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[micro_wake_word] Version 2 (#7032)

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This commit is contained in:
Kevin Ahrendt
2024-07-10 21:21:04 -04:00
committed by GitHub
parent 2da939c81c
commit 2873c6bbaf
11 changed files with 822 additions and 949 deletions
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esphome/components/micro_wake_word/__init__.py
+204 -58
View File
@@ -9,7 +9,7 @@ import requests
import esphome.config_validation as cv
import esphome.codegen as cg
from esphome.core import CORE, HexInt, EsphomeError
from esphome.core import CORE, HexInt
from esphome.components import esp32, microphone
from esphome import automation, git, external_files
@@ -41,9 +41,15 @@ CODEOWNERS = ["@kahrendt", "@jesserockz"]
DEPENDENCIES = ["microphone"]
DOMAIN = "micro_wake_word"
CONF_FEATURE_STEP_SIZE = "feature_step_size"
CONF_MODELS = "models"
CONF_ON_WAKE_WORD_DETECTED = "on_wake_word_detected"
CONF_PROBABILITY_CUTOFF = "probability_cutoff"
CONF_SLIDING_WINDOW_AVERAGE_SIZE = "sliding_window_average_size"
CONF_ON_WAKE_WORD_DETECTED = "on_wake_word_detected"
CONF_SLIDING_WINDOW_SIZE = "sliding_window_size"
CONF_TENSOR_ARENA_SIZE = "tensor_arena_size"
CONF_VAD = "vad"
TYPE_HTTP = "http"
@@ -98,12 +104,14 @@ GIT_SCHEMA = cv.All(
_process_git_source,
)
KEY_WAKE_WORD = "wake_word"
KEY_AUTHOR = "author"
KEY_WEBSITE = "website"
KEY_VERSION = "version"
KEY_MICRO = "micro"
KEY_MINIMUM_ESPHOME_VERSION = "minimum_esphome_version"
KEY_TRAINED_LANGUAGES = "trained_languages"
KEY_VERSION = "version"
KEY_WAKE_WORD = "wake_word"
KEY_WEBSITE = "website"
MANIFEST_SCHEMA_V1 = cv.Schema(
{
@@ -125,6 +133,29 @@ MANIFEST_SCHEMA_V1 = cv.Schema(
}
)
MANIFEST_SCHEMA_V2 = cv.Schema(
{
cv.Required(CONF_TYPE): "micro",
cv.Required(CONF_MODEL): cv.string,
cv.Required(KEY_AUTHOR): cv.string,
cv.Required(KEY_VERSION): cv.All(cv.int_, 2),
cv.Required(KEY_WAKE_WORD): cv.string,
cv.Required(KEY_TRAINED_LANGUAGES): cv.ensure_list(cv.string),
cv.Optional(KEY_WEBSITE): cv.url,
cv.Required(KEY_MICRO): cv.Schema(
{
cv.Required(CONF_FEATURE_STEP_SIZE): cv.int_range(min=0, max=30),
cv.Required(CONF_TENSOR_ARENA_SIZE): cv.int_,
cv.Required(CONF_PROBABILITY_CUTOFF): cv.float_,
cv.Required(CONF_SLIDING_WINDOW_SIZE): cv.positive_int,
cv.Required(KEY_MINIMUM_ESPHOME_VERSION): cv.All(
cv.version_number, cv.validate_esphome_version
),
}
),
}
)
def _compute_local_file_path(config: dict) -> Path:
url = config[CONF_URL]
@@ -135,6 +166,24 @@ def _compute_local_file_path(config: dict) -> Path:
return base_dir / key
def _convert_manifest_v1_to_v2(v1_manifest):
v2_manifest = v1_manifest.copy()
v2_manifest[KEY_VERSION] = 2
v2_manifest[KEY_MICRO][CONF_SLIDING_WINDOW_SIZE] = v1_manifest[KEY_MICRO][
CONF_SLIDING_WINDOW_AVERAGE_SIZE
]
del v2_manifest[KEY_MICRO][CONF_SLIDING_WINDOW_AVERAGE_SIZE]
v2_manifest[KEY_MICRO][
CONF_TENSOR_ARENA_SIZE
] = 45672 # Original Inception-based V1 manifest models require a minimum of 45672 bytes
v2_manifest[KEY_MICRO][
CONF_FEATURE_STEP_SIZE
] = 20 # Original Inception-based V1 manifest models use a 20 ms feature step size
return v2_manifest
def _download_file(url: str, path: Path) -> bytes:
if not external_files.has_remote_file_changed(url, path):
_LOGGER.debug("Remote file has not changed, skipping download")
@@ -155,6 +204,24 @@ def _download_file(url: str, path: Path) -> bytes:
return req.content
def _validate_manifest_version(manifest_data):
if manifest_version := manifest_data.get(KEY_VERSION):
if manifest_version == 1:
try:
MANIFEST_SCHEMA_V1(manifest_data)
except cv.Invalid as e:
raise cv.Invalid(f"Invalid manifest file: {e}") from e
elif manifest_version == 2:
try:
MANIFEST_SCHEMA_V2(manifest_data)
except cv.Invalid as e:
raise cv.Invalid(f"Invalid manifest file: {e}") from e
else:
raise cv.Invalid("Invalid manifest version")
else:
raise cv.Invalid("Invalid manifest file, missing 'version' key.")
def _process_http_source(config):
url = config[CONF_URL]
path = _compute_local_file_path(config)
@@ -167,11 +234,6 @@ def _process_http_source(config):
if not isinstance(manifest_data, dict):
raise cv.Invalid("Manifest file must contain a JSON object")
try:
MANIFEST_SCHEMA_V1(manifest_data)
except cv.Invalid as e:
raise cv.Invalid(f"Invalid manifest file: {e}") from e
model = manifest_data[CONF_MODEL]
model_url = urljoin(url, model)
@@ -206,7 +268,7 @@ def _validate_source_model_name(value):
return MODEL_SOURCE_SCHEMA(
{
CONF_TYPE: TYPE_HTTP,
CONF_URL: f"https://github.com/esphome/micro-wake-word-models/raw/main/models/{value}.json",
CONF_URL: f"https://github.com/esphome/micro-wake-word-models/raw/main/models/v2/{value}.json",
}
)
@@ -260,18 +322,55 @@ MODEL_SOURCE_SCHEMA = cv.Any(
msg="Not a valid model name, local path, http(s) url, or github shorthand",
)
MODEL_SCHEMA = cv.Schema(
{
cv.Optional(CONF_MODEL): MODEL_SOURCE_SCHEMA,
cv.Optional(CONF_PROBABILITY_CUTOFF): cv.percentage,
cv.Optional(CONF_SLIDING_WINDOW_SIZE): cv.positive_int,
cv.GenerateID(CONF_RAW_DATA_ID): cv.declare_id(cg.uint8),
}
)
# Provide a default VAD model that could be overridden
VAD_MODEL_SCHEMA = MODEL_SCHEMA.extend(
cv.Schema(
{
cv.Optional(
CONF_MODEL,
default="vad",
): MODEL_SOURCE_SCHEMA,
}
)
)
def _maybe_empty_vad_schema(value):
# Idea borrowed from uart/__init__.py's ``maybe_empty_debug`` function. Accessed 2 July 2024.
# Loads a default VAD model without any parameters overridden.
if value is None:
value = {}
return VAD_MODEL_SCHEMA(value)
CONFIG_SCHEMA = cv.All(
cv.Schema(
{
cv.GenerateID(): cv.declare_id(MicroWakeWord),
cv.GenerateID(CONF_MICROPHONE): cv.use_id(microphone.Microphone),
cv.Optional(CONF_PROBABILITY_CUTOFF): cv.percentage,
cv.Optional(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.positive_int,
cv.Required(CONF_MODELS): cv.ensure_list(MODEL_SCHEMA),
cv.Optional(CONF_ON_WAKE_WORD_DETECTED): automation.validate_automation(
single=True
),
cv.Required(CONF_MODEL): MODEL_SOURCE_SCHEMA,
cv.GenerateID(CONF_RAW_DATA_ID): cv.declare_id(cg.uint8),
cv.Optional(CONF_VAD): _maybe_empty_vad_schema,
cv.Optional(CONF_MODEL): cv.invalid(
f"The {CONF_MODEL} parameter has moved to be a list element under the {CONF_MODELS} parameter."
),
cv.Optional(CONF_PROBABILITY_CUTOFF): cv.invalid(
f"The {CONF_PROBABILITY_CUTOFF} parameter has moved to be a list element under the {CONF_MODELS} parameter."
),
cv.Optional(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.invalid(
f"The {CONF_SLIDING_WINDOW_AVERAGE_SIZE} parameter has been renamed to {CONF_SLIDING_WINDOW_SIZE} and moved to be a list element under the {CONF_MODELS} parameter."
),
}
).extend(cv.COMPONENT_SCHEMA),
cv.only_with_esp_idf,
@@ -282,45 +381,20 @@ def _load_model_data(manifest_path: Path):
with open(manifest_path, encoding="utf-8") as f:
manifest = json.load(f)
try:
MANIFEST_SCHEMA_V1(manifest)
except cv.Invalid as e:
raise EsphomeError(f"Invalid manifest file: {e}") from e
_validate_manifest_version(manifest)
model_path = manifest_path.parent / manifest[CONF_MODEL]
with open(model_path, "rb") as f:
model = f.read()
if manifest.get(KEY_VERSION) == 1:
manifest = _convert_manifest_v1_to_v2(manifest)
return manifest, model
async def to_code(config):
var = cg.new_Pvariable(config[CONF_ID])
await cg.register_component(var, config)
mic = await cg.get_variable(config[CONF_MICROPHONE])
cg.add(var.set_microphone(mic))
if on_wake_word_detection_config := config.get(CONF_ON_WAKE_WORD_DETECTED):
await automation.build_automation(
var.get_wake_word_detected_trigger(),
[(cg.std_string, "wake_word")],
on_wake_word_detection_config,
)
esp32.add_idf_component(
name="esp-tflite-micro",
repo="https://github.com/espressif/esp-tflite-micro",
ref="v1.3.1",
)
cg.add_build_flag("-DTF_LITE_STATIC_MEMORY")
cg.add_build_flag("-DTF_LITE_DISABLE_X86_NEON")
cg.add_build_flag("-DESP_NN")
model_config = config.get(CONF_MODEL)
data = []
def _model_config_to_manifest_data(model_config):
if model_config[CONF_TYPE] == TYPE_GIT:
# compute path to model file
key = f"{model_config[CONF_URL]}@{model_config.get(CONF_REF)}"
@@ -338,23 +412,95 @@ async def to_code(config):
else:
raise ValueError("Unsupported config type: {model_config[CONF_TYPE]}")
manifest, data = _load_model_data(file)
return _load_model_data(file)
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]
def _feature_step_size_validate(config):
features_step_size = None
for model_parameters in config[CONF_MODELS]:
model_config = model_parameters.get(CONF_MODEL)
manifest, _ = _model_config_to_manifest_data(model_config)
model_step_size = manifest[KEY_MICRO][CONF_FEATURE_STEP_SIZE]
if features_step_size is None:
features_step_size = model_step_size
elif features_step_size != model_step_size:
raise cv.Invalid("Cannot load models with different features step sizes.")
FINAL_VALIDATE_SCHEMA = _feature_step_size_validate
async def to_code(config):
var = cg.new_Pvariable(config[CONF_ID])
await cg.register_component(var, config)
mic = await cg.get_variable(config[CONF_MICROPHONE])
cg.add(var.set_microphone(mic))
esp32.add_idf_component(
name="esp-tflite-micro",
repo="https://github.com/espressif/esp-tflite-micro",
ref="v1.3.1",
)
cg.add(var.set_probability_cutoff(probability_cutoff))
sliding_window_average_size = config.get(
CONF_SLIDING_WINDOW_AVERAGE_SIZE,
manifest[KEY_MICRO][CONF_SLIDING_WINDOW_AVERAGE_SIZE],
)
cg.add(var.set_sliding_window_average_size(sliding_window_average_size))
cg.add(var.set_wake_word(manifest[KEY_WAKE_WORD]))
cg.add_build_flag("-DTF_LITE_STATIC_MEMORY")
cg.add_build_flag("-DTF_LITE_DISABLE_X86_NEON")
cg.add_build_flag("-DESP_NN")
if on_wake_word_detection_config := config.get(CONF_ON_WAKE_WORD_DETECTED):
await automation.build_automation(
var.get_wake_word_detected_trigger(),
[(cg.std_string, "wake_word")],
on_wake_word_detection_config,
)
if vad_model := config.get(CONF_VAD):
cg.add_define("USE_MICRO_WAKE_WORD_VAD")
# Use the general model loading code for the VAD codegen
config[CONF_MODELS].append(vad_model)
for model_parameters in config[CONF_MODELS]:
model_config = model_parameters.get(CONF_MODEL)
data = []
manifest, data = _model_config_to_manifest_data(model_config)
rhs = [HexInt(x) for x in data]
prog_arr = cg.progmem_array(model_parameters[CONF_RAW_DATA_ID], rhs)
probability_cutoff = model_parameters.get(
CONF_PROBABILITY_CUTOFF, manifest[KEY_MICRO][CONF_PROBABILITY_CUTOFF]
)
sliding_window_size = model_parameters.get(
CONF_SLIDING_WINDOW_SIZE,
manifest[KEY_MICRO][CONF_SLIDING_WINDOW_SIZE],
)
if manifest[KEY_WAKE_WORD] == "vad":
cg.add(
var.add_vad_model(
prog_arr,
probability_cutoff,
sliding_window_size,
manifest[KEY_MICRO][CONF_TENSOR_ARENA_SIZE],
)
)
else:
cg.add(
var.add_wake_word_model(
prog_arr,
probability_cutoff,
sliding_window_size,
manifest[KEY_WAKE_WORD],
manifest[KEY_MICRO][CONF_TENSOR_ARENA_SIZE],
)
)
cg.add(var.set_features_step_size(manifest[KEY_MICRO][CONF_FEATURE_STEP_SIZE]))
cg.add_library("kahrendt/ESPMicroSpeechFeatures", "1.0.0")
MICRO_WAKE_WORD_ACTION_SCHEMA = cv.Schema({cv.GenerateID(): cv.use_id(MicroWakeWord)})
esphome/components/micro_wake_word/audio_preprocessor_int8_model_data.h
-493
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[] = {
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} // namespace micro_wake_word
} // namespace esphome
#endif // USE_ESP_IDF
esphome/components/micro_wake_word/micro_wake_word.cpp
+236 -286
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File diff suppressed because it is too large Load Diff
esphome/components/micro_wake_word/micro_wake_word.h
+80 -110
View File
@@ -1,21 +1,18 @@
#pragma once
/**
* This is a workaround until we can figure out a way to get
* the tflite-micro idf component code available in CI
*
* */
//
#ifndef CLANG_TIDY
#ifdef USE_ESP_IDF
#include "preprocessor_settings.h"
#include "streaming_model.h"
#include "esphome/core/automation.h"
#include "esphome/core/component.h"
#include "esphome/core/ring_buffer.h"
#include "esphome/components/microphone/microphone.h"
#include <frontend_util.h>
#include <tensorflow/lite/core/c/common.h>
#include <tensorflow/lite/micro/micro_interpreter.h>
#include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
@@ -23,35 +20,6 @@
namespace esphome {
namespace micro_wake_word {
// The following are dictated by the preprocessor model
//
// The number of features the audio preprocessor generates per slice
static const uint8_t PREPROCESSOR_FEATURE_SIZE = 40;
// How frequently the preprocessor generates a new set of features
static const uint8_t FEATURE_STRIDE_MS = 20;
// Duration of each slice used as input into the preprocessor
static const uint8_t FEATURE_DURATION_MS = 30;
// Audio sample frequency in hertz
static const uint16_t AUDIO_SAMPLE_FREQUENCY = 16000;
// The number of old audio samples that are saved to be part of the next feature window
static const uint16_t HISTORY_SAMPLES_TO_KEEP =
((FEATURE_DURATION_MS - FEATURE_STRIDE_MS) * (AUDIO_SAMPLE_FREQUENCY / 1000));
// The number of new audio samples to receive to be included with the next feature window
static const uint16_t NEW_SAMPLES_TO_GET = (FEATURE_STRIDE_MS * (AUDIO_SAMPLE_FREQUENCY / 1000));
// The total number of audio samples included in the feature window
static const uint16_t SAMPLE_DURATION_COUNT = FEATURE_DURATION_MS * AUDIO_SAMPLE_FREQUENCY / 1000;
// Number of bytes in memory needed for the preprocessor arena
static const uint32_t PREPROCESSOR_ARENA_SIZE = 9528;
// The following configure the streaming wake word model
//
// The number of audio slices to process before accepting a positive detection
static const uint8_t MIN_SLICES_BEFORE_DETECTION = 74;
// Number of bytes in memory needed for the streaming wake word model
static const uint32_t STREAMING_MODEL_ARENA_SIZE = 64000;
static const uint32_t STREAMING_MODEL_VARIABLE_ARENA_SIZE = 1024;
enum State {
IDLE,
START_MICROPHONE,
@@ -61,6 +29,9 @@ enum State {
STOPPING_MICROPHONE,
};
// The number of audio slices to process before accepting a positive detection
static const uint8_t MIN_SLICES_BEFORE_DETECTION = 74;
class MicroWakeWord : public Component {
public:
void setup() override;
@@ -73,28 +44,21 @@ class MicroWakeWord : public Component {
bool is_running() const { return this->state_ != State::IDLE; }
bool initialize_models();
std::string get_wake_word() { return this->wake_word_; }
// Increasing either of these will reduce the rate of false acceptances while increasing the false rejection rate
void set_probability_cutoff(float probability_cutoff) { this->probability_cutoff_ = probability_cutoff; }
void set_sliding_window_average_size(size_t size);
void set_features_step_size(uint8_t step_size) { this->features_step_size_ = step_size; }
void set_microphone(microphone::Microphone *microphone) { this->microphone_ = microphone; }
Trigger<std::string> *get_wake_word_detected_trigger() const { return this->wake_word_detected_trigger_; }
void set_model_start(const uint8_t *model_start) { this->model_start_ = model_start; }
void set_wake_word(const std::string &wake_word) { this->wake_word_ = wake_word; }
void add_wake_word_model(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_average_size,
const std::string &wake_word, size_t tensor_arena_size);
#ifdef USE_MICRO_WAKE_WORD_VAD
void add_vad_model(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_size,
size_t tensor_arena_size);
#endif
protected:
void set_state_(State state);
int read_microphone_();
const uint8_t *model_start_;
std::string wake_word_;
microphone::Microphone *microphone_{nullptr};
Trigger<std::string> *wake_word_detected_trigger_ = new Trigger<std::string>();
State state_{State::IDLE};
@@ -102,85 +66,93 @@ class MicroWakeWord : public Component {
std::unique_ptr<RingBuffer> ring_buffer_;
int16_t *input_buffer_;
std::vector<WakeWordModel> wake_word_models_;
const tflite::Model *preprocessor_model_{nullptr};
const tflite::Model *streaming_model_{nullptr};
tflite::MicroInterpreter *streaming_interpreter_{nullptr};
tflite::MicroInterpreter *preprocessor_interperter_{nullptr};
#ifdef USE_MICRO_WAKE_WORD_VAD
std::unique_ptr<VADModel> vad_model_;
#endif
std::vector<float> recent_streaming_probabilities_;
size_t last_n_index_{0};
tflite::MicroMutableOpResolver<20> streaming_op_resolver_;
float probability_cutoff_{0.5};
size_t sliding_window_average_size_{10};
// Audio frontend handles generating spectrogram features
struct FrontendConfig frontend_config_;
struct FrontendState frontend_state_;
// When the wake word detection first starts or after the word has been detected once, we ignore this many audio
// feature slices before accepting a positive detection again
// When the wake word detection first starts, we ignore this many audio
// feature slices before accepting a positive detection
int16_t ignore_windows_{-MIN_SLICES_BEFORE_DETECTION};
uint8_t *streaming_var_arena_{nullptr};
uint8_t *streaming_tensor_arena_{nullptr};
uint8_t *preprocessor_tensor_arena_{nullptr};
int8_t *new_features_data_{nullptr};
uint8_t features_step_size_;
tflite::MicroResourceVariables *mrv_{nullptr};
// Stores audio fed into feature generator preprocessor
int16_t *preprocessor_audio_buffer_;
// Stores audio read from the microphone before being added to the ring buffer.
int16_t *input_buffer_{nullptr};
// Stores audio to be fed into the audio frontend for generating features.
int16_t *preprocessor_audio_buffer_{nullptr};
bool detected_{false};
std::string detected_wake_word_{""};
/** Detects if wake word has been said
void set_state_(State state);
/// @brief Tests if there are enough samples in the ring buffer to generate new features.
/// @return True if enough samples, false otherwise.
bool has_enough_samples_();
/** Reads audio from microphone into the ring buffer
*
* Audio data (16000 kHz with int16 samples) is read into the input_buffer_.
* Verifies the ring buffer has enough space for all audio data. If not, it logs
* a warning and resets the ring buffer entirely.
* @return Number of bytes written to the ring buffer
*/
size_t read_microphone_();
/// @brief Allocates memory for input_buffer_, preprocessor_audio_buffer_, and ring_buffer_
/// @return True if successful, false otherwise
bool allocate_buffers_();
/// @brief Frees memory allocated for input_buffer_ and preprocessor_audio_buffer_
void deallocate_buffers_();
/// @brief Loads streaming models and prepares the feature generation frontend
/// @return True if successful, false otherwise
bool load_models_();
/// @brief Deletes each model's TFLite interpreters and frees tensor arena memory. Frees memory used by the feature
/// generation frontend.
void unload_models_();
/** Performs inference with each configured model
*
* If enough audio samples are available, it will generate one slice of new features.
* If the streaming model predicts the wake word, then the nonstreaming model confirms it.
* @param ring_Buffer Ring buffer containing raw audio samples
* @return True if the wake word is detected, false otherwise
* It then loops through and performs inference with each of the loaded models.
*/
bool detect_wake_word_();
void update_model_probabilities_();
/// @brief Returns true if there are enough audio samples in the buffer to generate another slice of features
bool slice_available_();
/** Shifts previous feature slices over by one and generates a new slice of features
/** Checks every model's recent probabilities to determine if the wake word has been predicted
*
* @param ring_buffer ring buffer containing raw audio samples
* @return True if a new slice of features was generated, false otherwise
* Verifies the models have processed enough new samples for accurate predictions.
* Sets detected_wake_word_ to the wake word, if one is detected.
* @return True if a wake word is predicted, false otherwise
*/
bool update_features_();
bool detect_wake_words_();
/** Generates features from audio samples
/** Generates features for a window of audio samples
*
* Adapted from TFLite micro speech example
* @param audio_data Pointer to array with the audio samples
* @param audio_data_size The number of samples to use as input to the preprocessor model
* @param feature_output Array that will store the features
* Reads samples from the ring buffer and feeds them into the preprocessor frontend.
* Adapted from TFLite microspeech frontend.
* @param features int8_t array to store the audio features
* @return True if successful, false otherwise.
*/
bool generate_single_feature_(const int16_t *audio_data, int audio_data_size,
int8_t feature_output[PREPROCESSOR_FEATURE_SIZE]);
bool generate_features_for_window_(int8_t features[PREPROCESSOR_FEATURE_SIZE]);
/** Performs inference over the most recent feature slice with the streaming model
*
* @return Probability of the wake word between 0.0 and 1.0
*/
float perform_streaming_inference_();
/** Strides the audio samples by keeping the last 10 ms of the previous slice
*
* Adapted from the TFLite micro speech example
* @param ring_buffer Ring buffer containing raw audio samples
* @param audio_samples Pointer to an array that will store the strided audio samples
* @return True if successful, false otherwise
*/
bool stride_audio_samples_(int16_t **audio_samples);
/// @brief Returns true if successfully registered the preprocessor's TensorFlow operations
bool register_preprocessor_ops_(tflite::MicroMutableOpResolver<18> &op_resolver);
/// @brief Resets the ring buffer, ignore_windows_, and sliding window probabilities
void reset_states_();
/// @brief Returns true if successfully registered the streaming model's TensorFlow operations
bool register_streaming_ops_(tflite::MicroMutableOpResolver<17> &op_resolver);
bool register_streaming_ops_(tflite::MicroMutableOpResolver<20> &op_resolver);
inline uint16_t new_samples_to_get_() { return (this->features_step_size_ * (AUDIO_SAMPLE_FREQUENCY / 1000)); }
};
template<typename... Ts> class StartAction : public Action<Ts...>, public Parented<MicroWakeWord> {
@@ -202,5 +174,3 @@ template<typename... Ts> class IsRunningCondition : public Condition<Ts...>, pub
} // namespace esphome
#endif // USE_ESP_IDF
#endif // CLANG_TIDY
esphome/components/micro_wake_word/preprocessor_settings.h
+20
View File
@@ -0,0 +1,20 @@
#pragma once
#ifdef USE_ESP_IDF
#include <cstdint>
namespace esphome {
namespace micro_wake_word {
// The number of features the audio preprocessor generates per slice
static const uint8_t PREPROCESSOR_FEATURE_SIZE = 40;
// Duration of each slice used as input into the preprocessor
static const uint8_t FEATURE_DURATION_MS = 30;
// Audio sample frequency in hertz
static const uint16_t AUDIO_SAMPLE_FREQUENCY = 16000;
} // namespace micro_wake_word
} // namespace esphome
#endif
esphome/components/micro_wake_word/streaming_model.cpp
+189
View File
@@ -0,0 +1,189 @@
#ifdef USE_ESP_IDF
#include "streaming_model.h"
#include "esphome/core/hal.h"
#include "esphome/core/helpers.h"
#include "esphome/core/log.h"
static const char *const TAG = "micro_wake_word";
namespace esphome {
namespace micro_wake_word {
void WakeWordModel::log_model_config() {
ESP_LOGCONFIG(TAG, " - Wake Word: %s", this->wake_word_.c_str());
ESP_LOGCONFIG(TAG, " Probability cutoff: %.3f", this->probability_cutoff_);
ESP_LOGCONFIG(TAG, " Sliding window size: %d", this->sliding_window_size_);
}
void VADModel::log_model_config() {
ESP_LOGCONFIG(TAG, " - VAD Model");
ESP_LOGCONFIG(TAG, " Probability cutoff: %.3f", this->probability_cutoff_);
ESP_LOGCONFIG(TAG, " Sliding window size: %d", this->sliding_window_size_);
}
bool StreamingModel::load_model(tflite::MicroMutableOpResolver<20> &op_resolver) {
ExternalRAMAllocator<uint8_t> arena_allocator(ExternalRAMAllocator<uint8_t>::ALLOW_FAILURE);
if (this->tensor_arena_ == nullptr) {
this->tensor_arena_ = arena_allocator.allocate(this->tensor_arena_size_);
if (this->tensor_arena_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the streaming model's tensor arena.");
return false;
}
}
if (this->var_arena_ == nullptr) {
this->var_arena_ = arena_allocator.allocate(STREAMING_MODEL_VARIABLE_ARENA_SIZE);
if (this->var_arena_ == nullptr) {
ESP_LOGE(TAG, "Could not allocate the streaming model's variable tensor arena.");
return false;
}
this->ma_ = tflite::MicroAllocator::Create(this->var_arena_, STREAMING_MODEL_VARIABLE_ARENA_SIZE);
this->mrv_ = tflite::MicroResourceVariables::Create(this->ma_, 20);
}
const tflite::Model *model = tflite::GetModel(this->model_start_);
if (model->version() != TFLITE_SCHEMA_VERSION) {
ESP_LOGE(TAG, "Streaming model's schema is not supported");
return false;
}
if (this->interpreter_ == nullptr) {
this->interpreter_ = make_unique<tflite::MicroInterpreter>(
tflite::GetModel(this->model_start_), op_resolver, this->tensor_arena_, this->tensor_arena_size_, this->mrv_);
if (this->interpreter_->AllocateTensors() != kTfLiteOk) {
ESP_LOGE(TAG, "Failed to allocate tensors for the streaming model");
return false;
}
// Verify input tensor matches expected values
// Dimension 3 will represent the first layer stride, so skip it may vary
TfLiteTensor *input = this->interpreter_->input(0);
if ((input->dims->size != 3) || (input->dims->data[0] != 1) ||
(input->dims->data[2] != PREPROCESSOR_FEATURE_SIZE)) {
ESP_LOGE(TAG, "Streaming model tensor input dimensions has improper dimensions.");
return false;
}
if (input->type != kTfLiteInt8) {
ESP_LOGE(TAG, "Streaming model tensor input is not int8.");
return false;
}
// Verify output tensor matches expected values
TfLiteTensor *output = this->interpreter_->output(0);
if ((output->dims->size != 2) || (output->dims->data[0] != 1) || (output->dims->data[1] != 1)) {
ESP_LOGE(TAG, "Streaming model tensor output dimension is not 1x1.");
}
if (output->type != kTfLiteUInt8) {
ESP_LOGE(TAG, "Streaming model tensor output is not uint8.");
return false;
}
}
return true;
}
void StreamingModel::unload_model() {
this->interpreter_.reset();
ExternalRAMAllocator<uint8_t> arena_allocator(ExternalRAMAllocator<uint8_t>::ALLOW_FAILURE);
arena_allocator.deallocate(this->tensor_arena_, this->tensor_arena_size_);
this->tensor_arena_ = nullptr;
arena_allocator.deallocate(this->var_arena_, STREAMING_MODEL_VARIABLE_ARENA_SIZE);
this->var_arena_ = nullptr;
}
bool StreamingModel::perform_streaming_inference(const int8_t features[PREPROCESSOR_FEATURE_SIZE]) {
if (this->interpreter_ != nullptr) {
TfLiteTensor *input = this->interpreter_->input(0);
std::memmove(
(int8_t *) (tflite::GetTensorData<int8_t>(input)) + PREPROCESSOR_FEATURE_SIZE * this->current_stride_step_,
features, PREPROCESSOR_FEATURE_SIZE);
++this->current_stride_step_;
uint8_t stride = this->interpreter_->input(0)->dims->data[1];
if (this->current_stride_step_ >= stride) {
this->current_stride_step_ = 0;
TfLiteStatus invoke_status = this->interpreter_->Invoke();
if (invoke_status != kTfLiteOk) {
ESP_LOGW(TAG, "Streaming interpreter invoke failed");
return false;
}
TfLiteTensor *output = this->interpreter_->output(0);
++this->last_n_index_;
if (this->last_n_index_ == this->sliding_window_size_)
this->last_n_index_ = 0;
this->recent_streaming_probabilities_[this->last_n_index_] = output->data.uint8[0]; // probability;
}
return true;
}
ESP_LOGE(TAG, "Streaming interpreter is not initialized.");
return false;
}
void StreamingModel::reset_probabilities() {
for (auto &prob : this->recent_streaming_probabilities_) {
prob = 0;
}
}
WakeWordModel::WakeWordModel(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_average_size,
const std::string &wake_word, size_t tensor_arena_size) {
this->model_start_ = model_start;
this->probability_cutoff_ = probability_cutoff;
this->sliding_window_size_ = sliding_window_average_size;
this->recent_streaming_probabilities_.resize(sliding_window_average_size, 0);
this->wake_word_ = wake_word;
this->tensor_arena_size_ = tensor_arena_size;
};
bool WakeWordModel::determine_detected() {
int32_t sum = 0;
for (auto &prob : this->recent_streaming_probabilities_) {
sum += prob;
}
float sliding_window_average = static_cast<float>(sum) / static_cast<float>(255 * this->sliding_window_size_);
// Detect the wake word if the sliding window average is above the cutoff
if (sliding_window_average > this->probability_cutoff_) {
ESP_LOGD(TAG, "The '%s' model sliding average probability is %.3f and most recent probability is %.3f",
this->wake_word_.c_str(), sliding_window_average,
this->recent_streaming_probabilities_[this->last_n_index_] / (255.0));
return true;
}
return false;
}
VADModel::VADModel(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_size,
size_t tensor_arena_size) {
this->model_start_ = model_start;
this->probability_cutoff_ = probability_cutoff;
this->sliding_window_size_ = sliding_window_size;
this->recent_streaming_probabilities_.resize(sliding_window_size, 0);
this->tensor_arena_size_ = tensor_arena_size;
};
bool VADModel::determine_detected() {
uint8_t max = 0;
for (auto &prob : this->recent_streaming_probabilities_) {
max = std::max(prob, max);
}
return max > this->probability_cutoff_;
}
} // namespace micro_wake_word
} // namespace esphome
#endif
esphome/components/micro_wake_word/streaming_model.h
+84
View File
@@ -0,0 +1,84 @@
#pragma once
#ifdef USE_ESP_IDF
#include "preprocessor_settings.h"
#include <tensorflow/lite/core/c/common.h>
#include <tensorflow/lite/micro/micro_interpreter.h>
#include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
namespace esphome {
namespace micro_wake_word {
static const uint32_t STREAMING_MODEL_VARIABLE_ARENA_SIZE = 1024;
class StreamingModel {
public:
virtual void log_model_config() = 0;
virtual bool determine_detected() = 0;
bool perform_streaming_inference(const int8_t features[PREPROCESSOR_FEATURE_SIZE]);
/// @brief Sets all recent_streaming_probabilities to 0
void reset_probabilities();
/// @brief Allocates tensor and variable arenas and sets up the model interpreter
/// @param op_resolver MicroMutableOpResolver object that must exist until the model is unloaded
/// @return True if successful, false otherwise
bool load_model(tflite::MicroMutableOpResolver<20> &op_resolver);
/// @brief Destroys the TFLite interpreter and frees the tensor and variable arenas' memory
void unload_model();
protected:
uint8_t current_stride_step_{0};
float probability_cutoff_;
size_t sliding_window_size_;
size_t last_n_index_{0};
size_t tensor_arena_size_;
std::vector<uint8_t> recent_streaming_probabilities_;
const uint8_t *model_start_;
uint8_t *tensor_arena_{nullptr};
uint8_t *var_arena_{nullptr};
std::unique_ptr<tflite::MicroInterpreter> interpreter_;
tflite::MicroResourceVariables *mrv_{nullptr};
tflite::MicroAllocator *ma_{nullptr};
};
class WakeWordModel final : public StreamingModel {
public:
WakeWordModel(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_average_size,
const std::string &wake_word, size_t tensor_arena_size);
void log_model_config() override;
/// @brief Checks for the wake word by comparing the mean probability in the sliding window with the probability
/// cutoff
/// @return True if wake word is detected, false otherwise
bool determine_detected() override;
const std::string &get_wake_word() const { return this->wake_word_; }
protected:
std::string wake_word_;
};
class VADModel final : public StreamingModel {
public:
VADModel(const uint8_t *model_start, float probability_cutoff, size_t sliding_window_size, size_t tensor_arena_size);
void log_model_config() override;
/// @brief Checks for voice activity by comparing the max probability in the sliding window with the probability
/// cutoff
/// @return True if voice activity is detected, false otherwise
bool determine_detected() override;
};
} // namespace micro_wake_word
} // namespace esphome
#endif
esphome/core/defines.h
+1
View File
@@ -86,6 +86,7 @@
#define USE_ESP32_BLE_SERVER
#define USE_ESP32_CAMERA
#define USE_IMPROV
#define USE_MICRO_WAKE_WORD_VAD
#define USE_MICROPHONE
#define USE_PSRAM
#define USE_SOCKET_IMPL_BSD_SOCKETS
platformio.ini
+2 -1
View File
@@ -142,7 +142,8 @@ platform_packages =
framework = espidf
lib_deps =
${common:idf.lib_deps}
droscy/esp_wireguard@0.4.2 ; wireguard
droscy/esp_wireguard@0.4.2 ; wireguard
kahrendt/ESPMicroSpeechFeatures@1.0.0 ; micro_wake_word
build_flags =
${common:idf.build_flags}
-Wno-nonnull-compare
tests/components/micro_wake_word/common.yaml
+5 -1
View File
@@ -10,6 +10,10 @@ microphone:
pdm: true
micro_wake_word:
model: hey_jarvis
on_wake_word_detected:
- logger.log: "Wake word detected"
models:
- model: hey_jarvis
probability_cutoff: 0.7
- model: okay_nabu
sliding_window_size: 5
tests/components/micro_wake_word/test.esp32-idf.yaml
+1
View File
@@ -0,0 +1 @@
<<: !include common.yaml