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Using TensorFlowJS (Machine Learning) for Speech Recognition on ESP8266

April 14, 20192 min read

📺 Watch the Video View on YouTube --- Use TensorFlowJS via WebAudio API and WebGL GPU acceleration on Browser to recognize "keywords". In our case, w...

ESP8266 browser machine learning TensorFlowJS TensorFlow debashish sahu

Watch on YouTube

📺 Watch the Video

View on YouTube

Use TensorFlowJS via WebAudio API and WebGL GPU acceleration on Browser to recognize "keywords". In our case, without retraining "UP" turns on LED and "DOWN" turns it off.

FFT on ESP32

📺 Watch the Video

View on YouTube

GitHub: debsahu/SpeechRecognitionTensorFlowJS

Speech Recognition on Browser, AsyncWebServer served on ESP8266 to control LED_BUILTIN/GPIO16

Uses WebAudio API and WebGL GPU acceleration = speech recognition is done on the browser
http:// requests for microphone is blocked for chrome, use firefox instead
tf.min.js and speech-commands.min.js served from SPIFFs (1MB Program/3MB SPIFFs partition needed)
/upload and /update is a morden world's take on updates to ESP8266
Uses HTML templates to report LED_BUILTIN/GPIO16 status
Speech recognition: "UP" = ON and "DOWN" = OFF, "RIGHT" and "LEFT" ignored

Arduino Libraries needed

platformio.ini is included, use PlatformIO and it will take care of installing the following libraries.

Library	Link
ESPAsyncTCP	https://github.com/me-no-dev/ESPAsyncTCP
ESPAsyncWiFiManager	https://github.com/alanswx/ESPAsyncWiFiManager
ESP Async WebServer	https://github.com/me-no-dev/ESPAsyncWebServer

Credits

Inspiration(and major code with regards to JS): Rubik's Code
TensorFlowJS Documentation: Speech recognition
@RuiSantosdotme for HTML Templates

Technologies Used

ESP8266 browser machine learning TensorFlowJS TensorFlow debashish sahu

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