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[micro_wake_word] Version 2 (#7032)

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Kevin Ahrendt 2024-07-10 21:21:04 -04:00 committed by GitHub
parent 2da939c81c
commit 2873c6bbaf
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GPG Key ID: B5690EEEBB952194
11 changed files with 822 additions and 949 deletions
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256
esphome/components/micro_wake_word/__init__.py
View File

@ -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.Optional(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.positive_int,
cv.Required(CONF_MODELS): cv.ensure_list(MODEL_SCHEMA),
cv.Optional(CONF_ON_WAKE_WORD_DETECTED): automation.validate_automation(
single=True
),
cv.Required(CONF_MODEL): MODEL_SOURCE_SCHEMA,
cv.GenerateID(CONF_RAW_DATA_ID): cv.declare_id(cg.uint8),
cv.Optional(CONF_VAD): _maybe_empty_vad_schema,
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:
MANIFEST_SCHEMA_V1(manifest)
except cv.Invalid as e:
raise EsphomeError(f"Invalid manifest file: {e}") from e
_validate_manifest_version(manifest)
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):
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 = []
def _model_config_to_manifest_data(model_config):
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)
def _feature_step_size_validate(config):
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_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(config[CONF_RAW_DATA_ID], rhs)
cg.add(var.set_model_start(prog_arr))
prog_arr = cg.progmem_array(model_parameters[CONF_RAW_DATA_ID], rhs)
probability_cutoff = config.get(
probability_cutoff = model_parameters.get(
CONF_PROBABILITY_CUTOFF, manifest[KEY_MICRO][CONF_PROBABILITY_CUTOFF]
)
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],
sliding_window_size = model_parameters.get(
CONF_SLIDING_WINDOW_SIZE,
manifest[KEY_MICRO][CONF_SLIDING_WINDOW_SIZE],
)
cg.add(var.set_sliding_window_average_size(sliding_window_average_size))
cg.add(var.set_wake_word(manifest[KEY_WAKE_WORD]))
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)})

493
esphome/components/micro_wake_word/audio_preprocessor_int8_model_data.h
View File

@ -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,
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0x6e, 0x61, 0x6c, 0x57, 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x00, 0x00, 0x00, 0x00};
} // namespace micro_wake_word
} // namespace esphome
#endif // USE_ESP_IDF

498
esphome/components/micro_wake_word/micro_wake_word.cpp
View File

@ -1,12 +1,5 @@
#include "micro_wake_word.h"
/**
* 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
#include "streaming_model.h"
#ifdef USE_ESP_IDF
@ -14,13 +7,13 @@
#include "esphome/core/helpers.h"
#include "esphome/core/log.h"
#include "audio_preprocessor_int8_model_data.h"
#include <frontend.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>
#include <cinttypes>
#include <cmath>
namespace esphome {
@ -29,9 +22,9 @@ namespace micro_wake_word {
static const char *const TAG = "micro_wake_word";
static const size_t SAMPLE_RATE_HZ = 16000; // 16 kHz
static const size_t BUFFER_LENGTH = 500; // 0.5 seconds
static const size_t BUFFER_LENGTH = 64; // 0.064 seconds
static const size_t BUFFER_SIZE = SAMPLE_RATE_HZ / 1000 * BUFFER_LENGTH;
static const size_t INPUT_BUFFER_SIZE = 32 * SAMPLE_RATE_HZ / 1000; // 32ms * 16kHz / 1000ms
static const size_t INPUT_BUFFER_SIZE = 16 * SAMPLE_RATE_HZ / 1000; // 16ms * 16kHz / 1000ms
float MicroWakeWord::get_setup_priority() const { return setup_priority::AFTER_CONNECTION; }
@ -56,57 +49,55 @@ static const LogString *micro_wake_word_state_to_string(State state) {
void MicroWakeWord::dump_config() {
ESP_LOGCONFIG(TAG, "microWakeWord:");
ESP_LOGCONFIG(TAG, " Wake Word: %s", this->get_wake_word().c_str());
ESP_LOGCONFIG(TAG, " Probability cutoff: %.3f", this->probability_cutoff_);
ESP_LOGCONFIG(TAG, " Sliding window size: %d", this->sliding_window_average_size_);
ESP_LOGCONFIG(TAG, " models:");
for (auto &model : this->wake_word_models_) {
model.log_model_config();
}
#ifdef USE_MICRO_WAKE_WORD_VAD
this->vad_model_->log_model_config();
#endif
}
void MicroWakeWord::setup() {
ESP_LOGCONFIG(TAG, "Setting up microWakeWord...");
if (!this->initialize_models()) {
ESP_LOGE(TAG, "Failed to initialize models");
this->mark_failed();
return;
}
ExternalRAMAllocator<int16_t> allocator(ExternalRAMAllocator<int16_t>::ALLOW_FAILURE);
this->input_buffer_ = allocator.allocate(INPUT_BUFFER_SIZE * sizeof(int16_t));
if (this->input_buffer_ == nullptr) {
ESP_LOGW(TAG, "Could not allocate input buffer");
this->mark_failed();
return;
}
this->ring_buffer_ = RingBuffer::create(BUFFER_SIZE * sizeof(int16_t));
if (this->ring_buffer_ == nullptr) {
ESP_LOGW(TAG, "Could not allocate ring buffer");
if (!this->register_streaming_ops_(this->streaming_op_resolver_)) {
this->mark_failed();
return;
}
ESP_LOGCONFIG(TAG, "Micro Wake Word initialized");
this->frontend_config_.window.size_ms = FEATURE_DURATION_MS;
this->frontend_config_.window.step_size_ms = this->features_step_size_;
this->frontend_config_.filterbank.num_channels = PREPROCESSOR_FEATURE_SIZE;
this->frontend_config_.filterbank.lower_band_limit = 125.0;
this->frontend_config_.filterbank.upper_band_limit = 7500.0;
this->frontend_config_.noise_reduction.smoothing_bits = 10;
this->frontend_config_.noise_reduction.even_smoothing = 0.025;
this->frontend_config_.noise_reduction.odd_smoothing = 0.06;
this->frontend_config_.noise_reduction.min_signal_remaining = 0.05;
this->frontend_config_.pcan_gain_control.enable_pcan = 1;
this->frontend_config_.pcan_gain_control.strength = 0.95;
this->frontend_config_.pcan_gain_control.offset = 80.0;
this->frontend_config_.pcan_gain_control.gain_bits = 21;
this->frontend_config_.log_scale.enable_log = 1;
this->frontend_config_.log_scale.scale_shift = 6;
}
int MicroWakeWord::read_microphone_() {
size_t bytes_read = this->microphone_->read(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t));
if (bytes_read == 0) {
return 0;
void MicroWakeWord::add_wake_word_model(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->wake_word_models_.emplace_back(model_start, probability_cutoff, sliding_window_average_size, wake_word,
tensor_arena_size);
}
size_t bytes_free = this->ring_buffer_->free();
if (bytes_free < bytes_read) {
ESP_LOGW(TAG,
"Not enough free bytes in ring buffer to store incoming audio data (free bytes=%d, incoming bytes=%d). "
"Resetting the ring buffer. Wake word detection accuracy will be reduced.",
bytes_free, bytes_read);
this->ring_buffer_->reset();
}
return this->ring_buffer_->write((void *) this->input_buffer_, bytes_read);
#ifdef USE_MICRO_WAKE_WORD_VAD
void MicroWakeWord::add_vad_model(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_size,
size_t tensor_arena_size) {
this->vad_model_ = make_unique<VADModel>(model_start, probability_cutoff, sliding_window_size, tensor_arena_size);
}
#endif
void MicroWakeWord::loop() {
switch (this->state_) {
@ -124,9 +115,12 @@ void MicroWakeWord::loop() {
}
break;
case State::DETECTING_WAKE_WORD:
while (!this->has_enough_samples_()) {
this->read_microphone_();
if (this->detect_wake_word_()) {
ESP_LOGD(TAG, "Wake Word Detected");
}
this->update_model_probabilities_();
if (this->detect_wake_words_()) {
ESP_LOGD(TAG, "Wake Word '%s' Detected", (this->detected_wake_word_).c_str());
this->detected_ = true;
this->set_state_(State::STOP_MICROPHONE);
}
@ -136,13 +130,16 @@ void MicroWakeWord::loop() {
this->microphone_->stop();
this->set_state_(State::STOPPING_MICROPHONE);
this->high_freq_.stop();
this->unload_models_();
this->deallocate_buffers_();
break;
case State::STOPPING_MICROPHONE:
if (this->microphone_->is_stopped()) {
this->set_state_(State::IDLE);
if (this->detected_) {
this->wake_word_detected_trigger_->trigger(this->detected_wake_word_);
this->detected_ = false;
this->wake_word_detected_trigger_->trigger(this->wake_word_);
this->detected_wake_word_ = "";
}
}
break;
@ -150,14 +147,34 @@ void MicroWakeWord::loop() {
}
void MicroWakeWord::start() {
if (!this->is_ready()) {
ESP_LOGW(TAG, "Wake word detection can't start as the component hasn't been setup yet");
return;
}
if (this->is_failed()) {
ESP_LOGW(TAG, "Wake word component is marked as failed. Please check setup logs");
return;
}
if (!this->load_models_() || !this->allocate_buffers_()) {
ESP_LOGE(TAG, "Failed to load the wake word model(s) or allocate buffers");
this->status_set_error();
} else {
this->status_clear_error();
}
if (this->status_has_error()) {
ESP_LOGW(TAG, "Wake word component has an error. Please check logs");
return;
}
if (this->state_ != State::IDLE) {
ESP_LOGW(TAG, "Wake word is already running");
return;
}
this->reset_states_();
this->set_state_(State::START_MICROPHONE);
}
@ -179,289 +196,218 @@ void MicroWakeWord::set_state_(State state) {
this->state_ = state;
}
bool MicroWakeWord::initialize_models() {
ExternalRAMAllocator<uint8_t> arena_allocator(ExternalRAMAllocator<uint8_t>::ALLOW_FAILURE);
ExternalRAMAllocator<int8_t> features_allocator(ExternalRAMAllocator<int8_t>::ALLOW_FAILURE);
size_t MicroWakeWord::read_microphone_() {
size_t bytes_read = this->microphone_->read(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t));
if (bytes_read == 0) {
return 0;
}
size_t bytes_free = this->ring_buffer_->free();
if (bytes_free < bytes_read) {
ESP_LOGW(TAG,
"Not enough free bytes in ring buffer to store incoming audio data (free bytes=%d, incoming bytes=%d). "
"Resetting the ring buffer. Wake word detection accuracy will be reduced.",
bytes_free, bytes_read);
this->ring_buffer_->reset();
}
return this->ring_buffer_->write((void *) this->input_buffer_, bytes_read);
}
bool MicroWakeWord::allocate_buffers_() {
ExternalRAMAllocator<int16_t> audio_samples_allocator(ExternalRAMAllocator<int16_t>::ALLOW_FAILURE);
this->streaming_tensor_arena_ = arena_allocator.allocate(STREAMING_MODEL_ARENA_SIZE);
if (this->streaming_tensor_arena_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the streaming model's tensor arena.");
if (this->input_buffer_ == nullptr) {
this->input_buffer_ = audio_samples_allocator.allocate(INPUT_BUFFER_SIZE * sizeof(int16_t));
if (this->input_buffer_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate input buffer");
return false;
}
this->streaming_var_arena_ = arena_allocator.allocate(STREAMING_MODEL_VARIABLE_ARENA_SIZE);
if (this->streaming_var_arena_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the streaming model variable's tensor arena.");
return false;
}
this->preprocessor_tensor_arena_ = arena_allocator.allocate(PREPROCESSOR_ARENA_SIZE);
if (this->preprocessor_tensor_arena_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the audio preprocessor model's tensor arena.");
return false;
}
this->new_features_data_ = features_allocator.allocate(PREPROCESSOR_FEATURE_SIZE);
if (this->new_features_data_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the audio features buffer.");
return false;
}
this->preprocessor_audio_buffer_ = audio_samples_allocator.allocate(SAMPLE_DURATION_COUNT);
if (this->preprocessor_audio_buffer_ == nullptr) {
this->preprocessor_audio_buffer_ = audio_samples_allocator.allocate(this->new_samples_to_get_());
if (this->preprocessor_audio_buffer_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the audio preprocessor's buffer.");
return false;
}
this->preprocessor_model_ = tflite::GetModel(G_AUDIO_PREPROCESSOR_INT8_TFLITE);
if (this->preprocessor_model_->version() != TFLITE_SCHEMA_VERSION) {
ESP_LOGE(TAG, "Wake word's audio preprocessor model's schema is not supported");
return false;
}
this->streaming_model_ = tflite::GetModel(this->model_start_);
if (this->streaming_model_->version() != TFLITE_SCHEMA_VERSION) {
ESP_LOGE(TAG, "Wake word's streaming model's schema is not supported");
if (this->ring_buffer_ == nullptr) {
this->ring_buffer_ = RingBuffer::create(BUFFER_SIZE * sizeof(int16_t));
if (this->ring_buffer_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate ring buffer");
return false;
}
static tflite::MicroMutableOpResolver<18> preprocessor_op_resolver;
static tflite::MicroMutableOpResolver<17> streaming_op_resolver;
if (!this->register_preprocessor_ops_(preprocessor_op_resolver))
return false;
if (!this->register_streaming_ops_(streaming_op_resolver))
return false;
tflite::MicroAllocator *ma =
tflite::MicroAllocator::Create(this->streaming_var_arena_, STREAMING_MODEL_VARIABLE_ARENA_SIZE);
this->mrv_ = tflite::MicroResourceVariables::Create(ma, 15);
static tflite::MicroInterpreter static_preprocessor_interpreter(
this->preprocessor_model_, preprocessor_op_resolver, this->preprocessor_tensor_arena_, PREPROCESSOR_ARENA_SIZE);
static tflite::MicroInterpreter static_streaming_interpreter(this->streaming_model_, streaming_op_resolver,
this->streaming_tensor_arena_,
STREAMING_MODEL_ARENA_SIZE, this->mrv_);
this->preprocessor_interperter_ = &static_preprocessor_interpreter;
this->streaming_interpreter_ = &static_streaming_interpreter;
// Allocate tensors for each models.
if (this->preprocessor_interperter_->AllocateTensors() != kTfLiteOk) {
ESP_LOGE(TAG, "Failed to allocate tensors for the audio preprocessor");
return false;
}
if (this->streaming_interpreter_->AllocateTensors() != kTfLiteOk) {
ESP_LOGE(TAG, "Failed to allocate tensors for the streaming model");
return false;
}
// Verify input tensor matches expected values
TfLiteTensor *input = this->streaming_interpreter_->input(0);
if ((input->dims->size != 3) || (input->dims->data[0] != 1) || (input->dims->data[0] != 1) ||
(input->dims->data[1] != 1) || (input->dims->data[2] != PREPROCESSOR_FEATURE_SIZE)) {
ESP_LOGE(TAG, "Wake word detection model tensor input dimensions is not 1x1x%u", input->dims->data[2]);
return false;
}
if (input->type != kTfLiteInt8) {
ESP_LOGE(TAG, "Wake word detection model tensor input is not int8.");
return false;
}
// Verify output tensor matches expected values
TfLiteTensor *output = this->streaming_interpreter_->output(0);
if ((output->dims->size != 2) || (output->dims->data[0] != 1) || (output->dims->data[1] != 1)) {
ESP_LOGE(TAG, "Wake word detection model tensor output dimensions is not 1x1.");
}
if (output->type != kTfLiteUInt8) {
ESP_LOGE(TAG, "Wake word detection model tensor input is not uint8.");
return false;
}
this->recent_streaming_probabilities_.resize(this->sliding_window_average_size_, 0.0);
return true;
}
bool MicroWakeWord::update_features_() {
// Retrieve strided audio samples
int16_t *audio_samples = nullptr;
if (!this->stride_audio_samples_(&audio_samples)) {
return false;
}
// Compute the features for the newest audio samples
if (!this->generate_single_feature_(audio_samples, SAMPLE_DURATION_COUNT, this->new_features_data_)) {
return false;
}
return true;
}
float MicroWakeWord::perform_streaming_inference_() {
TfLiteTensor *input = this->streaming_interpreter_->input(0);
void MicroWakeWord::deallocate_buffers_() {
ExternalRAMAllocator<int16_t> audio_samples_allocator(ExternalRAMAllocator<int16_t>::ALLOW_FAILURE);
audio_samples_allocator.deallocate(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t));
this->input_buffer_ = nullptr;
audio_samples_allocator.deallocate(this->preprocessor_audio_buffer_, this->new_samples_to_get_());
this->preprocessor_audio_buffer_ = nullptr;
}
size_t bytes_to_copy = input->bytes;
memcpy((void *) (tflite::GetTensorData<int8_t>(input)), (const void *) (this->new_features_data_), bytes_to_copy);
uint32_t prior_invoke = millis();
TfLiteStatus invoke_status = this->streaming_interpreter_->Invoke();
if (invoke_status != kTfLiteOk) {
ESP_LOGW(TAG, "Streaming Interpreter Invoke failed");
bool MicroWakeWord::load_models_() {
// Setup preprocesor feature generator
if (!FrontendPopulateState(&this->frontend_config_, &this->frontend_state_, AUDIO_SAMPLE_FREQUENCY)) {
ESP_LOGD(TAG, "Failed to populate frontend state");
FrontendFreeStateContents(&this->frontend_state_);
return false;
}
ESP_LOGV(TAG, "Streaming Inference Latency=%" PRIu32 " ms", (millis() - prior_invoke));
TfLiteTensor *output = this->streaming_interpreter_->output(0);
return static_cast<float>(output->data.uint8[0]) / 255.0;
}
bool MicroWakeWord::detect_wake_word_() {
// Preprocess the newest audio samples into features
if (!this->update_features_()) {
// Setup streaming models
for (auto &model : this->wake_word_models_) {
if (!model.load_model(this->streaming_op_resolver_)) {
ESP_LOGE(TAG, "Failed to initialize a wake word model.");
return false;
}
}
#ifdef USE_MICRO_WAKE_WORD_VAD
if (!this->vad_model_->load_model(this->streaming_op_resolver_)) {
ESP_LOGE(TAG, "Failed to initialize VAD model.");
return false;
}
#endif
// Perform inference
float streaming_prob = this->perform_streaming_inference_();
// Add the most recent probability to the sliding window
this->recent_streaming_probabilities_[this->last_n_index_] = streaming_prob;
++this->last_n_index_;
if (this->last_n_index_ == this->sliding_window_average_size_)
this->last_n_index_ = 0;
float sum = 0.0;
for (auto &prob : this->recent_streaming_probabilities_) {
sum += prob;
return true;
}
float sliding_window_average = sum / static_cast<float>(this->sliding_window_average_size_);
void MicroWakeWord::unload_models_() {
FrontendFreeStateContents(&this->frontend_state_);
// Ensure we have enough samples since the last positive detection
for (auto &model : this->wake_word_models_) {
model.unload_model();
}
#ifdef USE_MICRO_WAKE_WORD_VAD
this->vad_model_->unload_model();
#endif
}
void MicroWakeWord::update_model_probabilities_() {
int8_t audio_features[PREPROCESSOR_FEATURE_SIZE];
if (!this->generate_features_for_window_(audio_features)) {
return;
}
// Increase the counter since the last positive detection
this->ignore_windows_ = std::min(this->ignore_windows_ + 1, 0);
for (auto &model : this->wake_word_models_) {
// Perform inference
model.perform_streaming_inference(audio_features);
}
#ifdef USE_MICRO_WAKE_WORD_VAD
this->vad_model_->perform_streaming_inference(audio_features);
#endif
}
bool MicroWakeWord::detect_wake_words_() {
// Verify we have processed samples since the last positive detection
if (this->ignore_windows_ < 0) {
return false;
}
// Detect the wake word if the sliding window average is above the cutoff
if (sliding_window_average > this->probability_cutoff_) {
this->ignore_windows_ = -MIN_SLICES_BEFORE_DETECTION;
for (auto &prob : this->recent_streaming_probabilities_) {
prob = 0;
}
#ifdef USE_MICRO_WAKE_WORD_VAD
bool vad_state = this->vad_model_->determine_detected();
#endif
ESP_LOGD(TAG, "Wake word sliding average probability is %.3f and most recent probability is %.3f",
sliding_window_average, streaming_prob);
for (auto &model : this->wake_word_models_) {
if (model.determine_detected()) {
#ifdef USE_MICRO_WAKE_WORD_VAD
if (vad_state) {
#endif
this->detected_wake_word_ = model.get_wake_word();
return true;
#ifdef USE_MICRO_WAKE_WORD_VAD
} else {
ESP_LOGD(TAG, "Wake word model predicts %s, but VAD model doesn't.", model.get_wake_word().c_str());
}
#endif
}
}
return false;
}
void MicroWakeWord::set_sliding_window_average_size(size_t size) {
this->sliding_window_average_size_ = size;
this->recent_streaming_probabilities_.resize(this->sliding_window_average_size_, 0.0);
bool MicroWakeWord::has_enough_samples_() {
return this->ring_buffer_->available() >=
(this->features_step_size_ * (AUDIO_SAMPLE_FREQUENCY / 1000)) * sizeof(int16_t);
}
bool MicroWakeWord::slice_available_() {
size_t available = this->ring_buffer_->available();
return available > (NEW_SAMPLES_TO_GET * sizeof(int16_t));
}
bool MicroWakeWord::stride_audio_samples_(int16_t **audio_samples) {
if (!this->slice_available_()) {
bool MicroWakeWord::generate_features_for_window_(int8_t features[PREPROCESSOR_FEATURE_SIZE]) {
// Ensure we have enough new audio samples in the ring buffer for a full window
if (!this->has_enough_samples_()) {
return false;
}
// Copy the last 320 bytes (160 samples over 10 ms) from the audio buffer to the start of the audio buffer
memcpy((void *) (this->preprocessor_audio_buffer_), (void *) (this->preprocessor_audio_buffer_ + NEW_SAMPLES_TO_GET),
HISTORY_SAMPLES_TO_KEEP * sizeof(int16_t));
// Copy 640 bytes (320 samples over 20 ms) from the ring buffer into the audio buffer offset 320 bytes (160 samples
// over 10 ms)
size_t bytes_read = this->ring_buffer_->read((void *) (this->preprocessor_audio_buffer_ + HISTORY_SAMPLES_TO_KEEP),
NEW_SAMPLES_TO_GET * sizeof(int16_t), pdMS_TO_TICKS(200));
size_t bytes_read = this->ring_buffer_->read((void *) (this->preprocessor_audio_buffer_),
this->new_samples_to_get_() * sizeof(int16_t), pdMS_TO_TICKS(200));
if (bytes_read == 0) {
ESP_LOGE(TAG, "Could not read data from Ring Buffer");
} else if (bytes_read < NEW_SAMPLES_TO_GET * sizeof(int16_t)) {
} else if (bytes_read < this->new_samples_to_get_() * sizeof(int16_t)) {
ESP_LOGD(TAG, "Partial Read of Data by Model");
ESP_LOGD(TAG, "Could only read %d bytes when required %d bytes ", bytes_read,
(int) (NEW_SAMPLES_TO_GET * sizeof(int16_t)));
(int) (this->new_samples_to_get_() * sizeof(int16_t)));
return false;
}
*audio_samples = this->preprocessor_audio_buffer_;
return true;
}
size_t num_samples_read;
struct FrontendOutput frontend_output = FrontendProcessSamples(
&this->frontend_state_, this->preprocessor_audio_buffer_, this->new_samples_to_get_(), &num_samples_read);
bool MicroWakeWord::generate_single_feature_(const int16_t *audio_data, const int audio_data_size,
int8_t feature_output[PREPROCESSOR_FEATURE_SIZE]) {
TfLiteTensor *input = this->preprocessor_interperter_->input(0);
TfLiteTensor *output = this->preprocessor_interperter_->output(0);
std::copy_n(audio_data, audio_data_size, tflite::GetTensorData<int16_t>(input));
if (this->preprocessor_interperter_->Invoke() != kTfLiteOk) {
ESP_LOGE(TAG, "Failed to preprocess audio for local wake word.");
return false;
for (size_t i = 0; i < frontend_output.size; ++i) {
// These scaling values are set to match the TFLite audio frontend int8 output.
// The feature pipeline outputs 16-bit signed integers in roughly a 0 to 670
// range. In training, these are then arbitrarily divided by 25.6 to get
// float values in the rough range of 0.0 to 26.0. This scaling is performed
// for historical reasons, to match up with the output of other feature
// generators.
// The process is then further complicated when we quantize the model. This
// means we have to scale the 0.0 to 26.0 real values to the -128 to 127
// signed integer numbers.
// All this means that to get matching values from our integer feature
// output into the tensor input, we have to perform:
// input = (((feature / 25.6) / 26.0) * 256) - 128
// To simplify this and perform it in 32-bit integer math, we rearrange to:
// input = (feature * 256) / (25.6 * 26.0) - 128
constexpr int32_t value_scale = 256;
constexpr int32_t value_div = 666; // 666 = 25.6 * 26.0 after rounding
int32_t value = ((frontend_output.values[i] * value_scale) + (value_div / 2)) / value_div;
value -= 128;
if (value < -128) {
value = -128;
}
if (value > 127) {
value = 127;
}
features[i] = value;
}
std::memcpy(feature_output, tflite::GetTensorData<int8_t>(output), PREPROCESSOR_FEATURE_SIZE * sizeof(int8_t));
return true;
}
bool MicroWakeWord::register_preprocessor_ops_(tflite::MicroMutableOpResolver<18> &op_resolver) {
if (op_resolver.AddReshape() != kTfLiteOk)
return false;
if (op_resolver.AddCast() != kTfLiteOk)
return false;
if (op_resolver.AddStridedSlice() != kTfLiteOk)
return false;
if (op_resolver.AddConcatenation() != kTfLiteOk)
return false;
if (op_resolver.AddMul() != kTfLiteOk)
return false;
if (op_resolver.AddAdd() != kTfLiteOk)
return false;
if (op_resolver.AddDiv() != kTfLiteOk)
return false;
if (op_resolver.AddMinimum() != kTfLiteOk)
return false;
if (op_resolver.AddMaximum() != kTfLiteOk)
return false;
if (op_resolver.AddWindow() != kTfLiteOk)
return false;
if (op_resolver.AddFftAutoScale() != kTfLiteOk)
return false;
if (op_resolver.AddRfft() != kTfLiteOk)
return false;
if (op_resolver.AddEnergy() != kTfLiteOk)
return false;
if (op_resolver.AddFilterBank() != kTfLiteOk)
return false;
if (op_resolver.AddFilterBankSquareRoot() != kTfLiteOk)
return false;
if (op_resolver.AddFilterBankSpectralSubtraction() != kTfLiteOk)
return false;
if (op_resolver.AddPCAN() != kTfLiteOk)
return false;
if (op_resolver.AddFilterBankLog() != kTfLiteOk)
return false;
return true;
void MicroWakeWord::reset_states_() {
ESP_LOGD(TAG, "Resetting buffers and probabilities");
this->ring_buffer_->reset();
this->ignore_windows_ = -MIN_SLICES_BEFORE_DETECTION;
for (auto &model : this->wake_word_models_) {
model.reset_probabilities();
}
#ifdef USE_MICRO_WAKE_WORD_VAD
this->vad_model_->reset_probabilities();
#endif
}
bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<17> &op_resolver) {
bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<20> &op_resolver) {
if (op_resolver.AddCallOnce() != kTfLiteOk)
return false;
if (op_resolver.AddVarHandle() != kTfLiteOk)
@ -496,6 +442,12 @@ bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<17> &
return false;
if (op_resolver.AddMaxPool2D() != kTfLiteOk)
return false;
if (op_resolver.AddPad() != kTfLiteOk)
return false;
if (op_resolver.AddPack() != kTfLiteOk)
return false;
if (op_resolver.AddSplitV() != kTfLiteOk)
return false;
return true;
}
@ -504,5 +456,3 @@ bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<17> &
} // namespace esphome
#endif // USE_ESP_IDF
#endif // CLANG_TIDY

190
esphome/components/micro_wake_word/micro_wake_word.h
View File

@ -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();
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_features_step_size(uint8_t step_size) { this->features_step_size_ = step_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 set_wake_word(const std::string &wake_word) { this->wake_word_ = wake_word; }
void add_wake_word_model(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_average_size,
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};
const tflite::Model *streaming_model_{nullptr};
tflite::MicroInterpreter *streaming_interpreter_{nullptr};
tflite::MicroInterpreter *preprocessor_interperter_{nullptr};
#ifdef USE_MICRO_WAKE_WORD_VAD
std::unique_ptr<VADModel> vad_model_;
#endif
std::vector<float> recent_streaming_probabilities_;
size_t last_n_index_{0};
tflite::MicroMutableOpResolver<20> streaming_op_resolver_;
float probability_cutoff_{0.5};
size_t sliding_window_average_size_{10};
// Audio frontend handles generating spectrogram features
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
// feature slices before accepting a positive detection again
// When the wake word detection first starts, we ignore this many audio
// feature slices before accepting a positive detection
int16_t ignore_windows_{-MIN_SLICES_BEFORE_DETECTION};
uint8_t *streaming_var_arena_{nullptr};
uint8_t *streaming_tensor_arena_{nullptr};
uint8_t *preprocessor_tensor_arena_{nullptr};
int8_t *new_features_data_{nullptr};
uint8_t features_step_size_;
tflite::MicroResourceVariables *mrv_{nullptr};
// Stores audio fed into feature generator preprocessor
int16_t *preprocessor_audio_buffer_;
// Stores audio read from the microphone before being added to the ring buffer.
int16_t *input_buffer_{nullptr};
// Stores audio to be fed into the audio frontend for generating features.
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.
* @param ring_Buffer Ring buffer containing raw audio samples
* @return True if the wake word is detected, false otherwise
* It then loops through and performs inference with each of the loaded models.
*/
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
bool slice_available_();
/** Shifts previous feature slices over by one and generates a new slice of features
/** Checks every model's recent probabilities to determine if the wake word has been predicted
*
* @param ring_buffer ring buffer containing raw audio samples
* @return True if a new slice of features was generated, false otherwise
* Verifies the models have processed enough new samples for accurate predictions.
* 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
* @param audio_data Pointer to array with the audio samples
* @param audio_data_size The number of samples to use as input to the preprocessor model
* @param feature_output Array that will store the features
* Reads samples from the ring buffer and feeds them into the preprocessor frontend.
* Adapted from TFLite microspeech frontend.
* @param features int8_t array to store the audio features
* @return True if successful, false otherwise.
*/
bool generate_single_feature_(const int16_t *audio_data, int audio_data_size,
int8_t feature_output[PREPROCESSOR_FEATURE_SIZE]);
bool generate_features_for_window_(int8_t features[PREPROCESSOR_FEATURE_SIZE]);
/** Performs inference over the most recent feature slice with the streaming model
*
* @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 Resets the ring buffer, ignore_windows_, and sliding window probabilities
void reset_states_();
/// @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

20
esphome/components/micro_wake_word/preprocessor_settings.h Normal file
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@ -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

189
esphome/components/micro_wake_word/streaming_model.cpp Normal file
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@ -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

84
esphome/components/micro_wake_word/streaming_model.h Normal file
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@ -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

1
esphome/core/defines.h
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@ -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

1
platformio.ini
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@ -143,6 +143,7 @@ framework = espidf
lib_deps =
${common:idf.lib_deps}
droscy/esp_wireguard@0.4.2 ; wireguard
kahrendt/ESPMicroSpeechFeatures@1.0.0 ; micro_wake_word
build_flags =
${common:idf.build_flags}
-Wno-nonnull-compare

6
tests/components/micro_wake_word/common.yaml
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@ -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

1
tests/components/micro_wake_word/test.esp32-idf.yaml Normal file
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@ -0,0 +1 @@
<<: !include common.yaml