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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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11 changed files with 822 additions and 949 deletions
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262
esphome/components/micro_wake_word/__init__.py
View File

@ -9,7 +9,7 @@ import requests
import esphome.config_validation as cv import esphome.config_validation as cv
import esphome.codegen as cg 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.components import esp32, microphone
from esphome import automation, git, external_files from esphome import automation, git, external_files
@ -41,9 +41,15 @@ CODEOWNERS = ["@kahrendt", "@jesserockz"]
DEPENDENCIES = ["microphone"] DEPENDENCIES = ["microphone"]
DOMAIN = "micro_wake_word" 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_PROBABILITY_CUTOFF = "probability_cutoff"
CONF_SLIDING_WINDOW_AVERAGE_SIZE = "sliding_window_average_size" 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" TYPE_HTTP = "http"
@ -98,12 +104,14 @@ GIT_SCHEMA = cv.All(
_process_git_source, _process_git_source,
) )
KEY_WAKE_WORD = "wake_word"
KEY_AUTHOR = "author" KEY_AUTHOR = "author"
KEY_WEBSITE = "website"
KEY_VERSION = "version"
KEY_MICRO = "micro" KEY_MICRO = "micro"
KEY_MINIMUM_ESPHOME_VERSION = "minimum_esphome_version" 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( 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: def _compute_local_file_path(config: dict) -> Path:
url = config[CONF_URL] url = config[CONF_URL]
@ -135,6 +166,24 @@ def _compute_local_file_path(config: dict) -> Path:
return base_dir / key 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: def _download_file(url: str, path: Path) -> bytes:
if not external_files.has_remote_file_changed(url, path): if not external_files.has_remote_file_changed(url, path):
_LOGGER.debug("Remote file has not changed, skipping download") _LOGGER.debug("Remote file has not changed, skipping download")
@ -155,6 +204,24 @@ def _download_file(url: str, path: Path) -> bytes:
return req.content 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): def _process_http_source(config):
url = config[CONF_URL] url = config[CONF_URL]
path = _compute_local_file_path(config) path = _compute_local_file_path(config)
@ -167,11 +234,6 @@ def _process_http_source(config):
if not isinstance(manifest_data, dict): if not isinstance(manifest_data, dict):
raise cv.Invalid("Manifest file must contain a JSON object") 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 = manifest_data[CONF_MODEL]
model_url = urljoin(url, model) model_url = urljoin(url, model)
@ -206,7 +268,7 @@ def _validate_source_model_name(value):
return MODEL_SOURCE_SCHEMA( return MODEL_SOURCE_SCHEMA(
{ {
CONF_TYPE: TYPE_HTTP, 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", 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( CONFIG_SCHEMA = cv.All(
cv.Schema( cv.Schema(
{ {
cv.GenerateID(): cv.declare_id(MicroWakeWord), cv.GenerateID(): cv.declare_id(MicroWakeWord),
cv.GenerateID(CONF_MICROPHONE): cv.use_id(microphone.Microphone), cv.GenerateID(CONF_MICROPHONE): cv.use_id(microphone.Microphone),
cv.Optional(CONF_PROBABILITY_CUTOFF): cv.percentage, cv.Required(CONF_MODELS): cv.ensure_list(MODEL_SCHEMA),
cv.Optional(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.positive_int,
cv.Optional(CONF_ON_WAKE_WORD_DETECTED): automation.validate_automation( cv.Optional(CONF_ON_WAKE_WORD_DETECTED): automation.validate_automation(
single=True single=True
), ),
cv.Required(CONF_MODEL): MODEL_SOURCE_SCHEMA, cv.Optional(CONF_VAD): _maybe_empty_vad_schema,
cv.GenerateID(CONF_RAW_DATA_ID): cv.declare_id(cg.uint8), cv.Optional(CONF_MODEL): cv.invalid(
f"The {CONF_MODEL} parameter has moved to be a list element under the {CONF_MODELS} parameter."
),
cv.Optional(CONF_PROBABILITY_CUTOFF): cv.invalid(
f"The {CONF_PROBABILITY_CUTOFF} parameter has moved to be a list element under the {CONF_MODELS} parameter."
),
cv.Optional(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.invalid(
f"The {CONF_SLIDING_WINDOW_AVERAGE_SIZE} parameter has been renamed to {CONF_SLIDING_WINDOW_SIZE} and moved to be a list element under the {CONF_MODELS} parameter."
),
} }
).extend(cv.COMPONENT_SCHEMA), ).extend(cv.COMPONENT_SCHEMA),
cv.only_with_esp_idf, 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: with open(manifest_path, encoding="utf-8") as f:
manifest = json.load(f) manifest = json.load(f)
try: _validate_manifest_version(manifest)
MANIFEST_SCHEMA_V1(manifest)
except cv.Invalid as e:
raise EsphomeError(f"Invalid manifest file: {e}") from e
model_path = manifest_path.parent / manifest[CONF_MODEL] model_path = manifest_path.parent / manifest[CONF_MODEL]
with open(model_path, "rb") as f: with open(model_path, "rb") as f:
model = f.read() model = f.read()
if manifest.get(KEY_VERSION) == 1:
manifest = _convert_manifest_v1_to_v2(manifest)
return manifest, model return manifest, model
async def to_code(config): def _model_config_to_manifest_data(model_config):
var = cg.new_Pvariable(config[CONF_ID])
await cg.register_component(var, config)
mic = await cg.get_variable(config[CONF_MICROPHONE])
cg.add(var.set_microphone(mic))
if on_wake_word_detection_config := config.get(CONF_ON_WAKE_WORD_DETECTED):
await automation.build_automation(
var.get_wake_word_detected_trigger(),
[(cg.std_string, "wake_word")],
on_wake_word_detection_config,
)
esp32.add_idf_component(
name="esp-tflite-micro",
repo="https://github.com/espressif/esp-tflite-micro",
ref="v1.3.1",
)
cg.add_build_flag("-DTF_LITE_STATIC_MEMORY")
cg.add_build_flag("-DTF_LITE_DISABLE_X86_NEON")
cg.add_build_flag("-DESP_NN")
model_config = config.get(CONF_MODEL)
data = []
if model_config[CONF_TYPE] == TYPE_GIT: if model_config[CONF_TYPE] == TYPE_GIT:
# compute path to model file # compute path to model file
key = f"{model_config[CONF_URL]}@{model_config.get(CONF_REF)}" key = f"{model_config[CONF_URL]}@{model_config.get(CONF_REF)}"
@ -338,23 +412,95 @@ async def to_code(config):
else: else:
raise ValueError("Unsupported config type: {model_config[CONF_TYPE]}") raise ValueError("Unsupported config type: {model_config[CONF_TYPE]}")
manifest, data = _load_model_data(file) return _load_model_data(file)
rhs = [HexInt(x) for x in data]
prog_arr = cg.progmem_array(config[CONF_RAW_DATA_ID], rhs)
cg.add(var.set_model_start(prog_arr))
probability_cutoff = config.get( def _feature_step_size_validate(config):
CONF_PROBABILITY_CUTOFF, manifest[KEY_MICRO][CONF_PROBABILITY_CUTOFF] features_step_size = None
for model_parameters in config[CONF_MODELS]:
model_config = model_parameters.get(CONF_MODEL)
manifest, _ = _model_config_to_manifest_data(model_config)
model_step_size = manifest[KEY_MICRO][CONF_FEATURE_STEP_SIZE]
if features_step_size is None:
features_step_size = model_step_size
elif features_step_size != model_step_size:
raise cv.Invalid("Cannot load models with different features step sizes.")
FINAL_VALIDATE_SCHEMA = _feature_step_size_validate
async def to_code(config):
var = cg.new_Pvariable(config[CONF_ID])
await cg.register_component(var, config)
mic = await cg.get_variable(config[CONF_MICROPHONE])
cg.add(var.set_microphone(mic))
esp32.add_idf_component(
name="esp-tflite-micro",
repo="https://github.com/espressif/esp-tflite-micro",
ref="v1.3.1",
) )
cg.add(var.set_probability_cutoff(probability_cutoff))
sliding_window_average_size = config.get(
CONF_SLIDING_WINDOW_AVERAGE_SIZE,
manifest[KEY_MICRO][CONF_SLIDING_WINDOW_AVERAGE_SIZE],
)
cg.add(var.set_sliding_window_average_size(sliding_window_average_size))
cg.add(var.set_wake_word(manifest[KEY_WAKE_WORD])) cg.add_build_flag("-DTF_LITE_STATIC_MEMORY")
cg.add_build_flag("-DTF_LITE_DISABLE_X86_NEON")
cg.add_build_flag("-DESP_NN")
if on_wake_word_detection_config := config.get(CONF_ON_WAKE_WORD_DETECTED):
await automation.build_automation(
var.get_wake_word_detected_trigger(),
[(cg.std_string, "wake_word")],
on_wake_word_detection_config,
)
if vad_model := config.get(CONF_VAD):
cg.add_define("USE_MICRO_WAKE_WORD_VAD")
# Use the general model loading code for the VAD codegen
config[CONF_MODELS].append(vad_model)
for model_parameters in config[CONF_MODELS]:
model_config = model_parameters.get(CONF_MODEL)
data = []
manifest, data = _model_config_to_manifest_data(model_config)
rhs = [HexInt(x) for x in data]
prog_arr = cg.progmem_array(model_parameters[CONF_RAW_DATA_ID], rhs)
probability_cutoff = model_parameters.get(
CONF_PROBABILITY_CUTOFF, manifest[KEY_MICRO][CONF_PROBABILITY_CUTOFF]
)
sliding_window_size = model_parameters.get(
CONF_SLIDING_WINDOW_SIZE,
manifest[KEY_MICRO][CONF_SLIDING_WINDOW_SIZE],
)
if manifest[KEY_WAKE_WORD] == "vad":
cg.add(
var.add_vad_model(
prog_arr,
probability_cutoff,
sliding_window_size,
manifest[KEY_MICRO][CONF_TENSOR_ARENA_SIZE],
)
)
else:
cg.add(
var.add_wake_word_model(
prog_arr,
probability_cutoff,
sliding_window_size,
manifest[KEY_WAKE_WORD],
manifest[KEY_MICRO][CONF_TENSOR_ARENA_SIZE],
)
)
cg.add(var.set_features_step_size(manifest[KEY_MICRO][CONF_FEATURE_STEP_SIZE]))
cg.add_library("kahrendt/ESPMicroSpeechFeatures", "1.0.0")
MICRO_WAKE_WORD_ACTION_SCHEMA = cv.Schema({cv.GenerateID(): cv.use_id(MicroWakeWord)}) 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,
0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x88, 0x00,
0x00, 0x00, 0xe0, 0x00, 0x00, 0x00, 0x80, 0x0e, 0x00, 0x00, 0x90, 0x0e, 0x00, 0x00, 0xcc, 0x1f, 0x00, 0x00, 0x03,
0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xe2, 0xeb, 0xff, 0xff, 0x0c, 0x00, 0x00, 0x00,
0x1c, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67,
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0xff, 0xff, 0x2a, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x6f, 0x75, 0x74, 0x70, 0x75,
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0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x43, 0x4f, 0x4e, 0x56, 0x45, 0x52, 0x53, 0x49, 0x4f,
0x4e, 0x5f, 0x4d, 0x45, 0x54, 0x41, 0x44, 0x41, 0x54, 0x41, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00,
0x08, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x6d, 0x69, 0x6e,
0x5f, 0x72, 0x75, 0x6e, 0x74, 0x69, 0x6d, 0x65, 0x5f, 0x76, 0x65, 0x72, 0x73, 0x69, 0x6f, 0x6e, 0x00, 0x2e, 0x00,
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0x09, 0x00, 0x00, 0x34, 0x09, 0x00, 0x00, 0x20, 0x09, 0x00, 0x00, 0x0c, 0x09, 0x00, 0x00, 0xf8, 0x08, 0x00, 0x00,
0xec, 0x07, 0x00, 0x00, 0x88, 0x07, 0x00, 0x00, 0x24, 0x07, 0x00, 0x00, 0xc0, 0x06, 0x00, 0x00, 0x38, 0x04, 0x00,
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0x01, 0x00, 0x00, 0x24, 0x01, 0x00, 0x00, 0x1c, 0x01, 0x00, 0x00, 0x14, 0x01, 0x00, 0x00, 0x0c, 0x01, 0x00, 0x00,
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0x00, 0xdc, 0x00, 0x00, 0x00, 0xd4, 0x00, 0x00, 0x00, 0xcc, 0x00, 0x00, 0x00, 0xc4, 0x00, 0x00, 0x00, 0xbc, 0x00,
0x00, 0x00, 0xb4, 0x00, 0x00, 0x00, 0xac, 0x00, 0x00, 0x00, 0xa4, 0x00, 0x00, 0x00, 0x9c, 0x00, 0x00, 0x00, 0x94,
0x00, 0x00, 0x00, 0x8c, 0x00, 0x00, 0x00, 0x6c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xf2, 0xf6, 0xff, 0xff,
0x04, 0x00, 0x00, 0x00, 0x58, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04,
0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x08, 0x00,
0x07, 0x00, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x0a, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00, 0x32, 0x2e, 0x31, 0x32, 0x2e, 0x30, 0x00,
0x00, 0x56, 0xf7, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x32, 0x2e, 0x38, 0x2e, 0x30, 0x00,
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} // namespace micro_wake_word
} // namespace esphome
#endif // USE_ESP_IDF

522
esphome/components/micro_wake_word/micro_wake_word.cpp
View File

@ -1,12 +1,5 @@
#include "micro_wake_word.h" #include "micro_wake_word.h"
#include "streaming_model.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 #ifdef USE_ESP_IDF
@ -14,13 +7,13 @@
#include "esphome/core/helpers.h" #include "esphome/core/helpers.h"
#include "esphome/core/log.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/core/c/common.h>
#include <tensorflow/lite/micro/micro_interpreter.h> #include <tensorflow/lite/micro/micro_interpreter.h>
#include <tensorflow/lite/micro/micro_mutable_op_resolver.h> #include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
#include <cinttypes>
#include <cmath> #include <cmath>
namespace esphome { namespace esphome {
@ -29,9 +22,9 @@ namespace micro_wake_word {
static const char *const TAG = "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 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 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; } float MicroWakeWord::get_setup_priority() const { return setup_priority::AFTER_CONNECTION; }
@ -56,58 +49,56 @@ static const LogString *micro_wake_word_state_to_string(State state) {
void MicroWakeWord::dump_config() { void MicroWakeWord::dump_config() {
ESP_LOGCONFIG(TAG, "microWakeWord:"); ESP_LOGCONFIG(TAG, "microWakeWord:");
ESP_LOGCONFIG(TAG, " Wake Word: %s", this->get_wake_word().c_str()); ESP_LOGCONFIG(TAG, " models:");
ESP_LOGCONFIG(TAG, " Probability cutoff: %.3f", this->probability_cutoff_); for (auto &model : this->wake_word_models_) {
ESP_LOGCONFIG(TAG, " Sliding window size: %d", this->sliding_window_average_size_); model.log_model_config();
}
#ifdef USE_MICRO_WAKE_WORD_VAD
this->vad_model_->log_model_config();
#endif
} }
void MicroWakeWord::setup() { void MicroWakeWord::setup() {
ESP_LOGCONFIG(TAG, "Setting up microWakeWord..."); ESP_LOGCONFIG(TAG, "Setting up microWakeWord...");
if (!this->initialize_models()) { if (!this->register_streaming_ops_(this->streaming_op_resolver_)) {
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");
this->mark_failed(); this->mark_failed();
return; return;
} }
ESP_LOGCONFIG(TAG, "Micro Wake Word initialized"); 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_() { void MicroWakeWord::add_wake_word_model(const uint8_t *model_start, float probability_cutoff,
size_t bytes_read = this->microphone_->read(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t)); size_t sliding_window_average_size, const std::string &wake_word,
if (bytes_read == 0) { size_t tensor_arena_size) {
return 0; 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() { void MicroWakeWord::loop() {
switch (this->state_) { switch (this->state_) {
case State::IDLE: case State::IDLE:
@ -124,9 +115,12 @@ void MicroWakeWord::loop() {
} }
break; break;
case State::DETECTING_WAKE_WORD: case State::DETECTING_WAKE_WORD:
this->read_microphone_(); while (!this->has_enough_samples_()) {
if (this->detect_wake_word_()) { this->read_microphone_();
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->detected_ = true;
this->set_state_(State::STOP_MICROPHONE); this->set_state_(State::STOP_MICROPHONE);
} }
@ -136,13 +130,16 @@ void MicroWakeWord::loop() {
this->microphone_->stop(); this->microphone_->stop();
this->set_state_(State::STOPPING_MICROPHONE); this->set_state_(State::STOPPING_MICROPHONE);
this->high_freq_.stop(); this->high_freq_.stop();
this->unload_models_();
this->deallocate_buffers_();
break; break;
case State::STOPPING_MICROPHONE: case State::STOPPING_MICROPHONE:
if (this->microphone_->is_stopped()) { if (this->microphone_->is_stopped()) {
this->set_state_(State::IDLE); this->set_state_(State::IDLE);
if (this->detected_) { if (this->detected_) {
this->wake_word_detected_trigger_->trigger(this->detected_wake_word_);
this->detected_ = false; this->detected_ = false;
this->wake_word_detected_trigger_->trigger(this->wake_word_); this->detected_wake_word_ = "";
} }
} }
break; break;
@ -150,14 +147,34 @@ void MicroWakeWord::loop() {
} }
void MicroWakeWord::start() { 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()) { if (this->is_failed()) {
ESP_LOGW(TAG, "Wake word component is marked as failed. Please check setup logs"); ESP_LOGW(TAG, "Wake word component is marked as failed. Please check setup logs");
return; 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) { if (this->state_ != State::IDLE) {
ESP_LOGW(TAG, "Wake word is already running"); ESP_LOGW(TAG, "Wake word is already running");
return; return;
} }
this->reset_states_();
this->set_state_(State::START_MICROPHONE); this->set_state_(State::START_MICROPHONE);
} }
@ -179,289 +196,218 @@ void MicroWakeWord::set_state_(State state) {
this->state_ = state; this->state_ = state;
} }
bool MicroWakeWord::initialize_models() { size_t MicroWakeWord::read_microphone_() {
ExternalRAMAllocator<uint8_t> arena_allocator(ExternalRAMAllocator<uint8_t>::ALLOW_FAILURE); size_t bytes_read = this->microphone_->read(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t));
ExternalRAMAllocator<int8_t> features_allocator(ExternalRAMAllocator<int8_t>::ALLOW_FAILURE); 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); ExternalRAMAllocator<int16_t> audio_samples_allocator(ExternalRAMAllocator<int16_t>::ALLOW_FAILURE);
this->streaming_tensor_arena_ = arena_allocator.allocate(STREAMING_MODEL_ARENA_SIZE); if (this->input_buffer_ == nullptr) {
if (this->streaming_tensor_arena_ == nullptr) { this->input_buffer_ = audio_samples_allocator.allocate(INPUT_BUFFER_SIZE * sizeof(int16_t));
ESP_LOGE(TAG, "Could not allocate the streaming model's tensor arena."); if (this->input_buffer_ == nullptr) {
return false; 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) { if (this->preprocessor_audio_buffer_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the audio preprocessor's buffer."); this->preprocessor_audio_buffer_ = audio_samples_allocator.allocate(this->new_samples_to_get_());
return false; 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->ring_buffer_ == nullptr) {
if (this->preprocessor_model_->version() != TFLITE_SCHEMA_VERSION) { this->ring_buffer_ = RingBuffer::create(BUFFER_SIZE * sizeof(int16_t));
ESP_LOGE(TAG, "Wake word's audio preprocessor model's schema is not supported"); if (this->ring_buffer_ == nullptr) {
return false; ESP_LOGE(TAG, "Could not allocate ring buffer");
} 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<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; return true;
} }
float MicroWakeWord::perform_streaming_inference_() { void MicroWakeWord::deallocate_buffers_() {
TfLiteTensor *input = this->streaming_interpreter_->input(0); ExternalRAMAllocator<int16_t> audio_samples_allocator(ExternalRAMAllocator<int16_t>::ALLOW_FAILURE);
audio_samples_allocator.deallocate(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t));
size_t bytes_to_copy = input->bytes; this->input_buffer_ = nullptr;
audio_samples_allocator.deallocate(this->preprocessor_audio_buffer_, this->new_samples_to_get_());
memcpy((void *) (tflite::GetTensorData<int8_t>(input)), (const void *) (this->new_features_data_), bytes_to_copy); this->preprocessor_audio_buffer_ = nullptr;
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=%" 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_() { bool MicroWakeWord::load_models_() {
// Preprocess the newest audio samples into features // Setup preprocesor feature generator
if (!this->update_features_()) { 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; return false;
} }
// Perform inference // Setup streaming models
float streaming_prob = this->perform_streaming_inference_(); 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
// Add the most recent probability to the sliding window return true;
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; void MicroWakeWord::unload_models_() {
for (auto &prob : this->recent_streaming_probabilities_) { FrontendFreeStateContents(&this->frontend_state_);
sum += prob;
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;
} }
float sliding_window_average = sum / static_cast<float>(this->sliding_window_average_size_); // Increase the counter since the last positive detection
// Ensure we have enough samples since the last positive detection
this->ignore_windows_ = std::min(this->ignore_windows_ + 1, 0); 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) { if (this->ignore_windows_ < 0) {
return false; return false;
} }
// Detect the wake word if the sliding window average is above the cutoff #ifdef USE_MICRO_WAKE_WORD_VAD
if (sliding_window_average > this->probability_cutoff_) { bool vad_state = this->vad_model_->determine_detected();
this->ignore_windows_ = -MIN_SLICES_BEFORE_DETECTION; #endif
for (auto &prob : this->recent_streaming_probabilities_) {
prob = 0;
}
ESP_LOGD(TAG, "Wake word sliding average probability is %.3f and most recent probability is %.3f", for (auto &model : this->wake_word_models_) {
sliding_window_average, streaming_prob); if (model.determine_detected()) {
return true; #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; return false;
} }
void MicroWakeWord::set_sliding_window_average_size(size_t size) { bool MicroWakeWord::has_enough_samples_() {
this->sliding_window_average_size_ = size; return this->ring_buffer_->available() >=
this->recent_streaming_probabilities_.resize(this->sliding_window_average_size_, 0.0); (this->features_step_size_ * (AUDIO_SAMPLE_FREQUENCY / 1000)) * sizeof(int16_t);
} }
bool MicroWakeWord::slice_available_() { bool MicroWakeWord::generate_features_for_window_(int8_t features[PREPROCESSOR_FEATURE_SIZE]) {
size_t available = this->ring_buffer_->available(); // Ensure we have enough new audio samples in the ring buffer for a full window
if (!this->has_enough_samples_()) {
return available > (NEW_SAMPLES_TO_GET * sizeof(int16_t));
}
bool MicroWakeWord::stride_audio_samples_(int16_t **audio_samples) {
if (!this->slice_available_()) {
return false; return false;
} }
// Copy the last 320 bytes (160 samples over 10 ms) from the audio buffer to the start of the audio buffer size_t bytes_read = this->ring_buffer_->read((void *) (this->preprocessor_audio_buffer_),
memcpy((void *) (this->preprocessor_audio_buffer_), (void *) (this->preprocessor_audio_buffer_ + NEW_SAMPLES_TO_GET), this->new_samples_to_get_() * sizeof(int16_t), pdMS_TO_TICKS(200));
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));
if (bytes_read == 0) { if (bytes_read == 0) {
ESP_LOGE(TAG, "Could not read data from Ring Buffer"); 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, "Partial Read of Data by Model");
ESP_LOGD(TAG, "Could only read %d bytes when required %d bytes ", bytes_read, 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; return false;
} }
*audio_samples = this->preprocessor_audio_buffer_; size_t num_samples_read;
return true; 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, for (size_t i = 0; i < frontend_output.size; ++i) {
int8_t feature_output[PREPROCESSOR_FEATURE_SIZE]) { // These scaling values are set to match the TFLite audio frontend int8 output.
TfLiteTensor *input = this->preprocessor_interperter_->input(0); // The feature pipeline outputs 16-bit signed integers in roughly a 0 to 670
TfLiteTensor *output = this->preprocessor_interperter_->output(0); // range. In training, these are then arbitrarily divided by 25.6 to get
std::copy_n(audio_data, audio_data_size, tflite::GetTensorData<int16_t>(input)); // 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
if (this->preprocessor_interperter_->Invoke() != kTfLiteOk) { // generators.
ESP_LOGE(TAG, "Failed to preprocess audio for local wake word."); // The process is then further complicated when we quantize the model. This
return false; // 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; return true;
} }
bool MicroWakeWord::register_preprocessor_ops_(tflite::MicroMutableOpResolver<18> &op_resolver) { void MicroWakeWord::reset_states_() {
if (op_resolver.AddReshape() != kTfLiteOk) ESP_LOGD(TAG, "Resetting buffers and probabilities");
return false; this->ring_buffer_->reset();
if (op_resolver.AddCast() != kTfLiteOk) this->ignore_windows_ = -MIN_SLICES_BEFORE_DETECTION;
return false; for (auto &model : this->wake_word_models_) {
if (op_resolver.AddStridedSlice() != kTfLiteOk) model.reset_probabilities();
return false; }
if (op_resolver.AddConcatenation() != kTfLiteOk) #ifdef USE_MICRO_WAKE_WORD_VAD
return false; this->vad_model_->reset_probabilities();
if (op_resolver.AddMul() != kTfLiteOk) #endif
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<17> &op_resolver) { bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<20> &op_resolver) {
if (op_resolver.AddCallOnce() != kTfLiteOk) if (op_resolver.AddCallOnce() != kTfLiteOk)
return false; return false;
if (op_resolver.AddVarHandle() != kTfLiteOk) if (op_resolver.AddVarHandle() != kTfLiteOk)
@ -496,6 +442,12 @@ bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<17> &
return false; return false;
if (op_resolver.AddMaxPool2D() != kTfLiteOk) if (op_resolver.AddMaxPool2D() != kTfLiteOk)
return false; 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; return true;
} }
@ -504,5 +456,3 @@ bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<17> &
} // namespace esphome } // namespace esphome
#endif // USE_ESP_IDF #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 #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 #ifdef USE_ESP_IDF
#include "preprocessor_settings.h"
#include "streaming_model.h"
#include "esphome/core/automation.h" #include "esphome/core/automation.h"
#include "esphome/core/component.h" #include "esphome/core/component.h"
#include "esphome/core/ring_buffer.h" #include "esphome/core/ring_buffer.h"
#include "esphome/components/microphone/microphone.h" #include "esphome/components/microphone/microphone.h"
#include <frontend_util.h>
#include <tensorflow/lite/core/c/common.h> #include <tensorflow/lite/core/c/common.h>
#include <tensorflow/lite/micro/micro_interpreter.h> #include <tensorflow/lite/micro/micro_interpreter.h>
#include <tensorflow/lite/micro/micro_mutable_op_resolver.h> #include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
@ -23,35 +20,6 @@
namespace esphome { namespace esphome {
namespace micro_wake_word { 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 { enum State {
IDLE, IDLE,
START_MICROPHONE, START_MICROPHONE,
@ -61,6 +29,9 @@ enum State {
STOPPING_MICROPHONE, 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 { class MicroWakeWord : public Component {
public: public:
void setup() override; void setup() override;
@ -73,28 +44,21 @@ class MicroWakeWord : public Component {
bool is_running() const { return this->state_ != State::IDLE; } bool is_running() const { return this->state_ != State::IDLE; }
bool initialize_models(); void set_features_step_size(uint8_t step_size) { this->features_step_size_ = step_size; }
std::string get_wake_word() { return this->wake_word_; }
// Increasing either of these will reduce the rate of false acceptances while increasing the false rejection rate
void set_probability_cutoff(float probability_cutoff) { this->probability_cutoff_ = probability_cutoff; }
void set_sliding_window_average_size(size_t size);
void set_microphone(microphone::Microphone *microphone) { this->microphone_ = microphone; } void set_microphone(microphone::Microphone *microphone) { this->microphone_ = microphone; }
Trigger<std::string> *get_wake_word_detected_trigger() const { return this->wake_word_detected_trigger_; } Trigger<std::string> *get_wake_word_detected_trigger() const { return this->wake_word_detected_trigger_; }
void set_model_start(const uint8_t *model_start) { this->model_start_ = model_start; } void add_wake_word_model(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_average_size,
void set_wake_word(const std::string &wake_word) { this->wake_word_ = wake_word; } const std::string &wake_word, size_t tensor_arena_size);
#ifdef USE_MICRO_WAKE_WORD_VAD
void add_vad_model(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_size,
size_t tensor_arena_size);
#endif
protected: protected:
void set_state_(State state);
int read_microphone_();
const uint8_t *model_start_;
std::string wake_word_;
microphone::Microphone *microphone_{nullptr}; microphone::Microphone *microphone_{nullptr};
Trigger<std::string> *wake_word_detected_trigger_ = new Trigger<std::string>(); Trigger<std::string> *wake_word_detected_trigger_ = new Trigger<std::string>();
State state_{State::IDLE}; State state_{State::IDLE};
@ -102,85 +66,93 @@ class MicroWakeWord : public Component {
std::unique_ptr<RingBuffer> ring_buffer_; std::unique_ptr<RingBuffer> ring_buffer_;
int16_t *input_buffer_; std::vector<WakeWordModel> wake_word_models_;
const tflite::Model *preprocessor_model_{nullptr}; #ifdef USE_MICRO_WAKE_WORD_VAD
const tflite::Model *streaming_model_{nullptr}; std::unique_ptr<VADModel> vad_model_;
tflite::MicroInterpreter *streaming_interpreter_{nullptr}; #endif
tflite::MicroInterpreter *preprocessor_interperter_{nullptr};
std::vector<float> recent_streaming_probabilities_; tflite::MicroMutableOpResolver<20> streaming_op_resolver_;
size_t last_n_index_{0};
float probability_cutoff_{0.5}; // Audio frontend handles generating spectrogram features
size_t sliding_window_average_size_{10}; struct FrontendConfig frontend_config_;
struct FrontendState frontend_state_;
// When the wake word detection first starts or after the word has been detected once, we ignore this many audio // When the wake word detection first starts, we ignore this many audio
// feature slices before accepting a positive detection again // feature slices before accepting a positive detection
int16_t ignore_windows_{-MIN_SLICES_BEFORE_DETECTION}; int16_t ignore_windows_{-MIN_SLICES_BEFORE_DETECTION};
uint8_t *streaming_var_arena_{nullptr}; uint8_t features_step_size_;
uint8_t *streaming_tensor_arena_{nullptr};
uint8_t *preprocessor_tensor_arena_{nullptr};
int8_t *new_features_data_{nullptr};
tflite::MicroResourceVariables *mrv_{nullptr}; // Stores audio read from the microphone before being added to the ring buffer.
int16_t *input_buffer_{nullptr};
// Stores audio fed into feature generator preprocessor // Stores audio to be fed into the audio frontend for generating features.
int16_t *preprocessor_audio_buffer_; int16_t *preprocessor_audio_buffer_{nullptr};
bool detected_{false}; 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 enough audio samples are available, it will generate one slice of new features.
* If the streaming model predicts the wake word, then the nonstreaming model confirms it. * It then loops through and performs inference with each of the loaded models.
* @param ring_Buffer Ring buffer containing raw audio samples
* @return True if the wake word is detected, false otherwise
*/ */
bool detect_wake_word_(); void update_model_probabilities_();
/// @brief Returns true if there are enough audio samples in the buffer to generate another slice of features /** Checks every model's recent probabilities to determine if the wake word has been predicted
bool slice_available_();
/** Shifts previous feature slices over by one and generates a new slice of features
* *
* @param ring_buffer ring buffer containing raw audio samples * Verifies the models have processed enough new samples for accurate predictions.
* @return True if a new slice of features was generated, false otherwise * Sets detected_wake_word_ to the wake word, if one is detected.
* @return True if a wake word is predicted, false otherwise
*/ */
bool update_features_(); bool detect_wake_words_();
/** Generates features from audio samples /** Generates features for a window of audio samples
* *
* Adapted from TFLite micro speech example * Reads samples from the ring buffer and feeds them into the preprocessor frontend.
* @param audio_data Pointer to array with the audio samples * Adapted from TFLite microspeech frontend.
* @param audio_data_size The number of samples to use as input to the preprocessor model * @param features int8_t array to store the audio features
* @param feature_output Array that will store the features
* @return True if successful, false otherwise. * @return True if successful, false otherwise.
*/ */
bool generate_single_feature_(const int16_t *audio_data, int audio_data_size, bool generate_features_for_window_(int8_t features[PREPROCESSOR_FEATURE_SIZE]);
int8_t feature_output[PREPROCESSOR_FEATURE_SIZE]);
/** Performs inference over the most recent feature slice with the streaming model /// @brief Resets the ring buffer, ignore_windows_, and sliding window probabilities
* void reset_states_();
* @return Probability of the wake word between 0.0 and 1.0
*/
float perform_streaming_inference_();
/** Strides the audio samples by keeping the last 10 ms of the previous slice
*
* Adapted from the TFLite micro speech example
* @param ring_buffer Ring buffer containing raw audio samples
* @param audio_samples Pointer to an array that will store the strided audio samples
* @return True if successful, false otherwise
*/
bool stride_audio_samples_(int16_t **audio_samples);
/// @brief Returns true if successfully registered the preprocessor's TensorFlow operations
bool register_preprocessor_ops_(tflite::MicroMutableOpResolver<18> &op_resolver);
/// @brief Returns true if successfully registered the streaming model's TensorFlow operations /// @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> { 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 } // namespace esphome
#endif // USE_ESP_IDF #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
View File

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

3
platformio.ini
View File

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

6
tests/components/micro_wake_word/common.yaml
View File

@ -10,6 +10,10 @@ microphone:
pdm: true pdm: true
micro_wake_word: micro_wake_word:
model: hey_jarvis
on_wake_word_detected: on_wake_word_detected:
- logger.log: "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