac)*prev + frac*next + cur; + g[st->band_bins[i] + j] = (1-frac)*bandE[i] + frac*bandE[i+1]; } } }