{"id":4488,"date":"2022-02-27T18:54:02","date_gmt":"2022-02-27T17:54:02","guid":{"rendered":"https:\/\/www.pschatzmann.ch\/home\/?p=4488"},"modified":"2022-04-07T13:06:13","modified_gmt":"2022-04-07T11:06:13","slug":"an-introduction-into-speech-recogntion-with-arduino","status":"publish","type":"post","link":"https:\/\/www.pschatzmann.ch\/home\/2022\/02\/27\/an-introduction-into-speech-recogntion-with-arduino\/","title":{"rendered":"An Introduction to Speech Recognition with Arduino"},"content":{"rendered":"<p>The staring point for doing <strong>speech recognition<\/strong> on an Arduino based board is <a href=\"https:\/\/www.tensorflow.org\/lite\/microcontrollers\/get_started_low_level#train_a_model\">TensorFlow Light For Microcontrollers<\/a> with the example sketch called micro_speech!<\/p>\n<p>There are quite a few <strong>alternative Arduino Libraries<\/strong> for <strong>TensorFlow Light<\/strong> but honestly the situation is rather dire and I could not find any version which would fit my requirements<\/p>\n<ul>\n<li>support multiple microcontrollers  (ESP32, RP2040 and Arduino Based MBED Implementations (e.g. Nano BLE Sense)) w\/o compile errors<\/li>\n<li>no further dependencies<\/li>\n<\/ul>\n<p>It took me quite some time to find the <strong>official repository<\/strong> on Github which is called <a href=\"https:\/\/github.com\/tensorflow\/tflite-micro-arduino-examples\">tflite-micro-arduino-examples<\/a>, but the only processor that seems to work out of the box is the <strong>Nano BLE Sense<\/strong>.<\/p>\n<p>So I cloned the repository to make some corrections for the RP2040 and ESP32. The updated repository can be found <a href=\"https:\/\/github.com\/pschatzmann\/tflite-micro-arduino-examples\">here<\/a>!<\/p>\n<p>Corrected Issues:<\/p>\n<ul>\n<li>std::fmax and std::fmin do not exist on ESP32<\/li>\n<li>TF_LITE_REMOVE_VIRTUAL_DELETE is not public for GreedyMemoryPlanner and MicroErrorReporter on ESP32<\/li>\n<li>RingBufferN is not available for all microcontrollers<\/li>\n<li>Missing implementation for DebugLog, SerialReadLine and SerialWrite for all processors except ARDUINO_ARDUINO_NANO33BLE<\/li>\n<\/ul>\n<p>The next challenge was to to <strong>understand the example<\/strong> and well, it is also quite confusing. Please note the provided examples are still not compiling because the audio provider has not been implemented. And honestly I think it is not worth the effort!<\/p>\n<p>Instead, I decided to try to go for an <strong>alternative design<\/strong> which follows the Arduino style of doing things: In my vision it should be very easy to implement a new <strong>micro-speech sketch<\/strong>. So a sketch might look as follows:<\/p>\n<pre><code>#include \"AudioTools.h\"\n#include \"AudioLibs\/AudioKit.h\"\n#include \"AudioLibs\/TfLiteAudioOutput.h\"\n#include \"model.h\" \/\/ tensorflow model\n\nAudioKitStream kit; \/\/Audio source\nTfLiteAudioFeatureProvider fp;\nTfLiteAudioOutput&lt;4&gt; tfl; \/\/ Audio sink\nconst char* kCategoryLabels[4] = {\n    \"silence\",\n    \"unknown\",\n    \"yes\",\n    \"no\",\n};\nStreamCopy copier(tfl, kit); \/\/ copy mic to tfl\nint channels = 2;\nint samples_per_second = 16000;\n\nvoid setup() {\n    Serial.begin(115200);\n    AudioLogger::instance().begin(Serial, AudioLogger::Warning);\n\n    \/\/ setup Audiokit\n    auto cfg = kit.defaultConfig(RX_MODE);\n    cfg.input_device = AUDIO_HAL_ADC_INPUT_LINE2;\n    cfg.channels = channels;\n    cfg.sample_rate = samples_per_second;\n    kit.begin(cfg);\n\n    \/\/ Setup tensorflow \n    fp.kAudioChannels = channels;\n    fp.kAudioSampleFrequency = samples_per_second;\n    tfl.begin(g_model, fp, kCategoryLabels, 10 * 1024);\n}\n\nvoid loop() {\n    copier.copy();\n}\n<\/code><\/pre>\n<p>In a nutshell, there is no more confusing logic distributed over 17 different files &#8211; but just a single simple Arduino Sketch with just a few lines of code: The TfLiteAudioOutput would implement the <strong>Arduino Print<\/strong> interface, so that we can write the audio data to it from any audio source and with the begin method we pass all relevant information which is needed for the processing!<\/p>\n<p>Keep tuned&#8230;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The staring point for doing speech recognition on an Arduino based board is TensorFlow Light For Microcontrollers with the example sketch called micro_speech! There are quite a few alternative Arduino Libraries for TensorFlow Light but honestly the situation is rather dire and I could not find any version which would [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":4489,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[20,14,22],"tags":[28],"class_list":["post-4488","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-arduino","category-machine-learning","category-machine-sound","tag-esp32audiokit"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>An Introduction to Speech Recognition with Arduino - Phil Schatzmann<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.pschatzmann.ch\/home\/2022\/02\/27\/an-introduction-into-speech-recogntion-with-arduino\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An Introduction to Speech Recognition with Arduino - Phil Schatzmann\" \/>\n<meta property=\"og:description\" content=\"The staring point for doing speech recognition on an Arduino based board is TensorFlow Light For Microcontrollers with the example sketch called micro_speech! 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