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Code Issues Projects Releases Packages Wiki Activity Actions 11

Add micro_wake_word component (#6136)

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Co-authored-by: Jesse Hills <3060199+jesserockz@users.noreply.github.com>
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kahrendt 2024-02-12 15:38:50 -05:00 committed by GitHub
parent 061d5b4979
commit e521662342
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1
CODEOWNERS
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@ -199,6 +199,7 @@ esphome/components/mcp9808/* @k7hpn
esphome/components/md5/* @esphome/core
esphome/components/mdns/* @esphome/core
esphome/components/media_player/* @jesserockz
esphome/components/micro_wake_word/* @jesserockz @kahrendt
esphome/components/micronova/* @jorre05
esphome/components/microphone/* @jesserockz
esphome/components/mics_4514/* @jesserockz

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esphome/components/micro_wake_word/__init__.py Normal file
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import logging
import json
import hashlib
from urllib.parse import urljoin
from pathlib import Path
import requests
import esphome.config_validation as cv
import esphome.codegen as cg
from esphome.core import CORE, HexInt, EsphomeError
from esphome.components import esp32, microphone
from esphome import automation, git, external_files
from esphome.automation import register_action, register_condition
from esphome.const import (
__version__,
CONF_ID,
CONF_MICROPHONE,
CONF_MODEL,
CONF_URL,
CONF_FILE,
CONF_PATH,
CONF_REF,
CONF_REFRESH,
CONF_TYPE,
CONF_USERNAME,
CONF_PASSWORD,
CONF_RAW_DATA_ID,
TYPE_GIT,
TYPE_LOCAL,
)
_LOGGER = logging.getLogger(__name__)
CODEOWNERS = ["@kahrendt", "@jesserockz"]
DEPENDENCIES = ["microphone"]
DOMAIN = "micro_wake_word"
CONF_PROBABILITY_CUTOFF = "probability_cutoff"
CONF_SLIDING_WINDOW_AVERAGE_SIZE = "sliding_window_average_size"
CONF_ON_WAKE_WORD_DETECTED = "on_wake_word_detected"
TYPE_HTTP = "http"
micro_wake_word_ns = cg.esphome_ns.namespace("micro_wake_word")
MicroWakeWord = micro_wake_word_ns.class_("MicroWakeWord", cg.Component)
StartAction = micro_wake_word_ns.class_("StartAction", automation.Action)
StopAction = micro_wake_word_ns.class_("StopAction", automation.Action)
IsRunningCondition = micro_wake_word_ns.class_(
"IsRunningCondition", automation.Condition
)
def _validate_json_filename(value):
value = cv.string(value)
if not value.endswith(".json"):
raise cv.Invalid("Manifest filename must end with .json")
return value
def _process_git_source(config):
repo_dir, _ = git.clone_or_update(
url=config[CONF_URL],
ref=config.get(CONF_REF),
refresh=config[CONF_REFRESH],
domain=DOMAIN,
username=config.get(CONF_USERNAME),
password=config.get(CONF_PASSWORD),
)
if not (repo_dir / config[CONF_FILE]).exists():
raise cv.Invalid("File does not exist in repository")
return config
CV_GIT_SCHEMA = cv.GIT_SCHEMA
if isinstance(CV_GIT_SCHEMA, dict):
CV_GIT_SCHEMA = cv.Schema(CV_GIT_SCHEMA)
GIT_SCHEMA = cv.All(
CV_GIT_SCHEMA.extend(
{
cv.Required(CONF_FILE): _validate_json_filename,
cv.Optional(CONF_REFRESH, default="1d"): cv.All(
cv.string, cv.source_refresh
),
}
),
_process_git_source,
)
KEY_WAKE_WORD = "wake_word"
KEY_AUTHOR = "author"
KEY_WEBSITE = "website"
KEY_VERSION = "version"
KEY_MICRO = "micro"
MANIFEST_SCHEMA_V1 = cv.Schema(
{
cv.Required(CONF_TYPE): "micro",
cv.Required(KEY_WAKE_WORD): cv.string,
cv.Required(KEY_AUTHOR): cv.string,
cv.Required(KEY_WEBSITE): cv.url,
cv.Required(KEY_VERSION): cv.All(cv.int_, 1),
cv.Required(CONF_MODEL): cv.string,
cv.Required(KEY_MICRO): cv.Schema(
{
cv.Required(CONF_PROBABILITY_CUTOFF): cv.float_,
cv.Required(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.positive_int,
}
),
}
)
def _compute_local_file_path(config: dict) -> Path:
url = config[CONF_URL]
h = hashlib.new("sha256")
h.update(url.encode())
key = h.hexdigest()[:8]
base_dir = external_files.compute_local_file_dir(DOMAIN)
return base_dir / key
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")
return path.read_bytes()
try:
req = requests.get(
url,
timeout=external_files.NETWORK_TIMEOUT,
headers={"User-agent": f"ESPHome/{__version__} (https://esphome.io)"},
)
req.raise_for_status()
except requests.exceptions.RequestException as e:
raise cv.Invalid(f"Could not download file from {url}: {e}") from e
path.parent.mkdir(parents=True, exist_ok=True)
path.write_bytes(req.content)
return req.content
def _process_http_source(config):
url = config[CONF_URL]
path = _compute_local_file_path(config)
json_path = path / "manifest.json"
json_contents = _download_file(url, json_path)
manifest_data = json.loads(json_contents)
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)
model_path = path / model
_download_file(str(model_url), model_path)
return config
HTTP_SCHEMA = cv.All(
{
cv.Required(CONF_URL): cv.url,
},
_process_http_source,
)
LOCAL_SCHEMA = cv.Schema(
{
cv.Required(CONF_PATH): cv.All(_validate_json_filename, cv.file_),
}
)
def _validate_source_model_name(value):
if not isinstance(value, str):
raise cv.Invalid("Model name must be a string")
if value.endswith(".json"):
raise cv.Invalid("Model name must not end with .json")
return MODEL_SOURCE_SCHEMA(
{
CONF_TYPE: TYPE_HTTP,
CONF_URL: f"https://github.com/esphome/micro-wake-word-models/raw/main/models/{value}.json",
}
)
def _validate_source_shorthand(value):
if not isinstance(value, str):
raise cv.Invalid("Shorthand only for strings")
try: # Test for model name
return _validate_source_model_name(value)
except cv.Invalid:
pass
try: # Test for local path
return MODEL_SOURCE_SCHEMA({CONF_TYPE: TYPE_LOCAL, CONF_PATH: value})
except cv.Invalid:
pass
try: # Test for http url
return MODEL_SOURCE_SCHEMA({CONF_TYPE: TYPE_HTTP, CONF_URL: value})
except cv.Invalid:
pass
git_file = git.GitFile.from_shorthand(value)
conf = {
CONF_TYPE: TYPE_GIT,
CONF_URL: git_file.git_url,
CONF_FILE: git_file.filename,
}
if git_file.ref:
conf[CONF_REF] = git_file.ref
try:
return MODEL_SOURCE_SCHEMA(conf)
except cv.Invalid as e:
raise cv.Invalid(
f"Could not find file '{git_file.filename}' in the repository. Please make sure it exists."
) from e
MODEL_SOURCE_SCHEMA = cv.Any(
_validate_source_shorthand,
cv.typed_schema(
{
TYPE_GIT: GIT_SCHEMA,
TYPE_LOCAL: LOCAL_SCHEMA,
TYPE_HTTP: HTTP_SCHEMA,
}
),
msg="Not a valid model name, local path, http(s) url, or github shorthand",
)
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.float_,
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.GenerateID(CONF_RAW_DATA_ID): cv.declare_id(cg.uint8),
}
).extend(cv.COMPONENT_SCHEMA),
cv.only_with_esp_idf,
)
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
model_path = urljoin(str(manifest_path), manifest[CONF_MODEL])
with open(model_path, "rb") as f:
model = f.read()
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",
)
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)}"
base_dir = Path(CORE.data_dir) / DOMAIN
h = hashlib.new("sha256")
h.update(key.encode())
file: Path = base_dir / h.hexdigest()[:8] / model_config[CONF_FILE]
elif model_config[CONF_TYPE] == TYPE_LOCAL:
file = model_config[CONF_PATH]
elif model_config[CONF_TYPE] == TYPE_HTTP:
file = _compute_local_file_path(model_config) / "manifest.json"
manifest, data = _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(
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],
)
cg.add(var.set_sliding_window_average_size(sliding_window_average_size))
cg.add(var.set_wake_word(manifest[KEY_WAKE_WORD]))
MICRO_WAKE_WORD_ACTION_SCHEMA = cv.Schema({cv.GenerateID(): cv.use_id(MicroWakeWord)})
@register_action("micro_wake_word.start", StartAction, MICRO_WAKE_WORD_ACTION_SCHEMA)
@register_action("micro_wake_word.stop", StopAction, MICRO_WAKE_WORD_ACTION_SCHEMA)
@register_condition(
"micro_wake_word.is_running", IsRunningCondition, MICRO_WAKE_WORD_ACTION_SCHEMA
)
async def micro_wake_word_action_to_code(config, action_id, template_arg, args):
var = cg.new_Pvariable(action_id, template_arg)
await cg.register_parented(var, config[CONF_ID])
return var

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esphome/components/micro_wake_word/audio_preprocessor_int8_model_data.h Normal file
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@ -0,0 +1,493 @@
#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,
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0x00, 0x02, 0x6c, 0xff, 0xff, 0xff, 0x2d, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2d, 0x0c, 0x00, 0x10, 0x00, 0x0f,
0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x35, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x35, 0x7c, 0xff, 0xff, 0xff, 0x20, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x20, 0x0c, 0x00, 0x00, 0x00, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x45, 0x6e, 0x65, 0x72, 0x67, 0x79, 0x00, 0x00,
0x00, 0x00, 0xa0, 0xff, 0xff, 0xff, 0x20, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x0a,
0x00, 0x00, 0x00, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x52, 0x66, 0x66, 0x74, 0x00, 0x00, 0xc0, 0xff, 0xff, 0xff,
0x20, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x12, 0x00, 0x00, 0x00, 0x53, 0x69, 0x67,
0x6e, 0x61, 0x6c, 0x46, 0x66, 0x74, 0x41, 0x75, 0x74, 0x6f, 0x53, 0x63, 0x61, 0x6c, 0x65, 0x00, 0x00, 0x0c, 0x00,
0x0c, 0x00, 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x16, 0x0c, 0x00, 0x10, 0x00, 0x0f, 0x00, 0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x20, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x20, 0x0c, 0x00, 0x00, 0x00, 0x53, 0x69, 0x67,
0x6e, 0x61, 0x6c, 0x57, 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x00, 0x00, 0x00, 0x00};
} // namespace micro_wake_word
} // namespace esphome
#endif // USE_ESP_IDF

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#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
#ifdef USE_ESP_IDF
#include "esphome/core/hal.h"
#include "esphome/core/helpers.h"
#include "esphome/core/log.h"
#include "audio_preprocessor_int8_model_data.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 <cmath>
namespace esphome {
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_SIZE = SAMPLE_RATE_HZ / 1000 * BUFFER_LENGTH;
static const size_t INPUT_BUFFER_SIZE = 32 * SAMPLE_RATE_HZ / 1000; // 32ms * 16kHz / 1000ms
float MicroWakeWord::get_setup_priority() const { return setup_priority::AFTER_CONNECTION; }
static const LogString *micro_wake_word_state_to_string(State state) {
switch (state) {
case State::IDLE:
return LOG_STR("IDLE");
case State::START_MICROPHONE:
return LOG_STR("START_MICROPHONE");
case State::STARTING_MICROPHONE:
return LOG_STR("STARTING_MICROPHONE");
case State::DETECTING_WAKE_WORD:
return LOG_STR("DETECTING_WAKE_WORD");
case State::STOP_MICROPHONE:
return LOG_STR("STOP_MICROPHONE");
case State::STOPPING_MICROPHONE:
return LOG_STR("STOPPING_MICROPHONE");
default:
return LOG_STR("UNKNOWN");
}
}
void MicroWakeWord::setup() {
ESP_LOGCONFIG(TAG, "Setting up Micro Wake Word...");
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(NEW_SAMPLES_TO_GET);
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");
this->mark_failed();
return;
}
ESP_LOGCONFIG(TAG, "Micro Wake Word initialized");
}
int MicroWakeWord::read_microphone_() {
size_t bytes_read = this->microphone_->read(this->input_buffer_, NEW_SAMPLES_TO_GET * sizeof(int16_t));
if (bytes_read == 0) {
return 0;
}
size_t bytes_written = this->ring_buffer_->write((void *) this->input_buffer_, bytes_read);
if (bytes_written != bytes_read) {
ESP_LOGW(TAG, "Failed to write some data to ring buffer (written=%d, expected=%d)", bytes_written, bytes_read);
}
return bytes_written;
}
void MicroWakeWord::loop() {
switch (this->state_) {
case State::IDLE:
break;
case State::START_MICROPHONE:
ESP_LOGD(TAG, "Starting Microphone");
this->microphone_->start();
this->set_state_(State::STARTING_MICROPHONE);
this->high_freq_.start();
break;
case State::STARTING_MICROPHONE:
if (this->microphone_->is_running()) {
this->set_state_(State::DETECTING_WAKE_WORD);
}
break;
case State::DETECTING_WAKE_WORD:
this->read_microphone_();
if (this->detect_wake_word_()) {
ESP_LOGD(TAG, "Wake Word Detected");
this->detected_ = true;
this->set_state_(State::STOP_MICROPHONE);
}
break;
case State::STOP_MICROPHONE:
ESP_LOGD(TAG, "Stopping Microphone");
this->microphone_->stop();
this->set_state_(State::STOPPING_MICROPHONE);
this->high_freq_.stop();
break;
case State::STOPPING_MICROPHONE:
if (this->microphone_->is_stopped()) {
this->set_state_(State::IDLE);
if (this->detected_) {
this->detected_ = false;
this->wake_word_detected_trigger_->trigger("");
}
}
break;
}
}
void MicroWakeWord::start() {
if (this->is_failed()) {
ESP_LOGW(TAG, "Wake word component is marked as failed. Please check setup logs");
return;
}
if (this->state_ != State::IDLE) {
ESP_LOGW(TAG, "Wake word is already running");
return;
}
this->set_state_(State::START_MICROPHONE);
}
void MicroWakeWord::stop() {
if (this->state_ == State::IDLE) {
ESP_LOGW(TAG, "Wake word is already stopped");
return;
}
if (this->state_ == State::STOPPING_MICROPHONE) {
ESP_LOGW(TAG, "Wake word is already stopping");
return;
}
this->set_state_(State::STOP_MICROPHONE);
}
void MicroWakeWord::set_state_(State state) {
ESP_LOGD(TAG, "State changed from %s to %s", LOG_STR_ARG(micro_wake_word_state_to_string(this->state_)),
LOG_STR_ARG(micro_wake_word_state_to_string(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);
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.");
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) {
ESP_LOGE(TAG, "Could not allocate the audio preprocessor's buffer.");
return false;
}
this->preprocessor_stride_buffer_ = audio_samples_allocator.allocate(HISTORY_SAMPLES_TO_KEEP);
if (this->preprocessor_stride_buffer_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the audio preprocessor's stride 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");
return false;
}
static tflite::MicroMutableOpResolver<18> preprocessor_op_resolver;
static tflite::MicroMutableOpResolver<14> 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_() {
// Verify we have enough samples for a feature slice
if (!this->slice_available_()) {
return false;
}
// 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);
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");
return false;
}
ESP_LOGV(TAG, "Streaming Inference Latency=%u 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_()) {
return false;
}
// Perform inference
uint32_t streaming_size = micros();
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;
}
float sliding_window_average = sum / static_cast<float>(this->sliding_window_average_size_);
// Ensure we have enough samples since the last positive detection
this->ignore_windows_ = std::min(this->ignore_windows_ + 1, 0);
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;
}
return true;
}
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::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) {
// Copy 320 bytes (160 samples over 10 ms) into preprocessor_audio_buffer_ from history in
// preprocessor_stride_buffer_
memcpy((void *) (this->preprocessor_audio_buffer_), (void *) (this->preprocessor_stride_buffer_),
HISTORY_SAMPLES_TO_KEEP * sizeof(int16_t));
if (this->ring_buffer_->available() < NEW_SAMPLES_TO_GET * sizeof(int16_t)) {
ESP_LOGD(TAG, "Audio Buffer not full enough");
return false;
}
// Copy 640 bytes (320 samples over 20 ms) from the ring buffer
// The first 320 bytes (160 samples over 10 ms) will be from history
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));
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)) {
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)));
return false;
}
// Copy the last 320 bytes (160 samples over 10 ms) from the audio buffer into history stride buffer for the next
// iteration
memcpy((void *) (this->preprocessor_stride_buffer_), (void *) (this->preprocessor_audio_buffer_ + NEW_SAMPLES_TO_GET),
HISTORY_SAMPLES_TO_KEEP * sizeof(int16_t));
*audio_samples = this->preprocessor_audio_buffer_;
return true;
}
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;
}
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;
}
bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<14> &op_resolver) {
if (op_resolver.AddCallOnce() != kTfLiteOk)
return false;
if (op_resolver.AddVarHandle() != kTfLiteOk)
return false;
if (op_resolver.AddReshape() != kTfLiteOk)
return false;
if (op_resolver.AddReadVariable() != kTfLiteOk)
return false;
if (op_resolver.AddStridedSlice() != kTfLiteOk)
return false;
if (op_resolver.AddConcatenation() != kTfLiteOk)
return false;
if (op_resolver.AddAssignVariable() != kTfLiteOk)
return false;
if (op_resolver.AddConv2D() != kTfLiteOk)
return false;
if (op_resolver.AddMul() != kTfLiteOk)
return false;
if (op_resolver.AddAdd() != kTfLiteOk)
return false;
if (op_resolver.AddMean() != kTfLiteOk)
return false;
if (op_resolver.AddFullyConnected() != kTfLiteOk)
return false;
if (op_resolver.AddLogistic() != kTfLiteOk)
return false;
if (op_resolver.AddQuantize() != kTfLiteOk)
return false;
return true;
}
} // namespace micro_wake_word
} // namespace esphome
#endif // USE_ESP_IDF
#endif // CLANG_TIDY

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#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 "esphome/core/automation.h"
#include "esphome/core/component.h"
#include "esphome/core/ring_buffer.h"
#include "esphome/components/microphone/microphone.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 {
// 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,
STARTING_MICROPHONE,
DETECTING_WAKE_WORD,
STOP_MICROPHONE,
STOPPING_MICROPHONE,
};
class MicroWakeWord : public Component {
public:
void setup() override;
void loop() override;
float get_setup_priority() const override;
void start();
void stop();
bool is_running() const { return this->state_ != State::IDLE; }
bool initialize_models();
// 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 set_wake_word(const std::string &wake_word) { this->wake_word_ = wake_word; }
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};
HighFrequencyLoopRequester high_freq_;
std::unique_ptr<RingBuffer> ring_buffer_;
int16_t *input_buffer_;
const tflite::Model *preprocessor_model_{nullptr};
const tflite::Model *streaming_model_{nullptr};
tflite::MicroInterpreter *streaming_interpreter_{nullptr};
tflite::MicroInterpreter *preprocessor_interperter_{nullptr};
std::vector<float> recent_streaming_probabilities_;
size_t last_n_index_{0};
float probability_cutoff_{0.5};
size_t sliding_window_average_size_{10};
// 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
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};
tflite::MicroResourceVariables *mrv_{nullptr};
// Stores audio fed into feature generator preprocessor
int16_t *preprocessor_audio_buffer_;
int16_t *preprocessor_stride_buffer_;
bool detected_{false};
/** Detects if wake word has been said
*
* 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
*/
bool detect_wake_word_();
/// @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
*
* @param ring_buffer ring buffer containing raw audio samples
* @return True if a new slice of features was generated, false otherwise
*/
bool update_features_();
/** Generates features from 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
* @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]);
/** 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 Returns true if successfully registered the streaming model's TensorFlow operations
bool register_streaming_ops_(tflite::MicroMutableOpResolver<14> &op_resolver);
};
template<typename... Ts> class StartAction : public Action<Ts...>, public Parented<MicroWakeWord> {
public:
void play(Ts... x) override { this->parent_->start(); }
};
template<typename... Ts> class StopAction : public Action<Ts...>, public Parented<MicroWakeWord> {
public:
void play(Ts... x) override { this->parent_->stop(); }
};
template<typename... Ts> class IsRunningCondition : public Condition<Ts...>, public Parented<MicroWakeWord> {
public:
bool check(Ts... x) override { return this->parent_->is_running(); }
};
} // namespace micro_wake_word
} // namespace esphome
#endif // USE_ESP_IDF
#endif // CLANG_TIDY

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tests/components/micro_wake_word/test.esp32-s3-idf.yaml Normal file
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i2s_audio:
i2s_lrclk_pin: GPIO18
i2s_bclk_pin: GPIO19
microphone:
- platform: i2s_audio
id: echo_microphone
i2s_din_pin: GPIO17
adc_type: external
pdm: true
micro_wake_word:
model: hey_jarvis
on_wake_word_detected:
- logger.log: "Wake word detected"