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Code Issues Packages Projects Releases Wiki Activity

Add micro_wake_word component (#6136)

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Co-authored-by: Jesse Hills <3060199+jesserockz@users.noreply.github.com>
This commit is contained in:
kahrendt
2024-02-13 09:38:50 +13:00
committed by GitHub
co-authored by Jesse Hills
parent 061d5b4979
commit e521662342
6 changed files with 1579 additions and 0 deletions
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CODEOWNERS
+1
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@@ -199,6 +199,7 @@ esphome/components/mcp9808/* @k7hpn
esphome/components/md5/* @esphome/core
esphome/components/mdns/* @esphome/core
esphome/components/media_player/* @jesserockz
esphome/components/micro_wake_word/* @jesserockz @kahrendt
esphome/components/micronova/* @jorre05
esphome/components/microphone/* @jesserockz
esphome/components/mics_4514/* @jesserockz
esphome/components/micro_wake_word/__init__.py
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@@ -0,0 +1,363 @@
import logging
import json
import hashlib
from urllib.parse import urljoin
from pathlib import Path
import requests
import esphome.config_validation as cv
import esphome.codegen as cg
from esphome.core import CORE, HexInt, EsphomeError
from esphome.components import esp32, microphone
from esphome import automation, git, external_files
from esphome.automation import register_action, register_condition
from esphome.const import (
__version__,
CONF_ID,
CONF_MICROPHONE,
CONF_MODEL,
CONF_URL,
CONF_FILE,
CONF_PATH,
CONF_REF,
CONF_REFRESH,
CONF_TYPE,
CONF_USERNAME,
CONF_PASSWORD,
CONF_RAW_DATA_ID,
TYPE_GIT,
TYPE_LOCAL,
)
_LOGGER = logging.getLogger(__name__)
CODEOWNERS = ["@kahrendt", "@jesserockz"]
DEPENDENCIES = ["microphone"]
DOMAIN = "micro_wake_word"
CONF_PROBABILITY_CUTOFF = "probability_cutoff"
CONF_SLIDING_WINDOW_AVERAGE_SIZE = "sliding_window_average_size"
CONF_ON_WAKE_WORD_DETECTED = "on_wake_word_detected"
TYPE_HTTP = "http"
micro_wake_word_ns = cg.esphome_ns.namespace("micro_wake_word")
MicroWakeWord = micro_wake_word_ns.class_("MicroWakeWord", cg.Component)
StartAction = micro_wake_word_ns.class_("StartAction", automation.Action)
StopAction = micro_wake_word_ns.class_("StopAction", automation.Action)
IsRunningCondition = micro_wake_word_ns.class_(
"IsRunningCondition", automation.Condition
)
def _validate_json_filename(value):
value = cv.string(value)
if not value.endswith(".json"):
raise cv.Invalid("Manifest filename must end with .json")
return value
def _process_git_source(config):
repo_dir, _ = git.clone_or_update(
url=config[CONF_URL],
ref=config.get(CONF_REF),
refresh=config[CONF_REFRESH],
domain=DOMAIN,
username=config.get(CONF_USERNAME),
password=config.get(CONF_PASSWORD),
)
if not (repo_dir / config[CONF_FILE]).exists():
raise cv.Invalid("File does not exist in repository")
return config
CV_GIT_SCHEMA = cv.GIT_SCHEMA
if isinstance(CV_GIT_SCHEMA, dict):
CV_GIT_SCHEMA = cv.Schema(CV_GIT_SCHEMA)
GIT_SCHEMA = cv.All(
CV_GIT_SCHEMA.extend(
{
cv.Required(CONF_FILE): _validate_json_filename,
cv.Optional(CONF_REFRESH, default="1d"): cv.All(
cv.string, cv.source_refresh
),
}
),
_process_git_source,
)
KEY_WAKE_WORD = "wake_word"
KEY_AUTHOR = "author"
KEY_WEBSITE = "website"
KEY_VERSION = "version"
KEY_MICRO = "micro"
MANIFEST_SCHEMA_V1 = cv.Schema(
{
cv.Required(CONF_TYPE): "micro",
cv.Required(KEY_WAKE_WORD): cv.string,
cv.Required(KEY_AUTHOR): cv.string,
cv.Required(KEY_WEBSITE): cv.url,
cv.Required(KEY_VERSION): cv.All(cv.int_, 1),
cv.Required(CONF_MODEL): cv.string,
cv.Required(KEY_MICRO): cv.Schema(
{
cv.Required(CONF_PROBABILITY_CUTOFF): cv.float_,
cv.Required(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.positive_int,
}
),
}
)
def _compute_local_file_path(config: dict) -> Path:
url = config[CONF_URL]
h = hashlib.new("sha256")
h.update(url.encode())
key = h.hexdigest()[:8]
base_dir = external_files.compute_local_file_dir(DOMAIN)
return base_dir / key
def _download_file(url: str, path: Path) -> bytes:
if not external_files.has_remote_file_changed(url, path):
_LOGGER.debug("Remote file has not changed, skipping download")
return path.read_bytes()
try:
req = requests.get(
url,
timeout=external_files.NETWORK_TIMEOUT,
headers={"User-agent": f"ESPHome/{__version__} (https://esphome.io)"},
)
req.raise_for_status()
except requests.exceptions.RequestException as e:
raise cv.Invalid(f"Could not download file from {url}: {e}") from e
path.parent.mkdir(parents=True, exist_ok=True)
path.write_bytes(req.content)
return req.content
def _process_http_source(config):
url = config[CONF_URL]
path = _compute_local_file_path(config)
json_path = path / "manifest.json"
json_contents = _download_file(url, json_path)
manifest_data = json.loads(json_contents)
if not isinstance(manifest_data, dict):
raise cv.Invalid("Manifest file must contain a JSON object")
try:
MANIFEST_SCHEMA_V1(manifest_data)
except cv.Invalid as e:
raise cv.Invalid(f"Invalid manifest file: {e}") from e
model = manifest_data[CONF_MODEL]
model_url = urljoin(url, model)
model_path = path / model
_download_file(str(model_url), model_path)
return config
HTTP_SCHEMA = cv.All(
{
cv.Required(CONF_URL): cv.url,
},
_process_http_source,
)
LOCAL_SCHEMA = cv.Schema(
{
cv.Required(CONF_PATH): cv.All(_validate_json_filename, cv.file_),
}
)
def _validate_source_model_name(value):
if not isinstance(value, str):
raise cv.Invalid("Model name must be a string")
if value.endswith(".json"):
raise cv.Invalid("Model name must not end with .json")
return MODEL_SOURCE_SCHEMA(
{
CONF_TYPE: TYPE_HTTP,
CONF_URL: f"https://github.com/esphome/micro-wake-word-models/raw/main/models/{value}.json",
}
)
def _validate_source_shorthand(value):
if not isinstance(value, str):
raise cv.Invalid("Shorthand only for strings")
try: # Test for model name
return _validate_source_model_name(value)
except cv.Invalid:
pass
try: # Test for local path
return MODEL_SOURCE_SCHEMA({CONF_TYPE: TYPE_LOCAL, CONF_PATH: value})
except cv.Invalid:
pass
try: # Test for http url
return MODEL_SOURCE_SCHEMA({CONF_TYPE: TYPE_HTTP, CONF_URL: value})
except cv.Invalid:
pass
git_file = git.GitFile.from_shorthand(value)
conf = {
CONF_TYPE: TYPE_GIT,
CONF_URL: git_file.git_url,
CONF_FILE: git_file.filename,
}
if git_file.ref:
conf[CONF_REF] = git_file.ref
try:
return MODEL_SOURCE_SCHEMA(conf)
except cv.Invalid as e:
raise cv.Invalid(
f"Could not find file '{git_file.filename}' in the repository. Please make sure it exists."
) from e
MODEL_SOURCE_SCHEMA = cv.Any(
_validate_source_shorthand,
cv.typed_schema(
{
TYPE_GIT: GIT_SCHEMA,
TYPE_LOCAL: LOCAL_SCHEMA,
TYPE_HTTP: HTTP_SCHEMA,
}
),
msg="Not a valid model name, local path, http(s) url, or github shorthand",
)
CONFIG_SCHEMA = cv.All(
cv.Schema(
{
cv.GenerateID(): cv.declare_id(MicroWakeWord),
cv.GenerateID(CONF_MICROPHONE): cv.use_id(microphone.Microphone),
cv.Optional(CONF_PROBABILITY_CUTOFF): cv.float_,
cv.Optional(CONF_SLIDING_WINDOW_AVERAGE_SIZE): cv.positive_int,
cv.Optional(CONF_ON_WAKE_WORD_DETECTED): automation.validate_automation(
single=True
),
cv.Required(CONF_MODEL): MODEL_SOURCE_SCHEMA,
cv.GenerateID(CONF_RAW_DATA_ID): cv.declare_id(cg.uint8),
}
).extend(cv.COMPONENT_SCHEMA),
cv.only_with_esp_idf,
)
def _load_model_data(manifest_path: Path):
with open(manifest_path, encoding="utf-8") as f:
manifest = json.load(f)
try:
MANIFEST_SCHEMA_V1(manifest)
except cv.Invalid as e:
raise EsphomeError(f"Invalid manifest file: {e}") from e
model_path = urljoin(str(manifest_path), manifest[CONF_MODEL])
with open(model_path, "rb") as f:
model = f.read()
return manifest, model
async def to_code(config):
var = cg.new_Pvariable(config[CONF_ID])
await cg.register_component(var, config)
mic = await cg.get_variable(config[CONF_MICROPHONE])
cg.add(var.set_microphone(mic))
if on_wake_word_detection_config := config.get(CONF_ON_WAKE_WORD_DETECTED):
await automation.build_automation(
var.get_wake_word_detected_trigger(),
[(cg.std_string, "wake_word")],
on_wake_word_detection_config,
)
esp32.add_idf_component(
name="esp-tflite-micro",
repo="https://github.com/espressif/esp-tflite-micro",
)
cg.add_build_flag("-DTF_LITE_STATIC_MEMORY")
cg.add_build_flag("-DTF_LITE_DISABLE_X86_NEON")
cg.add_build_flag("-DESP_NN")
model_config = config.get(CONF_MODEL)
data = []
if model_config[CONF_TYPE] == TYPE_GIT:
# compute path to model file
key = f"{model_config[CONF_URL]}@{model_config.get(CONF_REF)}"
base_dir = Path(CORE.data_dir) / DOMAIN
h = hashlib.new("sha256")
h.update(key.encode())
file: Path = base_dir / h.hexdigest()[:8] / model_config[CONF_FILE]
elif model_config[CONF_TYPE] == TYPE_LOCAL:
file = model_config[CONF_PATH]
elif model_config[CONF_TYPE] == TYPE_HTTP:
file = _compute_local_file_path(model_config) / "manifest.json"
manifest, data = _load_model_data(file)
rhs = [HexInt(x) for x in data]
prog_arr = cg.progmem_array(config[CONF_RAW_DATA_ID], rhs)
cg.add(var.set_model_start(prog_arr))
probability_cutoff = config.get(
CONF_PROBABILITY_CUTOFF, manifest[KEY_MICRO][CONF_PROBABILITY_CUTOFF]
)
cg.add(var.set_probability_cutoff(probability_cutoff))
sliding_window_average_size = config.get(
CONF_SLIDING_WINDOW_AVERAGE_SIZE,
manifest[KEY_MICRO][CONF_SLIDING_WINDOW_AVERAGE_SIZE],
)
cg.add(var.set_sliding_window_average_size(sliding_window_average_size))
cg.add(var.set_wake_word(manifest[KEY_WAKE_WORD]))
MICRO_WAKE_WORD_ACTION_SCHEMA = cv.Schema({cv.GenerateID(): cv.use_id(MicroWakeWord)})
@register_action("micro_wake_word.start", StartAction, MICRO_WAKE_WORD_ACTION_SCHEMA)
@register_action("micro_wake_word.stop", StopAction, MICRO_WAKE_WORD_ACTION_SCHEMA)
@register_condition(
"micro_wake_word.is_running", IsRunningCondition, MICRO_WAKE_WORD_ACTION_SCHEMA
)
async def micro_wake_word_action_to_code(config, action_id, template_arg, args):
var = cg.new_Pvariable(action_id, template_arg)
await cg.register_parented(var, config[CONF_ID])
return var
esphome/components/micro_wake_word/audio_preprocessor_int8_model_data.h
+493
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@@ -0,0 +1,493 @@
#pragma once
#ifdef USE_ESP_IDF
// Converted audio_preprocessor_int8.tflite
// From https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/micro_speech/models accessed
// January 2024
//
// Copyright 2023 The TensorFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
namespace esphome {
namespace micro_wake_word {
const unsigned char G_AUDIO_PREPROCESSOR_INT8_TFLITE[] = {
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} // namespace micro_wake_word
} // namespace esphome
#endif // USE_ESP_IDF
esphome/components/micro_wake_word/micro_wake_word.cpp
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esphome/components/micro_wake_word/micro_wake_word.h
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#pragma once
/**
* This is a workaround until we can figure out a way to get
* the tflite-micro idf component code available in CI
*
* */
//
#ifndef CLANG_TIDY
#ifdef USE_ESP_IDF
#include "esphome/core/automation.h"
#include "esphome/core/component.h"
#include "esphome/core/ring_buffer.h"
#include "esphome/components/microphone/microphone.h"
#include <tensorflow/lite/core/c/common.h>
#include <tensorflow/lite/micro/micro_interpreter.h>
#include <tensorflow/lite/micro/micro_mutable_op_resolver.h>
namespace esphome {
namespace micro_wake_word {
// The following are dictated by the preprocessor model
//
// The number of features the audio preprocessor generates per slice
static const uint8_t PREPROCESSOR_FEATURE_SIZE = 40;
// How frequently the preprocessor generates a new set of features
static const uint8_t FEATURE_STRIDE_MS = 20;
// Duration of each slice used as input into the preprocessor
static const uint8_t FEATURE_DURATION_MS = 30;
// Audio sample frequency in hertz
static const uint16_t AUDIO_SAMPLE_FREQUENCY = 16000;
// The number of old audio samples that are saved to be part of the next feature window
static const uint16_t HISTORY_SAMPLES_TO_KEEP =
((FEATURE_DURATION_MS - FEATURE_STRIDE_MS) * (AUDIO_SAMPLE_FREQUENCY / 1000));
// The number of new audio samples to receive to be included with the next feature window
static const uint16_t NEW_SAMPLES_TO_GET = (FEATURE_STRIDE_MS * (AUDIO_SAMPLE_FREQUENCY / 1000));
// The total number of audio samples included in the feature window
static const uint16_t SAMPLE_DURATION_COUNT = FEATURE_DURATION_MS * AUDIO_SAMPLE_FREQUENCY / 1000;
// Number of bytes in memory needed for the preprocessor arena
static const uint32_t PREPROCESSOR_ARENA_SIZE = 9528;
// The following configure the streaming wake word model
//
// The number of audio slices to process before accepting a positive detection
static const uint8_t MIN_SLICES_BEFORE_DETECTION = 74;
// Number of bytes in memory needed for the streaming wake word model
static const uint32_t STREAMING_MODEL_ARENA_SIZE = 64000;
static const uint32_t STREAMING_MODEL_VARIABLE_ARENA_SIZE = 1024;
enum State {
IDLE,
START_MICROPHONE,
STARTING_MICROPHONE,
DETECTING_WAKE_WORD,
STOP_MICROPHONE,
STOPPING_MICROPHONE,
};
class MicroWakeWord : public Component {
public:
void setup() override;
void loop() override;
float get_setup_priority() const override;
void start();
void stop();
bool is_running() const { return this->state_ != State::IDLE; }
bool initialize_models();
// Increasing either of these will reduce the rate of false acceptances while increasing the false rejection rate
void set_probability_cutoff(float probability_cutoff) { this->probability_cutoff_ = probability_cutoff; }
void set_sliding_window_average_size(size_t size);
void set_microphone(microphone::Microphone *microphone) { this->microphone_ = microphone; }
Trigger<std::string> *get_wake_word_detected_trigger() const { return this->wake_word_detected_trigger_; }
void set_model_start(const uint8_t *model_start) { this->model_start_ = model_start; }
void set_wake_word(const std::string &wake_word) { this->wake_word_ = wake_word; }
protected:
void set_state_(State state);
int read_microphone_();
const uint8_t *model_start_;
std::string wake_word_;
microphone::Microphone *microphone_{nullptr};
Trigger<std::string> *wake_word_detected_trigger_ = new Trigger<std::string>();
State state_{State::IDLE};
HighFrequencyLoopRequester high_freq_;
std::unique_ptr<RingBuffer> ring_buffer_;
int16_t *input_buffer_;
const tflite::Model *preprocessor_model_{nullptr};
const tflite::Model *streaming_model_{nullptr};
tflite::MicroInterpreter *streaming_interpreter_{nullptr};
tflite::MicroInterpreter *preprocessor_interperter_{nullptr};
std::vector<float> recent_streaming_probabilities_;
size_t last_n_index_{0};
float probability_cutoff_{0.5};
size_t sliding_window_average_size_{10};
// When the wake word detection first starts or after the word has been detected once, we ignore this many audio
// feature slices before accepting a positive detection again
int16_t ignore_windows_{-MIN_SLICES_BEFORE_DETECTION};
uint8_t *streaming_var_arena_{nullptr};
uint8_t *streaming_tensor_arena_{nullptr};
uint8_t *preprocessor_tensor_arena_{nullptr};
int8_t *new_features_data_{nullptr};
tflite::MicroResourceVariables *mrv_{nullptr};
// Stores audio fed into feature generator preprocessor
int16_t *preprocessor_audio_buffer_;
int16_t *preprocessor_stride_buffer_;
bool detected_{false};
/** Detects if wake word has been said
*
* If enough audio samples are available, it will generate one slice of new features.
* If the streaming model predicts the wake word, then the nonstreaming model confirms it.
* @param ring_Buffer Ring buffer containing raw audio samples
* @return True if the wake word is detected, false otherwise
*/
bool detect_wake_word_();
/// @brief Returns true if there are enough audio samples in the buffer to generate another slice of features
bool slice_available_();
/** Shifts previous feature slices over by one and generates a new slice of features
*
* @param ring_buffer ring buffer containing raw audio samples
* @return True if a new slice of features was generated, false otherwise
*/
bool update_features_();
/** Generates features from audio samples
*
* Adapted from TFLite micro speech example
* @param audio_data Pointer to array with the audio samples
* @param audio_data_size The number of samples to use as input to the preprocessor model
* @param feature_output Array that will store the features
* @return True if successful, false otherwise.
*/
bool generate_single_feature_(const int16_t *audio_data, int audio_data_size,
int8_t feature_output[PREPROCESSOR_FEATURE_SIZE]);
/** Performs inference over the most recent feature slice with the streaming model
*
* @return Probability of the wake word between 0.0 and 1.0
*/
float perform_streaming_inference_();
/** Strides the audio samples by keeping the last 10 ms of the previous slice
*
* Adapted from the TFLite micro speech example
* @param ring_buffer Ring buffer containing raw audio samples
* @param audio_samples Pointer to an array that will store the strided audio samples
* @return True if successful, false otherwise
*/
bool stride_audio_samples_(int16_t **audio_samples);
/// @brief Returns true if successfully registered the preprocessor's TensorFlow operations
bool register_preprocessor_ops_(tflite::MicroMutableOpResolver<18> &op_resolver);
/// @brief Returns true if successfully registered the streaming model's TensorFlow operations
bool register_streaming_ops_(tflite::MicroMutableOpResolver<14> &op_resolver);
};
template<typename... Ts> class StartAction : public Action<Ts...>, public Parented<MicroWakeWord> {
public:
void play(Ts... x) override { this->parent_->start(); }
};
template<typename... Ts> class StopAction : public Action<Ts...>, public Parented<MicroWakeWord> {
public:
void play(Ts... x) override { this->parent_->stop(); }
};
template<typename... Ts> class IsRunningCondition : public Condition<Ts...>, public Parented<MicroWakeWord> {
public:
bool check(Ts... x) override { return this->parent_->is_running(); }
};
} // namespace micro_wake_word
} // namespace esphome
#endif // USE_ESP_IDF
#endif // CLANG_TIDY
tests/components/micro_wake_word/test.esp32-s3-idf.yaml
+15
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i2s_audio:
i2s_lrclk_pin: GPIO18
i2s_bclk_pin: GPIO19
microphone:
- platform: i2s_audio
id: echo_microphone
i2s_din_pin: GPIO17
adc_type: external
pdm: true
micro_wake_word:
model: hey_jarvis
on_wake_word_detected:
- logger.log: "Wake word detected"