[micro_wake_word] Version 2 (#7032)

esphome/components/micro_wake_word/__init__.py

@@ -9,7 +9,7 @@ import requests
 import esphome.config_validation as cv
 import esphome.codegen as cg
 
-from esphome.core import CORE, HexInt, EsphomeError
+from esphome.core import CORE, HexInt
 
 from esphome.components import esp32, microphone
 from esphome import automation, git, external_files
@@ -41,9 +41,15 @@ CODEOWNERS = ["@kahrendt", "@jesserockz"]
 DEPENDENCIES = ["microphone"]
 DOMAIN = "micro_wake_word"
 
+
+CONF_FEATURE_STEP_SIZE = "feature_step_size"
+CONF_MODELS = "models"
+CONF_ON_WAKE_WORD_DETECTED = "on_wake_word_detected"
 CONF_PROBABILITY_CUTOFF = "probability_cutoff"
 CONF_SLIDING_WINDOW_AVERAGE_SIZE = "sliding_window_average_size"
-CONF_ON_WAKE_WORD_DETECTED = "on_wake_word_detected"
+CONF_SLIDING_WINDOW_SIZE = "sliding_window_size"
+CONF_TENSOR_ARENA_SIZE = "tensor_arena_size"
+CONF_VAD = "vad"
 
 TYPE_HTTP = "http"
 
@@ -98,12 +104,14 @@ GIT_SCHEMA = cv.All(
     _process_git_source,
 )
 
-KEY_WAKE_WORD = "wake_word"
+
 KEY_AUTHOR = "author"
-KEY_WEBSITE = "website"
-KEY_VERSION = "version"
 KEY_MICRO = "micro"
 KEY_MINIMUM_ESPHOME_VERSION = "minimum_esphome_version"
+KEY_TRAINED_LANGUAGES = "trained_languages"
+KEY_VERSION = "version"
+KEY_WAKE_WORD = "wake_word"
+KEY_WEBSITE = "website"
 
 MANIFEST_SCHEMA_V1 = cv.Schema(
     {
@@ -125,6 +133,29 @@ MANIFEST_SCHEMA_V1 = cv.Schema(
     }
 )
 
+MANIFEST_SCHEMA_V2 = cv.Schema(
+    {
+        cv.Required(CONF_TYPE): "micro",
+        cv.Required(CONF_MODEL): cv.string,
+        cv.Required(KEY_AUTHOR): cv.string,
+        cv.Required(KEY_VERSION): cv.All(cv.int_, 2),
+        cv.Required(KEY_WAKE_WORD): cv.string,
+        cv.Required(KEY_TRAINED_LANGUAGES): cv.ensure_list(cv.string),
+        cv.Optional(KEY_WEBSITE): cv.url,
+        cv.Required(KEY_MICRO): cv.Schema(
+            {
+                cv.Required(CONF_FEATURE_STEP_SIZE): cv.int_range(min=0, max=30),
+                cv.Required(CONF_TENSOR_ARENA_SIZE): cv.int_,
+                cv.Required(CONF_PROBABILITY_CUTOFF): cv.float_,
+                cv.Required(CONF_SLIDING_WINDOW_SIZE): cv.positive_int,
+                cv.Required(KEY_MINIMUM_ESPHOME_VERSION): cv.All(
+                    cv.version_number, cv.validate_esphome_version
+                ),
+            }
+        ),
+    }
+)
+
 
 def _compute_local_file_path(config: dict) -> Path:
     url = config[CONF_URL]
@@ -135,6 +166,24 @@ def _compute_local_file_path(config: dict) -> Path:
     return base_dir / key
 
 
+def _convert_manifest_v1_to_v2(v1_manifest):
+    v2_manifest = v1_manifest.copy()
+
+    v2_manifest[KEY_VERSION] = 2
+    v2_manifest[KEY_MICRO][CONF_SLIDING_WINDOW_SIZE] = v1_manifest[KEY_MICRO][
+        CONF_SLIDING_WINDOW_AVERAGE_SIZE
+    ]
+    del v2_manifest[KEY_MICRO][CONF_SLIDING_WINDOW_AVERAGE_SIZE]
+    v2_manifest[KEY_MICRO][
+        CONF_TENSOR_ARENA_SIZE
+    ] = 45672  # Original Inception-based V1 manifest models require a minimum of 45672 bytes
+    v2_manifest[KEY_MICRO][
+        CONF_FEATURE_STEP_SIZE
+    ] = 20  # Original Inception-based V1 manifest models use a 20 ms feature step size
+
+    return v2_manifest
+
+
 def _download_file(url: str, path: Path) -> bytes:
     if not external_files.has_remote_file_changed(url, path):
         _LOGGER.debug("Remote file has not changed, skipping download")
@@ -155,6 +204,24 @@ def _download_file(url: str, path: Path) -> bytes:
     return req.content
 
 
+def _validate_manifest_version(manifest_data):
+    if manifest_version := manifest_data.get(KEY_VERSION):
+        if manifest_version == 1:
+            try:
+                MANIFEST_SCHEMA_V1(manifest_data)
+            except cv.Invalid as e:
+                raise cv.Invalid(f"Invalid manifest file: {e}") from e
+        elif manifest_version == 2:
+            try:
+                MANIFEST_SCHEMA_V2(manifest_data)
+            except cv.Invalid as e:
+                raise cv.Invalid(f"Invalid manifest file: {e}") from e
+        else:
+            raise cv.Invalid("Invalid manifest version")
+    else:
+        raise cv.Invalid("Invalid manifest file, missing 'version' key.")
+
+
 def _process_http_source(config):
     url = config[CONF_URL]
     path = _compute_local_file_path(config)
@@ -167,11 +234,6 @@ def _process_http_source(config):
     if not isinstance(manifest_data, dict):
         raise cv.Invalid("Manifest file must contain a JSON object")
 
-    try:
-        MANIFEST_SCHEMA_V1(manifest_data)
-    except cv.Invalid as e:
-        raise cv.Invalid(f"Invalid manifest file: {e}") from e
-
     model = manifest_data[CONF_MODEL]
     model_url = urljoin(url, model)
 
@@ -206,7 +268,7 @@ def _validate_source_model_name(value):
     return MODEL_SOURCE_SCHEMA(
         {
             CONF_TYPE: TYPE_HTTP,
-            CONF_URL: f"https://github.com/esphome/micro-wake-word-models/raw/main/models/{value}.json",
+            CONF_URL: f"https://github.com/esphome/micro-wake-word-models/raw/main/models/v2/{value}.json",
         }
     )
 
@@ -260,18 +322,55 @@ MODEL_SOURCE_SCHEMA = cv.Any(
     msg="Not a valid model name, local path, http(s) url, or github shorthand",
 )
 
+MODEL_SCHEMA = cv.Schema(
+    {
+        cv.Optional(CONF_MODEL): MODEL_SOURCE_SCHEMA,
+        cv.Optional(CONF_PROBABILITY_CUTOFF): cv.percentage,
+        cv.Optional(CONF_SLIDING_WINDOW_SIZE): cv.positive_int,
+        cv.GenerateID(CONF_RAW_DATA_ID): cv.declare_id(cg.uint8),
+    }
+)
+
+# Provide a default VAD model that could be overridden
+VAD_MODEL_SCHEMA = MODEL_SCHEMA.extend(
+    cv.Schema(
+        {
+            cv.Optional(
+                CONF_MODEL,
+                default="vad",
+            ): MODEL_SOURCE_SCHEMA,
+        }
+    )
+)
+
+
+def _maybe_empty_vad_schema(value):
+    # Idea borrowed from uart/__init__.py's ``maybe_empty_debug`` function. Accessed 2 July 2024.
+    # Loads a default VAD model without any parameters overridden.
+    if value is None:
+        value = {}
+    return VAD_MODEL_SCHEMA(value)
+
+
 CONFIG_SCHEMA = cv.All(
     cv.Schema(
         {
             cv.GenerateID(): cv.declare_id(MicroWakeWord),
             cv.GenerateID(CONF_MICROPHONE): cv.use_id(microphone.Microphone),
-            cv.Optional(CONF_PROBABILITY_CUTOFF): cv.percentage,
+            cv.Required(CONF_MODELS): cv.ensure_list(MODEL_SCHEMA),
-            cv.Optional(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.positive_int,
             cv.Optional(CONF_ON_WAKE_WORD_DETECTED): automation.validate_automation(
                 single=True
             ),
-            cv.Required(CONF_MODEL): MODEL_SOURCE_SCHEMA,
+            cv.Optional(CONF_VAD): _maybe_empty_vad_schema,
-            cv.GenerateID(CONF_RAW_DATA_ID): cv.declare_id(cg.uint8),
+            cv.Optional(CONF_MODEL): cv.invalid(
+                f"The {CONF_MODEL} parameter has moved to be a list element under the {CONF_MODELS} parameter."
+            ),
+            cv.Optional(CONF_PROBABILITY_CUTOFF): cv.invalid(
+                f"The {CONF_PROBABILITY_CUTOFF} parameter has moved to be a list element under the {CONF_MODELS} parameter."
+            ),
+            cv.Optional(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.invalid(
+                f"The {CONF_SLIDING_WINDOW_AVERAGE_SIZE} parameter has been renamed to {CONF_SLIDING_WINDOW_SIZE} and moved to be a list element under the {CONF_MODELS} parameter."
+            ),
         }
     ).extend(cv.COMPONENT_SCHEMA),
     cv.only_with_esp_idf,
@@ -282,45 +381,20 @@ def _load_model_data(manifest_path: Path):
     with open(manifest_path, encoding="utf-8") as f:
         manifest = json.load(f)
 
-    try:
+    _validate_manifest_version(manifest)
-        MANIFEST_SCHEMA_V1(manifest)
-    except cv.Invalid as e:
-        raise EsphomeError(f"Invalid manifest file: {e}") from e
 
     model_path = manifest_path.parent / manifest[CONF_MODEL]
 
     with open(model_path, "rb") as f:
         model = f.read()
 
+    if manifest.get(KEY_VERSION) == 1:
+        manifest = _convert_manifest_v1_to_v2(manifest)
+
     return manifest, model
 
 
-async def to_code(config):
+def _model_config_to_manifest_data(model_config):
-    var = cg.new_Pvariable(config[CONF_ID])
-    await cg.register_component(var, config)
-
-    mic = await cg.get_variable(config[CONF_MICROPHONE])
-    cg.add(var.set_microphone(mic))
-
-    if on_wake_word_detection_config := config.get(CONF_ON_WAKE_WORD_DETECTED):
-        await automation.build_automation(
-            var.get_wake_word_detected_trigger(),
-            [(cg.std_string, "wake_word")],
-            on_wake_word_detection_config,
-        )
-
-    esp32.add_idf_component(
-        name="esp-tflite-micro",
-        repo="https://github.com/espressif/esp-tflite-micro",
-        ref="v1.3.1",
-    )
-
-    cg.add_build_flag("-DTF_LITE_STATIC_MEMORY")
-    cg.add_build_flag("-DTF_LITE_DISABLE_X86_NEON")
-    cg.add_build_flag("-DESP_NN")
-
-    model_config = config.get(CONF_MODEL)
-    data = []
     if model_config[CONF_TYPE] == TYPE_GIT:
         # compute path to model file
         key = f"{model_config[CONF_URL]}@{model_config.get(CONF_REF)}"
@@ -338,23 +412,95 @@ async def to_code(config):
     else:
         raise ValueError("Unsupported config type: {model_config[CONF_TYPE]}")
 
-    manifest, data = _load_model_data(file)
+    return _load_model_data(file)
 
-    rhs = [HexInt(x) for x in data]
-    prog_arr = cg.progmem_array(config[CONF_RAW_DATA_ID], rhs)
-    cg.add(var.set_model_start(prog_arr))
 
-    probability_cutoff = config.get(
+def _feature_step_size_validate(config):
-        CONF_PROBABILITY_CUTOFF, manifest[KEY_MICRO][CONF_PROBABILITY_CUTOFF]
+    features_step_size = None
+
+    for model_parameters in config[CONF_MODELS]:
+        model_config = model_parameters.get(CONF_MODEL)
+        manifest, _ = _model_config_to_manifest_data(model_config)
+
+        model_step_size = manifest[KEY_MICRO][CONF_FEATURE_STEP_SIZE]
+
+        if features_step_size is None:
+            features_step_size = model_step_size
+        elif features_step_size != model_step_size:
+            raise cv.Invalid("Cannot load models with different features step sizes.")
+
+
+FINAL_VALIDATE_SCHEMA = _feature_step_size_validate
+
+
+async def to_code(config):
+    var = cg.new_Pvariable(config[CONF_ID])
+    await cg.register_component(var, config)
+
+    mic = await cg.get_variable(config[CONF_MICROPHONE])
+    cg.add(var.set_microphone(mic))
+
+    esp32.add_idf_component(
+        name="esp-tflite-micro",
+        repo="https://github.com/espressif/esp-tflite-micro",
+        ref="v1.3.1",
     )
-    cg.add(var.set_probability_cutoff(probability_cutoff))
-    sliding_window_average_size = config.get(
-        CONF_SLIDING_WINDOW_AVERAGE_SIZE,
-        manifest[KEY_MICRO][CONF_SLIDING_WINDOW_AVERAGE_SIZE],
-    )
-    cg.add(var.set_sliding_window_average_size(sliding_window_average_size))
 
-    cg.add(var.set_wake_word(manifest[KEY_WAKE_WORD]))
+    cg.add_build_flag("-DTF_LITE_STATIC_MEMORY")
+    cg.add_build_flag("-DTF_LITE_DISABLE_X86_NEON")
+    cg.add_build_flag("-DESP_NN")
+
+    if on_wake_word_detection_config := config.get(CONF_ON_WAKE_WORD_DETECTED):
+        await automation.build_automation(
+            var.get_wake_word_detected_trigger(),
+            [(cg.std_string, "wake_word")],
+            on_wake_word_detection_config,
+        )
+
+    if vad_model := config.get(CONF_VAD):
+        cg.add_define("USE_MICRO_WAKE_WORD_VAD")
+
+        # Use the general model loading code for the VAD codegen
+        config[CONF_MODELS].append(vad_model)
+
+    for model_parameters in config[CONF_MODELS]:
+        model_config = model_parameters.get(CONF_MODEL)
+        data = []
+        manifest, data = _model_config_to_manifest_data(model_config)
+
+        rhs = [HexInt(x) for x in data]
+        prog_arr = cg.progmem_array(model_parameters[CONF_RAW_DATA_ID], rhs)
+
+        probability_cutoff = model_parameters.get(
+            CONF_PROBABILITY_CUTOFF, manifest[KEY_MICRO][CONF_PROBABILITY_CUTOFF]
+        )
+        sliding_window_size = model_parameters.get(
+            CONF_SLIDING_WINDOW_SIZE,
+            manifest[KEY_MICRO][CONF_SLIDING_WINDOW_SIZE],
+        )
+
+        if manifest[KEY_WAKE_WORD] == "vad":
+            cg.add(
+                var.add_vad_model(
+                    prog_arr,
+                    probability_cutoff,
+                    sliding_window_size,
+                    manifest[KEY_MICRO][CONF_TENSOR_ARENA_SIZE],
+                )
+            )
+        else:
+            cg.add(
+                var.add_wake_word_model(
+                    prog_arr,
+                    probability_cutoff,
+                    sliding_window_size,
+                    manifest[KEY_WAKE_WORD],
+                    manifest[KEY_MICRO][CONF_TENSOR_ARENA_SIZE],
+                )
+            )
+
+    cg.add(var.set_features_step_size(manifest[KEY_MICRO][CONF_FEATURE_STEP_SIZE]))
+    cg.add_library("kahrendt/ESPMicroSpeechFeatures", "1.0.0")
 
 
 MICRO_WAKE_WORD_ACTION_SCHEMA = cv.Schema({cv.GenerateID(): cv.use_id(MicroWakeWord)})

esphome/components/micro_wake_word/audio_preprocessor_int8_model_data.h

@@ -1,493 +0,0 @@
-#pragma once
-
-#ifdef USE_ESP_IDF
-
-// Converted audio_preprocessor_int8.tflite
-// From https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/micro_speech/models accessed
-// January 2024
-//
-// Copyright 2023 The TensorFlow Authors. All Rights Reserved.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//     http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-namespace esphome {
-namespace micro_wake_word {
-
-const unsigned char G_AUDIO_PREPROCESSOR_INT8_TFLITE[] = {
-    0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10,
-    0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x88, 0x00,
-    0x00, 0x00, 0xe0, 0x00, 0x00, 0x00, 0x80, 0x0e, 0x00, 0x00, 0x90, 0x0e, 0x00, 0x00, 0xcc, 0x1f, 0x00, 0x00, 0x03,
-    0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xe2, 0xeb, 0xff, 0xff, 0x0c, 0x00, 0x00, 0x00,
-    0x1c, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67,
-    0x5f, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x94, 0xff,
-    0xff, 0xff, 0x2a, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x6f, 0x75, 0x74, 0x70, 0x75,
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-    0x04, 0x00, 0x00, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x61, 0x75, 0x64, 0x69, 0x6f, 0x5f, 0x66, 0x72, 0x61, 0x6d, 0x65,
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-    0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x43, 0x4f, 0x4e, 0x56, 0x45, 0x52, 0x53, 0x49, 0x4f,
-    0x4e, 0x5f, 0x4d, 0x45, 0x54, 0x41, 0x44, 0x41, 0x54, 0x41, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00,
-    0x08, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x6d, 0x69, 0x6e,
-    0x5f, 0x72, 0x75, 0x6e, 0x74, 0x69, 0x6d, 0x65, 0x5f, 0x76, 0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x00, 0x2e, 0x00,
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-    0x09, 0x00, 0x00, 0x34, 0x09, 0x00, 0x00, 0x20, 0x09, 0x00, 0x00, 0x0c, 0x09, 0x00, 0x00, 0xf8, 0x08, 0x00, 0x00,
-    0xec, 0x07, 0x00, 0x00, 0x88, 0x07, 0x00, 0x00, 0x24, 0x07, 0x00, 0x00, 0xc0, 0x06, 0x00, 0x00, 0x38, 0x04, 0x00,
-    0x00, 0xb0, 0x01, 0x00, 0x00, 0x9c, 0x01, 0x00, 0x00, 0x88, 0x01, 0x00, 0x00, 0x74, 0x01, 0x00, 0x00, 0x60, 0x01,
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-    0x01, 0x00, 0x00, 0x24, 0x01, 0x00, 0x00, 0x1c, 0x01, 0x00, 0x00, 0x14, 0x01, 0x00, 0x00, 0x0c, 0x01, 0x00, 0x00,
-    0x04, 0x01, 0x00, 0x00, 0xfc, 0x00, 0x00, 0x00, 0xf4, 0x00, 0x00, 0x00, 0xec, 0x00, 0x00, 0x00, 0xe4, 0x00, 0x00,
-    0x00, 0xdc, 0x00, 0x00, 0x00, 0xd4, 0x00, 0x00, 0x00, 0xcc, 0x00, 0x00, 0x00, 0xc4, 0x00, 0x00, 0x00, 0xbc, 0x00,
-    0x00, 0x00, 0xb4, 0x00, 0x00, 0x00, 0xac, 0x00, 0x00, 0x00, 0xa4, 0x00, 0x00, 0x00, 0x9c, 0x00, 0x00, 0x00, 0x94,
-    0x00, 0x00, 0x00, 0x8c, 0x00, 0x00, 0x00, 0x6c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xf2, 0xf6, 0xff, 0xff,
-    0x04, 0x00, 0x00, 0x00, 0x58, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04,
-    0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x08, 0x00,
-    0x07, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
-    0x00, 0x0a, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
-    0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x32, 0x2e, 0x31, 0x32, 0x2e, 0x30, 0x00,
-    0x00, 0x56, 0xf7, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x32, 0x2e, 0x38, 0x2e, 0x30, 0x00,
-    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xd4, 0xe1, 0xff, 0xff, 0xd8, 0xe1, 0xff, 0xff, 0xdc,
-    0xe1, 0xff, 0xff, 0xe0, 0xe1, 0xff, 0xff, 0xe4, 0xe1, 0xff, 0xff, 0xe8, 0xe1, 0xff, 0xff, 0xec, 0xe1, 0xff, 0xff,
-    0xf0, 0xe1, 0xff, 0xff, 0xf4, 0xe1, 0xff, 0xff, 0xf8, 0xe1, 0xff, 0xff, 0xfc, 0xe1, 0xff, 0xff, 0x00, 0xe2, 0xff,
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-
-}  // namespace micro_wake_word
-}  // namespace esphome
-
-#endif  // USE_ESP_IDF

esphome/components/micro_wake_word/micro_wake_word.h

@@ -1,21 +1,18 @@
 #pragma once
 
-/**
- * This is a workaround until we can figure out a way to get
- * the tflite-micro idf component code available in CI
- *
- * */
-//
-#ifndef CLANG_TIDY
-
 #ifdef USE_ESP_IDF
 
+#include "preprocessor_settings.h"
+#include "streaming_model.h"
+
 #include "esphome/core/automation.h"
 #include "esphome/core/component.h"
 #include "esphome/core/ring_buffer.h"
 
 #include "esphome/components/microphone/microphone.h"
 
+#include <frontend_util.h>
+
 #include <tensorflow/lite/core/c/common.h>
 #include <tensorflow/lite/micro/micro_interpreter.h>
 #include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
@@ -23,35 +20,6 @@
 namespace esphome {
 namespace micro_wake_word {
 
-// The following are dictated by the preprocessor model
-//
-// The number of features the audio preprocessor generates per slice
-static const uint8_t PREPROCESSOR_FEATURE_SIZE = 40;
-// How frequently the preprocessor generates a new set of features
-static const uint8_t FEATURE_STRIDE_MS = 20;
-// Duration of each slice used as input into the preprocessor
-static const uint8_t FEATURE_DURATION_MS = 30;
-// Audio sample frequency in hertz
-static const uint16_t AUDIO_SAMPLE_FREQUENCY = 16000;
-// The number of old audio samples that are saved to be part of the next feature window
-static const uint16_t HISTORY_SAMPLES_TO_KEEP =
-    ((FEATURE_DURATION_MS - FEATURE_STRIDE_MS) * (AUDIO_SAMPLE_FREQUENCY / 1000));
-// The number of new audio samples to receive to be included with the next feature window
-static const uint16_t NEW_SAMPLES_TO_GET = (FEATURE_STRIDE_MS * (AUDIO_SAMPLE_FREQUENCY / 1000));
-// The total number of audio samples included in the feature window
-static const uint16_t SAMPLE_DURATION_COUNT = FEATURE_DURATION_MS * AUDIO_SAMPLE_FREQUENCY / 1000;
-// Number of bytes in memory needed for the preprocessor arena
-static const uint32_t PREPROCESSOR_ARENA_SIZE = 9528;
-
-// The following configure the streaming wake word model
-//
-// The number of audio slices to process before accepting a positive detection
-static const uint8_t MIN_SLICES_BEFORE_DETECTION = 74;
-
-// Number of bytes in memory needed for the streaming wake word model
-static const uint32_t STREAMING_MODEL_ARENA_SIZE = 64000;
-static const uint32_t STREAMING_MODEL_VARIABLE_ARENA_SIZE = 1024;
-
 enum State {
   IDLE,
   START_MICROPHONE,
@@ -61,6 +29,9 @@ enum State {
   STOPPING_MICROPHONE,
 };
 
+// The number of audio slices to process before accepting a positive detection
+static const uint8_t MIN_SLICES_BEFORE_DETECTION = 74;
+
 class MicroWakeWord : public Component {
  public:
   void setup() override;
@@ -73,28 +44,21 @@ class MicroWakeWord : public Component {
 
   bool is_running() const { return this->state_ != State::IDLE; }
 
-  bool initialize_models();
+  void set_features_step_size(uint8_t step_size) { this->features_step_size_ = step_size; }
-
-  std::string get_wake_word() { return this->wake_word_; }
-
-  // Increasing either of these will reduce the rate of false acceptances while increasing the false rejection rate
-  void set_probability_cutoff(float probability_cutoff) { this->probability_cutoff_ = probability_cutoff; }
-  void set_sliding_window_average_size(size_t size);
 
   void set_microphone(microphone::Microphone *microphone) { this->microphone_ = microphone; }
 
   Trigger<std::string> *get_wake_word_detected_trigger() const { return this->wake_word_detected_trigger_; }
 
-  void set_model_start(const uint8_t *model_start) { this->model_start_ = model_start; }
+  void add_wake_word_model(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_average_size,
-  void set_wake_word(const std::string &wake_word) { this->wake_word_ = wake_word; }
+                           const std::string &wake_word, size_t tensor_arena_size);
+
+#ifdef USE_MICRO_WAKE_WORD_VAD
+  void add_vad_model(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_size,
+                     size_t tensor_arena_size);
+#endif
 
  protected:
-  void set_state_(State state);
-  int read_microphone_();
-
-  const uint8_t *model_start_;
-  std::string wake_word_;
-
   microphone::Microphone *microphone_{nullptr};
   Trigger<std::string> *wake_word_detected_trigger_ = new Trigger<std::string>();
   State state_{State::IDLE};
@@ -102,85 +66,93 @@ class MicroWakeWord : public Component {
 
   std::unique_ptr<RingBuffer> ring_buffer_;
 
-  int16_t *input_buffer_;
+  std::vector<WakeWordModel> wake_word_models_;
 
-  const tflite::Model *preprocessor_model_{nullptr};
+#ifdef USE_MICRO_WAKE_WORD_VAD
-  const tflite::Model *streaming_model_{nullptr};
+  std::unique_ptr<VADModel> vad_model_;
-  tflite::MicroInterpreter *streaming_interpreter_{nullptr};
+#endif
-  tflite::MicroInterpreter *preprocessor_interperter_{nullptr};
 
-  std::vector<float> recent_streaming_probabilities_;
+  tflite::MicroMutableOpResolver<20> streaming_op_resolver_;
-  size_t last_n_index_{0};
 
-  float probability_cutoff_{0.5};
+  // Audio frontend handles generating spectrogram features
-  size_t sliding_window_average_size_{10};
+  struct FrontendConfig frontend_config_;
+  struct FrontendState frontend_state_;
 
-  // When the wake word detection first starts or after the word has been detected once, we ignore this many audio
+  // When the wake word detection first starts, we ignore this many audio
-  // feature slices before accepting a positive detection again
+  // feature slices before accepting a positive detection
   int16_t ignore_windows_{-MIN_SLICES_BEFORE_DETECTION};
 
-  uint8_t *streaming_var_arena_{nullptr};
+  uint8_t features_step_size_;
-  uint8_t *streaming_tensor_arena_{nullptr};
-  uint8_t *preprocessor_tensor_arena_{nullptr};
-  int8_t *new_features_data_{nullptr};
 
-  tflite::MicroResourceVariables *mrv_{nullptr};
+  // Stores audio read from the microphone before being added to the ring buffer.
-
+  int16_t *input_buffer_{nullptr};
-  // Stores audio fed into feature generator preprocessor
+  // Stores audio to be fed into the audio frontend for generating features.
-  int16_t *preprocessor_audio_buffer_;
+  int16_t *preprocessor_audio_buffer_{nullptr};
 
   bool detected_{false};
+  std::string detected_wake_word_{""};
 
-  /** Detects if wake word has been said
+  void set_state_(State state);
+
+  /// @brief Tests if there are enough samples in the ring buffer to generate new features.
+  /// @return True if enough samples, false otherwise.
+  bool has_enough_samples_();
+
+  /** Reads audio from microphone into the ring buffer
+   *
+   * Audio data (16000 kHz with int16 samples) is read into the input_buffer_.
+   * Verifies the ring buffer has enough space for all audio data. If not, it logs
+   * a warning and resets the ring buffer entirely.
+   * @return Number of bytes written to the ring buffer
+   */
+  size_t read_microphone_();
+
+  /// @brief Allocates memory for input_buffer_, preprocessor_audio_buffer_, and ring_buffer_
+  /// @return True if successful, false otherwise
+  bool allocate_buffers_();
+
+  /// @brief Frees memory allocated for input_buffer_ and preprocessor_audio_buffer_
+  void deallocate_buffers_();
+
+  /// @brief Loads streaming models and prepares the feature generation frontend
+  /// @return True if successful, false otherwise
+  bool load_models_();
+
+  /// @brief Deletes each model's TFLite interpreters and frees tensor arena memory. Frees memory used by the feature
+  /// generation frontend.
+  void unload_models_();
+
+  /** Performs inference with each configured model
    *
    * If enough audio samples are available, it will generate one slice of new features.
-   * If the streaming model predicts the wake word, then the nonstreaming model confirms it.
+   * It then loops through and performs inference with each of the loaded models.
-   * @param ring_Buffer Ring buffer containing raw audio samples
-   * @return True if the wake word is detected, false otherwise
    */
-  bool detect_wake_word_();
+  void update_model_probabilities_();
 
-  /// @brief Returns true if there are enough audio samples in the buffer to generate another slice of features
+  /** Checks every model's recent probabilities to determine if the wake word has been predicted
-  bool slice_available_();
-
-  /** Shifts previous feature slices over by one and generates a new slice of features
    *
-   * @param ring_buffer ring buffer containing raw audio samples
+   * Verifies the models have processed enough new samples for accurate predictions.
-   * @return True if a new slice of features was generated, false otherwise
+   * Sets detected_wake_word_ to the wake word, if one is detected.
+   * @return True if a wake word is predicted, false otherwise
    */
-  bool update_features_();
+  bool detect_wake_words_();
 
-  /** Generates features from audio samples
+  /** Generates features for a window of audio samples
    *
-   * Adapted from TFLite micro speech example
+   * Reads samples from the ring buffer and feeds them into the preprocessor frontend.
-   * @param audio_data Pointer to array with the audio samples
+   * Adapted from TFLite microspeech frontend.
-   * @param audio_data_size The number of samples to use as input to the preprocessor model
+   * @param features int8_t array to store the audio features
-   * @param feature_output Array that will store the features
    * @return True if successful, false otherwise.
    */
-  bool generate_single_feature_(const int16_t *audio_data, int audio_data_size,
+  bool generate_features_for_window_(int8_t features[PREPROCESSOR_FEATURE_SIZE]);
-                                int8_t feature_output[PREPROCESSOR_FEATURE_SIZE]);
 
-  /** Performs inference over the most recent feature slice with the streaming model
+  /// @brief Resets the ring buffer, ignore_windows_, and sliding window probabilities
-   *
+  void reset_states_();
-   * @return Probability of the wake word between 0.0 and 1.0
-   */
-  float perform_streaming_inference_();
-
-  /** Strides the audio samples by keeping the last 10 ms of the previous slice
-   *
-   * Adapted from the TFLite micro speech example
-   * @param ring_buffer Ring buffer containing raw audio samples
-   * @param audio_samples Pointer to an array that will store the strided audio samples
-   * @return True if successful, false otherwise
-   */
-  bool stride_audio_samples_(int16_t **audio_samples);
-
-  /// @brief Returns true if successfully registered the preprocessor's TensorFlow operations
-  bool register_preprocessor_ops_(tflite::MicroMutableOpResolver<18> &op_resolver);
 
   /// @brief Returns true if successfully registered the streaming model's TensorFlow operations
-  bool register_streaming_ops_(tflite::MicroMutableOpResolver<17> &op_resolver);
+  bool register_streaming_ops_(tflite::MicroMutableOpResolver<20> &op_resolver);
+
+  inline uint16_t new_samples_to_get_() { return (this->features_step_size_ * (AUDIO_SAMPLE_FREQUENCY / 1000)); }
 };
 
 template<typename... Ts> class StartAction : public Action<Ts...>, public Parented<MicroWakeWord> {
@@ -202,5 +174,3 @@ template<typename... Ts> class IsRunningCondition : public Condition<Ts...>, pub
 }  // namespace esphome
 
 #endif  // USE_ESP_IDF
-
-#endif  // CLANG_TIDY

esphome/components/micro_wake_word/preprocessor_settings.h

@@ -0,0 +1,20 @@
+#pragma once
+
+#ifdef USE_ESP_IDF
+
+#include <cstdint>
+
+namespace esphome {
+namespace micro_wake_word {
+
+// The number of features the audio preprocessor generates per slice
+static const uint8_t PREPROCESSOR_FEATURE_SIZE = 40;
+// Duration of each slice used as input into the preprocessor
+static const uint8_t FEATURE_DURATION_MS = 30;
+// Audio sample frequency in hertz
+static const uint16_t AUDIO_SAMPLE_FREQUENCY = 16000;
+
+}  // namespace micro_wake_word
+}  // namespace esphome
+
+#endif

esphome/components/micro_wake_word/streaming_model.cpp

@@ -0,0 +1,189 @@
+#ifdef USE_ESP_IDF
+
+#include "streaming_model.h"
+
+#include "esphome/core/hal.h"
+#include "esphome/core/helpers.h"
+#include "esphome/core/log.h"
+
+static const char *const TAG = "micro_wake_word";
+
+namespace esphome {
+namespace micro_wake_word {
+
+void WakeWordModel::log_model_config() {
+  ESP_LOGCONFIG(TAG, "    - Wake Word: %s", this->wake_word_.c_str());
+  ESP_LOGCONFIG(TAG, "      Probability cutoff: %.3f", this->probability_cutoff_);
+  ESP_LOGCONFIG(TAG, "      Sliding window size: %d", this->sliding_window_size_);
+}
+
+void VADModel::log_model_config() {
+  ESP_LOGCONFIG(TAG, "    - VAD Model");
+  ESP_LOGCONFIG(TAG, "      Probability cutoff: %.3f", this->probability_cutoff_);
+  ESP_LOGCONFIG(TAG, "      Sliding window size: %d", this->sliding_window_size_);
+}
+
+bool StreamingModel::load_model(tflite::MicroMutableOpResolver<20> &op_resolver) {
+  ExternalRAMAllocator<uint8_t> arena_allocator(ExternalRAMAllocator<uint8_t>::ALLOW_FAILURE);
+
+  if (this->tensor_arena_ == nullptr) {
+    this->tensor_arena_ = arena_allocator.allocate(this->tensor_arena_size_);
+    if (this->tensor_arena_ == nullptr) {
+      ESP_LOGE(TAG, "Could not allocate the streaming model's tensor arena.");
+      return false;
+    }
+  }
+
+  if (this->var_arena_ == nullptr) {
+    this->var_arena_ = arena_allocator.allocate(STREAMING_MODEL_VARIABLE_ARENA_SIZE);
+    if (this->var_arena_ == nullptr) {
+      ESP_LOGE(TAG, "Could not allocate the streaming model's variable tensor arena.");
+      return false;
+    }
+    this->ma_ = tflite::MicroAllocator::Create(this->var_arena_, STREAMING_MODEL_VARIABLE_ARENA_SIZE);
+    this->mrv_ = tflite::MicroResourceVariables::Create(this->ma_, 20);
+  }
+
+  const tflite::Model *model = tflite::GetModel(this->model_start_);
+  if (model->version() != TFLITE_SCHEMA_VERSION) {
+    ESP_LOGE(TAG, "Streaming model's schema is not supported");
+    return false;
+  }
+
+  if (this->interpreter_ == nullptr) {
+    this->interpreter_ = make_unique<tflite::MicroInterpreter>(
+        tflite::GetModel(this->model_start_), op_resolver, this->tensor_arena_, this->tensor_arena_size_, this->mrv_);
+    if (this->interpreter_->AllocateTensors() != kTfLiteOk) {
+      ESP_LOGE(TAG, "Failed to allocate tensors for the streaming model");
+      return false;
+    }
+
+    // Verify input tensor matches expected values
+    // Dimension 3 will represent the first layer stride, so skip it may vary
+    TfLiteTensor *input = this->interpreter_->input(0);
+    if ((input->dims->size != 3) || (input->dims->data[0] != 1) ||
+        (input->dims->data[2] != PREPROCESSOR_FEATURE_SIZE)) {
+      ESP_LOGE(TAG, "Streaming model tensor input dimensions has improper dimensions.");
+      return false;
+    }
+
+    if (input->type != kTfLiteInt8) {
+      ESP_LOGE(TAG, "Streaming model tensor input is not int8.");
+      return false;
+    }
+
+    // Verify output tensor matches expected values
+    TfLiteTensor *output = this->interpreter_->output(0);
+    if ((output->dims->size != 2) || (output->dims->data[0] != 1) || (output->dims->data[1] != 1)) {
+      ESP_LOGE(TAG, "Streaming model tensor output dimension is not 1x1.");
+    }
+
+    if (output->type != kTfLiteUInt8) {
+      ESP_LOGE(TAG, "Streaming model tensor output is not uint8.");
+      return false;
+    }
+  }
+
+  return true;
+}
+
+void StreamingModel::unload_model() {
+  this->interpreter_.reset();
+
+  ExternalRAMAllocator<uint8_t> arena_allocator(ExternalRAMAllocator<uint8_t>::ALLOW_FAILURE);
+
+  arena_allocator.deallocate(this->tensor_arena_, this->tensor_arena_size_);
+  this->tensor_arena_ = nullptr;
+  arena_allocator.deallocate(this->var_arena_, STREAMING_MODEL_VARIABLE_ARENA_SIZE);
+  this->var_arena_ = nullptr;
+}
+
+bool StreamingModel::perform_streaming_inference(const int8_t features[PREPROCESSOR_FEATURE_SIZE]) {
+  if (this->interpreter_ != nullptr) {
+    TfLiteTensor *input = this->interpreter_->input(0);
+
+    std::memmove(
+        (int8_t *) (tflite::GetTensorData<int8_t>(input)) + PREPROCESSOR_FEATURE_SIZE * this->current_stride_step_,
+        features, PREPROCESSOR_FEATURE_SIZE);
+    ++this->current_stride_step_;
+
+    uint8_t stride = this->interpreter_->input(0)->dims->data[1];
+
+    if (this->current_stride_step_ >= stride) {
+      this->current_stride_step_ = 0;
+
+      TfLiteStatus invoke_status = this->interpreter_->Invoke();
+      if (invoke_status != kTfLiteOk) {
+        ESP_LOGW(TAG, "Streaming interpreter invoke failed");
+        return false;
+      }
+
+      TfLiteTensor *output = this->interpreter_->output(0);
+
+      ++this->last_n_index_;
+      if (this->last_n_index_ == this->sliding_window_size_)
+        this->last_n_index_ = 0;
+      this->recent_streaming_probabilities_[this->last_n_index_] = output->data.uint8[0];  // probability;
+    }
+    return true;
+  }
+  ESP_LOGE(TAG, "Streaming interpreter is not initialized.");
+  return false;
+}
+
+void StreamingModel::reset_probabilities() {
+  for (auto &prob : this->recent_streaming_probabilities_) {
+    prob = 0;
+  }
+}
+
+WakeWordModel::WakeWordModel(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_average_size,
+                             const std::string &wake_word, size_t tensor_arena_size) {
+  this->model_start_ = model_start;
+  this->probability_cutoff_ = probability_cutoff;
+  this->sliding_window_size_ = sliding_window_average_size;
+  this->recent_streaming_probabilities_.resize(sliding_window_average_size, 0);
+  this->wake_word_ = wake_word;
+  this->tensor_arena_size_ = tensor_arena_size;
+};
+
+bool WakeWordModel::determine_detected() {
+  int32_t sum = 0;
+  for (auto &prob : this->recent_streaming_probabilities_) {
+    sum += prob;
+  }
+
+  float sliding_window_average = static_cast<float>(sum) / static_cast<float>(255 * this->sliding_window_size_);
+
+  // Detect the wake word if the sliding window average is above the cutoff
+  if (sliding_window_average > this->probability_cutoff_) {
+    ESP_LOGD(TAG, "The '%s' model sliding average probability is %.3f and most recent probability is %.3f",
+             this->wake_word_.c_str(), sliding_window_average,
+             this->recent_streaming_probabilities_[this->last_n_index_] / (255.0));
+    return true;
+  }
+  return false;
+}
+
+VADModel::VADModel(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_size,
+                   size_t tensor_arena_size) {
+  this->model_start_ = model_start;
+  this->probability_cutoff_ = probability_cutoff;
+  this->sliding_window_size_ = sliding_window_size;
+  this->recent_streaming_probabilities_.resize(sliding_window_size, 0);
+  this->tensor_arena_size_ = tensor_arena_size;
+};
+
+bool VADModel::determine_detected() {
+  uint8_t max = 0;
+  for (auto &prob : this->recent_streaming_probabilities_) {
+    max = std::max(prob, max);
+  }
+
+  return max > this->probability_cutoff_;
+}
+
+}  // namespace micro_wake_word
+}  // namespace esphome
+
+#endif

esphome/components/micro_wake_word/streaming_model.h

@@ -0,0 +1,84 @@
+#pragma once
+
+#ifdef USE_ESP_IDF
+
+#include "preprocessor_settings.h"
+
+#include <tensorflow/lite/core/c/common.h>
+#include <tensorflow/lite/micro/micro_interpreter.h>
+#include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
+
+namespace esphome {
+namespace micro_wake_word {
+
+static const uint32_t STREAMING_MODEL_VARIABLE_ARENA_SIZE = 1024;
+
+class StreamingModel {
+ public:
+  virtual void log_model_config() = 0;
+  virtual bool determine_detected() = 0;
+
+  bool perform_streaming_inference(const int8_t features[PREPROCESSOR_FEATURE_SIZE]);
+
+  /// @brief Sets all recent_streaming_probabilities to 0
+  void reset_probabilities();
+
+  /// @brief Allocates tensor and variable arenas and sets up the model interpreter
+  /// @param op_resolver MicroMutableOpResolver object that must exist until the model is unloaded
+  /// @return True if successful, false otherwise
+  bool load_model(tflite::MicroMutableOpResolver<20> &op_resolver);
+
+  /// @brief Destroys the TFLite interpreter and frees the tensor and variable arenas' memory
+  void unload_model();
+
+ protected:
+  uint8_t current_stride_step_{0};
+
+  float probability_cutoff_;
+  size_t sliding_window_size_;
+  size_t last_n_index_{0};
+  size_t tensor_arena_size_;
+  std::vector<uint8_t> recent_streaming_probabilities_;
+
+  const uint8_t *model_start_;
+  uint8_t *tensor_arena_{nullptr};
+  uint8_t *var_arena_{nullptr};
+  std::unique_ptr<tflite::MicroInterpreter> interpreter_;
+  tflite::MicroResourceVariables *mrv_{nullptr};
+  tflite::MicroAllocator *ma_{nullptr};
+};
+
+class WakeWordModel final : public StreamingModel {
+ public:
+  WakeWordModel(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_average_size,
+                const std::string &wake_word, size_t tensor_arena_size);
+
+  void log_model_config() override;
+
+  /// @brief Checks for the wake word by comparing the mean probability in the sliding window with the probability
+  /// cutoff
+  /// @return True if wake word is detected, false otherwise
+  bool determine_detected() override;
+
+  const std::string &get_wake_word() const { return this->wake_word_; }
+
+ protected:
+  std::string wake_word_;
+};
+
+class VADModel final : public StreamingModel {
+ public:
+  VADModel(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_size, size_t tensor_arena_size);
+
+  void log_model_config() override;
+
+  /// @brief Checks for voice activity by comparing the max probability in the sliding window with the probability
+  /// cutoff
+  /// @return True if voice activity is detected, false otherwise
+  bool determine_detected() override;
+};
+
+}  // namespace micro_wake_word
+}  // namespace esphome
+
+#endif

esphome/core/defines.h

@@ -86,6 +86,7 @@
 #define USE_ESP32_BLE_SERVER
 #define USE_ESP32_CAMERA
 #define USE_IMPROV
+#define USE_MICRO_WAKE_WORD_VAD
 #define USE_MICROPHONE
 #define USE_PSRAM
 #define USE_SOCKET_IMPL_BSD_SOCKETS

platformio.ini

@@ -142,7 +142,8 @@ platform_packages =
 framework = espidf
 lib_deps =
     ${common:idf.lib_deps}
-    droscy/esp_wireguard@0.4.2    ; wireguard
+    droscy/esp_wireguard@0.4.2              ; wireguard
+    kahrendt/ESPMicroSpeechFeatures@1.0.0   ; micro_wake_word
 build_flags =
     ${common:idf.build_flags}
     -Wno-nonnull-compare

tests/components/micro_wake_word/common.yaml

@@ -10,6 +10,10 @@ microphone:
     pdm: true
 
 micro_wake_word:
-  model: hey_jarvis
   on_wake_word_detected:
     - logger.log: "Wake word detected"
+  models:
+    - model: hey_jarvis
+      probability_cutoff: 0.7
+    - model: okay_nabu
+      sliding_window_size: 5

tests/components/micro_wake_word/test.esp32-idf.yaml

@@ -0,0 +1 @@
+<<: !include common.yaml