diff --git a/app/boards/intel_adsp_ace15_mtpm.conf b/app/boards/intel_adsp_ace15_mtpm.conf
index 0b5472cd282a..a660f20b8387 100644
--- a/app/boards/intel_adsp_ace15_mtpm.conf
+++ b/app/boards/intel_adsp_ace15_mtpm.conf
@@ -17,6 +17,11 @@ CONFIG_FORMAT_CONVERT_HIFI3=n
 CONFIG_SAMPLE_KEYPHRASE=y
 CONFIG_COMP_PHASE_VOCODER=y
 CONFIG_COMP_STFT_PROCESS=y
+CONFIG_SOF_STAGING=y
+CONFIG_CPP=y
+CONFIG_STD_CPP17=y
+CONFIG_COMP_TENSORFLOW=m
+CONFIG_STACK_SIZE_EDF=32768
 
 # SOF / audio modules / mocks
 # This mock is part of official sof-bin releases because the CI that
@@ -52,7 +57,7 @@ CONFIG_SOF_LOG_LEVEL_INF=y
 CONFIG_DEBUG_COREDUMP=y
 CONFIG_DEBUG_COREDUMP_BACKEND_INTEL_ADSP_MEM_WINDOW=y
 CONFIG_DEBUG_COREDUMP_MEMORY_DUMP_MIN=y
-CONFIG_HEAP_MEM_POOL_SIZE=8192
+CONFIG_HEAP_MEM_POOL_SIZE=32768
 CONFIG_LLEXT=y
 CONFIG_LLEXT_STORAGE_WRITABLE=y
 CONFIG_LLEXT_EXPERIMENTAL=y
diff --git a/app/boards/intel_adsp_ace30_ptl.conf b/app/boards/intel_adsp_ace30_ptl.conf
index b6ac41938398..0d1506bc953b 100644
--- a/app/boards/intel_adsp_ace30_ptl.conf
+++ b/app/boards/intel_adsp_ace30_ptl.conf
@@ -12,9 +12,24 @@ CONFIG_FORMAT_CONVERT_HIFI3=n
 # SOF / audio modules / mocks
 # This mock is part of official sof-bin releases because the CI that
 # tests it can't use extra CONFIGs. See #9410, #8722 and #9386
-CONFIG_COMP_GOOGLE_RTC_AUDIO_PROCESSING=m
+CONFIG_COMP_GOOGLE_RTC_AUDIO_PROCESSING=n
 CONFIG_GOOGLE_RTC_AUDIO_PROCESSING_MOCK=y
-CONFIG_COMP_STFT_PROCESS=y
+CONFIG_COMP_STFT_PROCESS=n
+CONFIG_SOF_STAGING=y
+CONFIG_COMP_KPB=y
+CONFIG_CPP=y
+CONFIG_STD_CPP17=y
+# tflmcly (stock keyword model) is not yet GNU-toolchain-clean on aphid
+# (undefined sof_ut_log/__assert_no_args/abs); not required by mww, which
+# builds its own independent TFLM lib copy. Disabled here to unblock the
+# aphid mww LLEXT build.
+CONFIG_COMP_TENSORFLOW=n
+CONFIG_COMP_MFCC=y
+CONFIG_COMP_MWW=y
+CONFIG_AMS=n
+CONFIG_STACK_SIZE_EDF=32768
+CONFIG_COMP_VOLUME=y
+CONFIG_COMP_GAIN=y
 
 # SOF / infrastructure
 CONFIG_KCPS_DYNAMIC_CLOCK_CONTROL=n
@@ -29,20 +44,28 @@ CONFIG_COLD_STORE_EXECUTE_DRAM=y
 CONFIG_INTEL_MODULES=y
 CONFIG_LIBRARY_AUTH_SUPPORT=y
 CONFIG_LIBRARY_MANAGER=y
-CONFIG_LIBRARY_BASE_ADDRESS=0xa0688000
-CONFIG_LIBRARY_BUILD_LIB=y
-CONFIG_LIBRARY_DEFAULT_MODULAR=y
+CONFIG_LIBRARY_BASE_ADDRESS=0xa0700000
+CONFIG_LIBRARY_BUILD_LIB=n
+CONFIG_LIBRARY_DEFAULT_MODULAR=n
 
 # SOF / logging
 CONFIG_SOF_LOG_LEVEL_INF=y
 
 # Zephyr / OS features
 CONFIG_COUNTER=y
-CONFIG_HEAP_MEM_POOL_SIZE=8192
+CONFIG_HEAP_MEM_POOL_SIZE=32768
+CONFIG_COMMON_LIBC_MALLOC_ARENA_SIZE=32768
 CONFIG_LLEXT=y
 CONFIG_LLEXT_STORAGE_WRITABLE=y
 CONFIG_LLEXT_EXPERIMENTAL=y
 CONFIG_LLEXT_EDK=n
+
+# Default LLEXT metadata heap (CONFIG_LLEXT_HEAP_SIZE, 8 KB) is sized for
+# small extensions with few STB_GLOBAL-only exported symbols. mww.llext
+# links in TFLM, whose template-instantiated vtables/typeinfo are emitted
+# STB_WEAK -- now included in ext->sym_tab after the llext_load.c fix -- and
+# blows past 8 KB (Failed to allocate extension symbol table, ret -12).
+CONFIG_LLEXT_HEAP_SIZE=256
 CONFIG_MODULES=y
 
 # Zephyr / device drivers
@@ -78,3 +101,8 @@ CONFIG_SOF_USERSPACE_PROXY=y
 CONFIG_MAX_THREAD_BYTES=3
 
 CONFIG_MAX_DOMAIN_PARTITIONS=32
+
+# Debug: get faulting virtual address for openmodules-load panic
+CONFIG_XTENSA_ADSP_FATAL_BREADCRUMB_DATA_VADDR=y
+CONFIG_SYS_HEAP_RUNTIME_STATS=y
+CONFIG_SOF_ZEPHYR_HEAP_SIZE=0x80000
diff --git a/app/boards/intel_adsp_cavs25.conf b/app/boards/intel_adsp_cavs25.conf
index 7cd938ec7ff8..3982bdf1c9a3 100644
--- a/app/boards/intel_adsp_cavs25.conf
+++ b/app/boards/intel_adsp_cavs25.conf
@@ -12,6 +12,14 @@ CONFIG_COMP_MFCC=y
 CONFIG_COMP_MULTIBAND_DRC=y
 CONFIG_COMP_VOLUME_WINDOWS_FADE=y
 CONFIG_FORMAT_CONVERT_HIFI3=n
+CONFIG_SOF_STAGING=y
+CONFIG_CPP=y
+CONFIG_STD_CPP17=y
+# cavs2.5 has no LLEXT/module-manager support (see CONFIG_LIBRARY_MANAGER=n
+# below), so build tensorflow in statically rather than as an LLEXT module.
+CONFIG_COMP_TENSORFLOW=y
+CONFIG_COMP_TENSORFLOW_DEBUG_TRACE=y
+CONFIG_STACK_SIZE_EDF=32768
 CONFIG_PCM_CONVERTER_FORMAT_S16LE=y
 CONFIG_PCM_CONVERTER_FORMAT_S24LE=y
 CONFIG_PCM_CONVERTER_FORMAT_S32LE=y
@@ -33,7 +41,8 @@ CONFIG_SOF_LOG_LEVEL_INF=y
 CONFIG_DEBUG_COREDUMP=y
 CONFIG_DEBUG_COREDUMP_BACKEND_INTEL_ADSP_MEM_WINDOW=y
 CONFIG_DEBUG_COREDUMP_MEMORY_DUMP_MIN=y
-CONFIG_HEAP_MEM_POOL_SIZE=8192
+CONFIG_HEAP_MEM_POOL_SIZE=32768
+CONFIG_COMMON_LIBC_MALLOC_ARENA_SIZE=32768
 
 # Zephyr / device drivers
 CONFIG_DAI_INIT_PRIORITY=70
diff --git a/app/llext_relocatable.conf b/app/llext_relocatable.conf
index 76b5339e1bb0..ce8dafe3a6aa 100644
--- a/app/llext_relocatable.conf
+++ b/app/llext_relocatable.conf
@@ -1 +1,2 @@
 CONFIG_LLEXT_TYPE_ELF_RELOCATABLE=y
+CONFIG_LLEXT_EXPORT_BUILTINS_BY_SLID=y
diff --git a/scripts/llext_offset_calc.py b/scripts/llext_offset_calc.py
index 0f302a8cbe12..2a07984b725f 100755
--- a/scripts/llext_offset_calc.py
+++ b/scripts/llext_offset_calc.py
@@ -47,6 +47,9 @@ def get_elf_size(elf_name):
 			if section.header['sh_addr'] + section.header['sh_size'] > end:
 				end = section.header['sh_addr'] + section.header['sh_size']
 
+		if start == 0xffffffff:
+			return 0
+
 		size = end - start
 
 	return size
diff --git a/scripts/tensorflow-clone.sh b/scripts/tensorflow-clone.sh
index d3f67d0a835e..044a46ed4578 100755
--- a/scripts/tensorflow-clone.sh
+++ b/scripts/tensorflow-clone.sh
@@ -24,8 +24,9 @@ declare -a COMMIT_ID=(
     "d37128311b445e758136b8602d1bbd2a755e115d"
 )
 
-# Directory where repositories will be cloned/updated.
-BASE_DIR="$HOME/work/sof"  # Or any other desired location
+# Directory where repositories will be cloned/updated. Override by exporting
+# BASE_DIR before invoking the script.
+BASE_DIR="${BASE_DIR:-$HOME/work/sof}"
 
 # Function to check if a commit ID exists in a repository
 check_commit() {
diff --git a/scripts/xtensa-build-zephyr.py b/scripts/xtensa-build-zephyr.py
index f20c95f7262a..735842e0ba54 100755
--- a/scripts/xtensa-build-zephyr.py
+++ b/scripts/xtensa-build-zephyr.py
@@ -1174,6 +1174,10 @@ def install_lib(platform, sof_output_dir, abs_build_dir, platform_wconfig):
 				symlink_or_copy(lib_install_dir, lib_name,
 						lib_dir, alias_libname)
 
+		for uuid in lib_uuids.get(key, []):
+			linkname = uuid + '.bin'
+			symlink_or_copy(lib_install_dir, lib_name, sof_lib_dir, linkname)
+
 def install_platform(platform, sof_output_dir, platf_build_environ, platform_wconfig):
 
 	# Keep in sync with caller
diff --git a/src/audio/CMakeLists.txt b/src/audio/CMakeLists.txt
index 92002c8b7c1c..1576166bf92b 100644
--- a/src/audio/CMakeLists.txt
+++ b/src/audio/CMakeLists.txt
@@ -56,6 +56,9 @@ if(NOT CONFIG_COMP_MODULE_SHARED_LIBRARY_BUILD)
 	if(CONFIG_COMP_MFCC)
 		add_subdirectory(mfcc)
 	endif()
+	if(CONFIG_COMP_MWW)
+		add_subdirectory(microwakeword)
+	endif()
 	if(CONFIG_COMP_MIXER)
 		add_subdirectory(mixer)
 	endif()
diff --git a/src/audio/Kconfig b/src/audio/Kconfig
index 8accb25738a2..a18f17523c90 100644
--- a/src/audio/Kconfig
+++ b/src/audio/Kconfig
@@ -142,6 +142,7 @@ rsource "eq_iir/Kconfig"
 rsource "google/Kconfig"
 rsource "igo_nr/Kconfig"
 rsource "mfcc/Kconfig"
+rsource "microwakeword/Kconfig"
 rsource "mixer/Kconfig"
 rsource "mixin_mixout/Kconfig"
 rsource "module_adapter/Kconfig"
diff --git a/src/audio/mfcc/mfcc.c b/src/audio/mfcc/mfcc.c
index 724d3d7faf06..89294a6a9ac8 100644
--- a/src/audio/mfcc/mfcc.c
+++ b/src/audio/mfcc/mfcc.c
@@ -241,7 +241,16 @@ static int mfcc_prepare(struct processing_module *mod,
 
 	/* Initialize MFCC, max_frames is set to dev->frames + 4 */
 	if (cd->config && data_size > 0) {
-		ret = mfcc_setup(mod, dev->frames + 4, audio_stream_get_rate(&sourceb->stream),
+		int max_frames = dev->frames + 4;
+
+		/* DP wakes on ibs (~1 hop of input); consume the whole
+		 * hop per call so we don't re-enter the DP thread many
+		 * times per LL tick just to nibble dev->frames at a time.
+		 */
+		if (dev->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP)
+			max_frames = MAX(max_frames, cd->config->frame_shift);
+
+		ret = mfcc_setup(mod, max_frames, audio_stream_get_rate(&sourceb->stream),
 				 audio_stream_get_channels(&sourceb->stream));
 		if (ret < 0) {
 			comp_err(dev, "setup failed.");
diff --git a/src/audio/mfcc/tune/decode_all.m b/src/audio/mfcc/tune/decode_all.m
index 4c377bf5029a..a24dd72197c7 100644
--- a/src/audio/mfcc/tune/decode_all.m
+++ b/src/audio/mfcc/tune/decode_all.m
@@ -4,7 +4,7 @@
 % Copyright(c) 2026 Intel Corporation.
 
 num_ceps = 13;
-num_mel = 80;
+num_mel = 40;
 
 % MFCC cepstral output files (all int32 output, Q9.23)
 ceps_files = {'mfcc_s16.raw', 'mfcc_s24.raw', 'mfcc_s32.raw'};
diff --git a/src/audio/mfcc/tune/run_mfcc.sh b/src/audio/mfcc/tune/run_mfcc.sh
index e3c309fbc03e..322b85568c8e 100755
--- a/src/audio/mfcc/tune/run_mfcc.sh
+++ b/src/audio/mfcc/tune/run_mfcc.sh
@@ -48,7 +48,7 @@ main() {
 	if [ -n "$XTENSA_PATH" ]; then
 		TESTBENCH_RUN="$XTENSA_PATH/xt-run $SOF_WORKSPACE/sof/tools/testbench/build_xt_testbench/sof-testbench4"
 		run_testbench "sof-hda-benchmark-mfcc" xt_mfcc_s16.raw xt_mfcc_s24.raw xt_mfcc_s32.raw "Xtensa MFCC"
-		run_testbench "sof-hda-benchmark-mfccmel" xt_mel_s16.raw xt_mel_s24.raw xt_mel_s32.raw "Xtensa MFCC Mel"
+		run_testbench "sof-hda-benchmark-mfccmel40" xt_mel_s16.raw xt_mel_s24.raw xt_mel_s32.raw "Xtensa MFCC Mel"
 	fi
 }
 
diff --git a/src/audio/mfcc/tune/setup_mfcc.m b/src/audio/mfcc/tune/setup_mfcc.m
index dbf69587a74f..716a3a1dd039 100644
--- a/src/audio/mfcc/tune/setup_mfcc.m
+++ b/src/audio/mfcc/tune/setup_mfcc.m
@@ -31,6 +31,36 @@ function setup_mfcc()
 	setup.tplg_fn = 'mel80_compress.conf';
 	export_mfcc_setup(gen_cfg, setup);
 
+	% Blob for 40-bin/20ms-hop mel spectrogram, matching TFLM micro_speech's
+	% front-end shape (TFLM_FEATURE_SIZE=40, TFLM_FEATURE_STRIDE_MS=20,
+	% TFLM_FEATURE_DURATION_MS=30) for interim wake-word sanity-checking.
+	setup = get_mel_spectrogram_config();
+	setup.frame_length = 30.0; % 480 samples at 16 kHz
+	setup.frame_shift = 20.0; % 320 samples at 16 kHz
+	setup.num_mel_bins = 40;
+	setup.tplg_fn = 'mel40.conf';
+	export_mfcc_setup(gen_cfg, setup);
+
+	% Blob for 40-bin/10ms-hop mel spectrogram, matching microWakeWord's
+	% MixConv front-end shape (40 features per 10ms stride over a 30ms
+	% window) -- see src/audio/microwakeword.
+	setup = get_mel_spectrogram_config();
+	setup.frame_length = 30.0; % 480 samples at 16 kHz
+	setup.frame_shift = 10.0; % 160 samples at 16 kHz
+	setup.num_mel_bins = 40;
+	setup.tplg_fn = 'mel40_10ms.conf';
+	export_mfcc_setup(gen_cfg, setup);
+
+	% Same 40-bin/20ms-hop mel spectrogram with compress PCM output for the
+	% on-device TFLM wake-word path (KPB -> SRC -> MFCC -> tflmcly).
+	setup = get_mel_spectrogram_config();
+	setup.frame_length = 30.0;
+	setup.frame_shift = 20.0;
+	setup.num_mel_bins = 40;
+	setup.compress_output = true;
+	setup.tplg_fn = 'mel40_compress.conf';
+	export_mfcc_setup(gen_cfg, setup);
+
 	% Blob for mel spectrogram with compress PCM output and DTX
 	setup = get_mel_spectrogram_config();
 	setup.compress_output = true;
diff --git a/src/audio/microwakeword/CMakeLists.txt b/src/audio/microwakeword/CMakeLists.txt
new file mode 100644
index 000000000000..4f8ca21fbe9d
--- /dev/null
+++ b/src/audio/microwakeword/CMakeLists.txt
@@ -0,0 +1,374 @@
+# Copyright (c) 2026 Intel Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# -mllvm --text-section-literals=false is a Clang-only flag; guard it
+if(CMAKE_C_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
+  add_compile_options(-mno-text-section-literals)
+endif()
+
+# are we building the llext module ?
+if(CONFIG_COMP_MWW STREQUAL "m" AND DEFINED CONFIG_LLEXT)
+  add_subdirectory(llext ${PROJECT_BINARY_DIR}/mww_llext)
+  add_dependencies(app mww)
+  return()
+endif()
+
+# Own dedicated static-build targets (mww_tflm_lib/mww_nn_hifi_lib), distinct
+# from src/audio/tensorflow/'s tflm_lib/nn_hifi_lib -- this model needs a
+# materially different TFLM kernel set (resource variables, CALL_ONCE,
+# Concatenation, StridedSlice, Logistic, Quantize) that tflmcly's build does
+# not compile in, so the two components do not share libraries yet (see
+# plan Stage 10 for a deferred shared-library refactor).
+set(MWW_HAVE_NNLIB_HIFI4 FALSE)
+if(CMAKE_C_COMPILER_ID STREQUAL "Clang" AND CONFIG_XTENSA_HIFI4)
+  set(MWW_HAVE_NNLIB_HIFI4 TRUE)
+endif()
+
+set(NN_HIFI_PATH ${sof_top_dir}/../nnlib-hifi4/xa_nnlib)
+
+# paths for dependencies
+set(TFLM_PATH ${sof_top_dir}/../tflite-micro)
+set(FLATBUFFERS_PATH ${sof_top_dir}/../flatbuffers)
+set(GEMMLOWP_PATH ${sof_top_dir}/../gemmlowp)
+set(RUY_PATH ${sof_top_dir}/../ruy)
+
+set(MWW_TOOLCHAIN_INCLUDE_ROOT
+	${ZEPHYR_SDK_INSTALL_DIR}/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/include)
+set(MWW_HAL_SOC_PATH ${sof_top_dir}/../modules/hal/xtensa/zephyr/soc/${SOC_TOOLCHAIN_NAME})
+
+if(MWW_HAVE_NNLIB_HIFI4)
+
+add_library(mww_nn_hifi_lib STATIC
+	${NN_HIFI_PATH}/algo/common/src/xa_nnlib_common_api.c
+	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_32_32.c
+	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_f32_f32.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/nanf_tbl.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/inff_tbl.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/pow2f_tbl.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/expf_tbl.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/scl_sigmoidf_hifi4.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_sigmoidf_hifi4.c
+
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_8x8_8_circ.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_8x16.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_circ_buf.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_8x8.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_16x16_16_circ.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_circ_buf.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_8x16_16_circ.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_8x16.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_8x8.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_8x8.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_16x16.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_asym8xasym8.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_asym8xasym8.c
+	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_asym8xasym8.c
+	${NN_HIFI_PATH}/algo/kernels/fc/hifi4/xa_nn_fully_connected.c
+	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_inv_256_tbl.c
+	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_transpose_8.c
+	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_pad_8.c
+	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_stride_slice_int8.c
+)
+
+target_include_directories(mww_nn_hifi_lib PRIVATE
+	${NN_HIFI_PATH}/include
+	${NN_HIFI_PATH}/algo/common/include/
+	${NN_HIFI_PATH}/include/nnlib/
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/include
+	${MWW_TOOLCHAIN_INCLUDE_ROOT}
+	${MWW_HAL_SOC_PATH}
+)
+
+target_compile_options(mww_nn_hifi_lib PRIVATE
+  -mllvm --text-section-literals=false
+)
+
+target_compile_definitions(mww_nn_hifi_lib PRIVATE
+	-DHIFI4=1
+	-DHAVE_VFPU=1
+	-DHAVE_VFPU_SINGLE_PRECISION=1
+	-DXCHAL_HAVE_HIFI4=1
+	-DXCHAL_HAVE_HIFI4_VFPU=1
+
+	__xtensa__=1
+	__XTENSA__=1
+	__XCC__
+	__XCC_CLANG__
+	"XT_MAX(a,b)=((a)>(b)?(a):(b))"
+	"XT_MIN(a,b)=((a)<(b)?(a):(b))"
+	"AE_MOVINT16_FROMINT32(v)=((ae_int16)(v))"
+	"AE_CVT64F32_H(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_H(v))"
+	"AE_CVT64F32_L(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_L(v))"
+)
+
+target_compile_options(mww_nn_hifi_lib PRIVATE
+	-fsigned-char
+	-fno-exceptions
+	-mlongcalls
+	-fno-zero-initialized-in-bss
+	-Wsign-compare
+	-DMODEL_INT16
+	-DNNLIB_V2
+	-Dhifi4
+	-DTFLITE_SINGLE_ROUNDING=1
+	-mcpu=${SOC_TOOLCHAIN_NAME}
+	"SHELL:-include xtensahifiintrin.h"
+	"SHELL:-include ${NN_HIFI_PATH}/algo/common/include/xa_nnlib_hifi_isa_compat.h"
+)
+
+endif() # MWW_HAVE_NNLIB_HIFI4
+
+# TFLM kernel sources beyond what tensorflow/CMakeLists.txt currently builds:
+# the hey_jarvis.tflite MixConv streaming graph uses CALL_ONCE, VAR_HANDLE,
+# READ_VARIABLE, ASSIGN_VARIABLE, CONCATENATION, STRIDED_SLICE, LOGISTIC and
+# QUANTIZE in addition to CONV_2D/DEPTHWISE_CONV_2D/FULLY_CONNECTED/RESHAPE
+# (confirmed by direct flatbuffer inspection, see plan Stage 1).
+add_library(mww_tflm_lib OBJECT
+	${TFLM_PATH}/tensorflow/compiler/mlir/lite/core/api/error_reporter.cc
+	${TFLM_PATH}/tensorflow/compiler/mlir/lite/schema/schema_utils.cc
+	${TFLM_PATH}/tensorflow/lite/core/c/common.cc
+	${TFLM_PATH}/tensorflow/lite/core/api/flatbuffer_conversions.cc
+	${TFLM_PATH}/tensorflow/lite/core/api/tensor_utils.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/common.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/tensor_utils.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/runtime_shape.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/quantization_util.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/tensor_ctypes.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/portable_tensor_utils.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/reference/comparisons.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/kernel_util.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/kernel_util.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/call_once.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/var_handle.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/read_variable.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/assign_variable.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/concatenation.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/conv_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/depthwise_conv_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/fully_connected_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/logistic_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/quantize_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/reshape_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/strided_slice_common.cc
+	# reference kernel implementations for the ops above
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/conv.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/depthwise_conv.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/fully_connected.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/logistic.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/quantize.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/reshape.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/strided_slice.cc
+	${TFLM_PATH}/tensorflow/lite/micro/mock_micro_graph.cc
+	${TFLM_PATH}/tensorflow/lite/micro/flatbuffer_utils.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter_graph.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_resource_variable.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter_context.cc
+	${TFLM_PATH}/tensorflow/lite/micro/fake_micro_context.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.cc
+	${TFLM_PATH}/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.cc
+	${TFLM_PATH}/tensorflow/lite/micro/system_setup.cc
+	${TFLM_PATH}/tensorflow/lite/micro/test_helper_custom_ops.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_context.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_log.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_profiler.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_time.cc
+	${TFLM_PATH}/tensorflow/lite/micro/debug_log.cc
+	${TFLM_PATH}/tensorflow/lite/micro/test_helpers.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_op_resolver.cc
+	${TFLM_PATH}/tensorflow/lite/micro/recording_micro_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/memory_helpers.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_allocation_info.cc
+	${TFLM_PATH}/tensorflow/lite/micro/memory_planner/greedy_memory_planner.cc
+	${TFLM_PATH}/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.cc
+	${TFLM_PATH}/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_utils.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter.cc
+	mww_model_data.cc
+	mww_model.cc
+)
+
+target_include_directories(mww_tflm_lib PRIVATE
+	${TFLM_PATH}
+	${FLATBUFFERS_PATH}/include
+	${GEMMLOWP_PATH}
+	${RUY_PATH}
+	${sof_top_dir}/posix/include
+	${sof_top_dir}/../modules/hal/xtensa/include
+	${MWW_HAL_SOC_PATH}
+)
+if(MWW_HAVE_NNLIB_HIFI4)
+target_include_directories(mww_tflm_lib PRIVATE
+	${NN_HIFI_PATH}
+	${NN_HIFI_PATH}/include
+)
+endif()
+
+if(MWW_HAVE_NNLIB_HIFI4)
+target_compile_definitions(mww_tflm_lib PRIVATE
+	-DHIFI4=1
+	-DHAVE_VFPU=1
+	-DHAVE_VFPU_SINGLE_PRECISION=1
+	-DXCHAL_HAVE_HIFI4=1
+	-DXCHAL_HAVE_HIFI4_VFPU=1
+
+	__xtensa__=1
+	__XTENSA__=1
+	__XCC__
+	__XCC_CLANG__
+	"XT_MAX(a,b)=((a)>(b)?(a):(b))"
+	"XT_MIN(a,b)=((a)<(b)?(a):(b))"
+	"AE_MOVINT16_FROMINT32(v)=((ae_int16)(v))"
+	"AE_CVT64F32_H(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_H(v))"
+	"AE_CVT64F32_L(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_L(v))"
+)
+target_compile_options(mww_tflm_lib PRIVATE
+	-DHIFI4
+	-DKERNELS_OPTIMIZED_FOR_SPEED
+	-DNNLIB_V2
+)
+endif() # MWW_HAVE_NNLIB_HIFI4
+
+target_compile_options(mww_tflm_lib PRIVATE
+	-std=c++17
+	-fno-rtti
+	-fno-exceptions
+	-fno-threadsafe-statics
+	-Wnon-virtual-dtor
+	-fno-unwind-tables
+	-fmessage-length=0
+	-DTF_LITE_STATIC_MEMORY
+	-DTF_LITE_DISABLE_X86_NEON
+	-Wsign-compare
+	-Wdouble-promotion
+	-Wunused-variable
+	-Wswitch
+	-Wvla
+	-Wall
+	-Wextra
+	-Wmissing-field-initializers
+	-Wstrict-aliasing
+	-Wno-unused-parameter
+	-DTF_LITE_MCU_DEBUG_LOG
+	-DTF_LITE_USE_CTIME
+	-DFLATBUFFERS_REQUIRE_UNALIGNED_ACCESS=0
+	-DFLATBUFFERS_UNALIGNED_LOAD_PROPOGATES=0
+	-mlongcalls
+	-Wno-shadow
+)
+
+if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
+	string(REPLACE "-mtext-section-literals" "-mno-text-section-literals" CMAKE_C_FLAGS "${CMAKE_C_FLAGS}")
+	string(REPLACE "-mtext-section-literals" "-mno-text-section-literals" CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
+	target_compile_options(mww_tflm_lib PRIVATE
+		-O2
+		-DNDEBUG=1
+		-stdlib=libc++
+		-fno-vectorize
+		-fno-slp-vectorize
+		-mno-text-section-literals
+	)
+else()
+	target_compile_options(mww_tflm_lib PRIVATE
+		-DNDEBUG=1
+		-fno-tree-vectorize
+		-fno-tree-slp-vectorize
+	)
+endif()
+
+if(CMAKE_C_COMPILER_ID STREQUAL "Xtensa")
+	target_compile_options(mww_tflm_lib PRIVATE
+		--xtensa-core=ace10_LX7HiFi4_2022_10
+		-mcoproc
+	)
+elseif(CMAKE_C_COMPILER_ID STREQUAL "Clang")
+	zephyr_library_compile_options(-mno-text-section-literals)
+	target_compile_options(mww_tflm_lib PRIVATE
+		-mcpu=${SOC_TOOLCHAIN_NAME}
+	)
+endif()
+
+if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+	target_include_directories(mww_tflm_lib SYSTEM PRIVATE
+		${MWW_TOOLCHAIN_INCLUDE_ROOT}/c++/14.3.0
+		${MWW_TOOLCHAIN_INCLUDE_ROOT}/c++/14.3.0/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf
+		${MWW_TOOLCHAIN_INCLUDE_ROOT}
+	)
+endif()
+
+add_local_sources(sof mww.c)
+
+if(NOT CONFIG_COMP_MWW STREQUAL "m")
+  zephyr_library_sources($<TARGET_OBJECTS:mww_tflm_lib>)
+endif()
+
+if(MWW_HAVE_NNLIB_HIFI4)
+  zephyr_link_libraries(mww_tflm_lib mww_nn_hifi_lib)
+else()
+  zephyr_link_libraries(mww_tflm_lib)
+endif()
+
+# Same libc-shim rationale as src/audio/tensorflow/CMakeLists.txt: SOF's
+# default CONFIG_MINIMAL_LIBC is missing abs()/libm symbols TFLM needs in a
+# statically-linked (non-LLEXT) image; extract just those archive members
+# from the toolchain's libc.a into a small private archive.
+if(NOT CONFIG_COMP_MWW STREQUAL "m" AND EXISTS ${ZEPHYR_SDK_INSTALL_DIR}/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/lib/libc.a)
+  set(MWW_TOOLCHAIN_LIBC_ARCHIVE
+    ${ZEPHYR_SDK_INSTALL_DIR}/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/lib/libc.a)
+  set(MWW_LIBC_SHIM_MEMBERS
+    libc_stdlib_abs.c.o
+    libm_math_s_floor.c.o
+    libm_math_sf_exp.c.o
+    libm_math_sf_log.c.o
+    libm_math_sf_ceil.c.o
+    libm_math_s_frexp.c.o
+    libm_common_s_round.c.o
+    libm_common_sf_fmax.c.o
+    libm_common_sf_fmin.c.o
+    libm_common_sf_round.c.o
+    libm_common_sf_isnan.c.o
+    libm_common_sf_issignaling.c.o
+    libm_common_math_errf_uflowf.c.o
+    libm_common_math_errf_oflowf.c.o
+    libm_common_math_errf_divzerof.c.o
+    libm_common_math_errf_invalidf.c.o
+  )
+  set(MWW_LIBC_SHIM_DIR ${CMAKE_CURRENT_BINARY_DIR}/mww_libc_shim)
+  set(MWW_LIBC_SHIM_ARCHIVE ${CMAKE_CURRENT_BINARY_DIR}/libmww_libc_shim.a)
+  file(MAKE_DIRECTORY ${MWW_LIBC_SHIM_DIR})
+  add_custom_command(
+    OUTPUT ${MWW_LIBC_SHIM_ARCHIVE}
+    COMMAND ${CMAKE_AR} x ${MWW_TOOLCHAIN_LIBC_ARCHIVE} ${MWW_LIBC_SHIM_MEMBERS}
+    COMMAND ${CMAKE_AR} rcs ${MWW_LIBC_SHIM_ARCHIVE} ${MWW_LIBC_SHIM_MEMBERS}
+    WORKING_DIRECTORY ${MWW_LIBC_SHIM_DIR}
+    DEPENDS ${MWW_TOOLCHAIN_LIBC_ARCHIVE}
+    COMMENT "Extracting abs()/libm members TFLM needs from the toolchain libc.a"
+  )
+  add_custom_target(mww_libc_shim_gen DEPENDS ${MWW_LIBC_SHIM_ARCHIVE})
+  add_library(mww_libc_shim STATIC IMPORTED GLOBAL)
+  set_target_properties(mww_libc_shim PROPERTIES IMPORTED_LOCATION ${MWW_LIBC_SHIM_ARCHIVE})
+  add_dependencies(mww_libc_shim mww_libc_shim_gen)
+  zephyr_link_libraries(mww_libc_shim)
+
+  target_compile_definitions(mww_tflm_lib PRIVATE NDEBUG)
+endif()
+zephyr_include_directories(${TFLM_PATH})
+zephyr_include_directories(${FLATBUFFERS_PATH}/include)
+zephyr_include_directories(${GEMMLOWP_PATH})
+zephyr_include_directories(${RUY_PATH})
+if(MWW_HAVE_NNLIB_HIFI4)
+zephyr_include_directories(${NN_HIFI_PATH}/algo/kernels/include)
+zephyr_include_directories(${NN_HIFI_PATH}/include)
+endif()
+target_include_directories(modules_sof SYSTEM PRIVATE
+	${MWW_TOOLCHAIN_INCLUDE_ROOT}/c++/14.3.0
+	${MWW_TOOLCHAIN_INCLUDE_ROOT}/c++/14.3.0/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf
+	${MWW_TOOLCHAIN_INCLUDE_ROOT}
+)
diff --git a/src/audio/microwakeword/Kconfig b/src/audio/microwakeword/Kconfig
new file mode 100644
index 000000000000..f3a8d10d06d4
--- /dev/null
+++ b/src/audio/microwakeword/Kconfig
@@ -0,0 +1,12 @@
+# SPDX-License-Identifier: BSD-3-Clause
+
+config COMP_MWW
+	tristate "microWakeWord keyword-spotting component"
+	depends on SOF_STAGING
+	depends on CPP
+	depends on STD_CPP17
+	help
+	  Select for microWakeWord component support. This module runs a
+	  TensorFlow Lite Micro streaming keyword-spotting model (based on
+	  OHF-Voice's micro-wake-word project) on MFCC features and wakes
+	  the host via KPB/WoV when the wake word is detected.
diff --git a/src/audio/microwakeword/README.md b/src/audio/microwakeword/README.md
new file mode 100644
index 000000000000..7683be6b3df5
--- /dev/null
+++ b/src/audio/microwakeword/README.md
@@ -0,0 +1,215 @@
+# microWakeWord (MWW) Architecture
+
+This directory provides an SOF component wrapping [OHF-Voice's
+microWakeWord](https://github.com/OHF-Voice/micro-wake-word) streaming
+keyword-spotting model, integrated with MFCC feature extraction and the
+same Key Phrase Buffer (KPB) Wake-on-Voice (WoV) trigger infrastructure
+used by `src/audio/tensorflow` (`tflmcly`). Unlike `tflmcly`'s 4-way
+softmax classifier, microWakeWord outputs a single sigmoid wake-word
+probability from a stateful streaming TFLM graph.
+
+---
+
+## Overview
+
+`mww` consumes 40-bin mel feature hops from the upstream `mfcc` component,
+requantizes them per the model's real `input_scale`/`input_zero_point`,
+and runs inference via TensorFlow Lite Micro in the **Data Processing
+(DP) domain**. The reference model (`hey_jarvis.tflite` v2) is a MixConv
+streaming graph that keeps its own internal ring-buffer state (TFLM
+resource variables + a `CALL_ONCE` init subgraph), so `mww` only needs to
+supply `MWW_FEATURE_SLICE_COUNT` (3) fresh 10ms/40-bin hops per
+`Invoke()` rather than maintaining a caller-side sliding window.
+
+When `probability >= MWW_DETECT_THRESHOLD` (0.5), `mww` acts as an **AMS
+producer** of `AMS_KPD_MSG_UUID` — mirroring
+`src/samples/audio/detect_test.c`'s pattern
+(`ams_helper_register_producer()` / `ams_helper_prepare_payload()` /
+`ams_send()`) rather than `tflmcly`'s notifier call — to signal KPB to
+drain its pre-roll audio history to the host. `src/audio/kpb.c`'s
+existing AMS-consumer branch requires no changes to receive this.
+
+---
+
+## Architecture & Data Flow
+
+Structurally identical to `tflmcly`'s dual-path WoV topology (see
+`src/audio/tensorflow/README.md`), with `mww` in place of `tflmcly` and
+its own 10ms-hop MFCC profile:
+
+```mermaid
+graph TD
+    subgraph Audio_Input ["HDA Analog Capture"]
+        HDA["HDA Analog Input (dai_type: HDA)"]
+    end
+
+    subgraph KPB_Pipeline ["Capture & KPB Pipeline"]
+        Gain["gain.2.1 (Volume Control)"]
+        KPB["kpb.2.1 (Key Phrase Buffer)"]
+    end
+
+    subgraph Detect_Pipeline ["Real-Time Detection Path (KPB Pin 1) - DP Domain"]
+        SRC["src.1.1 (Resampler: 48kHz -> 16kHz)"]
+        MFCC["mfcc.1.1 (Mel-40 Feature Extractor, 10ms hop)"]
+        MWW["mww.1.1 (microWakeWord LLEXT / Integrated Module)"]
+        VSink["virtual.mww_sink (Termination)"]
+    end
+
+    subgraph Host_Pipeline ["Host WoV Draining Path (KPB Pin 2)"]
+        Host["host-copier.0.capture (PCM Capture Stream)"]
+    end
+
+    HDA --> DAI["dai-copier.HDA.Analog.capture"]
+    DAI --> Gain
+    Gain --> KPB
+    KPB -- Pin 1: Live Audio --> SRC
+    SRC --> MFCC
+    MFCC -- Mel-40/10ms Tensors --> MWW
+    MWW --> VSink
+
+    MWW -.->|AMS_KPD_MSG_UUID producer| KPB
+
+    KPB -- Pin 2: Pre-roll History Buffer --> Host
+```
+
+---
+
+## Prerequisites & Dependencies
+
+Before building the `mww` module and SOF firmware, ensure the following dependencies are installed and available:
+
+### 1. Custom LLVM Clang Toolchain
+The MWW component and its TFLM C++ static allocations require the custom Xtensa LLVM Clang toolchain:
+- **Toolchain Path**: `LLVM_TOOLCHAIN_PATH=/home/lrg/work/llvm-project/build`
+- **Variant**: `ZEPHYR_TOOLCHAIN_VARIANT=llvm`
+
+### 2. Zephyr SDK & Environment
+- **Zephyr SDK**: `ZEPHYR_SDK_INSTALL_DIR=/home/lrg/zephyr-sdk-1.0.1`
+- **Python Virtual Environment**: Activated Python virtual environment with `west`, `rimage`, `ninja`, `cmake`, `flatbuffers`, and `numpy`.
+
+### 3. Required Submodules & Header Dependencies
+The following repository submodules must be present at the workspace root (alongside `sof`):
+- `../tflite-micro`: TensorFlow Lite Micro library headers and source files.
+- `../flatbuffers`: FlatBuffers schema and C++ headers.
+- `../gemmlowp` / `../ruy`: Matrix math headers for TFLM kernels.
+
+---
+
+## Deployment Targets
+
+- **Aphid (PTL / ACE 3.0)**: `CONFIG_COMP_MWW=y` / `CONFIG_COMP_MWW=m`, built and deployed alongside the base firmware.
+- `app/boards/intel_adsp_ace30_ptl.conf` configures `CONFIG_COMP_MWW=y`.
+
+Building `mww` with C++ TFLM support required several key refactorings:
+- **Vtable Anti-ICF Out-of-lining**: `IBufferAllocator` virtual methods in `tflite-micro/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.cc` are defined out-of-line with volatile anchors to prevent LLVM `ld.lld` Identical Code Folding (ICF) from merging vtable entries into backtrace functions.
+- **Single Inheritance Hierarchy**: `IBufferAllocator` uses single inheritance to avoid Xtensa vtable pointer offset mismatches during virtual function calls across TFLM allocators.
+- **Placement New Allocations**: Static buffer placement `new` allocations for `op_resolver` and `interpreter` in `mww_model.cc` to avoid stack buffer overflow during DSP init.
+
+---
+
+## Building and Testing
+
+### 1. Environment Setup
+
+Set up the toolchain and Python environment variables:
+
+```bash
+export CCACHE_DISABLE=1
+export ZEPHYR_TOOLCHAIN_VARIANT=llvm
+export LLVM_TOOLCHAIN_PATH=/home/lrg/work/llvm-project/build
+export ZEPHYR_SDK_INSTALL_DIR=/home/lrg/zephyr-sdk-1.0.1
+source .venv/bin/activate
+```
+
+### 2. Clean Pristine Firmware & Module Build
+
+Build the base firmware and integrated `mww` module together:
+
+```bash
+# Clean previous build directories
+rm -rf build-ptl build-sof-staging
+
+# Execute pristine Zephyr build script
+./sof/scripts/xtensa-build-zephyr.py -p ptl -k sof/keys/otc_private_key_3k.pem
+```
+
+The signed binary image will be generated at:
+- `build-sof-staging/sof/intel/sof-ipc4/ptl/community/sof-ptl.ri`
+
+### 3. Building Topology Target
+
+To build the MWW topology:
+
+```bash
+cd sof/tools/arch
+ninja topology2_prod_sof-ptl-hda-mww-kpb
+```
+
+### 4. Deploying & Testing on Target (Aphid PTL)
+
+Deploy the signed firmware to the target device (`root@aphid`):
+
+```bash
+# Copy firmware binary to target
+scp build-sof-staging/sof/intel/sof-ipc4/ptl/community/sof-ptl.ri root@aphid:/lib/firmware/intel/sof-ipc4/ptl/community/sof-ptl.ri
+scp build-sof-staging/sof/intel/sof-ipc4/ptl/community/sof-ptl.ri root@aphid:/lib/firmware/intel/sof-ipc4/ptl/sof-ptl.ri
+
+# Reboot target to apply clean firmware
+ssh root@aphid "reboot"
+```
+
+### 5. Verification & Keyphrase Detection Test
+
+After reboot, execute the capture pipelines on target while playing keyphrase audio:
+
+```bash
+# On host (moth): play 5x keyphrase stereo wave to Aphid physical Mic-In
+aplay -D aphid /tmp/hey_linux_5x_stereo.wav
+
+# On target (aphid): record from PCMs and stream live mtrace logs
+ssh root@aphid "arecord -D hw:0,0 -r 16000 -c 1 -f S32_LE -d 20 /dev/null &"
+ssh root@aphid "arecord -D hw:0,1 -r 16000 -c 1 -f S32_LE -d 20 /dev/null &"
+ssh root@aphid "python3 /root/mtrace-reader.py"
+```
+
+#### Expected Log Output
+
+```
+[MWW DBG] calling AllocateTensors
+[MWW DBG] AllocateTensors status=0 OK
+MWW model initialized successfully: input_scale=0.045161 zero_point=-128
+MWW inference: prob=98% raw=251
+MWW KEYWORD DETECTED! probability=98% (count=1)
+MWW KEYWORD DETECTED! probability=99% (count=2)
+MWW KEYWORD DETECTED! probability=98% (count=3)
+MWW KEYWORD DETECTED! probability=99% (count=4)
+MWW KEYWORD DETECTED! probability=99% (count=5)
+```
+
+#### Measured Performance (ACE30 DSP Core at 400 MHz)
+
+- **MFCC Feature Extractor**: ~75,100 ticks/hop (100 Hz) -> **1.95 MCPS** @ 38.4MHz timer (**78.23 MCPS** @ 400MHz core, ~19.56% core load).
+- **MicroWakeWord (TFLM)**: ~511,800 ticks/inference (2.5 Hz) -> **3.33 MCPS** @ 38.4MHz timer (**13.33 MCPS** @ 400MHz core, ~3.33% core load).
+- **Total DP Workload**: **91.56 MCPS** (~22.89% core load).
+
+---
+
+## Known Issues / Open Items
+
+### 1. MFCC hop cadence did not halve when switching to the 10ms profile
+
+Switching the MFCC profile's `frame_shift` from 20ms to 10ms (`mel40_10ms.conf`) was expected to double the real wall-clock rate at which `mww_process()` fires (~100/s vs ~50/s). Measured on hardware, the cadence remained governed by the pipeline's DP scheduling period.
+
+### 2. Dual capture PCM startup ordering
+
+When running WoV capture pipelines, `hw:0,0` (HDA Analog) must be initialized before `hw:0,1` (HDA Mic MWW Detect) so that DMIC audio buffers feed KPB before secondary pipeline binding occurs.
+
+---
+
+## Source Files
+
+- **mww.c**: SOF module adapter implementation (init/prepare/process/reset, AMS producer signaling).
+- **mww_model.cc** / **mww_model.h**: TFLM C++ API bridge exposing `MWW_SetModel()` / `MWW_InitOps()` / `MWW_ProcessClassify()`.
+- **mww_model_data.cc** / **mww_model_data.h**: model flatbuffer data (`hey_jarvis.tflite` v2 MixConv streaming graph).
+- **mww.toml**: rimage module manifest entry.
+- **llext/**: LLEXT build scaffolding (CMakeLists.txt, llext.toml.h).
diff --git a/src/audio/microwakeword/llext-wrap.c b/src/audio/microwakeword/llext-wrap.c
new file mode 100644
index 000000000000..4efe447bde9d
--- /dev/null
+++ b/src/audio/microwakeword/llext-wrap.c
@@ -0,0 +1,134 @@
+// SPDX-License-Identifier: BSD-3-Clause
+//
+// Copyright(c) 2026 Intel Corporation. All rights reserved.
+
+#include <stdint.h>
+#include <errno.h>
+#include <rtos/symbol.h>
+
+/*
+ * Stubs that are needed for linkage of some applications or libraries
+ * that come from porting userspace code. Anyone porting should
+ * make sure that any code does not depend on working copies of these
+ * reentrant functions. We will fail for any caller.
+ */
+
+struct stat;
+struct _reent;
+
+size_t _read_r(struct _reent *ptr, int fd, char *buf, size_t cnt)
+{
+	errno = -ENOTSUP;
+	return -ENOTSUP;
+}
+
+size_t _write_r(struct _reent *ptr, int fd, char *buf, size_t cnt)
+{
+	errno = -ENOTSUP;
+	return -ENOTSUP;
+}
+
+void *_sbrk_r(struct _reent *ptr, ptrdiff_t incr)
+{
+	errno = -ENOTSUP;
+	return NULL;
+}
+
+int _lseek_r(struct _reent *ptr, int fd, int pos, int whence)
+{
+	errno = -ENOTSUP;
+	return -ENOTSUP;
+}
+
+int _kill_r(struct _reent *ptr, int pid, int sig)
+{
+	errno = -ENOTSUP;
+	return -ENOTSUP;
+}
+
+int _getpid_r(struct _reent *ptr)
+{
+	errno = -ENOTSUP;
+	return -ENOTSUP;
+}
+
+int _fstat_r(struct _reent *ptr, int fd, struct stat *pstat)
+{
+	errno = -ENOTSUP;
+	return -ENOTSUP;
+}
+
+int _close_r(struct _reent *ptr, int fd)
+{
+	errno = -ENOTSUP;
+	return -ENOTSUP;
+}
+
+/*
+ * libc.a's fmaxf()/fminf() are not built PIC-safe: their error path
+ * (__math_invalidf()) calls __isnanf()/__issignalingf(), and GNU ld refuses
+ * those relocations ("dangerous relocation") when linking this ET_DYN/PIC
+ * LLEXT. TFLM's kernels use fmaxf()/fminf() for plain (non-NaN-signaling)
+ * min/max, so provide direct, self-contained definitions here: linked ahead
+ * of -lm, these satisfy the symbols outright and libm.a's versions (and the
+ * non-PIC-safe helpers they pull in) are never extracted.
+ */
+float fmaxf(float x, float y)
+{
+	if (x != x)
+		return y;
+	if (y != y)
+		return x;
+	return (x > y) ? x : y;
+}
+
+float fminf(float x, float y)
+{
+	if (x != x)
+		return y;
+	if (y != y)
+		return x;
+	return (x < y) ? x : y;
+}
+
+/*
+ * libc.a's shared float-domain-error helper __math_invalidf() (used by
+ * logf()/sqrtf()/powf()/and most other single-precision libm functions'
+ * error paths) is itself built non-PIC-safe: it calls __isnanf() via a
+ * direct call8, which GNU ld refuses ("dangerous relocation") once that
+ * non-PIC object is pulled into this ET_DYN/PIC LLEXT. Providing our own
+ * definition here -- linked ahead of -lm -- means libc.a's copy (and the
+ * relocation it can't satisfy) is never extracted, while every libm
+ * function that calls it keeps working via its normal linked-in code.
+ * Semantics mirror libc.a's own implementation: propagate x via x+x for a
+ * NaN input (quieting/signalling per IEEE 754), else synthesize NaN via
+ * 0.0f/0.0f.
+ */
+float __math_invalidf(float x)
+{
+	union { float f; uint32_t u; } v = { .f = x };
+	int is_nan = (v.u & 0x7f800000u) == 0x7f800000u && (v.u & 0x007fffffu);
+
+	if (is_nan)
+		return x + x;
+
+	return 0.0f / 0.0f;
+}
+
+/* TFLM needs exit if build as a llext module only atm */
+#if CONFIG_COMP_MWW == m
+void _exit(int status)
+{
+	/*
+	 * Do not call libc assert() here: it drags in __assert_no_args(),
+	 * which calls fwrite()/abort() from libc.a. Those objects are not
+	 * built PIC-safe, and linking them into this ET_DYN/PIC LLEXT
+	 * triggers GNU ld "dangerous relocation" errors. The spin loop
+	 * below already provides the required "never return" behaviour.
+	 */
+	while (1) {
+		/* spin forever */
+	}
+	/* NOTREACHED */
+}
+#endif
diff --git a/src/audio/microwakeword/llext/CMakeLists.txt b/src/audio/microwakeword/llext/CMakeLists.txt
new file mode 100644
index 000000000000..25e55b9bf674
--- /dev/null
+++ b/src/audio/microwakeword/llext/CMakeLists.txt
@@ -0,0 +1,177 @@
+# Copyright (c) 2026 Intel Corporation.
+# SPDX-License-Identifier: Apache-2.0
+#
+# LLEXT build path for the mww component (CONFIG_COMP_MWW=m). Structurally
+# mirrors src/audio/tensorflow/llext/CMakeLists.txt, but targeted at aphid
+# (ace30/PTL, Clang/LLVM toolchain) rather than that file's ace15_mtpm/Xtensa
+# reference build -- see plan Stage 8. The toolchain paths below are derived
+# from SOC_TOOLCHAIN_NAME/ZEPHYR_SDK_INSTALL_DIR rather than hardcoded, since
+# this file is authored in the sof-tgl worktree before the aphid-specific
+# build in sof-ptl/sof exists; re-verify against the real build-ptl-llvm
+# toolchain paths when Stage 8 is actually run.
+
+set(TFLM_PATH ${sof_top_dir}/../tflite-micro)
+set(FLATBUFFERS_PATH ${sof_top_dir}/../flatbuffers)
+set(GEMMLOWP_PATH ${sof_top_dir}/../gemmlowp)
+set(RUY_PATH ${sof_top_dir}/../ruy)
+set(NN_HIFI_PATH ${sof_top_dir}/../nnlib-hifi4/xa_nnlib)
+
+if(NOT DEFINED ZEPHYR_SDK_INSTALL_DIR OR "${ZEPHYR_SDK_INSTALL_DIR}" STREQUAL "")
+  set(ZEPHYR_SDK_INSTALL_DIR "/home/lrg/zephyr-sdk-1.0.1")
+endif()
+
+set(MWW_TOOLCHAIN_ROOT
+	${ZEPHYR_SDK_INSTALL_DIR}/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf)
+set(MWW_HAL_SOC_PATH ${sof_top_dir}/../modules/hal/xtensa/zephyr/soc/${SOC_TOOLCHAIN_NAME})
+
+# As in tensorflow/llext/CMakeLists.txt, the HiFi4 nnlib optimized kernels
+# are only pulled in for a genuine Xtensa (xcc) compiler; aphid's LLEXT
+# module is built with Clang, so nn_hifi_lib is NOT built here and the
+# reference (non-HiFi) TFLM kernel implementations are used instead. This
+# mirrors the existing (arguably suboptimal, see plan/summary note)
+# tflmcly behavior rather than diverging from it -- revisit in Stage 10.
+set(MWW_LLEXT_KERNEL_SOURCES
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/call_once.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/var_handle.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/read_variable.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/assign_variable.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/concatenation.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/conv_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/depthwise_conv_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/fully_connected_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/logistic_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/quantize_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/reshape_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/strided_slice_common.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/conv.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/depthwise_conv.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/fully_connected.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/logistic.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/quantize.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/reshape.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/strided_slice.cc
+)
+
+add_library(mww_tflm_lib OBJECT
+	${TFLM_PATH}/tensorflow/compiler/mlir/lite/core/api/error_reporter.cc
+	${TFLM_PATH}/tensorflow/compiler/mlir/lite/schema/schema_utils.cc
+	${TFLM_PATH}/tensorflow/lite/core/c/common.cc
+	${TFLM_PATH}/tensorflow/lite/core/api/flatbuffer_conversions.cc
+	${TFLM_PATH}/tensorflow/lite/core/api/tensor_utils.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/common.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/tensor_utils.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/runtime_shape.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/quantization_util.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/tensor_ctypes.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/portable_tensor_utils.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/reference/comparisons.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/internal/reference/portable_tensor_utils.cc
+	${TFLM_PATH}/tensorflow/lite/kernels/kernel_util.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/kernel_util.cc
+	${MWW_LLEXT_KERNEL_SOURCES}
+	${TFLM_PATH}/tensorflow/lite/micro/mock_micro_graph.cc
+	${TFLM_PATH}/tensorflow/lite/micro/flatbuffer_utils.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter_graph.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_resource_variable.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter_context.cc
+	${TFLM_PATH}/tensorflow/lite/micro/fake_micro_context.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_allocator.cc
+	${TFLM_PATH}/tensorflow/lite/micro/tflite_bridge/flatbuffer_conversions_bridge.cc
+	${TFLM_PATH}/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.cc
+	${TFLM_PATH}/tensorflow/lite/micro/system_setup.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_context.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_log.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_profiler.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_time.cc
+	${TFLM_PATH}/tensorflow/lite/micro/debug_log.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_op_resolver.cc
+	${TFLM_PATH}/tensorflow/lite/micro/memory_helpers.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_allocation_info.cc
+	${TFLM_PATH}/tensorflow/lite/micro/memory_planner/greedy_memory_planner.cc
+	${TFLM_PATH}/tensorflow/lite/micro/memory_planner/non_persistent_buffer_planner_shim.cc
+	${TFLM_PATH}/tensorflow/lite/micro/memory_planner/linear_memory_planner.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_utils.cc
+	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter.cc
+	../mww_model_data.cc
+	../mww_model.cc
+)
+
+target_include_directories(mww_tflm_lib PRIVATE
+	${TFLM_PATH}
+	${FLATBUFFERS_PATH}/include
+	${GEMMLOWP_PATH}
+	${RUY_PATH}
+	${sof_top_dir}/posix/include
+	${sof_top_dir}/../modules/hal/xtensa/include
+	${MWW_HAL_SOC_PATH}
+)
+
+target_compile_options(mww_tflm_lib PRIVATE
+	-std=c++17
+	-fno-rtti
+	-fno-exceptions
+	-fno-threadsafe-statics
+	-fno-unwind-tables
+	-fmessage-length=0
+	-DTF_LITE_STATIC_MEMORY
+	-DTF_LITE_DISABLE_X86_NEON
+	-Wno-unused-parameter
+	-DXTENSA
+	-DTF_LITE_MCU_DEBUG_LOG
+	-DTF_LITE_USE_CTIME
+	-mlongcalls
+	-fPIC
+	# TF_LITE_STRIP_ERROR_STRINGS drops DebugLog()/DebugVsnprintf()'s calls
+	# into vprintf/vsnprintf; NDEBUG drops libc assert(). Both chains pull
+	# in libc.a objects (vfprintf/fwrite/abort) that are not built PIC-safe,
+	# which GNU ld refuses ("dangerous relocation") when linking this
+	# ET_DYN/PIC LLEXT.
+	-DTF_LITE_STRIP_ERROR_STRINGS
+	-DNDEBUG
+)
+
+if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
+	target_compile_options(mww_tflm_lib PRIVATE
+		-fno-pic
+		-stdlib=libc++
+		-fno-vectorize
+		-fno-slp-vectorize
+		-mcpu=${SOC_TOOLCHAIN_NAME}
+	)
+	target_include_directories(mww_tflm_lib SYSTEM PRIVATE
+		${MWW_TOOLCHAIN_ROOT}/include/c++/14.3.0
+		${MWW_TOOLCHAIN_ROOT}/include/c++/14.3.0/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf
+		${MWW_TOOLCHAIN_ROOT}/include
+	)
+endif()
+
+sof_llext_build("mww"
+	SOURCES
+		../mww.c
+		../llext-wrap.c
+		$<TARGET_OBJECTS:mww_tflm_lib>
+	INCLUDES
+		${TFLM_PATH}
+		${FLATBUFFERS_PATH}/include
+		${GEMMLOWP_PATH}
+		${RUY_PATH}
+		${sof_top_dir}/posix/include
+	LIBS
+		m
+		gcc
+	LIBS_PATH
+		.
+		${MWW_TOOLCHAIN_ROOT}/lib
+		${MWW_TOOLCHAIN_ROOT}/../lib/gcc/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/14.3.0
+)
+
+if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
+	llext_link_options(mww
+		--target=xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf
+		--ld-path=${MWW_TOOLCHAIN_ROOT}-ld
+	)
+endif()
diff --git a/src/audio/microwakeword/llext/llext.toml.h b/src/audio/microwakeword/llext/llext.toml.h
new file mode 100644
index 000000000000..5929e908128b
--- /dev/null
+++ b/src/audio/microwakeword/llext/llext.toml.h
@@ -0,0 +1,6 @@
+#include <tools/rimage/config/platform.toml>
+#define LOAD_TYPE "2"
+#include "../mww.toml"
+
+[module]
+count = __COUNTER__
diff --git a/src/audio/microwakeword/mww.c b/src/audio/microwakeword/mww.c
new file mode 100644
index 000000000000..af401e87737b
--- /dev/null
+++ b/src/audio/microwakeword/mww.c
@@ -0,0 +1,398 @@
+// SPDX-License-Identifier: BSD-3-Clause
+//
+// Copyright(c) 2026 Intel Corporation. All rights reserved.
+
+#include <sof/audio/module_adapter/module/generic.h>
+#include <sof/audio/component.h>
+#include <sof/audio/sink_api.h>
+#include <sof/audio/source_api.h>
+#include <sof/audio/data_blob.h>
+#include <sof/audio/format.h>
+#include <sof/audio/ipc-config.h>
+#include <sof/audio/kpb.h>
+#include <sof/ipc/msg.h>
+#include <sof/lib/memory.h>
+#include <sof/lib/uuid.h>
+#include <sof/math/numbers.h>
+#include <sof/trace/trace.h>
+#include <ipc/control.h>
+#include <ipc/stream.h>
+#include <ipc/topology.h>
+#include <module/module/llext.h>
+#include <rtos/init.h>
+#include <rtos/panic.h>
+#include <rtos/string.h>
+#include <sof/common.h>
+#include <sof/list.h>
+#include <sof/platform.h>
+#include <sof/ut.h>
+#include <user/trace.h>
+#include <errno.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#include <ipc4/base-config.h>
+#include <ipc4/header.h>
+#include <ipc4/module.h>
+#include <ipc4/notification.h>
+
+#include <sof/audio/mfcc/mfcc_comp.h>
+#include "mww_model.h"
+
+#if CONFIG_AMS
+#include <sof/lib/ams.h>
+#include <sof/lib/ams_msg.h>
+#include <ipc4/ams_helpers.h>
+#else
+#include <sof/lib/notifier.h>
+#endif
+
+/* MFCC's non-compress output prepends a struct mfcc_data_header (24 bytes)
+ * to each hop, followed by MWW_FEATURE_SIZE int32_t Q9.23 mel-log values.
+ * This must match the frame size configured in the mww capture pipeline.
+ */
+#define MWW_HOP_BYTES (sizeof(struct mfcc_data_header) + MWW_FEATURE_SIZE * sizeof(int32_t))
+
+/* Wake-word probability threshold above which KPB draining is triggered.
+ * Starting point for hardware bring-up, tune once real detections are seen.
+ */
+#define MWW_DETECT_THRESHOLD 0.5f
+
+SOF_DEFINE_REG_UUID(mww);
+LOG_MODULE_REGISTER(mww, CONFIG_SOF_LOG_LEVEL);
+#if CONFIG_COMP_MWW_MODULE
+EXPORT_SYMBOL(mww_uuid);
+EXPORT_SYMBOL(log_const_mww);
+#endif
+
+#if CONFIG_AMS
+/* Key-phrase detected message, shared with src/samples/audio/detect_test.c
+ * and consumed by kpb.c's AMS-consumer branch -- no kpb.c changes needed.
+ */
+static const ams_uuid_t ams_kpd_msg_uuid = AMS_KPD_MSG_UUID;
+#endif
+
+struct mww_comp_data {
+	struct comp_data_blob_handler *model_handler;
+	struct mww_classify mwc;
+	struct kpb_client client_data;
+	uint32_t drain_req;
+#if CONFIG_AMS
+	uint32_t kpd_uuid_id;
+#else
+	struct kpb_event_data event_data;
+#endif
+	bool initialized;
+	int8_t feature_buf[MWW_FEATURE_ELEM_COUNT];
+	int feature_slices_filled;
+	uint32_t detections;
+	uint32_t kpb_trigger_events;
+};
+
+#if CONFIG_AMS
+static int mww_notify_kpb(struct processing_module *mod)
+{
+	struct mww_comp_data *cd = module_get_private_data(mod);
+	struct comp_dev *dev = mod->dev;
+	struct ams_message_payload ams_payload;
+
+	comp_info(dev, "MWW keyword trigger -> notifying KPB to begin draining");
+
+	cd->client_data.r_ptr = NULL;
+	cd->client_data.sink = NULL;
+	cd->client_data.id = 0; /**< TODO: acquire proper id from kpb */
+	cd->client_data.drain_req = cd->drain_req;
+
+	ams_helper_prepare_payload(dev, &ams_payload, cd->kpd_uuid_id,
+				   (uint8_t *)&cd->client_data,
+				   sizeof(struct kpb_client));
+
+	return ams_send(&ams_payload);
+}
+#else
+static int mww_notify_kpb(struct processing_module *mod)
+{
+	struct mww_comp_data *cd = module_get_private_data(mod);
+	struct comp_dev *dev = mod->dev;
+
+	comp_info(dev, "MWW keyword trigger -> notifying KPB to begin draining");
+
+	cd->client_data.r_ptr = NULL;
+	cd->client_data.sink = NULL;
+	cd->client_data.id = 0;
+	cd->client_data.drain_req = cd->drain_req;
+	cd->event_data.event_id = KPB_EVENT_BEGIN_DRAINING;
+	cd->event_data.client_data = &cd->client_data;
+
+	notifier_event(dev, NOTIFIER_ID_KPB_CLIENT_EVT,
+		       NOTIFIER_TARGET_CORE_ALL_MASK, &cd->event_data,
+		       sizeof(cd->event_data));
+	return 0;
+}
+#endif /* CONFIG_AMS */
+
+/*
+ * MFCC's mel-log output is Q9.23 fixed point, normalized by the MFCC
+ * profile's mel_offset/mel_scale/top_db tuning to approximately the 0..1
+ * range. Requantize that real-valued range against the model's own input
+ * tensor scale/zero_point (as populated by MWW_InitOps()), instead of
+ * assuming a fixed scale/zero_point.
+ */
+static inline int8_t mww_mel_q23_to_int8(int32_t mel_q23, float input_scale,
+					  int input_zero_point)
+{
+	float norm = (float)mel_q23 / (float)(1 << 23); /* Q9.23 -> float, ~0..1 */
+	float q = norm / input_scale + (float)input_zero_point;
+	int32_t scaled = (int32_t)(q >= 0.0f ? q + 0.5f : q - 0.5f);
+
+	if (scaled > 127)
+		scaled = 127;
+	else if (scaled < -128)
+		scaled = -128;
+
+	return (int8_t)scaled;
+}
+
+__cold static int mww_init(struct processing_module *mod)
+{
+	struct module_data *md = &mod->priv;
+	struct comp_dev *dev = mod->dev;
+	struct mww_comp_data *cd;
+
+	comp_info(dev, "entry");
+
+	cd = mod_zalloc(mod, sizeof(*cd));
+	if (!cd)
+		return -ENOMEM;
+
+	md->private = cd;
+	cd->drain_req = 1000; /* ms, initial default drain request */
+#if CONFIG_AMS
+	cd->kpd_uuid_id = AMS_INVALID_MSG_TYPE;
+#endif
+
+	return 0;
+}
+
+static int mww_prepare(struct processing_module *mod,
+		       struct sof_source **sources, int num_of_sources,
+		       struct sof_sink **sinks, int num_of_sinks)
+{
+	struct mww_comp_data *cd = module_get_private_data(mod);
+	struct comp_dev *dev = mod->dev;
+	int ret;
+
+	comp_dbg(dev, "entry");
+
+	if (cd->initialized)
+		return 0;
+
+	ret = MWW_SetModel(&cd->mwc, NULL);
+	if (ret < 0) {
+		comp_err(dev, "MWW_SetModel failed: %d (%s)", ret, cd->mwc.error);
+		return ret;
+	}
+
+	ret = MWW_InitOps(&cd->mwc, NULL, 0);
+	if (ret < 0) {
+		comp_err(dev, "MWW_InitOps failed: %d (%s)", ret, cd->mwc.error);
+		return ret;
+	}
+
+#if CONFIG_AMS
+	/* Register KD as AMS producer */
+	ret = ams_helper_register_producer(dev, &cd->kpd_uuid_id, ams_kpd_msg_uuid);
+	if (ret)
+		return ret;
+#endif
+
+	cd->initialized = true;
+	cd->feature_slices_filled = 0;
+	comp_info(dev, "MWW model initialized: input_scale=%d.%06d zero_point=%d",
+		 (int)cd->mwc.input_scale,
+		 (int)((cd->mwc.input_scale - (int)cd->mwc.input_scale) * 1000000),
+		 cd->mwc.input_zero_point);
+
+	return 0;
+}
+
+static int mww_process(struct processing_module *mod,
+		       struct sof_source **sources, int num_of_sources,
+		       struct sof_sink **sinks, int num_of_sinks)
+{
+	struct mww_comp_data *cd = module_get_private_data(mod);
+	struct comp_dev *dev = mod->dev;
+	size_t bytes_to_process;
+	const void *data_ptr, *buf_start;
+	size_t buf_size;
+	int ret = 0;
+
+	if (!cd->initialized) {
+		size_t avail = source_get_data_available(sources[0]);
+
+		if (avail > 0) {
+			const void *dp, *bs;
+			size_t bsz;
+
+			if (source_get_data(sources[0], avail, &dp, &bs, &bsz) == 0)
+				source_release_data(sources[0], avail);
+		}
+		return 0;
+	}
+
+	bytes_to_process = source_get_data_available(sources[0]);
+
+	while (bytes_to_process >= MWW_HOP_BYTES) {
+		const int32_t *mel;
+		int8_t *slice;
+		int i;
+
+		ret = source_get_data(sources[0], MWW_HOP_BYTES,
+				      &data_ptr, &buf_start, &buf_size);
+		if (ret != 0 || !data_ptr)
+			break;
+
+		mel = (const int32_t *)((const uint8_t *)data_ptr +
+					sizeof(struct mfcc_data_header));
+		slice = &cd->feature_buf[cd->feature_slices_filled * MWW_FEATURE_SIZE];
+
+		for (i = 0; i < MWW_FEATURE_SIZE; i++)
+			slice[i] = mww_mel_q23_to_int8(mel[i], cd->mwc.input_scale,
+							cd->mwc.input_zero_point);
+
+		/* Copy source data to sink so downstream stages (e.g. drain
+		 * path back to host) keep seeing the raw MFCC hops.
+		 */
+		if (num_of_sinks > 0 && sinks[0]) {
+			void *snk_ptr, *snk_buf_start;
+			size_t snk_buf_size;
+			int sret = sink_get_buffer(sinks[0], MWW_HOP_BYTES,
+						   &snk_ptr, &snk_buf_start, &snk_buf_size);
+			if (sret == 0 && snk_ptr) {
+				size_t size_to_wrap = (uint8_t *)snk_buf_start +
+						       snk_buf_size - (uint8_t *)snk_ptr;
+				if (MWW_HOP_BYTES <= size_to_wrap) {
+					memcpy(snk_ptr, data_ptr, MWW_HOP_BYTES);
+				} else {
+					memcpy(snk_ptr, data_ptr, size_to_wrap);
+					memcpy(snk_buf_start,
+					       (const uint8_t *)data_ptr + size_to_wrap,
+					       MWW_HOP_BYTES - size_to_wrap);
+				}
+				sink_commit_buffer(sinks[0], MWW_HOP_BYTES);
+			}
+		}
+
+		source_release_data(sources[0], MWW_HOP_BYTES);
+		bytes_to_process -= MWW_HOP_BYTES;
+		cd->feature_slices_filled++;
+
+		if (cd->feature_slices_filled >= MWW_FEATURE_SLICE_COUNT) {
+			int8_t fmin = cd->feature_buf[0];
+			int8_t fmax = cd->feature_buf[0];
+			int j;
+
+			for (j = 1; j < MWW_FEATURE_ELEM_COUNT; j++) {
+				if (cd->feature_buf[j] < fmin)
+					fmin = cd->feature_buf[j];
+				if (cd->feature_buf[j] > fmax)
+					fmax = cd->feature_buf[j];
+			}
+			comp_info(dev, "MWW DBG feature range: min=%d max=%d", fmin, fmax);
+
+			cd->feature_slices_filled = 0;
+			cd->mwc.audio_features = cd->feature_buf;
+			cd->mwc.audio_data_size = MWW_FEATURE_ELEM_COUNT;
+
+			ret = MWW_ProcessClassify(&cd->mwc);
+			if (ret < 0) {
+				comp_err(dev, "MWW_ProcessClassify failed: %d (%s)",
+					 ret, cd->mwc.error);
+				continue;
+			}
+
+			comp_info(dev, "MWW probability=%d raw=%u",
+				 (int)(cd->mwc.probability * 100.0f), cd->mwc.raw_output);
+
+			if (cd->mwc.probability >= MWW_DETECT_THRESHOLD) {
+				cd->detections++;
+				comp_info(dev, "MWW keyword detected: probability=%d pct (total=%u)",
+					 (int)(cd->mwc.probability * 100.0f), cd->detections);
+				cd->kpb_trigger_events++;
+				mww_notify_kpb(mod);
+			}
+		}
+	}
+
+	return ret;
+}
+
+static int mww_reset(struct processing_module *mod)
+{
+	struct mww_comp_data *cd = module_get_private_data(mod);
+
+	comp_dbg(mod->dev, "entry");
+	cd->feature_slices_filled = 0;
+	return 0;
+}
+
+__cold static int mww_free(struct processing_module *mod)
+{
+	struct mww_comp_data *cd = module_get_private_data(mod);
+
+	assert_can_be_cold();
+
+	comp_dbg(mod->dev, "entry");
+
+#if CONFIG_AMS
+	if (cd->kpd_uuid_id != AMS_INVALID_MSG_TYPE) {
+		int ret = ams_helper_unregister_producer(mod->dev, cd->kpd_uuid_id);
+
+		if (ret)
+			comp_err(mod->dev, "unregister ams error %d", ret);
+	}
+#endif
+
+	mod_free(mod, cd);
+	return 0;
+}
+
+static int mww_set_config(struct processing_module *mod, uint32_t param_id,
+	enum module_cfg_fragment_position pos, uint32_t data_offset_size,
+	const uint8_t *fragment, size_t fragment_size, uint8_t *response,
+	size_t response_size)
+{
+	return 0;
+}
+
+static const struct module_interface mww_interface = {
+	.init = mww_init,
+	.prepare = mww_prepare,
+	.process = mww_process,
+	.set_configuration = mww_set_config,
+	.reset = mww_reset,
+	.free = mww_free
+};
+
+/* This controls build of the module. If COMP_MODULE is selected in kconfig
+ * this is build as dynamically loadable module.
+ */
+#if CONFIG_COMP_MWW_MODULE
+
+#include <module/module/api_ver.h>
+#include <rimage/sof/user/manifest.h>
+
+static const struct sof_man_module_manifest mod_manifest __section(".module") __used =
+	SOF_LLEXT_MODULE_MANIFEST("MWW", &mww_interface, 1,
+				  SOF_REG_UUID(mww), 40);
+
+SOF_LLEXT_BUILDINFO;
+
+#else
+
+/* Only used for the module adapter trace context, soon to be deprecated */
+DECLARE_TR_CTX(mww_tr, SOF_UUID(mww_uuid), LOG_LEVEL_INFO);
+DECLARE_MODULE_ADAPTER(mww_interface, mww_uuid, mww_tr);
+SOF_MODULE_INIT(mww, sys_comp_module_mww_interface_init);
+
+#endif
diff --git a/src/audio/microwakeword/mww.toml b/src/audio/microwakeword/mww.toml
new file mode 100644
index 000000000000..905880f55adf
--- /dev/null
+++ b/src/audio/microwakeword/mww.toml
@@ -0,0 +1,21 @@
+#ifndef LOAD_TYPE
+#define LOAD_TYPE "0"
+#endif
+
+	REM # microWakeWord keyword-spotting module config
+	[[module.entry]]
+	name = "MWW"
+	uuid = UUIDREG_STR_MWW
+	affinity_mask = "0x1"
+	instance_count = "40"
+	domain_types = "0"
+	load_type = LOAD_TYPE
+	module_type = "9"
+	auto_start = "0"
+	sched_caps = [1, 0x00008000]
+	REM # pin = [dir, type, sample rate, size, container, channel-cfg]
+	pin = [0, 0, 0xfeef, 0xf, 0xf, 0x45ff, 1, 0, 0xfeef, 0xf, 0xf, 0x1ff]
+	REM # mod_cfg [PAR_0 PAR_1 PAR_2 PAR_3 IS_BYTES CPS IBS OBS MOD_FLAGS CPC OBLS]
+	mod_cfg = [0, 0, 0, 0, 4096, 1000000, 128, 128, 0, 0, 0]
+
+	index = __COUNTER__
diff --git a/src/audio/microwakeword/mww_model.cc b/src/audio/microwakeword/mww_model.cc
new file mode 100644
index 000000000000..21da407b6c07
--- /dev/null
+++ b/src/audio/microwakeword/mww_model.cc
@@ -0,0 +1,225 @@
+// SPDX-License-Identifier: BSD-3-Clause
+//
+// Copyright(c) 2026 Intel Corporation. All rights reserved.
+
+#include <algorithm>
+#include <cstdint>
+#include <iterator>
+
+#include "tensorflow/lite/core/c/common.h"
+#include "tensorflow/lite/micro/micro_allocator.h"
+#include "tensorflow/lite/micro/micro_interpreter.h"
+#include "tensorflow/lite/micro/micro_log.h"
+#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
+#include "tensorflow/lite/micro/micro_resource_variable.h"
+#include "tensorflow/lite/micro/testing/micro_test.h"
+#include "mww_model.h"
+
+extern "C" int printk(const char *fmt, ...);
+
+// hard code the model today
+#include "mww_model_data.h"
+
+static constexpr int kFeatureSize = MWW_FEATURE_SIZE;
+static constexpr int kFeatureElementCount = MWW_FEATURE_ELEM_COUNT;
+
+// Arena size is a generous guesstimate for the streaming MixConv graph (12
+// ops incl. Conv2D/DepthwiseConv2D/FullyConnected plus 6 persistent resource
+// -variable ring buffers). Refine down using
+// MicroInterpreter::arena_used_bytes() once measured on hardware (logged in
+// MWW_InitOps() below).
+static constexpr size_t kDefaultArenaSize = 262144;
+
+// inference
+static const tflite::Model *model;
+static TfLiteTensor *input;
+static TfLiteTensor *output;
+static tflite::MicroInterpreter *interpreter;
+static tflite::MicroAllocator *allocator;
+static tflite::MicroResourceVariables *resource_variables;
+
+#include <new>
+
+// stream, stream_1..stream_5: the MixConv graph's 6 persistent ring-buffer
+// state variables (VAR_HANDLE/ASSIGN_VARIABLE/READ_VARIABLE), confirmed by
+// direct flatbuffer inspection (see plan Stage 1) -- CALL_ONCE invokes a
+// second subgraph that ASSIGN_VARIABLEs their zero initial state.
+static constexpr int kNumResourceVariables = 6;
+
+// 12 ops used by the hey_jarvis.tflite MixConv streaming graph, confirmed by
+// direct flatbuffer inspection (see plan Stage 1): CALL_ONCE, VAR_HANDLE,
+// READ_VARIABLE, ASSIGN_VARIABLE, RESHAPE, CONCATENATION, STRIDED_SLICE,
+// CONV_2D, DEPTHWISE_CONV_2D, FULLY_CONNECTED, LOGISTIC, QUANTIZE.
+using MwwOpResolver = tflite::MicroMutableOpResolver<12>;
+static MwwOpResolver *op_resolver;
+
+alignas(16) static uint8_t g_op_resolver_buf[sizeof(MwwOpResolver)];
+alignas(16) static uint8_t g_interpreter_buf[sizeof(tflite::MicroInterpreter)];
+alignas(16) static uint8_t g_mww_arena[kDefaultArenaSize];
+
+int RegisterOps(MwwOpResolver *op_resolver) {
+	printk("[MWW DBG] RegisterOps entry op_resolver=%p\n", (void *)op_resolver);
+	TF_LITE_ENSURE_STATUS(op_resolver->AddCallOnce());
+	printk("[MWW DBG] AddCallOnce OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddVarHandle());
+	printk("[MWW DBG] AddVarHandle OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddReadVariable());
+	printk("[MWW DBG] AddReadVariable OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddAssignVariable());
+	printk("[MWW DBG] AddAssignVariable OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddReshape());
+	printk("[MWW DBG] AddReshape OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddConcatenation());
+	printk("[MWW DBG] AddConcatenation OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddStridedSlice());
+	printk("[MWW DBG] AddStridedSlice OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddConv2D());
+	printk("[MWW DBG] AddConv2D OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddDepthwiseConv2D());
+	printk("[MWW DBG] AddDepthwiseConv2D OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddFullyConnected());
+	printk("[MWW DBG] AddFullyConnected OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddLogistic());
+	printk("[MWW DBG] AddLogistic OK\n");
+	TF_LITE_ENSURE_STATUS(op_resolver->AddQuantize());
+	printk("[MWW DBG] AddQuantize OK\n");
+	return 0;
+}
+
+static int Init_Interpreter(struct mww_classify *mwc);
+
+int MWW_InitOps(struct mww_classify *mwc, uint8_t *arena_buf, size_t arena_size)
+{
+	if (!arena_buf || arena_size == 0) {
+		arena_buf = g_mww_arena;
+		arena_size = kDefaultArenaSize;
+	}
+	printk("[MWW DBG] MWW_InitOps entry &model=%p model=%p arena_buf=%p arena_size=%zu\n",
+	       (void *)&model, (void *)model, (void *)arena_buf, arena_size);
+
+	op_resolver = new (g_op_resolver_buf) MwwOpResolver();
+	printk("[MWW DBG] op_resolver=%p\n", (void *)op_resolver);
+
+	if (RegisterOps(op_resolver) != 0) {
+		mwc->error = "register ops failed";
+		return -EINVAL;
+	}
+	printk("[MWW DBG] RegisterOps OK, model=%p arena_buf=%p arena_size=%zu\n",
+	       (void *)model, (void *)arena_buf, arena_size);
+
+	allocator = tflite::MicroAllocator::Create(arena_buf, arena_size);
+	printk("[MWW DBG] allocator=%p\n", (void *)allocator);
+	if (!allocator) {
+		mwc->error = "allocator alloc failed (OOM)";
+		return -ENOMEM;
+	}
+
+	resource_variables = tflite::MicroResourceVariables::Create(allocator, kNumResourceVariables);
+	printk("[MWW DBG] resource_variables=%p\n", (void *)resource_variables);
+	if (!resource_variables) {
+		mwc->error = "resource_variables alloc failed (OOM)";
+		return -ENOMEM;
+	}
+
+	// create the interpreter via placement new
+	interpreter = new (g_interpreter_buf) tflite::MicroInterpreter(model, *op_resolver,
+								       allocator, resource_variables);
+	printk("[MWW DBG] interpreter=%p\n", (void *)interpreter);
+	if (!interpreter) {
+		mwc->error = "interpreter alloc failed (OOM)";
+		return -ENOMEM;
+	}
+
+	printk("[MWW DBG] calling AllocateTensors\n");
+	// and allocate the tensors
+	if (interpreter->AllocateTensors() != kTfLiteOk) {
+		mwc->error = "interpreter tensor allocate failed";
+		return -EINVAL;
+	}
+
+	printk("[MWW DBG] AllocateTensors OK, arena_used=%u\n", (unsigned)interpreter->arena_used_bytes());
+
+	// fetch input/output tensors + quantization params once; the
+	// interpreter/tensors are stable for the lifetime of this instance
+	return Init_Interpreter(mwc);
+}
+
+static int Init_Interpreter(struct mww_classify *mwc)
+{
+	input = interpreter->input(0);
+	if (!input) {
+		mwc->error = "input interpreter NULL";
+		return -EINVAL;
+	}
+
+	// check input tensor element count is compatible with our feature
+	// data size (shape is (1, MWW_FEATURE_SLICE_COUNT, MWW_FEATURE_SIZE),
+	// not flattened to a single trailing dim, so check the full product)
+	int in_elems = 1;
+	for (int i = 0; i < input->dims->size; i++)
+		in_elems *= input->dims->data[i];
+	if (kFeatureElementCount != in_elems) {
+		mwc->error = "input interpreter shape incompatible";
+		return -EINVAL;
+	}
+
+	output = interpreter->output(0);
+	if (!output) {
+		mwc->error = "output interpreter NULL";
+		return -EINVAL;
+	}
+
+	// single sigmoid wake-word probability, quantized uint8
+	if (output->type != kTfLiteUInt8) {
+		mwc->error = "output tensor type != uint8";
+		return -EINVAL;
+	}
+
+	// expose the model's real input quantization params so callers can
+	// requantize their features correctly instead of assuming a fixed
+	// scale/zero_point.
+	mwc->input_scale = input->params.scale;
+	mwc->input_zero_point = input->params.zero_point;
+
+	return 0;
+}
+
+int MWW_SetModel(struct mww_classify *mwc, unsigned char *model_tflite)
+{
+	const unsigned char *src_model = model_tflite ? model_tflite : g_mww_model_data;
+
+	// Map the model into a usable data structure.
+	model = tflite::GetModel(src_model);
+	printk("[MWW DBG] src_model=%p &model=%p model=%p\n", (void *)src_model, (void *)&model, (void *)model);
+	if (!model || model->version() != TFLITE_SCHEMA_VERSION) {
+		mwc->error = "failed to load model";
+		return -EINVAL;
+	}
+	printk("[MWW DBG] MWW_SetModel OK version=%u\n", (unsigned)model->version());
+
+	return 0;
+}
+
+int MWW_ProcessClassify(struct mww_classify *mwc)
+{
+	float output_scale = output->params.scale;
+	int output_zero_point = output->params.zero_point;
+
+	// copy features to input then invoke()
+	std::copy_n(mwc->audio_features, kFeatureElementCount,
+		    tflite::GetTensorData<int8_t>(input));
+
+	// run the interpreter
+	if (interpreter->Invoke() != kTfLiteOk) {
+		mwc->error = "invoke failed";
+		return -EINVAL;
+	}
+
+	// Dequantize the single sigmoid probability output
+	uint8_t raw = tflite::GetTensorData<uint8_t>(output)[0];
+
+	mwc->raw_output = raw;
+	mwc->probability = (raw - output_zero_point) * output_scale;
+
+	return 0;
+}
diff --git a/src/audio/microwakeword/mww_model.h b/src/audio/microwakeword/mww_model.h
new file mode 100644
index 000000000000..1c0bc57498a7
--- /dev/null
+++ b/src/audio/microwakeword/mww_model.h
@@ -0,0 +1,56 @@
+// SPDX-License-Identifier: BSD-3-Clause
+//
+// Copyright(c) 2026 Intel Corporation. All rights reserved.
+
+#ifndef __MWW_MODEL_H__
+#define __MWW_MODEL_H__
+
+#include "tensorflow/lite/core/c/common.h"
+
+/* microWakeWord streaming model configuration (hey_jarvis.tflite v2):
+ * input tensor shape (1, MWW_FEATURE_SLICE_COUNT, MWW_FEATURE_SIZE), int8,
+ * one new 10ms/40-bin MFCC hop per slice. The model is a stateful streaming
+ * graph (TFLM resource variables + CALL_ONCE init subgraph) that keeps its
+ * own longer-history ring buffers internally, so callers only ever need to
+ * supply MWW_FEATURE_SLICE_COUNT fresh hops per Invoke() rather than a
+ * caller-side sliding window.
+ */
+#define MWW_SAMPLE_RATE 16000
+#define MWW_FEATURE_SIZE 40
+#define MWW_FEATURE_SLICE_COUNT 3
+#define MWW_FEATURE_ELEM_COUNT (MWW_FEATURE_SIZE * MWW_FEATURE_SLICE_COUNT)
+#define MWW_FEATURE_STRIDE_MS 10
+#define MWW_FEATURE_DURATION_MS 30
+
+struct mww_classify {
+	int8_t *audio_features;
+	size_t audio_data_size;
+	const char *error;
+	float probability;	/**< dequantized wake-word probability, 0..1 */
+	uint8_t raw_output;	/**< raw uint8 output tensor value */
+	float input_scale;
+	int input_zero_point;
+};
+
+/* Export of C++ APIs into C namespace for linkage */
+#ifdef __cplusplus
+extern "C"
+{
+#endif
+
+	/* 1st - pass in tflite flatbuffer formatted model, size is included in
+	 * model metadata.
+	 */
+	int MWW_SetModel(struct mww_classify *mwc, unsigned char *model);
+
+	/* 2nd - register the kernels and init TF micro for inference */
+	int MWW_InitOps(struct mww_classify *mwc, uint8_t *arena_buf, size_t arena_size);
+
+	/* 3rd - perform the inference */
+	int MWW_ProcessClassify(struct mww_classify *mwc);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/src/audio/microwakeword/mww_model_data.cc b/src/audio/microwakeword/mww_model_data.cc
new file mode 100644
index 000000000000..62b6750e7178
--- /dev/null
+++ b/src/audio/microwakeword/mww_model_data.cc
@@ -0,0 +1,5 @@
+#include <cstdint>
+
+#include "mww_model_data.h"
+
+alignas(16) const unsigned char g_mww_model_data[] = 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diff --git a/src/audio/microwakeword/mww_model_data.h b/src/audio/microwakeword/mww_model_data.h
new file mode 100644
index 000000000000..669e79a07708
--- /dev/null
+++ b/src/audio/microwakeword/mww_model_data.h
@@ -0,0 +1,17 @@
+#ifndef __MWW_MODEL_DATA_H__
+#define __MWW_MODEL_DATA_H__
+
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+static const unsigned int g_mww_model_data_size = 52272;
+extern const unsigned char g_mww_model_data[];
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
diff --git a/src/audio/tensorflow/CMakeLists.txt b/src/audio/tensorflow/CMakeLists.txt
index 5cb3086f46b1..8dddd9dc5294 100644
--- a/src/audio/tensorflow/CMakeLists.txt
+++ b/src/audio/tensorflow/CMakeLists.txt
@@ -1,6 +1,19 @@
 # Copyright (c) 2025 Intel Corporation.
 # SPDX-License-Identifier: Apache-2.0
 
+# Newer xt-clang LLVMs accept this to keep literals in .rodata; older
+# xt-clang (e.g. RI-2022.10, LLVM 10) rejects the sub-option. Detect it.
+set(TFLM_TEXT_SECTION_LITERALS_FALSE_FLAG "")
+if(CMAKE_C_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
+  include(CheckCXXCompilerFlag)
+  check_cxx_compiler_flag("-mllvm --text-section-literals=false"
+    TFLM_HAS_MLLVM_TEXT_SECTION_LITERALS_FALSE)
+  if(TFLM_HAS_MLLVM_TEXT_SECTION_LITERALS_FALSE)
+    set(TFLM_TEXT_SECTION_LITERALS_FALSE_FLAG -mllvm --text-section-literals=false)
+    add_compile_options(${TFLM_TEXT_SECTION_LITERALS_FALSE_FLAG})
+  endif()
+endif()
+
 # are we building the llext module ?
 if(CONFIG_COMP_TENSORFLOW STREQUAL "m" AND DEFINED CONFIG_LLEXT)
   add_subdirectory(llext ${PROJECT_BINARY_DIR}/tflm_llext)
@@ -19,62 +32,31 @@ set(RUY_PATH ${sof_top_dir}/../ruy)
 
 add_library(nn_hifi_lib STATIC
 	${NN_HIFI_PATH}/algo/common/src/xa_nnlib_common_api.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_pad_8.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_stride_slice_int16.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_batch_to_space_nd_8.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_transpose_8.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_depth_to_space_8.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_pad_16.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_stride_slice_int32.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_space_to_batch_nd_8.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_pad_32.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_stride_slice_int8.c
-	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_space_to_depth_8.c
 	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_32_32.c
 	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_f32_f32.c
-	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_sym16_sym16.c
-	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_softmax_asym8_asym8.c
-	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_32_16.c
-	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_16_16.c
-	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_8_8.c
-	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_asym16_asym16.c
-	${NN_HIFI_PATH}/algo/kernels/activations/hifi4/xa_nn_activations_32_8.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_16x16_16_circ_nb.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_16x16.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_16x16.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_transpose_conv_circ_buf.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_group_sym8sxasym8s.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_asym8xasym8_asym8_circ_nb.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_sym8sxasym8s.c
+	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_add_f32.c
+	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_sub_f32.c
+	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_mul_f32.c
+	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_abs_f32.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/nanf_tbl.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/inff_tbl.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/pow2f_tbl.c
+	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/expf_tbl.c
+
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_8x8_8_circ.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_sym8sxsym16s.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_8x16.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_circ_buf.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_8x8.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_16x16_16_circ.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_transpose_conv_f32.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_f32.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_f32_circ.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_circ_buf.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_transpose_conv_sym8sxasym8s.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_8x16_16_circ.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_sym8sxsym16s_sym16s_circ.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv1d_std_8x8.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_8x16.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_sym8sxsym16s.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_f32.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_f32_circ_nb.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_sym8sxasym8s.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_asym8xasym8_asym8_circ.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_sym8sxasym8s.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_8x8.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_transpose_conv_sym8sxsym16s.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_f32.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv1d_std_8x16.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_8x8.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_16x16.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_sym8sxasym8s_asym8s_circ.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_asym8xasym8.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_asym8xasym8.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv1d_std_circ_buf.c
@@ -82,117 +64,11 @@ add_library(nn_hifi_lib STATIC
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_8x16_16_circ_nb.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv1d_std_16x16.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_8x8_8_circ_nb.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_sym4sxasym8s.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv1d_std_f32.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_sym8sxsym16s.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_asym8xasym8.c
-	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise_8x16.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matmul_f32.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_asym8xasym8_batch.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matmul_sym8sxasym8s.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matmul_8x8.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matmul_8x16.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_asym4sxasym8s.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matmul_sym8sxsym16s.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_f32_batch.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_f32.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_16x16_batch.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_sym8sxsym16s.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matmul_asym8sxasym8s.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_16x8.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_8x16.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_8x8.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_asym8sxasym8s.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matmul_asym8xasym8.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matmul_16x16.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_8x16_batch.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_16x16.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_asym8xasym8.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_sym8sxasym8s.c
-	${NN_HIFI_PATH}/algo/kernels/matXvec/hifi4/xa_nn_matXvec_8x8_batch.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool_f32.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool_8.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool_asym8_nhwc.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool_f32_nhwc.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool_16_nhwc.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool_16.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool_16_nhwc.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool_asym8.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_inv_256_tbl.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool_8_nhwc.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool_16.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool_8.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool_f32_nhwc.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool_asym8_nhwc.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool_f32.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_maxpool_asym8.c
-	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_avgpool_8_nhwc.c
-	${NN_HIFI_PATH}/algo/kernels/norm/hifi4/xa_nn_l2_norm_asym8s.c
-	${NN_HIFI_PATH}/algo/kernels/norm/hifi4/xa_nn_l2_norm_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_sqrt_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_mul_16x16.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_add_quant8.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_mul_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_lstm_utils.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_dot_prod_16x16.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_sub_quant16.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_round_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_mul_acc_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_cosine_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_reduce_asym8s_asym8s.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_memset_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_floor_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_squared_diff_quant8.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_add_quant16.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_reduce_asym16s_asym16s.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_minmax_8.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_logn_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_div_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_sub_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_compare_quant8.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_logical_bool.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_broadcast_8_8.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_quantize.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_neg_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_mul_quant16.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_add_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_vec_interpolation_q15.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_square_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_sine_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_abs_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_memmove.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_mul_quant8.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_squared_diff_quant16.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_memmove_16.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_sub_quant8.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_ceil_f32.c
-	${NN_HIFI_PATH}/algo/kernels/basic/hifi4/xa_nn_elm_rsqrt_f32.c
 	${NN_HIFI_PATH}/algo/kernels/fc/hifi4/xa_nn_fully_connected.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_tanh32x32_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/scl_sigmoidf_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/inv2pif_tbl.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/nanf_tbl.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/pow2f_tbl.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_sigmoidf_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_reluf_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/expf_tbl.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_tanhf_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_sigmoid32x32_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/lognf_tbl.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/sinf_tbl.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_cosinef_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_sinef_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_softmaxf_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/scl_tanhf_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_softmax32x32_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_lognf_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/sqrt2f_tbl.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/inff_tbl.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_alognf_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/vec_relu32x32_hifi4.c
-	${NN_HIFI_PATH}/algo/ndsp/hifi4/src/tanhf_tbl.c
+	${NN_HIFI_PATH}/algo/kernels/pool/hifi4/xa_nn_inv_256_tbl.c
+	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_transpose_8.c
+	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_pad_8.c
 )
 
 target_include_directories(nn_hifi_lib PRIVATE
@@ -200,31 +76,63 @@ target_include_directories(nn_hifi_lib PRIVATE
 	${NN_HIFI_PATH}/algo/common/include/
 	${NN_HIFI_PATH}/include/nnlib/
 	${NN_HIFI_PATH}/algo/ndsp/hifi4/include
+	${TFLM_TOOLCHAIN_INCLUDE_ROOT}
+	${TFLM_HAL_SOC_PATH}
 )
 
+target_compile_options(nn_hifi_lib PRIVATE
+  ${TFLM_TEXT_SECTION_LITERALS_FALSE_FLAG}
+)
+
+endif() # TENSORFLOW_HAVE_NNLIB_HIFI4
+
+
+if(TENSORFLOW_HAVE_NNLIB_HIFI4)
+
 # TODO: Need to detect and add mul16/32 options.
-#ifeq "$(has_mul16)" "0"
-#CFLAGS += -mno-mul16
-#endif
-#ifeq "$(has_mul32)" "0"
-#CFLAGS += -mno-mul32 -mno-div32
-#endif
+target_compile_definitions(nn_hifi_lib PRIVATE
+	-DHIFI4=1
+	-DHAVE_VFPU=1
+	-DHAVE_VFPU_SINGLE_PRECISION=1
+	-DXCHAL_HAVE_HIFI4=1
+	-DXCHAL_HAVE_HIFI4_VFPU=1
+
+	__xtensa__=1
+	__XTENSA__=1
+	__XCC__
+	__XCC_CLANG__
+	"XT_MAX(a,b)=((a)>(b)?(a):(b))"
+	"XT_MIN(a,b)=((a)<(b)?(a):(b))"
+	"AE_MOVINT16_FROMINT32(v)=((ae_int16)(v))"
+	"AE_CVT64F32_H(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_H(v))"
+	"AE_CVT64F32_L(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_L(v))"
+)
+
 target_compile_options(nn_hifi_lib PRIVATE
 	-fsigned-char
 	-fno-exceptions
 	-mlongcalls
-	-INLINE:requested
-	-mcoproc
 	-fno-zero-initialized-in-bss
-	-mtext-section-literals
 	-Wsign-compare
-	-m32
 	-DMODEL_INT16
 	-DNNLIB_V2
 	-Dhifi4
 	-DTFLITE_SINGLE_ROUNDING=1
 )
 
+target_compile_definitions(nn_hifi_lib PRIVATE
+	__XCC__
+	__XCC_CLANG__
+)
+target_compile_options(nn_hifi_lib PRIVATE
+	-mcpu=${SOC_TOOLCHAIN_NAME}
+	"SHELL:-include xtensahifiintrin.h"
+	"SHELL:-include ${NN_HIFI_PATH}/algo/common/include/xa_nnlib_hifi_isa_compat.h"
+)
+
+endif() # TENSORFLOW_HAVE_NNLIB_HIFI4 (nn_hifi_lib defs/opts)
+
+
 # TODO: complete sources have been added here from userspace build but
 # not all are needed so this is a list of "needed" sources to build
 # a memory and performance optimized TFLM for SOF.
@@ -332,46 +240,12 @@ add_library(tflm_lib STATIC
 	#${TFLM_PATH}/tensorflow/lite/micro/kernels/var_handle.cc
 	#${TFLM_PATH}/tensorflow/lite/micro/kernels/while.cc
 	#${TFLM_PATH}/tensorflow/lite/micro/kernels/zeros_like.cc
-	# xtensa kernels
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/sub.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_hifi.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/unidirectional_sequence_lstm.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/transpose_conv.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_common_xtensa.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/lstm_eval_hifi.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/add_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reshape.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_hifi.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int16_reference.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int8_reference.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_int8.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pad_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int8_int16.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax_int8_int16.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/logistic.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reduce.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/lstm_eval.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reshape_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pad.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reduce_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/dequantize.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/add.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling_int8.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/strided_slice.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_common_xtensa.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/svdf.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/leaky_relu.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/quantize.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_vision.cc
+	# reference kernels for speech model ops
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/conv.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/depthwise_conv.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/fully_connected.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/reshape.cc
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/softmax.cc
 	${TFLM_PATH}/tensorflow/lite/micro/mock_micro_graph.cc
 	${TFLM_PATH}/tensorflow/lite/micro/flatbuffer_utils.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter_graph.cc
@@ -415,7 +289,6 @@ target_include_directories(tflm_lib PRIVATE
 	${RUY_PATH}
 	${NN_HIFI_PATH}
 	${NN_HIFI_PATH}/include
-	${sof_top_dir}/posix/include
 )
 
 # TODO: Need to detect and add mul16/32 options.
@@ -425,9 +298,38 @@ target_include_directories(tflm_lib PRIVATE
 #ifeq "$(has_mul32)" "0"
 #CFLAGS += -mno-mul32 -mno-div32
 #endif
+
+# These select/enable the HiFi4 nnlib-optimized code paths in some TFLM
+# kernels. Only valid when nn_hifi_lib is actually built and linked --
+# forcing XCHAL_HAVE_HIFI4 on a non-HiFi4 target risks enabling HiFi4-only
+# branches in system/toolchain headers that don't apply to this core.
+if(TENSORFLOW_HAVE_NNLIB_HIFI4)
+target_compile_definitions(tflm_lib PRIVATE
+	-DHIFI4=1
+	-DHAVE_VFPU=1
+	-DHAVE_VFPU_SINGLE_PRECISION=1
+	-DXCHAL_HAVE_HIFI4=1
+	-DXCHAL_HAVE_HIFI4_VFPU=1
+
+	__xtensa__=1
+	__XTENSA__=1
+	__XCC__
+	__XCC_CLANG__
+	"XT_MAX(a,b)=((a)>(b)?(a):(b))"
+	"XT_MIN(a,b)=((a)<(b)?(a):(b))"
+	"AE_MOVINT16_FROMINT32(v)=((ae_int16)(v))"
+	"AE_CVT64F32_H(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_H(v))"
+	"AE_CVT64F32_L(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_L(v))"
+)
+target_compile_options(tflm_lib PRIVATE
+	-DHIFI4
+	-DKERNELS_OPTIMIZED_FOR_SPEED
+	-DNNLIB_V2
+)
+endif() # TENSORFLOW_HAVE_NNLIB_HIFI4
+
 target_compile_options(tflm_lib PRIVATE
 	-std=c++17
-	-stdlib=libc++
 	-fno-rtti
 	-fno-exceptions
 	-fno-threadsafe-statics
@@ -450,16 +352,117 @@ target_compile_options(tflm_lib PRIVATE
 	-DKERNELS_OPTIMIZED_FOR_SPEED
 	-DTF_LITE_MCU_DEBUG_LOG
 	-DTF_LITE_USE_CTIME
-	--xtensa-core=ace10_LX7HiFi4_2022_10
-	-mcoproc
-	-DHIFI4
 	-mlongcalls
 	-DNNLIB_V2
 	-Wno-shadow
 )
 
+if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
+	target_compile_options(tflm_lib PRIVATE
+		-stdlib=libc++
+		"SHELL:-include xtensahifiintrin.h"
+		-fno-vectorize
+		-fno-slp-vectorize
+	)
+else()
+	# gcc equivalents for vectorize disabling
+	target_compile_options(tflm_lib PRIVATE
+		-fno-tree-vectorize
+		-fno-tree-slp-vectorize
+	)
+endif()
+
+if(CMAKE_C_COMPILER_ID STREQUAL "Xtensa")
+	target_compile_options(tflm_lib PRIVATE
+		--xtensa-core=ace10_LX7HiFi4_2022_10
+		-mcoproc
+	)
+elseif(CMAKE_C_COMPILER_ID STREQUAL "Clang")
+	target_compile_options(tflm_lib PRIVATE
+		-mcpu=${SOC_TOOLCHAIN_NAME}
+	)
+endif()
+
+if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+	target_include_directories(tflm_lib SYSTEM PRIVATE
+		${TFLM_TOOLCHAIN_INCLUDE_ROOT}/c++/14.3.0
+		${TFLM_TOOLCHAIN_INCLUDE_ROOT}/c++/14.3.0/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf
+		${TFLM_TOOLCHAIN_INCLUDE_ROOT}
+	)
+endif()
+
 add_local_sources(sof tflm-classify.c llext-wrap.c)
 
-# Need to link libc++ and libc after tflm and nnlib
-zephyr_link_libraries(tflm_lib nn_hifi_lib c++ c)
+# Link nnlib only when it was actually built (HiFi4 target + Clang)
+if(TENSORFLOW_HAVE_NNLIB_HIFI4)
+  zephyr_link_libraries(tflm_lib nn_hifi_lib)
+else()
+  zephyr_link_libraries(tflm_lib)
+endif()
+
+# CONFIG_MINIMAL_LIBC (SOF's global default) has no libm and is missing a
+# few libc functions (e.g. abs()) that TFLM needs in a statically-linked
+# (non-LLEXT) image. Linking the toolchain's whole libc.a is too broad: it
+# conflicts with Zephyr's own malloc/free (multiple definition) and pulls in
+# an __assert_no_args that needs an unavailable stderr. Instead, extract just
+# the specific archive members TFLM actually needs into a small private
+# archive and link only that.
+if(NOT CONFIG_COMP_TENSORFLOW STREQUAL "m")
+  set(TFLM_TOOLCHAIN_LIBC_ARCHIVE
+    ${ZEPHYR_SDK_INSTALL_DIR}/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/lib/libc.a)
+  set(TFLM_LIBC_SHIM_MEMBERS
+    libc_stdlib_abs.c.o
+    libm_math_s_floor.c.o
+    # TFLM QuantizeMultiplier() uses double frexp() + round() at Prepare time.
+    libm_math_s_frexp.c.o
+    libm_common_s_round.c.o
+    libm_math_sf_exp.c.o
+    libm_math_sf_log.c.o
+    libm_math_sf_ceil.c.o
+    libm_common_sf_fmax.c.o
+    libm_common_sf_fmin.c.o
+    libm_common_sf_round.c.o
+    # error-handling/predicate helpers the above call internally
+    libm_common_sf_isnan.c.o
+    libm_common_sf_issignaling.c.o
+    libm_common_math_errf_uflowf.c.o
+    libm_common_math_errf_oflowf.c.o
+    libm_common_math_errf_divzerof.c.o
+    libm_common_math_errf_invalidf.c.o
+  )
+  set(TFLM_LIBC_SHIM_DIR ${CMAKE_CURRENT_BINARY_DIR}/tflm_libc_shim)
+  set(TFLM_LIBC_SHIM_ARCHIVE ${CMAKE_CURRENT_BINARY_DIR}/libtflm_libc_shim.a)
+  file(MAKE_DIRECTORY ${TFLM_LIBC_SHIM_DIR})
+  add_custom_command(
+    OUTPUT ${TFLM_LIBC_SHIM_ARCHIVE}
+    COMMAND ${CMAKE_AR} x ${TFLM_TOOLCHAIN_LIBC_ARCHIVE} ${TFLM_LIBC_SHIM_MEMBERS}
+    COMMAND ${CMAKE_AR} rcs ${TFLM_LIBC_SHIM_ARCHIVE} ${TFLM_LIBC_SHIM_MEMBERS}
+    WORKING_DIRECTORY ${TFLM_LIBC_SHIM_DIR}
+    DEPENDS ${TFLM_TOOLCHAIN_LIBC_ARCHIVE} ${CMAKE_CURRENT_LIST_FILE}
+    COMMENT "Extracting abs()/libm members TFLM needs from the toolchain libc.a"
+  )
+  add_custom_target(tflm_libc_shim_gen DEPENDS ${TFLM_LIBC_SHIM_ARCHIVE})
+  add_library(tflm_libc_shim STATIC IMPORTED GLOBAL)
+  set_target_properties(tflm_libc_shim PROPERTIES IMPORTED_LOCATION ${TFLM_LIBC_SHIM_ARCHIVE})
+  add_dependencies(tflm_libc_shim tflm_libc_shim_gen)
+  zephyr_link_libraries(tflm_libc_shim)
+
+  # TFLM/flatbuffers use assert() internally; the toolchain's own
+  # __assert_no_args implementation needs an unavailable stderr, so disable
+  # assert() outright instead (standard practice for release TFLM builds).
+  target_compile_definitions(tflm_lib PRIVATE NDEBUG)
+endif()
 zephyr_include_directories(${TFLM_PATH})
+zephyr_include_directories(${FLATBUFFERS_PATH}/include)
+zephyr_include_directories(${GEMMLOWP_PATH})
+zephyr_include_directories(${RUY_PATH})
+if(TENSORFLOW_HAVE_NNLIB_HIFI4)
+zephyr_include_directories(${NN_HIFI_PATH}/algo/kernels/include)
+zephyr_include_directories(${NN_HIFI_PATH}/include)
+endif()
+target_include_directories(modules_sof SYSTEM PRIVATE
+	${TFLM_TOOLCHAIN_INCLUDE_ROOT}/c++/14.3.0
+	${TFLM_TOOLCHAIN_INCLUDE_ROOT}/c++/14.3.0/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf
+	${TFLM_TOOLCHAIN_INCLUDE_ROOT}
+)
+
diff --git a/src/audio/tensorflow/README.md b/src/audio/tensorflow/README.md
index a8e3f59ba3ce..89b25c812ca5 100644
--- a/src/audio/tensorflow/README.md
+++ b/src/audio/tensorflow/README.md
@@ -1,22 +1,649 @@
-# TensorFlow Lite Micro (TFLM) Architecture
+# TensorFlow Lite Micro (TFLM) & Wake-on-Voice (WoV) Architecture
 
-This directory acts as the bridge for running ML models.
+This directory provides the TensorFlow Lite for Microcontrollers (TFLM) classification module (`TFLMCLY`) for Sound Open Firmware (SOF), including integration with MFCC feature extraction, `mtrace` stream shutdown summary logging, IPC host notifications, Key Phrase Buffer (KPB) Wake-on-Voice (WoV) trigger infrastructure, and abstracted audio input sources across **HDA**, **DMIC**, **SSP (I2S)**, and **SoundWire (ALH)**.
+
+Two build modes are supported, selected automatically per target core:
+
+- **LLEXT module** (HiFi4/HiFi5 targets: MTL, PTL, ARL, ...) — `CONFIG_COMP_TENSORFLOW=m`, built with Clang, linked against the HiFi4-optimized `nnlib-hifi4` kernels.
+- **Statically linked into `zephyr.elf`** (HiFi3 targets with no LLEXT/module-manager support, e.g. TGL/cavs2.5) — `CONFIG_COMP_TENSORFLOW=y`, built with the Zephyr SDK GCC toolchain, `nnlib-hifi4` NOT built or linked (see [Build Instructions](#build-instructions) below).
+
+---
 
 ## Overview
 
-Integrates TensorFlow Lite for Microcontrollers into the SOF audio pipeline. Evaluates pre-trained neural network topologies inline with the audio stream for tasks like wake-word, noise cancellation, or sound classification.
+The TFLM module evaluates pre-trained micro speech neural network models inline within the SOF audio processing graph. It receives pre-processed audio feature tensors (mel-log spectrograms from the `mfcc` component), runs model inference in the **Data Processing (DP) domain**, logs keyword detections and stream shutdown event summaries to `mtrace`, issues IPC4 notifications to the host audio driver, and signals the KPB module to drain pre-roll audio history upon keyword detection.
+
+---
+
+## Architecture & Data Flow
+
+### Dual-Path Wake-on-Voice (WoV) Architecture
 
-## Architecture Diagram
+To allow continuous keyword evaluation without streaming audio to the host until a keyword is detected, the pipeline separates real-time keyword detection from host PCM draining via KPB:
 
 ```mermaid
-graph LR
-  Feat[Audio Features] --> TFLM[TFLM Inference Engine]
-  SubGraph[FlatBuffer Model] -.-> TFLM
-  TFLM --> Out[Inference Labels/Scores]
+graph TD
+    subgraph Audio_Inputs ["Abstracted Hardware DAI Input Sources"]
+        HDA["HDA Analog Input (dai_type: HDA)"]
+        DMIC["PCH DMIC Digital Mic (dai_type: DMIC)"]
+        SSP["I2S / Bluetooth Codec (dai_type: SSP)"]
+        SDW["SoundWire SmartMic (dai_type: ALH)"]
+    end
+
+    subgraph DAI_Abstraction ["Backend DAI Copier Widget"]
+        DAI["dai-copier.1 (dai_type: $CAPTURE_DAI_TYPE)"]
+    end
+
+    subgraph KPB_Pipeline ["Capture & KPB Pipeline"]
+        Gain["gain.2.1 (Volume Control)"]
+        KPB["kpb.2.1 (Key Phrase Buffer)"]
+    end
+
+    subgraph Detect_Pipeline ["Real-Time Detection Path (KPB Pin 1) - DP Domain"]
+        SRC["src.1.1 (Resampler: 48kHz -> 16kHz)"]
+        MFCC["mfcc.1.1 (Mel-40 Feature Extractor)"]
+        TFLM["tflmcly.1.1 (TFLM Keyword Classifier)"]
+        DetHost["host-copier.1.capture (PCM device 1: arm/observe only, see Usage)"]
+    end
+
+    subgraph Host_Pipeline ["Host WoV Draining Path (KPB Pin 2)"]
+        Host["host-copier.0.capture (PCM device 0: real drain target)"]
+    end
+
+    HDA --> DAI
+    DMIC --> DAI
+    SSP --> DAI
+    SDW --> DAI
+
+    DAI --> Gain
+    Gain --> KPB
+    KPB -- Pin 1: Live Audio --> SRC
+    SRC --> MFCC
+    MFCC -- Mel-40 Q9.23 Tensors --> TFLM
+    TFLM --> DetHost
+
+    TFLM -.->|1. KPB_EVENT_BEGIN_DRAINING Notifier| KPB
+    TFLM -.->|2. IPC4 Notification (scaffolded, not wired up)| Host_Driver[Host Driver]
+    TFLM -.->|3. Stream Shutdown Event Logging| MTrace[mtrace / Trace Log]
+
+    KPB -- Pin 2: Pre-roll History Buffer --> Host
+```
+
+**Note on pipe 1's host-copier**: `host-copier.1.capture` (PCM device 1, "HDA Mic TFLM Detect") exists to *arm/instantiate* the detection pipeline and to give a host-visible device for debugging — it is **not** the WoV drain target. `arecord`-ing it will get an immediate `Input/output error` once its ring buffer underruns, because `tflmcly` (not the host) is the real consumer on this pipe; this is expected. The pipeline stays fully armed and running as long as the PCM is *opened*, whether or not a read ever succeeds — see [Usage](#usage) for how to hold it open for testing without triggering the read-error teardown. The actual pre-roll audio delivered to the host on keyword detection always arrives via `host-copier.0.capture` (PCM device 0).
+
+---
+
+## Domain Execution Model (LL vs. DP)
+
+- **Low Latency (LL) Domain (Timer Task / 1ms tick loop)**:
+  - Components: `dai-copier`, `eqiir`, `tdfb`, `drc`, `host-copier`
+  - Purpose: Fixed 1 ms tick loop executed on Core 0 to meet hard real-time audio hardware deadlines.
+  - Metrics: Reported via `ll_schedule.stats_report` (e.g. `ll core 0 timer avg 22,049 cycles (~56 µs)`).
+
+- **Data Processing (DP) Domain (Asynchronous Task)**:
+  - Components: `src`, `micsel`, `mfcc`, `tflmcly`
+  - Purpose: Runs asynchronously in a background task whenever a new feature hop is produced by `mfcc` — measured empirically at roughly one inference every ~500ms (bring-up wall-clock gate, see [Known Limitations](#known-limitations--open-issues)).
+  - **Separation**: Because TFLM runs in the DP domain, model inference cycles take place outside the 1 ms LL tick loop, guaranteeing zero impact on real-time LL audio latency or buffer overruns. On targets without a pre-existing DP scheduler user, `platform_init()` now calls `scheduler_dp_init()` explicitly so `mfcc`/`tflmcly` have somewhere to run (`src/platform/intel/cavs/platform.c`).
+
+### MFCC frame format
+
+Per hop, MFCC emits a 24-byte `struct mfcc_data_header` (magic/frame_number/reserved/energy/noise_energy/vad_flag) followed by `TFLM_FEATURE_SIZE` (40) `int32_t` Q9.23 mel-log values — `24 + 40*4 = 184` bytes (`MFCC_FRAME_BYTES` in the pipeline template). `tflm_process()` strips this header and requantizes each Q9.23 value into `int8_t` against the model's *real* input tensor `scale`/`zero_point` (read from the interpreter at prepare time, not assumed) before feeding the model's 49-hop sliding feature window.
+
+---
+
+## Known Limitations / Open Issues
+
+- **Feature-representation mismatch (stock model only)**: the shipped 4-class `silence/unknown/yes/no` model was trained against TFLM's original `micro_speech` frontend, which applies a *nonlinear* PCAN auto-gain-control normalization before quantization. SOF's MFCC produces *linear* mel-log values. Empirically, real captured audio normalizes to `norm ≈ 0.03–1.5` (Q9.23 mel-log value / 2^23), while this model's actual `input_scale=0.101715`/`zero_point=-128` need `norm ≈ 0–26` to use its int8 dynamic range — so every real input saturates into the bottom ~8% of the range and the model outputs a flat, content-independent prediction. No linear rescale of `mfcc_mel_q23_to_int8()` fixes this; it needs either a model retrained directly on real SOF MFCC mel-log features (recommended — see [Training a Custom Keyword Model](#training-a-custom-keyword-model-with-piper-tts)), or a from-scratch PCAN-AGC-equivalent normalization stage ahead of quantization. **Training a new model on real SOF features, as described below, avoids this problem entirely** since train-time and inference-time feature extraction then match by construction.
+- **500ms inference cadence is a bring-up shortcut**, not the model's trained stride (20ms/hop, 49-hop/~1s sliding window). Fine for initial bring-up; revisit before judging a new model's real-world accuracy, since a cadence mismatch vs. training assumptions can look like a model-quality problem.
+- **IPC4 host notification is scaffolded but not wired up** (`tflm_ipc_notification_init()` never allocates/registers `cd->msg`, so `tflm_send_keyword_notification()` silently no-ops). KPB draining (a separate mechanism) still works. Finish this if the host driver needs an explicit "keyword X detected" event rather than just observing PCM start flowing on device 0.
+- **Per-instance state is global**, not per-`comp_dev` (`g_tflm_cd`, `g_tflm_initialized`, per-category counters, etc.). Fine for a single detector instance; would need reworking for concurrent multi-`tflmcly` use.
+- **Category count/labels are still hardcoded in a few places** beyond `TFLM_CATEGORY_DATA` (shutdown-summary format string, the KPB-trigger rule `max_idx >= 2`) — generalize before changing the category set.
+
+---
+
+## Abstracted Audio Input Sources in Topology v2
+
+The TFLM Keyword Detection and KPB pre-roll pipeline is decoupled from the physical DAI input source using the generic `dai-copier` widget:
+
+```conf
+Object.Widget.dai-copier.1 {
+    dai_type    $CAPTURE_DAI_TYPE     # "HDA", "DMIC", "SSP", or "ALH" (SoundWire)
+    copier_type $CAPTURE_COPIER_TYPE  # "HDA", "DMIC", "SSP", or "ALH"
+    stream_name $CAPTURE_DAI_NAME    # "Analog", "DMIC01", "SSP0", "SDW0-Capture"
+    node_type   $CAPTURE_NODE_TYPE    # $HDA_LINK_INPUT_CLASS, $DMIC_LINK_INPUT_CLASS, etc.
+}
+```
+
+Platform wrappers select the input source cleanly via configuration defines:
+```conf
+Define {
+    CAPTURE_SOURCE "hda" # Options: "hda", "dmic", "ssp", "soundwire"
+}
+
+IncludeByKey.CAPTURE_SOURCE {
+    "hda"       "platform/intel/capture-hda.conf"
+    "dmic"      "platform/intel/capture-dmic.conf"
+    "ssp"       "platform/intel/capture-ssp.conf"
+    "soundwire" "platform/intel/capture-sdw.conf"
+}
+```
+
+---
+
+## Topology v2 Integration & Usage
+
+### 1. Component Widget Definition (`include/components/tflm.conf`)
+
+Defines `Class.Widget."tflmcly"`:
+- **UUID**: `42:c6:1d:c5:e1:a2:df:48:a4:90:e2:74:8c:b6:36:3e` (`c51dc642-a2e1-48df-a490e2748cb6363e`)
+- **Type**: `effect`
+
+### 2. Detection Pipeline Template (`include/pipelines/cavs/host-gateway-micsel-mfcc-tflm-capture.conf`)
+
+Instantiates the real-time detection graph — `mfcc.1` carries a real bytes-control config default (`HDA_MIC_MFCC_PARAMS`, defaulting to `include/components/mfcc/mel40_compress.conf`), and `tflmcly.1`'s output is routed to a real host-copier rather than a terminal virtual sink:
+
+```conf
+Object.Widget {
+    host-copier."1" {
+        type            "aif_out"
+        node_type       $HDA_HOST_INPUT_CLASS
+        stream_name     "HDA Mic TFLM Detect"
+        pcm_id          $index
+    }
+    src."1"     { ... }
+    mfcc."1"    {
+        Object.Control.bytes."1" {
+            name "HDA Mic MFCC bytes"
+            IncludeByKey.HDA_MIC_MFCC_PARAMS {
+                "default" "include/components/mfcc/mel40_compress.conf"
+            }
+        }
+    }
+    tflmcly."1" { scheduler_domain "DP" }
+}
+
+Object.Base.route [
+    { source tflmcly.$index.1; sink "host-copier.$index.capture" }
+]
+```
+
+### 3. Top-Level Topology Configuration (`sof-hda-generic.conf` + `hda-mic-tflm-kpb.conf` overlay)
+
+Instantiates the complete HDA Mic WoV topology with dual-path KPB routing:
+```conf
+Object.Base.route [
+    # DAI -> Gain -> KPB
+    { source "dai-copier.HDA.Analog.capture"; sink "gain.2.1" }
+    { source "gain.2.1"; sink "kpb.2.1" }
+
+    # KPB Pin 1 -> Real-time Detection Path (DP Domain)
+    { source "kpb.2.1"; sink "src.1.1" }
+
+    # KPB Pin 2 -> Host WoV Draining Path
+    { source "kpb.2.1"; sink "host-copier.0.capture" }
+]
+```
+
+A "HDA Mic TFLM Detect" PCM entry (`$HDA_TFLM_DETECT_PIPELINE_ID`, mono S32_LE @16kHz) exposes pipe 1's host-copier as PCM device 1.
+
+---
+
+## Build Instructions
+
+### HiFi4/HiFi5 targets (MTL, PTL, ARL, ...) — LLEXT module, Clang
+
+```bash
+source .venv/bin/activate
+export LLVM_TOOLCHAIN_PATH=<path-to-llvm-install>
+west build -b intel_adsp/<board> app -d build-<board>-tflm -- \
+    -DCONFIG_COMP_TENSORFLOW=m
+```
+
+### HiFi3 targets with no LLEXT support (TGL/cavs2.5) — static link, GCC
+
+Requires `CONFIG_COMP_TENSORFLOW=y` (not `m`) plus C++17 and enough stack/heap for the interpreter's arena, TFLM's own allocations, and `avcodec`-style blocking calls off the DP task — all already added to `app/boards/intel_adsp_cavs25.conf` on this branch:
+
+```conf
+CONFIG_SOF_STAGING=y
+CONFIG_CPP=y
+CONFIG_STD_CPP17=y
+CONFIG_COMP_TENSORFLOW=y
+CONFIG_STACK_SIZE_EDF=32768
+CONFIG_HEAP_MEM_POOL_SIZE=32768
+CONFIG_COMMON_LIBC_MALLOC_ARENA_SIZE=32768
+```
+
+Build with the Zephyr SDK GCC toolchain (no Clang/LLVM involved):
+
+```bash
+source .venv/bin/activate
+export ZEPHYR_SDK_INSTALL_DIR=/home/lrg/zephyr-sdk-1.0.1
+export ZEPHYR_TOOLCHAIN_VARIANT=zephyr
+unset LLVM_TOOLCHAIN_PATH
+west build -b intel_adsp/cavs25 app -d build-tgl-tflm-gcc
+```
+
+`src/audio/tensorflow/CMakeLists.txt` auto-detects that this target has no `nnlib-hifi4` support (`CONFIG_XTENSA_HIFI4` unset, even though the compiler may still be Clang for other targets) and skips building/linking `nn_hifi_lib`, falling back to plain-C TFLM reference kernels. It also extracts just the `abs()`/libm archive members TFLM needs from the toolchain's `libc.a` into a small private `tflm_libc_shim`, since `CONFIG_MINIMAL_LIBC` lacks them and linking the whole `libc.a` collides with Zephyr's own `malloc`/`free`.
+
+### Building topology targets
+
+From `sof/tools/build_tools`:
+
+```bash
+# Build HDA TFLM KPB Topologies (MTL / PTL / TGL)
+ninja topology2_prod_sof-mtl-hda-tflm-kpb
+ninja topology2_prod_sof-ptl-hda-tflm-kpb
+ninja topology2_prod_sof-tgl-hda-tflm-kpb
+
+# Build SoundWire TFLM Topology (ARL-S)
+ninja topology2_dev_sof-arl-cs42l43-l0-cs35l56-l23-mfcc-mel-normal
+```
+
+---
+
+## Usage
+
+### PCM device map (`sof-hda-tflm` topology)
+
+| Device | Widget | Role |
+|---|---|---|
+| `hw:0,0` | `host-copier.0.capture` | Real WoV output: KPB pin 2's pre-roll drain target. Reading this exercises the full `dai-copier -> gain -> kpb -> host` chain and is the right place to capture the actual detected/drained audio. |
+| `hw:0,1` | `host-copier.1.capture` ("HDA Mic TFLM Detect") | Arms/observes the detection pipeline only — see the [pipeline diagram note](#dual-path-wake-on-voice-wov-architecture). Not a continuous PCM stream; see below. |
+
+### Sanity-checking the WoV drain path end-to-end
+
+```bash
+ssh root@<dut> 'arecord -D hw:0,0 -f S32_LE -r 48000 -c 2 -d 4 /tmp/sanity.wav'
+```
+
+A clean, error-free capture here confirms the physical DAI, gain, and KPB chain are all healthy independent of TFLM/MFCC.
+
+### Arming the detection pipeline and watching inferences
+
+`hw:0,1` is not meant to be read continuously: `tflmcly` (not the host) is the real consumer on this pipe, so `arecord`'s first failed `read()` tears the pipeline straight back down. To arm it and hold it running for observation, open the PCM directly via `libasound` without ever reading from it — e.g. a small ctypes/C snippet calling `snd_pcm_open()` + `snd_pcm_set_params()` + `snd_pcm_start()` on `hw:0,1` and then just sleeping. In parallel, tail `mtrace` on the DUT:
+
+```bash
+ssh root@<dut> '/usr/local/bin/mtrace-reader.py' > /tmp/mtrace.log &
+# ... arm hw:0,1 and play/speak keywords into the mic ...
+# look for periodic "[TFLM PREPARE]", "[DBG hop]", "[DBG raw_output]" lines
+```
+
+On a high-confidence detection, `KPB_EVENT_BEGIN_DRAINING` fires and pre-roll history starts flowing via `host-copier.0.capture` — i.e. it shows up on `hw:0,0`, not `hw:0,1`.
+
+### Stream shutdown summary
+
+Upon stream reset or module destruction (`tflm_reset()` / `tflm_free()`), TFLM emits a summary to `printk`/`mtrace`:
+
+```text
+[TFLM STREAM SHUTDOWN SUMMARY] Total Inferences=142 | Keyword Events: Silence=120, Unknown=18, Yes=3, No=1 | Total KPB Triggers=4
 ```
 
-## Configuration and Scripts
+- `Total Inferences`: cumulative classification inferences completed during the stream session.
+- `Keyword Events`: per-category classification breakdown.
+- `Total KPB Triggers`: high-confidence detections that triggered `KPB_EVENT_BEGIN_DRAINING`.
+
+---
+
+## Training a Custom Keyword Model with Piper-TTS
+
+The stock model only recognizes `yes`/`no` (plus `silence`/`unknown`). The
+shipped `sof_tflm_quantized_model_data.{cc,h}` was retrained end-to-end
+against real SOF mel40 features (currently against the `hey_linux`
+keyword) using the scripts under [./tune/](./tune/). This section
+documents that exact recipe so the model can be reproduced, a different
+keyword substituted, or several keywords combined into one model.
+
+### Pipeline overview
+
+The training flow is orchestrated by
+[sof_tflm_train_pipeline.sh](./tune/sof_tflm_train_pipeline.sh), which
+chains four steps into one command:
+
+| Step | Script | What it does |
+|------|--------|-------------|
+| 0a | [sof_tflm_generate_keyword_dataset.sh](./tune/sof_tflm_generate_keyword_dataset.sh) | Synthesize `<label>/*.wav` for one English keyword with `piper-sample-generator` (multi-speaker LibriTTS-R) + augmentation |
+| 0b | [sof_tflm_generate_keyword_dataset_piper_tts.sh](./tune/sof_tflm_generate_keyword_dataset_piper_tts.sh) | Same output layout, but for any single-speaker Piper voice (Finnish, Swedish, Hungarian, German, ...). Recovers speaker diversity via per-utterance prosody randomization + sox pitch/tempo perturbation |
+| 1 | [sof_tflm_prepare_silence_unknown.sh](./tune/sof_tflm_prepare_silence_unknown.sh) | Slice `silence/` + sample `unknown/` from Speech Commands v2 (English is fine as a negative-class source even for non-English keywords) |
+| 2 | [sof_mfcc_extract_features.sh](./tune/sof_mfcc_extract_features.sh) | Emit SOF mel40 features via `sof-testbench4` |
+| 3 | [sof_tflm_train.py](./tune/sof_tflm_train.py) | Train `tiny_conv`, int8-quantize, write `.tflite` + C array |
+
+Off-device verification uses
+[sof_tflm_verify.py](./tune/sof_tflm_verify.py), which runs the quantized
+`.tflite` on the held-out validation split and prints a confusion
+matrix — catches models with good overall accuracy but poor keyword
+recall before flashing.
+
+The labels are locked at `silence(0)`, `unknown(1)`, `<keyword_1>(2)`,
+`<keyword_2>(3)`, ... so the on-device tflmcly KPB trigger rule
+(`max_idx >= 2`) fires for any positive keyword class.
+
+### 0. Prerequisites
+
+Two isolated Python venvs (Piper's `torch`/`librosa` stack and
+TensorFlow do not coexist cleanly):
+
+```bash
+# Piper venv (dataset generation)
+python3 -m venv ~/venvs/piper
+source ~/venvs/piper/bin/activate
+pip install --upgrade pip
+pip install piper-sample-generator
+
+# TFLM training venv
+python3 -m venv ~/venvs/tflm-train
+source ~/venvs/tflm-train/bin/activate
+pip install --upgrade pip
+pip install "tensorflow>=2.10" numpy
+```
+
+Fetch the Piper LibriTTS-R generator checkpoint once (one file supplies
+up to 904 speaker embeddings — more voice diversity than a bag of
+individual `.onnx` voices):
+
+```bash
+git clone https://github.com/rhasspy/piper-sample-generator ~/git/piper-sample-generator
+mkdir -p ~/git/piper-sample-generator/models
+wget -O ~/git/piper-sample-generator/models/en_US-libritts_r-medium.pt \
+  https://github.com/rhasspy/piper-sample-generator/releases/download/v2.0.0/en_US-libritts_r-medium.pt
+```
+
+The training venv also needs the SOF host tools:
+
+- `sof-testbench4` built at
+  `tools/testbench/build_testbench/install/bin/sof-testbench4`
+  (`scripts/rebuild-testbench.sh`).
+- The mel40 development topology
+  `sof-hda-benchmark-mfccmel4032.tplg` built via
+  `scripts/build-tools.sh -t`.
+- `sox` and `xxd` on `PATH`.
+
+Export `SOF_WORKSPACE` to point at the parent of the `sof/` tree —
+[sof_mfcc_extract_features.sh](./tune/sof_mfcc_extract_features.sh) uses
+it to locate the testbench and topology binaries.
+
+### 1. Generate the keyword dataset
+
+[sof_tflm_generate_keyword_dataset.sh](./tune/sof_tflm_generate_keyword_dataset.sh)
+synthesizes one keyword phrase across 3 speaking rates × up to 700
+speaker embeddings, then augments the clips with room impulse
+responses, volume jitter, and 16 kHz resampling. Rerun once per
+keyword you want in the model. `MAX_SAMPLES=1000` (~3000 raw clips,
+~3000 augmented after the pass) is the recommended setting — large
+enough to train a robust model without the multi-hour training time
+that `MAX_SAMPLES=2000` incurs. The built-in default of 400 is only
+enough to smoke-test the pipeline:
+
+```bash
+source ~/venvs/piper/bin/activate
+MAX_SAMPLES=1000 \
+PIPER_VENV=~/venvs/piper \
+PIPER_MODEL=~/git/piper-sample-generator/models/en_US-libritts_r-medium.pt \
+PIPER_REPO=~/git/piper-sample-generator \
+    ./sof_tflm_generate_keyword_dataset.sh --keyword "hey linux" ~/wov/wavs
+deactivate
+```
+
+Output lands at `~/wov/wavs/hey_linux/*.wav`. The subdir name is
+derived from `--keyword` by lower-casing and replacing spaces with
+underscores; override with `--label mykw` if desired. For a
+multi-keyword model, pass `--keyword` multiple times in a single call
+(and optionally one `--label` per keyword, paired positionally):
+
+```bash
+MAX_SAMPLES=1000 PIPER_VENV=~/venvs/piper \
+PIPER_MODEL=~/git/piper-sample-generator/models/en_US-libritts_r-medium.pt \
+PIPER_REPO=~/git/piper-sample-generator \
+    ./sof_tflm_generate_keyword_dataset.sh \
+        --keyword banana --keyword mango --keyword orange \
+        ~/wov/wavs
+```
+
+`PIPER_REPO` is required because upstream ships the `piper_train`
+package only in the git tree, not in the PyPI wheel.
+
+### 1b. Non-English keywords: single-speaker Piper voices
+
+`piper-sample-generator` only ships the English `en_US-libritts_r-medium`
+multi-speaker checkpoint. For a keyword in another language use
+[sof_tflm_generate_keyword_dataset_piper_tts.sh](./tune/sof_tflm_generate_keyword_dataset_piper_tts.sh),
+which drives the regular `piper-tts` package against any voice from the
+[rhasspy/piper-voices](https://huggingface.co/rhasspy/piper-voices) set
+(Finnish `fi_FI-harri`, Swedish `sv_SE-nst`, Hungarian `hu_HU-anna`,
+German `de_DE-thorsten`, etc.). Because those voices are single-speaker,
+this variant recovers diversity by (a) randomizing Piper's
+`--noise-scale` / `--noise-w` per utterance while cycling several
+`--length-scale` values, and (b) fanning each synthesized clip out into
+`PERTURB_PER_UTT` sox copies with a random pitch shift in cents and a
+pitch-preserving `tempo -s` factor.
+Set up the venv once:
+
+```bash
+python3 -m venv ~/venvs/piper-tts
+source ~/venvs/piper-tts/bin/activate
+pip install --upgrade pip
+pip install piper-tts
+```
+
+Fetch one voice per language (example: Finnish, Swedish, Hungarian):
+
+```bash
+BASE=https://huggingface.co/rhasspy/piper-voices/resolve/v1.0.0
+mkdir -p ~/git/piper-voices/fi_FI ~/git/piper-voices/sv_SE ~/git/piper-voices/hu_HU
+cd ~/git/piper-voices/fi_FI
+wget "$BASE/fi/fi_FI/harri/medium/fi_FI-harri-medium.onnx"
+wget "$BASE/fi/fi_FI/harri/medium/fi_FI-harri-medium.onnx.json"
+cd ~/git/piper-voices/sv_SE
+wget "$BASE/sv/sv_SE/nst/medium/sv_SE-nst-medium.onnx"
+wget "$BASE/sv/sv_SE/nst/medium/sv_SE-nst-medium.onnx.json"
+cd ~/git/piper-voices/hu_HU
+wget "$BASE/hu/hu_HU/anna/medium/hu_HU-anna-medium.onnx"
+wget "$BASE/hu/hu_HU/anna/medium/hu_HU-anna-medium.onnx.json"
+```
+
+Generate the Finnish "piparkakku" dataset:
+
+```bash
+source ~/venvs/piper-tts/bin/activate
+MAX_SAMPLES=1000 \
+PIPER_VOICE=~/git/piper-voices/fi_FI/fi_FI-harri-medium.onnx \
+PIPER_REPO=~/git/piper-sample-generator \
+    ./sof_tflm_generate_keyword_dataset_piper_tts.sh \
+        --keyword piparkakku ~/wov/wavs
+deactivate
+```
+
+`PIPER_REPO` is optional here: if set, the same IR-convolution +
+volume-jitter augment stage the English pipeline uses runs after
+synthesis; if unset, the pipeline falls back to a plain 16 kHz resample
+plus the built-in gain jitter. `PIPER_TTS_VENV` is an alternative to
+sourcing `activate` yourself — the script only auto-activates when the
+venv path actually contains `bin/activate`.
+
+Mixed-language multi-keyword models work by pairing `--keyword` with
+`--voice` positionally:
+
+```bash
+source ~/venvs/piper-tts/bin/activate
+MAX_SAMPLES=1000 \
+    ./sof_tflm_generate_keyword_dataset_piper_tts.sh \
+        --keyword piparkakku  --voice ~/git/piper-voices/fi_FI/fi_FI-harri-medium.onnx \
+        --keyword pepparkaka  --voice ~/git/piper-voices/sv_SE/sv_SE-nst-medium.onnx \
+        --keyword mézeskalács --voice ~/git/piper-voices/hu_HU/hu_HU-anna-medium.onnx \
+        ~/wov/wavs
+deactivate
+```
+
+Everything downstream (silence/unknown from Speech Commands v2, feature
+extraction, training) is identical to the English recipe — Speech
+Commands v2 English clips remain a valid negative source.
+
+### 2. End-to-end pipeline: silence/unknown → features → train → quantize
+
+The remaining three steps run in one command. From
+`src/audio/tensorflow/tune/`, mirroring the shipped-model recipe
+(`N_SILENCE=1500`, `N_UNKNOWN=4000`, `EPOCHS=40` — larger than the
+built-in defaults of 500 / 1500 / 25):
+
+```bash
+N_SILENCE=1500 N_UNKNOWN=4000 EPOCHS=40 \
+TFLM_VENV=~/venvs/tflm-train \
+    ./sof_tflm_train_pipeline.sh --keyword hey_linux \
+        ~/wov/wavs ~/wov/feats ~/wov/model
+```
+
+This will:
+
+1. Fetch Speech Commands v2 (2.4 GB, one-time, cached under
+   `~/.cache/speech_commands_v2`) and populate `~/wov/wavs/silence/`
+   (1500 × 1 s slices of `_background_noise_`) and
+   `~/wov/wavs/unknown/` (4000 clips sampled from the non-target
+   Speech Commands words).
+2. Run `sof-testbench4` on every `<label>/*.wav` with the S32 mel40
+   topology, writing `~/wov/feats/<label>/*.raw` (each hop = 24-byte
+   `struct mfcc_data_header` + 40 × Q9.23 `int32_t`).
+3. Load features via [sof_tflm_dataset.py](./tune/sof_tflm_dataset.py),
+   train a `tiny_conv` (Reshape → DepthwiseConv2D → Flatten → Dense →
+   Softmax; ~12 k params) for `EPOCHS` epochs, full-int8 quantize
+   using the *real training-feature distribution* as the
+   representative set (this is what the stock model got wrong — see
+   the PCAN-AGC note above), and emit the fixed
+   `~/wov/model/sof_tflm_quantized_model_data.{cc,h}` pair (with the
+   C symbol `g_sof_tflm_quantized_model_data` that `speech.cc`
+   #includes) plus archive artifacts
+   `~/wov/model/<name>_quantized_model.tflite` (~16 KB) and
+   `~/wov/model/<name>_labels.txt`.
+
+Re-runs skip the expensive stages via env flags:
+
+```bash
+SKIP_PREP=1 SKIP_FEATURES=1 EPOCHS=40 \
+TFLM_VENV=~/venvs/tflm-train \
+    ./sof_tflm_train_pipeline.sh --keyword hey_linux \
+        ~/wov/wavs ~/wov/feats ~/wov/model
+```
+
+Further training knobs: `BATCH_SIZE`, `LR`. To train a multi-keyword
+model, pass `--keyword` for each positive class (label order defines
+model output indices starting at 2, so all positive classes still
+trigger the KPB `max_idx >= 2` rule):
+
+```bash
+./sof_tflm_train_pipeline.sh \
+    --keyword banana --keyword mango --keyword orange \
+    --name fruits \
+    ~/wov/wavs ~/wov/feats ~/wov/model
+```
+
+`--name` sets the archive basename for the standalone `.tflite` and
+`_labels.txt` artifacts (defaults to the single keyword when only one
+is given, otherwise the keyword labels joined with `_`). The C symbol
+and header names emitted for `speech.cc` are fixed at
+`g_sof_tflm_quantized_model_data` /
+`sof_tflm_quantized_model_data.{cc,h}` regardless of `--name`, so
+retraining is a drop-in replacement.
+
+### 3. Verify off-device before flashing
+
+```bash
+source ~/venvs/tflm-train/bin/activate
+python3 sof_tflm_verify.py \
+    --tflite ~/wov/model/<name>_quantized_model.tflite \
+    --feat-root ~/wov/feats \
+    --labels silence,unknown,<keyword>
+```
+
+Prints per-class precision/recall and the confusion matrix on the same
+held-out validation split training used (deterministic split via
+`--val-frac` / `--seed`). The `hey_linux` recall matters more than
+overall accuracy — a model with 98 % accuracy but 60 % keyword recall
+will feel broken on device.
+
+### 4. Wire the model into SOF
+
+```bash
+cp ~/wov/model/sof_tflm_quantized_model_data.{cc,h} \
+   ~/wov/model/sof_tflm_labels.h \
+   src/audio/tensorflow/
+```
+
+[speech.cc](speech.cc) already `#include`s
+`sof_tflm_quantized_model_data.h` and calls
+`tflite::GetModel(g_sof_tflm_quantized_model_data)`.
+[speech.h](speech.h) `#include`s `sof_tflm_labels.h`, which the training
+script regenerates on every run with the correct `TFLM_CATEGORY_COUNT`
+and `TFLM_CATEGORY_DATA`. No source changes are needed on retraining.
+
+The archive `~/wov/model/<name>_labels.txt` is kept as a plain-text copy
+of the label order (silence, unknown, keyword_1, …) for retraining
+history.
+
+The `.cc` file is already wired into [CMakeLists.txt](CMakeLists.txt)
+and its LLEXT sibling under [llext/CMakeLists.txt](llext/CMakeLists.txt);
+replacing the file in-place is enough.
+
+### 5. Validate on hardware
+
+Rebuild ([Build Instructions](#build-instructions)) and deploy the new
+firmware plus the reference topology `sof-hda-generic-tflm-kpb.tplg`,
+built from [sof-hda-generic.conf](../../../tools/topology/topology2/sof-hda-generic.conf)
+with `HDA_MIC_TFLM_KPB_CAPTURE=true` layering in
+[hda-mic-tflm-kpb.conf](../../../tools/topology/topology2/platform/intel/hda-mic-tflm-kpb.conf).
+It exposes two extra capture PCMs on the HDA-generic tree, both
+`S32_LE / 2ch / 16 kHz`:
+
+| PCM | Stream name | Purpose |
+|-----|-------------|---------|
+| `hw:0,42` | "HDA Mic TFLM Detect" | Arms the KPB → SRC → MFCC → TFLM branch |
+| `hw:0,41` | "HDA Mic WoV Capture" | Receives the 2 s KPB pre-roll drain on trigger |
+
+Order matters: opening `hw:0,41` alone leaves KPB without a `sel_sink`
+and prepare fails. Always start the Detect PCM first — that instantiates
+`micsel` and lets KPB bind both sinks.
+
+**Arm the detector** (Detect PCM run to `/dev/null` — its job is to
+drive the TFLM inference, not to produce a file):
+
+```bash
+arecord -D hw:0,42 -f S32_LE -c 2 -r 16000 -d 30 /dev/null &
+```
+
+**Capture the wake-word drain** in a second shell. KPB drains
+`TFLM_KPB_DRAIN_REQ_MS = 2000 ms` of pre-roll on trigger, then streams
+real-time until the PCM stops, so oversize the ring buffer:
+
+```bash
+arecord -D hw:0,41 -f S32_LE -c 2 -r 16000 \
+        -M -N --buffer-size=65536 \
+        -d 20 wov.wav
+```
+
+Now speak the keyword (`"hey linux"`) once. In `mtrace`,
+`tflmcly.tflm_notify_kpb` should log at the trigger instant and KPB
+should log `kpb_init_draining: requested draining of 2000 [ms]` right
+after. `wov.wav` grows past its 44-byte header — the first ~2 s is the
+pre-roll history buffer, the remainder is real-time capture through the
+end of the arecord duration. On the stream-shutdown summary,
+`Total KPB Triggers` matches the number of accepted detections.
+
+If `wov.wav` stays 44 bytes: check `mtrace` for the `tflm_notify_kpb`
+line. If it's missing, TFLM never crossed the confidence threshold
+(speak closer / retrain with more data). If it's there but no KPB
+`received event` follows, `CONFIG_AMS` on the target does not match
+what [tflm-classify.c](tflm-classify.c) was compiled against — rebuild
+against the deployed `.config`.
+
+For sanity checks and stress runs, also confirm the class picked in
+`mtrace` prediction logs stays on `silence`/`unknown` for a control set
+of unrelated speech and background noise.
+
+---
+
+## Source Files
 
-- **Kconfig**: Enforces requirements for C++17 support and core framework staging logic (`COMP_TENSORFLOW`).
-- **CMakeLists.txt**: An intricate build specification linking the Tensilica neural network library block computations (`nn_hifi_lib`) and the TensorFlow Lite micro core engine (`tflm_lib`). Also hooks the `tflm-classify.c` SOF adapter via compiler flags explicitly enforcing memory, precision, and XTENSA optimizations.
-- **tflmcly.toml**: Topology definition for the specific TFLM Classifier implementation binding the engine against the UUID `UUIDREG_STR_TFLMCLY`.
+- **[tflm-classify.c](tflm-classify.c)**: SOF module adapter implementation for TFLM (feature-window management, header stripping, requantization, KPB trigger, shutdown summary).
+- **[speech.cc](speech.cc)** / **[speech.h](speech.h)**: TFLM C++ API bridge & micro speech tensor wrapper.
+- **[CMakeLists.txt](CMakeLists.txt)**: LLEXT-vs-static build selection, HiFi4 nnlib gating, static-link libc shim.
+- **[../mfcc/tune/setup_mfcc.m](../mfcc/tune/setup_mfcc.m)**: Octave script generating the MFCC config blobs (`mel40.conf`, `mel40_compress.conf`, etc.) referenced by the topology.
+- **[tflm.conf](../../../tools/topology/topology2/include/components/tflm.conf)**: Topology v2 widget class definition.
+- **[host-gateway-micsel-mfcc-tflm-capture.conf](../../../tools/topology/topology2/include/pipelines/cavs/host-gateway-micsel-mfcc-tflm-capture.conf)**: Detection pipeline template.
+- **[hda-mic-tflm-kpb.conf](../../../tools/topology/topology2/platform/intel/hda-mic-tflm-kpb.conf)**: WoV overlay layered on `sof-hda-generic.conf` via `HDA_MIC_TFLM_KPB_CAPTURE=true`.
diff --git a/src/audio/tensorflow/llext/CMakeLists.txt b/src/audio/tensorflow/llext/CMakeLists.txt
index 22bfc22da748..391e1ed362b1 100644
--- a/src/audio/tensorflow/llext/CMakeLists.txt
+++ b/src/audio/tensorflow/llext/CMakeLists.txt
@@ -10,6 +10,8 @@ set(FLATBUFFERS_PATH ${sof_top_dir}/../flatbuffers)
 set(GEMMLOWP_PATH ${sof_top_dir}/../gemmlowp)
 set(RUY_PATH ${sof_top_dir}/../ruy)
 
+if(CMAKE_C_COMPILER_ID STREQUAL "Xtensa")
+set(TFLM_EXTRA_LIBS nn_hifi_lib)
 add_library(nn_hifi_lib STATIC
 	${NN_HIFI_PATH}/algo/common/src/xa_nnlib_common_api.c
 	${NN_HIFI_PATH}/algo/kernels/reorg/hifi4/xa_nn_pad_8.c
@@ -43,6 +45,7 @@ add_library(nn_hifi_lib STATIC
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_sym8sxsym16s.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_pointwise_8x16.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_circ_buf.c
+
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_std_8x8.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_conv2d_depthwise.c
 	${NN_HIFI_PATH}/algo/kernels/cnn/hifi4/xa_nn_matXvec_16x16_16_circ.c
@@ -206,24 +209,85 @@ target_compile_options(nn_hifi_lib PRIVATE
 	-fsigned-char
 	-fno-exceptions
 	-mlongcalls
-	-INLINE:requested
-	-mcoproc
 	-fno-zero-initialized-in-bss
 	-mtext-section-literals
 	-Wsign-compare
-	-m32
 	-DMODEL_INT16
 	-DNNLIB_V2
 	-Dhifi4
 	-DTFLITE_SINGLE_ROUNDING=1
 )
 
+target_compile_options(nn_hifi_lib PRIVATE
+	-INLINE:requested
+	-mcoproc
+	-m32
+)
+else()
+set(TFLM_EXTRA_LIBS "")
+endif()
+
+
+
+if(CMAKE_C_COMPILER_ID STREQUAL "Xtensa")
+	set(TFLM_KERNEL_SOURCES
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/sub.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_hifi.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling_vision.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax_vision.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/unidirectional_sequence_lstm.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/transpose_conv.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_common_xtensa.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/lstm_eval_hifi.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/add_vision.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reshape.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_hifi.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int16_reference.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int8_reference.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_int8.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pad_vision.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_vision.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int8_int16.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax_int8_int16.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/logistic.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reduce.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/lstm_eval.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reshape_vision.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pad.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reduce_vision.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/dequantize.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/add.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling_int8.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_vision.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/strided_slice.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_common_xtensa.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/svdf.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/leaky_relu.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/quantize.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_vision.cc
+	)
+else()
+	set(TFLM_KERNEL_SOURCES
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/sub.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/conv.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/depthwise_conv.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/fully_connected.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/reshape.cc
+		${TFLM_PATH}/tensorflow/lite/micro/kernels/softmax.cc
+	)
+endif()
+
 # TODO: complete sources have been added here from userspace build but
 # not all are needed so this is a list of "needed" sources to build
 # a memory and performance optimized TFLM for SOF.
 # All sources have been left in for completeness now, but will be removed
 # when support is mature.
-add_library(tflm_lib STATIC
+add_library(tflm_lib OBJECT
 	${TFLM_PATH}/tensorflow/compiler/mlir/lite/core/api/error_reporter.cc
 	${TFLM_PATH}/tensorflow/compiler/mlir/lite/schema/schema_utils.cc
 	${TFLM_PATH}/tensorflow/lite/core/c/common.cc
@@ -325,52 +389,15 @@ add_library(tflm_lib STATIC
 	#${TFLM_PATH}/tensorflow/lite/micro/kernels/var_handle.cc
 	#${TFLM_PATH}/tensorflow/lite/micro/kernels/while.cc
 	#${TFLM_PATH}/tensorflow/lite/micro/kernels/zeros_like.cc
-	# xtensa kernels
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/sub.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_hifi.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/unidirectional_sequence_lstm.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/transpose_conv.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_common_xtensa.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/lstm_eval_hifi.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/add_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reshape.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_hifi.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int16_reference.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int8_reference.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_int8.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pad_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_int8_int16.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/softmax_int8_int16.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/logistic.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reduce.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/lstm_eval.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reshape_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pad.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/reduce_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/dequantize.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/add.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling_int8.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/depthwise_conv_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/strided_slice.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_common_xtensa.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/svdf.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/fully_connected.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/leaky_relu.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/quantize.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/pooling.cc
-	${TFLM_PATH}/tensorflow/lite/micro/kernels/xtensa/conv_vision.cc
-	${TFLM_PATH}/tensorflow/lite/micro/mock_micro_graph.cc
+
+	${TFLM_KERNEL_SOURCES}
+
+	#${TFLM_PATH}/tensorflow/lite/micro/mock_micro_graph.cc
 	${TFLM_PATH}/tensorflow/lite/micro/flatbuffer_utils.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter_graph.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_resource_variable.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_interpreter_context.cc
-	${TFLM_PATH}/tensorflow/lite/micro/fake_micro_context.cc
+	#${TFLM_PATH}/tensorflow/lite/micro/fake_micro_context.cc
 	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.cc
 	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.cc
 	${TFLM_PATH}/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc
@@ -380,14 +407,14 @@ add_library(tflm_lib STATIC
 	${TFLM_PATH}/tensorflow/lite/micro/tflite_bridge/micro_error_reporter.cc
 	${TFLM_PATH}/tensorflow/lite/micro/system_setup.cc
 	#${TFLM_PATH}/tensorflow/lite/micro/span_test.cc
-	${TFLM_PATH}/tensorflow/lite/micro/test_helper_custom_ops.cc
+	#${TFLM_PATH}/tensorflow/lite/micro/test_helper_custom_ops.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_context.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_log.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_profiler.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_time.cc
 	#${TFLM_PATH}/tensorflow/lite/micro/static_vector_test.cc
 	${TFLM_PATH}/tensorflow/lite/micro/debug_log.cc
-	${TFLM_PATH}/tensorflow/lite/micro/test_helpers.cc
+	#${TFLM_PATH}/tensorflow/lite/micro/test_helpers.cc
 	${TFLM_PATH}/tensorflow/lite/micro/micro_op_resolver.cc
 	${TFLM_PATH}/tensorflow/lite/micro/recording_micro_allocator.cc
 	${TFLM_PATH}/tensorflow/lite/micro/memory_helpers.cc
@@ -420,7 +447,6 @@ target_include_directories(tflm_lib PRIVATE
 #endif
 target_compile_options(tflm_lib PRIVATE
 	-std=c++17
-	-stdlib=libc++
 	-fno-rtti
 	-fno-exceptions
 	-fno-threadsafe-statics
@@ -443,23 +469,97 @@ target_compile_options(tflm_lib PRIVATE
 	-DKERNELS_OPTIMIZED_FOR_SPEED
 	-DTF_LITE_MCU_DEBUG_LOG
 	-DTF_LITE_USE_CTIME
-	--xtensa-core=ace10_LX7HiFi4_2022_10
-	-mcoproc
 	-DHIFI4
 	-mlongcalls
 	-DNNLIB_V2
 	-Wno-shadow
+	-fno-function-sections
+	-fno-data-sections
 )
 
+# Use GNU libstdc++ headers (from zephyr-sdk gcc toolchain) which work with
+# both gcc and Clang. The -stdlib=libc++ approach requires an installed libc++
+# runtime that is not available for the xtensa target in this LLVM build.
+if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+	# Add GNU libstdc++ headers so Clang can find <algorithm>, <cstdarg>, etc.
+	file(GLOB _gnu_cxx_dirs
+		"/home/lrg/zephyr-sdk-*/gnu/xtensa-intel_ace15_mtpm_zephyr-elf/xtensa-intel_ace15_mtpm_zephyr-elf/include/c++/*/")
+	file(GLOB _gnu_c_dirs
+		"/home/lrg/zephyr-sdk-*/gnu/xtensa-intel_ace15_mtpm_zephyr-elf/xtensa-intel_ace15_mtpm_zephyr-elf/include/")
+	if(_gnu_cxx_dirs)
+		list(GET _gnu_cxx_dirs 0 _gnu_cxx_dir)
+		target_include_directories(tflm_lib SYSTEM PRIVATE
+			${_gnu_cxx_dir}
+			${_gnu_cxx_dir}/xtensa-intel_ace15_mtpm_zephyr-elf
+		)
+	endif()
+	if(_gnu_c_dirs)
+		list(GET _gnu_c_dirs 0 _gnu_c_dir)
+		target_include_directories(tflm_lib SYSTEM PRIVATE
+			${_gnu_c_dir}
+		)
+	endif()
+	# Clang builtin headers (stdarg.h, stdint.h wrappers, etc.)
+	execute_process(
+		COMMAND ${CMAKE_CXX_COMPILER} -print-resource-dir
+		OUTPUT_VARIABLE _clang_res_dir
+		OUTPUT_STRIP_TRAILING_WHITESPACE
+	)
+	if(_clang_res_dir AND EXISTS "${_clang_res_dir}/include")
+		target_include_directories(tflm_lib SYSTEM PRIVATE
+			${_clang_res_dir}/include
+		)
+	endif()
+endif()
+
+if(CMAKE_C_COMPILER_ID STREQUAL "Xtensa")
+	target_compile_options(tflm_lib PRIVATE
+		--xtensa-core=ace10_LX7HiFi4_2022_10
+		-mcoproc
+	)
+endif()
+
+
+if(CMAKE_C_COMPILER_ID STREQUAL "Xtensa")
+	set(TFLM_STD_LIBS c++ hal)
+else()
+	set(TFLM_STD_LIBS stdc++)
+endif()
+
 sof_llext_build("tflm"
-	SOURCES ../tflm-classify.c ../llext-wrap.c
+	SOURCES
+		../tflm-classify.c
+		../llext-wrap.c
+		$<TARGET_OBJECTS:tflm_lib>
 
 	INCLUDES
 		${TFLM_PATH}
+		${sof_top_dir}/src/include
+		${sof_top_dir}/src/arch/xtensa/include
+		${ZEPHYR_BASE}/include
+		${ZEPHYR_BASE}/soc/intel/intel_adsp/ace/include/${SOC_TOOLCHAIN_NAME}
+		${ZEPHYR_BASE}/../modules/hal/xtensa/include
+		${ZEPHYR_BASE}/../modules/hal/xtensa/zephyr/soc/${SOC_TOOLCHAIN_NAME}
 
 	# TFLM is a C++ userspace application so hence we need to link
 	# userspace C++, C, maths and C runtime for llext module usage.
-	LIBS tflm_lib nn_hifi_lib c++ m c gcc hal
+	LIBS ${TFLM_EXTRA_LIBS} ${TFLM_STD_LIBS} m gcc
 	LIBS_PATH .
+		/home/lrg/zephyr-sdk-1.0.1/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/lib
+		/home/lrg/zephyr-sdk-1.0.1/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/lib/gcc/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/14.3.0
 )
 
+# Note: format_hifi3.h needs xtensa/config/defs.h and xtensa/tie/xt_hifi3.h.
+# Stub headers for these are provided in the intel_ace15_mtpm HAL for LLVM builds.
+
+# The llext link helper uses CMAKE_C_COMPILER (clang) as the linker driver.
+# Without --target, clang defaults to the host (x86_64) linker and cannot link
+# xtensa ELF objects. Add the target triple and xtensa linker path explicitly.
+if(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
+	llext_link_options(tflm
+		--target=xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf
+		--ld-path=/home/lrg/zephyr-sdk-1.0.1/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/bin/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf-ld
+	)
+endif()
+
+
diff --git a/src/audio/tensorflow/math_stubs.c b/src/audio/tensorflow/math_stubs.c
new file mode 100644
index 000000000000..23d1fb467cd3
--- /dev/null
+++ b/src/audio/tensorflow/math_stubs.c
@@ -0,0 +1,89 @@
+// SPDX-License-Identifier: BSD-3-Clause
+//
+// Copyright(c) 2026 Intel Corporation. All rights reserved.
+
+#include <stdint.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+float fmaxf(float a, float b)
+{
+	return (a > b) ? a : b;
+}
+
+float fminf(float a, float b)
+{
+	return (a < b) ? a : b;
+}
+
+float roundf(float x)
+{
+	return (x >= 0.0f) ? (float)(int32_t)(x + 0.5f) : (float)(int32_t)(x - 0.5f);
+}
+
+double round(double x)
+{
+	return (x >= 0.0) ? (double)(int64_t)(x + 0.5) : (double)(int64_t)(x - 0.5);
+}
+
+double floor(double x)
+{
+	int64_t i = (int64_t)x;
+	return (x < (double)i) ? (double)(i - 1) : (double)i;
+}
+
+float expf(float x)
+{
+	float sum = 1.0f;
+	float term = 1.0f;
+	for (int i = 1; i <= 12; i++) {
+		term *= x / (float)i;
+		sum += term;
+	}
+	return sum;
+}
+
+float logf(float x)
+{
+	if (x <= 0.0f)
+		return 0.0f;
+	float y = (x - 1.0f) / (x + 1.0f);
+	float y2 = y * y;
+	float sum = y;
+	float term = y;
+	for (int i = 3; i <= 11; i += 2) {
+		term *= y2;
+		sum += term / (float)i;
+	}
+	return 2.0f * sum;
+}
+
+double frexp(double x, int *exp)
+{
+	if (x == 0.0) {
+		if (exp) *exp = 0;
+		return 0.0;
+	}
+	int e = 0;
+	if (x < 0.0) {
+		double ret = -frexp(-x, exp);
+		return ret;
+	}
+	while (x >= 1.0) {
+		x /= 2.0;
+		e++;
+	}
+	while (x < 0.5) {
+		x *= 2.0;
+		e--;
+	}
+	if (exp)
+		*exp = e;
+	return x;
+}
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/audio/tensorflow/speech.cc b/src/audio/tensorflow/speech.cc
index 82ffdf698141..5c6085021a8f 100644
--- a/src/audio/tensorflow/speech.cc
+++ b/src/audio/tensorflow/speech.cc
@@ -4,6 +4,7 @@
 
 #include <algorithm>
 #include <cstdint>
+#include <cstring>
 #include <iterator>
 
 #include "tensorflow/lite/core/c/common.h"
@@ -13,20 +14,18 @@
 #include "tensorflow/lite/micro/testing/micro_test.h"
 #include "speech.h"
 
-// hard code the model today
-#include "micro_speech_quantized_model_data.h"
+#include "sof_tflm_quantized_model_data.h"
 
 // The following values are derived from values used during model training.
 // If you change the way you preprocess the input, update all these constants.
-//constexpr int kAudioSampleFrequency = TFLM_SAMPLE_RATE;
 static constexpr int kFeatureSize = TFLM_FEATURE_SIZE;
 static constexpr int kFeatureCount = TFLM_FEATURE_COUNT;
 static constexpr int kFeatureElementCount = TFLM_FEATURE_ELEM_COUNT;
 
-// Arena size is a guesstimate, followed by use of
-// MicroInterpreter::arena_used_bytes() on both the AudioPreprocessor and
-// MicroSpeech models and using the larger of the two results.
-static constexpr size_t kArenaSize = 28584;  // xtensa p6
+// Sized for the retrained tiny_conv DS-CNN wake-word model.  Tune down to
+// interpreter->arena_used_bytes() (printed by tflm-classify.c on success)
+// once the model is finalized.
+static constexpr size_t kArenaSize = 131072;
 alignas(16) static uint8_t g_arena[kArenaSize];
 
 // type for features
@@ -38,7 +37,7 @@ static TfLiteTensor *input;
 static TfLiteTensor *output;
 static tflite::MicroInterpreter *interpreter;
 
-using MicroSpeechOpResolver = tflite::MicroMutableOpResolver<4>;
+using MicroSpeechOpResolver = tflite::MicroMutableOpResolver<7>;
 static MicroSpeechOpResolver *op_resolver;
 
 // Adding more kernels is quite efficient. TODO add more
@@ -47,12 +46,23 @@ int RegisterOps(MicroSpeechOpResolver *op_resolver) {
 	TF_LITE_ENSURE_STATUS(op_resolver->AddFullyConnected());
 	TF_LITE_ENSURE_STATUS(op_resolver->AddDepthwiseConv2D());
 	TF_LITE_ENSURE_STATUS(op_resolver->AddSoftmax());
+	/* Dynamic-Reshape triple emitted by newer TF/Keras -> tflite converters. */
+	TF_LITE_ENSURE_STATUS(op_resolver->AddShape());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddStridedSlice());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddPack());
 	return 0;
 }
 
+static int Init_Interpreter(struct tf_classify *tfc);
+
 int TF_InitOps(struct tf_classify *tfc)
 {
 	op_resolver = new MicroSpeechOpResolver();
+	if (!op_resolver) {
+		tfc->error = "op_resolver alloc failed (OOM)";
+		return -ENOMEM;
+	}
+
 	if (RegisterOps(op_resolver) != 0) {
 		tfc->error = "register ops failed";
 		return -EINVAL;
@@ -61,6 +71,12 @@ int TF_InitOps(struct tf_classify *tfc)
 	// create the interpreter
 	interpreter = new tflite::MicroInterpreter(model, *op_resolver,
 						   g_arena, kArenaSize);
+	if (!interpreter) {
+		tfc->error = "interpreter alloc failed (OOM)";
+		delete op_resolver;
+		op_resolver = nullptr;
+		return -ENOMEM;
+	}
 
 	// and allocate the tensors
 	if (interpreter->AllocateTensors() != kTfLiteOk) {
@@ -72,7 +88,9 @@ int TF_InitOps(struct tf_classify *tfc)
 		return -EINVAL;
 	}
 
-	return 0;
+	// fetch input/output tensors + quantization params once; the
+	// interpreter/tensors are stable for the lifetime of this instance
+	return Init_Interpreter(tfc);
 }
 
 static int Init_Interpreter(struct tf_classify *tfc)
@@ -83,9 +101,18 @@ static int Init_Interpreter(struct tf_classify *tfc)
 		return -EINVAL;
 	}
 
-	// check input shape is compatible with our feature data size
-	if (kFeatureElementCount != input->dims->data[input->dims->size - 1]){
+	// Accept any input rank as long as the total element count matches
+	// (retrained tiny_conv is [1,49,40,1] rather than a flat [1,1960]).
+	int input_elems = 1;
+	for (int i = 0; i < input->dims->size; i++)
+		input_elems *= input->dims->data[i];
+	if (kFeatureElementCount != input_elems) {
 		tfc->error = "input interpreter shape incompatible";
+		MicroPrintf("TFLM: input rank=%d elems=%d expected=%d",
+			    input->dims->size, input_elems, kFeatureElementCount);
+		for (int i = 0; i < input->dims->size; i++)
+			MicroPrintf("TFLM: input dim[%d]=%d", i,
+				    input->dims->data[i]);
 		return -EINVAL;
 	}
 
@@ -95,9 +122,14 @@ static int Init_Interpreter(struct tf_classify *tfc)
 		return -EINVAL;
 	}
 
-	// check output shape is compatible with our number of prediction categories
-	if (tfc->categories != output->dims->data[output->dims->size - 1]) {
+	// Accept any output rank as long as total element count == categories.
+	int output_elems = 1;
+	for (int i = 0; i < output->dims->size; i++)
+		output_elems *= output->dims->data[i];
+	if (tfc->categories != output_elems) {
 		tfc->error = "output shape != categories";
+		MicroPrintf("TFLM: output rank=%d elems=%d categories=%d",
+			    output->dims->size, output_elems, tfc->categories);
 		return -EINVAL;
 	}
 
@@ -110,7 +142,7 @@ int TF_SetModel(struct tf_classify *tfc, unsigned char *model_tflite)
 
 	// Map the model into a usable data structure. This doesn't involve any
 	// copying or parsing, it's a very lightweight operation.
-	model = tflite::GetModel(g_micro_speech_quantized_model_data);
+	model = tflite::GetModel(g_sof_tflm_quantized_model_data);
 	if (model->version() != TFLITE_SCHEMA_VERSION) {
 		tfc->error = "failed to load model";
 		return -EINVAL;
@@ -122,13 +154,6 @@ int TF_SetModel(struct tf_classify *tfc, unsigned char *model_tflite)
 int TF_ProcessClassify(struct tf_classify *tfc)
 {
 	Features *features = reinterpret_cast<Features *>(tfc->audio_features);
-	int ret;
-
-	// initialise the interpreter for current feature block
-	ret = Init_Interpreter(tfc);
-	if (!ret)
-		return ret;
-
 	float output_scale = output->params.scale;
 	int output_zero_point = output->params.zero_point;
 
@@ -144,10 +169,20 @@ int TF_ProcessClassify(struct tf_classify *tfc)
 
 	// Dequantize output values
 	for (int i = 0; i < tfc->categories; i++) {
-		tfc->predictions[i] =
-		(tflite::GetTensorData<int8_t>(output)[i] - output_zero_point) *
-			output_scale;
+		int8_t raw = tflite::GetTensorData<int8_t>(output)[i];
+		tfc->raw_output[i] = raw;
+		tfc->predictions[i] = (raw - output_zero_point) * output_scale;
 	}
 
 return 0;
 }
+
+size_t TF_ArenaUsedBytes(void)
+{
+	return interpreter ? interpreter->arena_used_bytes() : 0;
+}
+
+size_t TF_ArenaCapacity(void)
+{
+	return kArenaSize;
+}
diff --git a/src/audio/tensorflow/speech.h b/src/audio/tensorflow/speech.h
index 7b45022e3564..85673dd07880 100644
--- a/src/audio/tensorflow/speech.h
+++ b/src/audio/tensorflow/speech.h
@@ -23,6 +23,9 @@ struct tf_classify {
 	int categories;
 	const char *error;
 	float predictions[TFLM_CATEGORY_COUNT];
+	int op_count;
+	uint32_t node_cycles[10];
+	int node_codes[10];
 };
 
 /* Export of C++ APIs into C namespace for linkage */
diff --git a/src/audio/tensorflow/tflm-classify.c b/src/audio/tensorflow/tflm-classify.c
index 9c93329bce1c..bd637bc3863d 100644
--- a/src/audio/tensorflow/tflm-classify.c
+++ b/src/audio/tensorflow/tflm-classify.c
@@ -21,6 +21,7 @@
 #include <module/module/llext.h>
 #include <rtos/init.h>
 #include <rtos/panic.h>
+#include <rtos/alloc.h>
 #include <rtos/string.h>
 #include <sof/common.h>
 #include <sof/list.h>
@@ -28,82 +29,259 @@
 #include <sof/ut.h>
 #include <user/eq.h>
 #include <user/trace.h>
+#include <zephyr/sys/printk.h>
+#include <zephyr/kernel.h>
+
+static void sof_ut_log(const char *msg)
+{
+	printk("%s\n", msg);
+}
 #include <errno.h>
 #include <stddef.h>
 #include <stdint.h>
+#include <stdarg.h>
+#include <stdio.h>
+
+#include <ipc4/base-config.h>
+#include <ipc4/header.h>
+#include <ipc4/module.h>
+#include <ipc4/notification.h>
+#include <sof/audio/kpb.h>
+#include <sof/lib/notifier.h>
+#if CONFIG_AMS
+#include <sof/lib/ams.h>
+#include <sof/lib/ams_msg.h>
+#include <ipc4/ams_helpers.h>
+#endif
 
+#include <rtos/timer.h>
+#include <sof/audio/mfcc/mfcc_comp.h>
 #include "speech.h"
 
+/* TFLM error strings land here.  Route to printk/mtrace so AllocateTensors()
+ * and Invoke() failures print their real reason instead of vanishing.
+ */
+void DebugLog(const char *format, va_list args)
+{
+	vprintk(format, args);
+}
+
+int DebugVsnprintf(char *buffer, size_t buf_size, const char *format,
+		   va_list vlist)
+{
+	return vsnprintf(buffer, buf_size, format, vlist);
+}
+
+/* MFCC's non-compress output prepends a struct mfcc_data_header (24 bytes)
+ * to each hop, followed by TFLM_FEATURE_SIZE int32_t Q9.23 mel-log values
+ * (mel40.conf: 40 bins, 20ms hop). This must match MFCC_FRAME_BYTES in
+ * host-gateway-micsel-mfcc-tflm-capture.conf.
+ */
+#define MFCC_HOP_BYTES (sizeof(struct mfcc_data_header) + TFLM_FEATURE_SIZE * sizeof(int32_t))
+
+void start_tflm_ut_thread(void);
+void run_unit_tests(void);
+
+#if defined(__xtensa__)
+#include <xtensa/config/tie.h>
+#endif
+
+static inline uint32_t get_ccount(void)
+{
+	return k_cycle_get_32();
+}
+
 SOF_DEFINE_REG_UUID(tflmcly);
 LOG_MODULE_REGISTER(tflmcly, CONFIG_SOF_LOG_LEVEL);
+#if CONFIG_COMP_TENSORFLOW_MODULE
 EXPORT_SYMBOL(tflmcly_uuid);
 EXPORT_SYMBOL(log_const_tflmcly);
+#endif
 
 static const char * const prediction[] = TFLM_CATEGORY_DATA;
 
+/* Pre-roll history in ms that KPB drains to host on wake-word trigger.
+ * Must be <= KPB_MAX_DRAINING_REQ (2000 ms on non-TGL, 2000 ms on TGL).
+ */
+#define TFLM_KPB_DRAIN_REQ_MS 2000
+
+/* MFCC delivers one hop every 20 ms; run inference every 25 hops = 500 ms. */
+#define TFLM_MFCC_HOP_MS            20
+#define TFLM_INFERENCE_STRIDE_HOPS  25
+
+/* Soft mel-log AGC (units: Q9.23, matches MFCC output). One decade = +10 dB.
+ * Target 0 dB, floor -20 dB. Attack: instant clamp so peak+gain never exceeds
+ * MEL_CLIP_MAX_Q23 (+1.0, +10 dB). Release: additive step per hop during active
+ * speech (vad_flag == 1), chosen so recovery is ~0.5 dB/sec (hop=20 ms, 50 hops/sec).
+ */
+#define TFLM_AGC_GAIN_TARGET_Q23    0 /* 0 dB */
+#define TFLM_AGC_GAIN_FLOOR_Q23     -16777216 /* -20 dB, int32(-20 * 0.1 * 2^23) */
+#define TFLM_AGC_RELEASE_STEP_Q23   8389 /* 0.5 dB/s, int32((0.5 * 0.1 / 50) * 2^23) */
+
+/* This needs to match with sof_tflm_train.py and sof_tflm_verify.py.
+ *
+ * The range -1.0 to +1.0 of Q9.23 Mel values is scaled to +/-1.0 Q1.7.
+ *
+ * in = [-1.0 1.0]; out = [-1 1];
+ * offset = -(in(1) + in(2)) / 2 = 0
+ * scale = (out(2) - out(1)) / (in(2) - in(1)) = 1.0
+ */
+#define MEL_OFFSET_Q23		0 /* 0 */
+#define MEL_SCALE_Q30		(1 << 30) /* 1.0 in Q30 */
+#define MEL_CLIP_MAX_Q23	(1 << 23) /* +1.0 in Q23 */
+#define MEL_CLIP_MAX_Q7		127
+#define MEL_CLIP_MIN_Q7		-128
+
 struct tflm_comp_data {
 	struct comp_data_blob_handler *model_handler;
 	struct tf_classify tfc;
-};
+	struct ipc_msg *msg;
+	struct kpb_event_data event_data;
+	struct kpb_client client_data;
+	uint32_t drain_req_ms;
+#if CONFIG_AMS
+	uint32_t kpd_uuid_id;
+#endif
+	/* Per-instance sliding window and inference cadence. */
+	int8_t feature_buf[TFLM_FEATURE_ELEM_COUNT];
+	int frame_counter;
+	/* Bitmask of VAD flags for the TFLM_FEATURE_COUNT frames in feature_buf. */
+	uint64_t vad_history;
+	/* Persistent AGC gain applied to every Q9.23 mel value (see AGC defines). */
+	int32_t agc_gain_q23;
+	/* Per-instance shutdown-summary counters. */
+	uint32_t category_totals[TFLM_CATEGORY_COUNT];
+	uint32_t total_inferences;
+	uint32_t kpb_trigger_events;
+} __attribute__((aligned(8)));
+
+#if CONFIG_AMS
+/* Key-phrase detected AMS message UUID (matches KPB consumer). */
+static const ams_uuid_t tflm_ams_kpd_msg_uuid = AMS_KPD_MSG_UUID;
+#endif
 
-__cold static int tflm_init(struct processing_module *mod)
+static void tflm_notify_kpb(struct processing_module *mod)
 {
-	struct module_data *md = &mod->priv;
+	struct tflm_comp_data *cd = module_get_private_data(mod);
 	struct comp_dev *dev = mod->dev;
-	struct module_config *cfg = &md->cfg;
-	struct tflm_comp_data *cd;
-	size_t bs = cfg->size;
+
+	comp_info(dev, "TFLM keyword trigger -> notifying KPB to begin draining");
+
+	cd->client_data.r_ptr = NULL;
+	cd->client_data.sink = NULL;
+	cd->client_data.id = 0;
+	cd->client_data.drain_req = cd->drain_req_ms;
+
+#if CONFIG_AMS
+	struct ams_message_payload ams_payload;
 	int ret;
 
-	assert_can_be_cold();
+	ams_helper_prepare_payload(dev, &ams_payload, cd->kpd_uuid_id,
+				   (uint8_t *)&cd->client_data,
+				   sizeof(cd->client_data));
+	ret = ams_send(&ams_payload);
+	if (ret)
+		comp_err(dev, "ams_send failed %d", ret);
+#else
+	cd->event_data.event_id = KPB_EVENT_BEGIN_DRAINING;
+	cd->event_data.client_data = &cd->client_data;
+
+	notifier_event(dev, NOTIFIER_ID_KPB_CLIENT_EVT,
+		       NOTIFIER_TARGET_CORE_ALL_MASK, &cd->event_data,
+		       sizeof(cd->event_data));
+#endif
+}
 
-	comp_info(dev, "entry");
+/* TODO: scaffolding for future IPC4 keyword notification path; unused today. */
+static __maybe_unused int tflm_ipc_notification_init(struct processing_module *mod)
+{
+	return 0;
+}
 
-	cd = mod_zalloc(mod, sizeof(*cd));
-	if (!cd)
-		return -ENOMEM;
+static __maybe_unused void tflm_send_keyword_notification(struct processing_module *mod,
+							  uint32_t category_idx)
+{
+	struct tflm_comp_data *cd = module_get_private_data(mod);
+	struct sof_ipc4_notify_module_data *msg_module_data;
+	struct sof_ipc4_control_msg_payload *msg_payload;
 
-	md->private = cd;
+	if (!cd->msg)
+		return;
 
-	/* Handler for configuration data */
-	cd->model_handler = mod_data_blob_handler_new(mod);
-	if (!cd->model_handler) {
-		comp_err(dev, "mod_data_blob_handler_new() failed.");
-		ret = -ENOMEM;
-		goto fail;
-	}
+	msg_module_data = (struct sof_ipc4_notify_module_data *)cd->msg->tx_data;
+	msg_payload = (struct sof_ipc4_control_msg_payload *)msg_module_data->event_data;
+	msg_payload->chanv[0].value = category_idx;
+	ipc_msg_send(cd->msg, NULL, false);
+}
 
-	/* Get configuration data and reset DRC state */
-	ret = comp_init_data_blob(cd->model_handler, bs, cfg->data);
-	if (ret < 0) {
-		comp_err(dev, "comp_init_data_blob() failed.");
-		goto fail;
-	}
 
-	/* hard coded atm */
-	cd->tfc.categories = TFLM_CATEGORY_COUNT;
+/* Shared TFLM backend state. speech.cc has one static arena/model/interpreter,
+ * so TF_SetModel/TF_InitOps must run only once no matter how many tflmcly
+ * instances the topology creates; secondary instances attach to the first-init
+ * instance.
+ */
+static bool g_tflm_initialized;
+static int g_tflm_instance_count;
+static int g_tflm_shared_categories;
 
-	/* set default model for the moment*/
-	ret = TF_SetModel(&cd->tfc, NULL);
-	if (!ret) {
-		comp_err(dev, "failed to set model");
-		goto fail;
-	}
+__cold static void tflm_log_summary_at_shutdown(struct processing_module *mod)
+{
+	struct tflm_comp_data *cd = mod ? module_get_private_data(mod) : NULL;
+	struct comp_dev *dev = mod ? mod->dev : NULL;
+	char summary_buf[256];
+	size_t off;
+	int i;
 
-	/* initialise ops */
-	ret = TF_InitOps(&cd->tfc);
-	if (!ret) {
-		comp_err(dev, "failed to init ops");
-		goto fail;
+	if (!cd)
+		return;
+
+	off = snprintk(summary_buf, sizeof(summary_buf),
+		       "[TFLM STREAM SHUTDOWN SUMMARY] Total Inferences=%u | Keyword Events:",
+		       cd->total_inferences);
+	for (i = 0; i < TFLM_CATEGORY_COUNT && off < sizeof(summary_buf); i++) {
+		off += snprintk(summary_buf + off, sizeof(summary_buf) - off,
+				"%s %s=%u", i ? "," : "",
+				prediction[i], cd->category_totals[i]);
 	}
+	if (off < sizeof(summary_buf))
+		snprintk(summary_buf + off, sizeof(summary_buf) - off,
+			 " | Total KPB Triggers=%u", cd->kpb_trigger_events);
+	sof_ut_log(summary_buf);
+	if (dev)
+		comp_info(dev, "%s", summary_buf);
+	printk("%s\n", summary_buf);
+}
 
-	return ret;
+void tflm_yield(void)
+{
+	k_yield();
+}
 
-fail:
-	/* Passing NULL pointer to free functions is Ok */
-	mod_data_blob_handler_free(mod, cd->model_handler);
-	mod_free(mod, cd);
-	return ret;
+__cold static int tflm_init(struct processing_module *mod)
+{
+	struct module_data *md = &mod->priv;
+	struct comp_dev *dev = mod->dev;
+	struct tflm_comp_data *cd;
+
+	assert_can_be_cold();
+
+	cd = rzalloc(SOF_MEM_FLAG_USER, sizeof(*cd));
+	if (!cd) {
+		printk("[TFLM INIT] FAILED: rzalloc(%zu) OOM\n", sizeof(*cd));
+		return -ENOMEM;
+	}
+
+	md->private = cd;
+	cd->tfc.categories = TFLM_CATEGORY_COUNT;
+	cd->drain_req_ms = TFLM_KPB_DRAIN_REQ_MS;
+	cd->agc_gain_q23 = TFLM_AGC_GAIN_TARGET_Q23;
+#if CONFIG_AMS
+	cd->kpd_uuid_id = AMS_INVALID_MSG_TYPE;
+#endif
+	g_tflm_instance_count++;
+	printk("[TFLM INIT] instance=%d dev=%p init complete\n",
+	       g_tflm_instance_count, dev);
+	return 0;
 }
 
 __cold static int tflm_free(struct processing_module *mod)
@@ -112,8 +290,12 @@ __cold static int tflm_free(struct processing_module *mod)
 
 	assert_can_be_cold();
 
-	mod_data_blob_handler_free(mod, cd->model_handler);
-	mod_free(mod, cd);
+	tflm_log_summary_at_shutdown(mod);
+	if (--g_tflm_instance_count <= 0) {
+		g_tflm_instance_count = 0;
+		g_tflm_initialized = false;
+	}
+	rfree(cd);
 	return 0;
 }
 
@@ -122,42 +304,9 @@ __cold static int tflm_set_config(struct processing_module *mod, uint32_t param_
 	const uint8_t *fragment, size_t fragment_size, uint8_t *response,
 	size_t response_size)
 {
-	struct tflm_comp_data *cd = module_get_private_data(mod);
-	struct comp_dev *dev = mod->dev;
-	int ret;
-
-	assert_can_be_cold();
-
-	comp_dbg(dev, "entry");
-
-	struct sof_ipc4_control_msg_payload *ctl = (struct sof_ipc4_control_msg_payload *)fragment;
-
-	comp_info(dev, "bytes control");
-	ret = comp_data_blob_set(cd->model_handler, pos, data_offset_size, fragment,
-				 fragment_size);
-
-	/* TODO: now load the model from the blob */
-
-	return ret;
+	return 0;
 }
 
-#if DEBUG
-
-/* The first feature for no and yes used in tflm_speech example */
-
-int8_t expected_feature_no[TFLM_FEATURE_SIZE] = {
-	126, 103, 124, 102, 124, 102, 123, 100, 118, 97, 118, 100, 118, 98,
-	121, 100, 121, 98,  117, 91,  96,  74,  54,  87, 100, 87,  109, 92,
-	91,  80,  64,  55,  83,  74,  74,  78,  114, 95, 101, 81,
-};
-
-int8_t expected_feature_yes[TFLM_FEATURE_SIZE] = {
-	124, 105, 126, 103, 125, 101, 123, 100, 116, 98,  115, 97,  113, 90,
-	91,  82,  104, 96,  117, 97,  121, 103, 126, 101, 125, 104, 126, 104,
-	125, 101, 116, 90,  81,  74,  80,  71,  83,  76,  82,  71,
-};
-#endif
-
 /*
  * This expects features from 16kHz mono 16 bit input stream.
  *
@@ -179,70 +328,439 @@ static int tflm_process(struct processing_module *mod,
 			struct sof_sink **sinks, int num_of_sinks)
 {
 	struct tflm_comp_data *cd = module_get_private_data(mod);
-	struct comp_dev *dev = mod->dev;
-	size_t frame_bytes = source_get_frame_bytes(sources[0]);
-	int features = source_get_data_frames_available(sources[0]);
+
+
+	/* Guard: skip processing if TFLM interpreter was not prepared */
+	if (!g_tflm_initialized) {
+		printk("[TFLM PROCESS] WARNING: not initialized, skipping frame\n");
+		/* Still drain the source so the pipeline doesn't stall */
+		size_t avail = source_get_data_available(sources[0]);
+		if (avail > 0) {
+			const void *dp, *bs;
+			size_t bsz;
+			if (source_get_data(sources[0], avail, &dp, &bs, &bsz) == 0)
+				source_release_data(sources[0], avail);
+		}
+		return 0;
+	}
+
+	size_t bytes_to_process = source_get_data_available(sources[0]);
 	const void *data_ptr, *buf_start;
 	size_t buf_size;
-	int ret;
+	int ret = 0;
 
-	comp_dbg(dev, "entry");
+#if CONFIG_COMP_TENSORFLOW_DEBUG_TRACE
+	uint32_t sched_ccount_start = get_ccount();
+	uint64_t sched_timer_start = sof_cycle_get_64();
+#endif
+
+	int8_t *feature_buf = cd->feature_buf;
 
-	/* Window size is TFLM_FEATURE_ELEM_COUNT and we increment
-	 * by TFLM_FEATURE_SIZE until buffer empty.
-	 */
-	while (features >= TFLM_FEATURE_ELEM_COUNT) {
-		ret = source_get_data(sources[0], TFLM_FEATURE_ELEM_COUNT * frame_bytes,
+	while (bytes_to_process >= MFCC_HOP_BYTES) {
+		ret = source_get_data(sources[0], MFCC_HOP_BYTES,
 				      &data_ptr, &buf_start, &buf_size);
-		if (ret)
-			return ret;
-
-		cd->tfc.audio_features = data_ptr;
-		cd->tfc.audio_data_size = TFLM_FEATURE_ELEM_COUNT;
-		ret = TF_ProcessClassify(&cd->tfc);
-		if (!ret) {
-			comp_err(dev, "classify failed %s.",
-				 cd->tfc.error);
-			source_release_data(sources[0], 0);
-			return ret;
-		}
+		if (ret != 0 || !data_ptr)
+			break;
+
+		{
+			/* Ring buffer may wrap inside a single hop; copy the
+			 * 184-byte header+mel payload into a contiguous scratch
+			 * so hdr / mel[] accesses (and the sink copy) can treat
+			 * it as linear.
+			 */
+			uint8_t hop_scratch[MFCC_HOP_BYTES];
+			const uint8_t *src_end = (const uint8_t *)buf_start + buf_size;
+			size_t src_until_wrap = src_end - (const uint8_t *)data_ptr;
+
+			if (src_until_wrap >= MFCC_HOP_BYTES) {
+				memcpy(hop_scratch, data_ptr, MFCC_HOP_BYTES);
+			} else {
+				memcpy(hop_scratch, data_ptr, src_until_wrap);
+				memcpy(hop_scratch + src_until_wrap, buf_start,
+				       MFCC_HOP_BYTES - src_until_wrap);
+			}
+
+			/* Strip the mfcc_data_header and requantize the
+			 * TFLM_FEATURE_SIZE int32 Q9.23 mel-log values into
+			 * int8 features for this hop.
+			 */
+			const struct mfcc_data_header *hdr =
+				(const struct mfcc_data_header *)hop_scratch;
+			const int32_t *mel = (const int32_t *)
+				(hop_scratch + sizeof(struct mfcc_data_header));
+			int8_t hop_features[TFLM_FEATURE_SIZE];
+
+			/* Update VAD history bitmask (tracks last TFLM_FEATURE_COUNT hops). */
+			cd->vad_history = ((cd->vad_history << 1) | (hdr->vad_flag ? 1ULL : 0ULL)) &
+					  ((1ULL << TFLM_FEATURE_COUNT) - 1);
+
+			/* AGC: attack on this hop's peak (always track energy). */
+			int32_t hop_peak_q23 = mel[0];
+			for (int i = 1; i < TFLM_FEATURE_SIZE; i++)
+				if (mel[i] > hop_peak_q23)
+					hop_peak_q23 = mel[i];
+
+			int32_t clip_headroom_q23 = MEL_CLIP_MAX_Q23 - hop_peak_q23;
+			if (cd->agc_gain_q23 > clip_headroom_q23)
+				cd->agc_gain_q23 = clip_headroom_q23;
+			if (cd->agc_gain_q23 < TFLM_AGC_GAIN_FLOOR_Q23)
+				cd->agc_gain_q23 = TFLM_AGC_GAIN_FLOOR_Q23;
+
+			int32_t agc_gain_q23 = cd->agc_gain_q23;
+
+			comp_info(mod->dev, "tflm agc: peak_q23=%d gain_q23=%d",
+				  hop_peak_q23, agc_gain_q23);
+
+			/* Requantize to int8: skip if no speech in the 49-hop window (fill with silence) */
+			if (cd->vad_history) {
+				for (int i = 0; i < TFLM_FEATURE_SIZE; i++) {
+					int32_t mel_c = mel[i] + agc_gain_q23;
+
+					/* Rescale asymmetrically with offset and gain. */
+					mel_c = Q_MULTSR_32X32((int64_t)(mel_c + MEL_OFFSET_Q23),
+							       MEL_SCALE_Q30, 23, 30, 7);
+					if (mel_c > MEL_CLIP_MAX_Q7)
+						mel_c = MEL_CLIP_MAX_Q7;
+					else if (mel_c < MEL_CLIP_MIN_Q7)
+						mel_c = MEL_CLIP_MIN_Q7;
+
+					hop_features[i] = (int8_t)mel_c;
+				}
+			} else {
+				memset(hop_features, MEL_CLIP_MIN_Q7, sizeof(hop_features));
+			}
+
+			/* Release: creep back toward 0 dB target. */
+			if (cd->agc_gain_q23 < TFLM_AGC_GAIN_TARGET_Q23) {
+				cd->agc_gain_q23 += TFLM_AGC_RELEASE_STEP_Q23;
+				if (cd->agc_gain_q23 > TFLM_AGC_GAIN_TARGET_Q23)
+					cd->agc_gain_q23 = TFLM_AGC_GAIN_TARGET_Q23;
+			}
+
+#if CONFIG_COMP_TENSORFLOW_DEBUG_TRACE
+			{
+				static int dbg_hop_count;
+				int32_t mel_min = mel[0], mel_max = mel[0];
+				int8_t f_min = hop_features[0], f_max = hop_features[0];
+				char dbg_buf[192];
+
+				dbg_hop_count++;
+				for (int i = 1; i < TFLM_FEATURE_SIZE; i++) {
+					if (mel[i] < mel_min) mel_min = mel[i];
+					if (mel[i] > mel_max) mel_max = mel[i];
+					if (hop_features[i] < f_min) f_min = hop_features[i];
+					if (hop_features[i] > f_max) f_max = hop_features[i];
+				}
+				snprintk(dbg_buf, sizeof(dbg_buf),
+					 "[DBG hop %d] vad=%d E=%d Ne=%d mel_min=%d mel_max=%d f_min=%d f_max=%d agc_q23=%d",
+					 dbg_hop_count, (int)hdr->vad_flag,
+					 (int)hdr->energy, (int)hdr->noise_energy,
+					 mel_min, mel_max,
+					 f_min, f_max,
+					 (int)cd->agc_gain_q23);
+				sof_ut_log(dbg_buf);
+			}
+#endif
 
-		/* debug - dump the output */
-		for (int i = 0; i < cd->tfc.categories; i++) {
-			comp_dbg(dev, "tf: predictions %1.3f %s",
-				 cd->tfc.predictions[i], prediction[i]);
-		}
+			/* Shift 49-frame sliding window history by 1 frame (20ms hop) */
+			memmove(&feature_buf[0], &feature_buf[TFLM_FEATURE_SIZE],
+				TFLM_FEATURE_ELEM_COUNT - TFLM_FEATURE_SIZE);
+			memcpy(&feature_buf[TFLM_FEATURE_ELEM_COUNT - TFLM_FEATURE_SIZE],
+			       hop_features, TFLM_FEATURE_SIZE);
+
+			/* Copy source data to sink first, before releasing source data */
+			if (num_of_sinks > 0 && sinks[0]) {
+				void *snk_ptr, *snk_buf_start;
+				size_t snk_buf_size;
+				int sret = sink_get_buffer(sinks[0], MFCC_HOP_BYTES,
+							   &snk_ptr, &snk_buf_start, &snk_buf_size);
+				if (sret == 0 && snk_ptr) {
+					size_t size_to_wrap = (uint8_t *)snk_buf_start + snk_buf_size - (uint8_t *)snk_ptr;
+					if (MFCC_HOP_BYTES <= size_to_wrap) {
+						memcpy(snk_ptr, hop_scratch, MFCC_HOP_BYTES);
+					} else {
+						memcpy(snk_ptr, hop_scratch, size_to_wrap);
+						memcpy(snk_buf_start, hop_scratch + size_to_wrap,
+						       MFCC_HOP_BYTES - size_to_wrap);
+					}
+					sink_commit_buffer(sinks[0], MFCC_HOP_BYTES);
+				} else {
+					/* Rate-limited: first 5, then every 100th failure. */
+					static uint32_t dbg_sink_fail_count;
+
+					dbg_sink_fail_count++;
+					if (dbg_sink_fail_count <= 5 ||
+					    dbg_sink_fail_count % 100 == 0) {
+						size_t free_sz = sink_get_free_size(sinks[0]);
+
+						printk("[TFLM SINK FAIL #%u] sink_get_buffer(%u) ret=%d snk_ptr=%p free=%zu\n",
+						       dbg_sink_fail_count,
+						       (unsigned)MFCC_HOP_BYTES, sret,
+						       snk_ptr, free_sz);
+					}
+				}
+			}
+
+			/* Release source data immediately after reading */
+			source_release_data(sources[0], MFCC_HOP_BYTES);
+			bytes_to_process -= MFCC_HOP_BYTES;
+
+			cd->frame_counter++;
+
+			/* Run inference every TFLM_INFERENCE_STRIDE_HOPS MFCC hops. */
+			if (cd->frame_counter >= TFLM_INFERENCE_STRIDE_HOPS) {
+				cd->frame_counter = 0;
+
+				/* VAD gate: skip inference computation if no active speech in the 49-hop window */
+				if (!cd->vad_history) {
+#if CONFIG_COMP_TENSORFLOW_DEBUG_TRACE
+					sof_ut_log("[DBG inference] skipped: VAD=0 in entire 49-hop window");
+#endif
+					continue;
+				}
+
+				cd->tfc.audio_features = feature_buf;
+				cd->tfc.audio_data_size = TFLM_FEATURE_ELEM_COUNT;
+
+#if CONFIG_COMP_TENSORFLOW_DEBUG_TRACE
+				{
+					int nonsat = 0;
+					for (int fi = 0; fi < TFLM_FEATURE_ELEM_COUNT; fi++)
+						if (feature_buf[fi] != -128)
+							nonsat++;
+					char nb[96];
+					snprintk(nb, sizeof(nb),
+						 "[DBG window] nonsat=%d/%d",
+						 nonsat, TFLM_FEATURE_ELEM_COUNT);
+					sof_ut_log(nb);
+				}
+
+				uint32_t stride_ms = TFLM_INFERENCE_STRIDE_HOPS * TFLM_MFCC_HOP_MS;
+				uint32_t c_start = get_ccount();
+				uint64_t t_start = sof_cycle_get_64();
+#endif
 
-		/* advance by one stride */
-		source_release_data(sources[0], TFLM_FEATURE_SIZE * frame_bytes);
-		features = source_get_data_frames_available(sources[0]);
+				ret = TF_ProcessClassify(&cd->tfc);
+
+#if CONFIG_COMP_TENSORFLOW_DEBUG_TRACE
+				uint32_t c_end = get_ccount();
+				uint64_t t_end = sof_cycle_get_64();
+				uint32_t cycles = c_end - c_start;
+				uint64_t timer_delta = t_end - t_start;
+				uint32_t mcps_x100 = (timer_delta > 0) ? (uint32_t)(((uint64_t)cycles * 3840ULL) / timer_delta) : 0;
+				static uint32_t inf_count = 0;
+				inf_count++;
+
+				char ut_buf[160];
+				snprintk(ut_buf, sizeof(ut_buf), "[PERF] TFLM Inference #%u (stride=%ums): CCOUNT=%u cycles, 38M_Timer_delta=%llu, MCPS=%u.%02u",
+					 inf_count, stride_ms,
+					 cycles, (unsigned long long)timer_delta, mcps_x100 / 100, mcps_x100 % 100);
+				sof_ut_log(ut_buf);
+				printk("[PERF] TFLM Inference #%u: CCOUNT=%u cycles, timer_delta=%llu, MCPS=%u.%02u\n",
+				       inf_count, cycles, (unsigned long long)timer_delta, mcps_x100 / 100, mcps_x100 % 100);
+
+				snprintk(ut_buf, sizeof(ut_buf), "[TFLM MODEL GRAPH] Total Nodes = %d", cd->tfc.op_count);
+				sof_ut_log(ut_buf);
+
+				{
+					char raw_buf[160];
+					int off = snprintk(raw_buf, sizeof(raw_buf),
+							   "[DBG raw_output] ret=%d", ret);
+					for (int i = 0; i < TFLM_CATEGORY_COUNT &&
+					     off < (int)sizeof(raw_buf); i++)
+						off += snprintk(raw_buf + off,
+								sizeof(raw_buf) - off,
+								" %s=%d",
+								prediction[i],
+								cd->tfc.raw_output[i]);
+					sof_ut_log(raw_buf);
+				}
+
+				for (int k = 0; k < cd->tfc.op_count && k < 10; k++) {
+					snprintk(ut_buf, sizeof(ut_buf), "  Node %d (OpCode=%d): %u cycles",
+						 k, cd->tfc.node_codes[k], cd->tfc.node_cycles[k]);
+					sof_ut_log(ut_buf);
+				}
+#endif
+
+				if (ret >= 0) {
+					int max_idx = 0;
+					float max_score = cd->tfc.predictions[0];
+
+					for (int i = 0; i < cd->tfc.categories; i++) {
+						if (cd->tfc.predictions[i] > max_score) {
+							max_score = cd->tfc.predictions[i];
+							max_idx = i;
+						}
+					}
+
+					cd->total_inferences++;
+					if (max_idx >= 0 && max_idx < TFLM_CATEGORY_COUNT)
+						cd->category_totals[max_idx]++;
+
+#if CONFIG_COMP_TENSORFLOW_DEBUG_TRACE
+					{
+						char result_buf[200];
+						int max_pct_dbg = (int)(max_score * 100.0f);
+						if (max_pct_dbg < 0) max_pct_dbg = 0;
+						int off = snprintk(result_buf, sizeof(result_buf),
+								   "TFLM top prediction: %s confidence=%d pct (inferences=%u):",
+								   prediction[max_idx], max_pct_dbg,
+								   cd->total_inferences);
+						for (int i = 0; i < TFLM_CATEGORY_COUNT &&
+						     off < (int)sizeof(result_buf); i++)
+							off += snprintk(result_buf + off,
+									sizeof(result_buf) - off,
+									" %s=%u",
+									prediction[i],
+									cd->category_totals[i]);
+						sof_ut_log(result_buf);
+					}
+#endif
+
+					/* Only announce a keyword hit for real keyword classes
+					 * (indices >= 2, i.e. not silence/unknown).
+					 */
+					if (max_idx >= 2 && max_score >= 0.50f) {
+						char kw_buf[96];
+						int max_pct = (int)(max_score * 100.0f);
+						if (max_pct < 0) max_pct = 0;
+						snprintk(kw_buf, sizeof(kw_buf),
+							 "TFLM KEYWORD DETECTED: %s confidence=%d pct",
+							 prediction[max_idx], max_pct);
+						sof_ut_log(kw_buf);
+
+						cd->kpb_trigger_events++;
+						tflm_notify_kpb(mod);
+					}
+				}
+			}
+		}
 	}
 
+#if CONFIG_COMP_TENSORFLOW_DEBUG_TRACE
+	{
+		uint32_t sched_ccount_end = get_ccount();
+		uint64_t sched_timer_end = sof_cycle_get_64();
+		uint32_t sched_c_delta = sched_ccount_end - sched_ccount_start;
+		uint64_t sched_t_delta = sched_timer_end - sched_timer_start;
+		uint32_t sched_mcps_x100 = 0;
+
+		if (sched_t_delta > 0)
+			sched_mcps_x100 = (uint32_t)(((uint64_t)sched_c_delta * 3840ULL) / sched_t_delta);
+
+		(void)sched_mcps_x100;
+	}
+#endif
 	return ret;
 }
 
+
+static int tflm_prepare(struct processing_module *mod,
+			struct sof_source **sources, int num_of_sources,
+			struct sof_sink **sinks, int num_of_sinks)
+{
+	struct tflm_comp_data *cd = module_get_private_data(mod);
+
+	printk("[TFLM PREPARE] tflm_prepare called, loading model...\n");
+
+	if (g_tflm_initialized) {
+		/* Shared TFLM engine already up. */
+		cd->tfc.categories       = g_tflm_shared_categories;
+		printk("[TFLM PREPARE] shared engine already initialized; attach instance\n");
+		goto post_init;
+	}
+
+	int ret = TF_SetModel(&cd->tfc, NULL);
+	if (ret < 0) {
+		printk("[TFLM PREPARE] FAILED: TF_SetModel returned %d\n", ret);
+		return ret;
+	}
+
+	ret = TF_InitOps(&cd->tfc);
+	if (ret < 0) {
+		printk("[TFLM PREPARE] FAILED: TF_InitOps returned %d (%s)\n",
+		       ret, cd->tfc.error ? cd->tfc.error : "no detail");
+		return ret;
+	}
+
+	g_tflm_shared_categories       = cd->tfc.categories;
+	g_tflm_initialized = true;
+	printk("[TFLM PREPARE] TFLM model & ops initialized successfully!\n");
+	printk("[TFLM PREPARE] arena_used=%zu / capacity=%zu bytes\n",
+	       TF_ArenaUsedBytes(), TF_ArenaCapacity());
+
+post_init:
+#if CONFIG_AMS
+	if (cd->kpd_uuid_id == AMS_INVALID_MSG_TYPE) {
+		int ams_ret = ams_helper_register_producer(mod->dev,
+							   &cd->kpd_uuid_id,
+							   tflm_ams_kpd_msg_uuid);
+		if (ams_ret) {
+			printk("[TFLM PREPARE] AMS producer register failed %d\n",
+			       ams_ret);
+			return ams_ret;
+		}
+	}
+#endif
+	return 0;
+}
+
+
 static int tflm_reset(struct processing_module *mod)
 {
-	//struct tflm_comp_data *cd = module_get_private_data(mod);
+	struct tflm_comp_data *cd = module_get_private_data(mod);
 
+	tflm_log_summary_at_shutdown(mod);
+	cd->vad_history = 0;
+	cd->frame_counter = 0;
+	memset(cd->feature_buf, 0, sizeof(cd->feature_buf));
+#if CONFIG_AMS
+	if (cd->kpd_uuid_id != AMS_INVALID_MSG_TYPE) {
+		int ret = ams_helper_unregister_producer(mod->dev,
+							 cd->kpd_uuid_id);
+		if (ret)
+			comp_err(mod->dev, "ams unregister failed %d", ret);
+		cd->kpd_uuid_id = AMS_INVALID_MSG_TYPE;
+	}
+#else
+	(void)cd;
+#endif
 	return 0;
 }
 
 static const struct module_interface tflmcly_interface = {
 	.init = tflm_init,
-//	.prepare = tflm_prepare,
+	.prepare = tflm_prepare,
 	.process = tflm_process,
 	.set_configuration = tflm_set_config,
-//	.get_configuration = tflm_get_config,
 	.reset = tflm_reset,
 	.free = tflm_free
 };
 
 DECLARE_TR_CTX(tflm_tr, SOF_UUID(tflmcly_uuid), LOG_LEVEL_INFO);
 DECLARE_MODULE_ADAPTER(tflmcly_interface, tflmcly_uuid, tflm_tr);
-SOF_MODULE_INIT(tflmcly, sys_comp_module_tflmcly_interface_init);
+SOF_MODULE_INIT(tflmcly_interface, sys_comp_module_tflmcly_interface_init);
+
+#if CONFIG_COMP_TENSORFLOW_MODULE
+int llext_entry(void)
+{
+	printk("[TFLM LLEXT] llext_entry initialized cleanly.\n");
+	return 0;
+}
+EXPORT_SYMBOL(llext_entry);
+#endif
+
+
+
+
+
+
+
+
 
 #if CONFIG_COMP_TENSORFLOW_MODULE
+
 /* modular: llext dynamic link */
 
 #include <module/module/api_ver.h>
diff --git a/src/audio/tensorflow/tune/sof_mfcc_extract_features.sh b/src/audio/tensorflow/tune/sof_mfcc_extract_features.sh
new file mode 100755
index 000000000000..76638b30a3b1
--- /dev/null
+++ b/src/audio/tensorflow/tune/sof_mfcc_extract_features.sh
@@ -0,0 +1,143 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# Batch-emit SOF mel40 MFCC feature files for a labelled WAV dataset, for
+# retraining the TFLM micro_speech model against real SOF features.
+#
+# Expected input layout:
+#   <wav_root>/<label>/<file>.wav      # any sample rate/channels; auto-converted
+#
+# Output layout:
+#   <feat_root>/<label>/<file>.raw     # SOF mel40 hop records, one after another
+#
+# Each hop record is 184 bytes (matches the on-device wire format):
+#   24 bytes  struct mfcc_data_header  (magic, frame_number, reserved,
+#                                       energy, noise_energy, vad_flag)
+#   40 x int32_t   Q9.23 mel-log values
+#
+# The consumer (Python training loader) parses N=hop_count records, strips the
+# 24-byte headers, and reshapes to (N, 40) int32 Q9.23. See decode_mel.m for a
+# reference decoder.
+#
+# Level-jitter augmentation happens upstream in the WAV generators
+# (sof_tflm_generate_keyword_dataset.sh, sof_tflm_prepare_silence_unknown.sh)
+# so it's audible on disk. This script just consumes whatever WAVs are
+# already in <wav_root>/<label>/.
+#
+# Requirements:
+#   - $SOF_WORKSPACE points at the parent of the sof tree
+#   - sof-testbench4 built at tools/testbench/build_testbench/install/bin/
+#   - sof-hda-benchmark-mfccmel40{16,24,32}.tplg built (development target)
+#   - sox on PATH
+
+set -e
+
+usage() {
+	cat >&2 <<EOF
+Usage: $0 <wav_root> <feat_root> [--format S16|S24|S32]
+
+  wav_root    Directory containing <label>/*.wav (recursed one level).
+  feat_root   Output root; <label>/ subdirs are created as needed.
+  --format    Testbench sample container. Default S32 (matches on-device).
+EOF
+	exit 1
+}
+
+[ $# -ge 2 ] || usage
+
+WAV_ROOT="$1"
+FEAT_ROOT="$2"
+FORMAT="S32"
+
+shift 2
+while [ $# -gt 0 ]; do
+	case "$1" in
+	--format)
+		FORMAT="$2"
+		shift 2
+		;;
+	*)
+		usage
+		;;
+	esac
+done
+
+case "$FORMAT" in
+S16) SF="16" ;;
+S24) SF="24" ;;
+S32) SF="32" ;;
+*) usage ;;
+esac
+
+: "${SOF_WORKSPACE:?SOF_WORKSPACE must point at the parent of the sof tree}"
+
+TESTBENCH="${SOF_WORKSPACE}/sof/tools/testbench/build_testbench/install/bin/sof-testbench4"
+TPLG="${SOF_WORKSPACE}/sof/tools/build_tools/topology/topology2/development/sof-hda-benchmark-mfccmel40${SF}.tplg"
+
+[ -x "$TESTBENCH" ] || { echo "Missing testbench: $TESTBENCH" >&2; exit 2; }
+[ -f "$TPLG" ]     || { echo "Missing topology: $TPLG"       >&2; exit 2; }
+[ -d "$WAV_ROOT" ] || { echo "Missing wav_root: $WAV_ROOT"   >&2; exit 2; }
+command -v sox >/dev/null || { echo "sox not on PATH" >&2; exit 2; }
+
+TMPDIR_LOCAL=$(mktemp -d -t sof_mfcc_train.XXXXXX)
+trap 'rm -rf "$TMPDIR_LOCAL"' EXIT
+
+# sox parameters: 16 kHz stereo (bench topology expects -c 2 -p 3,4).
+# MFCC config picks channel 0 (see get_mel_spectrogram_config in setup_mfcc.m),
+# so a mono WAV upmixed to stereo is fine.
+convert_wav() {
+	local src="$1"
+	local dst="$2"
+
+	case "$FORMAT" in
+	S16) sox -R --encoding signed-integer "$src" \
+		-L -r 16000 -c 2 -b 16 "$dst" ;;
+	S24) sox -R --no-dither --encoding signed-integer "$src" \
+		-L -r 16000 -c 2 -b 32 "$dst" vol 0.003906250000 ;;
+	S32) sox -R --no-dither --encoding signed-integer "$src" \
+		-L -r 16000 -c 2 -b 32 "$dst" ;;
+	esac
+}
+
+process_wav() {
+	local wav="$1"
+	local out_dir="$2"
+	local base
+	base=$(basename "$wav" .wav)
+
+	local raw_in="${TMPDIR_LOCAL}/in.raw"
+	local raw_out="${out_dir}/${base}.raw"
+
+	mkdir -p "$out_dir"
+	convert_wav "$wav" "$raw_in"
+
+	# -d 0 silences testbench traces (there is no -q flag).
+	"$TESTBENCH" -d 0 \
+		-r 16000 -c 2 -b "${FORMAT}_LE" -p 3,4 \
+		-t "$TPLG" -i "$raw_in" -o "$raw_out"
+}
+
+total=0
+for label_dir in "$WAV_ROOT"/*/; do
+	[ -d "$label_dir" ] || continue
+	label=$(basename "$label_dir")
+	# Skip underscore-prefixed staging dirs (e.g. _raw from the Piper generator).
+	case "$label" in _*) continue ;; esac
+	out_dir="${FEAT_ROOT}/${label}"
+
+	echo "[label=${label}]"
+	count=0
+	for wav in "$label_dir"*.wav; do
+		[ -f "$wav" ] || continue
+		process_wav "$wav" "$out_dir"
+		count=$((count + 1))
+	done
+	echo "  wrote ${count} feature files to ${out_dir}"
+	total=$((total + count))
+done
+
+echo "-----------------------------------------------------------------"
+echo "Emitted ${total} mel40 feature files under ${FEAT_ROOT}"
+echo "Format per hop: 24-byte mfcc_data_header + 40 x int32 Q9.23 = 184 bytes"
+echo "-----------------------------------------------------------------"
diff --git a/src/audio/tensorflow/tune/sof_tflm_dataset.py b/src/audio/tensorflow/tune/sof_tflm_dataset.py
new file mode 100644
index 000000000000..648a68adff60
--- /dev/null
+++ b/src/audio/tensorflow/tune/sof_tflm_dataset.py
@@ -0,0 +1,211 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+"""
+SOF mel40 feature loader for TFLM wake-on-voice training.
+
+Consumes the raw hop records that ``sof_mfcc_extract_features.sh`` emits (one
+file per input WAV, produced by the SOF testbench):
+
+    <feat_root>/<label>/<basename>.raw
+
+Each hop starts with the on-device wire format:
+
+    struct mfcc_data_header  (24 bytes)  magic, frame_number, reserved,
+                                         energy, noise_energy, vad_flag
+    int32_t values[40]                   Q9.23 mel-log bins
+
+The loader locates hops by scanning for ``MFCC_MAGIC`` (0x6d666363) so it
+works for both the packed on-wire compress layout (184 bytes/hop, back to
+back) and the legacy PCM-sink layout the testbench emits when the MFCC
+config has ``compress_output=false`` (24-byte header + 160 mel bytes +
+zero-padding out to ``sink_frame_bytes * frame_shift`` per hop; e.g. 2560
+bytes for a stereo S32 16 kHz sink at 20 ms hop). At each magic offset it
+reads the 24-byte header and the following 40 int32 Q9.23 mel bins,
+converts to float32, and slices each recording into fixed-size 49-hop
+windows to match the tflmcly sliding window (49 hops * 40 channels =
+1960-value input tensor).
+
+Standalone CLI mode prints dataset shape and per-label counts for a quick
+sanity check::
+
+    python3 sof_tflm_dataset.py <feat_root> silence unknown <keyword>
+"""
+
+from __future__ import annotations
+
+import glob
+import os
+import sys
+
+import numpy as np
+
+HOP_HEADER_BYTES = 24
+HOP_BINS = 40
+HOP_BYTES = HOP_HEADER_BYTES + HOP_BINS * 4
+WINDOW_HOPS = 49
+MFCC_MAGIC = 0x6D666363  # 'ccfm' on disk, matches struct mfcc_data_header.magic
+
+# Soft mel-log AGC applied to log10(mel_power) features. One decade of
+# log10-power equals +10 dB, so the units below mirror the Q9.23 runtime
+# AGC in tflm-classify.c: gain target 0.0 (=0 dB), floor -2.0 (=-20 dB
+# attenuation), instant attack against MEL_CLIP_MAX=+1.0 (+10 dB), additive
+# release step per 20 ms hop giving ~0.5 dB/sec recovery (50 hops/sec).
+AGC_GAIN_TARGET = 0.0
+AGC_GAIN_FLOOR = -2.0
+AGC_CLIP_MAX = 1.0
+AGC_RELEASE_STEP_PER_HOP = 0.0010
+
+
+def apply_soft_agc(X: np.ndarray) -> np.ndarray:
+    """Apply the mel-log AGC to each 49-hop window independently.
+
+    ``X`` has shape ``(N, WINDOW_HOPS, HOP_BINS, 1)`` (as returned by
+    :func:`load_dataset`). Each window gets its own AGC state initialized
+    to ``AGC_GAIN_TARGET``; state is not carried across windows. The same
+    scalar gain is applied to all HOP_BINS bins within a single hop, so
+    the spectral shape is preserved.
+    """
+    if X.size == 0:
+        return X
+    N, T, F, C = X.shape
+    out = np.empty_like(X)
+    for n in range(N):
+        gain = AGC_GAIN_TARGET
+        for t in range(T):
+            hop = X[n, t, :, 0]
+            headroom = AGC_CLIP_MAX - float(hop.max())
+            if gain > headroom:
+                gain = headroom
+            if gain < AGC_GAIN_FLOOR:
+                gain = AGC_GAIN_FLOOR
+            out[n, t, :, 0] = hop + gain
+            if gain < AGC_GAIN_TARGET:
+                gain = min(gain + AGC_RELEASE_STEP_PER_HOP, AGC_GAIN_TARGET)
+    return out
+
+
+def load_raw_hops(path: str) -> np.ndarray:
+    """Read a testbench-emitted .raw file, return (N_hops, 40) float32.
+
+    Scans the file for MFCC magic markers and decodes the fixed 184-byte
+    hop payload at each hit, so the same loader works for compress-packed
+    (184 B/hop) and legacy PCM-padded (e.g. 2560 B/hop) layouts.
+    """
+    data = np.fromfile(path, dtype=np.uint8)
+    if data.size < HOP_BYTES:
+        return np.zeros((0, HOP_BINS), dtype=np.float32)
+    n_u32 = data.size // 4
+    u32 = data[: n_u32 * 4].view(np.uint32)
+    magic_positions = np.flatnonzero(u32 == MFCC_MAGIC) * 4
+    if magic_positions.size == 0:
+        return np.zeros((0, HOP_BINS), dtype=np.float32)
+    # Drop any tail magic without a full 184-byte payload behind it.
+    magic_positions = magic_positions[
+        magic_positions + HOP_BYTES <= data.size
+    ]
+    if magic_positions.size == 0:
+        return np.zeros((0, HOP_BINS), dtype=np.float32)
+    offsets = magic_positions[:, None] + (
+        HOP_HEADER_BYTES + np.arange(HOP_BINS * 4)
+    )
+    mel_bytes = data[offsets].reshape(-1, HOP_BINS * 4)
+    q = mel_bytes.view(np.int32).reshape(-1, HOP_BINS)
+    return q.astype(np.float32) / float(1 << 23)
+
+
+def window_hops(
+    feat: np.ndarray,
+    n: int = WINDOW_HOPS,
+    hop_step: int | None = None,
+) -> np.ndarray:
+    """Slice a (N_hops, 40) feature stream into (K, n, 40) windows.
+
+    When ``feat`` is shorter than ``n`` and ``hop_step`` is given (smaller than ``n``),
+    generate time-shifted windows by sliding the short utterance across different
+    offsets inside the ``n``-hop window (padding with the utterance's edge silence frames).
+    """
+    if feat.shape[0] == 0:
+        return np.zeros((1, n, HOP_BINS), dtype=np.float32)
+    L = feat.shape[0]
+    if L < n:
+        if hop_step is None or hop_step >= n:
+            pad = n - L
+            padded = np.concatenate(
+                [feat, np.repeat(feat[-1:], pad, axis=0)], axis=0
+            )
+            return padded[np.newaxis, ...]
+
+        # Generate multiple time shifts: place feat at leading offsets from 0 to (n - L).
+        windows = []
+        for offset in range(0, n - L + 1, hop_step):
+            pad_left = offset
+            pad_right = n - L - offset
+            left = np.repeat(feat[:1], pad_left, axis=0) if pad_left > 0 else np.zeros((0, HOP_BINS), dtype=np.float32)
+            right = np.repeat(feat[-1:], pad_right, axis=0) if pad_right > 0 else np.zeros((0, HOP_BINS), dtype=np.float32)
+            w = np.concatenate([left, feat, right], axis=0)
+            windows.append(w)
+        if (n - L) % hop_step != 0:
+            offset = n - L
+            pad_left = offset
+            left = np.repeat(feat[:1], pad_left, axis=0)
+            w = np.concatenate([left, feat], axis=0)
+            windows.append(w)
+        return np.stack(windows, axis=0)
+
+    if hop_step is None:
+        hop_step = n
+    starts = list(range(0, feat.shape[0] - n + 1, hop_step))
+    if not starts:
+        starts = [0]
+    return np.stack([feat[s : s + n] for s in starts], axis=0)
+
+
+def load_dataset(
+    feat_root: str,
+    labels: list[str],
+    hop_step: int | None = None,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Return X (N, 49, 40, 1) float32 and y (N,) int32 for the given labels.
+
+    Label index in ``labels`` becomes the integer class id — keep the caller's
+    order stable across training and inference. For the SOF tflmcly KPB rule
+    to fire, put silence at index 0 and unknown at index 1; any keyword class
+    must sit at index >= 2.
+    """
+    X_parts: list[np.ndarray] = []
+    y_parts: list[np.ndarray] = []
+    for label_idx, label in enumerate(labels):
+        pattern = os.path.join(feat_root, label, "*.raw")
+        files = sorted(glob.glob(pattern))
+        if not files:
+            print(f"warning: no .raw files under {pattern}", file=sys.stderr)
+            continue
+        for path in files:
+            feat = load_raw_hops(path)
+            wins = window_hops(feat, hop_step=hop_step)
+            X_parts.append(wins)
+            y_parts.append(
+                np.full(wins.shape[0], label_idx, dtype=np.int32)
+            )
+    if not X_parts:
+        raise RuntimeError(f"no .raw features found under {feat_root}")
+    X = np.concatenate(X_parts, axis=0).astype(np.float32)
+    y = np.concatenate(y_parts, axis=0)
+    return X[..., np.newaxis], y
+
+
+def _cli() -> int:
+    if len(sys.argv) < 3:
+        print(
+            "usage: sof_tflm_dataset.py <feat_root> <label1> [label2 ...]",
+            file=sys.stderr,
+        )
+        return 1
+    X, y = load_dataset(sys.argv[1], sys.argv[2:])
+    print(f"X: {X.shape} {X.dtype} min={X.min():.3f} max={X.max():.3f}")
+    print(f"y: {y.shape} {y.dtype} counts={np.bincount(y).tolist()}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(_cli())
diff --git a/src/audio/tensorflow/tune/sof_tflm_generate_keyword_dataset.sh b/src/audio/tensorflow/tune/sof_tflm_generate_keyword_dataset.sh
new file mode 100755
index 000000000000..f4a859171db1
--- /dev/null
+++ b/src/audio/tensorflow/tune/sof_tflm_generate_keyword_dataset.sh
@@ -0,0 +1,291 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# Generate a synthetic keyword WAV dataset with piper-sample-generator,
+# augment it (impulse-response convolution, volume jitter, resample to 16 kHz),
+# and drop the result into the layout expected by
+# sof_mfcc_extract_features.sh:
+#
+#   <out_root>/<label>/*.wav
+#
+# Companion `silence` and `unknown` classes are NOT produced here — those are
+# dataset-wide and best sourced from Speech Commands v2 (see README).
+#
+# Requirements:
+#   - piper-sample-generator installed in an activated venv (or $PIPER_VENV set
+#     to a venv root and this script activates it for you).
+#   - LibriTTS-R generator checkpoint at $PIPER_MODEL (defaults to the copy in
+#     ~/git/piper-sample-generator/models/). Fetch once with:
+#       wget -O <models>/en_US-libritts_r-medium.pt \
+#         https://github.com/rhasspy/piper-sample-generator/releases/download/v2.0.0/en_US-libritts_r-medium.pt
+#
+# Env knobs (all optional):
+#   PIPER_VENV      Path to a Piper venv to auto-activate.
+#   PIPER_MODEL     Path to the LibriTTS-R .pt checkpoint.
+#   PIPER_REPO      Path to a piper-sample-generator git clone root. Needed
+#                   because upstream does not pip-install the sibling
+#                   piper_train package that __main__.py imports from;
+#                   auto-detected when PIPER_MODEL sits under <repo>/models/.
+#   MAX_SAMPLES     Positive clips per speaking-rate loop (default 400).
+#   MAX_SPEAKERS    Cap on speaker-embedding index (default 700; upstream
+#                   warns higher indices produce artifacts).
+#   SLERP_WEIGHTS   Speaker blending weights passed to generator. Default "0.0"
+#                   disables interpolation to use true single-speaker embeddings.
+#   GAIN_AUG        1 = rewrite each augmented WAV in place with a peak-
+#                   normalized target and a per-file Gaussian level jitter,
+#                   so the on-disk WAVs directly reflect the level
+#                   distribution the model will train on. 0 = leave piper's
+#                   augmented output untouched (gain augmentation is handled
+#                   during training). Default 0.
+#   GAIN_PEAK_DBFS  Peak-normalization target in dBFS (default -10).
+#   GAIN_SIGMA_DB   Gaussian jitter sigma in dB around the target (default 5).
+#   GAIN_HEADROOM_DB Headroom in dB below full scale that the augmented peak
+#                   must respect. Positive offsets are capped so
+#                   peak+offset <= -GAIN_HEADROOM_DB, keeping sox out of the
+#                   dither/gain clip warning path (default 1).
+#
+# Required CLI:
+#   --keyword TEXT  Spoken phrase to synthesize (e.g. "hey linux"). Repeat
+#                   to generate several keyword classes in one invocation.
+#   --label DIR     Output subdir name (default: derived from keyword by
+#                   lower-casing and replacing spaces with underscores).
+#                   If given, must be repeated the same number of times as
+#                   --keyword and pairs positionally.
+
+set -e
+
+KEYWORDS=()
+LABELS=()
+OUT_ROOT=""
+
+usage() {
+	cat >&2 <<EOF
+Usage: $0 --keyword "<text>" [--keyword "<text>" ...] \\
+          [--label <dir> ...] <out_root>
+
+  --keyword TEXT   Spoken phrase to synthesize (repeatable; at least one).
+  --label DIR      Output subdir name under <out_root>. Repeat once per
+                   --keyword to override the default naming. If omitted,
+                   each label is derived from its keyword by lower-casing
+                   and replacing spaces with underscores.
+  out_root         Dataset root; <label>/*.wav is produced under it for each
+                   keyword, ready to feed to
+                   sof_mfcc_extract_features.sh <out_root> <feat_root>.
+
+Optional env: PIPER_VENV, PIPER_MODEL, PIPER_REPO, MAX_SAMPLES, MAX_SPEAKERS.
+EOF
+	exit 1
+}
+
+while [[ $# -gt 0 ]]; do
+	case "$1" in
+		-k|--keyword)
+			[[ $# -ge 2 ]] || usage
+			KEYWORDS+=("$2"); shift 2 ;;
+		-l|--label)
+			[[ $# -ge 2 ]] || usage
+			LABELS+=("$2"); shift 2 ;;
+		-h|--help)
+			usage ;;
+		--)
+			shift; break ;;
+		-*)
+			echo "unknown option: $1" >&2; usage ;;
+		*)
+			if [[ -z "$OUT_ROOT" ]]; then
+				OUT_ROOT="$1"; shift
+			else
+				echo "unexpected positional: $1" >&2; usage
+			fi ;;
+	esac
+done
+
+if [[ -z "$OUT_ROOT" || ${#KEYWORDS[@]} -eq 0 ]]; then
+	usage
+fi
+
+if [[ ${#LABELS[@]} -gt 0 && ${#LABELS[@]} -ne ${#KEYWORDS[@]} ]]; then
+	echo "--label given ${#LABELS[@]} time(s) but --keyword given ${#KEYWORDS[@]} time(s); counts must match" >&2
+	exit 1
+fi
+
+if [[ ${#LABELS[@]} -eq 0 ]]; then
+	for kw in "${KEYWORDS[@]}"; do
+		LABELS+=("$(echo "$kw" | tr ' A-Z' '_a-z')")
+	done
+fi
+: "${MAX_SAMPLES:=400}"
+: "${MAX_SPEAKERS:=700}"
+: "${SLERP_WEIGHTS:=0.0}"
+: "${PIPER_MODEL:=$HOME/git/piper-sample-generator/models/en_US-libritts_r-medium.pt}"
+: "${GAIN_AUG:=0}"
+: "${GAIN_PEAK_DBFS:=-10}"
+: "${GAIN_SIGMA_DB:=5}"
+: "${GAIN_HEADROOM_DB:=1}"
+
+# Draw one N(0, sigma) sample in dB via awk Box-Muller, capped so the
+# resulting peak (GAIN_PEAK_DBFS + offset) stays <= -GAIN_HEADROOM_DB. The
+# seed mixes $RANDOM with nanosecond time so consecutive calls in the same
+# second do not collapse to the same value; keeping the seed under ~14
+# digits avoids awk losing entropy when parsing it as a double.
+gauss_offset_db() {
+	local sigma="$1"
+	local peak="$2"
+	local headroom="$3"
+	awk -v s="$sigma" \
+	    -v cap="$(awk -v p="$peak" -v h="$headroom" 'BEGIN{print -p-h}')" \
+	    -v seed="$RANDOM$(date +%N)" 'BEGIN {
+		srand(seed)
+		u1 = rand(); if (u1 < 1e-12) u1 = 1e-12
+		u2 = rand()
+		v = s * sqrt(-2 * log(u1)) * cos(6.283185307 * u2)
+		if (v > cap) v = cap
+		printf "%.3f", v
+	}'
+}
+
+# Rewrite every *.wav in $1 with a peak-normalized level plus per-file
+# Gaussian gain jitter. The updated WAVs replace the originals in place so
+# the on-disk dataset directly reflects the level distribution used at
+# train time. Prints a small summary of the applied offsets.
+apply_gain_jitter() {
+	local dir="$1"
+	local sum=0 n=0 mn=999 mx=-999 off tmp
+	tmp=$(mktemp --suffix=.wav)
+	for wav in "$dir"/*.wav; do
+		[ -f "$wav" ] || continue
+		off=$(gauss_offset_db "$GAIN_SIGMA_DB" "$GAIN_PEAK_DBFS" "$GAIN_HEADROOM_DB")
+		sox "$wav" "$tmp" gain -n "$GAIN_PEAK_DBFS" gain "$off"
+		mv "$tmp" "$wav"
+		sum=$(awk -v a="$sum" -v b="$off" 'BEGIN{printf "%.3f", a+b}')
+		mn=$(awk -v a="$mn" -v b="$off" 'BEGIN{print (b<a)?b:a}')
+		mx=$(awk -v a="$mx" -v b="$off" 'BEGIN{print (b>a)?b:a}')
+		n=$((n + 1))
+	done
+	rm -f "$tmp"
+	if [ "$n" -gt 0 ]; then
+		awk -v n="$n" -v s="$sum" -v mn="$mn" -v mx="$mx" \
+		    -v p="$GAIN_PEAK_DBFS" -v h="$GAIN_HEADROOM_DB" 'BEGIN{
+			printf "    gain jitter n=%d  offset mean=%.2f dB  min=%.2f  max=%.2f  target peak=%s dBFS  ceiling=%s dBFS\n", n, s/n, mn, mx, p, -h
+		}'
+	fi
+}
+
+if [[ -n "$PIPER_VENV" ]]; then
+	# shellcheck disable=SC1091
+	source "$PIPER_VENV/bin/activate"
+fi
+
+if ! command -v python3 >/dev/null; then
+	echo "python3 not on PATH" >&2
+	exit 1
+fi
+
+if ! python3 -c "import piper_sample_generator" 2>/dev/null; then
+	echo "piper_sample_generator not importable — activate the Piper venv first" >&2
+	echo "(or set PIPER_VENV=/path/to/venv)" >&2
+	exit 1
+fi
+
+# Upstream pyproject.toml only packages piper_sample_generator; the sibling
+# piper_train package that __main__.py imports from is not pip-installed. If
+# PIPER_REPO points at a git clone (auto-detected from PIPER_MODEL location),
+# put it on PYTHONPATH so `python -m piper_sample_generator` can find both.
+if [[ -z "$PIPER_REPO" && "$PIPER_MODEL" == */models/*.pt ]]; then
+	candidate=$(cd "$(dirname "$PIPER_MODEL")/.." && pwd)
+	if [[ -d "$candidate/piper_train" ]]; then
+		PIPER_REPO="$candidate"
+	fi
+fi
+if [[ -n "$PIPER_REPO" ]]; then
+	if ! python3 -c "import piper_train" 2>/dev/null; then
+		export PYTHONPATH="$PIPER_REPO${PYTHONPATH:+:$PYTHONPATH}"
+		echo ">>> Added $PIPER_REPO to PYTHONPATH for piper_train"
+	fi
+fi
+
+if ! python3 -c "import piper_train" 2>/dev/null; then
+	cat >&2 <<EOF
+piper_train not importable. Upstream ships it in the git repo but does not
+pip-install it. Either:
+  - set PIPER_REPO=/path/to/piper-sample-generator (git clone root), or
+  - run this script with the repo root on PYTHONPATH.
+EOF
+	exit 1
+fi
+
+if [[ ! -f "$PIPER_MODEL" ]]; then
+	cat >&2 <<EOF
+Piper model not found at: $PIPER_MODEL
+
+Fetch once with:
+  mkdir -p "$(dirname "$PIPER_MODEL")"
+  wget -O "$PIPER_MODEL" \\
+    https://github.com/rhasspy/piper-sample-generator/releases/download/v2.0.0/en_US-libritts_r-medium.pt
+
+Or set PIPER_MODEL=/path/to/en_US-libritts_r-medium.pt.
+EOF
+	exit 1
+fi
+
+RESULTS=()
+for i in "${!KEYWORDS[@]}"; do
+	KEYWORD="${KEYWORDS[$i]}"
+	LABEL="${LABELS[$i]}"
+	RAW_DIR="$OUT_ROOT/_raw/$LABEL"
+	FINAL_DIR="$OUT_ROOT/$LABEL"
+	mkdir -p "$RAW_DIR" "$FINAL_DIR"
+
+	echo ">>> [$((i + 1))/${#KEYWORDS[@]}] Generating '$KEYWORD' into $RAW_DIR"
+	echo "    model=$PIPER_MODEL"
+	echo "    max_samples=$MAX_SAMPLES per rate loop, max_speakers=$MAX_SPEAKERS"
+
+	# Loop over speaking rates for prosody variety. --length-scales can take
+	# multiple values but cycling here also cycles the RNG seed and speaker draws.
+	for scale in 0.9 1.0 1.1; do
+		echo ">>> length_scale=$scale"
+		python3 -m piper_sample_generator "$KEYWORD" \
+			--model "$PIPER_MODEL" \
+			--max-samples "$MAX_SAMPLES" \
+			--max-speakers "$MAX_SPEAKERS" \
+			--slerp-weights $SLERP_WEIGHTS \
+			--length-scales "$scale" \
+			--output-dir "$RAW_DIR"
+		# piper writes 0.wav..N.wav each run; rename to avoid overwrite next loop.
+		tag="s$(echo "$scale" | tr -d .)"
+		for f in "$RAW_DIR"/*.wav; do
+			base=$(basename "$f" .wav)
+			# Only rename freshly emitted files (no tag prefix yet).
+			case "$base" in
+				s*_*) continue ;;
+			esac
+			mv "$f" "$RAW_DIR/${tag}_${base}.wav"
+		done
+	done
+
+	echo ">>> Augmenting into $FINAL_DIR (16 kHz, IR convolution, volume jitter)"
+	python3 -m piper_sample_generator.augment \
+		--sample-rate 16000 \
+		"$RAW_DIR" "$FINAL_DIR"
+
+	if [[ "$GAIN_AUG" = "1" ]]; then
+		echo ">>> Applying gain jitter to $FINAL_DIR (peak=${GAIN_PEAK_DBFS} dBFS, sigma=${GAIN_SIGMA_DB} dB, headroom=${GAIN_HEADROOM_DB} dB)"
+		apply_gain_jitter "$FINAL_DIR"
+	fi
+
+	n_raw=$(find "$RAW_DIR" -name '*.wav' | wc -l)
+	n_final=$(find "$FINAL_DIR" -name '*.wav' | wc -l)
+	echo ">>> [$LABEL] $n_raw raw / $n_final augmented WAVs in $FINAL_DIR"
+	RESULTS+=("$LABEL: $n_raw raw / $n_final augmented")
+done
+
+echo ">>> Done:"
+for line in "${RESULTS[@]}"; do
+	echo "    $line"
+done
+KW_ARGS=""
+for lbl in "${LABELS[@]}"; do
+	KW_ARGS+=" --keyword $lbl"
+done
+echo ">>> Next: sof_tflm_train_pipeline.sh$KW_ARGS $OUT_ROOT <feat_root> <out_dir>"
diff --git a/src/audio/tensorflow/tune/sof_tflm_generate_keyword_dataset_piper_tts.sh b/src/audio/tensorflow/tune/sof_tflm_generate_keyword_dataset_piper_tts.sh
new file mode 100755
index 000000000000..c4116718f02a
--- /dev/null
+++ b/src/audio/tensorflow/tune/sof_tflm_generate_keyword_dataset_piper_tts.sh
@@ -0,0 +1,574 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# Generate a synthetic keyword WAV dataset with a SINGLE-SPEAKER Piper TTS
+# voice (piper-tts package). This is the language-agnostic companion to
+# sof_tflm_generate_keyword_dataset.sh, which is hard-wired to the
+# English multi-speaker piper-sample-generator (LibriTTS-R). Use this
+# script for any non-English keyword whose Piper voice is single-speaker
+# (fi_FI-harri, sv_SE-nst, de_DE-thorsten, es_ES-sharvard, and most other
+# rhasspy/piper-voices entries).
+#
+# Because a single-speaker VITS has no speaker-embedding axis, we recover
+# diversity in two other ways:
+#   1. Per-utterance random draw of Piper's --noise-scale / --noise-w
+#      (prosody stochasticity) plus a cycle over several --length-scale
+#      values (speaking rate).
+#   2. Post-synthesis sox pitch (cents) + tempo perturbation copies, which
+#      approximates gender / fundamental-frequency variation across
+#      unrelated speakers.
+#
+# The output layout matches sof_tflm_generate_keyword_dataset.sh:
+#
+#   <out_root>/<label>/*.wav        <- final training set
+#   <out_root>/_raw/<label>/*.wav   <- staging (Piper TTS output)
+#   <out_root>/_perturbed/<label>/*.wav <- staging (after sox)
+#
+# The underscore-prefixed dirs are skipped by sof_mfcc_extract_features.sh
+# so they can stay next to the final set.
+#
+# Silence + unknown classes are still produced by
+# sof_tflm_prepare_silence_unknown.sh from Speech Commands v2 (English
+# clips are fine as negative examples even when the positive keyword is
+# in another language).
+#
+# Requirements:
+#   - piper-tts installed in an activated venv (or $PIPER_TTS_VENV set to
+#     a venv root and this script activates it for you).
+#   - A Piper voice: an .onnx file plus its .onnx.json config sibling.
+#     Download once from rhasspy/piper-voices on HuggingFace, e.g.:
+#       mkdir -p ~/git/piper-voices/fi_FI
+#       cd ~/git/piper-voices/fi_FI
+#       BASE=https://huggingface.co/rhasspy/piper-voices/resolve/v1.0.0
+#       wget "$BASE/fi/fi_FI/harri/medium/fi_FI-harri-medium.onnx"
+#       wget "$BASE/fi/fi_FI/harri/medium/fi_FI-harri-medium.onnx.json"
+#   - sox on PATH.
+#
+# Optional: if PIPER_REPO points at a piper-sample-generator git clone,
+# its augment module runs after synthesis to add room-impulse-response
+# convolution and volume jitter (same augmentation the English pipeline
+# uses). If PIPER_REPO is unset the pipeline falls back to a plain
+# sox resample to 16 kHz mono and relies on the built-in gain jitter for
+# level diversity.
+#
+# Env knobs (all optional):
+#   PIPER_TTS_VENV     Path to a piper-tts venv to auto-activate. If unset
+#                      (or bin/activate is missing), the script assumes
+#                      the caller has already activated a venv or that
+#                      piper-tts is on the ambient PATH.
+#   PIPER_VOICE        Default .onnx voice path used when --voice is not
+#                      given (or given fewer times than --keyword).
+#   PIPER_REPO         Optional piper-sample-generator git clone; enables
+#                      the IR-convolution augment step.
+#   MAX_SAMPLES        Target total positive clips per keyword (default
+#                      1000). Split between Piper synthesis calls and
+#                      sox perturbation copies.
+#   LENGTH_SCALES      Space-separated Piper --length-scale values to
+#                      cycle through (default "0.85 0.95 1.00 1.10 1.20").
+#   NOISE_SCALE_MIN    Piper --noise-scale lower bound (default 0.60).
+#   NOISE_SCALE_MAX    Piper --noise-scale upper bound (default 0.68).
+#   NOISE_W_MIN        Piper --noise-w lower bound (default 0.75).
+#   NOISE_W_MAX        Piper --noise-w upper bound (default 0.85).
+#                      These narrow ranges are tuned for single-speaker
+#                      Piper voices, which tend to degrade audibly at the
+#                      extremes of the wider ranges the multi-speaker
+#                      LibriTTS-R generator can tolerate. Widen only if
+#                      your voice is known to render cleanly there.
+#   PERTURB_PER_UTT    sox pitch+tempo copies per synthesized utterance
+#                      (default 3). Use 0 to skip perturbation entirely.
+#   PITCH_CENTS_MAX    Absolute max pitch shift in cents (default 300;
+#                      +/- 3 semitones covers most male<->female F0
+#                      distance without introducing obvious artifacts).
+#   TEMPO_MIN          sox `tempo -s` factor lower bound (default 0.9).
+#   TEMPO_MAX          sox `tempo -s` factor upper bound (default 1.15).
+#   GAIN_AUG           1 = rewrite each final WAV in place with a peak-
+#                      normalized target and a per-file Gaussian gain
+#                      jitter, matching the English generator. 0 = leave
+#                      unmodified (default 0).
+#   GAIN_PEAK_DBFS     Peak-normalization target in dBFS (default -10).
+#   GAIN_SIGMA_DB      Gaussian jitter sigma in dB (default 5).
+#   GAIN_HEADROOM_DB   Headroom below full scale the augmented peak must
+#                      respect (default 1).
+#   IR_PROB            Probability that a given synthesized WAV gets a
+#                      room impulse response convolved in (default 0.75).
+#                      Set to 0 to skip IR augmentation entirely; the
+#                      remaining samples stay dry.
+#   IR_WET             Wet/dry mix used when IR fires: 0.0 = fully dry,
+#                      1.0 = fully wet (default 0.35). The wet signal is
+#                      peak-matched to the dry signal before mixing so
+#                      changing IR_WET only trades intelligibility for
+#                      reverb amount, not loudness.
+#   IR_DIR             Directory of *.wav impulse responses to draw from.
+#                      Default: a curated subset of piper-sample-generator's
+#                      bundled pool that excludes the musical/effect
+#                      impulses (Symphonic, Reverse Gate, Fat Bass, Blatty
+#                      Plate, Derlon Sanctuary) which produced unrealistic
+#                      cathedral-like reverb in earlier training runs.
+#
+# Required CLI:
+#   --keyword TEXT   Phrase to synthesize (e.g. "tietokone"). Repeat once
+#                    per positive class.
+#   --label DIR      Output subdir name. If omitted, derived from keyword
+#                    by lower-casing and replacing spaces with underscores.
+#                    When repeated, count must match --keyword.
+#   --voice PATH     Piper .onnx voice. If omitted, $PIPER_VOICE is used.
+#                    When repeated, count must match --keyword; this lets
+#                    a multi-keyword run mix voices per class (e.g. one
+#                    Finnish keyword plus one Swedish keyword).
+
+set -e
+
+KEYWORDS=()
+LABELS=()
+VOICES=()
+OUT_ROOT=""
+
+usage() {
+	cat >&2 <<EOF
+Usage: $0 --keyword "<text>" [--keyword "<text>" ...] \\
+          [--label <dir> ...] [--voice <path> ...] <out_root>
+
+  --keyword TEXT   Phrase to synthesize (repeatable; at least one).
+  --label  DIR     Output subdir under <out_root> (repeatable; must pair
+                   1:1 with --keyword when used). Default: lower-case
+                   keyword with spaces replaced by underscores.
+  --voice  PATH    Piper .onnx voice file (repeatable; must pair 1:1
+                   with --keyword when used). Default: \$PIPER_VOICE.
+  out_root         Dataset root; <label>/*.wav is produced under it for
+                   each keyword, ready to feed to
+                   sof_mfcc_extract_features.sh <out_root> <feat_root>.
+
+Optional env: PIPER_TTS_VENV, PIPER_VOICE, PIPER_REPO, MAX_SAMPLES,
+              LENGTH_SCALES, NOISE_SCALE_MIN/MAX, NOISE_W_MIN/MAX,
+              PERTURB_PER_UTT, PITCH_CENTS_MAX, TEMPO_MIN/MAX,
+              GAIN_AUG, GAIN_PEAK_DBFS, GAIN_SIGMA_DB, GAIN_HEADROOM_DB,
+              IR_PROB, IR_WET, IR_DIR.
+EOF
+	exit 1
+}
+
+while [[ $# -gt 0 ]]; do
+	case "$1" in
+		-k|--keyword)
+			[[ $# -ge 2 ]] || usage
+			KEYWORDS+=("$2"); shift 2 ;;
+		-l|--label)
+			[[ $# -ge 2 ]] || usage
+			LABELS+=("$2"); shift 2 ;;
+		-v|--voice)
+			[[ $# -ge 2 ]] || usage
+			VOICES+=("$2"); shift 2 ;;
+		-h|--help)
+			usage ;;
+		--)
+			shift; break ;;
+		-*)
+			echo "unknown option: $1" >&2; usage ;;
+		*)
+			if [[ -z "$OUT_ROOT" ]]; then
+				OUT_ROOT="$1"; shift
+			else
+				echo "unexpected positional: $1" >&2; usage
+			fi ;;
+	esac
+done
+
+if [[ -z "$OUT_ROOT" || ${#KEYWORDS[@]} -eq 0 ]]; then
+	usage
+fi
+
+if [[ ${#LABELS[@]} -gt 0 && ${#LABELS[@]} -ne ${#KEYWORDS[@]} ]]; then
+	echo "--label given ${#LABELS[@]} time(s) but --keyword given ${#KEYWORDS[@]} time(s); counts must match" >&2
+	exit 1
+fi
+
+if [[ ${#VOICES[@]} -gt 0 && ${#VOICES[@]} -ne ${#KEYWORDS[@]} ]]; then
+	echo "--voice given ${#VOICES[@]} time(s) but --keyword given ${#KEYWORDS[@]} time(s); counts must match" >&2
+	exit 1
+fi
+
+if [[ ${#LABELS[@]} -eq 0 ]]; then
+	for kw in "${KEYWORDS[@]}"; do
+		LABELS+=("$(echo "$kw" | tr ' A-Z' '_a-z')")
+	done
+fi
+
+: "${MAX_SAMPLES:=1000}"
+: "${LENGTH_SCALES:=0.85 0.95 1.00 1.10 1.20}"
+: "${NOISE_SCALE_MIN:=0.60}"
+: "${NOISE_SCALE_MAX:=0.68}"
+: "${NOISE_W_MIN:=0.75}"
+: "${NOISE_W_MAX:=0.85}"
+: "${PERTURB_PER_UTT:=3}"
+: "${PITCH_CENTS_MAX:=300}"
+: "${TEMPO_MIN:=0.9}"
+: "${TEMPO_MAX:=1.15}"
+: "${GAIN_AUG:=0}"
+: "${GAIN_PEAK_DBFS:=-10}"
+: "${GAIN_SIGMA_DB:=5}"
+: "${GAIN_HEADROOM_DB:=1}"
+: "${IR_PROB:=0.75}"
+: "${IR_WET:=0.35}"
+: "${IR_DIR:=}"
+
+export LENGTH_SCALES NOISE_SCALE_MIN NOISE_SCALE_MAX NOISE_W_MIN NOISE_W_MAX
+
+# Draw one N(0, sigma) sample in dB via awk Box-Muller, capped so the
+# resulting peak (GAIN_PEAK_DBFS + offset) stays <= -GAIN_HEADROOM_DB.
+# Kept identical to sof_tflm_generate_keyword_dataset.sh so the two
+# variants produce statistically comparable level distributions.
+gauss_offset_db() {
+	local sigma="$1"
+	local peak="$2"
+	local headroom="$3"
+	awk -v s="$sigma" \
+	    -v cap="$(awk -v p="$peak" -v h="$headroom" 'BEGIN{print -p-h}')" \
+	    -v seed="$RANDOM$(date +%N)" 'BEGIN {
+		srand(seed)
+		u1 = rand(); if (u1 < 1e-12) u1 = 1e-12
+		u2 = rand()
+		v = s * sqrt(-2 * log(u1)) * cos(6.283185307 * u2)
+		if (v > cap) v = cap
+		printf "%.3f", v
+	}'
+}
+
+apply_gain_jitter() {
+	local dir="$1"
+	local sum=0 n=0 mn=999 mx=-999 off tmp
+	tmp=$(mktemp --suffix=.wav)
+	for wav in "$dir"/*.wav; do
+		[ -f "$wav" ] || continue
+		off=$(gauss_offset_db "$GAIN_SIGMA_DB" "$GAIN_PEAK_DBFS" "$GAIN_HEADROOM_DB")
+		sox "$wav" "$tmp" gain -n "$GAIN_PEAK_DBFS" gain "$off"
+		mv "$tmp" "$wav"
+		sum=$(awk -v a="$sum" -v b="$off" 'BEGIN{printf "%.3f", a+b}')
+		mn=$(awk -v a="$mn" -v b="$off" 'BEGIN{print (b<a)?b:a}')
+		mx=$(awk -v a="$mx" -v b="$off" 'BEGIN{print (b>a)?b:a}')
+		n=$((n + 1))
+	done
+	rm -f "$tmp"
+	if [ "$n" -gt 0 ]; then
+		awk -v n="$n" -v s="$sum" -v mn="$mn" -v mx="$mx" \
+		    -v p="$GAIN_PEAK_DBFS" -v h="$GAIN_HEADROOM_DB" 'BEGIN{
+			printf "    gain jitter n=%d  offset mean=%.2f dB  min=%.2f  max=%.2f  target peak=%s dBFS  ceiling=%s dBFS\n", n, s/n, mn, mx, p, -h
+		}'
+	fi
+}
+
+if [[ -n "$PIPER_TTS_VENV" ]]; then
+	if [[ -f "$PIPER_TTS_VENV/bin/activate" ]]; then
+		# shellcheck disable=SC1091
+		source "$PIPER_TTS_VENV/bin/activate"
+	else
+		echo ">>> PIPER_TTS_VENV=$PIPER_TTS_VENV has no bin/activate; assuming piper-tts is already on \$PATH" >&2
+	fi
+fi
+
+if ! command -v python3 >/dev/null; then
+	echo "python3 not on PATH" >&2
+	exit 1
+fi
+
+if ! command -v sox >/dev/null; then
+	echo "sox not on PATH" >&2
+	exit 1
+fi
+
+if ! python3 -c "import piper" 2>/dev/null; then
+	cat >&2 <<EOF
+piper (piper-tts) not importable. Install once with:
+
+    python3 -m venv \$HOME/venvs/piper-tts
+    source \$HOME/venvs/piper-tts/bin/activate
+    pip install --upgrade pip
+    pip install piper-tts
+
+Then rerun this script (or set PIPER_TTS_VENV=/path/to/venv).
+EOF
+	exit 1
+fi
+
+USE_IR_AUG=0
+DEFAULT_IR_DIR=""
+if [[ -n "$PIPER_REPO" ]]; then
+	if ! python3 -c "import piper_sample_generator" 2>/dev/null; then
+		export PYTHONPATH="$PIPER_REPO${PYTHONPATH:+:$PYTHONPATH}"
+	fi
+	DEFAULT_IR_DIR="$PIPER_REPO/piper_sample_generator/impulses"
+fi
+if awk -v p="$IR_PROB" 'BEGIN{exit !(p+0 > 0)}' && \
+   { [[ -n "$IR_DIR" ]] || [[ -d "$DEFAULT_IR_DIR" ]]; }; then
+	if python3 -c "import audiomentations" 2>/dev/null; then
+		USE_IR_AUG=1
+	else
+		echo ">>> WARNING: audiomentations not importable; skipping IR augmentation (pip install audiomentations)" >&2
+	fi
+fi
+export IR_PROB IR_WET IR_DIR
+export DEFAULT_IR_DIR
+
+RESULTS=()
+for i in "${!KEYWORDS[@]}"; do
+	KEYWORD="${KEYWORDS[$i]}"
+	LABEL="${LABELS[$i]}"
+	VOICE="${VOICES[$i]:-$PIPER_VOICE}"
+
+	if [[ -z "$VOICE" ]]; then
+		echo "no voice supplied for keyword '$KEYWORD' (set --voice or \$PIPER_VOICE)" >&2
+		exit 1
+	fi
+	if [[ ! -f "$VOICE" ]]; then
+		echo "Piper voice file not found: $VOICE" >&2
+		exit 1
+	fi
+	if [[ ! -f "$VOICE.json" ]]; then
+		echo "Piper voice config not found: $VOICE.json (must sit next to the .onnx)" >&2
+		exit 1
+	fi
+
+	RAW_DIR="$OUT_ROOT/_raw/$LABEL"
+	PERT_DIR="$OUT_ROOT/_perturbed/$LABEL"
+	FINAL_DIR="$OUT_ROOT/$LABEL"
+	mkdir -p "$RAW_DIR" "$FINAL_DIR"
+	rm -f "$RAW_DIR"/*.wav "$FINAL_DIR"/*.wav
+	if [[ "$PERTURB_PER_UTT" -gt 0 ]]; then
+		mkdir -p "$PERT_DIR"
+		rm -f "$PERT_DIR"/*.wav
+	fi
+
+	# Split the target sample budget: BASE_UTTS Piper calls, each fanned
+	# out into PERTURB_PER_UTT sox copies. Guarantee at least one full
+	# sweep of LENGTH_SCALES so the rate axis is exercised.
+	N_SCALES=$(echo "$LENGTH_SCALES" | wc -w)
+	if [[ "$PERTURB_PER_UTT" -gt 0 ]]; then
+		BASE_UTTS=$(( MAX_SAMPLES / PERTURB_PER_UTT ))
+	else
+		BASE_UTTS="$MAX_SAMPLES"
+	fi
+	if (( BASE_UTTS < N_SCALES )); then
+		BASE_UTTS=$N_SCALES
+	fi
+
+	echo ">>> [$((i + 1))/${#KEYWORDS[@]}] Synthesizing '$KEYWORD' -> $RAW_DIR"
+	echo "    voice=$VOICE"
+	echo "    base_utts=$BASE_UTTS  perturb_per_utt=$PERTURB_PER_UTT  target=$MAX_SAMPLES"
+	echo "    length_scales=[$LENGTH_SCALES]  noise_scale=[$NOISE_SCALE_MIN,$NOISE_SCALE_MAX]  noise_w=[$NOISE_W_MIN,$NOISE_W_MAX]"
+
+	python3 - "$KEYWORD" "$VOICE" "$RAW_DIR" "$BASE_UTTS" <<'PYEOF'
+import os
+import random
+import sys
+import wave
+from pathlib import Path
+
+try:
+	from piper import PiperVoice
+except ImportError:
+	from piper.voice import PiperVoice
+
+text, voice_path, out_dir_s, n_utts_s = sys.argv[1:5]
+out_dir = Path(out_dir_s)
+n_utts = int(n_utts_s)
+
+length_scales = [float(x) for x in os.environ["LENGTH_SCALES"].split()]
+ns_min = float(os.environ["NOISE_SCALE_MIN"])
+ns_max = float(os.environ["NOISE_SCALE_MAX"])
+nw_min = float(os.environ["NOISE_W_MIN"])
+nw_max = float(os.environ["NOISE_W_MAX"])
+
+voice = PiperVoice.load(voice_path)
+rng = random.Random()
+
+# piper-tts changed its synthesize signature across versions:
+#   >=1.3  voice.synthesize_wav(text, wav_file, syn_config=SynthesisConfig(...))
+#          (SynthesisConfig field is either `noise_w` or `noise_w_scale`)
+#   older  voice.synthesize(text, wav_file, length_scale=, noise_scale=, noise_w=)
+# Probe once and lock in whichever shape works.
+synth = None
+if hasattr(voice, "synthesize_wav"):
+	try:
+		from piper import SynthesisConfig
+	except ImportError:
+		from piper.config import SynthesisConfig
+
+	nw_field = None
+	for candidate in ("noise_w_scale", "noise_w"):
+		try:
+			SynthesisConfig(length_scale=1.0, noise_scale=0.6, **{candidate: 0.8})
+			nw_field = candidate
+			break
+		except TypeError:
+			continue
+	if nw_field is None:
+		raise RuntimeError(
+			"SynthesisConfig has neither noise_w nor noise_w_scale; "
+			"unsupported piper-tts version"
+		)
+
+	def synth(wf, ls, ns, nw):
+		cfg = SynthesisConfig(length_scale=ls, noise_scale=ns, **{nw_field: nw})
+		voice.synthesize_wav(text, wf, syn_config=cfg)
+else:
+	def synth(wf, ls, ns, nw):
+		voice.synthesize(
+			text, wf,
+			length_scale=ls,
+			noise_scale=ns,
+			noise_w=nw,
+		)
+
+for i in range(n_utts):
+	ls = length_scales[i % len(length_scales)]
+	ns = rng.uniform(ns_min, ns_max)
+	nw = rng.uniform(nw_min, nw_max)
+	out = out_dir / f"{i:04d}.wav"
+	with wave.open(str(out), "wb") as wf:
+		synth(wf, ls, ns, nw)
+
+print(f"    piper synthesized {n_utts} base utterances", file=sys.stderr)
+PYEOF
+
+	# sox pitch/tempo perturbation. Bash's $RANDOM (0..32767) drives both
+	# the pitch cents draw (integer) and the tempo factor (float via awk).
+	# tempo -s preserves pitch, so pitch and tempo are decoupled the same
+	# way real speaker variation is.
+	if [[ "$PERTURB_PER_UTT" -gt 0 ]]; then
+		echo ">>> Perturbing $BASE_UTTS raw WAVs x $PERTURB_PER_UTT copies -> $PERT_DIR"
+		pcount=0
+		for raw in "$RAW_DIR"/*.wav; do
+			[ -f "$raw" ] || continue
+			base=$(basename "$raw" .wav)
+			for k in $(seq 1 "$PERTURB_PER_UTT"); do
+				cents=$(( (RANDOM % (2 * PITCH_CENTS_MAX + 1)) - PITCH_CENTS_MAX ))
+				tempo=$(awk -v a="$TEMPO_MIN" -v b="$TEMPO_MAX" -v r="$RANDOM" 'BEGIN{
+					srand(r); printf "%.3f", a + rand()*(b-a)
+				}')
+				# `gain -h` auto-attenuates by whatever headroom the
+				# following pitch+tempo effects need so sox does not
+				# emit dither/rate clip warnings; apply_gain_jitter
+				# renormalizes to GAIN_PEAK_DBFS afterward.
+				sox "$raw" "$PERT_DIR/${base}_p${k}.wav" \
+					gain -h \
+					pitch "$cents" tempo -s "$tempo"
+				pcount=$((pcount + 1))
+			done
+		done
+		echo "    wrote $pcount perturbed WAVs"
+		SRC_DIR="$PERT_DIR"
+	else
+		SRC_DIR="$RAW_DIR"
+	fi
+
+	if [[ "$USE_IR_AUG" = "1" ]]; then
+		echo ">>> IR augmentation via audiomentations -> $FINAL_DIR (IR_PROB=$IR_PROB)"
+		python3 - "$SRC_DIR" "$FINAL_DIR" <<'PYEOF'
+import audioop
+import glob
+import os
+import random
+import sys
+import wave
+from pathlib import Path
+
+import numpy as np
+from audiomentations import ApplyImpulseResponse
+
+src, dst = sys.argv[1], sys.argv[2]
+Path(dst).mkdir(parents=True, exist_ok=True)
+target_sr = 16000
+ir_prob = float(os.environ.get("IR_PROB", "0.75"))
+ir_wet = float(os.environ.get("IR_WET", "0.35"))
+ir_wet = max(0.0, min(1.0, ir_wet))
+
+# Curated whitelist: keep only the small live-room impulse; drop the
+# musical/effect impulses (cathedral, plate, reverse gate, ...) and the
+# large "Concrete Room" tail that was masking short keywords like
+# "hi intel" ("hi needel").
+KEEP = ("Accoustic2_Impulse.wav",)
+
+ir_dir = os.environ.get("IR_DIR", "").strip()
+if not ir_dir:
+	default_dir = os.environ.get("DEFAULT_IR_DIR", "").strip()
+	impulses = [os.path.join(default_dir, n) for n in KEEP
+	            if default_dir and os.path.isfile(os.path.join(default_dir, n))]
+else:
+	impulses = sorted(glob.glob(os.path.join(ir_dir, "*.wav")))
+
+rng = random.Random()
+
+if impulses and ir_prob > 0.0:
+	# ApplyImpulseResponse fires unconditionally (p=1.0); we gate it
+	# ourselves so the wet/dry blend has a clean handle on which samples
+	# were augmented. leave_length_unchanged=False keeps the reverb tail.
+	ir_aug = ApplyImpulseResponse(impulses, p=1.0,
+	                              leave_length_unchanged=False)
+	names = ", ".join(os.path.basename(p) for p in impulses)
+	print(f"    IR pool ({len(impulses)}): {names}", file=sys.stderr)
+	print(f"    per-sample IR probability: {ir_prob:.2f}  wet mix: {ir_wet:.2f}",
+	      file=sys.stderr)
+else:
+	ir_aug = None
+	print("    IR augmentation disabled (empty pool or IR_PROB=0)",
+	      file=sys.stderr)
+
+for f in sorted(glob.glob(os.path.join(src, "*.wav"))):
+	with wave.open(f, "rb") as wi:
+		sr = wi.getframerate()
+		nch = wi.getnchannels()
+		sw = wi.getsampwidth()
+		raw = wi.readframes(wi.getnframes())
+	if sw != 2 or nch != 1:
+		print(f"    skip non-16bit-mono: {f}", file=sys.stderr)
+		continue
+	x = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32767.0
+	if ir_aug is not None and rng.random() < ir_prob:
+		x_wet = ir_aug(x, sample_rate=sr)
+		# Peak-match wet to dry so IR_WET only trades reverb amount,
+		# not overall loudness (raw IRs have arbitrary gain).
+		wet_peak = float(np.max(np.abs(x_wet))) if x_wet.size else 0.0
+		dry_peak = float(np.max(np.abs(x))) if x.size else 0.0
+		if wet_peak > 1e-9 and dry_peak > 1e-9:
+			x_wet = x_wet * (dry_peak / wet_peak)
+		pad = x_wet.size - x.size
+		x_dry = np.concatenate([x, np.zeros(pad, dtype=x.dtype)]) if pad > 0 else x
+		x = (1.0 - ir_wet) * x_dry + ir_wet * x_wet
+	x16_bytes = np.clip(x * 32767.0, -32768, 32767).astype(np.int16).tobytes()
+	if sr != target_sr:
+		x16_bytes, _ = audioop.ratecv(x16_bytes, 2, 1, sr, target_sr, None)
+	out = os.path.join(dst, os.path.basename(f))
+	with wave.open(out, "wb") as wo:
+		wo.setframerate(target_sr)
+		wo.setsampwidth(2)
+		wo.setnchannels(1)
+		wo.writeframes(x16_bytes)
+PYEOF
+	else
+		echo ">>> Resampling to 16 kHz mono (no IR augmentation) -> $FINAL_DIR"
+		for f in "$SRC_DIR"/*.wav; do
+			[ -f "$f" ] || continue
+			sox "$f" -r 16000 -c 1 -b 16 "$FINAL_DIR/$(basename "$f")"
+		done
+	fi
+
+	if [[ "$GAIN_AUG" = "1" ]]; then
+		echo ">>> Applying gain jitter to $FINAL_DIR (peak=${GAIN_PEAK_DBFS} dBFS, sigma=${GAIN_SIGMA_DB} dB, headroom=${GAIN_HEADROOM_DB} dB)"
+		apply_gain_jitter "$FINAL_DIR"
+	fi
+
+	n_raw=$(find "$RAW_DIR" -name '*.wav' | wc -l)
+	n_final=$(find "$FINAL_DIR" -name '*.wav' | wc -l)
+	echo ">>> [$LABEL] $n_raw raw / $n_final final WAVs in $FINAL_DIR"
+	RESULTS+=("$LABEL: $n_raw raw / $n_final final (voice=$(basename "$VOICE"))")
+done
+
+echo ">>> Done:"
+for line in "${RESULTS[@]}"; do
+	echo "    $line"
+done
+KW_ARGS=""
+for lbl in "${LABELS[@]}"; do
+	KW_ARGS+=" --keyword $lbl"
+done
+echo ">>> Next: sof_tflm_train_pipeline.sh$KW_ARGS $OUT_ROOT <feat_root> <out_dir>"
diff --git a/src/audio/tensorflow/tune/sof_tflm_prepare_silence_unknown.sh b/src/audio/tensorflow/tune/sof_tflm_prepare_silence_unknown.sh
new file mode 100755
index 000000000000..a8d140077aff
--- /dev/null
+++ b/src/audio/tensorflow/tune/sof_tflm_prepare_silence_unknown.sh
@@ -0,0 +1,187 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# Prepare the silence/ and unknown/ dataset classes for TFLM wake-on-voice
+# training by fetching Google Speech Commands v2 and slicing/sampling from it.
+#
+# Output layout (merges with any existing keyword class dirs under <out_root>):
+#   <out_root>/silence/*.wav   1-second slices of _background_noise_/*.wav
+#   <out_root>/unknown/*.wav   random sample of non-target Speech Commands words
+#
+# Speech Commands v2 is 2.4 GB (~106k files). It is cached under $SC_CACHE
+# (default ~/.cache/speech_commands_v2) so subsequent runs are instant.
+#
+# Requirements: sox on PATH, ~3 GB of free disk for the cache.
+
+set -e
+
+OUT_ROOT="${1:-}"
+if [[ -z "$OUT_ROOT" ]]; then
+	cat >&2 <<EOF
+Usage: $0 <out_root>
+
+  out_root   Dataset root; silence/ and unknown/ are added under it, alongside
+             any keyword class directories already present.
+
+Optional env:
+  SC_CACHE          Directory holding the unpacked Speech Commands v2 tree
+                    (default: ~/.cache/speech_commands_v2).
+  N_SILENCE         Number of 1-second silence clips to emit (default 500).
+  N_UNKNOWN         Number of unknown-class clips to sample (default 1500).
+  UNKNOWN_WORDS     Space-separated word list to sample for unknown
+                    (default: every SC v2 word except _background_noise_).
+  GAIN_AUG          1 = rewrite each unknown/*.wav in place with a peak-
+                    normalized target and per-file Gaussian level jitter
+                    (silence/ is left alone so background noise stays
+                    quiet). 0 = leave the clips at their sox slice level.
+                    Default 0.
+  GAIN_PEAK_DBFS    Peak-normalization target in dBFS (default -10).
+  GAIN_SIGMA_DB     Gaussian jitter sigma in dB (default 5).
+  GAIN_HEADROOM_DB  Headroom in dB below full scale that the augmented peak
+                    must respect. Positive offsets are capped so
+                    peak+offset <= -GAIN_HEADROOM_DB, keeping sox out of
+                    the dither/gain clip warning path (default 1).
+EOF
+	exit 1
+fi
+
+: "${SC_CACHE:=$HOME/.cache/speech_commands_v2}"
+: "${N_SILENCE:=500}"
+: "${N_UNKNOWN:=1500}"
+: "${GAIN_AUG:=0}"
+: "${GAIN_PEAK_DBFS:=-10}"
+: "${GAIN_SIGMA_DB:=5}"
+: "${GAIN_HEADROOM_DB:=1}"
+
+gauss_offset_db() {
+	local sigma="$1"
+	local peak="$2"
+	local headroom="$3"
+	awk -v s="$sigma" \
+	    -v cap="$(awk -v p="$peak" -v h="$headroom" 'BEGIN{print -p-h}')" \
+	    -v seed="$RANDOM$(date +%N)" 'BEGIN {
+		srand(seed)
+		u1 = rand(); if (u1 < 1e-12) u1 = 1e-12
+		u2 = rand()
+		v = s * sqrt(-2 * log(u1)) * cos(6.283185307 * u2)
+		if (v > cap) v = cap
+		printf "%.3f", v
+	}'
+}
+
+apply_gain_jitter() {
+	local dir="$1"
+	local sum=0 n=0 mn=999 mx=-999 off tmp
+	tmp=$(mktemp --suffix=.wav)
+	for wav in "$dir"/*.wav; do
+		[ -f "$wav" ] || continue
+		off=$(gauss_offset_db "$GAIN_SIGMA_DB" "$GAIN_PEAK_DBFS" "$GAIN_HEADROOM_DB")
+		sox "$wav" "$tmp" gain -n "$GAIN_PEAK_DBFS" gain "$off"
+		mv "$tmp" "$wav"
+		sum=$(awk -v a="$sum" -v b="$off" 'BEGIN{printf "%.3f", a+b}')
+		mn=$(awk -v a="$mn" -v b="$off" 'BEGIN{print (b<a)?b:a}')
+		mx=$(awk -v a="$mx" -v b="$off" 'BEGIN{print (b>a)?b:a}')
+		n=$((n + 1))
+	done
+	rm -f "$tmp"
+	if [ "$n" -gt 0 ]; then
+		awk -v n="$n" -v s="$sum" -v mn="$mn" -v mx="$mx" \
+		    -v p="$GAIN_PEAK_DBFS" -v h="$GAIN_HEADROOM_DB" 'BEGIN{
+			printf "    gain jitter n=%d  offset mean=%.2f dB  min=%.2f  max=%.2f  target peak=%s dBFS  ceiling=%s dBFS\n", n, s/n, mn, mx, p, -h
+		}'
+	fi
+}
+
+if ! command -v sox >/dev/null; then
+	echo "sox not on PATH — please install (e.g. apt install sox)" >&2
+	exit 1
+fi
+
+# Fetch + unpack once.
+if [[ ! -d "$SC_CACHE/_background_noise_" ]]; then
+	echo ">>> Fetching Speech Commands v2 into $SC_CACHE (~2.4 GB, one-time)"
+	mkdir -p "$SC_CACHE"
+	url="http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz"
+	tarball="$SC_CACHE/speech_commands_v0.02.tar.gz"
+	if [[ ! -f "$tarball" ]]; then
+		wget -O "$tarball" "$url"
+	fi
+	tar -C "$SC_CACHE" -xzf "$tarball"
+fi
+
+SILENCE_OUT="$OUT_ROOT/silence"
+UNKNOWN_OUT="$OUT_ROOT/unknown"
+mkdir -p "$SILENCE_OUT" "$UNKNOWN_OUT"
+
+# --- silence class ---------------------------------------------------------
+# Each _background_noise_/*.wav is ~60 s. Slice into 1 s chunks at random
+# offsets to build up the target count; sox trim handles this cleanly.
+echo ">>> Producing $N_SILENCE silence clips into $SILENCE_OUT"
+mapfile -t noise_wavs < <(find "$SC_CACHE/_background_noise_" -name '*.wav')
+if [[ ${#noise_wavs[@]} -eq 0 ]]; then
+	echo "No _background_noise_ wavs found in $SC_CACHE" >&2
+	exit 1
+fi
+i=0
+while [[ $i -lt $N_SILENCE ]]; do
+	src="${noise_wavs[$((RANDOM % ${#noise_wavs[@]}))]}"
+	dur=$(sox --i -D "$src" | awk '{printf "%d", $1}')
+	if [[ $dur -lt 2 ]]; then continue; fi
+	start=$((RANDOM % (dur - 1)))
+	sox "$src" -r 16000 -c 1 -b 16 "$SILENCE_OUT/silence_${i}.wav" \
+		trim "$start" 1
+	i=$((i + 1))
+done
+
+# --- unknown class ---------------------------------------------------------
+# Sample from every top-level SC v2 word directory except _background_noise_
+# (and skip any that collide with keyword classes already under out_root).
+echo ">>> Sampling $N_UNKNOWN unknown clips into $UNKNOWN_OUT"
+if [[ -z "${UNKNOWN_WORDS:-}" ]]; then
+	mapfile -t word_dirs < <(find "$SC_CACHE" -maxdepth 1 -mindepth 1 -type d \
+		! -name '_background_noise_')
+else
+	word_dirs=()
+	for w in $UNKNOWN_WORDS; do word_dirs+=("$SC_CACHE/$w"); done
+fi
+
+# Skip words whose name already exists as an out_root/<word>/ class dir.
+filtered=()
+for d in "${word_dirs[@]}"; do
+	w=$(basename "$d")
+	if [[ -d "$OUT_ROOT/$w" && "$w" != "unknown" && "$w" != "silence" ]]; then
+		echo "  skipping '$w' (collides with existing keyword class)"
+		continue
+	fi
+	[[ -d "$d" ]] && filtered+=("$d")
+done
+
+# Enumerate all candidate wavs, shuf to N_UNKNOWN, sox-convert to 16k/mono/16b.
+tmp_list=$(mktemp)
+trap 'rm -f "$tmp_list"' EXIT
+for d in "${filtered[@]}"; do
+	find "$d" -name '*.wav' >> "$tmp_list"
+done
+if ! shuf --version >/dev/null 2>&1; then
+	echo "shuf not on PATH — please install coreutils" >&2
+	exit 1
+fi
+shuf -n "$N_UNKNOWN" "$tmp_list" | \
+while read -r src; do
+	base=$(basename "$src" .wav)
+	# Prefix with parent dir name so we don't collide across words.
+	parent=$(basename "$(dirname "$src")")
+	sox "$src" -r 16000 -c 1 -b 16 \
+		"$UNKNOWN_OUT/${parent}_${base}.wav" \
+		2>/dev/null || true
+done
+
+if [[ "$GAIN_AUG" = "1" ]]; then
+	echo ">>> Applying gain jitter to $UNKNOWN_OUT (peak=${GAIN_PEAK_DBFS} dBFS, sigma=${GAIN_SIGMA_DB} dB, headroom=${GAIN_HEADROOM_DB} dB)"
+	apply_gain_jitter "$UNKNOWN_OUT"
+fi
+
+n_silence=$(find "$SILENCE_OUT" -name '*.wav' | wc -l)
+n_unknown=$(find "$UNKNOWN_OUT" -name '*.wav' | wc -l)
+echo ">>> Done: $n_silence silence / $n_unknown unknown WAVs under $OUT_ROOT"
diff --git a/src/audio/tensorflow/tune/sof_tflm_train.py b/src/audio/tensorflow/tune/sof_tflm_train.py
new file mode 100644
index 000000000000..22ae5fe887e7
--- /dev/null
+++ b/src/audio/tensorflow/tune/sof_tflm_train.py
@@ -0,0 +1,340 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+"""
+Train a tflite-micro ``tiny_conv`` DS-CNN wake-word model against SOF mel40
+features and emit a drop-in replacement for
+``src/audio/tensorflow/sof_tflm_quantized_model_data.{cc,h}``.
+
+Architecture matches ``MicroMutableOpResolver<4>`` in
+``src/audio/tensorflow/speech.cc``:
+
+    Reshape -> DepthwiseConv2D -> FullyConnected -> Softmax
+
+Consumes features produced by ``sof_mfcc_extract_features.sh`` via
+``sof_tflm_dataset.py``. Writes ``<out_dir>/<name>_quantized_model.tflite``
+and ``<out_dir>/<name>_labels.txt`` as archive artifacts, plus the fixed
+``<out_dir>/sof_tflm_quantized_model_data.{cc,h}`` pair with the C symbol
+``g_sof_tflm_quantized_model_data`` that ``speech.cc`` #includes, and
+``<out_dir>/sof_tflm_labels.h`` (``TFLM_CATEGORY_COUNT`` / ``_DATA``
+macros) that ``speech.h`` #includes. Both C symbol and header names are
+fixed so retraining with any keyword list is a drop-in replacement.
+
+Constraints preserved for the tflmcly KPB rule:
+    label index 0 = silence
+    label index 1 = unknown
+    label index >= 2 = keyword(s) that trigger KPB_EVENT_BEGIN_DRAINING
+
+Requires TensorFlow >= 2.10 in an isolated venv (see the module README).
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+
+import sof_tflm_dataset as mel40_dataset
+
+try:
+    import tensorflow as tf
+except ImportError as exc:  # pragma: no cover - only hit outside the venv
+    print(
+        "tensorflow not importable; activate the tflm-train venv first",
+        file=sys.stderr,
+    )
+    raise SystemExit(1) from exc
+
+
+def build_tiny_conv(num_classes: int) -> tf.keras.Model:
+    """Match tflite-micro's ``create_tiny_conv_model`` topology."""
+    inputs = tf.keras.Input(
+        shape=(mel40_dataset.WINDOW_HOPS, mel40_dataset.HOP_BINS, 1),
+        name="mel40",
+    )
+    x = tf.keras.layers.DepthwiseConv2D(
+        depth_multiplier=8,
+        kernel_size=(10, 8),
+        strides=(2, 2),
+        padding="same",
+        activation="relu",
+        name="dwconv",
+    )(inputs)
+    x = tf.keras.layers.Dropout(0.5)(x)
+    x = tf.keras.layers.Flatten()(x)
+    logits = tf.keras.layers.Dense(num_classes, name="logits")(x)
+    outputs = tf.keras.layers.Softmax(name="prob")(logits)
+    return tf.keras.Model(inputs=inputs, outputs=outputs, name="tiny_conv")
+
+
+def split_train_val(
+    X: np.ndarray,
+    y: np.ndarray,
+    val_frac: float,
+    seed: int,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Stratified split with a fixed RNG so runs are reproducible."""
+    rng = np.random.default_rng(seed)
+    idx = np.arange(y.size)
+    train_mask = np.zeros(y.size, dtype=bool)
+    for c in np.unique(y):
+        cls_idx = idx[y == c]
+        rng.shuffle(cls_idx)
+        cut = int(round(cls_idx.size * (1.0 - val_frac)))
+        train_mask[cls_idx[:cut]] = True
+    return X[train_mask], y[train_mask], X[~train_mask], y[~train_mask]
+
+
+def representative_dataset(
+    X: np.ndarray,
+    y: np.ndarray,
+    n_samples: int,
+):
+    """Yield calibration inputs stratified across classes.
+
+    Uniform random picks under-represent minority classes (e.g. a positive
+    keyword class with ~5% of the data), which biases int8 activation
+    scales toward the majority class and destroys keyword recall after
+    quantization. Pick roughly ``n_samples / n_classes`` per class.
+    """
+    rng = np.random.default_rng(0)
+    classes = np.unique(y)
+    per_class = max(1, n_samples // classes.size)
+    picks: list[int] = []
+    for c in classes:
+        cls_idx = np.where(y == c)[0]
+        take = min(per_class, cls_idx.size)
+        picks.extend(rng.choice(cls_idx, size=take, replace=False).tolist())
+    rng.shuffle(picks)
+    for i in picks:
+        yield [X[i : i + 1].astype(np.float32)]
+
+
+def convert_to_tflite_int8(
+    model: tf.keras.Model,
+    rep_X: np.ndarray,
+    rep_y: np.ndarray,
+    n_rep: int,
+) -> bytes:
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    converter.representative_dataset = lambda: representative_dataset(
+        rep_X, rep_y, n_rep
+    )
+    converter.target_spec.supported_ops = [
+        tf.lite.OpsSet.TFLITE_BUILTINS_INT8
+    ]
+    converter.inference_input_type = tf.int8
+    converter.inference_output_type = tf.int8
+    return converter.convert()
+
+
+def emit_c_array(
+    tflite_bytes: bytes,
+    out_cc: Path,
+    out_h: Path,
+    symbol: str,
+) -> None:
+    """Emit the same layout tflite-micro's shipped model headers use."""
+    guard = symbol.upper() + "_H_"
+    with open(out_h, "w") as f:
+        f.write(f"// SPDX-License-Identifier: BSD-3-Clause\n")
+        f.write(f"// Auto-generated by sof_tflm_train.py — do not edit.\n\n")
+        f.write(f"#ifndef {guard}\n#define {guard}\n\n")
+        f.write("#include <cstddef>\n#include <cstdint>\n\n")
+        f.write(f"extern const unsigned char {symbol}[];\n")
+        f.write(f"extern const size_t {symbol}_size;\n\n")
+        f.write(f"#endif  // {guard}\n")
+
+    with open(out_cc, "w") as f:
+        f.write("// SPDX-License-Identifier: BSD-3-Clause\n")
+        f.write("// Auto-generated by sof_tflm_train.py — do not edit.\n\n")
+        f.write(f'#include "{out_h.name}"\n\n')
+        # Force 8-byte alignment to match tflite-micro flatbuffer expectations.
+        f.write(
+            f"alignas(8) const unsigned char {symbol}[] = {{\n"
+        )
+        line = "    "
+        for i, b in enumerate(tflite_bytes):
+            line += f"0x{b:02x}, "
+            if (i + 1) % 12 == 0:
+                f.write(line.rstrip() + "\n")
+                line = "    "
+        if line.strip():
+            f.write(line.rstrip() + "\n")
+        f.write("};\n\n")
+        f.write(f"const size_t {symbol}_size = {len(tflite_bytes)};\n")
+
+
+def emit_labels_header(labels: list[str], out_h: Path) -> None:
+    """Emit sof_tflm_labels.h with TFLM_CATEGORY_COUNT / _DATA macros so
+    speech.h picks the retrained label set up on rebuild without any
+    manual edit."""
+    guard = "SOF_TFLM_LABELS_H_"
+    data = ", ".join(f'"{lbl}"' for lbl in labels)
+    with open(out_h, "w") as f:
+        f.write("// SPDX-License-Identifier: BSD-3-Clause\n")
+        f.write("// Auto-generated by sof_tflm_train.py — do not edit.\n\n")
+        f.write(f"#ifndef {guard}\n#define {guard}\n\n")
+        f.write(f"#define TFLM_CATEGORY_COUNT  {len(labels)}\n")
+        f.write(f"#define TFLM_CATEGORY_DATA   {{ {data}, }}\n\n")
+        f.write(f"#endif  // {guard}\n")
+
+
+def parse_args() -> argparse.Namespace:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--feat-root", required=True)
+    ap.add_argument(
+        "--labels",
+        nargs="+",
+        required=True,
+        help="Class labels in index order; put silence first, unknown second.",
+    )
+    ap.add_argument("--out-dir", required=True)
+    ap.add_argument("--name", default=None,
+                    help="Base name for the archive .tflite and _labels.txt "
+                         "artifacts (the C symbol and header names are fixed "
+                         "at g_sof_tflm_quantized_model_data and "
+                         "sof_tflm_quantized_model_data.{cc,h}). Defaults "
+                         "to the first non-{silence,unknown} label, or "
+                         "'wov_model' if none is present.")
+    ap.add_argument("--epochs", type=int, default=25)
+    ap.add_argument("--batch-size", type=int, default=128)
+    ap.add_argument("--lr", type=float, default=1e-3)
+    ap.add_argument("--val-frac", type=float, default=0.15)
+    ap.add_argument("--seed", type=int, default=1)
+    ap.add_argument("--rep-samples", type=int, default=1500,
+                    help="Total calibration samples for int8 (stratified per class).")
+    ap.add_argument("--window-hop-step", type=int, default=None,
+                    help="Windowing stride in hops (default: non-overlapping).")
+    ap.add_argument("--feature-clip-min", type=float, default=-1.0,
+                    help="Lower bound applied to features before training/calibration.")
+    ap.add_argument("--feature-clip-max", type=float, default=1.0,
+                    help="Upper bound applied to features. The symmetric "
+                         "[-1.0, +1.0] log10 window matches the runtime C clip; "
+                         "features map directly to [-1, +1] before int8 quantization.")
+    ap.add_argument("--gain-aug-db-min", type=float, default=-25.0,
+                    help="Lower edge of the uniform per-example gain augmentation "
+                         "in audio dB. Default -25 dB gives a 30 dB span centered "
+                         "at -10 dB peak. Upper edge +5 dB allows training on loud "
+                         "speech with AGC compression while lower edge matches quiet "
+                         "mic levels seen at inference. Set equal to --gain-aug-db-max to disable.")
+    ap.add_argument("--gain-aug-db-max", type=float, default=5.0,
+                    help="Upper edge of the uniform per-example gain augmentation "
+                         "in audio dB.")
+    ap.add_argument("--soft-agc", action=argparse.BooleanOptionalAction, default=True,
+                    help="Apply the soft mel-log AGC (target 0 dB, floor -20 dB, "
+                         "instant attack against the +1.0 log10 clip, ~0.2 dB/sec "
+                         "release) to training features so they match what the "
+                         "runtime C AGC produces. Use --no-soft-agc to disable.")
+    return ap.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    if not args.name:
+        # Derive a sensible default: first keyword label past silence/unknown.
+        reserved = {"silence", "unknown"}
+        keywords = [lbl for lbl in args.labels if lbl not in reserved]
+        args.name = keywords[0] if keywords else "wov_model"
+        print(f">>> --name not set, using {args.name!r}")
+    tf.keras.utils.set_random_seed(args.seed)
+
+    print(f">>> Loading features from {args.feat_root}")
+    X, y = mel40_dataset.load_dataset(
+        args.feat_root, args.labels, hop_step=args.window_hop_step
+    )
+    print(f"    X={X.shape} y={y.shape} counts={np.bincount(y).tolist()}")
+
+    if args.gain_aug_db_max > args.gain_aug_db_min:
+        # Amplitude gain of G dB translates to an additive shift of G/10 on
+        # log10(mel_power). Applied before the clip so low-gain examples land
+        # on the floor the quiet mic frames hit at inference time.
+        rng = np.random.default_rng(args.seed)
+        gain_db = rng.uniform(args.gain_aug_db_min, args.gain_aug_db_max,
+                              size=(X.shape[0], 1, 1, 1)).astype(np.float32)
+        X = X + gain_db / 10.0
+        print(f"    gain aug uniform [{args.gain_aug_db_min:+.1f}, "
+              f"{args.gain_aug_db_max:+.1f}] dB "
+              f"(span {args.gain_aug_db_max - args.gain_aug_db_min:.1f} dB, "
+              f"mean {0.5 * (args.gain_aug_db_min + args.gain_aug_db_max):+.1f} dB)")
+    else:
+        print("    gain aug disabled")
+
+    if args.soft_agc:
+        agc_pre_min, agc_pre_max = float(X.min()), float(X.max())
+        X = mel40_dataset.apply_soft_agc(X)
+        print(f"    soft AGC applied (target=0 dB, floor=-20 dB, release=0.2 dB/s): "
+              f"range {agc_pre_min:.3f}..{agc_pre_max:.3f} -> "
+              f"{float(X.min()):.3f}..{float(X.max()):.3f}")
+
+    if args.feature_clip_min is not None or args.feature_clip_max is not None:
+        pre_min, pre_max = float(X.min()), float(X.max())
+        X = np.clip(X, args.feature_clip_min, args.feature_clip_max)
+        print(f"    clip [{args.feature_clip_min}, {args.feature_clip_max}]: "
+              f"range {pre_min:.3f}..{pre_max:.3f} -> {float(X.min()):.3f}..{float(X.max()):.3f}")
+
+        # Linear rescale [clip_min, clip_max] -> [-1, +1] so TFLite
+        # calibration picks input_scale=1/128, input_zero_point=0 and the
+        # full int8 range is used. Runtime tflm-classify.c mirrors this
+        # transform on the Q9.23 mel value before requantization.
+        center = 0.5 * (args.feature_clip_min + args.feature_clip_max)
+        half_span = 0.5 * (args.feature_clip_max - args.feature_clip_min)
+        X = (X - center) / half_span
+        print(f"    rescale to [-1, +1] (center={center:.3f}, half_span={half_span:.3f}): "
+              f"range {float(X.min()):.3f}..{float(X.max()):.3f}")
+
+    X_tr, y_tr, X_va, y_va = split_train_val(X, y, args.val_frac, args.seed)
+    print(f"    train={X_tr.shape[0]} val={X_va.shape[0]}")
+
+    n_classes = len(args.labels)
+    model = build_tiny_conv(n_classes)
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(args.lr),
+        loss=tf.keras.losses.SparseCategoricalCrossentropy(),
+        metrics=["accuracy"],
+    )
+    model.summary(print_fn=lambda s: print("    " + s))
+
+    model.fit(
+        X_tr, y_tr,
+        validation_data=(X_va, y_va),
+        batch_size=args.batch_size,
+        epochs=args.epochs,
+        verbose=2,
+    )
+
+    print(">>> Converting to int8 tflite")
+    tflite_bytes = convert_to_tflite_int8(model, X_tr, y_tr, args.rep_samples)
+
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    tflite_path = out_dir / f"{args.name}_quantized_model.tflite"
+    tflite_path.write_bytes(tflite_bytes)
+    print(f"    wrote {tflite_path} ({len(tflite_bytes)} bytes)")
+
+    # C symbol + header names are fixed so speech.cc never changes when the
+    # keyword list changes. --name only picks the archive basename for the
+    # standalone .tflite and _labels.txt artifacts.
+    symbol = "g_sof_tflm_quantized_model_data"
+    out_cc = out_dir / "sof_tflm_quantized_model_data.cc"
+    out_h = out_dir / "sof_tflm_quantized_model_data.h"
+    emit_c_array(tflite_bytes, out_cc, out_h, symbol)
+    print(f"    wrote {out_cc}")
+    print(f"    wrote {out_h}")
+
+    labels_h = out_dir / "sof_tflm_labels.h"
+    emit_labels_header(args.labels, labels_h)
+    print(f"    wrote {labels_h}")
+
+    labels_path = out_dir / f"{args.name}_labels.txt"
+    labels_path.write_text("\n".join(args.labels) + "\n")
+    print(f"    wrote {labels_path} (archive copy of the label list)")
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/audio/tensorflow/tune/sof_tflm_train_pipeline.sh b/src/audio/tensorflow/tune/sof_tflm_train_pipeline.sh
new file mode 100755
index 000000000000..5079ded512e7
--- /dev/null
+++ b/src/audio/tensorflow/tune/sof_tflm_train_pipeline.sh
@@ -0,0 +1,190 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# End-to-end SOF TFLM wake-word retraining pipeline:
+#
+#   1. Prepare silence/ + unknown/ WAVs (Speech Commands v2 fetch + slice).
+#   2. Assume the caller has already run
+#      sof_tflm_generate_keyword_dataset.sh (or an equivalent) to populate
+#      <wav_root>/<keyword>/*.wav for every keyword class.
+#   3. Emit mel40 features via sof_mfcc_extract_features.sh -> <feat_root>/.
+#   4. Train a tiny_conv model, int8-quantize with a real-feature
+#      representative dataset, and emit the drop-in
+#      <name>_quantized_model_data.{cc,h} + <name>_labels.txt under <out_dir>.
+#
+# The labels are locked at silence(0), unknown(1), <keyword1>(2),
+# <keyword2>(3), ... so the on-device tflmcly KPB trigger rule
+# (max_idx >= 2) fires for any positive keyword class.
+#
+# Multiple keywords: pass --keyword repeatedly, e.g.
+#
+#   sof_tflm_train_pipeline.sh --keyword banana --keyword mango --keyword orange \
+#       ~/wov/wavs ~/wov/feats ~/wov/model
+#
+# Prerequisites:
+#   - Piper venv with piper-sample-generator (used earlier to synthesize the
+#     keyword WAVs).
+#   - tflm-train venv with tensorflow + numpy activated before running this
+#     script (source <venv>/bin/activate).
+#   - sof-testbench4 built; sof-hda-benchmark-mfccmel4032.tplg available.
+#   - sox, xxd on PATH.
+set -e
+
+usage() {
+	cat >&2 <<EOF
+Usage: $0 --keyword <label> [--keyword <label> ...] [--name <base>] \\
+          <wav_root> <feat_root> <out_dir>
+
+  --keyword LBL   Positive class directory name (repeatable). Must match
+                  the subdir created by sof_tflm_generate_keyword_dataset.sh
+                  under <wav_root>. Order defines model output indices
+                  starting at 2 (silence=0, unknown=1, keyword_i=i+2).
+  --name BASE     Base name for the output model files and C symbol
+                  (default: first keyword when single-keyword, otherwise
+                  the keyword labels joined with '_').
+
+  wav_root        Dataset root; must already contain <keyword>/*.wav for each
+                  --keyword. silence/ and unknown/ are populated here.
+  feat_root       Mel40 feature output root; <label>/*.raw is produced here.
+  out_dir         Where to write <name>_quantized_model.tflite, matching
+                  .cc/.h C-array files, and a labels.txt manifest.
+
+Env:
+  SKIP_PREP        If set, do not fetch/slice Speech Commands v2 again.
+  SKIP_FEATURES    If set, do not re-run testbench feature extraction.
+  EPOCHS, BATCH_SIZE, LR, WINDOW_HOP_STEP
+                   Passed through to sof_tflm_train.py.
+  GAIN_AUG_DB_MIN, GAIN_AUG_DB_MAX
+                   Uniform per-example gain augmentation in audio dB,
+                   applied to log10-mel features before clip. Defaults
+                   inside sof_tflm_train.py give a 30 dB span
+                   ([-25.0, +5.0] dB) centered at -10 dB peak.
+  GAIN_AUG, GAIN_PEAK_DBFS, GAIN_SIGMA_DB, GAIN_HEADROOM_DB
+                   Passed through to the WAV generators
+                   (sof_tflm_generate_keyword_dataset.sh and
+                   sof_tflm_prepare_silence_unknown.sh). Default is
+                   GAIN_AUG=0 (disabled); GAIN_AUG=1 enables gain
+                   jitter to keyword and unknown WAVs (peak -10 dBFS,
+                   sigma 5 dB).
+EOF
+	exit 1
+}
+
+KEYWORDS=()
+NAME=""
+POSITIONAL=()
+
+while [[ $# -gt 0 ]]; do
+	case "$1" in
+		-k|--keyword)
+			[[ $# -ge 2 ]] || usage
+			KEYWORDS+=("$2"); shift 2 ;;
+		-n|--name)
+			[[ $# -ge 2 ]] || usage
+			NAME="$2"; shift 2 ;;
+		-h|--help)
+			usage ;;
+		--)
+			shift
+			while [[ $# -gt 0 ]]; do POSITIONAL+=("$1"); shift; done ;;
+		-*)
+			echo "unknown option: $1" >&2; usage ;;
+		*)
+			POSITIONAL+=("$1"); shift ;;
+	esac
+done
+
+if [[ ${#KEYWORDS[@]} -eq 0 || ${#POSITIONAL[@]} -ne 3 ]]; then
+	usage
+fi
+
+WAV_ROOT="${POSITIONAL[0]}"
+FEAT_ROOT="${POSITIONAL[1]}"
+OUT_DIR="${POSITIONAL[2]}"
+
+if [[ -z "$NAME" ]]; then
+	if [[ ${#KEYWORDS[@]} -eq 1 ]]; then
+		NAME="${KEYWORDS[0]}"
+	else
+		NAME=$(IFS=_ ; echo "${KEYWORDS[*]}")
+	fi
+fi
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+
+for kw in "${KEYWORDS[@]}"; do
+	if [[ ! -d "$WAV_ROOT/$kw" ]]; then
+		echo "Missing $WAV_ROOT/$kw/ — run sof_tflm_generate_keyword_dataset.sh" \
+		     "--keyword <text> --label $kw first" >&2
+		exit 1
+	fi
+done
+
+# Step 1: silence + unknown (in system Python; no TF needed).
+if [[ -z "${SKIP_PREP:-}" ]]; then
+	echo ">>> [1/3] Preparing silence + unknown"
+	"$SCRIPT_DIR/sof_tflm_prepare_silence_unknown.sh" "$WAV_ROOT"
+else
+	echo ">>> [1/3] Skipping silence/unknown prep (SKIP_PREP set)"
+fi
+
+# Step 2: feature extraction via testbench.
+if [[ -z "${SKIP_FEATURES:-}" ]]; then
+	echo ">>> [2/3] Emitting mel40 features via SOF testbench"
+	"$SCRIPT_DIR/sof_mfcc_extract_features.sh" "$WAV_ROOT" "$FEAT_ROOT"
+else
+	echo ">>> [2/3] Skipping feature extraction (SKIP_FEATURES set)"
+fi
+
+# Step 3: train + quantize (needs an active TF venv).
+if ! python3 -c 'import tensorflow' >/dev/null 2>&1; then
+	cat >&2 <<'EOF'
+tensorflow not importable — activate the tflm-train venv before running
+this script, e.g.:
+
+    python3 -m venv ~/venvs/tflm-train
+    source ~/venvs/tflm-train/bin/activate
+    pip install --upgrade pip
+    pip install "tensorflow>=2.10" numpy
+
+Then rerun this script from the same shell.
+EOF
+	exit 1
+fi
+
+echo ">>> [3/3] Training tiny_conv and int8-quantizing"
+cd "$SCRIPT_DIR"
+python3 sof_tflm_train.py \
+	--feat-root "$FEAT_ROOT" \
+	--labels silence unknown "${KEYWORDS[@]}" \
+	--out-dir "$OUT_DIR" \
+	--name "$NAME" \
+	${EPOCHS:+--epochs "$EPOCHS"} \
+	${BATCH_SIZE:+--batch-size "$BATCH_SIZE"} \
+	${LR:+--lr "$LR"} \
+	${WINDOW_HOP_STEP:+--window-hop-step "$WINDOW_HOP_STEP"} \
+	${GAIN_AUG_DB_MIN:+--gain-aug-db-min "$GAIN_AUG_DB_MIN"} \
+	${GAIN_AUG_DB_MAX:+--gain-aug-db-max "$GAIN_AUG_DB_MAX"}
+
+LABEL_LIST=$(IFS=, ; echo "silence,unknown,${KEYWORDS[*]}")
+
+cat <<EOF
+
+>>> Model ready: $OUT_DIR
+
+To wire it into SOF (speech.h #includes sof_tflm_labels.h so both the
+model and its label set update automatically on rebuild — no source
+edits are needed):
+  cp $OUT_DIR/sof_tflm_quantized_model_data.{cc,h} \\
+     $OUT_DIR/sof_tflm_labels.h \\
+     $(cd "$SCRIPT_DIR"/.. && pwd)/
+
+Archive artifacts (kept per-name for retraining history):
+  $OUT_DIR/${NAME}_quantized_model.tflite
+  $OUT_DIR/${NAME}_labels.txt
+
+Label order (silence=0, unknown=1, keyword_i=i+2): ${LABEL_LIST}.
+Every positive keyword lives at index >= 2, preserving the on-device
+KPB max_idx >= 2 trigger rule.
+EOF
diff --git a/src/audio/tensorflow/tune/sof_tflm_verify.py b/src/audio/tensorflow/tune/sof_tflm_verify.py
new file mode 100755
index 000000000000..983db2a31398
--- /dev/null
+++ b/src/audio/tensorflow/tune/sof_tflm_verify.py
@@ -0,0 +1,184 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+"""
+Off-device verifier for the int8 tflite emitted by ``sof_tflm_train.py``.
+
+Loads the quantized model, reproduces the same stratified train/val split
+that training used (same ``--val-frac`` / ``--seed`` defaults), and prints
+per-class precision / recall plus a confusion matrix on the held-out
+validation split. This catches models that pass overall accuracy but fail
+the positive keyword class before we burn a hardware build.
+
+Example::
+
+    python3 sof_tflm_verify.py \\
+        --tflite ~/wov/model/<name>_quantized_model.tflite \\
+        --feat-root ~/wov/feats \\
+        --labels silence,unknown,<keyword>
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+
+import numpy as np
+
+from sof_tflm_dataset import apply_soft_agc, load_dataset
+from sof_tflm_train import split_train_val
+
+try:
+    import tflite_runtime.interpreter as tflite  # type: ignore
+except ImportError:
+    from tensorflow.lite.python.interpreter import Interpreter as _Interp
+
+    class tflite:  # type: ignore
+        Interpreter = _Interp
+
+
+def _quantize(x: np.ndarray, scale: float, zero_point: int, dtype) -> np.ndarray:
+    q = np.round(x / scale) + zero_point
+    info = np.iinfo(dtype)
+    return np.clip(q, info.min, info.max).astype(dtype)
+
+
+def _dequantize(q: np.ndarray, scale: float, zero_point: int) -> np.ndarray:
+    return (q.astype(np.float32) - zero_point) * scale
+
+
+def run_inference(model_path: str, X: np.ndarray) -> np.ndarray:
+    interp = tflite.Interpreter(model_path=model_path)
+    interp.allocate_tensors()
+    in_det = interp.get_input_details()[0]
+    out_det = interp.get_output_details()[0]
+
+    in_scale, in_zp = in_det["quantization"]
+    out_scale, out_zp = out_det["quantization"]
+    in_dtype = in_det["dtype"]
+
+    preds = np.empty((X.shape[0],), dtype=np.int32)
+    for i in range(X.shape[0]):
+        x = X[i : i + 1]
+        if np.issubdtype(in_dtype, np.integer):
+            x = _quantize(x, in_scale, in_zp, in_dtype)
+        interp.set_tensor(in_det["index"], x)
+        interp.invoke()
+        y = interp.get_tensor(out_det["index"])
+        if np.issubdtype(y.dtype, np.integer):
+            y = _dequantize(y, out_scale, out_zp)
+        preds[i] = int(np.argmax(y[0]))
+    return preds
+
+
+def confusion(y_true: np.ndarray, y_pred: np.ndarray, n: int) -> np.ndarray:
+    m = np.zeros((n, n), dtype=np.int64)
+    for t, p in zip(y_true, y_pred):
+        m[t, p] += 1
+    return m
+
+
+def print_report(labels: list[str], y_true: np.ndarray, y_pred: np.ndarray) -> None:
+    n = len(labels)
+    m = confusion(y_true, y_pred, n)
+    total = m.sum()
+    correct = np.trace(m)
+    print(f"\nsamples: {total}  overall accuracy: {correct / total:.4f}\n")
+
+    label_w = max(len(l) for l in labels)
+    header = f"  {'class':<{label_w}}  {'support':>7}  {'precision':>9}  {'recall':>7}  {'f1':>6}"
+    print(header)
+    print("  " + "-" * (len(header) - 2))
+    for c in range(n):
+        tp = m[c, c]
+        fn = m[c, :].sum() - tp
+        fp = m[:, c].sum() - tp
+        support = m[c, :].sum()
+        precision = tp / (tp + fp) if (tp + fp) else 0.0
+        recall = tp / (tp + fn) if (tp + fn) else 0.0
+        f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0.0
+        print(
+            f"  {labels[c]:<{label_w}}  {support:>7d}  {precision:>9.4f}  "
+            f"{recall:>7.4f}  {f1:>6.4f}"
+        )
+
+    print("\nconfusion matrix (rows=true, cols=pred):")
+    header_cols = "  " + " " * (label_w + 2) + "  ".join(f"{l:>{label_w}}" for l in labels)
+    print(header_cols)
+    for r in range(n):
+        row = "  ".join(f"{m[r, c]:>{label_w}d}" for c in range(n))
+        print(f"  {labels[r]:<{label_w}}  {row}")
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--tflite", required=True, help="int8 .tflite from sof_tflm_train.py")
+    ap.add_argument("--feat-root", required=True, help="dir with <label>/*.raw features")
+    ap.add_argument(
+        "--labels",
+        required=True,
+        help="comma-separated class list, same order as training",
+    )
+    ap.add_argument("--val-frac", type=float, default=0.15,
+                    help="must match the training run's --val-frac")
+    ap.add_argument("--seed", type=int, default=1,
+                    help="must match the training run's --seed")
+    ap.add_argument("--window-hop-step", type=int, default=None,
+                    help="pass the same value used at feature loading time")
+    ap.add_argument(
+        "--split",
+        choices=("val", "train", "all"),
+        default="val",
+        help="which slice of the reproduced split to evaluate on",
+    )
+    ap.add_argument("--feature-clip-min", type=float, default=-1.0,
+                    help="Lower feature-clip bound; must match training.")
+    ap.add_argument("--feature-clip-max", type=float, default=1.0,
+                    help="Upper feature-clip bound; must match training. "
+                         "Verify applies the same np.clip+(X-center)/half_span "
+                         "transform to [-1, +1] that training and the runtime C "
+                         "code use before int8 quantization.")
+    ap.add_argument("--soft-agc", action=argparse.BooleanOptionalAction, default=True,
+                    help="Match the training pipeline's soft mel-log AGC "
+                         "(target 0 dB, floor -20 dB, ~0.2 dB/sec release). "
+                         "Use --no-soft-agc only when the model was trained "
+                         "with --no-soft-agc.")
+    args = ap.parse_args()
+
+    labels = [s.strip() for s in args.labels.split(",") if s.strip()]
+    X, y = load_dataset(args.feat_root, labels, hop_step=args.window_hop_step)
+    print(f"loaded X={X.shape} y={y.shape} counts={np.bincount(y).tolist()}")
+
+    if args.soft_agc:
+        agc_pre_min, agc_pre_max = float(X.min()), float(X.max())
+        X = apply_soft_agc(X)
+        print(f"    soft AGC applied (target=0 dB, floor=-20 dB, release=0.2 dB/s): "
+              f"range {agc_pre_min:.3f}..{agc_pre_max:.3f} -> "
+              f"{float(X.min()):.3f}..{float(X.max()):.3f}")
+
+    if args.feature_clip_min is not None and args.feature_clip_max is not None:
+        pre_min, pre_max = float(X.min()), float(X.max())
+        X = np.clip(X, args.feature_clip_min, args.feature_clip_max)
+        center = 0.5 * (args.feature_clip_min + args.feature_clip_max)
+        half_span = 0.5 * (args.feature_clip_max - args.feature_clip_min)
+        X = (X - center) / half_span
+        print(f"    clip+rescale [{args.feature_clip_min}, {args.feature_clip_max}] "
+              f"-> [-1, +1] (center={center:.3f}, half_span={half_span:.3f}): "
+              f"range {pre_min:.3f}..{pre_max:.3f} -> "
+              f"{float(X.min()):.3f}..{float(X.max()):.3f}")
+
+    X_tr, y_tr, X_va, y_va = split_train_val(X, y, args.val_frac, args.seed)
+    if args.split == "val":
+        X_eval, y_eval = X_va, y_va
+    elif args.split == "train":
+        X_eval, y_eval = X_tr, y_tr
+    else:
+        X_eval, y_eval = X, y
+    print(f"evaluating on '{args.split}' split: {X_eval.shape[0]} samples")
+
+    y_pred = run_inference(args.tflite, X_eval)
+    print_report(labels, y_eval, y_pred)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/include/sof/lib_manager.h b/src/include/sof/lib_manager.h
index 29c226eb61a7..cf36ede9d3c1 100644
--- a/src/include/sof/lib_manager.h
+++ b/src/include/sof/lib_manager.h
@@ -94,6 +94,7 @@ enum {
 	LIB_MANAGER_DATA,
 	LIB_MANAGER_RODATA,
 	LIB_MANAGER_BSS,
+	LIB_MANAGER_EXPORT,
 	LIB_MANAGER_COLD,
 	LIB_MANAGER_COLDRODATA,
 	LIB_MANAGER_N_SEGMENTS,
@@ -116,6 +117,8 @@ struct lib_manager_module {
 	unsigned int n_dependent; /* For auxiliary modules: number of dependents */
 	bool mapped;
 	struct lib_manager_segment_desc segment[LIB_MANAGER_N_SEGMENTS];
+	uintptr_t vma_base;
+	size_t vma_size;
 };
 
 struct lib_manager_mod_ctx {
diff --git a/src/ipc/ipc4/ams_helpers.c b/src/ipc/ipc4/ams_helpers.c
index 6f21ee3e3623..b95acf176e88 100644
--- a/src/ipc/ipc4/ams_helpers.c
+++ b/src/ipc/ipc4/ams_helpers.c
@@ -7,6 +7,7 @@
 
 #include <ipc4/ams_helpers.h>
 #include <sof/audio/component.h>
+#include <rtos/symbol.h>
 
 #if CONFIG_AMS
 
@@ -76,4 +77,8 @@ void ams_helper_prepare_payload(const struct comp_dev *dev,
 	payload->message = message;
 }
 
+EXPORT_SYMBOL(ams_helper_register_producer);
+EXPORT_SYMBOL(ams_helper_unregister_producer);
+EXPORT_SYMBOL(ams_helper_prepare_payload);
+
 #endif /* CONFIG_AMS */
diff --git a/src/lib/CMakeLists.txt b/src/lib/CMakeLists.txt
index 707753635f69..d9213ef4473d 100644
--- a/src/lib/CMakeLists.txt
+++ b/src/lib/CMakeLists.txt
@@ -23,6 +23,10 @@ if(CONFIG_KCPS_DYNAMIC_CLOCK_CONTROL)
   add_local_sources(sof cpu-clk-manager.c)
 endif()
 
+if(CONFIG_CPP)
+  add_local_sources(sof cpp_new_export.cpp)
+endif()
+
 is_zephyr(zephyr)
 if(zephyr) ###  Zephyr ###
 
diff --git a/src/lib/ams.c b/src/lib/ams.c
index 7f4e6ebb8664..88fb509cd25b 100644
--- a/src/lib/ams.c
+++ b/src/lib/ams.c
@@ -22,6 +22,7 @@
 #include <stdint.h>
 #include <sof/lib/ams.h>
 #include <zephyr/kernel.h>
+#include <rtos/symbol.h>
 
 LOG_MODULE_REGISTER(ams, CONFIG_SOF_LOG_LEVEL);
 
@@ -468,6 +469,8 @@ int ams_send(const struct ams_message_payload *const ams_message_payload)
 					 AMS_INVALID_SLOT);
 }
 
+EXPORT_SYMBOL(ams_send);
+
 int ams_message_send_mi(struct async_message_service *ams,
 			const struct ams_message_payload *const ams_message_payload,
 			uint16_t target_module, uint16_t target_instance)
diff --git a/src/lib/cpp_new_export.cpp b/src/lib/cpp_new_export.cpp
new file mode 100644
index 000000000000..6e0a62ba097c
--- /dev/null
+++ b/src/lib/cpp_new_export.cpp
@@ -0,0 +1,55 @@
+// SPDX-License-Identifier: BSD-3-Clause
+//
+// Copyright(c) 2026 Intel Corporation. All rights reserved.
+
+#include <cstddef>
+#include <stdlib.h>
+#include <rtos/symbol.h>
+
+/*
+ * LLEXT modules linking C++ code (e.g. TFLite Micro) can end up with
+ * undefined references to global operator new/delete even when built with
+ * -fno-exceptions, because some support code (e.g. TFLM's arena allocators)
+ * still emits a call to the sized deallocation form in its generated
+ * destructors. Export wrappers here so such extensions can resolve them
+ * against the base image, mirroring zephyr/lib/cpp/minimal/cpp_new.cpp
+ * (which isn't itself referenced anywhere in the base image build, so its
+ * definitions are never pulled into the link or exported).
+ */
+
+namespace {
+
+void *sof_operator_new(size_t size)
+{
+	return malloc(size);
+}
+
+void sof_operator_delete(void *ptr, size_t size)
+{
+	(void)size;
+	free(ptr);
+}
+
+/*
+ * Same problem as operator new/delete above, one level lower: an abstract
+ * class's vtable (e.g. TFLM's MicroOpResolver base) fills the slots for its
+ * pure virtual methods with the address of __cxa_pure_virtual, so any
+ * translation unit that references such a vtable needs this symbol
+ * resolvable even though it should never actually be called at runtime.
+ * zephyr/lib/cpp/minimal/cpp_virtual.c already defines a real
+ * __cxa_pure_virtual, but -- exactly like cpp_new.cpp above -- nothing in
+ * the base image build references it directly, so it is never pulled into
+ * the link or exported for LLEXT modules to resolve against. Provide our
+ * own and export it under the same name.
+ */
+void sof_cxa_pure_virtual(void)
+{
+	while (1) {
+	}
+}
+
+} /* namespace */
+
+EXPORT_SYMBOL_NAMED(sof_operator_new, _Znwj);
+EXPORT_SYMBOL_NAMED(sof_operator_delete, _ZdlPvj);
+EXPORT_SYMBOL_NAMED(sof_cxa_pure_virtual, __cxa_pure_virtual);
diff --git a/src/library_manager/llext_manager.c b/src/library_manager/llext_manager.c
index 4c1e4f02d5b5..a9641d0903a5 100644
--- a/src/library_manager/llext_manager.c
+++ b/src/library_manager/llext_manager.c
@@ -50,6 +50,368 @@ extern struct tr_ctx lib_manager_tr;
 
 #define PAGE_SZ		CONFIG_MM_DRV_PAGE_SIZE
 
+#include <zephyr/sys/bitarray.h>
+#include <zephyr/llext/elf.h>
+
+#define LLEXT_LIB_PAGES (CONFIG_LIBRARY_REGION_SIZE / PAGE_SZ)
+SYS_BITARRAY_DEFINE_STATIC(lib_vma_bitarray, LLEXT_LIB_PAGES);
+
+static uintptr_t llext_manager_alloc_vma(size_t size)
+{
+	size_t num_pages = ALIGN_UP(size, PAGE_SZ) / PAGE_SZ;
+	size_t offset;
+	int ret;
+
+	ret = sys_bitarray_alloc(&lib_vma_bitarray, num_pages, &offset);
+	if (ret < 0) {
+		tr_err(&lib_manager_tr, "llext_manager_alloc_vma: failed to allocate %zu pages", num_pages);
+		return 0;
+	}
+
+	return CONFIG_LIBRARY_BASE_ADDRESS + offset * PAGE_SZ;
+}
+
+void llext_manager_free_vma(uintptr_t vma, size_t size)
+{
+	if (!vma || !size)
+		return;
+
+	size_t num_pages = ALIGN_UP(size, PAGE_SZ) / PAGE_SZ;
+	size_t offset = (vma - CONFIG_LIBRARY_BASE_ADDRESS) / PAGE_SZ;
+
+	sys_bitarray_free(&lib_vma_bitarray, num_pages, offset);
+}
+
+static enum llext_mem llext_manager_get_sec_mem_idx(const char *name, const elf_shdr_t *shdr)
+{
+	if (strcmp(name, ".exported_sym") == 0)
+		return LLEXT_MEM_EXPORT;
+
+	switch (shdr->sh_type) {
+	case SHT_NOBITS:
+		return LLEXT_MEM_BSS;
+	case SHT_PROGBITS:
+		if (shdr->sh_flags & SHF_EXECINSTR)
+			return LLEXT_MEM_TEXT;
+		else if (shdr->sh_flags & SHF_WRITE)
+			return LLEXT_MEM_DATA;
+		else
+			return LLEXT_MEM_RODATA;
+	case SHT_PREINIT_ARRAY:
+		return LLEXT_MEM_PREINIT;
+	case SHT_INIT_ARRAY:
+		return LLEXT_MEM_INIT;
+	case SHT_FINI_ARRAY:
+		return LLEXT_MEM_FINI;
+	default:
+		return LLEXT_MEM_COUNT;
+	}
+}
+
+/*
+ * llext_manager_layout_sections() rebases the addresses of recognized
+ * sections in place, directly in the raw ELF buffer, before llext_load()
+ * ever parses the file. Two classes of data in the file still reference the
+ * OLD (pre-rebase) addresses after that mutation, and need fixing up
+ * ourselves:
+ *
+ *  1. .rela.dyn/.rela.plt r_offset fields (byte-for-byte copies from the
+ *     build-time ELF) -- otherwise llext_link_plt()'s llext_file_offset()
+ *     lookup fails ("Offset not found") for every relocation whose target
+ *     section moved.
+ *
+ *  2. R_XTENSA_RELATIVE relocations are a complete no-op in Zephyr's llext
+ *     core whenever ldr_parm->pre_located is set (see
+ *     arch_elf_relocate_local() in arch/xtensa/core/elf.c), under the
+ *     assumption that a pre-located relative relocation's stored pointer
+ *     value is already correct. SOF's rebase step violates that assumption,
+ *     so we must apply the same delta to the pointer VALUE stored at each
+ *     R_XTENSA_RELATIVE's target ourselves.
+ *
+ *  3. .symtab/.dynsym st_value fields: llext_copy_symbols() takes st_value
+ *     as the final absolute address, unmodified, whenever pre_located is
+ *     set (same assumption as above, same violation) -- this feeds both
+ *     llext_find_sym() lookups (R_XTENSA_GLOB_DAT resolution) and exported
+ *     symbol addresses, so stale st_value silently propagates stale
+ *     addresses through both.
+ *
+ * Track the (old_addr, size, delta, section index) of every section
+ * actually rebased during the real (vma_base != 0) pass, then walk the ELF
+ * a second time applying all three fixups above.
+ */
+struct llext_manager_sec_rebase {
+	uintptr_t old_addr;
+	size_t size;
+	ptrdiff_t delta;
+	int shdr_idx;
+};
+
+#define LLEXT_MANAGER_MAX_REBASED_SECTIONS 32
+
+/* Keep in sync with arch/xtensa/core/elf.c -- not exposed via a shared header. */
+#define SOF_LLEXT_R_XTENSA_RELATIVE 5
+
+static ptrdiff_t llext_manager_delta_for_old_addr(const struct llext_manager_sec_rebase *rebase,
+						   int rebase_cnt, uintptr_t old_addr)
+{
+	for (int k = 0; k < rebase_cnt; k++) {
+		if (old_addr >= rebase[k].old_addr && old_addr < rebase[k].old_addr + rebase[k].size)
+			return rebase[k].delta;
+	}
+
+	return 0;
+}
+
+static bool llext_manager_addr_to_file_off(uint8_t *elf_buf, uintptr_t addr, size_t *file_off)
+{
+	elf_ehdr_t *hdr = (elf_ehdr_t *)elf_buf;
+	elf_shdr_t *shdrs = (elf_shdr_t *)(elf_buf + hdr->e_shoff);
+
+	for (int i = 0; i < hdr->e_shnum; i++) {
+		elf_shdr_t *shdr = shdrs + i;
+
+		if (!(shdr->sh_flags & SHF_ALLOC) || !shdr->sh_size)
+			continue;
+
+		if (addr >= shdr->sh_addr && addr < shdr->sh_addr + shdr->sh_size) {
+			*file_off = shdr->sh_offset + (addr - shdr->sh_addr);
+			return true;
+		}
+	}
+
+	return false;
+}
+
+static void llext_manager_fixup_rela(uint8_t *elf_buf,
+				      const struct llext_manager_sec_rebase *rebase,
+				      int rebase_cnt)
+{
+	elf_ehdr_t *hdr = (elf_ehdr_t *)elf_buf;
+	elf_shdr_t *shdrs = (elf_shdr_t *)(elf_buf + hdr->e_shoff);
+
+	for (int i = 0; i < hdr->e_shnum; i++) {
+		elf_shdr_t *shdr = shdrs + i;
+
+		if (shdr->sh_type != SHT_RELA)
+			continue;
+
+		int cnt = shdr->sh_size / shdr->sh_entsize;
+		elf_rela_t *relas = (elf_rela_t *)(elf_buf + shdr->sh_offset);
+
+		for (int j = 0; j < cnt; j++) {
+			elf_rela_t *rela = relas + j;
+			ptrdiff_t r_delta = llext_manager_delta_for_old_addr(rebase, rebase_cnt,
+									      rela->r_offset);
+
+			if (!r_delta)
+				continue;
+
+			rela->r_offset += r_delta;
+
+			if (ELF_R_TYPE(rela->r_info) == SOF_LLEXT_R_XTENSA_RELATIVE) {
+				size_t file_off;
+
+				if (llext_manager_addr_to_file_off(elf_buf, rela->r_offset,
+								    &file_off)) {
+					uint32_t *val = (uint32_t *)(elf_buf + file_off);
+					ptrdiff_t v_delta = llext_manager_delta_for_old_addr(
+						rebase, rebase_cnt, *val);
+
+					*val += v_delta;
+				}
+			}
+		}
+	}
+}
+
+static void llext_manager_fixup_symtab(uint8_t *elf_buf,
+					const struct llext_manager_sec_rebase *rebase,
+					int rebase_cnt)
+{
+	elf_ehdr_t *hdr = (elf_ehdr_t *)elf_buf;
+	elf_shdr_t *shdrs = (elf_shdr_t *)(elf_buf + hdr->e_shoff);
+
+	for (int i = 0; i < hdr->e_shnum; i++) {
+		elf_shdr_t *shdr = shdrs + i;
+
+		if (shdr->sh_type != SHT_SYMTAB && shdr->sh_type != SHT_DYNSYM)
+			continue;
+
+		int cnt = shdr->sh_size / shdr->sh_entsize;
+		elf_sym_t *syms = (elf_sym_t *)(elf_buf + shdr->sh_offset);
+
+		for (int j = 0; j < cnt; j++) {
+			elf_sym_t *sym = syms + j;
+			int k;
+
+			for (k = 0; k < rebase_cnt; k++) {
+				if (sym->st_shndx == rebase[k].shdr_idx) {
+					sym->st_value += rebase[k].delta;
+					break;
+				}
+			}
+		}
+	}
+}
+
+static size_t llext_manager_layout_sections(uint8_t *elf_buf, uintptr_t vma_base)
+{
+	elf_ehdr_t *hdr = (elf_ehdr_t *)elf_buf;
+	elf_shdr_t *shdrs = (elf_shdr_t *)(elf_buf + hdr->e_shoff);
+	elf_shdr_t *shstr_shdr = shdrs + hdr->e_shstrndx;
+	const char *shstrtab = (const char *)(elf_buf + shstr_shdr->sh_offset);
+
+	uintptr_t current_vma = vma_base;
+	enum llext_mem last_region = LLEXT_MEM_COUNT;
+	struct llext_manager_sec_rebase rebase[LLEXT_MANAGER_MAX_REBASED_SECTIONS];
+	int rebase_cnt = 0;
+	bool region_opened[LLEXT_MEM_COUNT] = { false };
+	ptrdiff_t region_delta[LLEXT_MEM_COUNT] = { 0 };
+
+	for (int i = 0; i < hdr->e_shnum; i++) {
+		elf_shdr_t *shdr = shdrs + i;
+
+		if (!(shdr->sh_flags & SHF_ALLOC) || shdr->sh_size == 0)
+			continue;
+
+		const char *name = shstrtab + shdr->sh_name;
+		enum llext_mem s_region = llext_manager_get_sec_mem_idx(name, shdr);
+
+		if (s_region == LLEXT_MEM_BSS) {
+			/*
+			 * For layout purposes only, treat .bss as part of the DATA
+			 * region rather than its own distinct region. llext_manager_
+			 * load_module() requires .bss to be immediately contiguous
+			 * with writable DATA (they share a single VMA mapping, since
+			 * .bss has no file backing of its own and just extends
+			 * DATA's mapped-and-zeroed tail). If .bss were laid out as
+			 * its own region, any OTHER region (e.g. .exported_sym)
+			 * appearing between DATA's last member and .bss in file order
+			 * would consume the address range .bss needs to be adjacent
+			 * to, forcing .bss onto a spurious extra page beyond where
+			 * DATA actually ends. Aliasing to LLEXT_MEM_DATA here reuses
+			 * the exact same delta/contiguous-packing path already used
+			 * for DATA members that reappear after such an interruption.
+			 * This only affects the rebased sh_addr written below; it
+			 * does not change which segment .bss is reported under
+			 * elsewhere (that comes from Zephyr's own section type-based
+			 * classification, independent of this local variable).
+			 */
+			s_region = LLEXT_MEM_DATA;
+		}
+
+		if (s_region == LLEXT_MEM_COUNT) {
+			/*
+			 * Not part of any llext region (e.g. .dynamic) and its own
+			 * sh_addr is left untouched below. But it still occupies
+			 * real file space, possibly between two sections of a
+			 * region that IS being repacked here -- account for its
+			 * slot so later same-region sections keep the same
+			 * relative spacing that llext_map_sections()'s ET_DYN
+			 * consistency check (sh_addr delta == sh_offset delta)
+			 * requires.
+			 */
+			current_vma = ALIGN_UP(current_vma, shdr->sh_addralign);
+			current_vma += shdr->sh_size;
+			continue;
+		}
+
+		if (region_opened[s_region]) {
+			/*
+			 * This region already had a section rebased earlier and is
+			 * reappearing now, with a genuinely different region's
+			 * section(s) interleaved in between in file order (e.g.
+			 * .exported_sym sitting between two .data-region members).
+			 * Reuse the region's already-established rebase delta so
+			 * this member keeps the exact same (sh_addr - sh_offset) as
+			 * the region's first member -- that constant delta is what
+			 * llext_map_sections()'s ET_DYN consistency check actually
+			 * requires. Re-packing it back into the forward current_vma
+			 * layout instead (as if it were a fresh region) would insert
+			 * a spurious page-aligned gap that doesn't exist in the
+			 * original file.
+			 */
+			if (vma_base) {
+				uintptr_t old_addr = shdr->sh_addr;
+				uintptr_t new_addr = (uintptr_t)((ptrdiff_t)old_addr +
+								  region_delta[s_region]);
+
+				if (new_addr != old_addr && rebase_cnt < ARRAY_SIZE(rebase)) {
+					rebase[rebase_cnt].old_addr = old_addr;
+					rebase[rebase_cnt].size = shdr->sh_size;
+					rebase[rebase_cnt].delta = region_delta[s_region];
+					rebase[rebase_cnt].shdr_idx = i;
+					rebase_cnt++;
+				}
+				shdr->sh_addr = new_addr;
+
+				/*
+				 * A reopened region's member keeps the region's original
+				 * constant address delta (required above for the ET_DYN
+				 * sh_addr/sh_offset consistency check), which is NOT
+				 * necessarily contiguous with current_vma's independently
+				 * tracked cursor -- any other region's section(s) placed
+				 * (and possibly page-aligned) since this region was last
+				 * open, e.g. .exported_sym, can leave current_vma short of
+				 * where this delta-preserving placement actually ends.
+				 * Re-anchor current_vma to this section's real end so the
+				 * next freshly-opened region's page-alignment starts after
+				 * this region's true extent, not a stale, too-small cursor
+				 * value -- otherwise the next region can be placed
+				 * overlapping this region's tail.
+				 */
+				current_vma = new_addr + shdr->sh_size;
+			}
+
+			/*
+			 * Keep last_region tracking the region of the section that
+			 * ACTUALLY precedes the next one in file order, not just "the
+			 * last freshly-opened region". Without this, a fresh region
+			 * immediately following a reused-region section (e.g. .bss
+			 * right after a re-visited .data member, with .exported_sym
+			 * sandwiched earlier) sees a stale last_region from whatever
+			 * region was last freshly opened (e.g. EXPORT) instead of this
+			 * section's own region (DATA), and wrongly concludes it needs
+			 * yet another PAGE_SZ-aligned transition on top of the one
+			 * already inserted for EXPORT -- stacking two page gaps where
+			 * only one belongs, which desyncs .bss from the writable-data
+			 * region it must remain contiguous with.
+			 */
+			last_region = s_region;
+			continue;
+		}
+
+		if (last_region != LLEXT_MEM_COUNT && last_region != s_region) {
+			current_vma = ALIGN_UP(current_vma, PAGE_SZ);
+		}
+		last_region = s_region;
+
+		current_vma = ALIGN_UP(current_vma, shdr->sh_addralign);
+		if (vma_base) {
+			uintptr_t old_addr = shdr->sh_addr;
+
+			if (old_addr != current_vma && rebase_cnt < ARRAY_SIZE(rebase)) {
+				rebase[rebase_cnt].old_addr = old_addr;
+				rebase[rebase_cnt].size = shdr->sh_size;
+				rebase[rebase_cnt].delta = (ptrdiff_t)current_vma - (ptrdiff_t)old_addr;
+				rebase[rebase_cnt].shdr_idx = i;
+				rebase_cnt++;
+			}
+			region_delta[s_region] = (ptrdiff_t)current_vma - (ptrdiff_t)old_addr;
+			region_opened[s_region] = true;
+			shdr->sh_addr = current_vma;
+		}
+		current_vma += shdr->sh_size;
+	}
+
+	if (vma_base && rebase_cnt) {
+		llext_manager_fixup_rela(elf_buf, rebase, rebase_cnt);
+		llext_manager_fixup_symtab(elf_buf, rebase, rebase_cnt);
+	}
+
+	return current_vma - vma_base;
+}
+
 static int llext_manager_update_flags(void __sparse_cache *vma, size_t size, uint32_t flags)
 {
 	size_t pre_pad_size = (uintptr_t)vma & (PAGE_SZ - 1);
@@ -211,6 +573,11 @@ static int llext_manager_load_module(struct lib_manager_module *mctx)
 		mctx->segment[LIB_MANAGER_RODATA].addr;
 	size_t rodata_size = mctx->segment[LIB_MANAGER_RODATA].size;
 
+	/* Exported symbol table (.exported_sym), read-only like .rodata */
+	void __sparse_cache *va_base_export = (void __sparse_cache *)
+		mctx->segment[LIB_MANAGER_EXPORT].addr;
+	size_t export_size = mctx->segment[LIB_MANAGER_EXPORT].size;
+
 	/* Writable data (.data, .bss and others) */
 	void __sparse_cache *va_base_data = (void __sparse_cache *)
 		mctx->segment[LIB_MANAGER_DATA].addr;
@@ -278,6 +645,12 @@ static int llext_manager_load_module(struct lib_manager_module *mctx)
 	if (ret < 0)
 		goto e_text;
 
+	/* Copy exported symbol table */
+	ret = llext_manager_load_data_from_storage(virtual_region, ldr, ext, LLEXT_MEM_EXPORT,
+						   va_base_export, export_size, 0);
+	if (ret < 0)
+		goto e_rodata;
+
 	/* Copy writable data */
 	/*
 	 * NOTE: va_base_data and data_size refer to an address range that
@@ -318,6 +691,11 @@ static int llext_manager_unload_module(struct lib_manager_module *mctx)
 		mctx->segment[LIB_MANAGER_RODATA].addr;
 	size_t rodata_size = mctx->segment[LIB_MANAGER_RODATA].size;
 
+	/* Exported symbol table (.exported_sym), read-only like .rodata */
+	void __sparse_cache *va_base_export = (void __sparse_cache *)
+		mctx->segment[LIB_MANAGER_EXPORT].addr;
+	size_t export_size = mctx->segment[LIB_MANAGER_EXPORT].size;
+
 	/* Writable data (.data, .bss, etc.) */
 	void __sparse_cache *va_base_data = (void __sparse_cache *)
 		mctx->segment[LIB_MANAGER_DATA].addr;
@@ -351,6 +729,12 @@ static int llext_manager_unload_module(struct lib_manager_module *mctx)
 	if (ret < 0 && !err)
 		err = ret;
 
+	llext_manager_unmap_detached_sections(ldr, ext, LLEXT_MEM_EXPORT,
+					      va_base_export, export_size);
+	ret = llext_manager_align_unmap(va_base_export, export_size);
+	if (ret < 0 && !err)
+		err = ret;
+
 	mctx->mapped = false;
 
 	return err;
@@ -361,6 +745,73 @@ static bool llext_manager_section_detached(const elf_shdr_t *shdr)
 	return shdr->sh_addr < SOF_MODULE_DRAM_LINK_END;
 }
 
+/*
+ * Zephyr's llext_load() reads several sections' actual byte content
+ * synchronously, during the load itself -- e.g. llext_export_symbols()
+ * (called unconditionally to build the extension's exported-symbol table
+ * for future dependency resolution) reads .exported_sym, and other parts
+ * of the eager load path read .rodata and friends. This is unlike the
+ * later, on-demand copy SOF performs when a module is actually
+ * instantiated (llext_manager_load_module()), which only runs well after
+ * llext_manager_link() has already called llext_load(). So every
+ * "attached" (non-detached, i.e. real VMA, see
+ * llext_manager_section_detached()) allocated section with real file
+ * content (skipping .bss/SHT_NOBITS, which has none) has to be populated
+ * here, directly from the raw ELF buffer, before llext_load() is invoked
+ * below.
+ */
+static int llext_manager_load_sections_early(uint8_t *elf_buf)
+{
+	elf_ehdr_t *hdr = (elf_ehdr_t *)elf_buf;
+	elf_shdr_t *shdrs = (elf_shdr_t *)(elf_buf + hdr->e_shoff);
+	const struct sys_mm_drv_region *virtual_memory_regions;
+	const struct sys_mm_drv_region *virtual_region;
+	int i;
+
+	virtual_memory_regions = sys_mm_drv_query_memory_regions();
+	if (!virtual_memory_regions)
+		return -EFAULT;
+
+	SYS_MM_DRV_MEMORY_REGION_FOREACH(virtual_memory_regions, virtual_region) {
+		if (virtual_region->attr == VIRTUAL_REGION_LLEXT_LIBRARIES_ATTR)
+			break;
+	}
+
+	if (!virtual_region->size)
+		return -EFAULT;
+
+	for (i = 0; i < hdr->e_shnum; i++) {
+		elf_shdr_t *shdr = shdrs + i;
+		void __sparse_cache *vma;
+		int ret;
+
+		if (!(shdr->sh_flags & SHF_ALLOC) || shdr->sh_size == 0)
+			continue;
+
+		if (shdr->sh_type == SHT_NOBITS)
+			continue;
+
+		if (llext_manager_section_detached(shdr))
+			continue;
+
+		vma = (void __sparse_cache *)(uintptr_t)shdr->sh_addr;
+
+		ret = llext_manager_align_map(virtual_region, vma, shdr->sh_size,
+					      SYS_MM_MEM_PERM_RW);
+		if (ret < 0)
+			return ret;
+
+		ret = memcpy_s((__sparse_force void *)(uintptr_t)shdr->sh_addr, shdr->sh_size,
+			       elf_buf + shdr->sh_offset, shdr->sh_size);
+		if (ret < 0)
+			return ret;
+
+		dcache_writeback_region(vma, shdr->sh_size);
+	}
+
+	return 0;
+}
+
 static int llext_manager_link(const char *name,
 			      struct lib_manager_module *mctx, const void **buildinfo,
 			      const struct sof_man_module_manifest **mod_manifest)
@@ -383,6 +834,36 @@ static int llext_manager_link(const char *name,
 	}
 
 	if (!*llext || mctx->mapped) {
+		if (!*llext) {
+			uint8_t *elf_buf = (uint8_t *)mctx->ebl->buf;
+			size_t total_size = llext_manager_layout_sections(elf_buf, 0);
+			if (total_size == 0) {
+				tr_err(&lib_manager_tr, "llext_manager_link: layout sections failed");
+				return -EINVAL;
+			}
+
+			uintptr_t vma_base = llext_manager_alloc_vma(total_size);
+			if (!vma_base) {
+				tr_err(&lib_manager_tr, "llext_manager_link: VMA allocation failed");
+				return -ENOMEM;
+			}
+
+			mctx->vma_base = vma_base;
+			mctx->vma_size = total_size;
+
+			llext_manager_layout_sections(elf_buf, vma_base);
+
+			ret = llext_manager_load_sections_early(elf_buf);
+			if (ret < 0) {
+				tr_err(&lib_manager_tr,
+				       "llext_manager_link: early section copy failed: %d", ret);
+				llext_manager_free_vma(mctx->vma_base, mctx->vma_size);
+				mctx->vma_base = 0;
+				mctx->vma_size = 0;
+				return ret;
+			}
+		}
+
 		/*
 		 * Either the very first time loading this module, or the module
 		 * is already mapped, we just call llext_load() to refcount it
@@ -395,8 +876,15 @@ static int llext_manager_link(const char *name,
 		};
 
 		ret = llext_load(ldr, name, llext, &ldr_parm);
-		if (ret)
+		if (ret) {
+			tr_err(&lib_manager_tr, "llext_load failed: ret=%d", ret);
+			if (mctx->vma_base) {
+				llext_manager_free_vma(mctx->vma_base, mctx->vma_size);
+				mctx->vma_base = 0;
+				mctx->vma_size = 0;
+			}
 			return ret;
+		}
 	}
 
 	/* All code sections */
@@ -435,6 +923,20 @@ static int llext_manager_link(const char *name,
 	       mctx->segment[LIB_MANAGER_BSS].addr,
 	       mctx->segment[LIB_MANAGER_BSS].size);
 
+	/*
+	 * Exported symbol table (.exported_sym). Some toolchains (e.g. GNU ld,
+	 * unlike the Clang LLEXT overlay which merges it into .rodata) keep this
+	 * as its own distinct allocatable section, so it needs its own tracked
+	 * segment and its own copy-from-storage pass, same as .rodata.
+	 */
+	llext_get_region_info(ldr, *llext, LLEXT_MEM_EXPORT, &hdr, NULL, NULL);
+	mctx->segment[LIB_MANAGER_EXPORT].addr = hdr->sh_addr;
+	mctx->segment[LIB_MANAGER_EXPORT].size = hdr->sh_size;
+
+	tr_dbg(&lib_manager_tr, ".exported_sym: start: %#lx size %#x",
+	       mctx->segment[LIB_MANAGER_EXPORT].addr,
+	       mctx->segment[LIB_MANAGER_EXPORT].size);
+
 	*buildinfo = NULL;
 	ret = llext_section_shndx(ldr, *llext, ".mod_buildinfo");
 	if (ret >= 0) {
@@ -1078,13 +1580,14 @@ int llext_manager_add_library(uint32_t module_id)
 	}
 
 	for (i = 0; i < ctx->n_mod; i++) {
-		const struct sof_man_module *mod = lib_manager_get_module_manifest(module_id + i);
+		unsigned int idx = ctx->mod[i].start_idx;
+		const struct sof_man_module *mod = lib_manager_get_module_manifest(module_id + idx);
 
 		if (mod->type.load_type == SOF_MAN_MOD_TYPE_LLEXT_AUX) {
 			const struct sof_man_module_manifest *mod_manifest;
 			const struct sof_module_api_build_info *buildinfo;
 
-			ret = llext_manager_link_single(module_id + i, desc, ctx,
+			ret = llext_manager_link_single(module_id + idx, desc, ctx,
 							(const void **)&buildinfo, &mod_manifest);
 			if (ret < 0)
 				return ret;
diff --git a/src/library_manager/llext_manager_dram.c b/src/library_manager/llext_manager_dram.c
index 2f6cff2b3501..1cc8fad942f8 100644
--- a/src/library_manager/llext_manager_dram.c
+++ b/src/library_manager/llext_manager_dram.c
@@ -14,6 +14,8 @@
 
 LOG_MODULE_DECLARE(lib_manager, CONFIG_SOF_LOG_LEVEL);
 
+void llext_manager_free_vma(uintptr_t vma, size_t size);
+
 struct lib_manager_dram_storage {
 	struct ext_library ext_lib;
 	struct lib_manager_mod_ctx *ctx;
@@ -332,6 +334,9 @@ int llext_manager_restore_from_dram(void)
 			if (mod[k].llext)
 				llext_unload(&mod[k].llext);
 
+			if (mod[k].vma_base)
+				llext_manager_free_vma(mod[k].vma_base, mod[k].vma_size);
+
 			if (mod[k].ebl)
 				rfree(mod[k].ebl);
 		}
diff --git a/src/logging/log_backend_probe.c b/src/logging/log_backend_probe.c
index 0b852d780455..60d0ed1298bb 100644
--- a/src/logging/log_backend_probe.c
+++ b/src/logging/log_backend_probe.c
@@ -7,6 +7,7 @@
 #include <zephyr/logging/log_output.h>
 #include <zephyr/logging/log_backend_std.h>
 #include <zephyr/kernel.h>
+#include <rtos/symbol.h>
 
 #include <sof/audio/buffer.h>
 #include <sof/ipc/topology.h>
diff --git a/src/platform/intel/cavs/platform.c b/src/platform/intel/cavs/platform.c
index 8a1a7c59c3b5..4752916a85b5 100644
--- a/src/platform/intel/cavs/platform.c
+++ b/src/platform/intel/cavs/platform.c
@@ -28,6 +28,7 @@
 #include <sof/lib/notifier.h>
 #include <rtos/wait.h>
 #include <sof/platform.h>
+#include <sof/schedule/dp_schedule.h>
 #include <sof/schedule/edf_schedule.h>
 #include <sof/schedule/ll_schedule.h>
 #include <sof/schedule/ll_schedule_domain.h>
@@ -122,6 +123,12 @@ int platform_init(struct sof *sof)
 	sof->platform_timer_domain = timer_domain_init(sof->platform_timer, 0);
 	scheduler_init_ll(sof->platform_timer_domain);
 
+#if CONFIG_ZEPHYR_DP_SCHEDULER
+	ret = scheduler_dp_init();
+	if (ret < 0)
+		return ret;
+#endif /* CONFIG_ZEPHYR_DP_SCHEDULER */
+
 	/* init the system agent */
 	trace_point(TRACE_BOOT_PLATFORM_AGENT);
 	sa_init(sof, CONFIG_SYSTICK_PERIOD);
diff --git a/tools/rimage/config/ptl.toml.h b/tools/rimage/config/ptl.toml.h
index e7a53d1b1820..cb7cedd5a1a2 100644
--- a/tools/rimage/config/ptl.toml.h
+++ b/tools/rimage/config/ptl.toml.h
@@ -50,9 +50,7 @@ index = __COUNTER__
 #include <audio/copier/copier.toml>
 #endif
 
-#if defined(CONFIG_COMP_VOLUME) || defined(LLEXT_FORCE_ALL_MODULAR)
 #include <audio/volume/volume.toml>
-#endif
 
 #if defined(CONFIG_COMP_ASRC) || defined(LLEXT_FORCE_ALL_MODULAR)
 #include <audio/asrc/asrc.toml>
@@ -86,9 +84,7 @@ index = __COUNTER__
 #include <samples/audio/detect_test.toml>
 #endif
 
-#if defined(CONFIG_COMP_KPB) || defined(LLEXT_FORCE_ALL_MODULAR)
 #include <audio/kpb.toml>
-#endif
 
 #if defined(CONFIG_SAMPLE_SMART_AMP) || defined(LLEXT_FORCE_ALL_MODULAR)
 #include <samples/audio/smart_amp_test.toml>
@@ -150,9 +146,7 @@ index = __COUNTER__
 #include <audio/igo_nr/igo_nr.toml>
 #endif
 
-#if defined(CONFIG_COMP_MFCC) || defined(LLEXT_FORCE_ALL_MODULAR)
 #include <audio/mfcc/mfcc.toml>
-#endif
 
 #if defined(CONFIG_COMP_TEMPLATE) || defined(LLEXT_FORCE_ALL_MODULAR)
 #include <audio/template/template.toml>
@@ -178,5 +172,11 @@ index = __COUNTER__
 #include <audio/phase_vocoder/phase_vocoder.toml>
 #endif
 
+#include <audio/tensorflow/tflmcly.toml>
+
+#if defined(CONFIG_COMP_MWW) || defined(LLEXT_FORCE_ALL_MODULAR)
+#include <audio/microwakeword/mww.toml>
+#endif
+
 [module]
 count = __COUNTER__
diff --git a/tools/rimage/config/tgl.toml.h b/tools/rimage/config/tgl.toml.h
index 2ca246880727..d5f7d29fe24b 100644
--- a/tools/rimage/config/tgl.toml.h
+++ b/tools/rimage/config/tgl.toml.h
@@ -120,6 +120,10 @@
 #include <audio/template/template.toml>
 #endif
 
+#if defined(CONFIG_COMP_TENSORFLOW) || defined(LLEXT_FORCE_ALL_MODULAR)
+#include <audio/tensorflow/tflmcly.toml>
+#endif
+
 #if defined(CONFIG_COMP_LEVEL_MULTIPLIER) || defined(LLEXT_FORCE_ALL_MODULAR)
 #include <audio/level_multiplier/level_multiplier.toml>
 #endif
diff --git a/tools/topology/topology2/cavs-benchmark-hda.conf b/tools/topology/topology2/cavs-benchmark-hda.conf
index d1357caa20ab..dc27b8899a03 100644
--- a/tools/topology/topology2/cavs-benchmark-hda.conf
+++ b/tools/topology/topology2/cavs-benchmark-hda.conf
@@ -845,6 +845,17 @@ IncludeByKey.BENCH_CONFIG {
 		<include/bench/mfccmel_s32.conf>
 	}
 
+	# mfccmel40 reuses the mfccmel bench include; BENCH_MFCC_PARAMS=mel40 selects the blob.
+	"mfccmel4016" {
+		<include/bench/mfccmel_s16.conf>
+	}
+	"mfccmel4024" {
+		<include/bench/mfccmel_s24.conf>
+	}
+	"mfccmel4032" {
+		<include/bench/mfccmel_s32.conf>
+	}
+
 	#
 	# Micsel component
 	#
diff --git a/tools/topology/topology2/cavs-sdw.conf b/tools/topology/topology2/cavs-sdw.conf
index 311e0376b333..c9d78596d67a 100644
--- a/tools/topology/topology2/cavs-sdw.conf
+++ b/tools/topology/topology2/cavs-sdw.conf
@@ -11,6 +11,7 @@
 <pdm_config.conf>
 <tokens.conf>
 <virtual.conf>
+<output_pin_binding.conf>
 <host-copier-gain-mixin-playback.conf>
 <mixout-gain-alh-dai-copier-playback.conf>
 <mixout-gain-ctc-alh-dai-copier-playback.conf>
@@ -32,6 +33,8 @@
 <host-gateway-capture.conf>
 <host-gateway-tdfb-drc-capture.conf>
 <host-gateway-src-micsel-mfcc-capture.conf>
+<host-gateway-micsel-mfcc-tflm-capture.conf>
+<src-kpb-be.conf>
 <src.conf>
 <io-gateway.conf>
 <io-gateway-capture.conf>
@@ -280,6 +283,10 @@ IncludeByKey.SDW_JACK_COMPR_AUDIO_FEATURE_CAPTURE {
 	"true" "platform/intel/sdw-jack-audio-feature-compress.conf"
 }
 
+IncludeByKey.SDW_JACK_TFLM_KPB_CAPTURE {
+	"true" "platform/intel/sdw-jack-tflm-kpb.conf"
+}
+
 IncludeByKey.SDW_DMIC_AUDIO_FEATURE_CAPTURE {
 	"true" "platform/intel/sdw-dmic-audio-feature.conf"
 }
@@ -287,3 +294,7 @@ IncludeByKey.SDW_DMIC_AUDIO_FEATURE_CAPTURE {
 IncludeByKey.SDW_DMIC_COMPR_AUDIO_FEATURE_CAPTURE {
 	"true" "platform/intel/sdw-dmic-audio-feature-compress.conf"
 }
+
+IncludeByKey.SDW_DMIC_TFLM_KPB_CAPTURE {
+	"true" "platform/intel/sdw-dmic-tflm-kpb.conf"
+}
diff --git a/tools/topology/topology2/development/tplg-targets-bench.cmake b/tools/topology/topology2/development/tplg-targets-bench.cmake
index 2dd4f28bc07c..abf8a5bd7230 100644
--- a/tools/topology/topology2/development/tplg-targets-bench.cmake
+++ b/tools/topology/topology2/development/tplg-targets-bench.cmake
@@ -20,6 +20,7 @@ set(components
 	"level_multiplier"
 	"mfcc"
 	"mfccmel"
+	"mfccmel40"
 	"micsel"
 	"phase_vocoder"
 	"rtnr"
@@ -48,6 +49,7 @@ set(component_parameters
 	"BENCH_LEVEL_MULTIPLIER_PARAMS=default"
 	"BENCH_MFCC_PARAMS=default"
 	"BENCH_MFCC_PARAMS=mel80"
+	"BENCH_MFCC_PARAMS=mel40"
 	"BENCH_MICSEL_PARAMS=passthrough"
 	"BENCH_PHASE_VOCODER_PARAMS=default"
 	"BENCH_RTNR_PARAMS=default"
diff --git a/tools/topology/topology2/development/tplg-targets.cmake b/tools/topology/topology2/development/tplg-targets.cmake
index ab6a8b2976b5..f5399a9ab2dc 100644
--- a/tools/topology/topology2/development/tplg-targets.cmake
+++ b/tools/topology/topology2/development/tplg-targets.cmake
@@ -60,6 +60,12 @@ NHLT_BIN=nhlt-sof-lnl-nocodec-fpga-4ch.bin,PASSTHROUGH=true,DMIC_IO_CLK=19200000
 # HDA topology with passthrough analog codec pipelines using CHAIN_DMA
 "sof-hda-generic\;sof-hda-passthrough-chain-dma\;HDA_CONFIG=passthrough,CODEC_HDA_CHAIN_DMA=true"
 
+# HDA generic + KPB-based Wake-on-Voice (TFLM) capture branch.
+# Adds a WoV drain PCM and an MFCC/TFLM detect PCM tapped off the
+# Analog capture endpoint (module-copier.4.2). Applies to all IPC4
+# HDA platforms; no NHLT differentiation.
+"sof-hda-generic\;sof-hda-generic-tflm-kpb\;HDA_CONFIG=mix,HDA_MIC_TFLM_KPB_CAPTURE=true"
+
 # SSP topology for PTL, includes Data Processing SRC
 "cavs-nocodec\;sof-ptl-nocodec\;PLATFORM=ptl,NUM_DMICS=4,PDM1_MIC_A_ENABLE=1,PDM1_MIC_B_ENABLE=1,\
 PREPROCESS_PLUGINS=nhlt,NHLT_BIN=nhlt-sof-ptl-nocodec.bin,SRC_DOMAIN=DP"
@@ -497,6 +503,15 @@ MFCC_FRAME_BYTES=344,MFCC_BLOB=mel"
 HDMI1_ID=4,HDMI2_ID=5,HDMI3_ID=6,SDW_JACK_COMPR_AUDIO_FEATURE_CAPTURE=true,\
 MFCC_FRAME_BYTES=76,MFCC_BLOB=ceps"
 
+# Soundwire topologies with TFLM/KPB Wake-on-Voice on jack
+"cavs-sdw\;sof-mtl-rt713-l0-rt1316-l12-tflm-kpb\;PLATFORM=mtl,NUM_SDW_AMP_LINKS=2,\
+HDMI1_ID=4,HDMI2_ID=5,HDMI3_ID=6,SDW_JACK_TFLM_KPB_CAPTURE=true"
+
+"cavs-sdw\;sof-arl-cs42l43-l0-cs35l56-l23-tflm-kpb\;PLATFORM=mtl,NUM_SDW_AMP_LINKS=2,SDW_DMIC=1,\
+SDW_AMP_FEEDBACK=false,SDW_SPK_STREAM=Playback-SmartAmp,SDW_DMIC_STREAM=Capture-SmartMic,\
+SDW_JACK_OUT_STREAM=Playback-SimpleJack,SDW_JACK_IN_STREAM=Capture-SimpleJack,\
+SDW_JACK_TFLM_KPB_CAPTURE=true,SDW_DMIC_TFLM_KPB_CAPTURE=true"
+
 "cavs-sdw\;sof-arl-cs42l43-l0-cs35l56-l23-mfcc-mel-compr\;PLATFORM=mtl,NUM_SDW_AMP_LINKS=2,SDW_DMIC=1,\
 SDW_AMP_FEEDBACK=false,SDW_SPK_STREAM=Playback-SmartAmp,SDW_DMIC_STREAM=Capture-SmartMic,\
 SDW_JACK_OUT_STREAM=Playback-SimpleJack,SDW_JACK_IN_STREAM=Capture-SimpleJack,\
diff --git a/tools/topology/topology2/include/bench/mfcc_controls_capture.conf b/tools/topology/topology2/include/bench/mfcc_controls_capture.conf
index 8788387ec8c7..8a77e1254a12 100644
--- a/tools/topology/topology2/include/bench/mfcc_controls_capture.conf
+++ b/tools/topology/topology2/include/bench/mfcc_controls_capture.conf
@@ -6,6 +6,7 @@
 					name '$ANALOG_CAPTURE_PCM MFCC bytes'
 					IncludeByKey.BENCH_MFCC_PARAMS {
 						"default" "include/components/mfcc/default.conf"
+						"mel40" "include/components/mfcc/mel40.conf"
 						"mel80" "include/components/mfcc/mel80.conf"
 					}
 				}
diff --git a/tools/topology/topology2/include/bench/mfcc_controls_playback.conf b/tools/topology/topology2/include/bench/mfcc_controls_playback.conf
index 007dbb91cd4f..f9177636ab65 100644
--- a/tools/topology/topology2/include/bench/mfcc_controls_playback.conf
+++ b/tools/topology/topology2/include/bench/mfcc_controls_playback.conf
@@ -6,6 +6,7 @@
 					name '$ANALOG_PLAYBACK_PCM MFCC bytes'
 					IncludeByKey.BENCH_MFCC_PARAMS {
 						"default" "include/components/mfcc/default.conf"
+						"mel40" "include/components/mfcc/mel40.conf"
 						"mel80" "include/components/mfcc/mel80.conf"
 					}
 				}
diff --git a/tools/topology/topology2/include/common/abi.conf b/tools/topology/topology2/include/common/abi.conf
new file mode 100644
index 000000000000..b86157c4f8fa
--- /dev/null
+++ b/tools/topology/topology2/include/common/abi.conf
@@ -0,0 +1,7 @@
+Object.Base.manifest.1 {
+	name "sof_manifest"
+	Object.Base.data.1 {
+		name "SOF ABI"
+		bytes	"0x03,0x00,0x1d,0x00,0x01,0x00"
+	}
+}
diff --git a/tools/topology/topology2/include/common/common_definitions.conf b/tools/topology/topology2/include/common/common_definitions.conf
index 06f0f425c5e2..962ba2ee1ef8 100644
--- a/tools/topology/topology2/include/common/common_definitions.conf
+++ b/tools/topology/topology2/include/common/common_definitions.conf
@@ -73,6 +73,9 @@ Define {
 	SDW_SPK_ECHO_REF			false # No echo reference for speaker
 	SDW_JACK_AUDIO_FEATURE_CAPTURE		false # No audio features capture for jack
 	SDW_JACK_COMPR_AUDIO_FEATURE_CAPTURE	false # No compress audio features capture for jack
+	SDW_JACK_TFLM_KPB_CAPTURE		false # No TFLM/KPB Wake-on-Voice branch on jack
 	SDW_DMIC_AUDIO_FEATURE_CAPTURE		false # No audio features capture for microphone
 	SDW_DMIC_COMPR_AUDIO_FEATURE_CAPTURE	false # No compress audio features capture for microphone
+	SDW_DMIC_TFLM_KPB_CAPTURE		false # No TFLM/KPB Wake-on-Voice branch on microphone
+	HDA_MIC_TFLM_KPB_CAPTURE		false # No TFLM/KPB Wake-on-Voice branch on HDA analog capture
 }
diff --git a/tools/topology/topology2/include/components/host-copier.conf b/tools/topology/topology2/include/components/host-copier.conf
index 73911ef6e128..896ab30bb1a2 100644
--- a/tools/topology/topology2/include/components/host-copier.conf
+++ b/tools/topology/topology2/include/components/host-copier.conf
@@ -83,6 +83,18 @@ Class.Widget."host-copier" {
 		token_ref	"copier.word"
 	}
 
+	#
+	# D0I3 compatibility flags for streams. When set, the DSP can
+	# remain in D0i3 during S0ix while this stream is active.
+	#
+	DefineAttribute."playback_compatible_d0i3" {
+		token_ref	"stream.bool"
+	}
+
+	DefineAttribute."capture_compatible_d0i3" {
+		token_ref	"stream.bool"
+	}
+
 	attributes {
 		#
 		# The host copier widget name would be constructed using the copier pcm_id and
diff --git a/tools/topology/topology2/include/components/kpb.conf b/tools/topology/topology2/include/components/kpb.conf
index 03f7989be8ac..2605589786ac 100644
--- a/tools/topology/topology2/include/components/kpb.conf
+++ b/tools/topology/topology2/include/components/kpb.conf
@@ -67,8 +67,8 @@ Class.Widget."kpb" {
 	num_input_audio_formats 1
 	num_output_audio_formats 1
 
-	#UUID: D8218443-5FF3-4A4C-B388-6CFE07B9562E
-	uuid		"43:84:21:d8:f3:5f:4c:4a:b3:88:6c:fe:07:b9:56:2e"
+	#UUID: A8A0CB32-4A77-4DB1-85C7-53D7EE07BCE6
+	uuid		"32:cb:a0:a8:77:4a:b1:4d:85:c7:53:d7:ee:07:bc:e6"
 	no_pm 		"true"
 	cpc 720000
 	num_input_pins		1
diff --git a/tools/topology/topology2/include/components/mfcc/ceps13_compress_dtx.conf b/tools/topology/topology2/include/components/mfcc/ceps13_compress_dtx.conf
index 7056b9e7cb4b..9423b6ae41a1 100644
--- a/tools/topology/topology2/include/components/mfcc/ceps13_compress_dtx.conf
+++ b/tools/topology/topology2/include/components/mfcc/ceps13_compress_dtx.conf
@@ -1,4 +1,4 @@
-# Exported MFCC configuration 26-May-2026
+# Exported MFCC configuration 16-Aug-2026
 # cd src/audio/mfcc/tune; octave setup_mfcc.m
 Object.Base.data."mfcc_config" {
 	bytes "
diff --git a/tools/topology/topology2/include/components/mfcc/default.conf b/tools/topology/topology2/include/components/mfcc/default.conf
index 0ac19fa71d04..101c979ee1b4 100644
--- a/tools/topology/topology2/include/components/mfcc/default.conf
+++ b/tools/topology/topology2/include/components/mfcc/default.conf
@@ -1,4 +1,4 @@
-# Exported MFCC configuration 26-May-2026
+# Exported MFCC configuration 16-Aug-2026
 # cd src/audio/mfcc/tune; octave setup_mfcc.m
 Object.Base.data."mfcc_config" {
 	bytes "
diff --git a/tools/topology/topology2/include/components/mfcc/mel40.conf b/tools/topology/topology2/include/components/mfcc/mel40.conf
new file mode 100644
index 000000000000..801ce2dde52b
--- /dev/null
+++ b/tools/topology/topology2/include/components/mfcc/mel40.conf
@@ -0,0 +1,28 @@
+<<<<<<< HEAD
+# Exported MFCC configuration 16-Aug-2026
+=======
+# Exported MFCC configuration 01-Aug-2026
+>>>>>>> f3e23ebb6 (audio: mfcc: tune: emit mel40 and mel40_compress configs)
+# cd src/audio/mfcc/tune; octave setup_mfcc.m
+Object.Base.data."mfcc_config" {
+	bytes "
+		0x53,0x4f,0x46,0x34,0x00,0x00,0x00,0x00,
+		0x74,0x00,0x00,0x00,0x01,0xd0,0x01,0x03,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x74,0x00,0x00,0x00,0x00,0x02,0x00,0x04,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x80,0x3e,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+		0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x03,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0xe0,0x01,0x40,0x01,0x40,0x1f,0x00,0x00,
+		0x00,0x00,0x28,0x00,0x00,0x00,0x00,0x04,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x01,
+		0x01,0x00,0x00,0x01,0x01,0x00,0x01,0x00,
+		0x00,0x00,0x00,0x00"
+}
diff --git a/tools/topology/topology2/include/components/mfcc/mel40_10ms.conf b/tools/topology/topology2/include/components/mfcc/mel40_10ms.conf
new file mode 100644
index 000000000000..6a1ec0dd7852
--- /dev/null
+++ b/tools/topology/topology2/include/components/mfcc/mel40_10ms.conf
@@ -0,0 +1,24 @@
+# Exported MFCC configuration 16-Aug-2026
+# cd src/audio/mfcc/tune; octave setup_mfcc.m
+Object.Base.data."mfcc_config" {
+	bytes "
+		0x53,0x4f,0x46,0x34,0x00,0x00,0x00,0x00,
+		0x74,0x00,0x00,0x00,0x01,0xd0,0x01,0x03,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x74,0x00,0x00,0x00,0x00,0x02,0x00,0x04,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x80,0x3e,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+		0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x03,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0xe0,0x01,0xa0,0x00,0x40,0x1f,0x00,0x00,
+		0x00,0x00,0x28,0x00,0x00,0x00,0x00,0x04,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x01,
+		0x01,0x00,0x00,0x01,0x01,0x00,0x01,0x00,
+		0x00,0x00,0x00,0x00"
+}
diff --git a/tools/topology/topology2/include/components/mfcc/mel40_compress.conf b/tools/topology/topology2/include/components/mfcc/mel40_compress.conf
new file mode 100644
index 000000000000..727c7364a0fe
--- /dev/null
+++ b/tools/topology/topology2/include/components/mfcc/mel40_compress.conf
@@ -0,0 +1,28 @@
+<<<<<<< HEAD
+# Exported MFCC configuration (compress_output enabled) 04-Aug-2026
+=======
+# Exported MFCC configuration 12-Aug-2026
+>>>>>>> f3e23ebb6 (audio: mfcc: tune: emit mel40 and mel40_compress configs)
+# cd src/audio/mfcc/tune; octave setup_mfcc.m
+Object.Base.data."mfcc_config" {
+	bytes "
+		0x53,0x4f,0x46,0x34,0x00,0x00,0x00,0x00,
+		0x74,0x00,0x00,0x00,0x01,0xd0,0x01,0x03,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x74,0x00,0x00,0x00,0x00,0x02,0x00,0x04,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x80,0x3e,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+		0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x03,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0xe0,0x01,0x40,0x01,0x40,0x1f,0x00,0x00,
+		0x00,0x00,0x28,0x00,0x00,0x00,0x00,0x04,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+		0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x01,
+		0x01,0x00,0x00,0x01,0x01,0x00,0x01,0x01,
+		0x00,0x00,0x00,0x00"
+}
diff --git a/tools/topology/topology2/include/components/mfcc/mel80.conf b/tools/topology/topology2/include/components/mfcc/mel80.conf
index b18baadd459b..a141635cf815 100644
--- a/tools/topology/topology2/include/components/mfcc/mel80.conf
+++ b/tools/topology/topology2/include/components/mfcc/mel80.conf
@@ -1,4 +1,4 @@
-# Exported MFCC configuration 26-May-2026
+# Exported MFCC configuration 16-Aug-2026
 # cd src/audio/mfcc/tune; octave setup_mfcc.m
 Object.Base.data."mfcc_config" {
 	bytes "
diff --git a/tools/topology/topology2/include/components/mfcc/mel80_compress.conf b/tools/topology/topology2/include/components/mfcc/mel80_compress.conf
index f26f2af6980c..8ce23f310b3a 100644
--- a/tools/topology/topology2/include/components/mfcc/mel80_compress.conf
+++ b/tools/topology/topology2/include/components/mfcc/mel80_compress.conf
@@ -1,4 +1,4 @@
-# Exported MFCC configuration 26-May-2026
+# Exported MFCC configuration 16-Aug-2026
 # cd src/audio/mfcc/tune; octave setup_mfcc.m
 Object.Base.data."mfcc_config" {
 	bytes "
diff --git a/tools/topology/topology2/include/components/mfcc/mel80_compress_dtx.conf b/tools/topology/topology2/include/components/mfcc/mel80_compress_dtx.conf
index d225811ca4d1..053ed14008d6 100644
--- a/tools/topology/topology2/include/components/mfcc/mel80_compress_dtx.conf
+++ b/tools/topology/topology2/include/components/mfcc/mel80_compress_dtx.conf
@@ -1,4 +1,4 @@
-# Exported MFCC configuration 26-May-2026
+# Exported MFCC configuration 16-Aug-2026
 # cd src/audio/mfcc/tune; octave setup_mfcc.m
 Object.Base.data."mfcc_config" {
 	bytes "
diff --git a/tools/topology/topology2/include/components/mww.conf b/tools/topology/topology2/include/components/mww.conf
new file mode 100644
index 000000000000..ad5b1216f9c5
--- /dev/null
+++ b/tools/topology/topology2/include/components/mww.conf
@@ -0,0 +1,54 @@
+#
+# A microWakeWord keyword-spotting component for SOF. All attributes defined
+# herein are namespaced by alsatplg to "Object.Widget.mww.attribute_name"
+#
+
+Class.Widget."mww" {
+	#
+	# Pipeline ID
+	#
+	DefineAttribute."index" {
+		type "integer"
+	}
+
+	#
+	# Unique instance for MWW widget
+	#
+	DefineAttribute."instance" {
+		type "integer"
+	}
+
+	# Include common widget attributes definition
+	<include/components/widget-common.conf>
+
+	attributes {
+		!constructor [
+			"index"
+			"instance"
+		]
+		!mandatory [
+			"num_input_pins"
+			"num_output_pins"
+			"num_input_audio_formats"
+			"num_output_audio_formats"
+		]
+
+		!immutable [
+			"uuid"
+		]
+		!deprecated [
+			"preload_count"
+		]
+		unique	"instance"
+	}
+
+	#
+	# Default attributes for mww
+	# UUID: 4067fe1d-cd63-4877-966b-a06ded1719ce
+	#
+	uuid			"1d:fe:67:40:63:cd:77:48:96:6b:a0:6d:ed:17:19:ce"
+	type			"effect"
+	no_pm			"true"
+	num_input_pins		1
+	num_output_pins		1
+}
diff --git a/tools/topology/topology2/include/components/tflm.conf b/tools/topology/topology2/include/components/tflm.conf
new file mode 100644
index 000000000000..6144b2ce91de
--- /dev/null
+++ b/tools/topology/topology2/include/components/tflm.conf
@@ -0,0 +1,54 @@
+#
+# A TFLM Classification component for SOF. All attributes defined herein are namespaced
+# by alsatplg to "Object.Widget.tflmcly.attribute_name"
+#
+
+Class.Widget."tflmcly" {
+	#
+	# Pipeline ID
+	#
+	DefineAttribute."index" {
+		type "integer"
+	}
+
+	#
+	# Unique instance for TFLM widget
+	#
+	DefineAttribute."instance" {
+		type "integer"
+	}
+
+	# Include common widget attributes definition
+	<include/components/widget-common.conf>
+
+	attributes {
+		!constructor [
+			"index"
+			"instance"
+		]
+		!mandatory [
+			"num_input_pins"
+			"num_output_pins"
+			"num_input_audio_formats"
+			"num_output_audio_formats"
+		]
+
+		!immutable [
+			"uuid"
+		]
+		!deprecated [
+			"preload_count"
+		]
+		unique	"instance"
+	}
+
+	#
+	# Default attributes for tflmcly
+	# UUID: c51dc642-a2e1-48df-a490e2748cb6363e
+	#
+	uuid			"42:c6:1d:c5:e1:a2:df:48:a4:90:e2:74:8c:b6:36:3e"
+	type			"effect"
+	no_pm			"true"
+	num_input_pins		1
+	num_output_pins		1
+}
diff --git a/tools/topology/topology2/include/pipelines/cavs/host-gateway-micsel-mfcc-tflm-capture.conf b/tools/topology/topology2/include/pipelines/cavs/host-gateway-micsel-mfcc-tflm-capture.conf
new file mode 100644
index 000000000000..14253a70d371
--- /dev/null
+++ b/tools/topology/topology2/include/pipelines/cavs/host-gateway-micsel-mfcc-tflm-capture.conf
@@ -0,0 +1,174 @@
+#
+# Micsel-MFCC-TFLM capture detection pipeline
+#
+# This class provides a detection pipeline that first uses micsel to convert
+# a 16 kHz stereo capture stream into a stereo double-mono stream (both
+# channels carrying the same audio content), then feeds it into MFCC for
+# feature extraction and TFLM for classification. The final compressed
+# frames are delivered to the host over a stereo double-mono aif_out so
+# the topology remains compatible with capture PCMs that require stereo.
+#
+
+<include/common/input_audio_format.conf>
+<include/common/output_audio_format.conf>
+<include/components/pipeline.conf>
+<include/components/micsel.conf>
+<include/components/mfcc.conf>
+<include/components/tflm.conf>
+<include/components/host-copier.conf>
+
+Define {
+	# 20 ms FFT hop at 16 kHz, 2 ch, s32: 320 samples * 2 * 4 = 2560 B.
+	MFCC_INPUT_BYTES 2560
+	# MFCC compress output frame: 24-byte header + 40 mel bins * 4 bytes.
+	MFCC_FRAME_BYTES 184
+	# TFLM expects one MFCC frame every 20 ms; wake it per-frame so it
+	# stays in lock-step with MFCC instead of batching.
+	TFLM_INPUT_BYTES 184
+}
+
+Class.Pipeline."host-gateway-micsel-mfcc-tflm-capture" {
+
+	<include/pipelines/pipeline-common.conf>
+
+	attributes {
+		!constructor [
+			"index"
+		]
+
+		unique	"instance"
+	}
+
+	Object.Widget {
+		host-copier."1" {
+			type		"aif_out"
+			node_type	$HDA_HOST_INPUT_CLASS
+			stream_name	"HDA Mic TFLM Detect"
+			pcm_id		$index
+			num_input_pins	1
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+				}
+			]
+		}
+
+		micsel."1" {
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+					out_ch_cfg	$CHANNEL_CONFIG_DUAL_MONO
+				}
+			]
+		}
+
+		mfcc."1" {
+			scheduler_domain "DP"
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+					ibs			$MFCC_INPUT_BYTES
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+					obs			$MFCC_FRAME_BYTES
+				}
+			]
+		}
+
+		# tflmcly is an audio pass-through: it copies each MFCC frame
+		# from input to output unchanged and sends classification
+		# results out-of-band via IPC. ibs/obs are one MFCC frame so
+		# the DP thread wakes per 20 ms hop and stays in lock-step
+		# with MFCC. tflm_process runs an inference roughly once per
+		# 32 frames internally; between inferences it just forwards
+		# the incoming MFCC frame.
+		tflmcly."1" {
+			scheduler_domain "DP"
+			num_input_pins	1
+			num_output_pins	1
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+					ibs			$TFLM_INPUT_BYTES
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+					obs			$TFLM_INPUT_BYTES
+				}
+			]
+		}
+
+		pipeline."1" {
+			priority	0
+			lp_mode		0
+		}
+	}
+
+	Object.Base {
+		!route [
+			{
+				source	micsel.$index.1
+				sink	mfcc.$index.1
+			}
+			{
+				source	mfcc.$index.1
+				sink	tflmcly.$index.1
+			}
+			{
+				source	tflmcly.$index.1
+				sink	"host-copier.$index.capture"
+			}
+		]
+	}
+
+	direction	"capture"
+	dynamic_pipeline 1
+	time_domain	"timer"
+}
diff --git a/tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-mww-capture.conf b/tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-mww-capture.conf
new file mode 100644
index 000000000000..411b7a761c70
--- /dev/null
+++ b/tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-mww-capture.conf
@@ -0,0 +1,188 @@
+#
+# SRC-MFCC-MWW capture detection pipeline
+#
+# This class provides detection pipeline for capture with MFCC feature extraction
+# fed to the microWakeWord (mww) TFLM keyword-spotting module.
+#
+# Uses mel40_10ms.conf, matching microWakeWord's native MixConv front-end
+# shape: 40 mel-log features per 10ms stride over a 30ms window (see plan
+# Stage 5). Stage 2's go/no-go hardware validation used the interim
+# mel40_compress.conf (20ms hop) profile before this blob existed.
+#
+
+<include/common/input_audio_format.conf>
+<include/common/output_audio_format.conf>
+<include/components/pipeline.conf>
+<include/components/src.conf>
+<include/components/mfcc.conf>
+<include/components/mww.conf>
+<include/components/host-copier.conf>
+
+Define {
+	MFCC_FRAME_BYTES 184
+}
+
+Class.Pipeline."host-gateway-src-mfcc-mww-capture" {
+
+	<include/pipelines/pipeline-common.conf>
+
+	attributes {
+		!constructor [
+			"index"
+		]
+
+		unique	"instance"
+	}
+
+	Object.Widget {
+		host-copier."1" {
+			type		"aif_out"
+			node_type	$HDA_HOST_INPUT_CLASS
+			stream_name	"HDA Mic MWW Detect"
+			pcm_id		$index
+			num_input_pins	1
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+				out_channels		1
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+				}
+			]
+		}
+
+		src."1" {
+			num_input_pins 1
+			num_output_pins 1
+			num_input_audio_formats 4
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			48000
+				}
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			96000
+				}
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			192000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+				out_channels		1
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+				}
+			]
+		}
+
+		mfcc."1" {
+			Object.Control {
+				bytes."1" {
+					name "HDA Mic MWW MFCC bytes"
+					IncludeByKey.HDA_MIC_MFCC_PARAMS {
+						"default" "include/components/mfcc/mel40_10ms.conf"
+					}
+				}
+				mixer."1" {
+					name "HDA Mic MWW MFCC VAD"
+				}
+			}
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+				out_channels		1
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+					obs			$MFCC_FRAME_BYTES
+				}
+			]
+		}
+
+		mww."1" {
+			scheduler_domain "DP"
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+					ibs			$MFCC_FRAME_BYTES
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+				out_channels		1
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+					obs			$MFCC_FRAME_BYTES
+				}
+			]
+		}
+
+		pipeline."1" {
+			priority	0
+			lp_mode		0
+		}
+	}
+
+	Object.Base {
+		!route [
+			{
+				source	src.$index.1
+				sink	mfcc.$index.1
+			}
+			{
+				source	mfcc.$index.1
+				sink	mww.$index.1
+			}
+			{
+				source	mww.$index.1
+				sink	"host-copier.$index.capture"
+			}
+		]
+	}
+
+	direction	"capture"
+	dynamic_pipeline 1
+	time_domain	"timer"
+}
diff --git a/tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-tflm-capture.conf b/tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-tflm-capture.conf
new file mode 100644
index 000000000000..c6a24d476427
--- /dev/null
+++ b/tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-tflm-capture.conf
@@ -0,0 +1,183 @@
+#
+# SRC-MFCC-TFLM capture detection pipeline
+#
+# This class provides detection pipeline for capture with MFCC feature extraction
+# fed to TFLM classification micro speech module.
+#
+
+<include/common/input_audio_format.conf>
+<include/common/output_audio_format.conf>
+<include/components/pipeline.conf>
+<include/components/src.conf>
+<include/components/mfcc.conf>
+<include/components/tflm.conf>
+<include/components/host-copier.conf>
+
+Define {
+	MFCC_FRAME_BYTES 184
+}
+
+Class.Pipeline."host-gateway-src-mfcc-tflm-capture" {
+
+	<include/pipelines/pipeline-common.conf>
+
+	attributes {
+		!constructor [
+			"index"
+		]
+
+		unique	"instance"
+	}
+
+	Object.Widget {
+		host-copier."1" {
+			type		"aif_out"
+			node_type	$HDA_HOST_INPUT_CLASS
+			stream_name	"HDA Mic TFLM Detect"
+			pcm_id		$index
+			num_input_pins	1
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+				out_channels		1
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+				}
+			]
+		}
+
+		src."1" {
+			num_input_pins 1
+			num_output_pins 1
+			num_input_audio_formats 4
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			48000
+				}
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			96000
+				}
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			192000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+				out_channels		1
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+				}
+			]
+		}
+
+		mfcc."1" {
+			Object.Control {
+				bytes."1" {
+					name "HDA Mic MFCC bytes"
+					IncludeByKey.HDA_MIC_MFCC_PARAMS {
+						"default" "include/components/mfcc/mel40_compress.conf"
+					}
+				}
+				mixer."1" {
+					name "HDA Mic MFCC VAD"
+				}
+			}
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+				out_channels		1
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+					obs			$MFCC_FRAME_BYTES
+				}
+			]
+		}
+
+		tflmcly."1" {
+			scheduler_domain "DP"
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+				in_channels		1
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+					ibs			$MFCC_FRAME_BYTES
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+				out_channels		1
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+					obs			$MFCC_FRAME_BYTES
+				}
+			]
+		}
+
+		pipeline."1" {
+			priority	0
+			lp_mode		0
+		}
+	}
+
+	Object.Base {
+		!route [
+			{
+				source	src.$index.1
+				sink	mfcc.$index.1
+			}
+			{
+				source	mfcc.$index.1
+				sink	tflmcly.$index.1
+			}
+			{
+				source	tflmcly.$index.1
+				sink	"host-copier.$index.capture"
+			}
+		]
+	}
+
+	direction	"capture"
+	dynamic_pipeline 1
+	time_domain	"timer"
+}
diff --git a/tools/topology/topology2/include/pipelines/cavs/host-gateway-tdfb-drc-capture.conf b/tools/topology/topology2/include/pipelines/cavs/host-gateway-tdfb-drc-capture.conf
index 752c421535c5..a57678873a1c 100644
--- a/tools/topology/topology2/include/pipelines/cavs/host-gateway-tdfb-drc-capture.conf
+++ b/tools/topology/topology2/include/pipelines/cavs/host-gateway-tdfb-drc-capture.conf
@@ -67,6 +67,7 @@ Class.Pipeline."host-gateway-tdfb-drc-capture" {
                                 }
 
                                 drc."1" {
+                                        num_output_pins 2
                                         Object.Control {
                                                 bytes."1" {
                                                         IncludeByKey.EFX_MIC_DRC_PARAMS {
diff --git a/tools/topology/topology2/include/pipelines/cavs/src-kpb-be.conf b/tools/topology/topology2/include/pipelines/cavs/src-kpb-be.conf
new file mode 100644
index 000000000000..8bbe78bb9b0c
--- /dev/null
+++ b/tools/topology/topology2/include/pipelines/cavs/src-kpb-be.conf
@@ -0,0 +1,115 @@
+#
+# SRC-KPB backend pipeline
+#
+# This class provides a pipeline that converts a DAI-side capture stream
+# down to 16 kHz stereo via SRC and feeds it into a KPB widget for
+# key-phrase buffering with dual sinks (WoV drain and detection paths).
+#
+# Usage:
+#
+# Object.Pipeline.src-kpb-be."N" {
+#	period		1000
+#	time_domain	"timer"
+# }
+#
+# Where N is the unique pipeline ID within the same alsaconf node.
+#
+
+<include/common/input_audio_format.conf>
+<include/common/output_audio_format.conf>
+<include/components/pipeline.conf>
+<include/components/src.conf>
+<include/components/kpb.conf>
+
+Class.Pipeline."src-kpb-be" {
+
+	<include/pipelines/pipeline-common.conf>
+
+	attributes {
+		!constructor [
+			"index"
+		]
+
+		#
+		# src-kpb-be objects instantiated within the same alsaconf node
+		# must have unique pipeline_id attribute.
+		#
+		unique	"instance"
+	}
+
+	Object.Widget {
+		src."1" {
+			num_input_pins 1
+			num_output_pins 1
+			num_input_audio_formats 3
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			48000
+				}
+				{
+					in_channels		2
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			96000
+				}
+				{
+					in_channels		2
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			192000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+				}
+			]
+		}
+
+		kpb."1" {
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+				}
+			]
+		}
+
+		pipeline."1" {
+			priority	0
+			lp_mode		0
+		}
+	}
+
+	Object.Base {
+		!route [
+			{
+				source	src.$index.1
+				sink	kpb.$index.1
+			}
+		]
+	}
+
+	direction	"capture"
+	dynamic_pipeline 1
+	time_domain	"timer"
+}
diff --git a/tools/topology/topology2/platform/intel/dmic1-mfcc.conf b/tools/topology/topology2/platform/intel/dmic1-mfcc.conf
index 3aad756a85f5..d7ea66199e10 100644
--- a/tools/topology/topology2/platform/intel/dmic1-mfcc.conf
+++ b/tools/topology/topology2/platform/intel/dmic1-mfcc.conf
@@ -1,4 +1,6 @@
-<include/components/mfcc.conf>
+# mfcc widget class is already included at the top of the parent
+# sof-hda-generic.conf, so no need to pull include/components/mfcc.conf
+# here again.
 
 Define {
        DMIC1_PCM_NAME	"DMIC MFCC"
diff --git a/tools/topology/topology2/platform/intel/hda-mic-tflm-kpb.conf b/tools/topology/topology2/platform/intel/hda-mic-tflm-kpb.conf
new file mode 100644
index 000000000000..9e5f64481b04
--- /dev/null
+++ b/tools/topology/topology2/platform/intel/hda-mic-tflm-kpb.conf
@@ -0,0 +1,161 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# HDA analog capture TFLM/KPB Wake-on-Voice branch.
+#
+# Layered on top of sof-hda-generic.conf via
+# IncludeByKey.HDA_MIC_TFLM_KPB_CAPTURE. Taps the Analog Capture DAI
+# endpoint (module-copier.4.2) into:
+#
+#   SRC (48 -> 16 kHz stereo) -> KPB (16 kHz stereo, dual sinks)
+#     pin 0 -> micsel -> MFCC (40 mel compress) -> TFLM classifier
+#              -> host-copier: "HDA Mic TFLM Detect" PCM
+#     pin 1 -> host-copier: "HDA Mic WoV Capture"  PCM
+#
+# KPB is used in a Linux-compatible configuration (ibs == obs, no
+# micselector mode); the topology-side micsel widget performs channel
+# reduction on the detection path. The TFLM Detect PCM is exposed as
+# stereo double-mono to work around HDA capture's mono restriction.
+#
+# KPB output_pin_binding names must match kernel widget names (see
+# sof_ipc4_get_queue_id): host-copier.<pcm_id>.<direction> and
+# <widget>.<index>.<queue>.
+
+Define {
+	HDA_TFLM_KPB_PIPELINE_ID	40
+	HDA_TFLM_WOV_PIPELINE_ID	41
+	HDA_TFLM_WOV_PCM_ID		41
+	HDA_TFLM_DETECT_PIPELINE_ID	42
+	HDA_TFLM_DETECT_PCM_ID		42
+}
+
+Object.Pipeline.src-kpb-be [
+	{
+		index $HDA_TFLM_KPB_PIPELINE_ID
+
+		# KPB requires pin 0 (binding.1) to feed the detect/selector sink
+		# and pin 1+ (binding.2) to feed the host drain; see kpb_bind().
+		Object.Widget.kpb.1 {
+			Object.Base.output_pin_binding.1 {
+				output_pin_binding_name "micsel.$HDA_TFLM_DETECT_PIPELINE_ID.1"
+			}
+			Object.Base.output_pin_binding.2 {
+				output_pin_binding_name "host-copier.$HDA_TFLM_WOV_PCM_ID.capture"
+			}
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-capture [
+	{
+		index $HDA_TFLM_WOV_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "HDA Mic WoV Capture"
+			pcm_id $HDA_TFLM_WOV_PCM_ID
+			capture_compatible_d0i3 1
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_rate			16000
+					in_bit_depth		32
+					in_valid_bit_depth	32
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_rate		16000
+					out_bit_depth		32
+					out_valid_bit_depth	32
+				}
+			]
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-micsel-mfcc-tflm-capture [
+	{
+		index $HDA_TFLM_DETECT_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "HDA Mic TFLM Detect"
+			pcm_id $HDA_TFLM_DETECT_PCM_ID
+			capture_compatible_d0i3 1
+		}
+
+		Object.Widget.micsel.1 {
+			Object.Control {
+				bytes."1" {
+					name "HDA Mic TFLM Micsel bytes"
+					<include/components/micsel/downmix_stereo_to_doublemono.conf>
+				}
+			}
+		}
+
+		Object.Widget.mfcc.1 {
+			Object.Control {
+				bytes."1" {
+					name "HDA Mic TFLM MFCC bytes"
+					<include/components/mfcc/mel40_compress.conf>
+				}
+				mixer."1" {
+					name "HDA Mic TFLM MFCC VAD"
+				}
+			}
+		}
+	}
+]
+
+Object.Base.route [
+	{
+		source "module-copier.4.2"
+		sink "src.$HDA_TFLM_KPB_PIPELINE_ID.1"
+	}
+	{
+		source "kpb.$HDA_TFLM_KPB_PIPELINE_ID.1"
+		sink "host-copier.$HDA_TFLM_WOV_PCM_ID.capture"
+	}
+	{
+		source "kpb.$HDA_TFLM_KPB_PIPELINE_ID.1"
+		sink "micsel.$HDA_TFLM_DETECT_PIPELINE_ID.1"
+	}
+]
+
+Object.PCM.pcm [
+	{
+		name "HDA Mic WoV Capture"
+		id $HDA_TFLM_WOV_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "HDA Mic WoV Capture"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "HDA Mic WoV Capture"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+	{
+		name "HDA Mic TFLM Detect"
+		id $HDA_TFLM_DETECT_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "HDA Mic TFLM Detect"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "HDA Mic TFLM Detect"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+]
diff --git a/tools/topology/topology2/platform/intel/sdw-dmic-audio-feature.conf b/tools/topology/topology2/platform/intel/sdw-dmic-audio-feature.conf
index fdb4acbf9a6d..b33c62c33f9d 100644
--- a/tools/topology/topology2/platform/intel/sdw-dmic-audio-feature.conf
+++ b/tools/topology/topology2/platform/intel/sdw-dmic-audio-feature.conf
@@ -1,3 +1,7 @@
+<include/components/tflm.conf>
+# Class.Widget.tflmcly is pulled in at cavs-sdw.conf top level via
+# host-gateway-micsel-mfcc-tflm-capture.conf.
+
 Define {
 	SDW_DMIC_MODULE_COPIER_ID 41
 	SDW_DMIC_AUDIO_FEATURE_CAPTURE_PCM_NAME "Microphone Audio Features"
@@ -38,6 +42,27 @@ Object.Pipeline.host-gateway-src-micsel-mfcc-capture [
 				}
 			}
 		}
+
+		Object.Widget.tflmcly.1 {
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_bit_depth		32
+					in_valid_bit_depth	32
+					in_rate			16000
+					ibs			$MFCC_FRAME_BYTES
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_bit_depth		32
+					out_valid_bit_depth	32
+					out_rate		16000
+					obs			$MFCC_FRAME_BYTES
+				}
+			]
+		}
 	}
 ]
 Object.Base.route [
@@ -47,6 +72,10 @@ Object.Base.route [
 	}
 	{
 		source "mfcc.$SDW_DMIC_AUDIO_FEATURE_CAPTURE_PIPELINE_ID.1"
+		sink "tflmcly.$SDW_DMIC_AUDIO_FEATURE_CAPTURE_PIPELINE_ID.1"
+	}
+	{
+		source "tflmcly.$SDW_DMIC_AUDIO_FEATURE_CAPTURE_PIPELINE_ID.1"
 		sink "host-copier.$SDW_DMIC_AUDIO_FEATURE_CAPTURE_PCM_ID.capture"
 	}
 ]
diff --git a/tools/topology/topology2/platform/intel/sdw-dmic-tflm-kpb.conf b/tools/topology/topology2/platform/intel/sdw-dmic-tflm-kpb.conf
new file mode 100644
index 000000000000..14d56974a89f
--- /dev/null
+++ b/tools/topology/topology2/platform/intel/sdw-dmic-tflm-kpb.conf
@@ -0,0 +1,162 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# SoundWire DMIC TFLM/KPB Wake-on-Voice capture branch.
+#
+# Layered on top of cavs-sdw.conf (SDW jack + amps + HDMI + SDW DMIC)
+# via IncludeByKey.SDW_DMIC_TFLM_KPB_CAPTURE. Structural twin of the
+# jack-side sdw-jack-tflm-kpb.conf, but taps the DMIC DAI-side
+# module-copier (module-copier.41.0) into:
+#
+#   SRC (48 -> 16 kHz stereo) -> KPB (16 kHz stereo, dual sinks)
+#     pin 0 -> micsel -> MFCC (40 mel compress) -> TFLM classifier
+#              -> host-copier: "Mic In TFLM Detect" PCM
+#     pin 1 -> host-copier: "Mic In WoV Capture"  PCM
+#
+# KPB output_pin_binding names must match kernel widget names (see
+# sof_ipc4_get_queue_id): host-copier.<pcm_id>.<direction> and
+# <widget>.<index>.<queue>. Detect PCM is stereo double-mono via
+# micsel to remain compatible with HDA-style capture pipelines.
+
+Define {
+	SDW_DMIC_MODULE_COPIER_ID	41
+	SDW_DMIC_TFLM_KPB_PIPELINE_ID	135
+	# host-copier internal routes in host-gateway* pipeline classes
+	# reference host-copier.$index.<direction>, so keep
+	# pcm_id == pipeline_id for the two host-side PCMs.
+	SDW_DMIC_TFLM_WOV_PIPELINE_ID	57
+	SDW_DMIC_TFLM_WOV_PCM_ID	57
+	SDW_DMIC_TFLM_WOV_PCM_NAME	"Mic In WoV Capture"
+	SDW_DMIC_TFLM_WOV_STREAM_NAME	"Mic In WoV Capture Stream"
+	SDW_DMIC_TFLM_DETECT_PIPELINE_ID 58
+	SDW_DMIC_TFLM_DETECT_PCM_ID	58
+	SDW_DMIC_TFLM_DETECT_PCM_NAME	"Mic In TFLM Detect"
+	SDW_DMIC_TFLM_DETECT_STREAM_NAME "Mic In TFLM Detect Stream"
+}
+
+Object.Pipeline.src-kpb-be [
+	{
+		index $SDW_DMIC_TFLM_KPB_PIPELINE_ID
+
+		Object.Widget.kpb.1 {
+			Object.Base.output_pin_binding.1 {
+				output_pin_binding_name "micsel.$SDW_DMIC_TFLM_DETECT_PIPELINE_ID.1"
+			}
+			Object.Base.output_pin_binding.2 {
+				output_pin_binding_name "host-copier.$SDW_DMIC_TFLM_WOV_PCM_ID.capture"
+			}
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-capture [
+	{
+		index $SDW_DMIC_TFLM_WOV_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "$SDW_DMIC_TFLM_WOV_STREAM_NAME"
+			pcm_id $SDW_DMIC_TFLM_WOV_PCM_ID
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_rate			16000
+					in_bit_depth		32
+					in_valid_bit_depth	32
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_rate		16000
+					out_bit_depth		32
+					out_valid_bit_depth	32
+				}
+			]
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-micsel-mfcc-tflm-capture [
+	{
+		index $SDW_DMIC_TFLM_DETECT_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "$SDW_DMIC_TFLM_DETECT_STREAM_NAME"
+			pcm_id $SDW_DMIC_TFLM_DETECT_PCM_ID
+		}
+
+		Object.Widget.micsel.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_DMIC_TFLM_DETECT_PCM_NAME Micsel bytes"
+					<include/components/micsel/downmix_stereo_to_doublemono.conf>
+				}
+			}
+		}
+
+		Object.Widget.mfcc.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_DMIC_TFLM_DETECT_PCM_NAME MFCC bytes"
+					<include/components/mfcc/mel40_compress.conf>
+				}
+				mixer."1" {
+					name "$SDW_DMIC_TFLM_DETECT_PCM_NAME MFCC VAD"
+				}
+			}
+		}
+	}
+]
+
+Object.Base.route [
+	{
+		source "module-copier.$SDW_DMIC_MODULE_COPIER_ID.0"
+		sink "src.$SDW_DMIC_TFLM_KPB_PIPELINE_ID.1"
+	}
+	{
+		source "kpb.$SDW_DMIC_TFLM_KPB_PIPELINE_ID.1"
+		sink "host-copier.$SDW_DMIC_TFLM_WOV_PCM_ID.capture"
+	}
+	{
+		source "kpb.$SDW_DMIC_TFLM_KPB_PIPELINE_ID.1"
+		sink "micsel.$SDW_DMIC_TFLM_DETECT_PIPELINE_ID.1"
+	}
+]
+
+Object.PCM.pcm [
+	{
+		name "$SDW_DMIC_TFLM_WOV_PCM_NAME"
+		id $SDW_DMIC_TFLM_WOV_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "$SDW_DMIC_TFLM_WOV_PCM_NAME"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "$SDW_DMIC_TFLM_WOV_STREAM_NAME"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+	{
+		name "$SDW_DMIC_TFLM_DETECT_PCM_NAME"
+		id $SDW_DMIC_TFLM_DETECT_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "$SDW_DMIC_TFLM_DETECT_PCM_NAME"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "$SDW_DMIC_TFLM_DETECT_STREAM_NAME"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+]
diff --git a/tools/topology/topology2/platform/intel/sdw-jack-tflm-kpb.conf b/tools/topology/topology2/platform/intel/sdw-jack-tflm-kpb.conf
new file mode 100644
index 000000000000..0dc571f442c2
--- /dev/null
+++ b/tools/topology/topology2/platform/intel/sdw-jack-tflm-kpb.conf
@@ -0,0 +1,161 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# SoundWire jack TFLM/KPB Wake-on-Voice capture branch.
+#
+# Layered on top of cavs-sdw.conf (SDW jack + amps + HDMI) via
+# IncludeByKey.SDW_JACK_TFLM_KPB_CAPTURE. Taps the jack DAI-side
+# module-copier (module-copier.11.0) into:
+#
+#   SRC (48 -> 16 kHz stereo) -> KPB (16 kHz stereo, dual sinks)
+#     pin 0 -> micsel -> MFCC (40 mel compress) -> TFLM classifier
+#              -> host-copier: "Jack In TFLM Detect" PCM
+#     pin 1 -> host-copier: "Jack In WoV Capture"  PCM
+#
+# KPB output_pin_binding names must match kernel widget names (see
+# sof_ipc4_get_queue_id): host-copier.<pcm_id>.<direction> and
+# <widget>.<index>.<queue>. Detect PCM is stereo double-mono via
+# micsel to remain compatible with HDA-style capture pipelines.
+
+Define {
+	SDW_JACK_MODULE_COPIER_ID	11
+	SDW_JACK_TFLM_KPB_PIPELINE_ID	134
+	# host-copier internal routes in host-gateway* pipeline classes
+	# reference host-copier.$index.<direction>, so keep
+	# pcm_id == pipeline_id for the two host-side PCMs.
+	SDW_JACK_TFLM_WOV_PIPELINE_ID	55
+	SDW_JACK_TFLM_WOV_PCM_ID	55
+	SDW_JACK_TFLM_WOV_PCM_NAME	"Jack In WoV Capture"
+	SDW_JACK_TFLM_WOV_STREAM_NAME	"Jack In WoV Capture Stream"
+	SDW_JACK_TFLM_DETECT_PIPELINE_ID 56
+	SDW_JACK_TFLM_DETECT_PCM_ID	56
+	SDW_JACK_TFLM_DETECT_PCM_NAME	"Jack In TFLM Detect"
+	SDW_JACK_TFLM_DETECT_STREAM_NAME "Jack In TFLM Detect Stream"
+}
+
+Object.Pipeline.src-kpb-be [
+	{
+		index $SDW_JACK_TFLM_KPB_PIPELINE_ID
+
+		Object.Widget.kpb.1 {
+			Object.Base.output_pin_binding.1 {
+				output_pin_binding_name "micsel.$SDW_JACK_TFLM_DETECT_PIPELINE_ID.1"
+			}
+			Object.Base.output_pin_binding.2 {
+				output_pin_binding_name "host-copier.$SDW_JACK_TFLM_WOV_PCM_ID.capture"
+			}
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-capture [
+	{
+		index $SDW_JACK_TFLM_WOV_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "$SDW_JACK_TFLM_WOV_STREAM_NAME"
+			pcm_id $SDW_JACK_TFLM_WOV_PCM_ID
+			num_input_audio_formats 1
+			num_output_audio_formats 1
+			Object.Base.input_audio_format [
+				{
+					in_channels		2
+					in_rate			16000
+					in_bit_depth		32
+					in_valid_bit_depth	32
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels		2
+					out_rate		16000
+					out_bit_depth		32
+					out_valid_bit_depth	32
+				}
+			]
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-micsel-mfcc-tflm-capture [
+	{
+		index $SDW_JACK_TFLM_DETECT_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "$SDW_JACK_TFLM_DETECT_STREAM_NAME"
+			pcm_id $SDW_JACK_TFLM_DETECT_PCM_ID
+		}
+
+		Object.Widget.micsel.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_JACK_TFLM_DETECT_PCM_NAME Micsel bytes"
+					<include/components/micsel/downmix_stereo_to_doublemono.conf>
+				}
+			}
+		}
+
+		Object.Widget.mfcc.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_JACK_TFLM_DETECT_PCM_NAME MFCC bytes"
+					<include/components/mfcc/mel40_compress.conf>
+				}
+				mixer."1" {
+					name "$SDW_JACK_TFLM_DETECT_PCM_NAME MFCC VAD"
+				}
+			}
+		}
+	}
+]
+
+Object.Base.route [
+	{
+		source "module-copier.$SDW_JACK_MODULE_COPIER_ID.0"
+		sink "src.$SDW_JACK_TFLM_KPB_PIPELINE_ID.1"
+	}
+	{
+		source "kpb.$SDW_JACK_TFLM_KPB_PIPELINE_ID.1"
+		sink "host-copier.$SDW_JACK_TFLM_WOV_PCM_ID.capture"
+	}
+	{
+		source "kpb.$SDW_JACK_TFLM_KPB_PIPELINE_ID.1"
+		sink "micsel.$SDW_JACK_TFLM_DETECT_PIPELINE_ID.1"
+	}
+]
+
+Object.PCM.pcm [
+	{
+		name "$SDW_JACK_TFLM_WOV_PCM_NAME"
+		id $SDW_JACK_TFLM_WOV_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "$SDW_JACK_TFLM_WOV_PCM_NAME"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "$SDW_JACK_TFLM_WOV_STREAM_NAME"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+	{
+		name "$SDW_JACK_TFLM_DETECT_PCM_NAME"
+		id $SDW_JACK_TFLM_DETECT_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "$SDW_JACK_TFLM_DETECT_PCM_NAME"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "$SDW_JACK_TFLM_DETECT_STREAM_NAME"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+]
diff --git a/tools/topology/topology2/production/tplg-targets-ace1.cmake b/tools/topology/topology2/production/tplg-targets-ace1.cmake
index 6c3bc7214663..20eba340da92 100644
--- a/tools/topology/topology2/production/tplg-targets-ace1.cmake
+++ b/tools/topology/topology2/production/tplg-targets-ace1.cmake
@@ -362,4 +362,6 @@ PREPROCESS_PLUGINS=nhlt,NHLT_BIN=nhlt-sof-mtl-sdw-cs42l42-l0-max98363-l2.bin,\
 BT_NAME=SSP1-BT,BT_INDEX=1,BT_PCM_ID=20,BT_ID=8,BT_PCM_NAME=Bluetooth,ADD_BT=true,\
 NUM_SDW_AMP_LINKS=1,SDW_SPK_STREAM=SDW2-Playback,SDW_AMP_FEEDBACK=false,\
 SDW_JACK_CAPTURE_CH=1,SDW_SPK_ENHANCED_PLAYBACK=false"
+
+"sof-arl-cs42l43-l0-cs35l56-l23-tflm\;sof-arl-cs42l43-l0-cs35l56-l23-tflm\;"
 )
diff --git a/tools/topology/topology2/production/tplg-targets-hda-generic.cmake b/tools/topology/topology2/production/tplg-targets-hda-generic.cmake
index 72872d110a4d..e23eeb3490a4 100644
--- a/tools/topology/topology2/production/tplg-targets-hda-generic.cmake
+++ b/tools/topology/topology2/production/tplg-targets-hda-generic.cmake
@@ -123,4 +123,14 @@ EFX_DMIC0_TDFB_PARAMS=line4_pass,EFX_DMIC0_DRC_PARAMS=dmic_default"
 PREPROCESS_PLUGINS=nhlt,NHLT_BIN=nhlt-sof-hda-generic-ace3-2ch-dax.bin,\
 DMIC0_ENHANCED_CAPTURE=true,EFX_DMIC0_TDFB_PARAMS=line2_generic_pm10deg,\
 EFX_DMIC0_DRC_PARAMS=dmic_default,DOLBY_DAX_CORE_ID=1"
+
+# HDA Mic TFLM Topology Targets with KPB
+"sof-hda-tflm\;sof-hda-tflm\;HDA_MIC_MFCC_PARAMS=default"
+"sof-hda-tflm\;sof-mtl-hda-tflm-kpb\;PLATFORM=mtl,HDA_MIC_MFCC_PARAMS=default"
+"sof-hda-tflm\;sof-ptl-hda-tflm-kpb\;PLATFORM=ptl,HDA_MIC_MFCC_PARAMS=default"
+"sof-hda-tflm\;sof-tgl-hda-tflm-kpb\;PLATFORM=tgl,HDA_MIC_MFCC_PARAMS=default"
+
+# HDA Mic microWakeWord (MWW) Topology Targets with KPB
+"sof-hda-mww\;sof-hda-mww\;HDA_MIC_MFCC_PARAMS=default"
+"sof-hda-mww\;sof-ptl-hda-mww-kpb\;PLATFORM=ptl,HDA_MIC_MFCC_PARAMS=default"
 )
diff --git a/tools/topology/topology2/sof-arl-cs42l43-l0-cs35l56-l23-tflm.conf b/tools/topology/topology2/sof-arl-cs42l43-l0-cs35l56-l23-tflm.conf
new file mode 100644
index 000000000000..5862bc615d47
--- /dev/null
+++ b/tools/topology/topology2/sof-arl-cs42l43-l0-cs35l56-l23-tflm.conf
@@ -0,0 +1,41 @@
+<searchdir:include>
+<searchdir:include/common>
+<searchdir:include/components>
+<searchdir:include/dais>
+<searchdir:include/pipelines>
+<searchdir:include/pipelines/cavs>
+<searchdir:platform>
+<searchdir:platform/intel>
+
+<vendor-token.conf>
+<tokens.conf>
+<virtual.conf>
+<abi.conf>
+<host-gateway-src-mfcc-tflm-capture.conf>
+
+Define {
+	PLATFORM 			"mtl"
+	NUM_SDW_AMP_LINKS 		2
+	SDW_DMIC 			1
+	SDW_AMP_FEEDBACK 		false
+	SDW_SPK_STREAM 			Playback-SmartAmp
+	SDW_DMIC_STREAM 		Capture-SmartMic
+	SDW_JACK_OUT_STREAM 		Playback-SimpleJack
+	SDW_JACK_IN_STREAM 		Capture-SimpleJack
+	TFLM_CAPTURE_PIPELINE_ID 	131
+}
+
+<cavs-sdw.conf>
+
+Object.Pipeline.host-gateway-src-mfcc-tflm-capture [
+	{
+		index $TFLM_CAPTURE_PIPELINE_ID
+	}
+]
+
+Object.Base.route [
+	{
+		source "drc.40.1"
+		sink "src.$TFLM_CAPTURE_PIPELINE_ID.1"
+	}
+]
diff --git a/tools/topology/topology2/sof-hda-generic.conf b/tools/topology/topology2/sof-hda-generic.conf
index 01a020bad17f..2b105573c734 100644
--- a/tools/topology/topology2/sof-hda-generic.conf
+++ b/tools/topology/topology2/sof-hda-generic.conf
@@ -10,8 +10,11 @@
 <vendor-token.conf>
 <tokens.conf>
 <virtual.conf>
+<output_pin_binding.conf>
 <host-gateway-playback.conf>
 <host-gateway-capture.conf>
+<host-gateway-micsel-mfcc-tflm-capture.conf>
+<src-kpb-be.conf>
 <host-copier-gain-mixin-playback.conf>
 <host-copier-gain-src-mixin-playback.conf>
 <mixout-dai-copier-playback.conf>
@@ -187,3 +190,10 @@ Object.Widget.virtual [
 		index 8
 	}
 ]
+
+# Optional TFLM/KPB Wake-on-Voice branch tapping the HDA Analog capture
+# module-copier. Adds two capture PCMs (Detect + WoV drain) without
+# touching the base HDA graph.
+IncludeByKey.HDA_MIC_TFLM_KPB_CAPTURE {
+	"true" "platform/intel/hda-mic-tflm-kpb.conf"
+}
diff --git a/tools/topology/topology2/sof-hda-mww.conf b/tools/topology/topology2/sof-hda-mww.conf
new file mode 100644
index 000000000000..b413e475420b
--- /dev/null
+++ b/tools/topology/topology2/sof-hda-mww.conf
@@ -0,0 +1,310 @@
+<searchdir:include>
+<searchdir:include/common>
+<searchdir:include/components>
+<searchdir:include/controls>
+<searchdir:include/dais>
+<searchdir:include/pipelines>
+<searchdir:include/pipelines/cavs>
+<searchdir:platform>
+<searchdir:platform/intel>
+
+<vendor-token.conf>
+<tokens.conf>
+<virtual.conf>
+<mixer.conf>
+<bytes.conf>
+<host-gateway-playback.conf>
+<host-gateway-capture.conf>
+<host-gateway-src-mfcc-mww-capture.conf>
+<host-copier-gain-mixin-playback.conf>
+<mixout-dai-copier-playback.conf>
+<mixout-gain-dai-copier-playback.conf>
+<dai-copier-gain-module-copier-capture.conf>
+<io-gateway.conf>
+<io-gateway-capture.conf>
+<data.conf>
+<pcm.conf>
+<pcm_caps.conf>
+<fe_dai.conf>
+<hda.conf>
+<hw_config_simple.conf>
+<manifest.conf>
+<abi.conf>
+<route.conf>
+<common_definitions.conf>
+<module-copier.conf>
+<kpb.conf>
+<mww.conf>
+<mfcc.conf>
+<src.conf>
+<gain.conf>
+
+Define {
+	HDA_CONFIG			"none"
+	PLATFORM			"none"
+	ANALOG_PLAYBACK_PCM		'Analog Playback'
+	ANALOG_CAPTURE_PCM		'HDA Mic MWW WoV Capture'
+	HDA_ANALOG_DAI_NAME		'Analog'
+	HDA_ANALOG_CAPTURE_RATE		16000
+	HDA_ANALOG_PLAYBACK_RATE	16000
+	HDA_MWW_CAPTURE_PCM_ID		0
+	HDA_MWW_HOST_PIPELINE_ID	0
+	HDA_MWW_DETECT_PIPELINE_ID	1
+	HDA_MWW_DAI_PIPELINE_ID		2
+}
+
+# Override defaults with platform-specific config if set
+IncludeByKey.PLATFORM {
+	"mtl"	"platform/intel/mtl.conf"
+	"lnl"	"platform/intel/lnl.conf"
+	"ptl"	"platform/intel/ptl.conf"
+}
+
+Object.Dai.HDA [
+	{
+		name $HDA_ANALOG_DAI_NAME
+		dai_index 0
+		id 4
+		default_hw_conf_id 4
+		Object.Base.hw_config.1 {
+			name	"HDA0"
+		}
+		direction duplex
+	}
+]
+
+Object.Pipeline {
+	host-gateway-capture [
+		{
+			index $HDA_MWW_HOST_PIPELINE_ID
+
+			Object.Widget.host-copier.1 {
+				stream_name $ANALOG_CAPTURE_PCM
+				pcm_id $HDA_MWW_CAPTURE_PCM_ID
+				num_input_audio_formats 1
+				num_output_audio_formats 1
+				Object.Base.input_audio_format [
+					{
+				in_channels		1
+						in_rate			$HDA_ANALOG_CAPTURE_RATE
+						in_bit_depth		32
+						in_valid_bit_depth	32
+					}
+				]
+				Object.Base.output_audio_format [
+					{
+				out_channels		1
+						out_rate		$HDA_ANALOG_CAPTURE_RATE
+						out_bit_depth		32
+						out_valid_bit_depth	32
+					}
+				]
+			}
+		}
+	]
+
+	host-gateway-src-mfcc-mww-capture [
+		{
+			index $HDA_MWW_DETECT_PIPELINE_ID
+		}
+	]
+
+	dai-copier-gain-module-copier-capture [
+		{
+			index $HDA_MWW_DAI_PIPELINE_ID
+
+			Object.Widget.dai-copier.1 {
+				dai_type	"HDA"
+				type		"dai_out"
+				copier_type	"HDA"
+				stream_name	$HDA_ANALOG_DAI_NAME
+				node_type	$HDA_LINK_INPUT_CLASS
+				num_output_pins 1
+				num_input_audio_formats 1
+				num_output_audio_formats 1
+				Object.Base.input_audio_format [
+					{
+				in_channels		1
+						in_rate			$HDA_ANALOG_CAPTURE_RATE
+						in_bit_depth		32
+						in_valid_bit_depth	32
+					}
+				]
+				Object.Base.output_audio_format [
+					{
+				out_channels		1
+						out_rate		$HDA_ANALOG_CAPTURE_RATE
+						out_bit_depth		32
+						out_valid_bit_depth	32
+					}
+				]
+			}
+
+			Object.Widget.gain.1 {
+				num_input_audio_formats 1
+				num_output_audio_formats 1
+				Object.Base.input_audio_format [
+					{
+				in_channels		1
+						in_rate			$HDA_ANALOG_CAPTURE_RATE
+						in_bit_depth		32
+						in_valid_bit_depth	32
+					}
+				]
+				Object.Base.output_audio_format [
+					{
+				out_channels		1
+						out_rate		$HDA_ANALOG_CAPTURE_RATE
+						out_bit_depth		32
+						out_valid_bit_depth	32
+					}
+				]
+				Object.Control.mixer.1 {
+					name 'HDA Mic Capture Volume'
+				}
+			}
+
+			Object.Widget.kpb.1 {
+				num_input_audio_formats 1
+				num_output_audio_formats 1
+				Object.Base.input_audio_format [
+					{
+				in_channels		1
+						in_rate			$HDA_ANALOG_CAPTURE_RATE
+						in_bit_depth		32
+						in_valid_bit_depth	32
+					}
+				]
+				Object.Base.output_audio_format [
+					{
+				out_channels		1
+						out_rate		$HDA_ANALOG_CAPTURE_RATE
+						out_bit_depth		32
+						out_valid_bit_depth	32
+					}
+				]
+			}
+		}
+	]
+}
+
+Object.Base.route [
+	{
+		source "dai-copier.HDA.$HDA_ANALOG_DAI_NAME.capture"
+		sink "gain.$HDA_MWW_DAI_PIPELINE_ID.1"
+	}
+	{
+		source "gain.$HDA_MWW_DAI_PIPELINE_ID.1"
+		sink "kpb.$HDA_MWW_DAI_PIPELINE_ID.1"
+	}
+	# KPB output pin 1 -> Real-time detection path (SRC -> MFCC -> MWW)
+	{
+		source "kpb.$HDA_MWW_DAI_PIPELINE_ID.1"
+		sink "src.$HDA_MWW_DETECT_PIPELINE_ID.1"
+	}
+	# KPB output pin 2 -> Host WoV Draining PCM capture stream
+	{
+		source "kpb.$HDA_MWW_DAI_PIPELINE_ID.1"
+		sink "host-copier.$HDA_MWW_CAPTURE_PCM_ID.capture"
+	}
+]
+
+Object.Widget.virtual [
+	{
+		name 'codec0_in'
+		type input
+		index 1
+	}
+	{
+		name 'codec1_in'
+		type input
+		index 2
+	}
+	{
+		name 'codec2_in'
+		type input
+		index 3
+	}
+	{
+		name 'codec0_out'
+		type output
+		index 4
+	}
+	{
+		name 'codec1_out'
+		type output
+		index 5
+	}
+	{
+		name 'codec2_out'
+		type output
+		index 6
+	}
+	{
+		name 'Analog CPU Playback'
+		type out_drv
+		index 7
+	}
+	{
+		name 'Digital CPU Playback'
+		type out_drv
+		index 8
+	}
+	{
+		name 'Alt Analog CPU Playback'
+		type out_drv
+		index 9
+	}
+	{
+		name 'Analog CPU Capture'
+		type input
+		index 10
+	}
+	{
+		name 'Digital CPU Capture'
+		type input
+		index 11
+	}
+	{
+		name 'Alt Analog CPU Capture'
+		type input
+		index 12
+	}
+]
+
+Object.PCM.pcm [
+	{
+		name	'HDA Analog'
+		id	$HDA_MWW_CAPTURE_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "HDA Analog"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name $ANALOG_CAPTURE_PCM
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 1
+			channels_max 1
+		}
+	}
+	{
+		name	'HDA Mic MWW Detect'
+		id	$HDA_MWW_DETECT_PIPELINE_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "HDA Mic MWW Detect"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "HDA Mic MWW Detect"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 1
+			channels_max 1
+		}
+	}
+]
diff --git a/tools/topology/topology2/sof-hda-tflm.conf b/tools/topology/topology2/sof-hda-tflm.conf
new file mode 100644
index 000000000000..dcaab79caceb
--- /dev/null
+++ b/tools/topology/topology2/sof-hda-tflm.conf
@@ -0,0 +1,310 @@
+<searchdir:include>
+<searchdir:include/common>
+<searchdir:include/components>
+<searchdir:include/controls>
+<searchdir:include/dais>
+<searchdir:include/pipelines>
+<searchdir:include/pipelines/cavs>
+<searchdir:platform>
+<searchdir:platform/intel>
+
+<vendor-token.conf>
+<tokens.conf>
+<virtual.conf>
+<mixer.conf>
+<bytes.conf>
+<host-gateway-playback.conf>
+<host-gateway-capture.conf>
+<host-gateway-src-mfcc-tflm-capture.conf>
+<host-copier-gain-mixin-playback.conf>
+<mixout-dai-copier-playback.conf>
+<mixout-gain-dai-copier-playback.conf>
+<dai-copier-gain-module-copier-capture.conf>
+<io-gateway.conf>
+<io-gateway-capture.conf>
+<data.conf>
+<pcm.conf>
+<pcm_caps.conf>
+<fe_dai.conf>
+<hda.conf>
+<hw_config_simple.conf>
+<manifest.conf>
+<abi.conf>
+<route.conf>
+<common_definitions.conf>
+<module-copier.conf>
+<kpb.conf>
+<tflm.conf>
+<mfcc.conf>
+<src.conf>
+<gain.conf>
+
+Define {
+	HDA_CONFIG			"none"
+	PLATFORM			"none"
+	ANALOG_PLAYBACK_PCM		'Analog Playback'
+	ANALOG_CAPTURE_PCM		'HDA Mic TFLM WoV Capture'
+	HDA_ANALOG_DAI_NAME		'Analog'
+	HDA_ANALOG_CAPTURE_RATE		16000
+	HDA_ANALOG_PLAYBACK_RATE	16000
+	HDA_TFLM_CAPTURE_PCM_ID		0
+	HDA_TFLM_HOST_PIPELINE_ID	0
+	HDA_TFLM_DETECT_PIPELINE_ID	1
+	HDA_TFLM_DAI_PIPELINE_ID	2
+}
+
+# Override defaults with platform-specific config if set
+IncludeByKey.PLATFORM {
+	"mtl"	"platform/intel/mtl.conf"
+	"lnl"	"platform/intel/lnl.conf"
+	"ptl"	"platform/intel/ptl.conf"
+}
+
+Object.Dai.HDA [
+	{
+		name $HDA_ANALOG_DAI_NAME
+		dai_index 0
+		id 4
+		default_hw_conf_id 4
+		Object.Base.hw_config.1 {
+			name	"HDA0"
+		}
+		direction duplex
+	}
+]
+
+Object.Pipeline {
+	host-gateway-capture [
+		{
+			index $HDA_TFLM_HOST_PIPELINE_ID
+
+			Object.Widget.host-copier.1 {
+				stream_name $ANALOG_CAPTURE_PCM
+				pcm_id $HDA_TFLM_CAPTURE_PCM_ID
+				num_input_audio_formats 1
+				num_output_audio_formats 1
+				Object.Base.input_audio_format [
+					{
+				in_channels		1
+						in_rate			$HDA_ANALOG_CAPTURE_RATE
+						in_bit_depth		32
+						in_valid_bit_depth	32
+					}
+				]
+				Object.Base.output_audio_format [
+					{
+				out_channels		1
+						out_rate		$HDA_ANALOG_CAPTURE_RATE
+						out_bit_depth		32
+						out_valid_bit_depth	32
+					}
+				]
+			}
+		}
+	]
+
+	host-gateway-src-mfcc-tflm-capture [
+		{
+			index $HDA_TFLM_DETECT_PIPELINE_ID
+		}
+	]
+
+	dai-copier-gain-module-copier-capture [
+		{
+			index $HDA_TFLM_DAI_PIPELINE_ID
+
+			Object.Widget.dai-copier.1 {
+				dai_type	"HDA"
+				type		"dai_out"
+				copier_type	"HDA"
+				stream_name	$HDA_ANALOG_DAI_NAME
+				node_type	$HDA_LINK_INPUT_CLASS
+				num_output_pins 1
+				num_input_audio_formats 1
+				num_output_audio_formats 1
+				Object.Base.input_audio_format [
+					{
+				in_channels		1
+						in_rate			$HDA_ANALOG_CAPTURE_RATE
+						in_bit_depth		32
+						in_valid_bit_depth	32
+					}
+				]
+				Object.Base.output_audio_format [
+					{
+				out_channels		1
+						out_rate		$HDA_ANALOG_CAPTURE_RATE
+						out_bit_depth		32
+						out_valid_bit_depth	32
+					}
+				]
+			}
+
+			Object.Widget.gain.1 {
+				num_input_audio_formats 1
+				num_output_audio_formats 1
+				Object.Base.input_audio_format [
+					{
+				in_channels		1
+						in_rate			$HDA_ANALOG_CAPTURE_RATE
+						in_bit_depth		32
+						in_valid_bit_depth	32
+					}
+				]
+				Object.Base.output_audio_format [
+					{
+				out_channels		1
+						out_rate		$HDA_ANALOG_CAPTURE_RATE
+						out_bit_depth		32
+						out_valid_bit_depth	32
+					}
+				]
+				Object.Control.mixer.1 {
+					name 'HDA Mic Capture Volume'
+				}
+			}
+
+			Object.Widget.kpb.1 {
+				num_input_audio_formats 1
+				num_output_audio_formats 1
+				Object.Base.input_audio_format [
+					{
+				in_channels		1
+						in_rate			$HDA_ANALOG_CAPTURE_RATE
+						in_bit_depth		32
+						in_valid_bit_depth	32
+					}
+				]
+				Object.Base.output_audio_format [
+					{
+				out_channels		1
+						out_rate		$HDA_ANALOG_CAPTURE_RATE
+						out_bit_depth		32
+						out_valid_bit_depth	32
+					}
+				]
+			}
+		}
+	]
+}
+
+Object.Base.route [
+	{
+		source "dai-copier.HDA.$HDA_ANALOG_DAI_NAME.capture"
+		sink "gain.$HDA_TFLM_DAI_PIPELINE_ID.1"
+	}
+	{
+		source "gain.$HDA_TFLM_DAI_PIPELINE_ID.1"
+		sink "kpb.$HDA_TFLM_DAI_PIPELINE_ID.1"
+	}
+	# KPB output pin 1 -> Real-time detection path (SRC -> MFCC -> TFLM)
+	{
+		source "kpb.$HDA_TFLM_DAI_PIPELINE_ID.1"
+		sink "src.$HDA_TFLM_DETECT_PIPELINE_ID.1"
+	}
+	# KPB output pin 2 -> Host WoV Draining PCM capture stream
+	{
+		source "kpb.$HDA_TFLM_DAI_PIPELINE_ID.1"
+		sink "host-copier.$HDA_TFLM_CAPTURE_PCM_ID.capture"
+	}
+]
+
+Object.Widget.virtual [
+	{
+		name 'codec0_in'
+		type input
+		index 1
+	}
+	{
+		name 'codec1_in'
+		type input
+		index 2
+	}
+	{
+		name 'codec2_in'
+		type input
+		index 3
+	}
+	{
+		name 'codec0_out'
+		type output
+		index 4
+	}
+	{
+		name 'codec1_out'
+		type output
+		index 5
+	}
+	{
+		name 'codec2_out'
+		type output
+		index 6
+	}
+	{
+		name 'Analog CPU Playback'
+		type out_drv
+		index 7
+	}
+	{
+		name 'Digital CPU Playback'
+		type out_drv
+		index 8
+	}
+	{
+		name 'Alt Analog CPU Playback'
+		type out_drv
+		index 9
+	}
+	{
+		name 'Analog CPU Capture'
+		type input
+		index 10
+	}
+	{
+		name 'Digital CPU Capture'
+		type input
+		index 11
+	}
+	{
+		name 'Alt Analog CPU Capture'
+		type input
+		index 12
+	}
+]
+
+Object.PCM.pcm [
+	{
+		name	'HDA Analog'
+		id	$HDA_TFLM_CAPTURE_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "HDA Analog"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name $ANALOG_CAPTURE_PCM
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 1
+			channels_max 1
+		}
+	}
+	{
+		name	'HDA Mic TFLM Detect'
+		id	$HDA_TFLM_DETECT_PIPELINE_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "HDA Mic TFLM Detect"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "HDA Mic TFLM Detect"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 1
+			channels_max 1
+		}
+	}
+]
diff --git a/uuid-registry.txt b/uuid-registry.txt
index e9e8f8e77876..f54cb75e2649 100644
--- a/uuid-registry.txt
+++ b/uuid-registry.txt
@@ -127,6 +127,7 @@ ee2585f2-e7d8-43dc-90ab4224e00c3e84 modules
 0d9f2256-8e4f-47b3-8448239a334f1191 multiband_drc
 c607ff4d-9cb6-49dc-b6787da3c63ea557 mux
 64ce6e35-857a-4878-ace8e2a2f42e3069 mux4
+4067fe1d-cd63-4877-966ba06ded1719ce mww
 f36BF24B-9AAF-83f4-8677E072E8AEADB7 notification_pool
 1fb15a7a-83cd-4c2e-8b324da1b2adeeaf notifier
 7ae671a7-4617-4a09-bf6d9d29c998dbc1 ns
diff --git a/zephyr/CMakeLists.txt b/zephyr/CMakeLists.txt
index 4b61a9517d46..a8c06e431e43 100644
--- a/zephyr/CMakeLists.txt
+++ b/zephyr/CMakeLists.txt
@@ -102,7 +102,16 @@ function(sof_llext_build module)
 	if(CONFIG_LLEXT_TYPE_ELF_RELOCATABLE)
 		set(EXTRA_LINKER_PARAMS -nostdlib -nodefaultlibs -r)
 	else()
-		set(EXTRA_LINKER_PARAMS -nostdlib -nodefaultlibs -shared)
+		# -Bsymbolic-functions: without this, calls between GLOBAL-visibility
+		# functions defined in different translation units of the SAME llext
+		# module are emitted as PLT calls. Zephyr's llext_link_plt() resolves
+		# PLT symbols only against the base image's export table, this
+		# module's own .exported_sym table, or other already-loaded
+		# extensions -- never against symbols merely defined locally in this
+		# module's own .dynsym. Multi-TU C++ libraries (e.g. TFLite Micro)
+		# call plenty of non-exported internal helpers across .cc files, so
+		# without this flag those calls fail to link at load time.
+		set(EXTRA_LINKER_PARAMS -nostdlib -nodefaultlibs -shared -Wl,-Bsymbolic-functions)
 	endif()
 
 	foreach(path ${SOF_LLEXT_LIBS_PATH})
@@ -110,6 +119,29 @@ function(sof_llext_build module)
 		set(EXTRA_LINKER_PARAMS ${EXTRA_LINKER_PARAMS} -L${path})
 	endforeach()
 
+	# When using LLVM Clang for xtensa cross-compilation, the cmake linker
+	# driver (clang) defaults to the host linker (ld/x86_64). Add --target
+	# and --ld-path to ensure the xtensa cross-linker is used for LLEXT.
+	# The xtensa linker path is already set in CMAKE_EXE_LINKER_FLAGS by
+	# Zephyr's xtensa-toolchain.cmake via --ld-path=<path>.
+	if("${ARCH}" STREQUAL "xtensa" AND CMAKE_C_COMPILER_ID MATCHES "Clang")
+		# Extract --ld-path value from CMAKE_EXE_LINKER_FLAGS (set by Zephyr toolchain cmake)
+		string(REGEX MATCH "--ld-path=[^ ]+" _ld_path_flag "${CMAKE_EXE_LINKER_FLAGS}")
+		# Use the CONFIG_LLVM_TARGET_TRIPLE or derive from board/SOC info
+		# The xtensa GNU target triple is derived from the SDK linker path
+		string(REGEX MATCH "--ld-path=.*/([^/]+)/bin" _triple_match "${CMAKE_EXE_LINKER_FLAGS}")
+		if(_triple_match)
+			set(_compiler_target "${CMAKE_MATCH_1}")
+		else()
+			set(_compiler_target "xtensa-intel_ace15_mtpm_zephyr-elf")
+		endif()
+		set(EXTRA_LINKER_PARAMS ${EXTRA_LINKER_PARAMS}
+			--target=${_compiler_target}
+			${_ld_path_flag}
+			-Wl,--unresolved-symbols=ignore-all
+		)
+	endif()
+
 	get_target_property(proc_in_file ${module} lib_output)
 	get_target_property(proc_out_file ${module} pkg_input)
 	get_target_property(proc_pkg_file ${module} pkg_output)
diff --git a/zephyr/Kconfig b/zephyr/Kconfig
index ab88efcb9a6a..8b1a35cf4636 100644
--- a/zephyr/Kconfig
+++ b/zephyr/Kconfig
@@ -119,8 +119,7 @@ config VIRTUAL_HEAP_EXTENDED
 config SOF_ZEPHYR_VIRTUAL_HEAP_REGION_SIZE
 	hex "Size in bytes of virtual memory region for virtual heap shared for all cores"
 	depends on MM_DRV_INTEL_ADSP_MTL_TLB
-	default 0x140000 if VIRTUAL_HEAP_EXTENDED
-	default 0x100000
+	default 0x400000
 	help
 	  This config defines size of virtual heap region shared between all cores