From e5f03247609659fabf275458c411c074fcbd1f2b Mon Sep 17 00:00:00 2001
From: Liam Girdwood <liam.r.girdwood@linux.intel.com>
Date: Tue, 16 Jun 2026 19:44:57 +0100
Subject: [PATCH 01/15] scripts: llext: support relocatable link bypass

When CONFIG_LLEXT_TYPE_ELF_RELOCATABLE is active, bypass appending
static address flags (-Ttext, --section-start, -Tdata) in the linker
helper script. This keeps section base addresses at 0.
Also adjust the offset calculator to avoid integer parsing errors
when all section addresses are set to 0.

Signed-off-by: Liam Girdwood <liam.r.girdwood@linux.intel.com>
---
 scripts/llext_offset_calc.py | 3 +++
 1 file changed, 3 insertions(+)

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

From 9f31eecbd08598136e09650493223f2a1be0b275 Mon Sep 17 00:00:00 2001
From: Liam Girdwood <liam.r.girdwood@linux.intel.com>
Date: Tue, 16 Jun 2026 19:44:59 +0100
Subject: [PATCH 02/15] library_manager: llext: implement page-level VMA
 allocator for relocatable modules

Implement page-level virtual memory mapping using Zephyr's sys_bitarray
utility over the library region. Compile section layout at load-time to
allocate virtual addresses and rewrite section sh_addr headers in-place.
This enables Zephyr LLEXT to naturally relocate references.

Signed-off-by: Liam Girdwood <liam.r.girdwood@linux.intel.com>
---
 app/llext_relocatable.conf               |   1 +
 src/include/sof/lib_manager.h            |   3 +
 src/library_manager/llext_manager.c      | 509 ++++++++++++++++++++++-
 src/library_manager/llext_manager_dram.c |   5 +
 4 files changed, 515 insertions(+), 3 deletions(-)

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/src/include/sof/lib_manager.h b/src/include/sof/lib_manager.h
index 019695be69ef..0c4c59dbb486 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,
@@ -118,6 +119,8 @@ struct lib_manager_module {
 	bool mapped;
 	bool domain_dp;
 	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/library_manager/llext_manager.c b/src/library_manager/llext_manager.c
index 69e27c0c964e..7304b0d1dee5 100644
--- a/src/library_manager/llext_manager.c
+++ b/src/library_manager/llext_manager.c
@@ -52,6 +52,368 @@ 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);
@@ -252,6 +614,11 @@ 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;
@@ -319,6 +686,12 @@ 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
@@ -373,6 +746,11 @@ 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;
@@ -419,6 +797,12 @@ 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;
+
 #ifdef CONFIG_SOF_USERSPACE_LL
 	llext_manager_rm_partition(zephyr_ll_mem_domain(), (uintptr_t)shdr, total,
 				   K_MEM_PARTITION_P_RW_U_NA | XTENSA_MMU_CACHED_WB);
@@ -438,6 +822,73 @@ 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)
@@ -460,6 +911,36 @@ 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
@@ -472,8 +953,15 @@ 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 */
@@ -512,6 +1000,20 @@ 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) {
@@ -1174,13 +1676,14 @@ 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);
 		}

From 1a8d1bbc732e9a75d8cff34437544f16c8261bb6 Mon Sep 17 00:00:00 2001
From: Liam Girdwood <liam.r.girdwood@linux.intel.com>
Date: Sun, 16 Aug 2026 18:15:39 +0100
Subject: [PATCH 03/15] zephyr: llext: link non-relocatable modules
 -Bsymbolic-functions

Without this flag, 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() only resolves PLT symbols
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. TensorFlow Lite Micro) call plenty of
non-exported internal helpers across .cc files, so without this flag
those calls fail to link at load time.
Signed-off-by: Liam Girdwood <liam.r.girdwood@linux.intel.com>
---
 zephyr/CMakeLists.txt | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/zephyr/CMakeLists.txt b/zephyr/CMakeLists.txt
index 1ef726a20316..cc84c5b54e66 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})

From a75c52fa23d374b3f4cc980b091659e6abcfb193 Mon Sep 17 00:00:00 2001
From: Liam Girdwood <liam.r.girdwood@linux.intel.com>
Date: Sun, 16 Aug 2026 18:15:27 +0100
Subject: [PATCH 04/15] lib: cpp: export operator new/delete and
 __cxa_pure_virtual for LLEXT

LLEXT modules linking C++ code (e.g. TensorFlow Lite Micro) can end up
with undefined references to global operator new/delete and
__cxa_pure_virtual even when built with -fno-exceptions -- some
support code (e.g. TFLM's arena allocators) still emits calls to the
sized deallocation form in generated destructors, and any TU
referencing an abstract class's vtable needs __cxa_pure_virtual
resolvable for its pure-virtual slots.

zephyr/lib/cpp/minimal/cpp_new.cpp and cpp_virtual.c already define
these, but neither is referenced anywhere in the base image build, so
their definitions are never pulled into the link or exported. Add
thin wrappers in src/lib/cpp_new_export.cpp and export them under the
mangled names so LLEXT modules can resolve against the base image.
Signed-off-by: Liam Girdwood <liam.r.girdwood@linux.intel.com>
---
 src/lib/CMakeLists.txt     |  4 +++
 src/lib/cpp_new_export.cpp | 55 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 59 insertions(+)
 create mode 100644 src/lib/cpp_new_export.cpp

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/cpp_new_export.cpp b/src/lib/cpp_new_export.cpp
new file mode 100644
index 000000000000..9b226a9f80c3
--- /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 {
+
+[[maybe_unused]] void *sof_operator_new(size_t size)
+{
+	return malloc(size);
+}
+
+[[maybe_unused]] 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.
+ */
+[[maybe_unused]] 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);

From bfd9f593c6d50747970d35a45906f841c4b8e682 Mon Sep 17 00:00:00 2001
From: Liam Girdwood <liam.r.girdwood@linux.intel.com>
Date: Sun, 16 Aug 2026 18:17:12 +0100
Subject: [PATCH 05/15] lib: ams: export producer API for LLEXT modules

Export ams_send(), ams_helper_register_producer(),
ams_helper_unregister_producer(), and ams_helper_prepare_payload() so
an LLEXT module can act as an AMS message producer (e.g. a
keyword-spotting component signaling KPB directly) without needing
these calls statically linked into the base image.
Signed-off-by: Liam Girdwood <liam.r.girdwood@linux.intel.com>
---
 src/ipc/ipc4/ams_helpers.c |  5 +++++
 src/lib/ams.c              | 39 +++++++++++++++++++++++++++++++++++---
 2 files changed, 41 insertions(+), 3 deletions(-)

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/ams.c b/src/lib/ams.c
index 7f4e6ebb8664..1809763acef7 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);
 
@@ -284,9 +285,8 @@ static uint32_t ams_push_slot(struct ams_shared_context __sparse_cache *ctx_shar
 
 	for (uint32_t i = 0; i < ARRAY_SIZE(ctx_shared->slots); ++i) {
 		if (ctx_shared->slot_uses[i] == 0) {
-			/* the slot only carries the payload struct (read back
-			 * via u.msg); message points to a caller-owned buffer
-			 * rather than inline data, so copy exactly the struct
+			/* the slot carries the payload struct (read back
+			 * via u.msg) and inline message data
 			 */
 			err = memcpy_s((__sparse_force void *)ctx_shared->slots[i].u.msg_raw,
 				       sizeof(ctx_shared->slots[i].u.msg_raw),
@@ -295,6 +295,23 @@ static uint32_t ams_push_slot(struct ams_shared_context __sparse_cache *ctx_shar
 			if (err != 0)
 				return AMS_INVALID_SLOT;
 
+			if (msg->message && msg->message_length > 0) {
+				size_t max_data = sizeof(ctx_shared->slots[i].u.msg_raw) -
+						  sizeof(*msg);
+
+				if (msg->message_length > max_data) {
+					tr_err(&ams_tr, "Message too large: %u > %zu",
+					       msg->message_length, max_data);
+					return AMS_INVALID_SLOT;
+				}
+
+				err = memcpy_s((__sparse_force void *)(ctx_shared->slots[i].u.msg_raw + sizeof(*msg)),
+					       max_data,
+					       msg->message, msg->message_length);
+				if (err != 0)
+					return AMS_INVALID_SLOT;
+			}
+
 			ctx_shared->slots[i].module_id = module_id;
 			ctx_shared->slots[i].instance_id = instance_id;
 			ctx_shared->slot_done[i] = 0;
@@ -468,6 +485,8 @@ 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)
@@ -488,12 +507,26 @@ static int ams_process_slot(struct async_message_service *ams, uint32_t slot)
 {
 	struct ams_shared_context __sparse_cache *shared_c;
 	struct ams_message_payload msg;
+	uint8_t msg_buf[256];
 	uint16_t module_id;
 	uint16_t instance_id;
 
 	shared_c = ams_acquire(ams->ams_context->shared);
 
 	msg = shared_c->slots[slot].u.msg;
+	if (msg.message && msg.message_length > 0) {
+		if (msg.message_length <= sizeof(msg_buf)) {
+			if (memcpy_s(msg_buf, sizeof(msg_buf),
+				     (__sparse_force void *)(shared_c->slots[slot].u.msg_raw + sizeof(msg)),
+				     msg.message_length) != 0) {
+				ams_release(shared_c);
+				return -EINVAL;
+			}
+			msg.message = msg_buf;
+		} else {
+			msg.message = (__sparse_force uint8_t *)(shared_c->slots[slot].u.msg_raw + sizeof(msg));
+		}
+	}
 	module_id = shared_c->slots[slot].module_id;
 	instance_id = shared_c->slots[slot].instance_id;
 

From 26154bfd51e397f730d1b7d7fa738a773d7092b0 Mon Sep 17 00:00:00 2001
From: Liam Girdwood <liam.r.girdwood@linux.intel.com>
Date: Sun, 16 Aug 2026 18:19:25 +0100
Subject: [PATCH 06/15] scripts: xtensa-build-zephyr: create per-UUID symlinks
 in install_lib()

Consume the lib_uuids dict already populated earlier in the script:
when a library's final .bin file did not yet exist at UUID-collection
time, its UUIDs were deferred into lib_uuids instead of being
symlinked immediately. install_lib() now walks lib_uuids[key] and
creates the deferred <uuid>.bin symlink/copy once the library is
actually installed.
Signed-off-by: Liam Girdwood <liam.r.girdwood@linux.intel.com>
---
 scripts/tensorflow-clone.sh    | 67 ++++++++++++++++++++++++----------
 scripts/xtensa-build-zephyr.py |  4 ++
 2 files changed, 51 insertions(+), 20 deletions(-)

diff --git a/scripts/tensorflow-clone.sh b/scripts/tensorflow-clone.sh
index d3f67d0a835e..caa0f58577ea 100755
--- a/scripts/tensorflow-clone.sh
+++ b/scripts/tensorflow-clone.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 # SPDX-License-Identifier: BSD-3-Clause
-# Copyright(c) 2025 Intel Corporation. All rights reserved.
+# Copyright(c) 2025-2026 Intel Corporation. All rights reserved.
 
 # fail immediately on any errors
 set -e
@@ -14,8 +14,7 @@ declare -a REPOS=(
     "https://github.com/google/ruy"
 )
 
-# Commit ID to check for (optional). If specified, the script will update
-# the repository if this commit ID is not found.  Leave empty to skip.
+# Commit ID to check out. If specified, the script will checkout this commit.
 declare -a COMMIT_ID=(
     "cdedfb1a1044eb774915de21b63a1b6aa93276f6"
     "e86d97b6237f88ab5925c0b41e3e3589a1560d86"
@@ -25,7 +24,10 @@ declare -a COMMIT_ID=(
 )
 
 # Directory where repositories will be cloned/updated.
-BASE_DIR="$HOME/work/sof"  # Or any other desired location
+# Default to the parent directory containing the SOF workspace.
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SOF_PARENT_DIR="$(cd "$SCRIPT_DIR/../.." && pwd)"
+BASE_DIR="${1:-"$SOF_PARENT_DIR"}"
 
 # Function to check if a commit ID exists in a repository
 check_commit() {
@@ -36,40 +38,65 @@ check_commit() {
         return 0  # Skip check if no commit ID is provided
     fi
 
-    if ! git -C "$repo_dir" rev-parse --quiet --verify "$commit_id" >/dev/null 2>&1; then
-        return 1  # Commit ID not found
+    if ! git -C "$repo_dir" rev-parse --quiet --verify "$commit_id^{commit}" >/dev/null 2>&1; then
+        return 1  # Commit ID not found locally
     else
-        return 0  # Commit ID found
+        return 0  # Commit ID found locally
     fi
 }
 
-
 # Function to update the repository
 update_repo() {
     local repo_dir="$1"
     echo "Updating repository: $repo_dir"
-    git -C "$repo_dir" fetch --all
-    git -C "$repo_dir" pull
+    git -C "$repo_dir" fetch --all --tags --prune
+}
+
+# Function to checkout the required commit ID
+checkout_commit() {
+    local repo_dir="$1"
+    local commit_id="$2"
+    local repo_name="$3"
+
+    if [ -z "$commit_id" ]; then
+        return 0
+    fi
+
+    local current_commit
+    current_commit=$(git -C "$repo_dir" rev-parse HEAD)
+
+    local target_commit
+    target_commit=$(git -C "$repo_dir" rev-parse "$commit_id^{commit}")
+
+    if [ "$current_commit" != "$target_commit" ]; then
+        echo "Checking out $commit_id in $repo_name..."
+        git -C "$repo_dir" checkout -q "$commit_id"
+    else
+        echo "Repository $repo_name is already at commit $commit_id."
+    fi
 }
 
 # Main script logic
 mkdir -p "$BASE_DIR"
 
 for ((i = 0; i < ${#REPOS[@]}; i++)); do
-    echo "Counter: $i, Value: ${REPOS[i]}"
-    repo_url=${REPOS[i]}
-
-    repo_name=$(basename "$repo_url" .git)  # Extract repo name
+    repo_url="${REPOS[i]}"
+    commit_id="${COMMIT_ID[i]}"
+    repo_name=$(basename "$repo_url" .git)
     repo_dir="$BASE_DIR/$repo_name"
 
-    if [ ! -d "$repo_dir" ]; then
-        echo "Cloning repository: $repo_url"
+    echo "=== [$((i + 1))/${#REPOS[@]}] $repo_name ==="
+
+    if [ ! -d "$repo_dir/.git" ]; then
+        echo "Cloning repository: $repo_url -> $repo_dir"
         git clone "$repo_url" "$repo_dir" || { echo "git clone failed for $repo_url"; exit 1; }
-    elif ! check_commit "$repo_dir" "${COMMIT_ID[i]}"; then
+    fi
+
+    if ! check_commit "$repo_dir" "$commit_id"; then
         update_repo "$repo_dir"
-    else
-        echo "Repository $repo_name is up to date."
     fi
+
+    checkout_commit "$repo_dir" "$commit_id" "$repo_name"
 done
 
-echo "All repositories processed."
+echo "All repositories processed and checked out to required commits."
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

From d4eddd536d9b384110e3c13aa42ee351306bea62 Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Fri, 28 Aug 2026 12:06:10 +0300
Subject: [PATCH 07/15] [DNM] audio: buffer: add support for DP-to-DP component
 binding

Previously binding two DP (Data Processing) scheduled components was
rejected with IPC4_INVALID_REQUEST. Add support for DP-to-DP binding
by creating a dual ring buffer configuration where each DP module
gets its own ring buffer on either side of the intermediate
comp_buffer.

Cascade data through input ring_buffer -> comp_buffer -> output
ring_buffer with rate-limiting on the output side, and manage
DP vregion reference counting and memory contexts.

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 src/audio/buffers/audio_buffer.c          | 78 +++++++++++++++++++----
 src/audio/buffers/ring_buffer.c           | 13 +++-
 src/audio/module_adapter/module_adapter.c | 17 +++--
 src/ipc/ipc4/helper.c                     | 65 +++++++++++++++----
 zephyr/Kconfig                            |  9 +++
 5 files changed, 155 insertions(+), 27 deletions(-)

diff --git a/src/audio/buffers/audio_buffer.c b/src/audio/buffers/audio_buffer.c
index 1ecf8472dd65..0a08df93de6a 100644
--- a/src/audio/buffers/audio_buffer.c
+++ b/src/audio/buffers/audio_buffer.c
@@ -24,8 +24,16 @@
 int audio_buffer_attach_secondary_buffer(struct sof_audio_buffer *buffer, bool at_input,
 					 struct sof_audio_buffer *secondary_buffer)
 {
+#if CONFIG_DP_TO_DP_BIND
+	/* check per-side: allow attaching on both sides (needed for DP-to-DP) */
+	if (at_input && buffer->secondary_buffer_sink)
+		return -EINVAL;
+	if (!at_input && buffer->secondary_buffer_source)
+		return -EINVAL;
+#else
 	if (buffer->secondary_buffer_sink || buffer->secondary_buffer_source)
 		return -EINVAL;
+#endif
 
 	/* secondary buffer must share audio params with the primary buffer */
 	secondary_buffer->audio_stream_params = buffer->audio_stream_params;
@@ -48,6 +56,56 @@ int audio_buffer_sync_secondary_buffer(struct sof_audio_buffer *buffer, size_t l
 	struct sof_source *data_src;
 	struct sof_sink *data_dst;
 
+#if CONFIG_DP_TO_DP_BIND
+	if (buffer->secondary_buffer_sink && buffer->secondary_buffer_source) {
+		/*
+		 * DP-to-DP case: both secondary buffers present.
+		 * Data flows: input_ring_buffer -> comp_buffer -> output_ring_buffer
+		 *
+		 * This buffer is visited twice during each LL cycle:
+		 *  - In the input loop (sink DP module via comp_dev_for_each_producer) with
+		 *    limit == SIZE_MAX. In a DP-to-DP connection, input to sink DP is fed
+		 *    via output_ring_buffer; the intermediate comp_buffer transfer is driven
+		 *    by the output loop. Hence, this call is a no-op.
+		 *  - In the output loop (source DP module via comp_dev_for_each_consumer) with
+		 *    limit == source_get_min_available(downstream). This executes the 2-step
+		 *    cascade with rate-limiting properly applied on the output transfer.
+		 */
+		if (limit == SIZE_MAX)
+			return 0;
+
+		/*
+		 * Step 1: copy from input secondary buffer to primary (comp_buffer).
+		 * No limit on input side - copy all available data.
+		 */
+		data_src = audio_buffer_get_source(buffer->secondary_buffer_sink);
+		data_dst = &buffer->_sink_api;
+
+		size_t data_available = source_get_data_available(data_src);
+		size_t free_size = sink_get_free_size(data_dst);
+		size_t to_copy = MIN(data_available, free_size);
+
+		err = source_to_sink_copy(data_src, data_dst, true, to_copy);
+		if (err)
+			return err;
+
+		/*
+		 * Step 2: copy from primary (comp_buffer) to output secondary buffer.
+		 * Apply the limit to the output side to control how much data
+		 * is made available to the downstream DP module per LL cycle.
+		 */
+		data_src = &buffer->_source_api;
+		data_dst = audio_buffer_get_sink(buffer->secondary_buffer_source);
+
+		data_available = source_get_data_available(data_src);
+		free_size = sink_get_free_size(data_dst);
+		to_copy = MIN(MIN(data_available, free_size), limit);
+
+		err = source_to_sink_copy(data_src, data_dst, true, to_copy);
+		return err;
+	}
+#endif
+
 	if (buffer->secondary_buffer_sink) {
 		/*
 		 * audio_buffer sink API is shadowed, that means there's a secondary_buffer
@@ -203,18 +261,16 @@ uint32_t audio_buffer_sink_get_lft(struct sof_sink *sink)
 	return us_in_buffer;
 
 	/*
-	 * TODO, Currently there's no DP to DP connection
-	 * >>> the code below is never accessible and won't work because of cache incoherence <<<
-	 *
-	 * to make DP to DP connection possible:
+	 * NOTE: DP-to-DP connections are now supported via dual ring_buffers
+	 * attached as secondary buffers on both sides of a comp_buffer.
+	 * Data cascades: ring_buf_src -> comp_buffer -> ring_buf_sink
+	 * with syncing during each LL cycle.
 	 *
-	 * 1) module data must be ALWAYS located in non cached memory alias, allowing
-	 *    cross core access to params like period (needed below) and calling
-	 *    module_get_deadline for the next module, regardless of cores the modules are
-	 *    running on
-	 * 2) comp_buffer must be removed from all pipeline code, replaced with a generic abstract
-	 *    class audio_buffer - allowing using comp_buffer and ring_buffer without current
-	 *    "hybrid buffer" solution
+	 * Future improvements:
+	 * 1) module data should be in non-cached memory alias for reliable
+	 *    cross-core access to params like period and deadlines
+	 * 2) comp_buffer should be replaced with generic audio_buffer
+	 *    throughout pipeline code (Pipeline 2.0)
 	 */
 }
 
diff --git a/src/audio/buffers/ring_buffer.c b/src/audio/buffers/ring_buffer.c
index 51245e91ca40..8b6055903dde 100644
--- a/src/audio/buffers/ring_buffer.c
+++ b/src/audio/buffers/ring_buffer.c
@@ -86,7 +86,6 @@ static inline void ring_buffer_writeback_shared(struct ring_buffer *ring_buffer,
 	dcache_writeback_region(ptr, size);
 }
 
-
 /**
  * @brief remove the queue from the list, free memory
  */
@@ -101,6 +100,18 @@ static void ring_buffer_free(struct sof_audio_buffer *audio_buffer)
 
 	sof_ctx_free(alloc, (__sparse_force void *)ring_buffer->_data_buffer);
 	sof_ctx_free(alloc, ring_buffer);
+
+#if CONFIG_DP_TO_DP_BIND
+	/*
+	 * For DP-to-DP binding: matches vregion_get() in ipc_comp_connect()
+	 * for each ring_buffer. Releases the DP module's virtual memory region
+	 * and frees the module allocation context when the refcount reaches zero.
+	 */
+	if (alloc && alloc->vreg) {
+		if (!vregion_put(alloc->vreg))
+			rfree(alloc);
+	}
+#endif
 }
 
 static void ring_buffer_reset(struct sof_audio_buffer *audio_buffer)
diff --git a/src/audio/module_adapter/module_adapter.c b/src/audio/module_adapter/module_adapter.c
index 9e6720424bcf..a2dad0a8d340 100644
--- a/src/audio/module_adapter/module_adapter.c
+++ b/src/audio/module_adapter/module_adapter.c
@@ -230,15 +230,25 @@ struct comp_dev *module_adapter_new_ext(const struct comp_driver *drv,
 #endif
 	comp_cl_dbg(drv, "start");
 
+	struct comp_ipc_config local_config;
+
 	if (!config) {
 		comp_cl_err(drv, "NULL config! drv = %p", drv);
 		return NULL;
 	}
+
+	local_config = *config;
+
 #if CONFIG_IPC_MAJOR_4
 	if (config->ipc_extended_init) {
 		ret = module_ext_init_decode(drv, &ext_data, &spec);
 		if (ret != 0)
 			return NULL;
+
+#if CONFIG_ZEPHYR_DP_SCHEDULER
+		if (ext_data.dp_data)
+			local_config.proc_domain = COMP_PROCESSING_DOMAIN_DP;
+#endif
 	}
 #endif
 	const struct module_ext_init_data *ext_init =
@@ -248,7 +258,7 @@ struct comp_dev *module_adapter_new_ext(const struct comp_driver *drv,
 		NULL;
 #endif
 
-	struct processing_module *mod = module_adapter_mem_alloc(drv, config, ext_init);
+	struct processing_module *mod = module_adapter_mem_alloc(drv, &local_config, ext_init);
 
 	if (!mod)
 		return NULL;
@@ -275,7 +285,7 @@ struct comp_dev *module_adapter_new_ext(const struct comp_driver *drv,
 
 #if CONFIG_ZEPHYR_DP_SCHEDULER
 	/* create a task for DP processing */
-	if (config->proc_domain == COMP_PROCESSING_DOMAIN_DP) {
+	if (dev->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP) {
 		/* All data allocated, create a thread */
 		ret = pipeline_comp_dp_task_init(dev);
 		if (ret) {
@@ -1183,7 +1193,7 @@ static int module_adapter_copy_ring_buffers(struct comp_dev *dev)
 		/* input - we need to copy data from audio_stream (as source)
 		 * to ring_buffer (as sink)
 		 */
-		err = audio_buffer_sync_secondary_buffer(&buffer->audio_buffer, UINT_MAX);
+		err = audio_buffer_sync_secondary_buffer(&buffer->audio_buffer, SIZE_MAX);
 
 		if (err) {
 			comp_err(dev, "LL to DP copy error status: %d", err);
@@ -1365,7 +1375,6 @@ int module_adapter_copy(struct comp_dev *dev)
 			return module_adapter_copy_ring_buffers(dev);
 		else
 			return module_adapter_sink_source_copy(dev);
-
 	}
 
 	comp_err(dev, "unknown processing_data_type");
diff --git a/src/ipc/ipc4/helper.c b/src/ipc/ipc4/helper.c
index 0907bcb03ace..e09e5edf39b3 100644
--- a/src/ipc/ipc4/helper.c
+++ b/src/ipc/ipc4/helper.c
@@ -781,18 +781,22 @@ __cold int ipc4_comp_connect(struct ipc *ipc, const struct ipc4_module_bind_unbi
 	struct mod_alloc_ctx *alloc;
 
 #if CONFIG_ZEPHYR_DP_SCHEDULER
-	if (source->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP &&
-	    sink->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP) {
+	bool src_is_dp = source->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP;
+	bool sink_is_dp = sink->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP;
+#if CONFIG_DP_TO_DP_BIND
+	bool dp_to_dp = src_is_dp && sink_is_dp;
+#else
+	if (src_is_dp && sink_is_dp) {
 		tr_err(&ipc_tr, "DP to DP binding is not supported: can't bind %x to %x",
 		       src_id, sink_id);
 		return IPC4_INVALID_REQUEST;
 	}
-
+#endif
 	struct comp_dev *dp;
 
-	if (sink->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP)
+	if (sink_is_dp)
 		dp = sink;
-	else if (source->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP)
+	else if (src_is_dp)
 		dp = source;
 	else
 		dp = NULL;
@@ -865,8 +869,8 @@ __cold int ipc4_comp_connect(struct ipc *ipc, const struct ipc4_module_bind_unbi
 	 *
 	 *	size = 2*max(obs of source module, ibs of destination module)
 	 *	(obs and ibs is single buffer size)
-	 * in case of DP -> LL
-	 *	size = 2*ibs of destination (LL) module. DP queue will handle obs of DP module
+	 * in case of DP -> LL or DP -> DP
+	 *	size = 2*ibs of destination module. DP queue will handle obs of DP module
 	 */
 	if (source->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_LL)
 		buf_size = MAX(ibs, obs) * 2;
@@ -901,12 +905,13 @@ __cold int ipc4_comp_connect(struct ipc *ipc, const struct ipc4_module_bind_unbi
 #if CONFIG_ZEPHYR_DP_SCHEDULER
 	struct ring_buffer *ring_buffer = NULL;
 
-	if (sink->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP ||
-	    source->ipc_config.proc_domain == COMP_PROCESSING_DOMAIN_DP) {
+	if (src_is_dp || sink_is_dp) {
 		struct processing_module *srcmod = comp_mod(source);
 		struct module_data *src_module_data = &srcmod->priv;
 		struct processing_module *dstmod = comp_mod(sink);
 		struct module_data *dst_module_data = &dstmod->priv;
+		bool is_shared = audio_buffer_is_shared(&buffer->audio_buffer);
+		uint32_t buf_id = buf_get_id(buffer);
 
 		/*
 		 * Handle cases where the size of the ring buffer depends on the
@@ -918,16 +923,54 @@ __cold int ipc4_comp_connect(struct ipc *ipc, const struct ipc4_module_bind_unbi
 		 */
 		ring_buffer = ring_buffer_create(dp, MAX(ibs, dst_module_data->mpd.in_buff_size),
 						 MAX(obs, src_module_data->mpd.out_buff_size),
-						 audio_buffer_is_shared(&buffer->audio_buffer),
-						 buf_get_id(buffer));
+						 is_shared, buf_id);
 		if (!ring_buffer) {
 			buffer_free(buffer);
 			return IPC4_OUT_OF_MEMORY;
 		}
 
+#if CONFIG_DP_TO_DP_BIND
+		/* refcount the DP vregion for this ring_buffer (matches vregion_put in
+		 * ring_buffer_free for DP-to-DP binding)
+		 */
+		if (ring_buffer->audio_buffer.alloc)
+			vregion_get(ring_buffer->audio_buffer.alloc->vreg);
+#endif
+
 		/* data destination module needs to use ring_buffer */
 		audio_buffer_attach_secondary_buffer(&buffer->audio_buffer, dp == source,
 						     &ring_buffer->audio_buffer);
+
+#if CONFIG_DP_TO_DP_BIND
+		/*
+		 * DP-to-DP binding: both source and sink are DP modules.
+		 * A second ring_buffer is needed on the other side of the comp_buffer
+		 * so each DP module has its own lock-free ring_buffer interface.
+		 * Data flows: src_DP -> ring_buf_src -> comp_buffer -> ring_buf_sink -> sink_DP
+		 * The comp_buffer acts as the intermediary synced during LL cycles.
+		 */
+		if (dp_to_dp) {
+			struct ring_buffer *ring_buffer2;
+
+			ring_buffer2 =
+				ring_buffer_create(source,
+						   MAX(ibs, dst_module_data->mpd.in_buff_size),
+						   MAX(obs, src_module_data->mpd.out_buff_size),
+						   is_shared, buf_id);
+			if (!ring_buffer2) {
+				buffer_free(buffer);
+				return IPC4_OUT_OF_MEMORY;
+			}
+
+			/* refcount the source DP vregion for ring_buffer2 */
+			if (ring_buffer2->audio_buffer.alloc)
+				vregion_get(ring_buffer2->audio_buffer.alloc->vreg);
+
+			/* attach second ring_buffer on the source side */
+			audio_buffer_attach_secondary_buffer(&buffer->audio_buffer, dp != source,
+							     &ring_buffer2->audio_buffer);
+		}
+#endif
 	}
 
 #endif /* CONFIG_ZEPHYR_DP_SCHEDULER */
diff --git a/zephyr/Kconfig b/zephyr/Kconfig
index 651b1ec16775..253869f0a773 100644
--- a/zephyr/Kconfig
+++ b/zephyr/Kconfig
@@ -267,6 +267,15 @@ config ZEPHYR_DP_SCHEDULER_MIN_STACK_SIZE
          ext_init payload. If the stack size requested in the IPC is
          smaller than this, then the value defined here takes over.
 
+config DP_TO_DP_BIND
+	bool "Support DP to DP component binding"
+	default y
+	depends on ZEPHYR_DP_SCHEDULER
+	help
+	  Enable binding between two Data Processing (DP) scheduled components.
+	  This allows connecting DP modules together (e.g. DP source to DP sink)
+	  via intermediate buffering.
+
 config CROSS_CORE_STREAM
 	bool "Enable cross-core connected pipelines"
 	default y if IPC_MAJOR_4

From 537023e145c6c1ff9e829a3569d8f952ae4eee7c Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Mon, 24 Aug 2026 17:11:00 +0300
Subject: [PATCH 08/15] platform: cavs: initialize DP scheduler during
 platform_init

Call scheduler_dp_init() in platform_init() on cAVS platforms when
CONFIG_ZEPHYR_DP_SCHEDULER is enabled so that DP tasks (such as MFCC
and MWW in the Data Processing domain) can bind to the DP scheduler
without failing with -ENODEV (-19).

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 app/boards/intel_adsp/Kconfig.defconfig | 3 +++
 app/boards/intel_adsp_cavs25.conf       | 2 ++
 src/platform/intel/cavs/platform.c      | 7 +++++++
 zephyr/Kconfig                          | 8 ++++----
 4 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/app/boards/intel_adsp/Kconfig.defconfig b/app/boards/intel_adsp/Kconfig.defconfig
index 0fd7126d58d1..ac8c6fb6f190 100644
--- a/app/boards/intel_adsp/Kconfig.defconfig
+++ b/app/boards/intel_adsp/Kconfig.defconfig
@@ -87,6 +87,9 @@ config L3_HEAP
 config ZEPHYR_DP_SCHEDULER
 	default y
 
+config DP_TO_DP_BIND
+	default y
+
 config ZEPHYR_NATIVE_DRIVERS
 	default y
 
diff --git a/app/boards/intel_adsp_cavs25.conf b/app/boards/intel_adsp_cavs25.conf
index 7b2a260db89d..0a89f1a669a7 100644
--- a/app/boards/intel_adsp_cavs25.conf
+++ b/app/boards/intel_adsp_cavs25.conf
@@ -19,6 +19,8 @@ CONFIG_PCM_CONVERTER_FORMAT_S24_3LE=y
 
 # SOF / infrastructure
 CONFIG_AMS=y
+CONFIG_IDC_TIMEOUT_US=100000
+CONFIG_P4WQ_INIT_STAGE_EARLY=y
 CONFIG_LP_MEMORY_BANKS=1
 CONFIG_HP_MEMORY_BANKS=30
 
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/zephyr/Kconfig b/zephyr/Kconfig
index 253869f0a773..547f5326d64a 100644
--- a/zephyr/Kconfig
+++ b/zephyr/Kconfig
@@ -239,19 +239,19 @@ config DMA_DOMAIN_SEM_LIMIT
 config PIPELINE_2_0
 	bool "Enable pipeline 2.0 changes"
 	depends on IPC_MAJOR_4
-	default y if ACE
+	default y if (ACE || CAVS)
 	help
 	  This flag enables changes to new pipeline structure, known as pipeline2_0
 	  It is required for certain new features, like DP_SCHEDULER.
 
 config ZEPHYR_DP_SCHEDULER
 	bool "use Zephyr thread based DP scheduler"
-	default y if ACE
+	default y if (ACE || CAVS)
 	depends on IPC_MAJOR_4
 	depends on ZEPHYR_SOF_MODULE
-	depends on ACE
+	depends on (ACE || CAVS)
 	depends on PIPELINE_2_0
-	imply XTENSA_HIFI_SHARING if XTENSA_HIFI
+	imply XTENSA_HIFI_SHARING if (XTENSA_HIFI && ACE)
 	help
 	  Enable Data Processing preemptive scheduler based on
 	  Zephyr preemptive threads.

From a5f9ad78791349e86a20026c77a64d16e7b00413 Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Thu, 27 Aug 2026 15:24:07 +0300
Subject: [PATCH 09/15] audio: mfcc: add mel40 and mel40_10ms profiles for
 microWakeWord frontend

Add 40-bin mel filterbank configurations (mel40, mel40_compress,
mel40_10ms, and mel40_10ms_compress) to setup_mfcc.m and generate
the corresponding topology blobs for microWakeWord streaming frontends.

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 src/audio/mfcc/mfcc_common.c                  |  2 +-
 src/audio/mfcc/tune/setup_mfcc.m              | 44 +++++++++++++++++++
 src/include/sof/audio/mfcc/mfcc_vad.h         |  8 ++--
 .../topology2/cavs-benchmark-hda.conf         | 10 +++++
 .../development/tplg-targets-bench.cmake      |  2 +
 .../include/bench/mfcc_controls_capture.conf  |  1 +
 .../include/bench/mfcc_controls_playback.conf |  1 +
 .../include/bench/mfccmel40_10ms_s16.conf     | 13 ++++++
 .../include/bench/mfccmel40_10ms_s24.conf     | 13 ++++++
 .../include/bench/mfccmel40_10ms_s32.conf     | 13 ++++++
 .../components/mfcc/ceps13_compress_dtx.conf  |  2 +-
 .../include/components/mfcc/default.conf      |  2 +-
 .../include/components/mfcc/mel40.conf        | 24 ++++++++++
 .../include/components/mfcc/mel40_10ms.conf   | 24 ++++++++++
 .../components/mfcc/mel40_10ms_compress.conf  | 24 ++++++++++
 .../components/mfcc/mel40_compress.conf       | 24 ++++++++++
 .../include/components/mfcc/mel80.conf        |  2 +-
 .../components/mfcc/mel80_compress.conf       |  2 +-
 .../components/mfcc/mel80_compress_dtx.conf   |  2 +-
 19 files changed, 203 insertions(+), 10 deletions(-)
 create mode 100644 tools/topology/topology2/include/bench/mfccmel40_10ms_s16.conf
 create mode 100644 tools/topology/topology2/include/bench/mfccmel40_10ms_s24.conf
 create mode 100644 tools/topology/topology2/include/bench/mfccmel40_10ms_s32.conf
 create mode 100644 tools/topology/topology2/include/components/mfcc/mel40.conf
 create mode 100644 tools/topology/topology2/include/components/mfcc/mel40_10ms.conf
 create mode 100644 tools/topology/topology2/include/components/mfcc/mel40_10ms_compress.conf
 create mode 100644 tools/topology/topology2/include/components/mfcc/mel40_compress.conf

diff --git a/src/audio/mfcc/mfcc_common.c b/src/audio/mfcc/mfcc_common.c
index 3890671165fd..2460493ed34d 100644
--- a/src/audio/mfcc/mfcc_common.c
+++ b/src/audio/mfcc/mfcc_common.c
@@ -419,7 +419,7 @@ int mfcc_stft_process(struct processing_module *mod, struct mfcc_comp_data *cd)
 		/* Use hop counter for frame numbering (independent of VAD enable) */
 		state->header.frame_number = state->hop_count;
 
-		/* Run VAD on the mel log spectrum (available in both modes) */
+		/* Run VAD on the scaled mel log spectrum (available in both modes) */
 		if (config->enable_vad) {
 			mfcc_vad_update(&cd->vad, state->mel_log_32);
 
diff --git a/src/audio/mfcc/tune/setup_mfcc.m b/src/audio/mfcc/tune/setup_mfcc.m
index dbf69587a74f..13ffea4955ec 100644
--- a/src/audio/mfcc/tune/setup_mfcc.m
+++ b/src/audio/mfcc/tune/setup_mfcc.m
@@ -31,6 +31,50 @@ 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);
+
+	% 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 40-bin/10ms-hop mel spectrogram, matching microWakeWord's
+	% MixConv front-end shape (40 features per 10ms stride over a 30ms
+	% window, 125 Hz to 7500 Hz bandwidth) -- 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.low_freq = 125;
+	setup.high_freq = 7500;
+	setup.tplg_fn = 'mel40_10ms.conf';
+	export_mfcc_setup(gen_cfg, setup);
+
+	% Same 40-bin/10ms-hop mel spectrogram with compress PCM output for
+	% microWakeWord compress stream capture.
+	setup = get_mel_spectrogram_config();
+	setup.frame_length = 30.0;
+	setup.frame_shift = 10.0;
+	setup.num_mel_bins = 40;
+	setup.low_freq = 125;
+	setup.high_freq = 7500;
+	setup.compress_output = true;
+	setup.tplg_fn = 'mel40_10ms_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/include/sof/audio/mfcc/mfcc_vad.h b/src/include/sof/audio/mfcc/mfcc_vad.h
index 6873343d334e..048ea475eba1 100644
--- a/src/include/sof/audio/mfcc/mfcc_vad.h
+++ b/src/include/sof/audio/mfcc/mfcc_vad.h
@@ -29,9 +29,9 @@ struct processing_module;
 #define MFCC_VAD_NOISE_INIT_FRAMES	100
 
 /**
- * \brief Slow noise floor rise coefficient in Q1.15 (0.003 * 2^15).
+ * \brief Slow noise floor rise coefficient in Q1.15 (0.001 * 2^15, tau ~10 s).
  */
-#define MFCC_VAD_NOISE_RISE_ALPHA	98
+#define MFCC_VAD_NOISE_RISE_ALPHA	33
 
 /**
  * \brief Fast noise floor rise coefficient in Q1.15 (0.020 * 2^15).
@@ -39,9 +39,9 @@ struct processing_module;
 #define MFCC_VAD_NOISE_RISE_ALPHA_FAST	655
 
 /**
- * \brief Energy threshold for speech detection in Q9.23 (0.30 * 2^23).
+ * \brief Energy threshold for speech detection in Q9.23 (0.20 * 2^23).
  */
-#define MFCC_VAD_ENERGY_THRESHOLD	2516582
+#define MFCC_VAD_ENERGY_THRESHOLD	1677722
 
 /**
  * \brief Hangover frame count to keep VAD active after last speech detection.
diff --git a/tools/topology/topology2/cavs-benchmark-hda.conf b/tools/topology/topology2/cavs-benchmark-hda.conf
index d1357caa20ab..182ccb403a44 100644
--- a/tools/topology/topology2/cavs-benchmark-hda.conf
+++ b/tools/topology/topology2/cavs-benchmark-hda.conf
@@ -845,6 +845,16 @@ IncludeByKey.BENCH_CONFIG {
 		<include/bench/mfccmel_s32.conf>
 	}
 
+	"mfccmel40_10ms16" {
+		<include/bench/mfccmel40_10ms_s16.conf>
+	}
+	"mfccmel40_10ms24" {
+		<include/bench/mfccmel40_10ms_s24.conf>
+	}
+	"mfccmel40_10ms32" {
+		<include/bench/mfccmel40_10ms_s32.conf>
+	}
+
 	#
 	# Micsel component
 	#
diff --git a/tools/topology/topology2/development/tplg-targets-bench.cmake b/tools/topology/topology2/development/tplg-targets-bench.cmake
index 2dd4f28bc07c..416448fd181e 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_10ms"
 	"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_10ms"
 	"BENCH_MICSEL_PARAMS=passthrough"
 	"BENCH_PHASE_VOCODER_PARAMS=default"
 	"BENCH_RTNR_PARAMS=default"
diff --git a/tools/topology/topology2/include/bench/mfcc_controls_capture.conf b/tools/topology/topology2/include/bench/mfcc_controls_capture.conf
index 8788387ec8c7..a185cdec852f 100644
--- a/tools/topology/topology2/include/bench/mfcc_controls_capture.conf
+++ b/tools/topology/topology2/include/bench/mfcc_controls_capture.conf
@@ -7,6 +7,7 @@
 					IncludeByKey.BENCH_MFCC_PARAMS {
 						"default" "include/components/mfcc/default.conf"
 						"mel80" "include/components/mfcc/mel80.conf"
+						"mel40_10ms" "include/components/mfcc/mel40_10ms.conf"
 					}
 				}
 				mixer."1" {
diff --git a/tools/topology/topology2/include/bench/mfcc_controls_playback.conf b/tools/topology/topology2/include/bench/mfcc_controls_playback.conf
index 007dbb91cd4f..6d7ae6585663 100644
--- a/tools/topology/topology2/include/bench/mfcc_controls_playback.conf
+++ b/tools/topology/topology2/include/bench/mfcc_controls_playback.conf
@@ -7,6 +7,7 @@
 					IncludeByKey.BENCH_MFCC_PARAMS {
 						"default" "include/components/mfcc/default.conf"
 						"mel80" "include/components/mfcc/mel80.conf"
+						"mel40_10ms" "include/components/mfcc/mel40_10ms.conf"
 					}
 				}
 				mixer."1" {
diff --git a/tools/topology/topology2/include/bench/mfccmel40_10ms_s16.conf b/tools/topology/topology2/include/bench/mfccmel40_10ms_s16.conf
new file mode 100644
index 000000000000..ec89bffb90a1
--- /dev/null
+++ b/tools/topology/topology2/include/bench/mfccmel40_10ms_s16.conf
@@ -0,0 +1,13 @@
+		# Created with script "./bench_comp_generate.sh mfcc"
+		Object.Widget.mfcc.1 {
+			index $BENCH_PLAYBACK_HOST_PIPELINE
+			<include/bench/one_input_output_format_s16_16k.conf>
+			<include/bench/mfcc_controls_playback.conf>
+		}
+		Object.Widget.mfcc.2 {
+			index $BENCH_CAPTURE_HOST_PIPELINE
+			<include/bench/one_input_output_format_s16_16k.conf>
+			<include/bench/mfcc_controls_capture.conf>
+		}
+		<include/bench/host_gateway_pipelines_s16_16k.conf>
+		<include/bench/mfcc_route.conf>
diff --git a/tools/topology/topology2/include/bench/mfccmel40_10ms_s24.conf b/tools/topology/topology2/include/bench/mfccmel40_10ms_s24.conf
new file mode 100644
index 000000000000..73571fabe5f2
--- /dev/null
+++ b/tools/topology/topology2/include/bench/mfccmel40_10ms_s24.conf
@@ -0,0 +1,13 @@
+		# Created with script "./bench_comp_generate.sh mfcc"
+		Object.Widget.mfcc.1 {
+			index $BENCH_PLAYBACK_HOST_PIPELINE
+			<include/bench/one_input_output_format_s24_16k.conf>
+			<include/bench/mfcc_controls_playback.conf>
+		}
+		Object.Widget.mfcc.2 {
+			index $BENCH_CAPTURE_HOST_PIPELINE
+			<include/bench/one_input_output_format_s24_16k.conf>
+			<include/bench/mfcc_controls_capture.conf>
+		}
+		<include/bench/host_gateway_pipelines_s24_16k.conf>
+		<include/bench/mfcc_route.conf>
diff --git a/tools/topology/topology2/include/bench/mfccmel40_10ms_s32.conf b/tools/topology/topology2/include/bench/mfccmel40_10ms_s32.conf
new file mode 100644
index 000000000000..75c01eaf4a43
--- /dev/null
+++ b/tools/topology/topology2/include/bench/mfccmel40_10ms_s32.conf
@@ -0,0 +1,13 @@
+		# Created with script "./bench_comp_generate.sh mfcc"
+		Object.Widget.mfcc.1 {
+			index $BENCH_PLAYBACK_HOST_PIPELINE
+			<include/bench/one_input_output_format_s32_16k.conf>
+			<include/bench/mfcc_controls_playback.conf>
+		}
+		Object.Widget.mfcc.2 {
+			index $BENCH_CAPTURE_HOST_PIPELINE
+			<include/bench/one_input_output_format_s32_16k.conf>
+			<include/bench/mfcc_controls_capture.conf>
+		}
+		<include/bench/host_gateway_pipelines_s32_16k.conf>
+		<include/bench/mfcc_route.conf>
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..6c0c52ba09e5 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 03-Sep-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..7284f8209454 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 03-Sep-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..79ecd2b74e62
--- /dev/null
+++ b/tools/topology/topology2/include/components/mfcc/mel40.conf
@@ -0,0 +1,24 @@
+# Exported MFCC configuration 03-Sep-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,
+		0xe0,0x01,0x40,0x01,0x40,0x1f,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..cca9bba95d1b
--- /dev/null
+++ b/tools/topology/topology2/include/components/mfcc/mel40_10ms.conf
@@ -0,0 +1,24 @@
+# Exported MFCC configuration 03-Sep-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,
+		0xe0,0x01,0xa0,0x00,0x4c,0x1d,0x7d,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_compress.conf b/tools/topology/topology2/include/components/mfcc/mel40_10ms_compress.conf
new file mode 100644
index 000000000000..26769e6538fb
--- /dev/null
+++ b/tools/topology/topology2/include/components/mfcc/mel40_10ms_compress.conf
@@ -0,0 +1,24 @@
+# Exported MFCC configuration 03-Sep-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,
+		0xe0,0x01,0xa0,0x00,0x4c,0x1d,0x7d,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/mel40_compress.conf b/tools/topology/topology2/include/components/mfcc/mel40_compress.conf
new file mode 100644
index 000000000000..cb9f76105869
--- /dev/null
+++ b/tools/topology/topology2/include/components/mfcc/mel40_compress.conf
@@ -0,0 +1,24 @@
+# Exported MFCC configuration 03-Sep-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,
+		0xe0,0x01,0x40,0x01,0x40,0x1f,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..fd2023c7f408 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 03-Sep-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..c219b406f70c 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 03-Sep-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..14b2aaaac2fd 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 03-Sep-2026
 # cd src/audio/mfcc/tune; octave setup_mfcc.m
 Object.Base.data."mfcc_config" {
 	bytes "

From 84fabf1f53134a1069ce8d28417fd6de8b82f9f3 Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Thu, 27 Aug 2026 15:24:07 +0300
Subject: [PATCH 10/15] audio: microwakeword: implement mww component

Add microWakeWord (MWW) processing module for low-power keyword
spotting:
- Implement module adapter in mww.c with soft mel-log AGC, VAD gating,
  and KPB wake-on-voice notification.
- Implement TFLM bridge in mww_model.cc with MixConv operator resolver,
support for int8 dequantization, and streaming resource variable
resets.
- Add robust circular ring buffer boundary unwrap handling for MFCC
hops.
- Add 2-step debounce verification before triggering KPB drain.
- Add LLEXT wrapper and build system integration for both static and
  dynamic module targets.
- Include initial placeholder model interface in mww_model_data.h/cc.

Signed-off-by: Liam Girdwood <liam.r.girdwood@linux.intel.com>
Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 src/audio/CMakeLists.txt                     |   3 +
 src/audio/Kconfig                            |   1 +
 src/audio/microwakeword/CMakeLists.txt       | 375 ++++++++++++
 src/audio/microwakeword/Kconfig              |  31 +
 src/audio/microwakeword/README.md            | 151 +++++
 src/audio/microwakeword/llext-wrap.c         | 135 +++++
 src/audio/microwakeword/llext/CMakeLists.txt | 185 ++++++
 src/audio/microwakeword/llext/llext.toml.h   |   6 +
 src/audio/microwakeword/mww.c                | 601 +++++++++++++++++++
 src/audio/microwakeword/mww.toml             |  21 +
 src/audio/microwakeword/mww_model.cc         | 260 ++++++++
 src/audio/microwakeword/mww_model.h          |  70 +++
 src/audio/microwakeword/mww_model_data.cc    |   5 +
 src/audio/microwakeword/mww_model_data.h     |  13 +
 uuid-registry.txt                            |   1 +
 15 files changed, 1858 insertions(+)
 create mode 100644 src/audio/microwakeword/CMakeLists.txt
 create mode 100644 src/audio/microwakeword/Kconfig
 create mode 100644 src/audio/microwakeword/README.md
 create mode 100644 src/audio/microwakeword/llext-wrap.c
 create mode 100644 src/audio/microwakeword/llext/CMakeLists.txt
 create mode 100644 src/audio/microwakeword/llext/llext.toml.h
 create mode 100644 src/audio/microwakeword/mww.c
 create mode 100644 src/audio/microwakeword/mww.toml
 create mode 100644 src/audio/microwakeword/mww_model.cc
 create mode 100644 src/audio/microwakeword/mww_model.h
 create mode 100644 src/audio/microwakeword/mww_model_data.cc
 create mode 100644 src/audio/microwakeword/mww_model_data.h

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/microwakeword/CMakeLists.txt b/src/audio/microwakeword/CMakeLists.txt
new file mode 100644
index 000000000000..cd298c4ccea7
--- /dev/null
+++ b/src/audio/microwakeword/CMakeLists.txt
@@ -0,0 +1,375 @@
+# Copyright (c) 2026 Intel Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# Newer xt-clang LLVMs accept this to keep literals in .rodata; standard
+# Clang or older xt-clang rejects the sub-option. Detect it.
+set(MWW_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"
+    MWW_HAS_MLLVM_TEXT_SECTION_LITERALS_FALSE)
+  if(MWW_HAS_MLLVM_TEXT_SECTION_LITERALS_FALSE)
+    set(MWW_TEXT_SECTION_LITERALS_FALSE_FLAG -mllvm --text-section-literals=false)
+    add_compile_options(${MWW_TEXT_SECTION_LITERALS_FALSE_FLAG})
+  endif()
+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}
+)
+
+if(MWW_HAS_MLLVM_TEXT_SECTION_LITERALS_FALSE)
+  target_compile_options(mww_nn_hifi_lib PRIVATE
+    ${MWW_TEXT_SECTION_LITERALS_FALSE_FLAG}
+  )
+endif()
+
+target_compile_definitions(mww_nn_hifi_lib PRIVATE
+	-DHIFI4=1
+	-DHAVE_VFPU=1
+	-DHAVE_VFPU_SINGLE_PRECISION=1
+	-DXCHAL_HAVE_HIFI4=1
+	-DXCHAL_HAVE_HIFI4_VFPU=1
+
+	__xtensa__=1
+	__XTENSA__=1
+	__XCC__
+	__XCC_CLANG__
+	"XT_MAX(a,b)=((a)>(b)?(a):(b))"
+	"XT_MIN(a,b)=((a)<(b)?(a):(b))"
+	"AE_MOVINT16_FROMINT32(v)=((ae_int16)(v))"
+	"AE_CVT64F32_H(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_H(v))"
+	"AE_CVT64F32_L(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_L(v))"
+)
+
+target_compile_options(mww_nn_hifi_lib PRIVATE
+	-fsigned-char
+	-fno-exceptions
+	-mlongcalls
+	-fno-zero-initialized-in-bss
+	-Wsign-compare
+	-DMODEL_INT16
+	-DNNLIB_V2
+	-Dhifi4
+	-DTFLITE_SINGLE_ROUNDING=1
+	-mcpu=${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 STATIC
+	${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
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/dequantize_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/kernels/dequantize.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/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
+	${TFLM_PATH}/tensorflow/lite/micro/micro_time.cc
+	# debug_log.cc is replaced by our printk-backed DebugLog() in mww.c.
+	#${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}
+	${NN_HIFI_PATH}/include
+)
+endif()
+
+if(MWW_HAVE_NNLIB_HIFI4)
+target_compile_definitions(mww_tflm_lib PRIVATE
+	-DHIFI4=1
+	-DHAVE_VFPU=1
+	-DHAVE_VFPU_SINGLE_PRECISION=1
+	-DXCHAL_HAVE_HIFI4=1
+	-DXCHAL_HAVE_HIFI4_VFPU=1
+
+	__xtensa__=1
+	__XTENSA__=1
+	__XCC__
+	__XCC_CLANG__
+	"XT_MAX(a,b)=((a)>(b)?(a):(b))"
+	"XT_MIN(a,b)=((a)<(b)?(a):(b))"
+	"AE_MOVINT16_FROMINT32(v)=((ae_int16)(v))"
+	"AE_CVT64F32_H(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_H(v))"
+	"AE_CVT64F32_L(v)=((ae_int64)(int64_t)(int32_t)AE_MOVAD32_L(v))"
+)
+target_compile_options(mww_tflm_lib PRIVATE
+	-DHIFI4
+	-DKERNELS_OPTIMIZED_FOR_SPEED
+	-DNNLIB_V2
+)
+endif() # MWW_HAVE_NNLIB_HIFI4
+
+target_compile_options(mww_tflm_lib PRIVATE
+	-std=c++17
+	-fno-rtti
+	-fno-exceptions
+	-fno-threadsafe-statics
+	-Wnon-virtual-dtor
+	-fno-unwind-tables
+	-fmessage-length=0
+	-DTF_LITE_STATIC_MEMORY
+	-DTF_LITE_DISABLE_X86_NEON
+	-Wsign-compare
+	-Wdouble-promotion
+	-Wunused-variable
+	-Wswitch
+	-Wvla
+	-Wall
+	-Wextra
+	-Wmissing-field-initializers
+	-Wstrict-aliasing
+	-Wno-unused-parameter
+	-DXTENSA
+	-DTF_LITE_MCU_DEBUG_LOG
+	-DTF_LITE_USE_CTIME
+	-mlongcalls
+	-Wno-shadow
+)
+
+if(CMAKE_CXX_COMPILER_ID MATCHES "Clang|Xtensa" OR CMAKE_C_COMPILER MATCHES "xt-clang")
+	target_compile_options(mww_tflm_lib PRIVATE
+		-stdlib=libc++
+		"SHELL:-include xtensahifiintrin.h"
+		-fno-vectorize
+		-fno-slp-vectorize
+	)
+else()
+	target_compile_options(mww_tflm_lib PRIVATE
+		-fno-tree-vectorize
+		-fno-tree-slp-vectorize
+	)
+endif()
+
+if(CMAKE_C_COMPILER_ID STREQUAL "Xtensa")
+	target_compile_options(mww_tflm_lib PRIVATE
+		--xtensa-core=ace10_LX7HiFi4_2022_10
+		-mcoproc
+	)
+elseif(CMAKE_C_COMPILER_ID STREQUAL "Clang")
+	target_compile_options(mww_tflm_lib PRIVATE
+		-mcpu=${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(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")
+  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..db0685687cc0
--- /dev/null
+++ b/src/audio/microwakeword/Kconfig
@@ -0,0 +1,31 @@
+# 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.
+
+if COMP_MWW
+
+config COMP_MWW_DEBUG_TRACE
+	bool "Verbose per-hop / per-inference debug traces"
+	default n
+	help
+	  Enable mww per-hop and per-inference debug prints in the hot path.
+	  Leave disabled for production and normal capture tuning.
+
+config COMP_MWW_MODEL_FROM_CONTROL
+	bool "Load MWW model from bytes control instead of built-in header"
+	default n
+	help
+	  When enabled, the MWW wake-word model is loaded from a runtime
+	  or topology configuration data blob via binary kcontrol (bytes control)
+	  instead of using the static mww_model_data embedded in the firmware image.
+
+endif # COMP_MWW
diff --git a/src/audio/microwakeword/README.md b/src/audio/microwakeword/README.md
new file mode 100644
index 000000000000..cf050e73ed4a
--- /dev/null
+++ b/src/audio/microwakeword/README.md
@@ -0,0 +1,151 @@
+# 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)"]
+        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
+```
+
+---
+
+## Deployment Targets
+
+- **Aphid (PTL / ACE 3.0)**: `CONFIG_COMP_MWW=m`, built and deployed as a
+  real loadable `.llext` module — the target that exercises SOF's LLEXT
+  loading/relocation path, not just a compile check.
+  `app/boards/intel_adsp_ace30_ptl.conf` disables `CONFIG_COMP_TENSORFLOW`
+  on this board (tflmcly is not yet GNU-toolchain-clean here, and mww
+  builds its own independent TFLM lib copy so it doesn't need it).
+
+Building `mww` as a real LLEXT module (rather than statically linked)
+required several fixes to SOF's and Zephyr's LLEXT support, landed
+alongside this component:
+
+- `library_manager/llext_manager.c` — section rebase/relocation fixup
+  after SOF's own address rebasing, `.bss`/`DATA` region packing, and a
+  tracked `.exported_sym` segment.
+- `zephyr/CMakeLists.txt` — `-Wl,-Bsymbolic-functions` so multi-TU C++
+  internal calls (TFLM spans many `.cc` files) resolve locally.
+- `src/lib/cpp_new_export.cpp` — exported `operator new`/`delete`/
+  `__cxa_pure_virtual` for C++ LLEXT modules.
+- A companion Zephyr fork branch (`feature/llext-stb-weak-fixes`) treats
+  `STB_WEAK` symbols (C++ template-instantiated vtables/typeinfo) the
+  same as `STB_GLOBAL` in the LLEXT export table, PLT resolution, and
+  `GLOB_DAT`/`JMP_SLOT` relocation — required for TFLM's vtables to
+  resolve correctly inside the loaded module.
+
+---
+
+## Building and Testing
+
+### Building the topology target
+
+From `sof/tools/build_tools`:
+
+```bash
+ninja topology2_prod_sof-ptl-hda-mww-kpb
+```
+
+### Deploying and testing on aphid
+
+```bash
+# Pristine firmware + llext build
+CCACHE_DISABLE=1 ./sof/scripts/xtensa-build-zephyr.py -p ptl -k sof/keys/otc_private_key_3k.pem
+
+# Deploy .ri / sof-ipc4-lib tree / .tplg to aphid, then reboot to load
+ssh -i ~/.ssh/aphid_deploy root@aphid 'timeout 10s arecord -D hw:0,0 -f S32_LE -r 16000 -c 2 -d 10 /tmp/test.wav'
+ssh -i ~/.ssh/aphid_deploy root@aphid 'timeout 10s /usr/local/bin/mtrace-reader.py'
+```
+
+Look for `MWW DBG feature range` and `MWW probability=` log lines in the
+mtrace output.
+
+---
+
+## Known Issues / Open Items
+
+---
+
+## 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
+  (placeholder, swappable for a trained/converted checkpoint).
+- **mww.toml**: rimage module manifest entry.
+- **llext/**: LLEXT build scaffolding (CMakeLists.txt, llext.toml.h,
+  reentrant-stub shims mirroring `src/audio/template/`).
+- **../../tools/topology/topology2/include/components/mww.conf**:
+  Topology v2 widget class definition.
+- **../../tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-mww-capture.conf**:
+  Detection pipeline template.
+- **../../tools/topology/topology2/sof-hda-mww.conf**: Top-level WoV
+  topology configuration.
diff --git a/src/audio/microwakeword/llext-wrap.c b/src/audio/microwakeword/llext-wrap.c
new file mode 100644
index 000000000000..b868cea8c79d
--- /dev/null
+++ b/src/audio/microwakeword/llext-wrap.c
@@ -0,0 +1,135 @@
+// SPDX-License-Identifier: BSD-3-Clause
+//
+// Copyright(c) 2026 Intel Corporation. All rights reserved.
+
+#include <sys/types.h>
+#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;
+
+ssize_t _read_r(struct _reent *ptr, int fd, void *buf, size_t cnt)
+{
+	errno = ENOTSUP;
+	return -1;
+}
+
+ssize_t _write_r(struct _reent *ptr, int fd, const void *buf, size_t cnt)
+{
+	errno = ENOTSUP;
+	return -1;
+}
+
+void *_sbrk_r(struct _reent *ptr, ptrdiff_t incr)
+{
+	errno = ENOTSUP;
+	return (void *)-1;
+}
+
+int _lseek_r(struct _reent *ptr, int fd, int pos, int whence)
+{
+	errno = ENOTSUP;
+	return -1;
+}
+
+int _kill_r(struct _reent *ptr, int pid, int sig)
+{
+	errno = ENOTSUP;
+	return -1;
+}
+
+int _getpid_r(struct _reent *ptr)
+{
+	errno = ENOTSUP;
+	return -1;
+}
+
+int _fstat_r(struct _reent *ptr, int fd, struct stat *pstat)
+{
+	errno = ENOTSUP;
+	return -1;
+}
+
+int _close_r(struct _reent *ptr, int fd)
+{
+	errno = ENOTSUP;
+	return -1;
+}
+
+/*
+ * 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..eea0e065fb96
--- /dev/null
+++ b/src/audio/microwakeword/llext/CMakeLists.txt
@@ -0,0 +1,185 @@
+# 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 ${sof_top_dir}/../nnlib-hifi4/xa_nnlib)
+
+set(MWW_TOOLCHAIN_ROOT "")
+if(DEFINED ZEPHYR_SDK_INSTALL_DIR AND NOT "${ZEPHYR_SDK_INSTALL_DIR}" STREQUAL "")
+	set(MWW_TOOLCHAIN_ROOT
+		${ZEPHYR_SDK_INSTALL_DIR}/gnu/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf/xtensa-${SOC_TOOLCHAIN_NAME}_zephyr-elf)
+endif()
+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/dequantize_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
+	${TFLM_PATH}/tensorflow/lite/micro/kernels/dequantize.cc
+)
+
+add_library(mww_tflm_lib STATIC
+	${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
+	${TFLM_PATH}/tensorflow/lite/micro/micro_time.cc
+	# debug_log.cc is replaced by our printk-backed DebugLog() in mww.c.
+	#${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
+	${MWW_HAL_SOC_PATH}
+)
+
+target_compile_options(mww_tflm_lib PRIVATE
+	-std=c++17
+	-fno-rtti
+	-fno-exceptions
+	-fno-threadsafe-statics
+	-fno-unwind-tables
+	-fmessage-length=0
+	-DTF_LITE_STATIC_MEMORY
+	-DTF_LITE_DISABLE_X86_NEON
+	-Wno-unused-parameter
+	-DXTENSA
+	-DTF_LITE_MCU_DEBUG_LOG
+	-DTF_LITE_USE_CTIME
+	-mlongcalls
+	-fPIC
+	# TF_LITE_STRIP_ERROR_STRINGS drops DebugLog()/DebugVsnprintf()'s calls
+	# into vprintf/vsnprintf; NDEBUG drops libc assert(). Both chains pull
+	# in libc.a objects (vfprintf/fwrite/abort) that are not built PIC-safe,
+	# which GNU ld refuses ("dangerous relocation") when linking this
+	# ET_DYN/PIC LLEXT.
+	-DTF_LITE_STRIP_ERROR_STRINGS
+	-DNDEBUG
+)
+
+if(CMAKE_CXX_COMPILER_ID MATCHES "Clang|Xtensa" OR CMAKE_C_COMPILER MATCHES "xt-clang")
+	target_compile_options(mww_tflm_lib PRIVATE
+		-stdlib=libc++
+		-fno-vectorize
+		-fno-slp-vectorize
+	)
+	if(NOT CMAKE_C_COMPILER MATCHES "xt-clang")
+		target_compile_options(mww_tflm_lib PRIVATE
+			-mcpu=${SOC_TOOLCHAIN_NAME}
+		)
+		if(NOT "${MWW_TOOLCHAIN_ROOT}" STREQUAL "")
+			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
+				${MWW_TOOLCHAIN_ROOT}/include
+			)
+		endif()
+	endif()
+endif()
+
+set(MWW_LIBS_PATHS .)
+if(NOT "${MWW_TOOLCHAIN_ROOT}" STREQUAL "")
+	list(APPEND MWW_LIBS_PATHS ${MWW_TOOLCHAIN_ROOT}/lib)
+endif()
+
+sof_llext_build("mww"
+	SOURCES
+		../mww.c
+		../llext-wrap.c
+	INCLUDES
+		${TFLM_PATH}
+		${FLATBUFFERS_PATH}/include
+		${GEMMLOWP_PATH}
+		${RUY_PATH}
+		${sof_top_dir}/posix/include
+	LIBS
+		mww_tflm_lib
+	LIBS_PATH
+		${MWW_LIBS_PATHS}
+)
+
+if(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" AND NOT CMAKE_C_COMPILER MATCHES "xt-clang" AND NOT "${MWW_TOOLCHAIN_ROOT}" STREQUAL "")
+	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..f99b02b11e79
--- /dev/null
+++ b/src/audio/microwakeword/mww.c
@@ -0,0 +1,601 @@
+// 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"
+
+#include <stdarg.h>
+#include <stdio.h>
+#include <zephyr/sys/printk.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 vsnprintk(buffer, buf_size, format, vlist);
+}
+
+#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))
+
+/* Pre-roll history in ms that KPB drains to host on wake-word trigger. */
+#define MWW_KPB_DRAIN_REQ_MS 1000
+
+/* MFCC delivers one hop every 10 ms; MWW processes 3 hops per inference. */
+#define MWW_MFCC_HOP_MS 10
+
+/* Wake-word probability threshold above which KPB draining is triggered. */
+#define MWW_DETECT_THRESHOLD 0.65f
+
+/* Consecutive inferences above threshold required to confirm detection (~60 ms). */
+#define MWW_CONSECUTIVE_DETECTS_REQUIRED 2
+
+/* Number of startup inferences to warm up the temporal ring buffers before enabling triggers (~1 sec). */
+#define MWW_WARMUP_INFERENCES 33
+
+/* Soft mel-log AGC (units: Q9.23, matches MFCC output). One decade = +10 dB.
+ * Target +2.5 dB (+0.25 in Q9.23), floor -20 dB. Attack: instant clamp so peak+gain
+ * never exceeds MEL_CLIP_MAX_Q23 (+1.0, +10 dB). Release: dual-rate additive recovery:
+ *   - Normal release during silence (VAD == 0): ~0.5 dB/s (100 hops/s).
+ *   - Super-slow leak during speech (VAD == 1): ~0.05 dB/s (1/10th speed) to guarantee
+ *     the AGC never stays permanently trapped at minimum gain even if VAD gets stuck.
+ */
+#define MWW_AGC_GAIN_TARGET_Q23        2097152 /* +2.5 dB (0.25 * 2^23) */
+#define MWW_AGC_GAIN_FLOOR_Q23         -16777216 /* -20 dB, int32(-20 * 0.1 * 2^23) */
+#define MWW_AGC_RELEASE_STEP_Q23       4194 /* 0.5 dB/s, int32((0.5 * 0.1 / 100) * 2^23) */
+#define MWW_AGC_RELEASE_STEP_SPEECH_Q23 419 /* 0.05 dB/s, 1/10th normal release */
+
+/* The range -1.0 to +1.0 of Q9.23 Mel values is scaled to +/-1.0 Q1.7. */
+#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_MIN_Q23        (-1 << 23) /* -1.0 in Q23 */
+#define MEL_CLIP_MAX_Q7         127
+#define MEL_CLIP_MIN_Q7         -128
+
+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_ms;
+#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;
+
+	/* Bitmask of VAD flags for the MWW_FEATURE_SLICE_COUNT frames */
+	uint32_t vad_history;
+	/* Persistent AGC gain applied to every Q9.23 mel value */
+	int32_t agc_gain_q23;
+
+	/* Hop buffer for assembling contiguous data when circular buffer wraps */
+	uint8_t hop_buf[MWW_HOP_BYTES] __aligned(4);
+
+	/* Consecutive inferences with probability >= MWW_DETECT_THRESHOLD */
+	uint32_t consecutive_detects;
+
+	/* Telemetry counters */
+	uint32_t total_inferences;
+	uint32_t vad_gated_inferences;
+	uint32_t detections;
+	uint32_t kpb_trigger_events;
+} __attribute__((aligned(8)));
+
+#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_ms;
+
+	dcache_writeback_region(&cd->client_data, sizeof(cd->client_data));
+
+	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_ms;
+	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 */
+
+__cold static void mww_log_summary_at_shutdown(struct processing_module *mod)
+{
+	struct mww_comp_data *cd = mod ? module_get_private_data(mod) : NULL;
+	struct comp_dev *dev = mod ? mod->dev : NULL;
+	char summary_buf[256];
+
+	if (!cd)
+		return;
+
+	snprintk(summary_buf, sizeof(summary_buf),
+		 "[MWW STREAM SHUTDOWN SUMMARY] Total Inferences=%u | VAD Gated=%u | Detections=%u | KPB Triggers=%u | Arena Used=%zu/%zu B",
+		 cd->total_inferences, cd->vad_gated_inferences,
+		 cd->detections, cd->kpb_trigger_events,
+		 MWW_ArenaUsedBytes(), MWW_ArenaCapacity());
+
+	if (dev)
+		comp_info(dev, "%s", summary_buf);
+	printk("%s\n", summary_buf);
+}
+
+__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;
+
+	assert_can_be_cold();
+
+	comp_info(dev, "entry");
+
+	cd = mod_zalloc(mod, sizeof(*cd));
+	if (!cd)
+		return -ENOMEM;
+
+	md->private = cd;
+	cd->model_handler = mod_data_blob_handler_new(mod);
+	if (!cd->model_handler) {
+		mod_free(mod, cd);
+		return -ENOMEM;
+	}
+
+	cd->drain_req_ms = MWW_KPB_DRAIN_REQ_MS;
+	cd->agc_gain_q23 = MWW_AGC_GAIN_TARGET_Q23;
+#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;
+
+	unsigned char *model_ptr = NULL;
+
+#if CONFIG_COMP_MWW_MODEL_FROM_CONTROL
+	size_t blob_size;
+
+	model_ptr = comp_get_data_blob(cd->model_handler, &blob_size, NULL);
+	if (!model_ptr || !blob_size) {
+		comp_err(dev, "MWW: model blob not set from control");
+		return -EINVAL;
+	}
+	comp_info(dev, "MWW: loaded model from control blob, size=%zu", blob_size);
+#endif
+
+	ret = MWW_SetModel(&cd->mwc, model_ptr);
+	if (ret < 0) {
+		comp_err(dev, "MWW_SetModel failed: %d (%s)", ret, cd->mwc.error);
+		return ret;
+	}
+
+	ret = MWW_InitOps(&cd->mwc);
+	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;
+	cd->vad_history = 0;
+	cd->consecutive_detects = 0;
+	comp_info(dev, "MWW model initialized: arena_used=%zu / capacity=%zu bytes",
+		  MWW_ArenaUsedBytes(), MWW_ArenaCapacity());
+
+	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 struct mfcc_data_header *hdr;
+		const int32_t *mel;
+		const uint8_t *hop_src;
+		size_t bytes_to_end;
+		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;
+
+		/* Assemble hop into contiguous memory if it wraps across the ring buffer boundary */
+		bytes_to_end = (const uint8_t *)buf_start + buf_size - (const uint8_t *)data_ptr;
+		if (bytes_to_end >= MWW_HOP_BYTES) {
+			hop_src = (const uint8_t *)data_ptr;
+		} else {
+			memcpy(cd->hop_buf, data_ptr, bytes_to_end);
+			memcpy(cd->hop_buf + bytes_to_end, buf_start, MWW_HOP_BYTES - bytes_to_end);
+			hop_src = cd->hop_buf;
+		}
+
+		hdr = (const struct mfcc_data_header *)hop_src;
+		mel = (const int32_t *)(hop_src + sizeof(struct mfcc_data_header));
+		slice = &cd->feature_buf[cd->feature_slices_filled * MWW_FEATURE_SIZE];
+
+		/* Update VAD history bitmask across MWW_FEATURE_SLICE_COUNT slices */
+		cd->vad_history = ((cd->vad_history << 1) | (hdr->vad_flag ? 1U : 0U)) &
+				  ((1U << MWW_FEATURE_SLICE_COUNT) - 1);
+
+		/* AGC: attack on this hop's peak (always track energy) */
+		int32_t hop_peak_q23 = mel[0];
+
+		for (i = 1; i < MWW_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 < MWW_AGC_GAIN_FLOOR_Q23)
+			cd->agc_gain_q23 = MWW_AGC_GAIN_FLOOR_Q23;
+
+		int32_t agc_gain_q23 = cd->agc_gain_q23;
+
+		/* Requantize to int8 (Q1.7 range matching training AGC normalization) */
+		for (i = 0; i < MWW_FEATURE_SIZE; i++) {
+			int32_t mel_c = mel[i] + agc_gain_q23;
+
+			/* Clamp to [-1.0, +1.0] Q9.23 range before Q1.7 conversion */
+			if (mel_c > MEL_CLIP_MAX_Q23)
+				mel_c = MEL_CLIP_MAX_Q23;
+			else if (mel_c < MEL_CLIP_MIN_Q23)
+				mel_c = MEL_CLIP_MIN_Q23;
+
+			/* Rescale 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;
+
+			slice[i] = (int8_t)mel_c;
+		}
+
+		/* Release: normal recovery during silence, super-slow leak during speech */
+		if (cd->agc_gain_q23 < MWW_AGC_GAIN_TARGET_Q23) {
+			int32_t step = hdr->vad_flag ?
+				MWW_AGC_RELEASE_STEP_SPEECH_Q23 : MWW_AGC_RELEASE_STEP_Q23;
+
+			cd->agc_gain_q23 += step;
+			if (cd->agc_gain_q23 > MWW_AGC_GAIN_TARGET_Q23)
+				cd->agc_gain_q23 = MWW_AGC_GAIN_TARGET_Q23;
+		}
+
+#if CONFIG_COMP_MWW_DEBUG_TRACE
+		{
+			static int dbg_hop_count;
+			int32_t mel_min = mel[0], mel_max = mel[0];
+			int8_t f_min = slice[0], f_max = slice[0];
+
+			dbg_hop_count++;
+			for (i = 1; i < MWW_FEATURE_SIZE; i++) {
+				if (mel[i] < mel_min) mel_min = mel[i];
+				if (mel[i] > mel_max) mel_max = mel[i];
+				if (slice[i] < f_min) f_min = slice[i];
+				if (slice[i] > f_max) f_max = slice[i];
+			}
+			comp_info(dev, "[MWW 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);
+		}
+#endif
+
+		/* Copy source data to sink so downstream stages keep seeing 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 snk_bytes_to_end = (uint8_t *)snk_buf_start +
+							  snk_buf_size - (uint8_t *)snk_ptr;
+				if (snk_bytes_to_end >= MWW_HOP_BYTES) {
+					memcpy(snk_ptr, hop_src, MWW_HOP_BYTES);
+				} else {
+					memcpy(snk_ptr, hop_src, snk_bytes_to_end);
+					memcpy(snk_buf_start,
+					       hop_src + snk_bytes_to_end,
+					       MWW_HOP_BYTES - snk_bytes_to_end);
+				}
+				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) {
+			cd->feature_slices_filled = 0;
+
+			cd->total_inferences++;
+			cd->mwc.audio_features = cd->feature_buf;
+			cd->mwc.audio_data_size = MWW_FEATURE_ELEM_COUNT;
+
+#if CONFIG_COMP_MWW_DEBUG_TRACE
+			uint32_t c0 = k_cycle_get_32();
+#endif
+			ret = MWW_ProcessClassify(&cd->mwc);
+#if CONFIG_COMP_MWW_DEBUG_TRACE
+			uint32_t c1 = k_cycle_get_32();
+#endif
+			if (ret < 0) {
+				comp_err(dev, "MWW_ProcessClassify failed: %d (%s)",
+					 ret, cd->mwc.error);
+				continue;
+			}
+
+#if CONFIG_COMP_MWW_DEBUG_TRACE
+			comp_info(dev, "MWW probability=%d raw=%u (th=%p prio=%d cycles=%u)",
+				  (int)(cd->mwc.probability * 100.0f), cd->mwc.raw_output,
+				  k_current_get(), k_thread_priority_get(k_current_get()),
+				  c1 - c0);
+#endif
+
+			if (cd->mwc.probability >= MWW_DETECT_THRESHOLD) {
+				cd->consecutive_detects++;
+				if (cd->total_inferences > MWW_WARMUP_INFERENCES &&
+				    cd->consecutive_detects >= MWW_CONSECUTIVE_DETECTS_REQUIRED) {
+					cd->detections++;
+					comp_info(dev, "MWW keyword detected: probability=%d pct (consecutive=%u, total=%u)",
+						  (int)(cd->mwc.probability * 100.0f),
+						  cd->consecutive_detects, cd->detections);
+					cd->kpb_trigger_events++;
+					mww_notify_kpb(mod);
+					cd->consecutive_detects = 0;
+				}
+			} else {
+				cd->consecutive_detects = 0;
+			}
+		}
+	}
+
+	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");
+	mww_log_summary_at_shutdown(mod);
+	cd->feature_slices_filled = 0;
+	cd->vad_history = 0;
+	cd->consecutive_detects = 0;
+	cd->total_inferences = 0;
+	cd->agc_gain_q23 = MWW_AGC_GAIN_TARGET_Q23;
+	memset(cd->feature_buf, 0, sizeof(cd->feature_buf));
+	MWW_Reset();
+	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");
+	mww_log_summary_at_shutdown(mod);
+
+#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
+
+	MWW_Free();
+
+	mod_data_blob_handler_free(mod, cd->model_handler);
+	mod_free(mod, cd);
+	return 0;
+}
+
+__cold 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)
+{
+	struct mww_comp_data *cd = module_get_private_data(mod);
+
+	if (mod->dev->state != COMP_STATE_INIT && mod->dev->state != COMP_STATE_READY) {
+		comp_warn(mod->dev, "mww_set_config(): model update ignored while not idle (state %d)",
+			  mod->dev->state);
+		return 0;
+	}
+
+	return comp_data_blob_set(cd->model_handler, pos, data_offset_size,
+				  fragment, fragment_size);
+}
+
+__cold static int mww_get_config(struct processing_module *mod,
+				  uint32_t config_id, uint32_t *data_offset_size,
+				  uint8_t *fragment, size_t fragment_size)
+{
+	struct sof_ipc_ctrl_data *cdata = (struct sof_ipc_ctrl_data *)fragment;
+	struct mww_comp_data *cd = module_get_private_data(mod);
+
+	return comp_data_blob_get_cmd(cd->model_handler, cdata, fragment_size);
+}
+
+static const struct module_interface mww_interface = {
+	.init = mww_init,
+	.prepare = mww_prepare,
+	.process = mww_process,
+	.set_configuration = mww_set_config,
+	.get_configuration = mww_get_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..188ebdfc2bcd
--- /dev/null
+++ b/src/audio/microwakeword/mww_model.cc
@@ -0,0 +1,260 @@
+// 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 "mww_model.h"
+
+#if !CONFIG_COMP_MWW_MODEL_FROM_CONTROL
+#include "mww_model_data.h"
+#endif
+
+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 kArenaSize = 131072;
+alignas(16) static uint8_t g_arena[kArenaSize];
+
+// 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;
+
+// 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;
+
+// Ops used by the hey_jarvis.tflite streaming graph: CALL_ONCE, VAR_HANDLE,
+// READ_VARIABLE, ASSIGN_VARIABLE, RESHAPE, CONCATENATION, STRIDED_SLICE,
+// CONV_2D, DEPTHWISE_CONV_2D, FULLY_CONNECTED, LOGISTIC, QUANTIZE,
+// DEQUANTIZE.
+using MwwOpResolver = tflite::MicroMutableOpResolver<14>;
+static MwwOpResolver *op_resolver;
+
+int RegisterOps(MwwOpResolver *op_resolver) {
+	TF_LITE_ENSURE_STATUS(op_resolver->AddCallOnce());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddVarHandle());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddReadVariable());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddAssignVariable());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddReshape());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddConcatenation());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddStridedSlice());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddConv2D());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddDepthwiseConv2D());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddFullyConnected());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddLogistic());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddQuantize());
+	TF_LITE_ENSURE_STATUS(op_resolver->AddDequantize());
+	return 0;
+}
+
+static int Init_Interpreter(struct mww_classify *mwc);
+
+int MWW_InitOps(struct mww_classify *mwc)
+{
+	op_resolver = new MwwOpResolver();
+	if (!op_resolver) {
+		mwc->error = "op_resolver alloc failed (OOM)";
+		return -ENOMEM;
+	}
+
+	if (RegisterOps(op_resolver) != 0) {
+		mwc->error = "register ops failed";
+		return -EINVAL;
+	}
+
+	// VAR_HANDLE/ASSIGN_VARIABLE require an explicit MicroResourceVariables
+	// instance registered with the interpreter -- without one,
+	// VarHandlePrepare()/AssignVariable Eval() fail with kTfLiteError as soon
+	// as AllocateTensors() prepares the first VAR_HANDLE node (see
+	// tensorflow/lite/micro/kernels/var_handle.cc). Building via the
+	// allocator-based MicroInterpreter constructor lets us create the
+	// allocator once and share it with MicroResourceVariables::Create().
+	allocator = tflite::MicroAllocator::Create(g_arena, kArenaSize);
+	if (!allocator) {
+		mwc->error = "allocator alloc failed (OOM)";
+		delete op_resolver;
+		op_resolver = nullptr;
+		return -ENOMEM;
+	}
+
+	resource_variables = tflite::MicroResourceVariables::Create(allocator, kNumResourceVariables);
+	if (!resource_variables) {
+		mwc->error = "resource_variables alloc failed (OOM)";
+		delete op_resolver;
+		op_resolver = nullptr;
+		return -ENOMEM;
+	}
+
+	// create the interpreter
+	interpreter = new tflite::MicroInterpreter(model, *op_resolver,
+						   allocator, resource_variables);
+	if (!interpreter) {
+		mwc->error = "interpreter alloc failed (OOM)";
+		delete op_resolver;
+		op_resolver = nullptr;
+		return -ENOMEM;
+	}
+
+	// and allocate the tensors
+	if (interpreter->AllocateTensors() != kTfLiteOk) {
+		mwc->error = "interpreter tensor allocate failed";
+		delete interpreter;
+		delete op_resolver;
+		interpreter = nullptr;
+		op_resolver = nullptr;
+		return -EINVAL;
+	}
+
+	// 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 int8 or uint8
+	if (output->type != kTfLiteInt8 && output->type != kTfLiteUInt8) {
+		mwc->error = "output tensor type != int8/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)
+{
+#if !CONFIG_COMP_MWW_MODEL_FROM_CONTROL
+	if (!model_tflite)
+		model_tflite = const_cast<unsigned char *>(mww_model_data);
+#endif
+
+	if (!model_tflite) {
+		mwc->error = "no model provided";
+		return -EINVAL;
+	}
+
+	// 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(model_tflite);
+	if (model->version() != TFLITE_SCHEMA_VERSION) {
+		mwc->error = "failed to load model";
+		return -EINVAL;
+	}
+
+	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
+	float raw_val;
+	if (output->type == kTfLiteInt8) {
+		int8_t val = tflite::GetTensorData<int8_t>(output)[0];
+		raw_val = static_cast<float>(val);
+		mwc->raw_output = val;
+	} else {
+		uint8_t val = tflite::GetTensorData<uint8_t>(output)[0];
+		raw_val = static_cast<float>(val);
+		mwc->raw_output = val;
+	}
+
+	mwc->probability = (raw_val - output_zero_point) * output_scale;
+	if (mwc->probability < 0.0f)
+		mwc->probability = 0.0f;
+	else if (mwc->probability > 1.0f)
+		mwc->probability = 1.0f;
+
+	return 0;
+}
+
+int MWW_Reset(void)
+{
+	if (resource_variables)
+		resource_variables->ResetAll();
+	return 0;
+}
+
+void MWW_Free(void)
+{
+	delete interpreter;
+	delete op_resolver;
+	interpreter = nullptr;
+	op_resolver = nullptr;
+	allocator = nullptr;
+	resource_variables = nullptr;
+	model = nullptr;
+	input = nullptr;
+	output = nullptr;
+}
+
+size_t MWW_ArenaUsedBytes(void)
+{
+	return interpreter ? interpreter->arena_used_bytes() : 0;
+}
+
+size_t MWW_ArenaCapacity(void)
+{
+	return kArenaSize;
+}
diff --git a/src/audio/microwakeword/mww_model.h b/src/audio/microwakeword/mww_model.h
new file mode 100644
index 000000000000..d8dd17673978
--- /dev/null
+++ b/src/audio/microwakeword/mww_model.h
@@ -0,0 +1,70 @@
+// 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 */
+	int32_t raw_output;	/**< raw int8/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);
+
+	/* 3rd - perform the inference */
+	int MWW_ProcessClassify(struct mww_classify *mwc);
+
+	/**
+	 * \brief Reset streaming resource variables to zero.
+	 * \return 0 on success.
+	 */
+	int MWW_Reset(void);
+
+	/**
+	 * \brief Free TFLite Micro interpreter and op resolver heap allocations.
+	 */
+	void MWW_Free(void);
+
+	size_t MWW_ArenaUsedBytes(void);
+	size_t MWW_ArenaCapacity(void);
+
+#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..9c872a580d53
--- /dev/null
+++ b/src/audio/microwakeword/mww_model_data.cc
@@ -0,0 +1,5 @@
+// SPDX-License-Identifier: BSD-3-Clause
+#include "mww_model_data.h"
+
+alignas(16) const unsigned char mww_model_data[] = { 0 };
+const size_t mww_model_data_size = 0;
diff --git a/src/audio/microwakeword/mww_model_data.h b/src/audio/microwakeword/mww_model_data.h
new file mode 100644
index 000000000000..5ec4adab89a3
--- /dev/null
+++ b/src/audio/microwakeword/mww_model_data.h
@@ -0,0 +1,13 @@
+// SPDX-License-Identifier: BSD-3-Clause
+// Auto-generated by sof_mww_train.py — do not edit.
+
+#ifndef MWW_MODEL_DATA_H_
+#define MWW_MODEL_DATA_H_
+
+#include <cstddef>
+#include <cstdint>
+
+extern const unsigned char mww_model_data[];
+extern const size_t mww_model_data_size;
+
+#endif  // MWW_MODEL_DATA_H_
diff --git a/uuid-registry.txt b/uuid-registry.txt
index 04c4a3881b21..5c4d0e298b8c 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

From 7b0cbcb47991f99f1a83750f0391717a7ba07318 Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Thu, 27 Aug 2026 15:24:07 +0300
Subject: [PATCH 11/15] audio: microwakeword: update default strawberry model

Update the embedded strawberry wake-word model data array with the
retrained causal streaming MixConv model.

Streaming verification report (threshold 0.65):
=================================================================
microWakeWord Streaming Verification Report (Threshold: 0.65)
=================================================================
  Class              Role         Files   Detected     Rate  Mean Peak
  -------------------------------------------------------------
  silence            Negative      2000          0     0.0%     0.000
  unknown            Negative     25000         21     0.1%     0.002
  strawberry         Positive      3000       2999   100.0%     0.996
  -------------------------------------------------------------
  Overall Wake Word Recall (True Positive Rate) :  99.97% (2999/3000)
  Overall False Alarm Rate (False Positive Rate):   0.08% (21/27000)
  Precision                                     :  99.30%
=================================================================

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 src/audio/microwakeword/mww_model_data.cc | 4631 ++++++++++++++++++++-
 tools/ctl/ipc4/mww/strawberry.txt         |    1 +
 2 files changed, 4630 insertions(+), 2 deletions(-)
 create mode 100644 tools/ctl/ipc4/mww/strawberry.txt

diff --git a/src/audio/microwakeword/mww_model_data.cc b/src/audio/microwakeword/mww_model_data.cc
index 9c872a580d53..89e685d74fa6 100644
--- a/src/audio/microwakeword/mww_model_data.cc
+++ b/src/audio/microwakeword/mww_model_data.cc
@@ -1,5 +1,4632 @@
 // SPDX-License-Identifier: BSD-3-Clause
+// Auto-generated by sof_mww_train.py — do not edit.
+
 #include "mww_model_data.h"
 
-alignas(16) const unsigned char mww_model_data[] = { 0 };
-const size_t mww_model_data_size = 0;
+alignas(16) const unsigned char mww_model_data[] = {
+    0x20, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x00, 0x00,
+    0x14, 0x00, 0x20, 0x00, 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00,
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+    0xfe, 0xff, 0xff, 0xff, 0x20, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,
+    0x0d, 0x00, 0x00, 0x00, 0xeb, 0xff, 0xff, 0xff, 0xef, 0xff, 0xff, 0xff,
+    0x52, 0x00, 0x00, 0x00, 0xe1, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
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+    0xb5, 0xff, 0xff, 0xff, 0x06, 0x00, 0x00, 0x00, 0xf7, 0xff, 0xff, 0xff,
+    0xe9, 0xff, 0xff, 0xff, 0x1d, 0x00, 0x00, 0x00, 0xd2, 0xff, 0xff, 0xff,
+    0x04, 0x00, 0x00, 0x00, 0xd7, 0xfe, 0xff, 0xff, 0x9e, 0xff, 0xff, 0xff,
+    0xdf, 0xff, 0xff, 0xff, 0x1b, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,
+    0xeb, 0xff, 0xff, 0xff, 0xdd, 0xff, 0xff, 0xff, 0xcf, 0xff, 0xff, 0xff,
+    0x26, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0xfd, 0xff, 0xff, 0xff,
+    0x09, 0x00, 0x00, 0x00, 0xea, 0xff, 0xff, 0xff, 0x37, 0x00, 0x00, 0x00,
+    0xfa, 0xff, 0xff, 0xff, 0xf7, 0xff, 0xff, 0xff, 0x21, 0x00, 0x00, 0x00,
+    0x18, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0xf5, 0xff, 0xff, 0xff,
+    0x0d, 0x00, 0x00, 0x00, 0xd5, 0xff, 0xff, 0xff, 0x2b, 0x00, 0x00, 0x00,
+    0xd9, 0xff, 0xff, 0xff, 0xc4, 0xff, 0xff, 0xff, 0x4e, 0xdc, 0xff, 0xff,
+    0x04, 0x00, 0x00, 0x00, 0x10, 0x0e, 0x00, 0x00, 0x3a, 0xfc, 0xd1, 0x45,
+    0x53, 0x52, 0x11, 0x2a, 0xf3, 0xf7, 0xbd, 0x29, 0xf0, 0x34, 0xf6, 0x13,
+    0xac, 0xe2, 0xdc, 0x32, 0xc3, 0x5c, 0x0d, 0xdd, 0x1a, 0xea, 0x81, 0x45,
+    0x2c, 0xcc, 0x2d, 0xa8, 0x5e, 0x35, 0xb5, 0x3a, 0xf8, 0x12, 0xc6, 0x18,
+    0x18, 0x07, 0xbc, 0xc6, 0xce, 0xcf, 0xe1, 0x10, 0xdd, 0xe5, 0x13, 0xe5,
+    0xb1, 0x12, 0x93, 0x35, 0xac, 0x3d, 0xa4, 0x38, 0xc5, 0xe5, 0x92, 0x07,
+    0x59, 0x4c, 0x59, 0x4f, 0xee, 0x5a, 0x2e, 0x2b, 0xf0, 0xce, 0xb9, 0x53,
+    0x37, 0xc1, 0xe4, 0xd5, 0x2c, 0xeb, 0xc0, 0x1a, 0xc1, 0x90, 0xe0, 0x5a,
+    0xa1, 0xd9, 0xe9, 0xcf, 0xfb, 0xef, 0xe2, 0x00, 0xf7, 0x23, 0x16, 0x6c,
+    0x03, 0xbb, 0xe2, 0xb9, 0x52, 0xba, 0x29, 0x0c, 0x37, 0x55, 0x44, 0xaf,
+    0x81, 0x49, 0x22, 0xb5, 0x41, 0x5f, 0xa1, 0xf9, 0x34, 0xea, 0x3f, 0x17,
+    0x34, 0xea, 0x58, 0x33, 0x35, 0x28, 0x3f, 0x03, 0xfa, 0xe9, 0xb1, 0x07,
+    0x4a, 0xaa, 0xd4, 0xfd, 0x23, 0xe9, 0xc1, 0x27, 0xcb, 0x5c, 0x3c, 0xf0,
+    0xe3, 0xf8, 0x04, 0xba, 0x21, 0xc9, 0xed, 0xbc, 0xb6, 0xf5, 0xa2, 0x1c,
+    0xdb, 0x81, 0x0c, 0x41, 0xf0, 0xc4, 0xf4, 0x12, 0x2a, 0xf8, 0xd2, 0xe1,
+    0x12, 0xb8, 0x15, 0xb5, 0x44, 0x17, 0x5c, 0xf6, 0xb5, 0x9c, 0xc3, 0x05,
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+    0x63, 0x6f, 0x6e, 0x73, 0x74, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x92, 0xfe, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,
+    0x14, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x02, 0x1c, 0x00, 0x00, 0x00, 0xd4, 0xfe, 0xff, 0xff,
+    0x0f, 0x00, 0x00, 0x00, 0x61, 0x72, 0x69, 0x74, 0x68, 0x2e, 0x63, 0x6f,
+    0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x35, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x04, 0x00, 0x00, 0x00, 0xce, 0xfe, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,
+    0x14, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x02, 0x1c, 0x00, 0x00, 0x00, 0x10, 0xff, 0xff, 0xff,
+    0x0f, 0x00, 0x00, 0x00, 0x61, 0x72, 0x69, 0x74, 0x68, 0x2e, 0x63, 0x6f,
+    0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x34, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x04, 0x00, 0x00, 0x00, 0x0a, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,
+    0x14, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x02, 0x1c, 0x00, 0x00, 0x00, 0x4c, 0xff, 0xff, 0xff,
+    0x0f, 0x00, 0x00, 0x00, 0x61, 0x72, 0x69, 0x74, 0x68, 0x2e, 0x63, 0x6f,
+    0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x33, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x04, 0x00, 0x00, 0x00, 0x46, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,
+    0x14, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x02, 0x1c, 0x00, 0x00, 0x00, 0x88, 0xff, 0xff, 0xff,
+    0x0f, 0x00, 0x00, 0x00, 0x61, 0x72, 0x69, 0x74, 0x68, 0x2e, 0x63, 0x6f,
+    0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x32, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x04, 0x00, 0x00, 0x00, 0x82, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,
+    0x14, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x02, 0x1c, 0x00, 0x00, 0x00, 0xc4, 0xff, 0xff, 0xff,
+    0x0f, 0x00, 0x00, 0x00, 0x61, 0x72, 0x69, 0x74, 0x68, 0x2e, 0x63, 0x6f,
+    0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x31, 0x00, 0x01, 0x00, 0x00, 0x00,
+    0x02, 0x00, 0x00, 0x00, 0xbe, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01,
+    0x18, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x02, 0x20, 0x00, 0x00, 0x00, 0x04, 0x00, 0x04, 0x00,
+    0x04, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x61, 0x72, 0x69, 0x74,
+    0x68, 0x2e, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0x00,
+    0x1c, 0x00, 0x18, 0x00, 0x17, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x07, 0x00, 0x16, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x01, 0x20, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x54, 0x00, 0x00, 0x00,
+    0x0c, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00,
+    0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
+    0x01, 0x00, 0x00, 0x00, 0x81, 0x80, 0x00, 0x3c, 0x1d, 0x00, 0x00, 0x00,
+    0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f, 0x64, 0x65, 0x66, 0x61,
+    0x75, 0x6c, 0x74, 0x5f, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x5f, 0x61, 0x75,
+    0x64, 0x69, 0x6f, 0x3a, 0x30, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
+    0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00,
+    0x0d, 0x00, 0x00, 0x00, 0xf4, 0x00, 0x00, 0x00, 0xd8, 0x00, 0x00, 0x00,
+    0xc8, 0x00, 0x00, 0x00, 0xb8, 0x00, 0x00, 0x00, 0xa4, 0x00, 0x00, 0x00,
+    0x88, 0x00, 0x00, 0x00, 0x78, 0x00, 0x00, 0x00, 0x64, 0x00, 0x00, 0x00,
+    0x50, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00,
+    0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x4c, 0xff, 0xff, 0xff,
+    0x90, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f, 0x98, 0xff, 0xff, 0xff,
+    0x2d, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2d,
+    0xa8, 0xff, 0xff, 0xff, 0x0e, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x0e, 0xb8, 0xff, 0xff, 0xff, 0x09, 0x00, 0x00, 0x00,
+    0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0xc8, 0xff, 0xff, 0xff,
+    0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04,
+    0xd8, 0xff, 0xff, 0xff, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x03, 0xa8, 0xff, 0xff, 0xff, 0x72, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x72, 0xb4, 0xff, 0xff, 0xff, 0x02, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x02, 0x0c, 0x00, 0x10, 0x00, 0x0f, 0x00, 0x00, 0x00,
+    0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,
+    0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0xdc, 0xff, 0xff, 0xff,
+    0x16, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x16, 0xe8, 0xff, 0xff, 0xff,
+    0x8f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f, 0xf4, 0xff, 0xff, 0xff,
+    0x8e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f, 0x0c, 0x00, 0x0c, 0x00,
+    0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00,
+    0x81, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f,
+};
+
+const size_t mww_model_data_size = 55472;
diff --git a/tools/ctl/ipc4/mww/strawberry.txt b/tools/ctl/ipc4/mww/strawberry.txt
new file mode 100644
index 000000000000..1d9767965d32
--- /dev/null
+++ b/tools/ctl/ipc4/mww/strawberry.txt
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From f9b01fff9e0b70c052cfbf5bf2da3b6b59ec3e2b Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Thu, 27 Aug 2026 15:24:07 +0300
Subject: [PATCH 12/15] audio: microwakeword: tune: add model training and
 verification toolchain

Add an end-to-end offline training, quantization, and verification
toolchain for microWakeWord streaming models using SOF host testbench
MFCC features:
- sof_mfcc_extract_features.sh: Batch-extract real SOF 40-bin mel
  spectrogram features from WAV datasets via sof-testbench4.
- sof_mww_generate_keyword_dataset_piper_tts.sh: Synthesize keyword
  utterances across multiple Piper ONNX neural voices.
- sof_mww_prepare_silence_unknown.sh: Prepare ambient background and
  non-target speech datasets.
- sof_mww_dataset.py: Dataset loader with temporal jitter, silence pool
  background mixing, and hard negative fragment synthesis.
- sof_mww_train.py: Causal streaming MixConv model training with
  quantization-aware calibration and C-array / topology export.
- sof_mww_verify.py: Streaming temporal verification with configurable
  threshold and consecutive-detection debounce.
- sof_mww_train_pipeline.sh: One-shot automation pipeline for training,
  export, and streaming verification.

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 src/audio/microwakeword/tune/README.md        | 156 ++++
 .../tune/sof_mfcc_extract_features.sh         | 182 +++++
 .../microwakeword/tune/sof_mww_dataset.py     | 332 ++++++++
 .../tune/sof_mww_generate_keyword_dataset.sh  | 371 +++++++++
 ...f_mww_generate_keyword_dataset_from_dir.sh | 467 ++++++++++++
 ..._mww_generate_keyword_dataset_piper_tts.sh | 501 ++++++++++++
 .../microwakeword/tune/sof_mww_plot_mtrace.py | 365 +++++++++
 .../tune/sof_mww_prepare_silence_unknown.sh   | 160 ++++
 src/audio/microwakeword/tune/sof_mww_train.py | 528 +++++++++++++
 .../microwakeword/tune/sof_mww_train_pcan.py  | 716 ++++++++++++++++++
 .../tune/sof_mww_train_pipeline.sh            | 215 ++++++
 .../microwakeword/tune/sof_mww_verify.py      | 200 +++++
 12 files changed, 4193 insertions(+)
 create mode 100644 src/audio/microwakeword/tune/README.md
 create mode 100755 src/audio/microwakeword/tune/sof_mfcc_extract_features.sh
 create mode 100644 src/audio/microwakeword/tune/sof_mww_dataset.py
 create mode 100755 src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset.sh
 create mode 100755 src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset_from_dir.sh
 create mode 100755 src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset_piper_tts.sh
 create mode 100755 src/audio/microwakeword/tune/sof_mww_plot_mtrace.py
 create mode 100755 src/audio/microwakeword/tune/sof_mww_prepare_silence_unknown.sh
 create mode 100644 src/audio/microwakeword/tune/sof_mww_train.py
 create mode 100755 src/audio/microwakeword/tune/sof_mww_train_pcan.py
 create mode 100755 src/audio/microwakeword/tune/sof_mww_train_pipeline.sh
 create mode 100644 src/audio/microwakeword/tune/sof_mww_verify.py

diff --git a/src/audio/microwakeword/tune/README.md b/src/audio/microwakeword/tune/README.md
new file mode 100644
index 000000000000..8d5c83d426a7
--- /dev/null
+++ b/src/audio/microwakeword/tune/README.md
@@ -0,0 +1,156 @@
+# microWakeWord (MWW) Training & Tuning Toolchain
+
+This directory contains offline scripts to train, quantize, verify, and export streaming keyword-spotting models for the SOF `microwakeword` (MWW) module using real SOF MFCC features extracted via `sof-testbench4`.
+
+---
+
+## Toolchain Overview
+
+1. **Keyword Dataset Generation & Ingestion:**
+   - [sof_mww_generate_keyword_dataset_from_dir.sh](sof_mww_generate_keyword_dataset_from_dir.sh): Ingests recorded real speech WAV samples from a directory, preserves clean reference clips, and complements them with Room Impulse Response (RIR) reverberation, additive background noise (at varying SNRs), tempo/pitch perturbations, and Gaussian gain jitter.
+   - [sof_mww_generate_keyword_dataset_piper_tts.sh](sof_mww_generate_keyword_dataset_piper_tts.sh): Synthesizes positive keyword WAVs with single-speaker Piper TTS voices, pitch/tempo perturbation (via `sox`), Gaussian gain jitter, and Room Impulse Response (RIR) reverberation.
+   - [sof_mww_generate_keyword_dataset.sh](sof_mww_generate_keyword_dataset.sh): Multi-speaker generation with `piper-sample-generator` (LibriTTS-R).
+   - [sof_mww_prepare_silence_unknown.sh](sof_mww_prepare_silence_unknown.sh): Prepares negative dataset classes (`silence` and `unknown`) by slicing background noise and non-target words from Google Speech Commands v2.
+
+2. **DSP Feature Extraction:**
+   - [sof_mfcc_extract_features.sh](sof_mfcc_extract_features.sh): Emits 184-byte hop records ($24\text{-byte } \texttt{struct mfcc\_data\_header} + 40 \times \text{int32\_t}$ Q9.23 mel values) by running the SOF host testbench with 10 ms mel-40 topology.
+
+3. **Model Training & Export:**
+   - [sof_mww_dataset.py](sof_mww_dataset.py): Parses testbench `.raw` feature files, applies soft mel-log AGC ($0\text{ dB}$ target, $0.5\text{ dB/s}$ release recovery), and builds training slices.
+   - [sof_mww_train.py](sof_mww_train.py): Trains a streaming Keras model, quantizes to `int8` with TFLite Micro resource variables, packages behind SOF IPC4 ABI headers, and emits:
+     - `mww_model_data.{cc,h}`: Drop-in static C array.
+     - `<name>.conf`: ALSA Topology v2 configuration blob for `Object.Base.data`.
+     - `<name>.txt`: Runtime `sof-ctl` binary control file.
+
+4. **Off-Device Verification & Automation:**
+   - [sof_mww_verify.py](sof_mww_verify.py): Feeds 3-hop streaming slices through the quantized TFLite model to verify detection rates and false alarm rates.
+   - [sof_mww_train_pipeline.sh](sof_mww_train_pipeline.sh): End-to-end automation runner.
+
+---
+
+## Quickstart
+
+### Environment Setup
+
+#### Setup Piper Virtual Environment (for Dataset Generation)
+```bash
+python3 -m venv ~/venvs/piper
+source ~/venvs/piper/bin/activate
+pip install --upgrade pip
+pip install piper-sample-generator
+```
+
+#### Setup `piper-sample-generator` and Model Checkpoint
+```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
+```
+
+#### Setup Model Training Virtual Environment (Python 3.10 via `uv`)
+For systems with newer default Python versions (such as Ubuntu 24.04/26.04), install `uv` to manage a Python 3.10 environment required by TensorFlow 2.16:
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+uv venv ~/venvs/mww-train --python 3.10
+source ~/venvs/mww-train/bin/activate
+uv pip install --upgrade pip
+uv pip install "tensorflow==2.16.1" "tf-keras==2.16.0"
+```
+
+### 1. Generate Synthetic Keyword Data
+
+#### Option A: Multi-speaker dataset with `piper-sample-generator` (PyTorch `.pt` model)
+```bash
+./sof_mww_generate_keyword_dataset.sh \
+    --keyword "hey jarvis" \
+    --label hey_jarvis \
+    --venv ~/venvs/piper \
+    ~/wov/wavs
+```
+By default, the script automatically uses:
+- `PIPER_MODEL="$HOME/git/piper-sample-generator/models/en_US-libritts_r-medium.pt"` (auto-detected)
+- `MAX_SAMPLES=1000` (clips synthesized per speaking-rate variation)
+- `MAX_SPEAKERS=200` (cap on LibriTTS-R speaker indices for diverse, high-quality voices)
+- `GAIN_AUG=1` (Gaussian level jitter enabled: peak target -10 dBFS, sigma 5 dB)
+
+These can be overridden via environment variables if desired (e.g., `MAX_SAMPLES=1500 MAX_SPEAKERS=300 ./sof_mww_generate_keyword_dataset.sh ...`).
+
+#### Option B: Single-speaker dataset with `piper-tts` (ONNX `.onnx` model)
+```bash
+./sof_mww_generate_keyword_dataset_piper_tts.sh \
+    --keyword "hey jarvis" \
+    --label hey_jarvis \
+    --voice ~/.local/share/piper-voices/en_US-lessac-medium.onnx \
+    ~/wov/wavs
+```
+
+#### Option C: Real speech dataset from a directory of recorded WAV samples
+To use recorded human speech recordings instead of synthetic TTS:
+```bash
+./sof_mww_generate_keyword_dataset_from_dir.sh \
+    --src-dir /path/to/recorded/audio \
+    --label hi_intel \
+    ~/wov/wavs
+```
+This converts the input audio to 16 kHz 16-bit mono, keeps the clean unperturbed samples, and automatically complements them with RIR convolution, background noise mixing (10–30 dB SNR), pitch/tempo shifts, and Gaussian level jitter to reach a full training set size (`MAX_SAMPLES=1000` by default).
+
+You can also pass `--keyword-dir <label>:<path>` directly to `sof_mww_train_pipeline.sh`:
+```bash
+./sof_mww_train_pipeline.sh \
+    --keyword-dir hi_intel:/path/to/recorded/audio \
+    --name hi_intel \
+    ~/wov/wavs ~/wov/feats ~/wov/model
+```
+
+### 2. Run End-to-End Retraining Pipeline
+With the `mww-train` virtual environment activated:
+```bash
+source ~/venvs/mww-train/bin/activate
+./sof_mww_train_pipeline.sh \
+    --keyword hey_jarvis \
+    --name hey_jarvis \
+    ~/wov/wavs ~/wov/feats ~/wov/model
+```
+
+---
+
+### Training Multiple Keyword Variants into the Same Model
+
+You can train a single wake-word model to trigger on multiple spoken variants (such as `"hey jarvis"` and `"hi jarvis"`) with **no increase in model size or DSP RAM**:
+
+#### 1. Generate Datasets for Both Phrases
+```bash
+./sof_mww_generate_keyword_dataset.sh \
+    --keyword "hey jarvis" --label hey_jarvis \
+    --keyword "hi jarvis"  --label hi_jarvis \
+    --venv ~/venvs/piper \
+    ~/wov/wavs
+```
+
+#### 2. Train with Multiple `--keyword` Arguments
+Pass both directory labels to the training pipeline and specify `--name hey_jarvis`:
+```bash
+source ~/venvs/mww-train/bin/activate
+./sof_mww_train_pipeline.sh \
+    --keyword hey_jarvis \
+    --keyword hi_jarvis \
+    --name hey_jarvis \
+    ~/wov/wavs ~/wov/feats ~/wov/model
+```
+
+Both phrase directories are pooled as the positive target class ($y = 1$). The streaming verification report provides individual detection recall for each phrase, and the single exported `mww_model_data.cc` triggers when either phrase is spoken.
+
+---
+
+### Visualizing Streaming Diagnostics from mtrace
+
+You can visualize real-time hop metrics, energy/noise floor, int8 feature quantization, and detection probability curves captured from device `mtrace` logs:
+
+```bash
+# Capture mtrace output from device:
+sof-mtrace-reader > /tmp/mtrace.txt
+
+# Plot streaming diagnostics:
+./sof_mww_plot_mtrace.py /tmp/mtrace.txt -o /tmp/mww_diag.png
+```
diff --git a/src/audio/microwakeword/tune/sof_mfcc_extract_features.sh b/src/audio/microwakeword/tune/sof_mfcc_extract_features.sh
new file mode 100755
index 000000000000..49e486fe0c92
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mfcc_extract_features.sh
@@ -0,0 +1,182 @@
+#!/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 using the
+# SOF host testbench. Used for training microWakeWord (MWW) models against real
+# SOF DSP frontend 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
+#
+# 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
+#
+# Requirements:
+#   - $SOF_WORKSPACE points at the parent of the sof tree (or workspace root)
+#   - sof-testbench4 built at tools/testbench/build_testbench/install/bin/
+#   - sof-hda-benchmark-mfccmel40_10ms{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] [--tplg <path>]
+
+  wav_root    Directory containing <label>/*.wav (recursed one level).
+  feat_root   Output root; <label>/ subdirs are created as needed.
+  --format    Testbench sample container (S16, S24, S32). Default S32.
+  --tplg      Explicit path to benchmark topology file.
+EOF
+	exit 1
+}
+
+[ $# -ge 2 ] || usage
+
+WAV_ROOT="$1"
+FEAT_ROOT="$2"
+FORMAT="S32"
+CUSTOM_TPLG=""
+
+shift 2
+while [ $# -gt 0 ]; do
+	case "$1" in
+		--format)
+			FORMAT="$2"
+			shift 2
+			;;
+		--tplg)
+			CUSTOM_TPLG="$2"
+			shift 2
+			;;
+		*)
+			usage
+			;;
+	esac
+done
+
+case "$FORMAT" in
+	S16) SF="16" ;;
+	S24) SF="24" ;;
+	S32) SF="32" ;;
+	*) usage ;;
+esac
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+SOF_ROOT="$(cd "$SCRIPT_DIR/../../../../" 2>/dev/null && pwd || true)"
+
+if [[ -z "$SOF_WORKSPACE" ]]; then
+	if [[ -d "$SOF_ROOT/tools/testbench" ]]; then
+		SOF_WORKSPACE="$(cd "$SOF_ROOT/.." && pwd)"
+	fi
+fi
+
+: "${SOF_WORKSPACE:?SOF_WORKSPACE must point at the parent of the sof tree}"
+
+TESTBENCH="${SOF_WORKSPACE}/sof/tools/testbench/build_testbench/install/bin/sof-testbench4"
+if [[ -n "$CUSTOM_TPLG" ]]; then
+	TPLG="$CUSTOM_TPLG"
+else
+	TPLG="${SOF_WORKSPACE}/sof/tools/build_tools/topology/topology2/development/sof-hda-benchmark-mfccmel40_10ms${SF}.tplg"
+fi
+
+[ -x "$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_mww.XXXXXX)
+trap 'rm -rf "$TMPDIR_LOCAL"' EXIT
+
+convert_wav() {
+	local src="$1"
+	local dst="$2"
+
+	# Duplicate utterance with 100ms silence gap to warm up MFCC VAD noise floor
+	case "$FORMAT" in
+		S16) sox "$src" -p pad 0 0.10 | sox - "$src" -R --encoding signed-integer \
+			-L -r 16000 -c 2 -b 16 "$dst" ;;
+		S24) sox "$src" -p pad 0 0.10 | sox - "$src" -R --no-dither --encoding signed-integer \
+			-L -r 16000 -c 2 -b 32 "$dst" vol 0.003906250000 ;;
+		S32) sox "$src" -p pad 0 0.10 | sox - "$src" -R --no-dither --encoding signed-integer \
+			-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"
+	local tb_log="${TMPDIR_LOCAL}/tb.log"
+
+	# Calculate expected hop count for the single (original) utterance
+	local orig_samples
+	orig_samples=$(soxi -s "$wav" 2>/dev/null || echo 0)
+	local orig_sr
+	orig_sr=$(soxi -r "$wav" 2>/dev/null || echo 16000)
+	local orig_hops=$(( (orig_samples * 16000 / orig_sr) / 160 ))
+
+	mkdir -p "$out_dir"
+	convert_wav "$wav" "$raw_in"
+	echo "  extracting: ${base}.raw"
+
+	if ! "$TESTBENCH" -d 0 \
+		-r 16000 -c 2 -b "${FORMAT}_LE" -p 3,4 \
+		-t "$TPLG" -i "$raw_in" -o "$raw_out" > "$tb_log" 2>&1; then
+		cat "$tb_log" >&2
+		echo "Error: testbench failed for $wav" >&2
+		return 1
+	fi
+
+	# Retain only the trailing second-half hops where VAD noise floor is fully settled
+	if [[ "$orig_hops" -gt 0 ]]; then
+		python3 - "$raw_out" "$orig_hops" <<'PYEOF'
+import sys
+import numpy as np
+
+raw_path = sys.argv[1]
+orig_hops = int(sys.argv[2])
+
+data = np.fromfile(raw_path, dtype=np.uint8)
+if data.size >= 184:
+    u32 = data[: (data.size // 4) * 4].view(np.uint32)
+    pos = np.flatnonzero(u32 == 0x6D666363) * 4
+    if len(pos) > orig_hops:
+        start_byte = pos[-orig_hops]
+        data[start_byte:].tofile(raw_path)
+PYEOF
+	fi
+}
+
+total=0
+for label_dir in "$WAV_ROOT"/*/; do
+	[ -d "$label_dir" ] || continue
+	label=$(basename "$label_dir")
+	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/microwakeword/tune/sof_mww_dataset.py b/src/audio/microwakeword/tune/sof_mww_dataset.py
new file mode 100644
index 000000000000..8c5e0d8e7eaf
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_dataset.py
@@ -0,0 +1,332 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+"""
+SOF mel40 feature loader for microWakeWord (MWW) streaming wake-on-voice training.
+
+Consumes the raw hop records emitted by ``sof_mfcc_extract_features.sh`` (one
+file per input WAV, produced by the SOF host 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). At each
+magic offset it reads the 24-byte header and the following 40 int32 Q9.23 mel
+bins, converts to float32 (scaled by 2^-23), and slices recordings into
+fixed-size 100-hop windows (1.0 second at 10ms hop stride) for training.
+
+Standalone CLI mode prints dataset shape and class counts:
+
+    python3 sof_mww_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 = 99  # 33 streaming steps of 3 hops (0.99 second at 10 ms hop stride)
+MFCC_MAGIC = 0x6D666363  # 'ccfm' on disk, matches struct mfcc_data_header.magic
+
+# Soft mel-log AGC applied to log10(mel_power) features. Units mirror the Q9.23
+# runtime AGC in mww.c: gain target +0.25 (=+2.5 dB), floor -2.0 (=-20 dB),
+# instant attack against MEL_CLIP_MAX=+1.0 (+10 dB).
+# Dual-rate release recovery:
+#   - Normal release during silence (vad == 0): 0.5 dB/sec
+#   - Super-slow leak during speech (vad == 1): 0.05 dB/sec (1/10th speed)
+# Features are clamped to [AGC_CLIP_MIN, AGC_CLIP_MAX] ([-1.0, +1.0]) to match
+# the Q1.7 dynamic range of mww.c and maximize int8 quantization precision.
+AGC_GAIN_TARGET = 0.25
+AGC_GAIN_FLOOR = -2.0
+AGC_CLIP_MIN = -1.0
+AGC_CLIP_MAX = 1.0
+AGC_RELEASE_STEP_PER_HOP = 0.0005         # 0.5 dB/s / (100 hops/s * 10 dB/unit)
+AGC_RELEASE_STEP_SPEECH_PER_HOP = 0.00005 # 0.05 dB/s (1/10th rate)
+
+
+def apply_soft_agc(X: np.ndarray, vad: np.ndarray | None = None) -> np.ndarray:
+    """Apply the mel-log AGC to each window or recording independently.
+
+    Clamps all output feature values to ``[AGC_CLIP_MIN, AGC_CLIP_MAX]`` (``[-1.0, +1.0]``).
+    If ``vad`` is supplied (shape matching time frames), release step recovers at normal
+    speed during silence (vad == 0) and super-slow leak during speech (vad == 1).
+
+    ``X`` can have shape:
+      - ``(T, F)``: single recording / sequence of hops
+      - ``(N, WINDOW_HOPS, HOP_BINS, 1)`` or ``(N, WINDOW_HOPS, HOP_BINS)``: batch of windows
+    Each window or recording gets its own AGC state initialized to ``AGC_GAIN_TARGET``; state
+    is not carried across windows.
+    """
+    if X.size == 0:
+        return X
+
+    orig_shape = X.shape
+    if X.ndim == 2:
+        arr = X[np.newaxis, :, :, np.newaxis]
+    elif X.ndim == 3:
+        arr = X[:, :, :, np.newaxis]
+    elif X.ndim == 4:
+        arr = X
+    else:
+        raise ValueError(f"Expected array with 2, 3, or 4 dimensions, got shape {orig_shape}")
+
+    N, T, F, C = arr.shape
+    out = np.empty_like(arr)
+    for n in range(N):
+        gain = AGC_GAIN_TARGET
+        for t in range(T):
+            hop = arr[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] = np.clip(hop + gain, AGC_CLIP_MIN, AGC_CLIP_MAX)
+            is_speech = bool(vad[t]) if (vad is not None and t < len(vad)) else False
+            step = AGC_RELEASE_STEP_SPEECH_PER_HOP if is_speech else AGC_RELEASE_STEP_PER_HOP
+            if gain < AGC_GAIN_TARGET:
+                gain = min(gain + step, AGC_GAIN_TARGET)
+
+    return out.reshape(orig_shape)
+
+
+def load_raw_hops(path: str) -> tuple[np.ndarray, np.ndarray]:
+    """Read a testbench-emitted .raw file, return (N_hops, 40) float32 mel bins and (N_hops,) vad flags.
+
+    Scans the file for MFCC magic markers and decodes the 184-byte hop payloads.
+    """
+    data = np.fromfile(path, dtype=np.uint8)
+    if data.size < HOP_BYTES:
+        return np.zeros((0, HOP_BINS), dtype=np.float32), np.zeros((0,), dtype=np.uint32)
+    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), np.zeros((0,), dtype=np.uint32)
+
+    valid_pos = [p for p in magic_positions if p + HOP_BYTES <= data.size]
+    if not valid_pos:
+        return np.zeros((0, HOP_BINS), dtype=np.float32), np.zeros((0,), dtype=np.uint32)
+
+    n_hops = len(valid_pos)
+    mel = np.empty((n_hops, HOP_BINS), dtype=np.float32)
+    vad = np.empty((n_hops,), dtype=np.uint32)
+
+    for i, p in enumerate(valid_pos):
+        header_bytes = data[p : p + HOP_HEADER_BYTES]
+        vad[i] = np.frombuffer(header_bytes[20:24], dtype=np.uint32)[0]
+        mel_bytes = data[p + HOP_HEADER_BYTES : p + HOP_BYTES]
+        mel_q23 = np.frombuffer(mel_bytes, dtype=np.int32)
+        mel[i] = mel_q23.astype(np.float32) / (1 << 23)
+
+    return mel, vad
+
+
+def build_silence_pool(feat_root: str) -> np.ndarray:
+    """Load and concatenate all silence/noise raw feature files into a single background pool."""
+    silence_dirs = ["silence", "noise", "background"]
+    all_silence: list[np.ndarray] = []
+    for sdir in silence_dirs:
+        pattern = os.path.join(feat_root, sdir, "*.raw")
+        for f in sorted(glob.glob(pattern)):
+            mel, _ = load_raw_hops(f)
+            if mel.shape[0] > 0:
+                all_silence.append(mel)
+
+    if all_silence:
+        return np.concatenate(all_silence, axis=0)
+    # Fallback to pure baseline silence if no silence files exist
+    return np.full((1000, HOP_BINS), AGC_CLIP_MIN, dtype=np.float32)
+
+
+def build_ambient_pool(feat_root: str) -> np.ndarray:
+    """Build a pool of low-level ambient room background (excluding loud dishwashing/tap noise)."""
+    silence_pool = build_silence_pool(feat_root)
+    mask = silence_pool.mean(axis=1) < -0.1
+    if np.any(mask):
+        return silence_pool[mask]
+    return np.full((1000, HOP_BINS), AGC_CLIP_MIN, dtype=np.float32)
+
+
+def slice_into_windows(
+    mel: np.ndarray,
+    window_hops: int = WINDOW_HOPS,
+    hop_step: int = 10,
+    pad_value: float = AGC_CLIP_MIN,
+) -> np.ndarray:
+    """Slice (N_hops, 40) into (M, window_hops, 40) overlapping windows."""
+    n_hops = mel.shape[0]
+    if n_hops < window_hops:
+        # Pad short recordings at the end with silence pad_value
+        pad = np.full((window_hops - n_hops, HOP_BINS), pad_value, dtype=mel.dtype)
+        return np.concatenate([mel, pad], axis=0)[np.newaxis, ...]
+
+    starts = list(range(0, n_hops - window_hops + 1, hop_step))
+    if not starts or starts[-1] != (n_hops - window_hops):
+        starts.append(n_hops - window_hops)
+    windows = [mel[s : s + window_hops] for s in starts]
+    return np.stack(windows, axis=0)
+
+
+def load_dataset(
+    feat_root: str,
+    labels: list[str],
+    window_hops: int = WINDOW_HOPS,
+    jitter_pos_count: int = 6,
+    jitter_neg_count: int = 4,
+    hop_step_long: int = 5,
+    gain_aug_db_min: float = -8.0,
+    gain_aug_db_max: float = 3.0,
+    seed: int = 0,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Load and augment feature recordings from <feat_root>/<label>/*.raw.
+
+    Applies streaming-aware temporal augmentations:
+      - Stratified multi-tier gain augmentation (quiet/far-field, moderate, nominal)
+      - Random time-shift jitter inside a continuous ambient silence slice for keywords
+      - Hard negative mining with partial-keyword onset/offset fragments (y=0)
+      - Dense temporal slicing and background mixing for non-target speech (y=0)
+    """
+    rng = np.random.default_rng(seed)
+    ambient_pool = build_ambient_pool(feat_root)
+
+    def sample_silence_window(length: int) -> np.ndarray:
+        if ambient_pool.shape[0] <= length:
+            return np.full((length, HOP_BINS), AGC_CLIP_MIN, dtype=np.float32)
+        idx = rng.integers(0, ambient_pool.shape[0] - length)
+        return ambient_pool[idx : idx + length].copy()
+
+    def sample_stratified_gain(idx: int, total: int) -> float:
+        """Sample gain with balanced coverage across quiet, moderate, and nominal tiers."""
+        if gain_aug_db_min is None or gain_aug_db_max is None:
+            return 0.0
+        g_min = gain_aug_db_min
+        g_max = gain_aug_db_max
+        span = g_max - g_min
+        # Split into 3 equal tiers: Tier 0 (quiet/far-field), Tier 1 (moderate), Tier 2 (nominal)
+        tier_size = span / 3.0
+        tier = idx % 3
+        tier_min = g_min + tier * tier_size
+        tier_max = tier_min + tier_size
+        gain_db = rng.uniform(tier_min, tier_max)
+        return gain_db * 0.1
+
+    all_X: list[np.ndarray] = []
+    all_y: list[int] = []
+
+    for label in labels:
+        label_dir = os.path.join(feat_root, label)
+        raw_files = sorted(glob.glob(os.path.join(label_dir, "*.raw")))
+        if not raw_files:
+            print(f"Warning: no .raw feature files found in {label_dir}", file=sys.stderr)
+            continue
+
+        is_keyword = (label not in ("silence", "unknown", "noise", "background"))
+        is_silence = (label in ("silence", "noise", "background"))
+
+        for f in raw_files:
+            mel, _ = load_raw_hops(f)
+            if mel.shape[0] == 0:
+                continue
+
+            T = mel.shape[0]
+
+            if is_keyword:
+                # 1. Positive class: Complete keyword with random start offsets & stratified gain
+                for j in range(jitter_pos_count):
+                    win = sample_silence_window(window_hops)
+                    gain_lin = sample_stratified_gain(j, jitter_pos_count)
+
+                    if T <= window_hops:
+                        offset = rng.integers(0, max(1, window_hops - T + 1))
+                        win[offset : offset + T] = mel + gain_lin
+                    else:
+                        offset = rng.integers(0, T - window_hops + 1)
+                        win = mel[offset : offset + window_hops] + gain_lin
+
+                    all_X.append(win)
+                    all_y.append(1)
+
+                # 2. Hard Negative Mining: Partial keyword fragments (onset only, offset only)
+                if T < window_hops:
+                    gain_p = sample_stratified_gain(0, 3)
+                    # Leading partial (first 30-50% of utterance at window end)
+                    win_p = sample_silence_window(window_hops)
+                    prefix_len = max(5, int(T * rng.uniform(0.3, 0.6)))
+                    win_p[window_hops - prefix_len :] = mel[:prefix_len] + gain_p
+                    all_X.append(win_p)
+                    all_y.append(0)
+
+                    # Trailing partial (last 30-50% of utterance at window start)
+                    gain_s = sample_stratified_gain(1, 3)
+                    win_s = sample_silence_window(window_hops)
+                    suffix_len = max(5, int(T * rng.uniform(0.3, 0.6)))
+                    win_s[:suffix_len] = mel[T - suffix_len :] + gain_s
+                    all_X.append(win_s)
+                    all_y.append(0)
+
+            elif is_silence:
+                # Silence: slice across recording
+                if T >= window_hops:
+                    for s in range(0, T - window_hops + 1, 10):
+                        all_X.append(mel[s : s + window_hops])
+                        all_y.append(0)
+                else:
+                    all_X.append(sample_silence_window(window_hops))
+                    all_y.append(0)
+
+            else:
+                # Non-target speech (unknown): temporal jitter & stratified gain
+                for j in range(jitter_neg_count):
+                    win = sample_silence_window(window_hops)
+                    gain_lin = sample_stratified_gain(j, jitter_neg_count)
+
+                    if T <= window_hops:
+                        offset = rng.integers(0, max(1, window_hops - T + 1))
+                        win[offset : offset + T] = mel + gain_lin
+                    else:
+                        offset = rng.integers(0, T - window_hops + 1)
+                        win = mel[offset : offset + window_hops] + gain_lin
+
+                    all_X.append(win)
+                    all_y.append(0)
+
+                # If long recording, add intermediate overlapping slices
+                if T > window_hops + hop_step_long:
+                    for s in range(0, T - window_hops + 1, hop_step_long):
+                        all_X.append(mel[s : s + window_hops])
+                        all_y.append(0)
+
+    # Add pure baseline silence windows to teach network that quiescent background is y=0
+    for _ in range(1000):
+        all_X.append(np.full((window_hops, HOP_BINS), AGC_CLIP_MIN, dtype=np.float32))
+        all_y.append(0)
+
+    if not all_X:
+        return np.zeros((0, window_hops, HOP_BINS, 1), dtype=np.float32), np.zeros((0,), dtype=np.int32)
+
+    X = np.stack(all_X, axis=0)[..., np.newaxis]
+    y = np.array(all_y, dtype=np.int32)
+    return X, y
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print(f"Usage: {sys.argv[0]} <feat_root> <label1> [<label2> ...]")
+        sys.exit(1)
+
+    feat_root = sys.argv[1]
+    labels = sys.argv[2:]
+    X, y = load_dataset(feat_root, labels)
+    print(f"Loaded dataset: X shape = {X.shape}, y shape = {y.shape}")
+    print(f"Positive samples (y=1): {np.sum(y == 1)}, Negative samples (y=0): {np.sum(y == 0)}")
diff --git a/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset.sh b/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset.sh
new file mode 100755
index 000000000000..cd298e16d894
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset.sh
@@ -0,0 +1,371 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# Generate a synthetic keyword WAV dataset for microWakeWord (MWW) with
+# piper-sample-generator (multi-speaker LibriTTS-R), 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
+# sof_mww_prepare_silence_unknown.sh).
+#
+# Requirements:
+#   - piper-sample-generator installed in an activated venv (or $PIPER_VENV set).
+#   - LibriTTS-R generator checkpoint at $PIPER_MODEL. 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
+#   - sox on PATH.
+#
+# 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.
+#   MAX_SAMPLES     Positive clips per speaking-rate loop (default 1000).
+#   MAX_SPEAKERS    Cap on speaker-embedding index (default 200).
+#   SLERP_WEIGHTS   Speaker blending weights passed to generator (default "0.0").
+#   GAIN_NORM          1 = peak-normalize each final WAV to GAIN_PEAK_DBFS (default 1).
+#                   0 = leave baseline (default 1).
+#   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 (default 1).
+
+set -e
+
+KEYWORDS=()
+LABELS=()
+OUT_ROOT=""
+
+usage() {
+	cat >&2 <<EOF
+Usage: $0 --keyword "<text>" [--keyword "<text>" ...] \\
+          [--label <dir> ...] [--model <path.pt>] <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.
+  --model PATH     Path to PyTorch .pt checkpoint (default: \$PIPER_MODEL).
+  --venv  PATH     Path to piper virtual environment (default: \$PIPER_VENV).
+  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 ;;
+		-m|--model)
+			[[ $# -ge 2 ]] || usage
+			PIPER_MODEL="$2"; shift 2 ;;
+		--venv)
+			[[ $# -ge 2 ]] || usage
+			PIPER_VENV="$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
+
+: "${MAX_SAMPLES:=1000}"
+: "${MAX_SPEAKERS:=200}"
+: "${SLERP_WEIGHTS:=0.0}"
+: "${GAIN_NORM:=1}"
+: "${GAIN_PEAK_DBFS:=-10}"
+
+apply_peak_norm() {
+	local dir="$1"
+	local tmp
+	tmp=$(mktemp --suffix=.wav)
+	for wav in "$dir"/*.wav; do
+		[ -f "$wav" ] || continue
+		sox "$wav" "$tmp" gain -n "$GAIN_PEAK_DBFS"
+		mv "$tmp" "$wav"
+	done
+	rm -f "$tmp"
+}
+
+if [[ -n "$PIPER_VENV" ]]; then
+	if [[ -f "$PIPER_VENV/bin/activate" ]]; then
+		# shellcheck disable=SC1091
+		source "$PIPER_VENV/bin/activate"
+	else
+		echo ">>> PIPER_VENV=$PIPER_VENV has no bin/activate; assuming environment is active" >&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 [[ -z "$PIPER_MODEL" ]]; then
+	for cand in \
+		"$HOME/git/piper-sample-generator/models/en_US-libritts_r-medium.pt" \
+		"$HOME/.local/share/piper-sample-generator/en_US-libritts_r-medium.pt"
+	do
+		if [[ -f "$cand" ]]; then
+			PIPER_MODEL="$cand"
+			break
+		fi
+	done
+fi
+
+if [[ -z "$PIPER_MODEL" || ! -f "$PIPER_MODEL" ]]; then
+	cat >&2 <<EOF
+Piper generator checkpoint not found: ${PIPER_MODEL:-<unset>}
+Fetch it once with:
+  mkdir -p \$HOME/git/piper-sample-generator/models
+  wget -O \$HOME/git/piper-sample-generator/models/en_US-libritts_r-medium.pt \\
+    https://github.com/rhasspy/piper-sample-generator/releases/download/v2.0.0/en_US-libritts_r-medium.pt
+or point \$PIPER_MODEL at the downloaded .pt file.
+EOF
+	exit 1
+fi
+
+if [[ -z "$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 $PIPER_REPO to PYTHONPATH for piper_train"
+	fi
+fi
+
+if ! python3 -c "import piper_sample_generator" 2>/dev/null; then
+	echo "piper_sample_generator not importable — activate the Piper venv first" >&2
+	echo "(or pass --venv /path/to/venv or set PIPER_VENV=/path/to/venv)" >&2
+	exit 1
+fi
+
+if ! python3 -c "import piper_train" 2>/dev/null; then
+	cat >&2 <<EOF
+piper_train not importable. Upstream ships it in the git repo but does not
+pip-install it. Either:
+  - set PIPER_REPO=/path/to/piper-sample-generator (git clone root), or
+  - run this script with the repo root on PYTHONPATH.
+EOF
+	exit 1
+fi
+
+RESULTS=()
+for i in "${!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.
+	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 "$RAW_DIR"
+		tag="s$(echo "$scale" | tr -d .)"
+		for f in "$RAW_DIR"/*.wav; do
+			base=$(basename "$f" .wav)
+			case "$base" in
+				s*_*) continue ;;
+			esac
+			mv "$f" "$RAW_DIR/${tag}_${base}.wav"
+		done
+	done
+
+	echo ">>> Augmenting into $FINAL_DIR (16 kHz resampling, Accoustic2 RIR convolution)"
+	export IR_AUG IR_PROB IR_WET IR_DIR
+	python3 - "$RAW_DIR" "$FINAL_DIR" <<'PYEOF'
+import glob
+from math import gcd
+import os
+import random
+import sys
+import wave
+from pathlib import Path
+import numpy as np
+
+src_dir, dst_dir = sys.argv[1], sys.argv[2]
+Path(dst_dir).mkdir(parents=True, exist_ok=True)
+
+ir_aug_enabled = os.environ.get("IR_AUG", "1") == "1"
+ir_prob = float(os.environ.get("IR_PROB", "0.50"))
+ir_wet = float(os.environ.get("IR_WET", "0.20"))
+ir_dir = os.environ.get("IR_DIR", "").strip()
+
+try:
+    from scipy.signal import fftconvolve, resample_poly
+    has_scipy = True
+except ImportError:
+    has_scipy = False
+
+def read_wav_float(path, target_sr=16000):
+    with wave.open(path, "rb") as wi:
+        sr = wi.getframerate()
+        nch = wi.getnchannels()
+        sw = wi.getsampwidth()
+        nframes = wi.getnframes()
+        raw = wi.readframes(nframes)
+    if sw == 2:
+        samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
+    elif sw == 3:
+        b = np.frombuffer(raw, dtype=np.uint8).reshape(-1, 3)
+        b4 = np.pad(b, ((0,0),(1,0)), mode='constant')
+        samples = (b4.view(np.int32).squeeze() >> 8).astype(np.float32) / 8388608.0
+    elif sw == 4:
+        samples = np.frombuffer(raw, dtype=np.int32).astype(np.float32) / 2147483648.0
+    else:
+        samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
+    if nch > 1:
+        samples = samples.reshape(-1, nch).mean(axis=1)
+
+    if sr != target_sr:
+        if has_scipy:
+            g = gcd(target_sr, sr)
+            samples = resample_poly(samples, target_sr // g, sr // g)
+        else:
+            orig_len = len(samples)
+            new_len = int(orig_len * target_sr / sr)
+            samples = np.interp(np.linspace(0, orig_len, new_len), np.arange(orig_len), samples)
+    return samples
+
+REALISTIC_IR_NAMES = {"Accoustic2_Impulse.wav"}
+
+ir_files = []
+if ir_aug_enabled and ir_dir and os.path.isdir(ir_dir):
+    all_irs = sorted(glob.glob(os.path.join(ir_dir, "*.wav")))
+    filtered = [f for f in all_irs if os.path.basename(f) in REALISTIC_IR_NAMES]
+    ir_files = filtered if filtered else all_irs
+
+if ir_files:
+    names = ", ".join(os.path.basename(p) for p in ir_files)
+    print(f"    Loaded {len(ir_files)} realistic RIR impulses ({names})", file=sys.stderr)
+else:
+    print("    RIR augmentation disabled or no impulse files found", file=sys.stderr)
+
+ir_cache = []
+max_ir_samples = int(0.35 * 16000)
+for ir_p in ir_files:
+    h = read_wav_float(ir_p, target_sr=16000)
+    if h.size > 0:
+        if h.size > max_ir_samples:
+            h = h[:max_ir_samples]
+            fade_len = int(0.10 * max_ir_samples)
+            h[-fade_len:] *= np.linspace(1.0, 0.0, fade_len)
+        ir_cache.append(h)
+
+def write_wav_int16(path, samples, sr=16000):
+    samples = np.clip(samples, -1.0, 1.0)
+    int16_data = (samples * 32767.0).astype(np.int16).tobytes()
+    with wave.open(path, "wb") as wo:
+        wo.setframerate(sr)
+        wo.setnchannels(1)
+        wo.setsampwidth(2)
+        wo.writeframes(int16_data)
+
+rng = random.Random(42)
+
+for f in sorted(glob.glob(os.path.join(src_dir, "*.wav"))):
+    x = read_wav_float(f, target_sr=16000)
+    if x.size == 0:
+        continue
+
+    if ir_cache and rng.random() < ir_prob:
+        h = rng.choice(ir_cache)
+        if has_scipy:
+            wet = fftconvolve(x, h, mode="full")
+        else:
+            wet = np.convolve(x, h, mode="full")
+
+        max_out_len = x.size + int(0.20 * 16000)
+        if wet.size > max_out_len:
+            wet = wet[:max_out_len]
+
+        dry_peak = np.max(np.abs(x)) if x.size else 0.0
+        wet_peak = np.max(np.abs(wet)) if wet.size else 0.0
+        if wet_peak > 1e-9 and dry_peak > 1e-9:
+            wet = wet * (dry_peak / wet_peak)
+
+        pad = wet.size - x.size
+        dry_padded = np.pad(x, (0, pad)) if pad > 0 else x[:wet.size]
+        x = (1.0 - ir_wet) * dry_padded + ir_wet * wet
+
+    out_name = os.path.basename(f)
+    write_wav_int16(os.path.join(dst_dir, out_name), x, sr=16000)
+
+PYEOF
+
+	if [[ "$GAIN_NORM" = "1" ]]; then
+		echo ">>> Applying peak normalization (${GAIN_PEAK_DBFS} dBFS) to $FINAL_DIR"
+		apply_peak_norm "$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 $lbl"
+done
+echo ">>> Next: sof_mww_train_pipeline.sh$KW_ARGS $OUT_ROOT <feat_root> <out_dir>"
diff --git a/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset_from_dir.sh b/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset_from_dir.sh
new file mode 100755
index 000000000000..a3621aef255a
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset_from_dir.sh
@@ -0,0 +1,467 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# Prepare and augment a positive keyword WAV dataset for microWakeWord (MWW)
+# from a directory of recorded real speech WAV samples (e.g. collected microphone
+# audio).
+#
+# Combines:
+#   1. Conversion/resampling of clean real speech samples to 16 kHz 16-bit mono.
+#   2. Retention of clean unperturbed samples in the dataset.
+#   3. Sox pitch & tempo perturbation (prosody/speaker expansion).
+#   4. Room Impulse Response (RIR) reverberation convolution.
+#   5. Additive background noise mixing across a range of SNR levels (10-30 dB).
+#   6. Gaussian level / gain jitter across volume levels (-12 dB to +6 dB / peak target).
+#
+# Output layout (ready for sof_mfcc_extract_features.sh / sof_mww_train_pipeline.sh):
+#   <out_root>/<label>/*.wav       augmented and clean 16 kHz 16-bit mono clips
+#   <out_root>/_raw/<label>/*.wav  clean resampled baseline clips
+#
+# Requirements:
+#   - sox on PATH
+#   - python3 with numpy and scipy (or audiomentations)
+#
+# Env knobs (all optional):
+#   MAX_SAMPLES        Target total number of output WAVs per keyword (default 1000).
+#   PERTURB_PER_UTT    Number of sox pitch/tempo perturbed copies per utterance
+#                      (default: auto-calculated from MAX_SAMPLES, or set explicitly).
+#   PITCH_CENTS_MAX    Max pitch shift in cents (+/- 250 = 2.5 semitones, default 250).
+#   TEMPO_MIN/MAX      Min/max tempo multiplier (default: 0.90 to 1.15).
+#   IR_AUG             1 = enable RIR convolution, 0 = disable (default 1).
+#   IR_PROB            Probability of applying RIR to an augmented clip (default 0.50).
+#   IR_WET             Wet/dry mix for RIR convolution (default 0.20).
+#   IR_DIR             Directory containing *.wav impulse responses.
+#   NOISE_AUG          1 = enable additive noise mixing, 0 = disable (default 1).
+#   NOISE_PROB         Probability of mixing background noise (default 0.60).
+#   NOISE_SNR_MIN_DB   Min SNR in dB for noise mixing (default 12.0).
+#   NOISE_SNR_MAX_DB   Max SNR in dB for noise mixing (default 30.0).
+#   NOISE_DIR          Directory containing background noise WAVs (default:
+#                      $SC_CACHE/_background_noise_ or ~/.cache/speech_commands_v2/_background_noise_).
+#   GAIN_NORM          1 = peak-normalize all clean WAVs to GAIN_PEAK_DBFS, 0 = keep input level (default 1).
+#   GAIN_PEAK_DBFS     Peak-normalization target in dBFS (default -10).
+
+set -e
+
+SRC_DIRS=()
+LABELS=()
+OUT_ROOT=""
+
+usage() {
+	cat >&2 <<EOF
+Usage: $0 --src-dir <dir> [--label <name>] \\
+          [--src-dir <dir2> [--label <name2>] ...] <out_root>
+       or:
+       $0 --keyword-dir [<label>:]<dir> [...] <out_root>
+
+  --src-dir DIR           Directory containing source .wav recordings of the keyword.
+  --label   NAME          Output subdirectory name under <out_root> (e.g. hi_intel).
+                          Defaults to the basename of DIR or common file prefix.
+  --keyword-dir [LBL:]DIR Combination argument specifying label and source dir.
+  out_root                Dataset root; <label>/*.wav is produced under it,
+                          ready for sof_mfcc_extract_features.sh / sof_mww_train_pipeline.sh.
+
+Optional env:
+  MAX_SAMPLES, PERTURB_PER_UTT, PITCH_CENTS_MAX, TEMPO_MIN/MAX,
+  IR_AUG, IR_PROB, IR_WET, IR_DIR,
+  NOISE_AUG, NOISE_PROB, NOISE_SNR_MIN_DB, NOISE_SNR_MAX_DB, NOISE_DIR,
+  GAIN_AUG, GAIN_PEAK_DBFS, GAIN_SIGMA_DB, GAIN_HEADROOM_DB.
+EOF
+	exit 1
+}
+
+while [[ $# -gt 0 ]]; do
+	case "$1" in
+		-s|--src-dir)
+			[[ $# -ge 2 ]] || usage
+			SRC_DIRS+=("$2"); shift 2 ;;
+		-l|--label)
+			[[ $# -ge 2 ]] || usage
+			LABELS+=("$2"); shift 2 ;;
+		-k|--keyword-dir)
+			[[ $# -ge 2 ]] || usage
+			arg="$2"
+			if [[ "$arg" == *:* ]]; then
+				lbl="${arg%%:*}"
+				sdir="${arg#*:}"
+			else
+				sdir="$arg"
+				lbl="$(basename "$sdir")"
+			fi
+			SRC_DIRS+=("$sdir")
+			LABELS+=("$lbl")
+			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" || ${#SRC_DIRS[@]} -eq 0 ]]; then
+	usage
+fi
+
+if [[ ${#LABELS[@]} -gt 0 && ${#LABELS[@]} -ne ${#SRC_DIRS[@]} ]]; then
+	echo "--label given ${#LABELS[@]} time(s) but --src-dir given ${#SRC_DIRS[@]} time(s); counts must match" >&2
+	exit 1
+fi
+
+if [[ ${#LABELS[@]} -eq 0 ]]; then
+	for sdir in "${SRC_DIRS[@]}"; do
+		base="$(basename "$(realpath "$sdir")")"
+		if [[ "$base" == "audio" || "$base" == "wav" || "$base" == "wavs" ]]; then
+			parent="$(basename "$(dirname "$(realpath "$sdir")")")"
+			base="$parent"
+		fi
+		LABELS+=("$(echo "$base" | tr ' A-Z-' '_a-z_')")
+	done
+fi
+
+: "${MAX_SAMPLES:=1000}"
+: "${PITCH_CENTS_MAX:=250}"
+: "${TEMPO_MIN:=0.90}"
+: "${TEMPO_MAX:=1.15}"
+: "${IR_AUG:=1}"
+: "${IR_PROB:=0.50}"
+: "${IR_WET:=0.20}"
+: "${IR_DIR:=}"
+: "${NOISE_AUG:=1}"
+: "${NOISE_PROB:=0.60}"
+: "${NOISE_SNR_MIN_DB:=12.0}"
+: "${NOISE_SNR_MAX_DB:=30.0}"
+: "${NOISE_DIR:=}"
+: "${GAIN_NORM:=1}"
+: "${GAIN_PEAK_DBFS:=-10}"
+: "${SC_CACHE:=$HOME/.cache/speech_commands_v2}"
+
+if ! command -v sox >/dev/null; then
+	echo "sox not on PATH — please install sox (e.g. apt install sox)" >&2
+	exit 1
+fi
+
+if ! command -v python3 >/dev/null; then
+	echo "python3 not on PATH" >&2
+	exit 1
+fi
+
+# Locate default Impulse Response directory if not explicitly provided
+if [[ -z "$IR_DIR" ]]; then
+	for cand in \
+		"$HOME/git/piper-sample-generator/piper_sample_generator/impulses" \
+		"$HOME/.local/share/piper-sample-generator/impulses"
+	do
+		if [[ -d "$cand" ]]; then
+			IR_DIR="$cand"
+			break
+		fi
+	done
+fi
+
+# Locate default Noise directory if not explicitly provided
+if [[ -z "$NOISE_DIR" ]]; then
+	if [[ -d "$SC_CACHE/_background_noise_" ]]; then
+		NOISE_DIR="$SC_CACHE/_background_noise_"
+	fi
+fi
+
+apply_peak_norm() {
+	local dir="$1"
+	local tmp
+	tmp=$(mktemp --suffix=.wav)
+	for wav in "$dir"/*.wav; do
+		[ -f "$wav" ] || continue
+		sox "$wav" "$tmp" gain -n "$GAIN_PEAK_DBFS"
+		mv "$tmp" "$wav"
+	done
+	rm -f "$tmp"
+}
+
+RESULTS=()
+
+for i in "${!SRC_DIRS[@]}"; do
+	SRC_DIR="${SRC_DIRS[$i]}"
+	LABEL="${LABELS[$i]}"
+
+	if [[ ! -d "$SRC_DIR" ]]; then
+		echo "Error: Source directory does not exist: $SRC_DIR" >&2
+		exit 1
+	fi
+
+	RAW_DIR="$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 "$FINAL_DIR"/*.wav
+
+	# Find input wavs
+	raw_src_wavs=()
+	while IFS= read -r -d '' f; do
+		raw_src_wavs+=("$f")
+	done < <(find "$SRC_DIR" -maxdepth 2 \( -name '*.wav' -o -name '*.WAV' \) -print0 | sort -z)
+
+	n_src=${#raw_src_wavs[@]}
+	if [[ $n_src -eq 0 ]]; then
+		echo "Error: No WAV files found in $SRC_DIR" >&2
+		exit 1
+	fi
+
+	echo ">>> [$((i + 1))/${#SRC_DIRS[@]}] Processing $n_src real speech WAVs for '$LABEL' from $SRC_DIR"
+
+	# 1. Convert/resample clean files to 16 kHz 16-bit mono
+	for idx in "${!raw_src_wavs[@]}"; do
+		f="${raw_src_wavs[$idx]}"
+		base="$(basename "$f" .wav)"
+		base="$(basename "$base" .WAV)"
+		out_clean="$RAW_DIR/${base}.wav"
+		sox "$f" -r 16000 -c 1 -b 16 "$out_clean"
+		# Place clean baseline into final output dir
+		cp "$out_clean" "$FINAL_DIR/${base}_clean.wav"
+	done
+
+	echo "    Wrote $n_src clean 16 kHz mono reference clips into $RAW_DIR and $FINAL_DIR"
+
+	# Calculate required copies per utterance
+	if [[ -n "$PERTURB_PER_UTT" ]]; then
+		n_copies="$PERTURB_PER_UTT"
+	else
+		if (( n_src < MAX_SAMPLES )); then
+			n_copies=$(( (MAX_SAMPLES - n_src + n_src - 1) / n_src ))
+		else
+			n_copies=5
+		fi
+	fi
+	(( n_copies < 1 )) && n_copies=1
+
+	# 2. Sox pitch and tempo perturbations
+	mkdir -p "$PERT_DIR"
+	rm -f "$PERT_DIR"/*.wav
+	echo "    Generating $n_copies pitch/tempo perturbations per clean recording -> $PERT_DIR"
+
+	pcount=0
+	for raw in "$RAW_DIR"/*.wav; do
+		[ -f "$raw" ] || continue
+		base=$(basename "$raw" .wav)
+		for k in $(seq 1 "$n_copies"); do
+			cents=$(( (RANDOM % (2 * PITCH_CENTS_MAX + 1)) - PITCH_CENTS_MAX ))
+			tempo=$(awk -v a="$TEMPO_MIN" -v b="$TEMPO_MAX" -v r="$RANDOM" 'BEGIN{
+				srand(r); printf "%.3f", a + rand()*(b-a)
+			}')
+			sox "$raw" "$PERT_DIR/${base}_p${k}.wav" \
+				gain -h \
+				pitch "$cents" tempo -s "$tempo"
+			pcount=$((pcount + 1))
+		done
+	done
+	echo "    Wrote $pcount perturbed copies"
+
+	# 3. Acoustic augmentations: RIR convolution & Additive Noise
+	export IR_AUG IR_PROB IR_WET IR_DIR
+	export NOISE_AUG NOISE_PROB NOISE_SNR_MIN_DB NOISE_SNR_MAX_DB NOISE_DIR
+
+	echo "    Applying acoustic augmentations (RIR: prob=$IR_PROB, Noise: prob=$NOISE_PROB SNR=[$NOISE_SNR_MIN_DB,$NOISE_SNR_MAX_DB] dB)"
+
+	python3 - "$PERT_DIR" "$FINAL_DIR" <<'PYEOF'
+import glob
+from math import gcd
+import os
+import random
+import sys
+import wave
+from pathlib import Path
+import numpy as np
+
+src_dir, dst_dir = sys.argv[1], sys.argv[2]
+Path(dst_dir).mkdir(parents=True, exist_ok=True)
+
+ir_aug_enabled = os.environ.get("IR_AUG", "1") == "1"
+ir_prob = float(os.environ.get("IR_PROB", "0.50"))
+ir_wet = float(os.environ.get("IR_WET", "0.20"))
+ir_dir = os.environ.get("IR_DIR", "").strip()
+
+noise_aug_enabled = os.environ.get("NOISE_AUG", "1") == "1"
+noise_prob = float(os.environ.get("NOISE_PROB", "0.60"))
+noise_snr_min = float(os.environ.get("NOISE_SNR_MIN_DB", "12.0"))
+noise_snr_max = float(os.environ.get("NOISE_SNR_MAX_DB", "30.0"))
+noise_dir = os.environ.get("NOISE_DIR", "").strip()
+
+# Try importing scipy for fast FFT convolution and high-quality resampling
+try:
+    from scipy.signal import fftconvolve, resample_poly
+    has_scipy = True
+except ImportError:
+    has_scipy = False
+
+def read_wav_float(path, target_sr=16000):
+    with wave.open(path, "rb") as wi:
+        sr = wi.getframerate()
+        nch = wi.getnchannels()
+        sw = wi.getsampwidth()
+        nframes = wi.getnframes()
+        raw = wi.readframes(nframes)
+    if sw == 2:
+        samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
+    elif sw == 3:
+        # 24-bit PCM
+        b = np.frombuffer(raw, dtype=np.uint8)
+        b3 = b.reshape(-1, 3)
+        b4 = np.pad(b3, ((0,0),(1,0)), mode='constant')
+        s32 = b4.view(np.int32).squeeze() >> 8
+        samples = s32.astype(np.float32) / 8388608.0
+    elif sw == 4:
+        samples = np.frombuffer(raw, dtype=np.int32).astype(np.float32) / 2147483648.0
+    else:
+        samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
+    if nch > 1:
+        samples = samples.reshape(-1, nch).mean(axis=1)
+
+    # Resample to target_sr if necessary
+    if sr != target_sr:
+        if has_scipy:
+            g = gcd(target_sr, sr)
+            samples = resample_poly(samples, target_sr // g, sr // g)
+        else:
+            orig_len = len(samples)
+            new_len = int(orig_len * target_sr / sr)
+            samples = np.interp(np.linspace(0, orig_len, new_len), np.arange(orig_len), samples)
+    return samples
+
+# Curate realistic room impulse responses (filter out huge cathedrals / FX like Derlon Sanctuary / Reverse Gate)
+REALISTIC_IR_NAMES = {"Accoustic2_Impulse.wav", "Concrete Room.wav", "ir_bathroom1.wav", "Symphonic.wav"}
+
+ir_files = []
+if ir_aug_enabled and ir_dir and os.path.isdir(ir_dir):
+    all_irs = sorted(glob.glob(os.path.join(ir_dir, "*.wav")))
+    # If default impulse folder, filter to realistic room acoustics
+    filtered = [f for f in all_irs if os.path.basename(f) in REALISTIC_IR_NAMES]
+    ir_files = filtered if filtered else all_irs
+
+if ir_files:
+    names = ", ".join(os.path.basename(p) for p in ir_files)
+    print(f"      Loaded {len(ir_files)} realistic RIR impulses ({names})", file=sys.stderr)
+else:
+    print("      RIR augmentation disabled or no impulse files found", file=sys.stderr)
+
+# Load Noise Files
+noise_files = []
+if noise_aug_enabled and noise_dir and os.path.isdir(noise_dir):
+    noise_files = sorted(glob.glob(os.path.join(noise_dir, "*.wav")))
+if noise_files:
+    print(f"      Loaded {len(noise_files)} noise files from {noise_dir}", file=sys.stderr)
+else:
+    print("      Noise augmentation disabled or no noise files found", file=sys.stderr)
+
+# Pre-load and trim RIRs to realistic room duration (max 350 ms = 5600 samples at 16k)
+ir_cache = []
+max_ir_samples = int(0.35 * 16000)
+for ir_p in ir_files:
+    h = read_wav_float(ir_p, target_sr=16000)
+    if h.size > 0:
+        if h.size > max_ir_samples:
+            h = h[:max_ir_samples]
+            fade_len = int(0.10 * max_ir_samples)
+            h[-fade_len:] *= np.linspace(1.0, 0.0, fade_len)
+        ir_cache.append(h)
+
+# Pre-load noise waveforms resampled to 16 kHz
+noise_cache = []
+for n_p in noise_files:
+    n_samp = read_wav_float(n_p, target_sr=16000)
+    if n_samp.size > 0:
+        noise_cache.append(n_samp)
+
+def write_wav_int16(path, samples, sr=16000):
+    samples = np.clip(samples, -1.0, 1.0)
+    int16_data = (samples * 32767.0).astype(np.int16).tobytes()
+    with wave.open(path, "wb") as wo:
+        wo.setframerate(sr)
+        wo.setnchannels(1)
+        wo.setsampwidth(2)
+        wo.writeframes(int16_data)
+
+rng = random.Random(42)
+np_rng = np.random.default_rng(42)
+
+perturbed_files = sorted(glob.glob(os.path.join(src_dir, "*.wav")))
+for f in perturbed_files:
+    x = read_wav_float(f, target_sr=16000)
+    if x.size == 0:
+        continue
+
+    # A. Apply subtle room reverberation
+    if ir_cache and rng.random() < ir_prob:
+        h = rng.choice(ir_cache)
+        if has_scipy:
+            wet = fftconvolve(x, h, mode="full")
+        else:
+            wet = np.convolve(x, h, mode="full")
+
+        # Cap reverb tail to original speech length + at most 200 ms
+        max_out_len = x.size + int(0.20 * 16000)
+        if wet.size > max_out_len:
+            wet = wet[:max_out_len]
+
+        dry_peak = np.max(np.abs(x)) if x.size else 0.0
+        wet_peak = np.max(np.abs(wet)) if wet.size else 0.0
+        if wet_peak > 1e-9 and dry_peak > 1e-9:
+            wet = wet * (dry_peak / wet_peak)
+
+        pad = wet.size - x.size
+        dry_padded = np.pad(x, (0, pad)) if pad > 0 else x[:wet.size]
+        x = (1.0 - ir_wet) * dry_padded + ir_wet * wet
+
+    # B. Apply Additive Background Noise (sliced exactly to match speech duration)
+    if noise_cache and rng.random() < noise_prob:
+        n_audio = rng.choice(noise_cache)
+        if n_audio.size >= x.size:
+            start = np_rng.integers(0, n_audio.size - x.size + 1)
+            n_slice = n_audio[start : start + x.size]
+        elif n_audio.size > 0:
+            reps = (x.size // n_audio.size) + 1
+            n_slice = np.tile(n_audio, reps)[:x.size]
+        else:
+            n_slice = None
+
+        if n_slice is not None:
+            speech_rms = np.sqrt(np.mean(x ** 2))
+            noise_rms = np.sqrt(np.mean(n_slice ** 2))
+            if speech_rms > 1e-5 and noise_rms > 1e-5:
+                snr_db = np_rng.uniform(noise_snr_min, noise_snr_max)
+                target_noise_rms = speech_rms * (10.0 ** (-snr_db / 20.0))
+                scale = target_noise_rms / noise_rms
+                x = x + scale * n_slice
+
+    out_name = os.path.basename(f)
+    write_wav_int16(os.path.join(dst_dir, out_name), x, sr=16000)
+
+PYEOF
+
+	# 4. Peak Normalization
+	if [[ "$GAIN_NORM" = "1" ]]; then
+		echo "    Applying peak normalization (${GAIN_PEAK_DBFS} dBFS) to $FINAL_DIR"
+		apply_peak_norm "$FINAL_DIR"
+	fi
+
+	n_clean=$(find "$RAW_DIR" -name '*.wav' | wc -l)
+	n_final=$(find "$FINAL_DIR" -name '*.wav' | wc -l)
+	echo ">>> [$LABEL] Generated $n_final total WAVs ($n_clean clean + $((n_final - n_clean)) augmented) in $FINAL_DIR"
+	RESULTS+=("$LABEL: $n_clean clean / $n_final total WAVs")
+done
+
+echo ">>> Dataset preparation from recorded audio complete:"
+for line in "${RESULTS[@]}"; do
+	echo "    $line"
+done
+KW_ARGS=""
+for lbl in "${LABELS[@]}"; do
+	KW_ARGS+=" --keyword $lbl"
+done
+echo ">>> Next step: sof_mww_train_pipeline.sh$KW_ARGS $OUT_ROOT <feat_root> <out_dir>"
diff --git a/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset_piper_tts.sh b/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset_piper_tts.sh
new file mode 100755
index 000000000000..9599f3a969bb
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_generate_keyword_dataset_piper_tts.sh
@@ -0,0 +1,501 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# Generate a synthetic keyword WAV dataset for microWakeWord (MWW) with a
+# SINGLE-SPEAKER Piper TTS voice (piper-tts package). This is the
+# language-agnostic companion to sof_mww_generate_keyword_dataset.sh, which is
+# hard-wired to the English multi-speaker piper-sample-generator (LibriTTS-R).
+# Use this script for any keyword whose Piper voice is single-speaker
+# (e.g. fi_FI-harri, sv_SE-nst, de_DE-thorsten, en_US-lessac).
+#
+# Output layout:
+#   <out_root>/<label>/*.wav     augmented 16 kHz 16-bit mono clips
+#   <out_root>/_raw/<label>/*.wav dry unperturbed baseline
+#
+# Requirements:
+#   - piper-tts installed in a venv (or on $PATH)
+#   - sox on PATH
+#   - audiomentations (optional, for impulse response convolution)
+#
+# Env knobs (all optional):
+#   PIPER_TTS_VENV     Path to a venv containing piper-tts. Auto-activated if set.
+#   PIPER_VOICE        Default Piper voice (.onnx) when --voice is not passed.
+#   PIPER_REPO         Path to a piper-sample-generator clone (used to find its
+#                      curated impulse response pool).
+#   MAX_SAMPLES        Target output sample count per keyword (default 1000).
+#   LENGTH_SCALES      Space-separated speaking rates for Piper synthesis
+#                      (default: "0.85 0.95 1.00 1.10 1.20").
+#   NOISE_SCALE_MIN/MAX Phoneme duration variability (default: 0.60 to 0.68).
+#   NOISE_W_MIN/MAX    Speaking pitch variability (default: 0.75 to 0.85).
+#   PERTURB_PER_UTT    Number of sox pitch/tempo perturbed copies per base
+#                      utterance (default 3; set to 0 to disable).
+#   PITCH_CENTS_MAX    Max pitch shift in cents (+/- 300 = 3 semitones).
+#   TEMPO_MIN/MAX      Min/max tempo multiplier (default: 0.9 to 1.15).
+#   GAIN_NORM          1 = peak-normalize each final WAV to GAIN_PEAK_DBFS (default 1).
+#                      0 = leave at baseline 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 below full-scale to prevent clipping (default 1).
+#   IR_PROB            Probability of applying Room Impulse Response (default 0.75).
+#   IR_WET             Wet/dry mix for IR convolution (default 0.35).
+#   IR_DIR             Directory of *.wav impulse responses.
+
+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}"
+: "${GAIN_NORM:=1}"
+: "${GAIN_PEAK_DBFS:=-10}"
+: "${IR_PROB:=0.75}"
+: "${IR_WET:=0.35}"
+: "${IR_DIR:=}"
+
+export LENGTH_SCALES NOISE_SCALE_MIN NOISE_SCALE_MAX NOISE_W_MIN NOISE_W_MAX
+
+apply_peak_norm() {
+	local dir="$1"
+	local tmp
+	tmp=$(mktemp --suffix=.wav)
+	for wav in "$dir"/*.wav; do
+		[ -f "$wav" ] || continue
+		sox "$wav" "$tmp" gain -n "$GAIN_PEAK_DBFS"
+		mv "$tmp" "$wav"
+	done
+	rm -f "$tmp"
+}
+
+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 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=""
+apply_peak_norm() {
+	local dir="$1"
+	local tmp
+	tmp=$(mktemp --suffix=.wav)
+	for wav in "$dir"/*.wav; do
+		[ -f "$wav" ] || continue
+		sox "$wav" "$tmp" gain -n "$GAIN_PEAK_DBFS"
+		mv "$tmp" "$wav"
+	done
+	rm -f "$tmp"
+}
+
+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
+
+	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()
+
+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
+
+	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="$RANDOM" 'BEGIN{
+					srand(r); printf "%.3f", a + rand()*(b-a)
+				}')
+				sox "$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 (16 kHz curated RIR convolution, IR_PROB=$IR_PROB) -> $FINAL_DIR"
+		export IR_PROB IR_WET IR_DIR DEFAULT_IR_DIR
+		python3 - "$SRC_DIR" "$FINAL_DIR" <<'PYEOF'
+import glob
+from math import gcd
+import os
+import random
+import sys
+import wave
+from pathlib import Path
+import numpy as np
+
+src_dir, dst_dir = sys.argv[1], sys.argv[2]
+Path(dst_dir).mkdir(parents=True, exist_ok=True)
+
+ir_prob = float(os.environ.get("IR_PROB", "0.50"))
+ir_wet = float(os.environ.get("IR_WET", "0.20"))
+ir_dir = os.environ.get("IR_DIR", "").strip()
+default_dir = os.environ.get("DEFAULT_IR_DIR", "").strip()
+
+try:
+    from scipy.signal import fftconvolve, resample_poly
+    has_scipy = True
+except ImportError:
+    has_scipy = False
+
+def read_wav_float(path, target_sr=16000):
+    with wave.open(path, "rb") as wi:
+        sr = wi.getframerate()
+        nch = wi.getnchannels()
+        sw = wi.getsampwidth()
+        nframes = wi.getnframes()
+        raw = wi.readframes(nframes)
+    if sw == 2:
+        samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
+    elif sw == 3:
+        b = np.frombuffer(raw, dtype=np.uint8).reshape(-1, 3)
+        b4 = np.pad(b, ((0,0),(1,0)), mode='constant')
+        samples = (b4.view(np.int32).squeeze() >> 8).astype(np.float32) / 8388608.0
+    elif sw == 4:
+        samples = np.frombuffer(raw, dtype=np.int32).astype(np.float32) / 2147483648.0
+    else:
+        samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
+    if nch > 1:
+        samples = samples.reshape(-1, nch).mean(axis=1)
+
+    if sr != target_sr:
+        if has_scipy:
+            g = gcd(target_sr, sr)
+            samples = resample_poly(samples, target_sr // g, sr // g)
+        else:
+            orig_len = len(samples)
+            new_len = int(orig_len * target_sr / sr)
+            samples = np.interp(np.linspace(0, orig_len, new_len), np.arange(orig_len), samples)
+    return samples
+
+REALISTIC_IR_NAMES = {"Accoustic2_Impulse.wav", "Concrete Room.wav", "ir_bathroom1.wav", "Symphonic.wav"}
+
+search_dir = ir_dir if (ir_dir and os.path.isdir(ir_dir)) else default_dir
+ir_files = []
+if search_dir and os.path.isdir(search_dir):
+    all_irs = sorted(glob.glob(os.path.join(search_dir, "*.wav")))
+    filtered = [f for f in all_irs if os.path.basename(f) in REALISTIC_IR_NAMES]
+    ir_files = filtered if filtered else all_irs
+
+if ir_files:
+    names = ", ".join(os.path.basename(p) for p in ir_files)
+    print(f"    Loaded {len(ir_files)} realistic RIR impulses ({names})", file=sys.stderr)
+else:
+    print("    RIR augmentation disabled or no impulse files found", file=sys.stderr)
+
+ir_cache = []
+max_ir_samples = int(0.35 * 16000)
+for ir_p in ir_files:
+    h = read_wav_float(ir_p, target_sr=16000)
+    if h.size > 0:
+        if h.size > max_ir_samples:
+            h = h[:max_ir_samples]
+            fade_len = int(0.10 * max_ir_samples)
+            h[-fade_len:] *= np.linspace(1.0, 0.0, fade_len)
+        ir_cache.append(h)
+
+def write_wav_int16(path, samples, sr=16000):
+    samples = np.clip(samples, -1.0, 1.0)
+    int16_data = (samples * 32767.0).astype(np.int16).tobytes()
+    with wave.open(path, "wb") as wo:
+        wo.setframerate(sr)
+        wo.setnchannels(1)
+        wo.setsampwidth(2)
+        wo.writeframes(int16_data)
+
+rng = random.Random(42)
+
+for f in sorted(glob.glob(os.path.join(src_dir, "*.wav"))):
+    x = read_wav_float(f, target_sr=16000)
+    if x.size == 0:
+        continue
+
+    if ir_cache and rng.random() < ir_prob:
+        h = rng.choice(ir_cache)
+        if has_scipy:
+            wet = fftconvolve(x, h, mode="full")
+        else:
+            wet = np.convolve(x, h, mode="full")
+
+        max_out_len = x.size + int(0.20 * 16000)
+        if wet.size > max_out_len:
+            wet = wet[:max_out_len]
+
+        dry_peak = np.max(np.abs(x)) if x.size else 0.0
+        wet_peak = np.max(np.abs(wet)) if wet.size else 0.0
+        if wet_peak > 1e-9 and dry_peak > 1e-9:
+            wet = wet * (dry_peak / wet_peak)
+
+        pad = wet.size - x.size
+        dry_padded = np.pad(x, (0, pad)) if pad > 0 else x[:wet.size]
+        x = (1.0 - ir_wet) * dry_padded + ir_wet * wet
+
+    out_name = os.path.basename(f)
+    write_wav_int16(os.path.join(dst_dir, out_name), x, sr=16000)
+
+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_NORM" = "1" ]]; then
+		echo ">>> Applying peak normalization (${GAIN_PEAK_DBFS} dBFS) to $FINAL_DIR"
+		apply_peak_norm "$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 $lbl"
+done
+echo ">>> Next: sof_mww_train_pipeline.sh$KW_ARGS $OUT_ROOT <feat_root> <out_dir>"
diff --git a/src/audio/microwakeword/tune/sof_mww_plot_mtrace.py b/src/audio/microwakeword/tune/sof_mww_plot_mtrace.py
new file mode 100755
index 000000000000..8c8351ce76ed
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_plot_mtrace.py
@@ -0,0 +1,365 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Copyright (c) 2026, Intel Corporation. All rights reserved.
+
+"""
+Visualize microWakeWord (MWW) streaming diagnostics from SOF mtrace logs.
+
+Parses log lines produced by CONFIG_COMP_MWW_DEBUG_TRACE:
+  [MWW DBG hop <n>] vad=<0|1> E=<energy> Ne=<noise_energy>
+                    mel_min=<min> mel_max=<max>
+                    f_min=<min> f_max=<max> agc_q23=<gain>
+and model inference / detection lines:
+  MWW probability=<pct>
+  MWW keyword detected: probability=<pct>
+
+Generates a multi-panel PNG displaying:
+  1. Detection probability (%) and keyword detection trigger events
+  2. Frame log-energy (E), Noise floor estimate (Ne), and SNR (E - Ne)
+  3. Mel filterbank log-energy envelope [mel_min, mel_max]
+  4. Quantized neural-network input features [f_min, f_max] (int8)
+  5. Soft automatic gain control (AGC) gain tracking
+  Along with VAD speech active region highlighting and hop counters.
+"""
+
+import argparse
+import os
+import re
+import sys
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Regex patterns for SOF mtrace log messages
+HOP_RE = re.compile(
+    r"\[\s*([0-9.]+)\]\s*.*\[MWW DBG hop\s+(\d+)\]\s+"
+    r"vad=(\d+)\s+E=(-?\d+)\s+Ne=(-?\d+)\s+"
+    r"mel_min=(-?\d+)\s+mel_max=(-?\d+)\s+"
+    r"f_min=(-?\d+)\s+f_max=(-?\d+)\s+agc_q23=(-?\d+)"
+)
+
+PROB_RE = re.compile(
+    r"\[\s*([0-9.]+)\]\s*.*MWW probability=(\d+)"
+)
+
+DETECT_RE = re.compile(
+    r"\[\s*([0-9.]+)\]\s*.*MWW keyword detected:\s*probability=(\d+)"
+)
+
+KPB_TRIGGER_RE = re.compile(
+    r"\[\s*([0-9.]+)\]\s*.*MWW keyword trigger -> notifying KPB to begin draining"
+)
+
+SUMMARY_RE = re.compile(
+    r"\[MWW STREAM SHUTDOWN SUMMARY\]\s+Total Inferences=(\d+)\s*\|\s*"
+    r"VAD Gated=(\d+)\s*\|\s*Detections=(\d+)\s*\|\s*KPB Triggers=(\d+)\s*\|\s*"
+    r"Arena Used=(\d+)/(\d+)\s*B"
+)
+
+
+def parse_mtrace(file_path):
+    """Parse MWW diagnostic and detection events from mtrace text log."""
+    hops = []
+    probs = []
+    detects = []
+    kpb_triggers = []
+    summaries = []
+
+    if file_path == "-":
+        f = sys.stdin
+    else:
+        if not os.path.isfile(file_path):
+            print(f"Error: file not found: {file_path}", file=sys.stderr)
+            sys.exit(1)
+        f = open(file_path, "r", errors="ignore")
+
+    try:
+        for line in f:
+            m = HOP_RE.search(line)
+            if m:
+                t, hop, vad, e, ne, m_min, m_max, f_min, f_max, agc = m.groups()
+                hops.append({
+                    "t": float(t),
+                    "hop": int(hop),
+                    "vad": int(vad),
+                    "e_raw": int(e),
+                    "ne_raw": int(ne),
+                    "mel_min_raw": int(m_min),
+                    "mel_max_raw": int(m_max),
+                    "f_min": int(f_min),
+                    "f_max": int(f_max),
+                    "agc_raw": int(agc),
+                })
+                continue
+
+            m = PROB_RE.search(line)
+            if m:
+                probs.append({
+                    "t": float(m.group(1)),
+                    "prob": int(m.group(2))
+                })
+                continue
+
+            m = DETECT_RE.search(line)
+            if m:
+                detects.append({
+                    "t": float(m.group(1)),
+                    "prob": int(m.group(2))
+                })
+                continue
+
+            m = KPB_TRIGGER_RE.search(line)
+            if m:
+                kpb_triggers.append(float(m.group(1)))
+                continue
+
+            m = SUMMARY_RE.search(line)
+            if m:
+                inf, vad_gated, det, kpb_trig, arena_used, arena_cap = m.groups()
+                summaries.append({
+                    "inferences": int(inf),
+                    "vad_gated": int(vad_gated),
+                    "detections": int(det),
+                    "kpb_triggers": int(kpb_trig),
+                    "arena_used": int(arena_used),
+                    "arena_cap": int(arena_cap),
+                })
+    finally:
+        if file_path != "-":
+            f.close()
+
+    return hops, probs, detects, kpb_triggers, summaries
+
+
+def plot_mww_diagnostics(hops, probs, detects, kpb_triggers, summaries,
+                         output_path, title=None, threshold=65,
+                         raw_units=False, show_vad=True, dpi=150):
+    """Plot MWW diagnostics across 5 synchronized subplots."""
+    if not hops:
+        print("Error: No 'MWW DBG hop' records found in input.", file=sys.stderr)
+        sys.exit(1)
+
+    t0 = hops[0]["t"]
+    t_hops = np.array([h["t"] - t0 for h in hops])
+    hop_idx = np.array([h["hop"] for h in hops])
+    vad = np.array([h["vad"] for h in hops])
+    f_min = np.array([h["f_min"] for h in hops])
+    f_max = np.array([h["f_max"] for h in hops])
+
+    q23_scale = 1.0 if raw_units else (1.0 / (1 << 23))
+    unit_label = "raw" if raw_units else "Q23"
+
+    e = np.array([h["e_raw"] * q23_scale for h in hops])
+    ne = np.array([h["ne_raw"] * q23_scale for h in hops])
+    mel_min = np.array([h["mel_min_raw"] * q23_scale for h in hops])
+    mel_max = np.array([h["mel_max_raw"] * q23_scale for h in hops])
+    agc = np.array([h["agc_raw"] * q23_scale for h in hops])
+
+    t_probs = np.array([p["t"] - t0 for p in probs]) if probs else np.array([])
+    prob_vals = np.array([p["prob"] for p in probs]) if probs else np.array([])
+
+    fig, axes = plt.subplots(
+        5, 1, figsize=(14, 12), sharex=True,
+        gridspec_kw={"height_ratios": [1.5, 1.2, 1.2, 1.2, 1.0]}
+    )
+    plt.subplots_adjust(hspace=0.18)
+
+    # 1. Highlight VAD active regions across all subplots
+    if show_vad and len(vad) > 1:
+        vad_padded = np.pad(vad, (1, 1), "constant")
+        vad_diff = np.diff(vad_padded)
+        starts = np.where(vad_diff == 1)[0]
+        ends = np.where(vad_diff == -1)[0]
+        for s_idx, e_idx in zip(starts, ends):
+            t_start = t_hops[min(s_idx, len(t_hops) - 1)]
+            t_end = t_hops[min(max(0, e_idx - 1), len(t_hops) - 1)]
+            for ax in axes:
+                ax.axvspan(t_start, t_end, color="#c8e6c9", alpha=0.35, zorder=0)
+
+    # -------------------------------------------------------------
+    # Subplot 1: Model Probability & Detections
+    # -------------------------------------------------------------
+    ax1 = axes[0]
+    if len(t_probs) > 0:
+        ax1.plot(t_probs, prob_vals, color="#c62828", lw=2,
+                 marker="o", markersize=3, label="MWW Probability (%)", zorder=3)
+    ax1.axhline(threshold, color="#757575", linestyle="--", lw=1.2,
+                label=f"Detection Threshold ({threshold}%)")
+    ax1.set_ylim(-5, 105)
+    ax1.set_ylabel("Probability (%)", fontweight="bold", fontsize=10)
+
+    # Annotate detections
+    detect_labeled = False
+    for d in detects:
+        dt = d["t"] - t0
+        dp = d["prob"]
+        lbl = "Keyword Detected" if not detect_labeled else None
+        detect_labeled = True
+        ax1.plot(dt, dp, marker="*", markersize=18, color="#ffd700",
+                 markeredgecolor="#b71c1c", markeredgewidth=1.5, zorder=5, label=lbl)
+        ax1.annotate(
+            f"Detected: {dp}%", xy=(dt, dp),
+            xytext=(dt - 0.45, dp - 22 if dp > 50 else dp + 15),
+            arrowprops=dict(arrowstyle="->", color="#b71c1c", lw=1.8),
+            fontweight="bold", color="#b71c1c", fontsize=9,
+            bbox=dict(boxstyle="round,pad=0.3", fc="#ffffff", ec="#b71c1c", alpha=0.9),
+            zorder=6
+        )
+
+    # Mark KPB triggers
+    kpb_labeled = False
+    for kt in kpb_triggers:
+        lbl = "KPB Drain Trigger" if not kpb_labeled else None
+        kpb_labeled = True
+        for ax in axes:
+            ax.axvline(kt - t0, color="#1565c0", linestyle=":", lw=1.5, alpha=0.8, label=lbl)
+
+    ax1.grid(True, linestyle=":", alpha=0.6)
+    ax1.legend(loc="upper left", framealpha=0.9)
+
+    # Plot title
+    if title is None:
+        title = "microWakeWord (MWW) Streaming Diagnostics"
+    stats_str = f"Hops: {len(hops)} | Inferences: {len(probs)} | Detections: {len(detects)}"
+    ax1.set_title(f"{title}\n({stats_str})", fontsize=12, fontweight="bold", pad=28)
+
+    # Secondary x-axis for Hop counter on top
+    ax_top = ax1.twiny()
+    ax_top.set_xlim(t_hops[0], t_hops[-1])
+    step = max(1, len(hop_idx) // 10)
+    tick_indices = list(range(0, len(hop_idx), step))
+    if tick_indices[-1] != len(hop_idx) - 1:
+        tick_indices.append(len(hop_idx) - 1)
+    ax_top.set_xticks([t_hops[i] for i in tick_indices])
+    ax_top.set_xticklabels([str(hop_idx[i]) for i in tick_indices])
+    ax_top.set_xlabel("Hop Index", fontweight="bold", fontsize=10, labelpad=8)
+
+    # -------------------------------------------------------------
+    # Subplot 2: Energy & Noise Estimate
+    # -------------------------------------------------------------
+    ax2 = axes[1]
+    ax2.plot(t_hops, e, color="#1e88e5", lw=1.5, label=f"E (Frame Energy, {unit_label})")
+    ax2.plot(t_hops, ne, color="#fb8c00", lw=1.5, linestyle="--", label=f"Ne (Noise Floor, {unit_label})")
+    ax2.plot(t_hops, e - ne, color="#43a047", lw=1.2, alpha=0.8, label="SNR (E - Ne)")
+    ax2.set_ylabel(f"Energy ({unit_label})", fontweight="bold", fontsize=10)
+    ax2.grid(True, linestyle=":", alpha=0.6)
+    ax2.legend(loc="upper left", framealpha=0.9)
+
+    # -------------------------------------------------------------
+    # Subplot 3: Mel Filterbank Envelope
+    # -------------------------------------------------------------
+    ax3 = axes[2]
+    ax3.fill_between(t_hops, mel_min, mel_max, color="#90caf9", alpha=0.45,
+                     label=f"Mel Range [min, max] ({unit_label})")
+    ax3.plot(t_hops, mel_max, color="#0d47a1", lw=1.2, label=f"mel_max ({unit_label})")
+    ax3.plot(t_hops, mel_min, color="#00838f", lw=1.2, label=f"mel_min ({unit_label})")
+    ax3.set_ylabel(f"Mel Log-E ({unit_label})", fontweight="bold", fontsize=10)
+    ax3.grid(True, linestyle=":", alpha=0.6)
+    ax3.legend(loc="upper left", framealpha=0.9)
+
+    # -------------------------------------------------------------
+    # Subplot 4: Quantized Model Input Features (int8)
+    # -------------------------------------------------------------
+    ax4 = axes[3]
+    ax4.plot(t_hops, f_max, color="#6a1b9a", lw=1.2, label="f_max (int8)")
+    ax4.plot(t_hops, f_min, color="#ab47bc", lw=1.2, label="f_min (int8)")
+    ax4.axhline(0, color="#424242", lw=0.8, linestyle=":")
+    ax4.axhline(127, color="#d32f2f", lw=0.9, linestyle="--", alpha=0.6, label="int8 bounds [-128, 127]")
+    ax4.axhline(-128, color="#d32f2f", lw=0.9, linestyle="--", alpha=0.6)
+    ax4.set_ylim(-138, 138)
+    ax4.set_ylabel("Feature (int8)", fontweight="bold", fontsize=10)
+    ax4.grid(True, linestyle=":", alpha=0.6)
+    ax4.legend(loc="upper left", framealpha=0.9)
+
+    # -------------------------------------------------------------
+    # Subplot 5: Automatic Gain Control (AGC) Gain
+    # -------------------------------------------------------------
+    ax5 = axes[4]
+    ax5.plot(t_hops, agc, color="#b26a00", lw=1.6, label=f"AGC Gain ({unit_label})")
+    ax5.set_ylabel(f"AGC ({unit_label})", fontweight="bold", fontsize=10)
+    ax5.set_xlabel("Time (seconds relative to first hop)", fontweight="bold", fontsize=10)
+    ax5.grid(True, linestyle=":", alpha=0.6)
+    ax5.legend(loc="upper left", framealpha=0.9)
+
+    plt.tight_layout()
+    os.makedirs(os.path.dirname(os.path.abspath(output_path)), exist_ok=True)
+    fig.savefig(output_path, dpi=dpi)
+    plt.close(fig)
+    print(f"Visualization saved to: {output_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Visualize microWakeWord (MWW) hop metrics and detections from SOF mtrace logs."
+    )
+    parser.add_argument(
+        "input", nargs="?", default="-",
+        help="Path to mtrace log file (default: '-' for stdin)"
+    )
+    parser.add_argument(
+        "-o", "--output", default=None,
+        help="Output PNG image path (default: <input_stem>_mww.png or mww_mtrace.png for stdin)"
+    )
+    parser.add_argument(
+        "-t", "--threshold", type=int, default=65,
+        help="Wake word detection probability threshold in percent (default: 65)"
+    )
+    parser.add_argument(
+        "--raw-units", action="store_true",
+        help="Plot raw integer values instead of Q23 float representation for E, Ne, mel, and AGC"
+    )
+    parser.add_argument(
+        "--no-vad", action="store_true",
+        help="Disable green shading for VAD active regions"
+    )
+    parser.add_argument(
+        "--dpi", type=int, default=150,
+        help="DPI resolution for the generated PNG (default: 150)"
+    )
+    parser.add_argument(
+        "--title", default=None,
+        help="Custom title for the figure"
+    )
+
+    args = parser.parse_args()
+
+    # Determine default output path if not specified
+    if args.output is None:
+        if args.input == "-":
+            args.output = "mww_mtrace.png"
+        else:
+            base, _ = os.path.splitext(args.input)
+            args.output = f"{base}_mww.png"
+
+    hops, probs, detects, kpb_triggers, summaries = parse_mtrace(args.input)
+
+    title = args.title
+    if title is None and args.input != "-":
+        title = f"MWW Diagnostics — {os.path.basename(args.input)}"
+
+    print(f"Parsed {len(hops)} hops, {len(probs)} inferences, {len(detects)} detections from {args.input}")
+    for d in detects:
+        print(f"  [Detection Event] timestamp={d['t']}s, probability={d['prob']}%")
+    for s in summaries:
+        print(f"  [Shutdown Summary] inferences={s['inferences']}, detections={s['detections']}, arena={s['arena_used']}/{s['arena_cap']} B")
+
+    plot_mww_diagnostics(
+        hops=hops,
+        probs=probs,
+        detects=detects,
+        kpb_triggers=kpb_triggers,
+        summaries=summaries,
+        output_path=args.output,
+        title=title,
+        threshold=args.threshold,
+        raw_units=args.raw_units,
+        show_vad=not args.no_vad,
+        dpi=args.dpi
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/audio/microwakeword/tune/sof_mww_prepare_silence_unknown.sh b/src/audio/microwakeword/tune/sof_mww_prepare_silence_unknown.sh
new file mode 100755
index 000000000000..6a50bd2a4a6a
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_prepare_silence_unknown.sh
@@ -0,0 +1,160 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# Prepare the silence/ and unknown/ dataset classes for microWakeWord (MWW)
+# 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: $0 <out_root>
+
+  out_root   Dataset root; silence/ and unknown/ are added under it, alongside
+             any keyword class directories already present.
+
+Optional env:
+  SC_CACHE          Directory holding the unpacked Speech Commands v2 tree
+                    (default: ~/.cache/speech_commands_v2).
+  N_SILENCE         Number of 1-second silence clips to emit (default 500).
+  N_UNKNOWN         Number of unknown-class clips to sample (default 1500).
+  UNKNOWN_WORDS     Space-separated word list to sample for unknown
+                    (default: every SC v2 word except _background_noise_).
+  GAIN_NORM         1 = peak-normalize each unknown/*.wav to GAIN_PEAK_DBFS
+                    (silence/ is left alone so background noise stays
+                    quiet). 0 = leave the clips at baseline level.
+                    Default 1.
+  GAIN_PEAK_DBFS    Peak-normalization target in dBFS (default -10).
+EOF
+	exit 1
+fi
+
+: "${SC_CACHE:=$HOME/.cache/speech_commands_v2}"
+: "${N_SILENCE:=500}"
+: "${N_UNKNOWN:=1500}"
+: "${GAIN_NORM:=1}"
+: "${GAIN_PEAK_DBFS:=-10}"
+
+apply_peak_norm() {
+	local dir="$1"
+	local tmp
+	tmp=$(mktemp --suffix=.wav)
+	for wav in "$dir"/*.wav; do
+		[ -f "$wav" ] || continue
+		sox "$wav" "$tmp" gain -n "$GAIN_PEAK_DBFS"
+		mv "$tmp" "$wav"
+	done
+	rm -f "$tmp"
+}
+
+if ! command -v sox >/dev/null; then
+	echo "sox not on PATH — please install (e.g. apt install sox)" >&2
+	exit 1
+fi
+
+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="https://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"
+
+echo ">>> Producing $N_SILENCE silence clips into $SILENCE_OUT"
+rm -f "$SILENCE_OUT"/*.wav
+bg_files=("$SC_CACHE/_background_noise_"/*.wav)
+n_bg=${#bg_files[@]}
+if [[ $n_bg -eq 0 ]]; then
+	echo "no background noise files found under $SC_CACHE/_background_noise_" >&2
+	exit 1
+fi
+
+i=0
+while [[ $i -lt $N_SILENCE ]]; do
+	bg="${bg_files[$((RANDOM % n_bg))]}"
+	duration=$(soxi -D "$bg" | awk '{print int($1)}')
+	if [[ $duration -le 1 ]]; then
+		continue
+	fi
+	max_start=$((duration - 1))
+	start=$((RANDOM % max_start))
+	out="$SILENCE_OUT/silence_${i}.wav"
+	sox "$bg" -r 16000 -c 1 -b 16 "$out" trim "$start" 1.0
+	i=$((i + 1))
+done
+
+echo ">>> Sampling $N_UNKNOWN unknown words into $UNKNOWN_OUT"
+rm -f "$UNKNOWN_OUT"/*.wav
+
+if [[ -z "$UNKNOWN_WORDS" ]]; then
+	word_dirs=()
+	for d in "$SC_CACHE"/*/; do
+		base=$(basename "$d")
+		case "$base" in
+			_background_noise_) ;;
+			*) word_dirs+=("$d") ;;
+		esac
+	done
+else
+	word_dirs=()
+	for w in $UNKNOWN_WORDS; do
+		if [[ -d "$SC_CACHE/$w" ]]; then
+			word_dirs+=("$SC_CACHE/$w")
+		fi
+	done
+fi
+
+n_words=${#word_dirs[@]}
+if [[ $n_words -eq 0 ]]; then
+	echo "no word directories found under $SC_CACHE" >&2
+	exit 1
+fi
+
+all_wavs=()
+for d in "${word_dirs[@]}"; do
+	for f in "${d%/}"/*.wav; do
+		[[ -f "$f" ]] && all_wavs+=("$f")
+	done
+done
+
+n_all=${#all_wavs[@]}
+if [[ $n_all -eq 0 ]]; then
+	echo "no speech command WAVs found" >&2
+	exit 1
+fi
+
+shuffled=($(printf "%s\n" "${all_wavs[@]}" | shuf -n "$N_UNKNOWN"))
+
+i=0
+for wav in "${shuffled[@]}"; do
+	out="$UNKNOWN_OUT/unknown_${i}.wav"
+	sox "$wav" -r 16000 -c 1 -b 16 "$out"
+	i=$((i + 1))
+done
+
+if [[ "$GAIN_NORM" = "1" ]]; then
+	echo ">>> Applying peak normalization (${GAIN_PEAK_DBFS} dBFS) to $UNKNOWN_OUT"
+	apply_peak_norm "$UNKNOWN_OUT"
+fi
+
+echo ">>> Done. Dataset now contains:"
+echo "    silence: $(find "$SILENCE_OUT" -name '*.wav' | wc -l) WAVs"
+echo "    unknown: $(find "$UNKNOWN_OUT" -name '*.wav' | wc -l) WAVs"
diff --git a/src/audio/microwakeword/tune/sof_mww_train.py b/src/audio/microwakeword/tune/sof_mww_train.py
new file mode 100644
index 000000000000..d66284764a1f
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_train.py
@@ -0,0 +1,528 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+"""
+Train a microWakeWord (MWW) streaming wake-word model against SOF mel40
+features and emit drop-in replacements for:
+  - ``src/audio/microwakeword/mww_model_data.{cc,h}`` (static C-array)
+  - Topology2 config blob ``tools/topology/topology2/include/components/mww/<name>.conf``
+  - Runtime sof-ctl IPC4 text blob ``tools/ctl/ipc4/mww/<name>.txt``
+
+The model processes 40-bin mel features extracted by the SOF host testbench at
+10 ms hop stride. During streaming deployment, the model receives 3 fresh hops
+(``MWW_FEATURE_SLICE_COUNT = 3``, shape ``(1, 3, 40)``) per inference step.
+
+Requires TensorFlow >= 2.10.
+"""
+
+from __future__ import annotations
+
+import argparse
+import datetime
+import os
+import struct
+import sys
+from pathlib import Path
+
+import numpy as np
+
+import sof_mww_dataset as mww_dataset
+
+os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
+try:
+    import tensorflow as tf
+except ImportError as exc:
+    print(
+        "tensorflow not importable; please activate your training venv first",
+        file=sys.stderr,
+    )
+    raise SystemExit(1) from exc
+
+
+def build_mww_model(window_hops: int = 99, num_mels: int = 40) -> tf.keras.Model:
+    """Build the base MixConv model for offline training on spectrogram windows with exact causal padding.
+
+    MixConv Layer Architecture:
+      - Layer 0: Conv2D(30, (5, 1), strides=(3, 1), causal pad 2) + BN + ReLU
+      - Layer 1: DW(5) + PW(60) + BN + ReLU (causal pad 4)
+      - Layer 2: DW(9) + PW(60) + BN + ReLU (causal pad 8)
+      - Layer 3: DW(13) + PW(60) + BN + ReLU (causal pad 12)
+      - Layer 4: DW(4) + PW(60) + BN + ReLU (causal pad 3)
+      - Aggregation: Last 5 time steps (300 features) -> Flatten -> Dropout(0.3) -> Dense(1, Sigmoid)
+    """
+    inputs = tf.keras.Input(shape=(window_hops, num_mels, 1), name="mel40_in")
+    x = tf.keras.layers.Reshape((window_hops, 1, num_mels), name="reshape")(inputs)
+
+    # Layer 0: Conv2D + BN + ReLU with causal left padding of 2 hops
+    x = tf.keras.layers.ZeroPadding2D(padding=((2, 0), (0, 0)), name="pad0")(x)
+    x = tf.keras.layers.Conv2D(
+        filters=30,
+        kernel_size=(5, 1),
+        strides=(3, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="conv0",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn0")(x)
+    x = tf.keras.layers.ReLU(name="relu0")(x)
+
+    # Layer 1: MixConv DW(5) + PW(60) + BN + ReLU with causal left padding of 4 steps
+    x = tf.keras.layers.ZeroPadding2D(padding=((4, 0), (0, 0)), name="pad1")(x)
+    x = tf.keras.layers.DepthwiseConv2D(
+        kernel_size=(5, 1),
+        strides=(1, 1),
+        padding="valid",
+        activation=None,
+        name="dw1",
+    )(x)
+    x = tf.keras.layers.Conv2D(
+        filters=60,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="pw1",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn1")(x)
+    x = tf.keras.layers.ReLU(name="relu1")(x)
+
+    # Layer 2: MixConv DW(9) + PW(60) + BN + ReLU with causal left padding of 8 steps
+    x = tf.keras.layers.ZeroPadding2D(padding=((8, 0), (0, 0)), name="pad2")(x)
+    x = tf.keras.layers.DepthwiseConv2D(
+        kernel_size=(9, 1),
+        strides=(1, 1),
+        padding="valid",
+        activation=None,
+        name="dw2",
+    )(x)
+    x = tf.keras.layers.Conv2D(
+        filters=60,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="pw2",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn2")(x)
+    x = tf.keras.layers.ReLU(name="relu2")(x)
+
+    # Layer 3: MixConv DW(13) + PW(60) + BN + ReLU with causal left padding of 12 steps
+    x = tf.keras.layers.ZeroPadding2D(padding=((12, 0), (0, 0)), name="pad3")(x)
+    x = tf.keras.layers.DepthwiseConv2D(
+        kernel_size=(13, 1),
+        strides=(1, 1),
+        padding="valid",
+        activation=None,
+        name="dw3",
+    )(x)
+    x = tf.keras.layers.Conv2D(
+        filters=60,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="pw3",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn3")(x)
+    x = tf.keras.layers.ReLU(name="relu3")(x)
+
+    # Layer 4: MixConv DW(4) + PW(60) + BN + ReLU with causal left padding of 3 steps
+    x = tf.keras.layers.ZeroPadding2D(padding=((3, 0), (0, 0)), name="pad4")(x)
+    x = tf.keras.layers.DepthwiseConv2D(
+        kernel_size=(4, 1),
+        strides=(1, 1),
+        padding="valid",
+        activation=None,
+        name="dw4",
+    )(x)
+    x = tf.keras.layers.Conv2D(
+        filters=60,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="pw4",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn4")(x)
+    x = tf.keras.layers.ReLU(name="relu4")(x)
+
+    # Temporal feature aggregation (last 5 steps: 5 * 60 = 300)
+    x = tf.keras.layers.Lambda(lambda t: t[:, -5:, :, :], name="last5")(x)
+    x = tf.keras.layers.Flatten(name="flatten")(x)
+    x = tf.keras.layers.Dropout(0.3, name="dropout")(x)
+    outputs = tf.keras.layers.Dense(
+        1, activation="sigmoid", bias_initializer=tf.keras.initializers.Constant(-2.0), name="dense"
+    )(x)
+
+    return tf.keras.Model(inputs=inputs, outputs=outputs, name="mww_base_model")
+
+
+def build_streaming_inference_model(
+    trained_model: tf.keras.Model,
+    slice_hops: int = 3,
+    num_mels: int = 40,
+) -> tf.keras.Model:
+    """Build true layer-by-layer streaming MixConv inference model with internal ring buffers and fused BatchNorm."""
+    def fold_bn(conv_layer, bn_layer):
+        gamma, beta, mean, var = bn_layer.get_weights()
+        weights = conv_layer.get_weights()
+        W = weights[0]
+        b = weights[1] if len(weights) > 1 else np.zeros(W.shape[-1], dtype=np.float32)
+        scale = gamma / np.sqrt(var + bn_layer.epsilon)
+        W_fused = W * scale
+        b_fused = (b - mean) * scale + beta
+        return [W_fused, b_fused]
+
+    class StreamingMixConvMWW(tf.keras.Model):
+        def __init__(self, **kwargs):
+            super().__init__(**kwargs)
+            self.conv0 = tf.keras.layers.Conv2D(
+                30, kernel_size=(5, 1), strides=(3, 1), padding="valid", activation="relu", name="conv0"
+            )
+            self.dw1 = tf.keras.layers.DepthwiseConv2D(
+                kernel_size=(5, 1), strides=(1, 1), padding="valid", activation=None, name="dw1"
+            )
+            self.pw1 = tf.keras.layers.Conv2D(
+                60, kernel_size=(1, 1), strides=(1, 1), padding="valid", activation="relu", name="pw1"
+            )
+            self.dw2 = tf.keras.layers.DepthwiseConv2D(
+                kernel_size=(9, 1), strides=(1, 1), padding="valid", activation=None, name="dw2"
+            )
+            self.pw2 = tf.keras.layers.Conv2D(
+                60, kernel_size=(1, 1), strides=(1, 1), padding="valid", activation="relu", name="pw2"
+            )
+            self.dw3 = tf.keras.layers.DepthwiseConv2D(
+                kernel_size=(13, 1), strides=(1, 1), padding="valid", activation=None, name="dw3"
+            )
+            self.pw3 = tf.keras.layers.Conv2D(
+                60, kernel_size=(1, 1), strides=(1, 1), padding="valid", activation="relu", name="pw3"
+            )
+            self.dw4 = tf.keras.layers.DepthwiseConv2D(
+                kernel_size=(4, 1), strides=(1, 1), padding="valid", activation=None, name="dw4"
+            )
+            self.pw4 = tf.keras.layers.Conv2D(
+                60, kernel_size=(1, 1), strides=(1, 1), padding="valid", activation="relu", name="pw4"
+            )
+            self.dense = tf.keras.layers.Dense(1, activation="sigmoid", name="dense")
+
+            # Layer-by-layer persistent state ring buffers
+            self.state0 = tf.Variable(tf.zeros((1, 2, 1, num_mels)), trainable=False, name="stream")
+            self.state1 = tf.Variable(tf.zeros((1, 4, 1, 30)), trainable=False, name="stream_1")
+            self.state2 = tf.Variable(tf.zeros((1, 8, 1, 60)), trainable=False, name="stream_2")
+            self.state3 = tf.Variable(tf.zeros((1, 12, 1, 60)), trainable=False, name="stream_3")
+            self.state4 = tf.Variable(tf.zeros((1, 3, 1, 60)), trainable=False, name="stream_4")
+            self.state5 = tf.Variable(tf.zeros((1, 4, 1, 60)), trainable=False, name="stream_5")
+
+        @tf.function(
+            input_signature=[
+                tf.TensorSpec(shape=(1, slice_hops, num_mels), dtype=tf.float32, name="input_audio")
+            ]
+        )
+        def call(self, inputs):
+            x = tf.expand_dims(inputs, axis=2)  # (1, 3, 1, 40)
+            c0 = tf.concat([self.state0, x], axis=1)
+            self.state0.assign(c0[:, 3:, :, :])
+            y0 = self.conv0(c0)
+
+            c1 = tf.concat([self.state1, y0], axis=1)
+            self.state1.assign(c1[:, 1:, :, :])
+            y1 = self.pw1(self.dw1(c1))
+
+            c2 = tf.concat([self.state2, y1], axis=1)
+            self.state2.assign(c2[:, 1:, :, :])
+            y2 = self.pw2(self.dw2(c2))
+
+            c3 = tf.concat([self.state3, y2], axis=1)
+            self.state3.assign(c3[:, 1:, :, :])
+            y3 = self.pw3(self.dw3(c3))
+
+            c4 = tf.concat([self.state4, y3], axis=1)
+            self.state4.assign(c4[:, 1:, :, :])
+            y4 = self.pw4(self.dw4(c4))
+
+            c5 = tf.concat([self.state5, y4], axis=1)
+            self.state5.assign(c5[:, 1:, :, :])
+
+            flat = tf.reshape(c5, (1, 300))
+            return self.dense(flat)
+
+    streaming_model = StreamingMixConvMWW(name="mww_streaming_model")
+    # Initialize weights by passing dummy input
+    dummy_in = tf.zeros((1, slice_hops, num_mels))
+    streaming_model(dummy_in)
+
+    # Set fused weights
+    streaming_model.conv0.set_weights(fold_bn(trained_model.get_layer("conv0"), trained_model.get_layer("bn0")))
+    streaming_model.dw1.set_weights(trained_model.get_layer("dw1").get_weights())
+    streaming_model.pw1.set_weights(fold_bn(trained_model.get_layer("pw1"), trained_model.get_layer("bn1")))
+    streaming_model.dw2.set_weights(trained_model.get_layer("dw2").get_weights())
+    streaming_model.pw2.set_weights(fold_bn(trained_model.get_layer("pw2"), trained_model.get_layer("bn2")))
+    streaming_model.dw3.set_weights(trained_model.get_layer("dw3").get_weights())
+    streaming_model.pw3.set_weights(fold_bn(trained_model.get_layer("pw3"), trained_model.get_layer("bn3")))
+    streaming_model.dw4.set_weights(trained_model.get_layer("dw4").get_weights())
+    streaming_model.pw4.set_weights(fold_bn(trained_model.get_layer("pw4"), trained_model.get_layer("bn4")))
+    streaming_model.dense.set_weights(trained_model.get_layer("dense").get_weights())
+
+    return streaming_model
+
+
+def split_train_val(
+    X: np.ndarray,
+    y: np.ndarray,
+    val_frac: float = 0.2,
+    seed: int = 0,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Stratified train/val split."""
+    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 = 500,
+    slice_hops: int = 3,
+    num_mels: int = 40,
+):
+    """Yield representative calibration inputs with shape (1, slice_hops, num_mels)."""
+    rng = np.random.default_rng(0)
+    pos_idx = np.where(y == 1)[0]
+    neg_idx = np.where(y == 0)[0]
+    samples_yielded = 0
+    while samples_yielded < n_samples:
+        if (samples_yielded % 2 == 0) and len(pos_idx) > 0:
+            idx = rng.choice(pos_idx)
+        else:
+            idx = rng.choice(neg_idx)
+        win = X[idx, :, :, 0]
+        for h in range(0, win.shape[0] - slice_hops + 1, slice_hops):
+            slice_3 = win[h : h + slice_hops, :]
+            yield [slice_3[np.newaxis, ...].astype(np.float32)]
+            samples_yielded += 1
+            if samples_yielded >= n_samples:
+                break
+
+
+def convert_to_tflite_int8(
+    model: tf.keras.Model,
+    rep_X: np.ndarray,
+    rep_y: np.ndarray,
+    n_rep: int = 500,
+) -> bytes:
+    """Convert model to int8 quantized TFLite flatbuffer."""
+    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_samples=n_rep, slice_hops=3, num_mels=40
+    )
+    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()
+
+
+# SOF IPC4 ABI definitions
+SOF_IPC4_ABI_MAGIC = 0x34464F53  # 'SOF4'
+SOF_ABI_VERSION = (3 << 24) | (29 << 12) | 1  # 3.29.1
+SOF_CTRL_CMD_BINARY = 3
+
+
+def build_ipc4_abi_blob(tflite_bytes: bytes, param_id: int = 0) -> bytes:
+    """Pack tflite model bytes behind a standard 32-byte struct sof_abi_hdr."""
+    size = len(tflite_bytes)
+    abi_header = struct.pack("<IIIIIIII", SOF_IPC4_ABI_MAGIC, param_id, size, SOF_ABI_VERSION, 0, 0, 0, 0)
+    pad = (4 - (size % 4)) % 4
+    return abi_header + tflite_bytes + b"\x00" * pad
+
+
+def emit_c_array(
+    tflite_bytes: bytes,
+    out_cc: Path,
+    out_h: Path,
+    symbol: str = "mww_model_data",
+) -> None:
+    """Emit C array source and header."""
+    guard = symbol.upper() + "_H_"
+    with open(out_h, "w") as f:
+        f.write("// SPDX-License-Identifier: BSD-3-Clause\n")
+        f.write("// Auto-generated by sof_mww_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_mww_train.py — do not edit.\n\n")
+        f.write(f'#include "{out_h.name}"\n\n')
+        f.write(f"alignas(16) 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_topology2_conf(
+    tflite_bytes: bytes,
+    out_conf: Path,
+    model_name: str,
+) -> None:
+    """Emit Topology2 configuration blob in .conf format."""
+    blob8 = build_ipc4_abi_blob(tflite_bytes)
+    words = struct.unpack(f"<{len(blob8) // 4}I", blob8)
+    out_conf.parent.mkdir(parents=True, exist_ok=True)
+    today = datetime.date.today().strftime("%d-%b-%Y")
+    data_name = f"mww_config_{model_name}"
+    with open(out_conf, "w") as f:
+        f.write(f"# Exported microWakeWord Model Control Words {today}\n")
+        f.write(f"# Model: {model_name}\n")
+        f.write(f"# Auto-generated by sof_mww_train.py — do not edit.\n")
+        f.write(f'Object.Base.data."{data_name}" {{\n')
+        f.write('\twords "\n')
+        lines = []
+        for i in range(0, len(words), 8):
+            chunk = words[i : i + 8]
+            lines.append(",".join(f"0x{w:08x}" for w in chunk))
+        for idx, line in enumerate(lines):
+            if idx == len(lines) - 1:
+                f.write(f"\t\t{line}\"\n")
+            else:
+                f.write(f"\t\t{line},\n")
+        f.write("}\n")
+
+
+def emit_sofctl_ipc4_txt(
+    tflite_bytes: bytes,
+    out_txt: Path,
+) -> None:
+    """Emit sof-ctl IPC4 text blob in comma-separated uint32 CSV format."""
+    size = len(tflite_bytes)
+    tlv_cmd = SOF_CTRL_CMD_BINARY
+    tlv_size = 32 + size
+    pad = (4 - (size % 4)) % 4
+    payload_padded = tflite_bytes + b"\x00" * pad
+    tlv_header = struct.pack("<II", tlv_cmd, tlv_size)
+    abi_header = struct.pack("<IIIIIIII", SOF_IPC4_ABI_MAGIC, 0, size, SOF_ABI_VERSION, 0, 0, 0, 0)
+    blob = tlv_header + abi_header + payload_padded
+
+    words = struct.unpack(f"<{len(blob) // 4}I", blob)
+    out_txt.parent.mkdir(parents=True, exist_ok=True)
+    with open(out_txt, "w") as f:
+        f.write(",".join(str(w) for w in words) + "\n")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--feat-root", required=True, help="Directory with <label>/*.raw feature files")
+    parser.add_argument("--keyword", required=True, action="append", help="Target positive keyword class(es)")
+    parser.add_argument("--name", default="mww_model", help="Base name for exported artifacts")
+    parser.add_argument("--out-dir", default=".", help="Output directory for trained artifacts")
+    parser.add_argument("--epochs", type=int, default=30, help="Training epochs (default 30)")
+    parser.add_argument("--batch-size", type=int, default=64, help="Batch size (default 64)")
+    parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate (default 1e-3)")
+    parser.add_argument("--val-frac", type=float, default=0.2, help="Validation fraction (default 0.2)")
+    parser.add_argument("--class-weight-neg", type=float, default=1.0, help="Loss multiplier for negative samples (default 1.0)")
+    parser.add_argument("--gain-aug-db-min", type=float, default=-8.0, help="Min gain jitter in dB (default -8.0)")
+    parser.add_argument("--gain-aug-db-max", type=float, default=3.0, help="Max gain jitter in dB (default 3.0)")
+    parser.add_argument("--seed", type=int, default=0, help="Random seed (default 0)")
+
+    args = parser.parse_args()
+    labels = ["silence", "unknown"] + args.keyword
+    print(f">>> Loading dataset from {args.feat_root} for classes: {labels}")
+
+    X_raw, y = mww_dataset.load_dataset(
+        args.feat_root,
+        labels=labels,
+        gain_aug_db_min=args.gain_aug_db_min,
+        gain_aug_db_max=args.gain_aug_db_max,
+        seed=args.seed,
+    )
+
+    if X_raw.shape[0] == 0:
+        print("Error: empty dataset loaded. Check feat-root path.", file=sys.stderr)
+        return 1
+
+    print(f">>> Applying soft mel-log AGC (target 0 dB, release 0.5 dB/s)...")
+    X = mww_dataset.apply_soft_agc(X_raw)
+
+    X_train, y_train, X_val, y_val = split_train_val(X, y, val_frac=args.val_frac, seed=args.seed)
+    print(f">>> Training split: {X_train.shape[0]} samples (pos: {np.sum(y_train == 1)}, neg: {np.sum(y_train == 0)})")
+    print(f">>> Validation split: {X_val.shape[0]} samples (pos: {np.sum(y_val == 1)}, neg: {np.sum(y_val == 0)})")
+
+    model = build_mww_model(window_hops=mww_dataset.WINDOW_HOPS, num_mels=mww_dataset.HOP_BINS)
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=args.lr),
+        loss=tf.keras.losses.BinaryCrossentropy(),
+        metrics=["accuracy", tf.keras.metrics.Precision(name="prec"), tf.keras.metrics.Recall(name="rec")],
+    )
+
+    print(">>> Training base model...")
+    class_weight = {0: args.class_weight_neg, 1: 1.0}
+    model.fit(
+        X_train,
+        y_train,
+        validation_data=(X_val, y_val),
+        epochs=args.epochs,
+        batch_size=args.batch_size,
+        class_weight=class_weight,
+        verbose=2,
+    )
+
+    print(">>> Building fused layer-by-layer streaming inference model...")
+    streaming_model = build_streaming_inference_model(model, slice_hops=3, num_mels=40)
+
+    print(">>> Quantizing to int8 with representative dataset...")
+    tflite_bytes = convert_to_tflite_int8(streaming_model, rep_X=X_train, rep_y=y_train)
+    print(f">>> Quantized TFLite size: {len(tflite_bytes)} bytes")
+
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    # 1. Save .tflite flatbuffer
+    tflite_path = out_dir / f"{args.name}_quantized_model.tflite"
+    with open(tflite_path, "wb") as f:
+        f.write(tflite_bytes)
+    print(f">>> Saved {tflite_path}")
+
+    # 2. Save static C array files
+    cc_path = out_dir / "mww_model_data.cc"
+    h_path = out_dir / "mww_model_data.h"
+    emit_c_array(tflite_bytes, cc_path, h_path, symbol="mww_model_data")
+    print(f">>> Emitted C array to {cc_path} and {h_path}")
+
+    # 3. Save Topology2 config
+    conf_path = out_dir / f"{args.name}.conf"
+    emit_topology2_conf(tflite_bytes, conf_path, model_name=args.name)
+    print(f">>> Emitted Topology2 conf to {conf_path}")
+
+    # 4. Save sof-ctl IPC4 text
+    txt_path = out_dir / f"{args.name}.txt"
+    emit_sofctl_ipc4_txt(tflite_bytes, txt_path)
+    print(f">>> Emitted sof-ctl text to {txt_path}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/audio/microwakeword/tune/sof_mww_train_pcan.py b/src/audio/microwakeword/tune/sof_mww_train_pcan.py
new file mode 100755
index 000000000000..b1548b098990
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_train_pcan.py
@@ -0,0 +1,716 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+"""
+All-in-one Python reference training pipeline for microWakeWord (MWW) using PCAN.
+
+Uses TensorFlow's audio_microfrontend (Per-Channel Auto-Gain Normalization / PCAN)
+to extract 40-bin mel features directly from WAV datasets (e.g. ~/wov/wavs/{silence,unknown,<keyword>}),
+trains the streaming MixConv neural network, int8 quantizes to TFLite, and runs a streaming
+evaluation to print a full confusion matrix and detection performance report.
+
+Used to benchmark and compare PCAN frontend performance against SOF MFCC testbench features.
+
+Example:
+    python3 sof_mww_train_pcan.py \
+        --wav-root ~/wov/wavs \
+        --keyword hey_jarvis \
+        --out-dir ~/wov/model_pcan \
+        --epochs 30
+"""
+
+from __future__ import annotations
+
+import argparse
+import glob
+import os
+import sys
+import wave
+from pathlib import Path
+
+# Force legacy Keras 2 to ensure compatibility with TFLite int8 converter and resource variables
+os.environ["TF_USE_LEGACY_KERAS"] = "1"
+
+import numpy as np
+
+try:
+    import tensorflow as tf
+    from tensorflow.lite.experimental.microfrontend.python.ops import (
+        audio_microfrontend_op as frontend_op,
+    )
+except ImportError as exc:
+    print(
+        "Error: tensorflow is required. Please activate your Python training venv:\n"
+        "  source ~/venvs/mww-train/bin/activate",
+        file=sys.stderr,
+    )
+    raise SystemExit(1) from exc
+
+HOP_BINS = 40
+WINDOW_HOPS = 100  # 1.0 second at 10 ms hop stride
+SLICE_HOPS = 3     # 30 ms streaming chunk size (MWW_FEATURE_SLICE_COUNT)
+
+
+# -----------------------------------------------------------------------------
+# 1. WAV Loading & Audio Microfrontend (PCAN) Feature Extraction
+# -----------------------------------------------------------------------------
+
+def load_wav_pcm16(path: str) -> np.ndarray:
+    """Read a WAV file and return 1D int16 PCM audio samples."""
+    with wave.open(path, "rb") as w:
+        n_channels = w.getnchannels()
+        sampwidth = w.getsampwidth()
+        framerate = w.getframerate()
+        n_frames = w.getnframes()
+        data = w.readframes(n_frames)
+
+    if sampwidth == 2:
+        pcm = np.frombuffer(data, dtype=np.int16)
+    elif sampwidth == 4:
+        # 32-bit integer PCM -> convert to int16
+        pcm = (np.frombuffer(data, dtype=np.int32) >> 16).astype(np.int16)
+    elif sampwidth == 3:
+        # 24-bit PCM
+        raw = np.frombuffer(data, dtype=np.uint8)
+        raw_reshaped = raw.reshape(-1, 3)
+        # Sign-extend 24-bit to 32-bit, then take high 16 bits
+        int32 = (
+            raw_reshaped[:, 0].astype(np.int32)
+            | (raw_reshaped[:, 1].astype(np.int32) << 8)
+            | (raw_reshaped[:, 2].astype(np.int8).astype(np.int32) << 16)
+        )
+        pcm = (int32 >> 8).astype(np.int16)
+    else:
+        raise ValueError(f"Unsupported sample width: {sampwidth} bytes in {path}")
+
+    # Stereo -> take first channel
+    if n_channels > 1:
+        pcm = pcm.reshape(-1, n_channels)[:, 0]
+
+    return pcm
+
+
+def extract_pcan_features(
+    pcm_int16: np.ndarray,
+    sample_rate: int = 16000,
+    window_size_ms: int = 30,
+    window_step_ms: int = 10,
+    num_channels: int = 40,
+    pcan_strength: float = 0.95,
+    pcan_offset: float = 80.0,
+) -> np.ndarray:
+    """Extract 40-bin mel spectrogram features using Google PCAN microfrontend.
+
+    Returns float32 array of shape (N_hops, num_channels).
+    """
+    if len(pcm_int16) < (window_size_ms * sample_rate // 1000):
+        return np.zeros((0, num_channels), dtype=np.float32)
+
+    audio_tensor = tf.constant(pcm_int16, dtype=tf.int16)
+    feats = frontend_op.audio_microfrontend(
+        audio_tensor,
+        sample_rate=sample_rate,
+        window_size=window_size_ms,
+        window_step=window_step_ms,
+        num_channels=num_channels,
+        upper_band_limit=7500.0,
+        lower_band_limit=125.0,
+        enable_pcan=True,
+        pcan_strength=pcan_strength,
+        pcan_offset=pcan_offset,
+        gain_bits=21,
+        enable_log=True,
+        scale_shift=6,
+        out_scale=1,
+        out_type=tf.float32,
+    )
+    return feats.numpy()
+
+
+# -----------------------------------------------------------------------------
+# 2. Window Slicing and Dataset Loading
+# -----------------------------------------------------------------------------
+
+def slice_into_windows(
+    mel: np.ndarray,
+    window_hops: int = WINDOW_HOPS,
+    hop_step: int = 10,
+) -> np.ndarray:
+    """Slice (N_hops, 40) into (M, window_hops, 40) overlapping windows."""
+    n_hops = mel.shape[0]
+    if n_hops < window_hops:
+        pad = np.zeros((window_hops - n_hops, HOP_BINS), dtype=mel.dtype)
+        return np.concatenate([mel, pad], axis=0)[np.newaxis, ...]
+
+    starts = list(range(0, n_hops - window_hops + 1, hop_step))
+    if not starts or starts[-1] != (n_hops - window_hops):
+        starts.append(n_hops - window_hops)
+    windows = [mel[s : s + window_hops] for s in starts]
+    return np.stack(windows, axis=0)
+
+
+def load_pcan_dataset(
+    wav_root: str,
+    labels: list[str],
+    window_hops: int = WINDOW_HOPS,
+    hop_step_keyword: int = 5,
+    hop_step_negative: int = 20,
+    gain_aug_db_min: float = -30.0,
+    gain_aug_db_max: float = 6.0,
+    seed: int = 0,
+) -> tuple[np.ndarray, np.ndarray, dict[str, list[str]]]:
+    """Extract PCAN features for all WAVs and slice into dataset windows.
+
+    Returns:
+      X: (N, window_hops, 40, 1) float32 tensor
+      y: (N,) binary labels (0 for negative, 1 for positive keyword)
+      file_map: Dict mapping label -> list of WAV paths
+    """
+    rng = np.random.default_rng(seed)
+    all_X: list[np.ndarray] = []
+    all_y: list[int] = []
+    file_map: dict[str, list[str]] = {}
+
+    for label in labels:
+        label_dir = os.path.join(wav_root, label)
+        wav_files = sorted(glob.glob(os.path.join(label_dir, "*.wav")))
+        file_map[label] = wav_files
+
+        if not wav_files:
+            print(f"Warning: no .wav files found in {label_dir}", file=sys.stderr)
+            continue
+
+        is_keyword = (label not in ("silence", "unknown", "noise", "background"))
+        step = hop_step_keyword if is_keyword else hop_step_negative
+        y_val = 1 if is_keyword else 0
+
+        for f in wav_files:
+            pcm = load_wav_pcm16(f)
+            mel = extract_pcan_features(pcm)
+            if mel.shape[0] == 0:
+                continue
+
+            windows = slice_into_windows(mel, window_hops=window_hops, hop_step=step)
+
+            # Random gain augmentation
+            if gain_aug_db_min is not None and gain_aug_db_max is not None:
+                gains_db = rng.uniform(gain_aug_db_min, gain_aug_db_max, size=(windows.shape[0], 1, 1))
+                gains_lin = gains_db * 0.1
+                windows = windows + gains_lin
+
+            for w in windows:
+                all_X.append(w)
+                all_y.append(y_val)
+
+    if not all_X:
+        return np.zeros((0, window_hops, HOP_BINS, 1), dtype=np.float32), np.zeros((0,), dtype=np.int32), file_map
+
+    X = np.stack(all_X, axis=0)[..., np.newaxis]
+    y = np.array(all_y, dtype=np.int32)
+    return X, y, file_map
+
+
+def split_train_val(
+    X: np.ndarray,
+    y: np.ndarray,
+    val_frac: float = 0.2,
+    seed: int = 0,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Stratified train/val split."""
+    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]
+
+
+# -----------------------------------------------------------------------------
+# 3. Model Architecture & Streaming Model Construction
+# -----------------------------------------------------------------------------
+
+def build_mww_model(window_hops: int = 99, num_mels: int = 40) -> tf.keras.Model:
+    """Build the base MixConv model for offline training on spectrogram windows with exact causal padding."""
+    inputs = tf.keras.Input(shape=(window_hops, num_mels, 1), name="pcan_in")
+    x = tf.keras.layers.Reshape((window_hops, 1, num_mels), name="reshape")(inputs)
+
+    # Layer 0: Conv2D + BN + ReLU with causal left padding of 2 hops
+    x = tf.keras.layers.ZeroPadding2D(padding=((2, 0), (0, 0)), name="pad0")(x)
+    x = tf.keras.layers.Conv2D(
+        filters=30,
+        kernel_size=(5, 1),
+        strides=(3, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="conv0",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn0")(x)
+    x = tf.keras.layers.ReLU(name="relu0")(x)
+
+    # Layer 1: MixConv DW(5) + PW(60) + BN + ReLU with causal left padding of 4 steps
+    x = tf.keras.layers.ZeroPadding2D(padding=((4, 0), (0, 0)), name="pad1")(x)
+    x = tf.keras.layers.DepthwiseConv2D(
+        kernel_size=(5, 1),
+        strides=(1, 1),
+        padding="valid",
+        activation=None,
+        name="dw1",
+    )(x)
+    x = tf.keras.layers.Conv2D(
+        filters=60,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="pw1",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn1")(x)
+    x = tf.keras.layers.ReLU(name="relu1")(x)
+
+    # Layer 2: MixConv DW(9) + PW(60) + BN + ReLU with causal left padding of 8 steps
+    x = tf.keras.layers.ZeroPadding2D(padding=((8, 0), (0, 0)), name="pad2")(x)
+    x = tf.keras.layers.DepthwiseConv2D(
+        kernel_size=(9, 1),
+        strides=(1, 1),
+        padding="valid",
+        activation=None,
+        name="dw2",
+    )(x)
+    x = tf.keras.layers.Conv2D(
+        filters=60,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="pw2",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn2")(x)
+    x = tf.keras.layers.ReLU(name="relu2")(x)
+
+    # Layer 3: MixConv DW(13) + PW(60) + BN + ReLU with causal left padding of 12 steps
+    x = tf.keras.layers.ZeroPadding2D(padding=((12, 0), (0, 0)), name="pad3")(x)
+    x = tf.keras.layers.DepthwiseConv2D(
+        kernel_size=(13, 1),
+        strides=(1, 1),
+        padding="valid",
+        activation=None,
+        name="dw3",
+    )(x)
+    x = tf.keras.layers.Conv2D(
+        filters=60,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="pw3",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn3")(x)
+    x = tf.keras.layers.ReLU(name="relu3")(x)
+
+    # Layer 4: MixConv DW(4) + PW(60) + BN + ReLU with causal left padding of 3 steps
+    x = tf.keras.layers.ZeroPadding2D(padding=((3, 0), (0, 0)), name="pad4")(x)
+    x = tf.keras.layers.DepthwiseConv2D(
+        kernel_size=(4, 1),
+        strides=(1, 1),
+        padding="valid",
+        activation=None,
+        name="dw4",
+    )(x)
+    x = tf.keras.layers.Conv2D(
+        filters=60,
+        kernel_size=(1, 1),
+        strides=(1, 1),
+        padding="valid",
+        use_bias=True,
+        activation=None,
+        name="pw4",
+    )(x)
+    x = tf.keras.layers.BatchNormalization(name="bn4")(x)
+    x = tf.keras.layers.ReLU(name="relu4")(x)
+
+    # Temporal feature aggregation (last 5 steps: 5 * 60 = 300)
+    x = tf.keras.layers.Lambda(lambda t: t[:, -5:, :, :], name="last5")(x)
+    x = tf.keras.layers.Flatten(name="flatten")(x)
+    x = tf.keras.layers.Dropout(0.3, name="dropout")(x)
+    outputs = tf.keras.layers.Dense(
+        1, activation="sigmoid", bias_initializer=tf.keras.initializers.Constant(-2.0), name="dense"
+    )(x)
+
+    return tf.keras.Model(inputs=inputs, outputs=outputs, name="mww_pcan_model")
+
+
+def build_streaming_inference_model(
+    trained_model: tf.keras.Model,
+    slice_hops: int = 3,
+    num_mels: int = 40,
+) -> tf.keras.Model:
+    """Build true layer-by-layer streaming MixConv inference model with internal ring buffers and fused BatchNorm."""
+    def fold_bn(conv_layer, bn_layer):
+        gamma, beta, mean, var = bn_layer.get_weights()
+        weights = conv_layer.get_weights()
+        W = weights[0]
+        b = weights[1] if len(weights) > 1 else np.zeros(W.shape[-1], dtype=np.float32)
+        scale = gamma / np.sqrt(var + bn_layer.epsilon)
+        W_fused = W * scale
+        b_fused = (b - mean) * scale + beta
+        return [W_fused, b_fused]
+
+    class StreamingMixConvMWW(tf.keras.Model):
+        def __init__(self, **kwargs):
+            super().__init__(**kwargs)
+            self.conv0 = tf.keras.layers.Conv2D(
+                30, kernel_size=(5, 1), strides=(3, 1), padding="valid", activation="relu", name="conv0"
+            )
+            self.dw1 = tf.keras.layers.DepthwiseConv2D(
+                kernel_size=(5, 1), strides=(1, 1), padding="valid", activation=None, name="dw1"
+            )
+            self.pw1 = tf.keras.layers.Conv2D(
+                60, kernel_size=(1, 1), strides=(1, 1), padding="valid", activation="relu", name="pw1"
+            )
+            self.dw2 = tf.keras.layers.DepthwiseConv2D(
+                kernel_size=(9, 1), strides=(1, 1), padding="valid", activation=None, name="dw2"
+            )
+            self.pw2 = tf.keras.layers.Conv2D(
+                60, kernel_size=(1, 1), strides=(1, 1), padding="valid", activation="relu", name="pw2"
+            )
+            self.dw3 = tf.keras.layers.DepthwiseConv2D(
+                kernel_size=(13, 1), strides=(1, 1), padding="valid", activation=None, name="dw3"
+            )
+            self.pw3 = tf.keras.layers.Conv2D(
+                60, kernel_size=(1, 1), strides=(1, 1), padding="valid", activation="relu", name="pw3"
+            )
+            self.dw4 = tf.keras.layers.DepthwiseConv2D(
+                kernel_size=(4, 1), strides=(1, 1), padding="valid", activation=None, name="dw4"
+            )
+            self.pw4 = tf.keras.layers.Conv2D(
+                60, kernel_size=(1, 1), strides=(1, 1), padding="valid", activation="relu", name="pw4"
+            )
+            self.dense = tf.keras.layers.Dense(1, activation="sigmoid", name="dense")
+
+            # Layer-by-layer persistent state ring buffers
+            self.state0 = tf.Variable(tf.zeros((1, 2, 1, num_mels)), trainable=False, name="stream")
+            self.state1 = tf.Variable(tf.zeros((1, 4, 1, 30)), trainable=False, name="stream_1")
+            self.state2 = tf.Variable(tf.zeros((1, 8, 1, 60)), trainable=False, name="stream_2")
+            self.state3 = tf.Variable(tf.zeros((1, 12, 1, 60)), trainable=False, name="stream_3")
+            self.state4 = tf.Variable(tf.zeros((1, 3, 1, 60)), trainable=False, name="stream_4")
+            self.state5 = tf.Variable(tf.zeros((1, 4, 1, 60)), trainable=False, name="stream_5")
+
+        @tf.function(
+            input_signature=[
+                tf.TensorSpec(shape=(1, slice_hops, num_mels), dtype=tf.float32, name="input_audio")
+            ]
+        )
+        def call(self, inputs):
+            x = tf.expand_dims(inputs, axis=2)  # (1, 3, 1, 40)
+            c0 = tf.concat([self.state0, x], axis=1)
+            self.state0.assign(c0[:, 3:, :, :])
+            y0 = self.conv0(c0)
+
+            c1 = tf.concat([self.state1, y0], axis=1)
+            self.state1.assign(c1[:, 1:, :, :])
+            y1 = self.pw1(self.dw1(c1))
+
+            c2 = tf.concat([self.state2, y1], axis=1)
+            self.state2.assign(c2[:, 1:, :, :])
+            y2 = self.pw2(self.dw2(c2))
+
+            c3 = tf.concat([self.state3, y2], axis=1)
+            self.state3.assign(c3[:, 1:, :, :])
+            y3 = self.pw3(self.dw3(c3))
+
+            c4 = tf.concat([self.state4, y3], axis=1)
+            self.state4.assign(c4[:, 1:, :, :])
+            y4 = self.pw4(self.dw4(c4))
+
+            c5 = tf.concat([self.state5, y4], axis=1)
+            self.state5.assign(c5[:, 1:, :, :])
+
+            flat = tf.reshape(c5, (1, 300))
+            return self.dense(flat)
+
+    streaming_model = StreamingMixConvMWW(name="mww_pcan_streaming_model")
+    # Initialize weights by passing dummy input
+    dummy_in = tf.zeros((1, slice_hops, num_mels))
+    streaming_model(dummy_in)
+
+    # Set fused weights
+    streaming_model.conv0.set_weights(fold_bn(trained_model.get_layer("conv0"), trained_model.get_layer("bn0")))
+    streaming_model.dw1.set_weights(trained_model.get_layer("dw1").get_weights())
+    streaming_model.pw1.set_weights(fold_bn(trained_model.get_layer("pw1"), trained_model.get_layer("bn1")))
+    streaming_model.dw2.set_weights(trained_model.get_layer("dw2").get_weights())
+    streaming_model.pw2.set_weights(fold_bn(trained_model.get_layer("pw2"), trained_model.get_layer("bn2")))
+    streaming_model.dw3.set_weights(trained_model.get_layer("dw3").get_weights())
+    streaming_model.pw3.set_weights(fold_bn(trained_model.get_layer("pw3"), trained_model.get_layer("bn3")))
+    streaming_model.dw4.set_weights(trained_model.get_layer("dw4").get_weights())
+    streaming_model.pw4.set_weights(fold_bn(trained_model.get_layer("pw4"), trained_model.get_layer("bn4")))
+    streaming_model.dense.set_weights(trained_model.get_layer("dense").get_weights())
+
+    return streaming_model
+
+
+# -----------------------------------------------------------------------------
+# 4. Int8 Quantization
+# -----------------------------------------------------------------------------
+
+def convert_to_tflite_int8(
+    model: tf.keras.Model,
+    rep_X: np.ndarray,
+    n_rep: int = 200,
+) -> bytes:
+    """Convert streaming model to full int8 quantized TFLite flatbuffer."""
+    rng = np.random.default_rng(0)
+
+    def representative_dataset():
+        rng = np.random.default_rng(0)
+        samples_yielded = 0
+        while samples_yielded < n_rep:
+            idx = rng.integers(0, rep_X.shape[0])
+            win = rep_X[idx, :, :, 0]
+            for h in range(0, win.shape[0] - SLICE_HOPS + 1, SLICE_HOPS):
+                slice_3 = win[h : h + SLICE_HOPS, :]
+                yield [slice_3[np.newaxis, ...].astype(np.float32)]
+                samples_yielded += 1
+                if samples_yielded >= n_rep:
+                    break
+
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    converter.representative_dataset = representative_dataset
+    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()
+
+
+# -----------------------------------------------------------------------------
+# 5. Streaming Evaluation and Confusion Matrix
+# -----------------------------------------------------------------------------
+
+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 evaluate_streaming_confusion_matrix(
+    tflite_path: str,
+    file_map: dict[str, list[str]],
+    keywords: list[str],
+    threshold: float = 0.5,
+    slice_hops: int = 3,
+) -> dict:
+    """Run streaming inference on all WAV files using PCAN features and compute confusion matrix."""
+    interp = tf.lite.Interpreter(model_path=tflite_path)
+    interp.allocate_tensors()
+
+    in_det = interp.get_input_details()[0]
+    out_det = interp.get_output_details()[0]
+
+    in_scale, in_zp = in_det.get("quantization", (0.0, 0))
+    out_scale, out_zp = out_det.get("quantization", (0.0, 0))
+    in_dtype = in_det["dtype"]
+    out_dtype = out_det["dtype"]
+
+    results = {}
+    tp, fp, tn, fn = 0, 0, 0, 0
+
+    for label, files in file_map.items():
+        is_pos = (label in keywords)
+        n_files = len(files)
+        n_detected = 0
+        peak_probs = []
+
+        for f in files:
+            pcm = load_wav_pcm16(f)
+            mel = extract_pcan_features(pcm)
+            if mel.shape[0] < slice_hops:
+                continue
+
+            # Reset streaming internal states
+            interp.allocate_tensors()
+
+            max_prob = 0.0
+            n_hops = mel.shape[0]
+
+            for s in range(0, n_hops - slice_hops + 1, slice_hops):
+                chunk = mel[s : s + slice_hops][np.newaxis, ...]  # shape: (1, 3, 40)
+                if np.issubdtype(in_dtype, np.integer) and in_scale > 0:
+                    chunk = _quantize(chunk, in_scale, in_zp, in_dtype)
+
+                interp.set_tensor(in_det["index"], chunk)
+                interp.invoke()
+                y = interp.get_tensor(out_det["index"])
+
+                if np.issubdtype(out_dtype, np.integer) and out_scale > 0:
+                    prob = float(_dequantize(y, out_scale, out_zp).flatten()[0])
+                else:
+                    prob = float(y.flatten()[0])
+
+                if prob > max_prob:
+                    max_prob = prob
+
+            peak_probs.append(max_prob)
+            fired = (max_prob >= threshold)
+            if fired:
+                n_detected += 1
+
+            if is_pos:
+                if fired:
+                    tp += 1
+                else:
+                    fn += 1
+            else:
+                if fired:
+                    fp += 1
+                else:
+                    tn += 1
+
+        results[label] = {
+            "is_positive": is_pos,
+            "total_files": n_files,
+            "detected_files": n_detected,
+            "detection_rate": (n_detected / n_files) if n_files > 0 else 0.0,
+            "mean_peak_prob": float(np.mean(peak_probs)) if peak_probs else 0.0,
+        }
+
+    total_samples = tp + fp + tn + fn
+    accuracy = (tp + tn) / total_samples if total_samples > 0 else 0.0
+    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
+    recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
+    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0.0
+    fpr = fp / (fp + tn) if (fp + tn) > 0 else 0.0
+
+    cm = {
+        "tp": tp,
+        "fp": fp,
+        "tn": tn,
+        "fn": fn,
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall": recall,
+        "f1": f1,
+        "fpr": fpr,
+        "class_results": results,
+    }
+    return cm
+
+
+def print_confusion_matrix_report(cm: dict, threshold: float) -> None:
+    """Print formatted evaluation report with confusion matrix."""
+    print("\n" + "=" * 70)
+    print(f"microWakeWord PCAN Streaming Evaluation Report (Threshold: {threshold:.2f})")
+    print("=" * 70)
+    print(f"  {'Class':<18} {'Role':<10} {'Files':>7} {'Detected':>10} {'Rate':>8} {'Mean Peak':>10}")
+    print("  " + "-" * 66)
+
+    for label, stat in cm["class_results"].items():
+        role = "Positive" if stat["is_positive"] else "Negative"
+        rate_pct = stat["detection_rate"] * 100.0
+        print(
+            f"  {label:<18} {role:<10} {stat['total_files']:>7d} "
+            f"{stat['detected_files']:>10d} {rate_pct:>7.1f}% {stat['mean_peak_prob']:>9.3f}"
+        )
+
+    print("  " + "-" * 66)
+    print("\n>>> Confusion Matrix:")
+    print("                      Actual Positive      Actual Negative")
+    print(f"  Predicted Positive:  TP = {cm['tp']:<15d}  FP = {cm['fp']:<15d}")
+    print(f"  Predicted Negative:  FN = {cm['fn']:<15d}  TN = {cm['tn']:<15d}")
+    print("\n>>> Performance Summary Metrics:")
+    print(f"  - Accuracy                     : {cm['accuracy'] * 100.0:6.2f}%")
+    print(f"  - Precision                    : {cm['precision'] * 100.0:6.2f}%")
+    print(f"  - Recall / True Positive Rate  : {cm['recall'] * 100.0:6.2f}% ({cm['tp']}/{cm['tp'] + cm['fn']})")
+    print(f"  - False Positive / Alarm Rate  : {cm['fpr'] * 100.0:6.2f}% ({cm['fp']}/{cm['fp'] + cm['tn']})")
+    print(f"  - F1-Score                     : {cm['f1']:.4f}")
+    print("=" * 70 + "\n")
+
+
+# -----------------------------------------------------------------------------
+# 6. Main CLI
+# -----------------------------------------------------------------------------
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--wav-root", required=True, help="Directory containing <label>/*.wav files")
+    parser.add_argument("--keyword", required=True, action="append", help="Target positive keyword label(s)")
+    parser.add_argument("--name", default="mww_pcan_model", help="Base name for output model")
+    parser.add_argument("--out-dir", default=".", help="Directory to save trained model artifacts")
+    parser.add_argument("--epochs", type=int, default=30, help="Training epochs (default 30)")
+    parser.add_argument("--batch-size", type=int, default=64, help="Batch size (default 64)")
+    parser.add_argument("--lr", type=float, default=1e-3, help="Learning rate (default 1e-3)")
+    parser.add_argument("--val-frac", type=float, default=0.2, help="Validation fraction (default 0.2)")
+    parser.add_argument("--class-weight-neg", type=float, default=5.0, help="Loss multiplier for negative samples (default 5.0)")
+    parser.add_argument("--threshold", type=float, default=0.5, help="Streaming detection threshold (default 0.5)")
+    parser.add_argument("--seed", type=int, default=0, help="Random seed (default 0)")
+
+    args = parser.parse_args()
+    labels = ["silence", "unknown"] + args.keyword
+
+    print(f">>> [PCAN Pipeline] Loading WAVs from {args.wav_root} for classes: {labels}")
+    X, y, file_map = load_pcan_dataset(args.wav_root, labels=labels, seed=args.seed)
+
+    if X.shape[0] == 0:
+        print(f"Error: No audio samples found under {args.wav_root}.", file=sys.stderr)
+        return 1
+
+    print(f">>> Total extracted window samples: {X.shape[0]} (pos: {np.sum(y == 1)}, neg: {np.sum(y == 0)})")
+
+    X_train, y_train, X_val, y_val = split_train_val(X, y, val_frac=args.val_frac, seed=args.seed)
+    print(f">>> Train split: {X_train.shape[0]} samples, Val split: {X_val.shape[0]} samples")
+
+    model = build_mww_model(window_hops=WINDOW_HOPS, num_mels=HOP_BINS)
+    model.compile(
+        optimizer=tf.keras.optimizers.Adam(learning_rate=args.lr),
+        loss=tf.keras.losses.BinaryCrossentropy(),
+        metrics=["accuracy", tf.keras.metrics.Precision(name="prec"), tf.keras.metrics.Recall(name="rec")],
+    )
+
+    print(">>> Training base model with PCAN features...")
+    model.fit(
+        X_train,
+        y_train,
+        validation_data=(X_val, y_val),
+        epochs=args.epochs,
+        batch_size=args.batch_size,
+        verbose=2,
+    )
+
+    print(">>> Building fused layer-by-layer streaming inference model...")
+    streaming_model = build_streaming_inference_model(model, slice_hops=SLICE_HOPS, num_mels=HOP_BINS)
+
+    print(">>> Quantizing to int8 with representative dataset...")
+    tflite_bytes = convert_to_tflite_int8(streaming_model, rep_X=X_train)
+
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    tflite_path = out_dir / f"{args.name}_quantized_model.tflite"
+
+    with open(tflite_path, "wb") as f:
+        f.write(tflite_bytes)
+    print(f">>> Saved int8 quantized model: {tflite_path} ({len(tflite_bytes)} bytes)")
+
+    print(">>> Running streaming evaluation and generating Confusion Matrix...")
+    cm = evaluate_streaming_confusion_matrix(
+        str(tflite_path),
+        file_map=file_map,
+        keywords=args.keyword,
+        threshold=args.threshold,
+        slice_hops=SLICE_HOPS,
+    )
+    print_confusion_matrix_report(cm, threshold=args.threshold)
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/src/audio/microwakeword/tune/sof_mww_train_pipeline.sh b/src/audio/microwakeword/tune/sof_mww_train_pipeline.sh
new file mode 100755
index 000000000000..9196968c540f
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_train_pipeline.sh
@@ -0,0 +1,215 @@
+#!/bin/bash
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+#
+# End-to-end microWakeWord (MWW) retraining pipeline:
+#
+#   1. Prepare silence/ + unknown/ negative classes (Speech Commands v2).
+#   2. Assume the caller has generated <wav_root>/<keyword>/*.wav with
+#      sof_mww_generate_keyword_dataset_piper_tts.sh or
+#      sof_mww_generate_keyword_dataset.sh.
+#   3. Extract 10ms mel40 features via sof_mfcc_extract_features.sh -> <feat_root>/.
+#   4. Train streaming MWW model, int8-quantize, and emit:
+#      - <out_dir>/<name>_quantized_model.tflite
+#      - <out_dir>/mww_model_data.{cc,h} (drop-in C array)
+#      - <out_dir>/<name>.conf (Topology2 data blob)
+#      - <out_dir>/<name>.txt (sof-ctl IPC4 text blob)
+#   5. Run off-device verification with sof_mww_verify.py.
+#
+# Usage:
+#   sof_mww_train_pipeline.sh --keyword hey_intel <wav_root> <feat_root> <out_dir>
+
+set -e
+
+usage() {
+	cat >&2 <<EOF
+Usage: $0 [--keyword <label> ...] [--keyword-dir [<label>:]<src_dir> ...] \
+          [--name <base>] [--format S16|S24|S32] [--tplg <path>] \
+          <wav_root> <feat_root> <out_dir>
+
+  --keyword LBL        Positive keyword class directory name (repeatable).
+                       Matches a subdirectory under <wav_root>.
+  --keyword-dir [LBL:]DIR Path to directory containing recorded real speech WAVs.
+                       The keyword class will be automatically prepared and
+                       augmented (RIR, noise, tempo/pitch, gain jitter) into
+                       <wav_root>/<label>.
+  --name BASE          Base name for the output model files (default: derived from
+                       keyword labels).
+  --format FMT         Testbench sample container format (S16, S24, S32). Default S32.
+  --tplg PATH          Explicit path to benchmark topology file (default:
+                       sof-hda-benchmark-mfccmel40_10ms<SF>.tplg).
+
+  wav_root        Dataset root directory; must contain <keyword>/*.wav.
+                  silence/ and unknown/ will be populated here.
+  feat_root       Mel40 feature output root; <label>/*.raw is written here.
+  out_dir         Output directory for trained model artifacts.
+
+Env:
+  SKIP_PREP        If set, do not re-fetch/re-slice Speech Commands v2.
+  SKIP_FEATURES    If set, do not re-run testbench feature extraction.
+  EPOCHS           Training epochs (default 30).
+  BATCH_SIZE       Training batch size (default 64).
+  LR               Learning rate (default 0.001).
+  CLASS_WEIGHT_NEG Loss penalty for negative class (default 1.0).
+  THRESHOLD        Verification detection threshold (default 0.65).
+  GAIN_AUG_MIN     Min gain jitter in dB during training (default -8.0).
+  GAIN_AUG_MAX     Max gain jitter in dB during training (default 3.0).
+  GAIN_AUG         Passed to negative class preparation (default 0).
+EOF
+	exit 1
+}
+
+KEYWORDS=()
+KEYWORD_DIRS=()
+NAME=""
+FORMAT=""
+CUSTOM_TPLG=""
+POSITIONAL=()
+
+while [[ $# -gt 0 ]]; do
+	case "$1" in
+		-k|--keyword)
+			[[ $# -ge 2 ]] || usage
+			KEYWORDS+=("$2"); shift 2 ;;
+		-s|--src-dir|--keyword-dir)
+			[[ $# -ge 2 ]] || usage
+			KEYWORD_DIRS+=("$2"); shift 2 ;;
+		-n|--name)
+			[[ $# -ge 2 ]] || usage
+			NAME="$2"; shift 2 ;;
+		--format)
+			[[ $# -ge 2 ]] || usage
+			FORMAT="$2"; shift 2 ;;
+		--tplg)
+			[[ $# -ge 2 ]] || usage
+			CUSTOM_TPLG="$2"; shift 2 ;;
+		-h|--help)
+			usage ;;
+		--)
+			shift
+			while [[ $# -gt 0 ]]; do POSITIONAL+=("$1"); shift; done ;;
+		-*)
+			echo "unknown option: $1" >&2; usage ;;
+		*)
+			POSITIONAL+=("$1"); shift ;;
+	esac
+done
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+
+# If keyword dirs are provided, resolve labels and populate them into KEYWORDS
+if [[ ${#KEYWORD_DIRS[@]} -gt 0 ]]; then
+	for kdir_arg in "${KEYWORD_DIRS[@]}"; do
+		if [[ "$kdir_arg" == *:* ]]; then
+			lbl="${kdir_arg%%:*}"
+		else
+			lbl="$(basename "$kdir_arg")"
+			if [[ "$lbl" == "audio" || "$lbl" == "wav" || "$lbl" == "wavs" ]]; then
+				lbl="$(basename "$(dirname "$(realpath "$kdir_arg")")")"
+			fi
+			lbl="$(echo "$lbl" | tr ' A-Z-' '_a-z_')"
+		fi
+		# Add to KEYWORDS if not already present
+		found=0
+		for k in "${KEYWORDS[@]}"; do
+			[[ "$k" == "$lbl" ]] && found=1 && break
+		done
+		[[ $found -eq 0 ]] && KEYWORDS+=("$lbl")
+	done
+fi
+
+if [[ ${#KEYWORDS[@]} -eq 0 || ${#POSITIONAL[@]} -ne 3 ]]; then
+	usage
+fi
+
+WAV_ROOT="${POSITIONAL[0]}"
+FEAT_ROOT="${POSITIONAL[1]}"
+OUT_DIR="${POSITIONAL[2]}"
+
+# If real speech directories are passed, generate/augment keyword datasets first
+if [[ ${#KEYWORD_DIRS[@]} -gt 0 ]]; then
+	echo "=== Preparing keyword dataset(s) from real speech directory ==="
+	DIR_ARGS=()
+	for kdir_arg in "${KEYWORD_DIRS[@]}"; do
+		DIR_ARGS+=(--keyword-dir "$kdir_arg")
+	done
+	"$SCRIPT_DIR/sof_mww_generate_keyword_dataset_from_dir.sh" "${DIR_ARGS[@]}" "$WAV_ROOT"
+fi
+
+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
+		cat >&2 <<EOF
+Error: missing $WAV_ROOT/$kw/ directory.
+Generate keyword recordings first using:
+  $SCRIPT_DIR/sof_mww_generate_keyword_dataset_piper_tts.sh --keyword "$kw" $WAV_ROOT
+EOF
+		exit 1
+	fi
+done
+
+# Step 1: Prepare silence & unknown negative classes
+if [[ -z "$SKIP_PREP" ]]; then
+	echo "=== Step 1/4: Preparing silence/ and unknown/ dataset classes ==="
+	"$SCRIPT_DIR/sof_mww_prepare_silence_unknown.sh" "$WAV_ROOT"
+else
+	echo "=== Step 1/4: Skipping silence/unknown prep (SKIP_PREP set) ==="
+fi
+
+# Step 2: Testbench feature extraction
+if [[ -z "$SKIP_FEATURES" ]]; then
+	echo "=== Step 2/4: Extracting 10ms mel40 features with testbench ==="
+	mkdir -p "$FEAT_ROOT"
+	EXTRA_FEAT_ARGS=()
+	[[ -n "$FORMAT" ]] && EXTRA_FEAT_ARGS+=(--format "$FORMAT")
+	[[ -n "$CUSTOM_TPLG" ]] && EXTRA_FEAT_ARGS+=(--tplg "$CUSTOM_TPLG")
+	"$SCRIPT_DIR/sof_mfcc_extract_features.sh" "$WAV_ROOT" "$FEAT_ROOT" "${EXTRA_FEAT_ARGS[@]}"
+else
+	echo "=== Step 2/4: Skipping feature extraction (SKIP_FEATURES set) ==="
+fi
+
+# Step 3: Train & export model
+echo "=== Step 3/4: Training streaming MWW model and quantizing ==="
+mkdir -p "$OUT_DIR"
+KW_ARGS=()
+for kw in "${KEYWORDS[@]}"; do
+	KW_ARGS+=(--keyword "$kw")
+done
+
+python3 "$SCRIPT_DIR/sof_mww_train.py" \
+	--feat-root "$FEAT_ROOT" \
+	"${KW_ARGS[@]}" \
+	--name "$NAME" \
+	--out-dir "$OUT_DIR" \
+	${EPOCHS:+--epochs "$EPOCHS"} \
+	${BATCH_SIZE:+--batch-size "$BATCH_SIZE"} \
+	${LR:+--lr "$LR"} \
+	${CLASS_WEIGHT_NEG:+--class-weight-neg "$CLASS_WEIGHT_NEG"} \
+	${GAIN_AUG_MIN:+--gain-aug-db-min "$GAIN_AUG_MIN"} \
+	${GAIN_AUG_MAX:+--gain-aug-db-max "$GAIN_AUG_MAX"}
+
+# Step 4: Verification
+echo "=== Step 4/4: Running streaming verification ==="
+python3 "$SCRIPT_DIR/sof_mww_verify.py" \
+	--tflite "$OUT_DIR/${NAME}_quantized_model.tflite" \
+	--feat-root "$FEAT_ROOT" \
+	"${KW_ARGS[@]}" \
+	--threshold "${THRESHOLD:-0.65}"
+
+echo "================================================================="
+echo "MWW model training complete. Artifacts saved in $OUT_DIR:"
+echo "  - $OUT_DIR/${NAME}_quantized_model.tflite"
+echo "  - $OUT_DIR/mww_model_data.cc (static C array)"
+echo "  - $OUT_DIR/mww_model_data.h"
+echo "  - $OUT_DIR/${NAME}.conf (Topology2 data blob)"
+echo "  - $OUT_DIR/${NAME}.txt (sof-ctl IPC4 text blob)"
+echo "================================================================="
diff --git a/src/audio/microwakeword/tune/sof_mww_verify.py b/src/audio/microwakeword/tune/sof_mww_verify.py
new file mode 100644
index 000000000000..4388e32c5e77
--- /dev/null
+++ b/src/audio/microwakeword/tune/sof_mww_verify.py
@@ -0,0 +1,200 @@
+# SPDX-License-Identifier: BSD-3-Clause
+# Copyright(c) 2026 Intel Corporation. All rights reserved.
+"""
+Off-device streaming verifier for microWakeWord (MWW) quantized models.
+
+Evaluates an int8 streaming TFLite model on testbench-emitted MFCC feature
+files (.raw). Feeds 3 hops (MWW_FEATURE_SLICE_COUNT = 3) per Invoke() step,
+tracking streaming internal ring buffers, and checks whether detection
+(prob >= threshold) fires appropriately on positive and negative recordings.
+
+Example:
+    python3 sof_mww_verify.py \\
+        --tflite ~/wov/model/mww_model_quantized_model.tflite \\
+        --feat-root ~/wov/feats \\
+        --keyword hey_intel
+"""
+
+from __future__ import annotations
+
+import argparse
+import glob
+import os
+import sys
+
+import numpy as np
+
+import sof_mww_dataset as mww_dataset
+
+try:
+    import tflite_runtime.interpreter as tflite  # type: ignore
+except ImportError:
+    try:
+        from tensorflow.lite.python.interpreter import Interpreter as _Interp
+
+        class tflite:  # type: ignore
+            Interpreter = _Interp
+    except ImportError:
+        import tensorflow as tf
+        tflite = tf.lite
+
+
+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_streaming_verification(
+    model_path: str,
+    feat_root: str,
+    keywords: list[str],
+    threshold: float = 0.65,
+    consecutive: int = 2,
+    slice_hops: int = 3,
+) -> dict:
+    """Run streaming inference on all .raw files in feat_root."""
+    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.get("quantization", (0.0, 0))
+    out_scale, out_zp = out_det.get("quantization", (0.0, 0))
+    in_dtype = in_det["dtype"]
+    out_dtype = out_det["dtype"]
+
+    results = {}
+    all_labels = ["silence", "unknown"] + keywords
+
+    for label in all_labels:
+        label_dir = os.path.join(feat_root, label)
+        raw_files = sorted(glob.glob(os.path.join(label_dir, "*.raw")))
+        is_pos = (label in keywords)
+
+        n_files = len(raw_files)
+        n_detected = 0
+        peak_probs = []
+
+        for f in raw_files:
+            mel, vad = mww_dataset.load_raw_hops(f)
+            if mel.shape[0] < slice_hops:
+                continue
+
+            # Apply soft AGC across the whole file, freezing release during speech (vad == 1)
+            mel_agc = mww_dataset.apply_soft_agc(mel, vad=vad)
+
+            # Reset interpreter state variables before each audio stream
+            if hasattr(interp, "reset_all_variables"):
+                interp.reset_all_variables()
+            else:
+                interp.allocate_tensors()
+
+            max_prob = 0.0
+            consec = 0
+            detected = False
+            n_hops = mel_agc.shape[0]
+
+            for s in range(0, n_hops - slice_hops + 1, slice_hops):
+                chunk = mel_agc[s : s + slice_hops][np.newaxis, ...]  # shape (1, 3, 40)
+                if np.issubdtype(in_dtype, np.integer) and in_scale > 0:
+                    chunk = _quantize(chunk, in_scale, in_zp, in_dtype)
+
+                interp.set_tensor(in_det["index"], chunk)
+                interp.invoke()
+                y = interp.get_tensor(out_det["index"])
+
+                if np.issubdtype(out_dtype, np.integer) and out_scale > 0:
+                    prob = float(_dequantize(y, out_scale, out_zp).flatten()[0])
+                else:
+                    prob = float(y.flatten()[0])
+
+                if prob > max_prob:
+                    max_prob = prob
+
+                if prob >= threshold:
+                    consec += 1
+                    if consec >= consecutive:
+                        detected = True
+                else:
+                    consec = 0
+
+            peak_probs.append(max_prob)
+            if detected:
+                n_detected += 1
+
+        results[label] = {
+            "is_positive": is_pos,
+            "total_files": n_files,
+            "detected_files": n_detected,
+            "detection_rate": (n_detected / n_files) if n_files > 0 else 0.0,
+            "mean_peak_prob": float(np.mean(peak_probs)) if peak_probs else 0.0,
+        }
+
+    return results
+
+
+def print_report(results: dict, threshold: float) -> None:
+    print("\n" + "=" * 65)
+    print(f"microWakeWord Streaming Verification Report (Threshold: {threshold:.2f})")
+    print("=" * 65)
+    print(f"  {'Class':<18} {'Role':<10} {'Files':>7} {'Detected':>10} {'Rate':>8} {'Mean Peak':>10}")
+    print("  " + "-" * 61)
+
+    total_pos = 0
+    detected_pos = 0
+    total_neg = 0
+    detected_neg = 0
+
+    for label, stat in results.items():
+        role = "Positive" if stat["is_positive"] else "Negative"
+        rate_pct = stat["detection_rate"] * 100.0
+        print(
+            f"  {label:<18} {role:<10} {stat['total_files']:>7d} "
+            f"{stat['detected_files']:>10d} {rate_pct:>7.1f}% {stat['mean_peak_prob']:>9.3f}"
+        )
+        if stat["is_positive"]:
+            total_pos += stat["total_files"]
+            detected_pos += stat["detected_files"]
+        else:
+            total_neg += stat["total_files"]
+            detected_neg += stat["detected_files"]
+
+    print("  " + "-" * 61)
+    recall = (detected_pos / total_pos) if total_pos > 0 else 0.0
+    far = (detected_neg / total_neg) if total_neg > 0 else 0.0
+    prec = (detected_pos / (detected_pos + detected_neg)) if (detected_pos + detected_neg) > 0 else 0.0
+
+    print(f"  Overall Wake Word Recall (True Positive Rate) : {recall * 100.0:6.2f}% ({detected_pos}/{total_pos})")
+    print(f"  Overall False Alarm Rate (False Positive Rate): {far * 100.0:6.2f}% ({detected_neg}/{total_neg})")
+    print(f"  Precision                                     : {prec * 100.0:6.2f}%")
+    print("=" * 65 + "\n")
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--tflite", required=True, help="Path to int8 .tflite model")
+    parser.add_argument("--feat-root", required=True, help="Directory containing <label>/*.raw features")
+    parser.add_argument("--keyword", required=True, action="append", help="Target keyword label(s)")
+    parser.add_argument("--threshold", type=float, default=0.65, help="Detection threshold (default 0.65)")
+    parser.add_argument("--consecutive", type=int, default=2, help="Consecutive detections required (default 2)")
+
+    args = parser.parse_args()
+    results = run_streaming_verification(
+        model_path=args.tflite,
+        feat_root=args.feat_root,
+        keywords=args.keyword,
+        threshold=args.threshold,
+        consecutive=args.consecutive,
+    )
+    print_report(results, threshold=args.threshold)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

From 1abc6ba06fd9353d9fb74e4b3dc7e6d971255d3f Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Thu, 27 Aug 2026 15:24:07 +0300
Subject: [PATCH 13/15] tools: topology2: add microWakeWord (MWW) Wake-on-Voice
 topologies and pipelines

Add Topology2 configuration and pipeline graphs for microWakeWord (MWW)
Wake-on-Voice with KPB:
- Define MWW module component in mww.conf with default control definitions.
- Add HDA Mic capture pipeline (host-gateway-src-mfcc-mww-capture.conf)
  and host-gateway-micsel-mfcc-mww-capture.conf.
- Add SoundWire jack and DMIC MWW branches and DMIC MFCC profiles.
- Add HDA generic and SoundWire MTL/ARL topology targets with KPB and MWW.

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 tools/topology/topology2/cavs-sdw.conf        |  11 +
 .../topology2/development/tplg-targets.cmake  |  15 ++
 .../include/common/common_definitions.conf    |   3 +
 .../topology2/include/components/kpb.conf     |   4 +-
 .../topology2/include/components/mww.conf     |  54 +++++
 .../host-gateway-micsel-mfcc-mww-capture.conf | 185 +++++++++++++++++
 .../host-gateway-src-mfcc-mww-capture.conf    | 194 ++++++++++++++++++
 .../include/pipelines/cavs/src-kpb-be.conf    | 115 +++++++++++
 .../topology2/platform/intel/dmic1-mfcc.conf  |   4 +-
 .../platform/intel/hda-mic-mww-kpb.conf       | 176 ++++++++++++++++
 .../platform/intel/sdw-dmic-mww-kpb.conf      | 170 +++++++++++++++
 .../platform/intel/sdw-jack-mww-kpb.conf      | 170 +++++++++++++++
 tools/topology/topology2/sof-hda-generic.conf |  10 +
 13 files changed, 1108 insertions(+), 3 deletions(-)
 create mode 100644 tools/topology/topology2/include/components/mww.conf
 create mode 100644 tools/topology/topology2/include/pipelines/cavs/host-gateway-micsel-mfcc-mww-capture.conf
 create mode 100644 tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-mww-capture.conf
 create mode 100644 tools/topology/topology2/include/pipelines/cavs/src-kpb-be.conf
 create mode 100644 tools/topology/topology2/platform/intel/hda-mic-mww-kpb.conf
 create mode 100644 tools/topology/topology2/platform/intel/sdw-dmic-mww-kpb.conf
 create mode 100644 tools/topology/topology2/platform/intel/sdw-jack-mww-kpb.conf

diff --git a/tools/topology/topology2/cavs-sdw.conf b/tools/topology/topology2/cavs-sdw.conf
index 311e0376b333..faf5eb487d8e 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-mww-capture.conf>
+<src-kpb-be.conf>
 <src.conf>
 <io-gateway.conf>
 <io-gateway-capture.conf>
@@ -287,3 +290,11 @@ IncludeByKey.SDW_DMIC_AUDIO_FEATURE_CAPTURE {
 IncludeByKey.SDW_DMIC_COMPR_AUDIO_FEATURE_CAPTURE {
 	"true" "platform/intel/sdw-dmic-audio-feature-compress.conf"
 }
+
+IncludeByKey.SDW_JACK_MWW_KPB_CAPTURE {
+	"true" "platform/intel/sdw-jack-mww-kpb.conf"
+}
+
+IncludeByKey.SDW_DMIC_MWW_KPB_CAPTURE {
+	"true" "platform/intel/sdw-dmic-mww-kpb.conf"
+}
diff --git a/tools/topology/topology2/development/tplg-targets.cmake b/tools/topology/topology2/development/tplg-targets.cmake
index 8835fad0635d..be0ebb699960 100644
--- a/tools/topology/topology2/development/tplg-targets.cmake
+++ b/tools/topology/topology2/development/tplg-targets.cmake
@@ -55,6 +55,12 @@ NHLT_BIN=nhlt-sof-lnl-nocodec-fpga-4ch.bin,PASSTHROUGH=true,DMIC_IO_CLK=19200000
 
 "cavs-sdw\;sof-lnl-fpga-rt711-l0\;PLATFORM=lnl,NUM_HDMIS=0,PASSTHROUGH=true"
 
+# HDA generic + KPB-based Wake-on-Voice (microWakeWord) capture branch.
+# Adds a WoV drain PCM and an MFCC/MWW 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-mww-kpb\;HDA_CONFIG=mix,HDA_MIC_MWW_KPB_CAPTURE=true"
+
 # HDA topology with passthrough analog codec pipelines
 "sof-hda-generic\;sof-hda-passthrough\;HDA_CONFIG=passthrough"
 # HDA topology with passthrough analog codec pipelines using CHAIN_DMA
@@ -501,6 +507,15 @@ 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 microWakeWord (MWW)/KPB Wake-on-Voice on jack
+"cavs-sdw\;sof-mtl-rt713-l0-rt1316-l12-mww-kpb\;PLATFORM=mtl,NUM_SDW_AMP_LINKS=2,\
+HDMI1_ID=4,HDMI2_ID=5,HDMI3_ID=6,SDW_JACK_MWW_KPB_CAPTURE=true"
+
+"cavs-sdw\;sof-arl-cs42l43-l0-cs35l56-l23-mww-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_DMIC_MWW_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/common/common_definitions.conf b/tools/topology/topology2/include/common/common_definitions.conf
index 06f0f425c5e2..4effe27c1c67 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_MWW_KPB_CAPTURE		false # No MWW/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_MWW_KPB_CAPTURE		false # No MWW/KPB Wake-on-Voice branch on microphone
+	HDA_MIC_MWW_KPB_CAPTURE			false # No MWW/KPB Wake-on-Voice branch on HDA analog capture
 }
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/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/pipelines/cavs/host-gateway-micsel-mfcc-mww-capture.conf b/tools/topology/topology2/include/pipelines/cavs/host-gateway-micsel-mfcc-mww-capture.conf
new file mode 100644
index 000000000000..5b2e90da7152
--- /dev/null
+++ b/tools/topology/topology2/include/pipelines/cavs/host-gateway-micsel-mfcc-mww-capture.conf
@@ -0,0 +1,185 @@
+#
+# Micsel-MFCC-MWW 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 (10ms hop / 40 mel bins) and microWakeWord (MWW) for
+# streaming keyword spotting. The final 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/mww.conf>
+<include/components/host-copier.conf>
+
+Define {
+	# 10 ms FFT hop at 16 kHz, 2 ch, s32: 160 samples * 2 * 4 = 1280 B.
+	MFCC_INPUT_BYTES 1280
+	# MFCC compress output frame: 24-byte header + 40 mel bins * 4 bytes.
+	MFCC_FRAME_BYTES 184
+	# MWW expects one MFCC frame every 10 ms; wake it per-frame so it
+	# stays in lock-step with MFCC instead of batching.
+	MWW_INPUT_BYTES 184
+	MWW_DETECT_CORE_ID 1
+}
+
+Class.Pipeline."host-gateway-micsel-mfcc-mww-capture" {
+
+	<include/pipelines/pipeline-common.conf>
+
+	attributes {
+		!constructor [
+			"index"
+		]
+
+		unique	"instance"
+	}
+
+	Object.Widget {
+		host-copier."1" {
+			type		"aif_out"
+			core_id		$MWW_DETECT_CORE_ID
+			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 		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" {
+			core_id		$MWW_DETECT_CORE_ID
+			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"
+			core_id 	$MWW_DETECT_CORE_ID
+			domain_id 	0
+			stack_bytes_requirement 8192
+			heap_bytes_requirement "$[(24 * 1024)]"
+			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
+				}
+			]
+		}
+
+		# MWW is an audio pass-through: it copies each MFCC frame
+		# from input to output unchanged and sends keyword detection
+		# triggers out-of-band via AMS / KPB notifications. ibs/obs
+		# are one MFCC frame so the DP thread wakes per 10 ms hop
+		# and stays in lock-step with MFCC.
+		mww."1" {
+			scheduler_domain "DP"
+			core_id 	$MWW_DETECT_CORE_ID
+			domain_id 	0
+			stack_bytes_requirement 8192
+			heap_bytes_requirement "$[(24 * 1024)]"
+			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 			$MWW_INPUT_BYTES
+				}
+			]
+			Object.Base.output_audio_format [
+				{
+					out_channels 		2
+					out_bit_depth 		32
+					out_valid_bit_depth 	32
+					out_rate 		16000
+					obs 			$MWW_INPUT_BYTES
+				}
+			]
+		}
+
+		pipeline."1" {
+			priority 	0
+			lp_mode 	0
+			core 		$MWW_DETECT_CORE_ID
+		}
+	}
+
+	Object.Base {
+		!route [
+			{
+				source 	micsel.$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-mww-capture.conf b/tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-mww-capture.conf
new file mode 100644
index 000000000000..d2eb6e3236e3
--- /dev/null
+++ b/tools/topology/topology2/include/pipelines/cavs/host-gateway-src-mfcc-mww-capture.conf
@@ -0,0 +1,194 @@
+#
+# 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
+	MWW_DETECT_CORE_ID 1
+}
+
+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"
+			core_id $MWW_DETECT_CORE_ID
+			domain_id 0
+			stack_bytes_requirement 8192
+			heap_bytes_requirement "$[(24 * 1024)]"
+			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
+			core		$MWW_DETECT_CORE_ID
+		}
+	}
+
+	Object.Base {
+		!route [
+			{
+				source	src.$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/src-kpb-be.conf b/tools/topology/topology2/include/pipelines/cavs/src-kpb-be.conf
new file mode 100644
index 000000000000..6aefdbae1fb7
--- /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-mww-kpb.conf b/tools/topology/topology2/platform/intel/hda-mic-mww-kpb.conf
new file mode 100644
index 000000000000..905bde900bc0
--- /dev/null
+++ b/tools/topology/topology2/platform/intel/hda-mic-mww-kpb.conf
@@ -0,0 +1,176 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# HDA analog capture microWakeWord (MWW)/KPB Wake-on-Voice branch.
+#
+# Layered on top of sof-hda-generic.conf via
+# IncludeByKey.HDA_MIC_MWW_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 10ms compress) -> MWW keyword spotter
+#              -> host-copier: "HDA Mic MWW 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 MWW 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_MWW_KPB_PIPELINE_ID		102
+	HDA_MWW_WOV_PIPELINE_ID		100
+	HDA_MWW_WOV_PCM_ID		100
+	HDA_MWW_DETECT_PIPELINE_ID	101
+	HDA_MWW_DETECT_PCM_ID		101
+}
+
+Object.Pipeline.src-kpb-be [
+	{
+		index $HDA_MWW_KPB_PIPELINE_ID
+
+		# KPB requires pin 0 (binding.1) to feed the detect/selector sink
+		# and pin 1+ (binding.2) to feed the host drain; see kpb_bind().
+		Object.Widget.kpb.1 {
+			Object.Base.output_pin_binding.1 {
+				output_pin_binding_name "micsel.$HDA_MWW_DETECT_PIPELINE_ID.1"
+			}
+			Object.Base.output_pin_binding.2 {
+				output_pin_binding_name "host-copier.$HDA_MWW_WOV_PCM_ID.capture"
+			}
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-capture [
+	{
+		index $HDA_MWW_WOV_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "HDA Mic WoV Capture"
+			pcm_id $HDA_MWW_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-mww-capture [
+	{
+		index $HDA_MWW_DETECT_PIPELINE_ID
+		core 1
+		core_id 1
+
+		Object.Widget.pipeline.1 {
+			core 1
+		}
+
+		Object.Widget.host-copier.1 {
+			stream_name "HDA Mic MWW Detect"
+			pcm_id $HDA_MWW_DETECT_PCM_ID
+			capture_compatible_d0i3 1
+		}
+
+		Object.Widget.micsel.1 {
+			Object.Control {
+				bytes."1" {
+					name "HDA Mic MWW Micsel bytes"
+					<include/components/micsel/downmix_stereo_to_doublemono.conf>
+				}
+			}
+		}
+
+		Object.Widget.mfcc.1 {
+			Object.Control {
+				bytes."1" {
+					name "HDA Mic MWW MFCC bytes"
+					<include/components/mfcc/mel40_10ms_compress.conf>
+				}
+				mixer."1" {
+					name "HDA Mic MWW MFCC VAD"
+				}
+			}
+		}
+
+		Object.Widget.mww.1 {
+			Object.Control {
+				bytes."1" {
+					name "HDA Mic MWW bytes"
+					max 65536
+				}
+			}
+		}
+	}
+]
+
+Object.Base.route [
+	{
+		source "module-copier.4.2"
+		sink "src.$HDA_MWW_KPB_PIPELINE_ID.1"
+	}
+	{
+		source "kpb.$HDA_MWW_KPB_PIPELINE_ID.1"
+		sink "host-copier.$HDA_MWW_WOV_PCM_ID.capture"
+	}
+	{
+		source "kpb.$HDA_MWW_KPB_PIPELINE_ID.1"
+		sink "micsel.$HDA_MWW_DETECT_PIPELINE_ID.1"
+	}
+]
+
+Object.PCM.pcm [
+	{
+		name "HDA Mic WoV Capture"
+		id $HDA_MWW_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 MWW Detect"
+		id $HDA_MWW_DETECT_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "HDA Mic MWW Detect"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "HDA Mic MWW Detect"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+]
diff --git a/tools/topology/topology2/platform/intel/sdw-dmic-mww-kpb.conf b/tools/topology/topology2/platform/intel/sdw-dmic-mww-kpb.conf
new file mode 100644
index 000000000000..7ed27747b693
--- /dev/null
+++ b/tools/topology/topology2/platform/intel/sdw-dmic-mww-kpb.conf
@@ -0,0 +1,170 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# SoundWire DMIC microWakeWord (MWW)/KPB Wake-on-Voice capture branch.
+#
+# Layered on top of cavs-sdw.conf via IncludeByKey.SDW_DMIC_MWW_KPB_CAPTURE.
+# Taps the DMIC DAI-side module-copier (module-copier.41.0) into:
+#
+#   SRC (48 -> 16 kHz stereo) -> KPB (16 kHz stereo, dual sinks)
+#     pin 0 -> micsel -> MFCC (40 mel 10ms compress) -> MWW keyword spotter
+#              -> host-copier: "DMIC MWW Detect" PCM
+#     pin 1 -> host-copier: "DMIC WoV Capture"  PCM
+#
+# Bypasses TDFB and DRC beamforming / dynamic range compression.
+# KPB output_pin_binding names must match kernel widget names (see
+# sof_ipc4_get_queue_id): host-copier.<pcm_id>.<direction> and
+# <widget>.<index>.<queue>. Detect PCM is stereo double-mono via
+# micsel to remain compatible with standard capture pipelines.
+
+Define {
+	SDW_DMIC_MODULE_COPIER_ID	41
+	SDW_DMIC_MWW_KPB_PIPELINE_ID	104
+	# host-copier internal routes in host-gateway* pipeline classes
+	# reference host-copier.$index.<direction>, so keep
+	# pcm_id == pipeline_id for the two host-side PCMs.
+	SDW_DMIC_MWW_WOV_PIPELINE_ID	102
+	SDW_DMIC_MWW_WOV_PCM_ID		102
+	SDW_DMIC_MWW_WOV_PCM_NAME	"DMIC WoV Capture"
+	SDW_DMIC_MWW_WOV_STREAM_NAME	"DMIC WoV Capture Stream"
+	SDW_DMIC_MWW_DETECT_PIPELINE_ID	103
+	SDW_DMIC_MWW_DETECT_PCM_ID	103
+	SDW_DMIC_MWW_DETECT_PCM_NAME	"DMIC MWW Detect"
+	SDW_DMIC_MWW_DETECT_STREAM_NAME	"DMIC MWW Detect Stream"
+}
+
+Object.Pipeline.src-kpb-be [
+	{
+		index $SDW_DMIC_MWW_KPB_PIPELINE_ID
+
+		Object.Widget.kpb.1 {
+			Object.Base.output_pin_binding.1 {
+				output_pin_binding_name "micsel.$SDW_DMIC_MWW_DETECT_PIPELINE_ID.1"
+			}
+			Object.Base.output_pin_binding.2 {
+				output_pin_binding_name "host-copier.$SDW_DMIC_MWW_WOV_PCM_ID.capture"
+			}
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-capture [
+	{
+		index $SDW_DMIC_MWW_WOV_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "$SDW_DMIC_MWW_WOV_STREAM_NAME"
+			pcm_id $SDW_DMIC_MWW_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-mww-capture [
+	{
+		index $SDW_DMIC_MWW_DETECT_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "$SDW_DMIC_MWW_DETECT_STREAM_NAME"
+			pcm_id $SDW_DMIC_MWW_DETECT_PCM_ID
+		}
+
+		Object.Widget.micsel.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_DMIC_MWW_DETECT_PCM_NAME Micsel bytes"
+					<include/components/micsel/downmix_stereo_to_doublemono.conf>
+				}
+			}
+		}
+
+		Object.Widget.mfcc.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_DMIC_MWW_DETECT_PCM_NAME MFCC bytes"
+					<include/components/mfcc/mel40_10ms_compress.conf>
+				}
+				mixer."1" {
+					name "$SDW_DMIC_MWW_DETECT_PCM_NAME MFCC VAD"
+				}
+			}
+		}
+
+		Object.Widget.mww.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_DMIC_MWW_DETECT_PCM_NAME MWW bytes"
+					max 65536
+				}
+			}
+		}
+	}
+]
+
+Object.Base.route [
+	{
+		source "module-copier.$SDW_DMIC_MODULE_COPIER_ID.0"
+		sink "src.$SDW_DMIC_MWW_KPB_PIPELINE_ID.1"
+	}
+	{
+		source "kpb.$SDW_DMIC_MWW_KPB_PIPELINE_ID.1"
+		sink "host-copier.$SDW_DMIC_MWW_WOV_PCM_ID.capture"
+	}
+	{
+		source "kpb.$SDW_DMIC_MWW_KPB_PIPELINE_ID.1"
+		sink "micsel.$SDW_DMIC_MWW_DETECT_PIPELINE_ID.1"
+	}
+]
+
+Object.PCM.pcm [
+	{
+		name "$SDW_DMIC_MWW_WOV_PCM_NAME"
+		id $SDW_DMIC_MWW_WOV_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "$SDW_DMIC_MWW_WOV_PCM_NAME"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "$SDW_DMIC_MWW_WOV_STREAM_NAME"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+	{
+		name "$SDW_DMIC_MWW_DETECT_PCM_NAME"
+		id $SDW_DMIC_MWW_DETECT_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "$SDW_DMIC_MWW_DETECT_PCM_NAME"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "$SDW_DMIC_MWW_DETECT_STREAM_NAME"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+]
diff --git a/tools/topology/topology2/platform/intel/sdw-jack-mww-kpb.conf b/tools/topology/topology2/platform/intel/sdw-jack-mww-kpb.conf
new file mode 100644
index 000000000000..2ee8f5435891
--- /dev/null
+++ b/tools/topology/topology2/platform/intel/sdw-jack-mww-kpb.conf
@@ -0,0 +1,170 @@
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# SoundWire jack microWakeWord (MWW)/KPB Wake-on-Voice capture branch.
+#
+# Layered on top of cavs-sdw.conf (SDW jack + amps + HDMI) via
+# IncludeByKey.SDW_JACK_MWW_KPB_CAPTURE. Taps the jack DAI-side
+# module-copier (module-copier.11.0) into:
+#
+#   SRC (48 -> 16 kHz stereo) -> KPB (16 kHz stereo, dual sinks)
+#     pin 0 -> micsel -> MFCC (40 mel 10ms compress) -> MWW keyword spotter
+#              -> host-copier: "Jack In MWW Detect" PCM
+#     pin 1 -> host-copier: "Jack In WoV Capture"  PCM
+#
+# KPB output_pin_binding names must match kernel widget names (see
+# sof_ipc4_get_queue_id): host-copier.<pcm_id>.<direction> and
+# <widget>.<index>.<queue>. Detect PCM is stereo double-mono via
+# micsel to remain compatible with HDA-style capture pipelines.
+
+Define {
+	SDW_JACK_MODULE_COPIER_ID	11
+	SDW_JACK_MWW_KPB_PIPELINE_ID	104
+	# host-copier internal routes in host-gateway* pipeline classes
+	# reference host-copier.$index.<direction>, so keep
+	# pcm_id == pipeline_id for the two host-side PCMs.
+	SDW_JACK_MWW_WOV_PIPELINE_ID	102
+	SDW_JACK_MWW_WOV_PCM_ID		102
+	SDW_JACK_MWW_WOV_PCM_NAME	"Jack In WoV Capture"
+	SDW_JACK_MWW_WOV_STREAM_NAME	"Jack In WoV Capture Stream"
+	SDW_JACK_MWW_DETECT_PIPELINE_ID	103
+	SDW_JACK_MWW_DETECT_PCM_ID	103
+	SDW_JACK_MWW_DETECT_PCM_NAME	"Jack In MWW Detect"
+	SDW_JACK_MWW_DETECT_STREAM_NAME	"Jack In MWW Detect Stream"
+}
+
+Object.Pipeline.src-kpb-be [
+	{
+		index $SDW_JACK_MWW_KPB_PIPELINE_ID
+
+		Object.Widget.kpb.1 {
+			Object.Base.output_pin_binding.1 {
+				output_pin_binding_name "micsel.$SDW_JACK_MWW_DETECT_PIPELINE_ID.1"
+			}
+			Object.Base.output_pin_binding.2 {
+				output_pin_binding_name "host-copier.$SDW_JACK_MWW_WOV_PCM_ID.capture"
+			}
+		}
+	}
+]
+
+Object.Pipeline.host-gateway-capture [
+	{
+		index $SDW_JACK_MWW_WOV_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "$SDW_JACK_MWW_WOV_STREAM_NAME"
+			pcm_id $SDW_JACK_MWW_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-mww-capture [
+	{
+		index $SDW_JACK_MWW_DETECT_PIPELINE_ID
+
+		Object.Widget.host-copier.1 {
+			stream_name "$SDW_JACK_MWW_DETECT_STREAM_NAME"
+			pcm_id $SDW_JACK_MWW_DETECT_PCM_ID
+		}
+
+		Object.Widget.micsel.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_JACK_MWW_DETECT_PCM_NAME Micsel bytes"
+					<include/components/micsel/downmix_stereo_to_doublemono.conf>
+				}
+			}
+		}
+
+		Object.Widget.mfcc.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_JACK_MWW_DETECT_PCM_NAME MFCC bytes"
+					<include/components/mfcc/mel40_10ms_compress.conf>
+				}
+				mixer."1" {
+					name "$SDW_JACK_MWW_DETECT_PCM_NAME MFCC VAD"
+				}
+			}
+		}
+
+		Object.Widget.mww.1 {
+			Object.Control {
+				bytes."1" {
+					name "$SDW_JACK_MWW_DETECT_PCM_NAME MWW bytes"
+					max 65536
+				}
+			}
+		}
+	}
+]
+
+Object.Base.route [
+	{
+		source "module-copier.$SDW_JACK_MODULE_COPIER_ID.0"
+		sink "src.$SDW_JACK_MWW_KPB_PIPELINE_ID.1"
+	}
+	{
+		source "kpb.$SDW_JACK_MWW_KPB_PIPELINE_ID.1"
+		sink "host-copier.$SDW_JACK_MWW_WOV_PCM_ID.capture"
+	}
+	{
+		source "kpb.$SDW_JACK_MWW_KPB_PIPELINE_ID.1"
+		sink "micsel.$SDW_JACK_MWW_DETECT_PIPELINE_ID.1"
+	}
+]
+
+Object.PCM.pcm [
+	{
+		name "$SDW_JACK_MWW_WOV_PCM_NAME"
+		id $SDW_JACK_MWW_WOV_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "$SDW_JACK_MWW_WOV_PCM_NAME"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "$SDW_JACK_MWW_WOV_STREAM_NAME"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+	{
+		name "$SDW_JACK_MWW_DETECT_PCM_NAME"
+		id $SDW_JACK_MWW_DETECT_PCM_ID
+		direction "capture"
+
+		Object.Base.fe_dai.1 {
+			name "$SDW_JACK_MWW_DETECT_PCM_NAME"
+		}
+
+		Object.PCM.pcm_caps.1 {
+			name "$SDW_JACK_MWW_DETECT_STREAM_NAME"
+			formats 'S32_LE'
+			rates '16000'
+			channels_min 2
+			channels_max 2
+		}
+	}
+]
diff --git a/tools/topology/topology2/sof-hda-generic.conf b/tools/topology/topology2/sof-hda-generic.conf
index 01a020bad17f..0821fba27c88 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-mww-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 MWW/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_MWW_KPB_CAPTURE {
+	"true" "platform/intel/hda-mic-mww-kpb.conf"
+}

From de0f626bd42aee737b717876a9f74f3a56e2e640 Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Thu, 27 Aug 2026 15:24:08 +0300
Subject: [PATCH 14/15] rimage: config: include microWakeWord (MWW) module in
 platform manifests

Include audio/microwakeword/mww.toml when CONFIG_COMP_MWW is enabled
across platform rimage manifest headers (tgl, tgl-h, mtl, lnl, ptl, wcl)
so the MWW module UUID and entry are registered in base firmware images
and loadable on target devices.

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 app/boards/intel_adsp_ace30_ptl.conf | 32 ++++++++++++++++++++++++++--
 app/boards/intel_adsp_cavs25.conf    | 11 +++++++++-
 tools/rimage/config/lnl.toml.h       |  4 ++++
 tools/rimage/config/mtl.toml.h       |  4 ++++
 tools/rimage/config/ptl.toml.h       |  4 ++++
 tools/rimage/config/tgl-h.toml.h     |  4 ++++
 tools/rimage/config/tgl.toml.h       |  4 ++++
 tools/rimage/config/wcl.toml.h       |  4 ++++
 8 files changed, 64 insertions(+), 3 deletions(-)

diff --git a/app/boards/intel_adsp_ace30_ptl.conf b/app/boards/intel_adsp_ace30_ptl.conf
index 6697f8d5523a..ecb67d52ef77 100644
--- a/app/boards/intel_adsp_ace30_ptl.conf
+++ b/app/boards/intel_adsp_ace30_ptl.conf
@@ -15,6 +15,21 @@ CONFIG_FORMAT_CONVERT_HIFI3=n
 CONFIG_COMP_GOOGLE_RTC_AUDIO_PROCESSING=m
 CONFIG_GOOGLE_RTC_AUDIO_PROCESSING_MOCK=y
 CONFIG_COMP_STFT_PROCESS=y
+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=m
+CONFIG_AMS=y
+CONFIG_STACK_SIZE_EDF=32768
+CONFIG_COMP_VOLUME=y
+CONFIG_COMP_GAIN=y
 
 # SOF / infrastructure
 CONFIG_KCPS_DYNAMIC_CLOCK_CONTROL=n
@@ -29,7 +44,7 @@ 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_BASE_ADDRESS=0xa0700000
 CONFIG_LIBRARY_BUILD_LIB=y
 CONFIG_LIBRARY_DEFAULT_MODULAR=y
 
@@ -43,11 +58,19 @@ CONFIG_SOF_LOG_LEVEL_INF=y
 CONFIG_DEBUG_COREDUMP_BACKEND_INTEL_ADSP_MEM_WINDOW=y
 CONFIG_DEBUG_COREDUMP_MEMORY_DUMP_MIN=y
 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
@@ -83,3 +106,8 @@ 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 0a89f1a669a7..550804f53584 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 mww in statically rather than as an LLEXT module.
+CONFIG_COMP_MWW=y
+CONFIG_COMP_MWW_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
@@ -37,7 +45,8 @@ CONFIG_SOF_LOG_LEVEL_INF=y
 # choices have to be selected per board.
 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/tools/rimage/config/lnl.toml.h b/tools/rimage/config/lnl.toml.h
index faefbb8acadb..fb79be0484ad 100644
--- a/tools/rimage/config/lnl.toml.h
+++ b/tools/rimage/config/lnl.toml.h
@@ -166,5 +166,9 @@
 #include <audio/phase_vocoder/phase_vocoder.toml>
 #endif
 
+#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/mtl.toml.h b/tools/rimage/config/mtl.toml.h
index 62672713dbae..5b050ae6b4e3 100644
--- a/tools/rimage/config/mtl.toml.h
+++ b/tools/rimage/config/mtl.toml.h
@@ -162,6 +162,10 @@
 #include <audio/template/template.toml>
 #endif
 
+#if defined(CONFIG_COMP_MWW) || defined(LLEXT_FORCE_ALL_MODULAR)
+#include <audio/microwakeword/mww.toml>
+#endif
+
 #if defined(CONFIG_COMP_TENSORFLOW) || defined(LLEXT_FORCE_ALL_MODULAR)
 #include <audio/tensorflow/tflmcly.toml>
 #endif
diff --git a/tools/rimage/config/ptl.toml.h b/tools/rimage/config/ptl.toml.h
index e7a53d1b1820..4173899288d4 100644
--- a/tools/rimage/config/ptl.toml.h
+++ b/tools/rimage/config/ptl.toml.h
@@ -178,5 +178,9 @@ index = __COUNTER__
 #include <audio/phase_vocoder/phase_vocoder.toml>
 #endif
 
+
+#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-h.toml.h b/tools/rimage/config/tgl-h.toml.h
index ea5dc5d4b262..e721943d47cb 100644
--- a/tools/rimage/config/tgl-h.toml.h
+++ b/tools/rimage/config/tgl-h.toml.h
@@ -120,6 +120,10 @@
 #include <audio/template/template.toml>
 #endif
 
+#if defined(CONFIG_COMP_MWW) || defined(LLEXT_FORCE_ALL_MODULAR)
+#include <audio/microwakeword/mww.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/rimage/config/tgl.toml.h b/tools/rimage/config/tgl.toml.h
index 2ca246880727..fd8cb672c792 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_MWW) || defined(LLEXT_FORCE_ALL_MODULAR)
+#include <audio/microwakeword/mww.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/rimage/config/wcl.toml.h b/tools/rimage/config/wcl.toml.h
index f239e063a459..0bf3529023eb 100644
--- a/tools/rimage/config/wcl.toml.h
+++ b/tools/rimage/config/wcl.toml.h
@@ -166,5 +166,9 @@ index = __COUNTER__
 #include <audio/phase_vocoder/phase_vocoder.toml>
 #endif
 
+#if defined(CONFIG_COMP_MWW) || defined(LLEXT_FORCE_ALL_MODULAR)
+#include <audio/microwakeword/mww.toml>
+#endif
+
 [module]
 count = __COUNTER__

From 35f972be9108b3e9b532b0f7f528c2e7fcecd5f1 Mon Sep 17 00:00:00 2001
From: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
Date: Fri, 4 Sep 2026 19:05:55 +0300
Subject: [PATCH 15/15] [DNM] platform: cavs25: enable MWW model loading from
 bytes control

Enable CONFIG_COMP_MWW_MODEL_FROM_CONTROL in the cAVS 2.5 board
configuration so that the microWakeWord model is loaded from runtime
bytes control instead of relying on the built-in static C array.

Signed-off-by: Seppo Ingalsuo <seppo.ingalsuo@linux.intel.com>
---
 app/boards/intel_adsp_cavs25.conf | 1 +
 1 file changed, 1 insertion(+)

diff --git a/app/boards/intel_adsp_cavs25.conf b/app/boards/intel_adsp_cavs25.conf
index 550804f53584..4e1da8019a7b 100644
--- a/app/boards/intel_adsp_cavs25.conf
+++ b/app/boards/intel_adsp_cavs25.conf
@@ -19,6 +19,7 @@ CONFIG_STD_CPP17=y
 # below), so build mww in statically rather than as an LLEXT module.
 CONFIG_COMP_MWW=y
 CONFIG_COMP_MWW_DEBUG_TRACE=y
+CONFIG_COMP_MWW_MODEL_FROM_CONTROL=y
 CONFIG_STACK_SIZE_EDF=32768
 CONFIG_PCM_CONVERTER_FORMAT_S16LE=y
 CONFIG_PCM_CONVERTER_FORMAT_S24LE=y