diff --git a/esphome/components/micro_wake_word/__init__.py b/esphome/components/micro_wake_word/__init__.py index 35ee3cfedc..3d3459ccab 100644 --- a/esphome/components/micro_wake_word/__init__.py +++ b/esphome/components/micro_wake_word/__init__.py @@ -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)}) diff --git a/esphome/components/micro_wake_word/audio_preprocessor_int8_model_data.h b/esphome/components/micro_wake_word/audio_preprocessor_int8_model_data.h deleted file mode 100644 index 918e76045f..0000000000 --- a/esphome/components/micro_wake_word/audio_preprocessor_int8_model_data.h +++ /dev/null @@ -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, - 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-} // namespace micro_wake_word -} // namespace esphome - -#endif // USE_ESP_IDF diff --git a/esphome/components/micro_wake_word/micro_wake_word.cpp b/esphome/components/micro_wake_word/micro_wake_word.cpp index 5a89708127..b58c7ec434 100644 --- a/esphome/components/micro_wake_word/micro_wake_word.cpp +++ b/esphome/components/micro_wake_word/micro_wake_word.cpp @@ -1,12 +1,5 @@ #include "micro_wake_word.h" - -/** - * This is a workaround until we can figure out a way to get - * the tflite-micro idf component code available in CI - * - * */ -// -#ifndef CLANG_TIDY +#include "streaming_model.h" #ifdef USE_ESP_IDF @@ -14,13 +7,13 @@ #include "esphome/core/helpers.h" #include "esphome/core/log.h" -#include "audio_preprocessor_int8_model_data.h" +#include +#include #include #include #include -#include #include namespace esphome { @@ -29,9 +22,9 @@ namespace micro_wake_word { static const char *const TAG = "micro_wake_word"; static const size_t SAMPLE_RATE_HZ = 16000; // 16 kHz -static const size_t BUFFER_LENGTH = 500; // 0.5 seconds +static const size_t BUFFER_LENGTH = 64; // 0.064 seconds static const size_t BUFFER_SIZE = SAMPLE_RATE_HZ / 1000 * BUFFER_LENGTH; -static const size_t INPUT_BUFFER_SIZE = 32 * SAMPLE_RATE_HZ / 1000; // 32ms * 16kHz / 1000ms +static const size_t INPUT_BUFFER_SIZE = 16 * SAMPLE_RATE_HZ / 1000; // 16ms * 16kHz / 1000ms float MicroWakeWord::get_setup_priority() const { return setup_priority::AFTER_CONNECTION; } @@ -56,58 +49,56 @@ static const LogString *micro_wake_word_state_to_string(State state) { void MicroWakeWord::dump_config() { ESP_LOGCONFIG(TAG, "microWakeWord:"); - ESP_LOGCONFIG(TAG, " Wake Word: %s", this->get_wake_word().c_str()); - ESP_LOGCONFIG(TAG, " Probability cutoff: %.3f", this->probability_cutoff_); - ESP_LOGCONFIG(TAG, " Sliding window size: %d", this->sliding_window_average_size_); + ESP_LOGCONFIG(TAG, " models:"); + for (auto &model : this->wake_word_models_) { + model.log_model_config(); + } +#ifdef USE_MICRO_WAKE_WORD_VAD + this->vad_model_->log_model_config(); +#endif } void MicroWakeWord::setup() { ESP_LOGCONFIG(TAG, "Setting up microWakeWord..."); - if (!this->initialize_models()) { - ESP_LOGE(TAG, "Failed to initialize models"); - this->mark_failed(); - return; - } - - ExternalRAMAllocator allocator(ExternalRAMAllocator::ALLOW_FAILURE); - this->input_buffer_ = allocator.allocate(INPUT_BUFFER_SIZE * sizeof(int16_t)); - if (this->input_buffer_ == nullptr) { - ESP_LOGW(TAG, "Could not allocate input buffer"); - this->mark_failed(); - return; - } - - this->ring_buffer_ = RingBuffer::create(BUFFER_SIZE * sizeof(int16_t)); - if (this->ring_buffer_ == nullptr) { - ESP_LOGW(TAG, "Could not allocate ring buffer"); + if (!this->register_streaming_ops_(this->streaming_op_resolver_)) { this->mark_failed(); return; } ESP_LOGCONFIG(TAG, "Micro Wake Word initialized"); + + this->frontend_config_.window.size_ms = FEATURE_DURATION_MS; + this->frontend_config_.window.step_size_ms = this->features_step_size_; + this->frontend_config_.filterbank.num_channels = PREPROCESSOR_FEATURE_SIZE; + this->frontend_config_.filterbank.lower_band_limit = 125.0; + this->frontend_config_.filterbank.upper_band_limit = 7500.0; + this->frontend_config_.noise_reduction.smoothing_bits = 10; + this->frontend_config_.noise_reduction.even_smoothing = 0.025; + this->frontend_config_.noise_reduction.odd_smoothing = 0.06; + this->frontend_config_.noise_reduction.min_signal_remaining = 0.05; + this->frontend_config_.pcan_gain_control.enable_pcan = 1; + this->frontend_config_.pcan_gain_control.strength = 0.95; + this->frontend_config_.pcan_gain_control.offset = 80.0; + this->frontend_config_.pcan_gain_control.gain_bits = 21; + this->frontend_config_.log_scale.enable_log = 1; + this->frontend_config_.log_scale.scale_shift = 6; } -int MicroWakeWord::read_microphone_() { - size_t bytes_read = this->microphone_->read(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t)); - if (bytes_read == 0) { - return 0; - } - - 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); +void MicroWakeWord::add_wake_word_model(const uint8_t *model_start, float probability_cutoff, + size_t sliding_window_average_size, const std::string &wake_word, + size_t tensor_arena_size) { + this->wake_word_models_.emplace_back(model_start, probability_cutoff, sliding_window_average_size, wake_word, + tensor_arena_size); } +#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(model_start, probability_cutoff, sliding_window_size, tensor_arena_size); +} +#endif + void MicroWakeWord::loop() { switch (this->state_) { case State::IDLE: @@ -124,9 +115,12 @@ void MicroWakeWord::loop() { } break; case State::DETECTING_WAKE_WORD: - this->read_microphone_(); - if (this->detect_wake_word_()) { - ESP_LOGD(TAG, "Wake Word Detected"); + while (!this->has_enough_samples_()) { + this->read_microphone_(); + } + this->update_model_probabilities_(); + if (this->detect_wake_words_()) { + ESP_LOGD(TAG, "Wake Word '%s' Detected", (this->detected_wake_word_).c_str()); this->detected_ = true; this->set_state_(State::STOP_MICROPHONE); } @@ -136,13 +130,16 @@ void MicroWakeWord::loop() { this->microphone_->stop(); this->set_state_(State::STOPPING_MICROPHONE); this->high_freq_.stop(); + this->unload_models_(); + this->deallocate_buffers_(); break; case State::STOPPING_MICROPHONE: if (this->microphone_->is_stopped()) { this->set_state_(State::IDLE); if (this->detected_) { + this->wake_word_detected_trigger_->trigger(this->detected_wake_word_); this->detected_ = false; - this->wake_word_detected_trigger_->trigger(this->wake_word_); + this->detected_wake_word_ = ""; } } break; @@ -150,14 +147,34 @@ void MicroWakeWord::loop() { } void MicroWakeWord::start() { + if (!this->is_ready()) { + ESP_LOGW(TAG, "Wake word detection can't start as the component hasn't been setup yet"); + return; + } + if (this->is_failed()) { ESP_LOGW(TAG, "Wake word component is marked as failed. Please check setup logs"); return; } + + if (!this->load_models_() || !this->allocate_buffers_()) { + ESP_LOGE(TAG, "Failed to load the wake word model(s) or allocate buffers"); + this->status_set_error(); + } else { + this->status_clear_error(); + } + + if (this->status_has_error()) { + ESP_LOGW(TAG, "Wake word component has an error. Please check logs"); + return; + } + if (this->state_ != State::IDLE) { ESP_LOGW(TAG, "Wake word is already running"); return; } + + this->reset_states_(); this->set_state_(State::START_MICROPHONE); } @@ -179,289 +196,218 @@ void MicroWakeWord::set_state_(State state) { this->state_ = state; } -bool MicroWakeWord::initialize_models() { - ExternalRAMAllocator arena_allocator(ExternalRAMAllocator::ALLOW_FAILURE); - ExternalRAMAllocator features_allocator(ExternalRAMAllocator::ALLOW_FAILURE); +size_t MicroWakeWord::read_microphone_() { + size_t bytes_read = this->microphone_->read(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t)); + if (bytes_read == 0) { + return 0; + } + + size_t bytes_free = this->ring_buffer_->free(); + + if (bytes_free < bytes_read) { + ESP_LOGW(TAG, + "Not enough free bytes in ring buffer to store incoming audio data (free bytes=%d, incoming bytes=%d). " + "Resetting the ring buffer. Wake word detection accuracy will be reduced.", + bytes_free, bytes_read); + + this->ring_buffer_->reset(); + } + + return this->ring_buffer_->write((void *) this->input_buffer_, bytes_read); +} + +bool MicroWakeWord::allocate_buffers_() { ExternalRAMAllocator audio_samples_allocator(ExternalRAMAllocator::ALLOW_FAILURE); - this->streaming_tensor_arena_ = arena_allocator.allocate(STREAMING_MODEL_ARENA_SIZE); - if (this->streaming_tensor_arena_ == nullptr) { - ESP_LOGE(TAG, "Could not allocate the streaming model's tensor arena."); - return false; + if (this->input_buffer_ == nullptr) { + this->input_buffer_ = audio_samples_allocator.allocate(INPUT_BUFFER_SIZE * sizeof(int16_t)); + if (this->input_buffer_ == nullptr) { + ESP_LOGE(TAG, "Could not allocate input buffer"); + return false; + } } - this->streaming_var_arena_ = arena_allocator.allocate(STREAMING_MODEL_VARIABLE_ARENA_SIZE); - if (this->streaming_var_arena_ == nullptr) { - ESP_LOGE(TAG, "Could not allocate the streaming model variable's tensor arena."); - return false; - } - - this->preprocessor_tensor_arena_ = arena_allocator.allocate(PREPROCESSOR_ARENA_SIZE); - if (this->preprocessor_tensor_arena_ == nullptr) { - ESP_LOGE(TAG, "Could not allocate the audio preprocessor model's tensor arena."); - return false; - } - - this->new_features_data_ = features_allocator.allocate(PREPROCESSOR_FEATURE_SIZE); - if (this->new_features_data_ == nullptr) { - ESP_LOGE(TAG, "Could not allocate the audio features buffer."); - return false; - } - - this->preprocessor_audio_buffer_ = audio_samples_allocator.allocate(SAMPLE_DURATION_COUNT); if (this->preprocessor_audio_buffer_ == nullptr) { - ESP_LOGE(TAG, "Could not allocate the audio preprocessor's buffer."); - return false; + this->preprocessor_audio_buffer_ = audio_samples_allocator.allocate(this->new_samples_to_get_()); + if (this->preprocessor_audio_buffer_ == nullptr) { + ESP_LOGE(TAG, "Could not allocate the audio preprocessor's buffer."); + return false; + } } - this->preprocessor_model_ = tflite::GetModel(G_AUDIO_PREPROCESSOR_INT8_TFLITE); - if (this->preprocessor_model_->version() != TFLITE_SCHEMA_VERSION) { - ESP_LOGE(TAG, "Wake word's audio preprocessor model's schema is not supported"); - return false; - } - - this->streaming_model_ = tflite::GetModel(this->model_start_); - if (this->streaming_model_->version() != TFLITE_SCHEMA_VERSION) { - ESP_LOGE(TAG, "Wake word's streaming model's schema is not supported"); - 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; + if (this->ring_buffer_ == nullptr) { + this->ring_buffer_ = RingBuffer::create(BUFFER_SIZE * sizeof(int16_t)); + if (this->ring_buffer_ == nullptr) { + ESP_LOGE(TAG, "Could not allocate ring buffer"); + return false; + } } return true; } -float MicroWakeWord::perform_streaming_inference_() { - TfLiteTensor *input = this->streaming_interpreter_->input(0); - - size_t bytes_to_copy = input->bytes; - - memcpy((void *) (tflite::GetTensorData(input)), (const void *) (this->new_features_data_), bytes_to_copy); - - uint32_t prior_invoke = millis(); - - TfLiteStatus invoke_status = this->streaming_interpreter_->Invoke(); - if (invoke_status != kTfLiteOk) { - ESP_LOGW(TAG, "Streaming Interpreter Invoke failed"); - return false; - } - - ESP_LOGV(TAG, "Streaming Inference Latency=%" PRIu32 " ms", (millis() - prior_invoke)); - - TfLiteTensor *output = this->streaming_interpreter_->output(0); - - return static_cast(output->data.uint8[0]) / 255.0; +void MicroWakeWord::deallocate_buffers_() { + ExternalRAMAllocator audio_samples_allocator(ExternalRAMAllocator::ALLOW_FAILURE); + audio_samples_allocator.deallocate(this->input_buffer_, INPUT_BUFFER_SIZE * sizeof(int16_t)); + this->input_buffer_ = nullptr; + audio_samples_allocator.deallocate(this->preprocessor_audio_buffer_, this->new_samples_to_get_()); + this->preprocessor_audio_buffer_ = nullptr; } -bool MicroWakeWord::detect_wake_word_() { - // Preprocess the newest audio samples into features - if (!this->update_features_()) { +bool MicroWakeWord::load_models_() { + // Setup preprocesor feature generator + if (!FrontendPopulateState(&this->frontend_config_, &this->frontend_state_, AUDIO_SAMPLE_FREQUENCY)) { + ESP_LOGD(TAG, "Failed to populate frontend state"); + FrontendFreeStateContents(&this->frontend_state_); return false; } - // Perform inference - float streaming_prob = this->perform_streaming_inference_(); + // Setup streaming models + for (auto &model : this->wake_word_models_) { + if (!model.load_model(this->streaming_op_resolver_)) { + ESP_LOGE(TAG, "Failed to initialize a wake word model."); + return false; + } + } +#ifdef USE_MICRO_WAKE_WORD_VAD + if (!this->vad_model_->load_model(this->streaming_op_resolver_)) { + ESP_LOGE(TAG, "Failed to initialize VAD model."); + return false; + } +#endif - // Add the most recent probability to the sliding window - this->recent_streaming_probabilities_[this->last_n_index_] = streaming_prob; - ++this->last_n_index_; - if (this->last_n_index_ == this->sliding_window_average_size_) - this->last_n_index_ = 0; + return true; +} - float sum = 0.0; - for (auto &prob : this->recent_streaming_probabilities_) { - sum += prob; +void MicroWakeWord::unload_models_() { + FrontendFreeStateContents(&this->frontend_state_); + + 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(this->sliding_window_average_size_); - - // Ensure we have enough samples since the last positive detection + // Increase the counter since the last positive detection this->ignore_windows_ = std::min(this->ignore_windows_ + 1, 0); + + for (auto &model : this->wake_word_models_) { + // Perform inference + model.perform_streaming_inference(audio_features); + } +#ifdef USE_MICRO_WAKE_WORD_VAD + this->vad_model_->perform_streaming_inference(audio_features); +#endif +} + +bool MicroWakeWord::detect_wake_words_() { + // Verify we have processed samples since the last positive detection if (this->ignore_windows_ < 0) { return false; } - // Detect the wake word if the sliding window average is above the cutoff - if (sliding_window_average > this->probability_cutoff_) { - this->ignore_windows_ = -MIN_SLICES_BEFORE_DETECTION; - for (auto &prob : this->recent_streaming_probabilities_) { - prob = 0; - } +#ifdef USE_MICRO_WAKE_WORD_VAD + bool vad_state = this->vad_model_->determine_detected(); +#endif - ESP_LOGD(TAG, "Wake word sliding average probability is %.3f and most recent probability is %.3f", - sliding_window_average, streaming_prob); - return true; + for (auto &model : this->wake_word_models_) { + if (model.determine_detected()) { +#ifdef USE_MICRO_WAKE_WORD_VAD + if (vad_state) { +#endif + this->detected_wake_word_ = model.get_wake_word(); + return true; +#ifdef USE_MICRO_WAKE_WORD_VAD + } else { + ESP_LOGD(TAG, "Wake word model predicts %s, but VAD model doesn't.", model.get_wake_word().c_str()); + } +#endif + } } return false; } -void MicroWakeWord::set_sliding_window_average_size(size_t size) { - this->sliding_window_average_size_ = size; - this->recent_streaming_probabilities_.resize(this->sliding_window_average_size_, 0.0); +bool MicroWakeWord::has_enough_samples_() { + return this->ring_buffer_->available() >= + (this->features_step_size_ * (AUDIO_SAMPLE_FREQUENCY / 1000)) * sizeof(int16_t); } -bool MicroWakeWord::slice_available_() { - size_t available = this->ring_buffer_->available(); - - return available > (NEW_SAMPLES_TO_GET * sizeof(int16_t)); -} - -bool MicroWakeWord::stride_audio_samples_(int16_t **audio_samples) { - if (!this->slice_available_()) { +bool MicroWakeWord::generate_features_for_window_(int8_t features[PREPROCESSOR_FEATURE_SIZE]) { + // Ensure we have enough new audio samples in the ring buffer for a full window + if (!this->has_enough_samples_()) { return false; } - // Copy the last 320 bytes (160 samples over 10 ms) from the audio buffer to the start of the audio buffer - memcpy((void *) (this->preprocessor_audio_buffer_), (void *) (this->preprocessor_audio_buffer_ + NEW_SAMPLES_TO_GET), - HISTORY_SAMPLES_TO_KEEP * sizeof(int16_t)); - - // Copy 640 bytes (320 samples over 20 ms) from the ring buffer into the audio buffer offset 320 bytes (160 samples - // over 10 ms) - size_t bytes_read = this->ring_buffer_->read((void *) (this->preprocessor_audio_buffer_ + HISTORY_SAMPLES_TO_KEEP), - NEW_SAMPLES_TO_GET * sizeof(int16_t), pdMS_TO_TICKS(200)); + size_t bytes_read = this->ring_buffer_->read((void *) (this->preprocessor_audio_buffer_), + this->new_samples_to_get_() * sizeof(int16_t), pdMS_TO_TICKS(200)); if (bytes_read == 0) { ESP_LOGE(TAG, "Could not read data from Ring Buffer"); - } else if (bytes_read < NEW_SAMPLES_TO_GET * sizeof(int16_t)) { + } else if (bytes_read < this->new_samples_to_get_() * sizeof(int16_t)) { ESP_LOGD(TAG, "Partial Read of Data by Model"); ESP_LOGD(TAG, "Could only read %d bytes when required %d bytes ", bytes_read, - (int) (NEW_SAMPLES_TO_GET * sizeof(int16_t))); + (int) (this->new_samples_to_get_() * sizeof(int16_t))); return false; } - *audio_samples = this->preprocessor_audio_buffer_; - return true; -} + size_t num_samples_read; + struct FrontendOutput frontend_output = FrontendProcessSamples( + &this->frontend_state_, this->preprocessor_audio_buffer_, this->new_samples_to_get_(), &num_samples_read); -bool MicroWakeWord::generate_single_feature_(const int16_t *audio_data, const int audio_data_size, - int8_t feature_output[PREPROCESSOR_FEATURE_SIZE]) { - TfLiteTensor *input = this->preprocessor_interperter_->input(0); - TfLiteTensor *output = this->preprocessor_interperter_->output(0); - std::copy_n(audio_data, audio_data_size, tflite::GetTensorData(input)); - - if (this->preprocessor_interperter_->Invoke() != kTfLiteOk) { - ESP_LOGE(TAG, "Failed to preprocess audio for local wake word."); - return false; + for (size_t i = 0; i < frontend_output.size; ++i) { + // These scaling values are set to match the TFLite audio frontend int8 output. + // The feature pipeline outputs 16-bit signed integers in roughly a 0 to 670 + // range. In training, these are then arbitrarily divided by 25.6 to get + // float values in the rough range of 0.0 to 26.0. This scaling is performed + // for historical reasons, to match up with the output of other feature + // generators. + // The process is then further complicated when we quantize the model. This + // means we have to scale the 0.0 to 26.0 real values to the -128 to 127 + // signed integer numbers. + // All this means that to get matching values from our integer feature + // output into the tensor input, we have to perform: + // input = (((feature / 25.6) / 26.0) * 256) - 128 + // To simplify this and perform it in 32-bit integer math, we rearrange to: + // input = (feature * 256) / (25.6 * 26.0) - 128 + constexpr int32_t value_scale = 256; + constexpr int32_t value_div = 666; // 666 = 25.6 * 26.0 after rounding + int32_t value = ((frontend_output.values[i] * value_scale) + (value_div / 2)) / value_div; + value -= 128; + if (value < -128) { + value = -128; + } + if (value > 127) { + value = 127; + } + features[i] = value; } - std::memcpy(feature_output, tflite::GetTensorData(output), PREPROCESSOR_FEATURE_SIZE * sizeof(int8_t)); return true; } -bool MicroWakeWord::register_preprocessor_ops_(tflite::MicroMutableOpResolver<18> &op_resolver) { - if (op_resolver.AddReshape() != kTfLiteOk) - return false; - if (op_resolver.AddCast() != kTfLiteOk) - return false; - if (op_resolver.AddStridedSlice() != kTfLiteOk) - return false; - if (op_resolver.AddConcatenation() != kTfLiteOk) - return false; - if (op_resolver.AddMul() != kTfLiteOk) - return false; - if (op_resolver.AddAdd() != kTfLiteOk) - return false; - if (op_resolver.AddDiv() != kTfLiteOk) - return false; - if (op_resolver.AddMinimum() != kTfLiteOk) - return false; - if (op_resolver.AddMaximum() != kTfLiteOk) - return false; - if (op_resolver.AddWindow() != kTfLiteOk) - return false; - if (op_resolver.AddFftAutoScale() != kTfLiteOk) - return false; - if (op_resolver.AddRfft() != kTfLiteOk) - return false; - if (op_resolver.AddEnergy() != kTfLiteOk) - return false; - if (op_resolver.AddFilterBank() != kTfLiteOk) - return false; - if (op_resolver.AddFilterBankSquareRoot() != kTfLiteOk) - return false; - if (op_resolver.AddFilterBankSpectralSubtraction() != kTfLiteOk) - return false; - if (op_resolver.AddPCAN() != kTfLiteOk) - return false; - if (op_resolver.AddFilterBankLog() != kTfLiteOk) - return false; - - return true; +void MicroWakeWord::reset_states_() { + ESP_LOGD(TAG, "Resetting buffers and probabilities"); + this->ring_buffer_->reset(); + this->ignore_windows_ = -MIN_SLICES_BEFORE_DETECTION; + for (auto &model : this->wake_word_models_) { + model.reset_probabilities(); + } +#ifdef USE_MICRO_WAKE_WORD_VAD + this->vad_model_->reset_probabilities(); +#endif } -bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<17> &op_resolver) { +bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<20> &op_resolver) { if (op_resolver.AddCallOnce() != kTfLiteOk) return false; if (op_resolver.AddVarHandle() != kTfLiteOk) @@ -496,6 +442,12 @@ bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<17> & return false; if (op_resolver.AddMaxPool2D() != kTfLiteOk) return false; + if (op_resolver.AddPad() != kTfLiteOk) + return false; + if (op_resolver.AddPack() != kTfLiteOk) + return false; + if (op_resolver.AddSplitV() != kTfLiteOk) + return false; return true; } @@ -504,5 +456,3 @@ bool MicroWakeWord::register_streaming_ops_(tflite::MicroMutableOpResolver<17> & } // namespace esphome #endif // USE_ESP_IDF - -#endif // CLANG_TIDY diff --git a/esphome/components/micro_wake_word/micro_wake_word.h b/esphome/components/micro_wake_word/micro_wake_word.h index 1d7c18d686..0c805b75fc 100644 --- a/esphome/components/micro_wake_word/micro_wake_word.h +++ b/esphome/components/micro_wake_word/micro_wake_word.h @@ -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 + #include #include #include @@ -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 *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 *wake_word_detected_trigger_ = new Trigger(); State state_{State::IDLE}; @@ -102,85 +66,93 @@ class MicroWakeWord : public Component { std::unique_ptr ring_buffer_; - int16_t *input_buffer_; + std::vector 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 vad_model_; +#endif - std::vector 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 class StartAction : public Action, public Parented { @@ -202,5 +174,3 @@ template class IsRunningCondition : public Condition, pub } // namespace esphome #endif // USE_ESP_IDF - -#endif // CLANG_TIDY diff --git a/esphome/components/micro_wake_word/preprocessor_settings.h b/esphome/components/micro_wake_word/preprocessor_settings.h new file mode 100644 index 0000000000..03f4fb5230 --- /dev/null +++ b/esphome/components/micro_wake_word/preprocessor_settings.h @@ -0,0 +1,20 @@ +#pragma once + +#ifdef USE_ESP_IDF + +#include + +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 diff --git a/esphome/components/micro_wake_word/streaming_model.cpp b/esphome/components/micro_wake_word/streaming_model.cpp new file mode 100644 index 0000000000..013fa2ce6e --- /dev/null +++ b/esphome/components/micro_wake_word/streaming_model.cpp @@ -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 arena_allocator(ExternalRAMAllocator::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::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 arena_allocator(ExternalRAMAllocator::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(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(sum) / static_cast(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 diff --git a/esphome/components/micro_wake_word/streaming_model.h b/esphome/components/micro_wake_word/streaming_model.h new file mode 100644 index 0000000000..0d85579f35 --- /dev/null +++ b/esphome/components/micro_wake_word/streaming_model.h @@ -0,0 +1,84 @@ +#pragma once + +#ifdef USE_ESP_IDF + +#include "preprocessor_settings.h" + +#include +#include +#include + +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 recent_streaming_probabilities_; + + const uint8_t *model_start_; + uint8_t *tensor_arena_{nullptr}; + uint8_t *var_arena_{nullptr}; + std::unique_ptr 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 diff --git a/esphome/core/defines.h b/esphome/core/defines.h index 1e6f3517db..4831ed2c9e 100644 --- a/esphome/core/defines.h +++ b/esphome/core/defines.h @@ -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 diff --git a/platformio.ini b/platformio.ini index a72bf598c5..f07889526f 100644 --- a/platformio.ini +++ b/platformio.ini @@ -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 diff --git a/tests/components/micro_wake_word/common.yaml b/tests/components/micro_wake_word/common.yaml index c0f3593cc6..8bd7345307 100644 --- a/tests/components/micro_wake_word/common.yaml +++ b/tests/components/micro_wake_word/common.yaml @@ -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 diff --git a/tests/components/micro_wake_word/test.esp32-idf.yaml b/tests/components/micro_wake_word/test.esp32-idf.yaml new file mode 100644 index 0000000000..dade44d145 --- /dev/null +++ b/tests/components/micro_wake_word/test.esp32-idf.yaml @@ -0,0 +1 @@ +<<: !include common.yaml