Deploy TensorFlow Lite Micro Voice Recognition on ESP32 with I2S Mic Input

Deploying a TensorFlow Lite Micro Voice Recognition Model on ESP32 with I2S Microphone Input

In this tutorial, we will guide you through the process of deploying a TensorFlow Lite Micro voice recognition model on an ESP32 microcontroller using an I2S microphone as input. This project will help you build a voice recognition system that can run locally on the ESP32 without requiring an internet connection.

Prerequisites

• Basic knowledge of programming in C/C++.
• Familiarity with Arduino IDE and ESP32 development.
• Installed Arduino IDE with ESP32 board support.
• TensorFlow Lite Micro library.
• I2S microphone (e.g., INMP441).
• ESP32 development board.

Parts/Tools

• ESP32 Development Board
• I2S Microphone Module (e.g., INMP441)
• Jumper wires
• Arduino IDE
• TensorFlow Lite Micro library (installed via Arduino Library Manager)

Steps

1. Set Up Arduino IDE for ESP32
   1. Open Arduino IDE and go to File > Preferences.
   2. In the “Additional Board Manager URLs” field, add the ESP32 board URL:

      https://dl.espressif.com/dl/package_esp32_index.json

   3. Go to Tools > Board > Board Manager, search for “ESP32”, and install the latest version.
2. Wire the I2S Microphone to ESP32
   1. Connect the INMP441 microphone pins to the ESP32 as follows:
      • VCC to 3.3V
      • GND to GND
      • SD to GPIO 34
      • LRCK to GPIO 25
      • BCLK to GPIO 26
3. Install TensorFlow Lite Micro Library
   1. In Arduino IDE, navigate to Tools > Manage Libraries.
   2. Search for “TensorFlow Lite Micro” and install it.
4. Prepare the Voice Recognition Model
   1. Train your voice recognition model using TensorFlow and convert it to TensorFlow Lite format.
   2. Use the TensorFlow Lite Converter to generate the model files.
   3. Your model should be a `.tflite` file.
5. Write the ESP32 Code
   1. Create a new sketch in Arduino IDE and include the necessary libraries:

      #include 
      #include 
      #include 
      #include 
      #include 
      #include 
      #include 

   2. Initialize I2S for the microphone input:

      void setup() {
          i2s_config_t i2s_config = {
              .intr_alloc = 0,
              .mode = I2S_MODE_MASTER | I2S_MODE_RX,
              .sample_rate = 44100,
              .bits_per_sample = I2S_BITS_PER_SAMPLE_16BIT,
              .channel_format = I2S_CHANNEL_FMT_RIGHT_LEFT,
              .communication_format = I2S_COMM_FORMAT_I2S,
              .dma_buf_count = 8,
              .dma_buf_len = 1024,
              .use_apll = false,
          };
          i2s_driver_install(I2S_NUM_0, &i2s_config, 0, NULL);
      }

   3. Load the TensorFlow Lite model and set up the interpreter:

      const tflite::Model* model = tflite::GetModel(your_model_data);
      tflite::MicroInterpreter interpreter(model, tensor_arena, kTensorArenaSize, resolver, &error_reporter);

   4. Implement the loop function to read audio data and process it:

      void loop() {
          int16_t audio_buffer[buffer_size];
          size_t bytes_read;
          i2s_read(I2S_NUM_0, audio_buffer, sizeof(audio_buffer), &bytes_read, portMAX_DELAY);
          
          // Process audio with TensorFlow Lite
          interpreter.Invoke();
      }

6. Upload and Test
   1. Connect your ESP32 to your computer and select the correct board and port in Arduino IDE.
   2. Click on the upload button to compile and upload your code.
   3. Open the Serial Monitor to view the output and test the voice recognition.

Troubleshooting

• Microphone Not Responding: Ensure all connections are secure and the microphone is powered.
• Model Not Loading: Verify that the model path and file are correctly specified in the code.
• Audio Quality Issues: Check the I2S configuration and ensure the sample rate matches your model’s requirements.

Conclusion

By following this tutorial, you have successfully deployed a TensorFlow Lite Micro voice recognition model on an ESP32 using an I2S microphone. This project can be expanded for various applications, such as voice-activated controls or custom voice commands. Experiment with different models and configurations to enhance your project further!