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ESP32-S3 + TensorFlow Lite Micro: A Practical Guide to Local Wake Word & Edge AI Inference

#machinelearning #esp32 #edgeai #tensorflow

This post breaks down how we deploy TensorFlow Lite Micro (TFLM) on ESP32-S3 to run real-time wake word detection and other edge-AI workloads.
If you're exploring embedded ML on MCUs, this is a practical reference.

Why ESP32-S3 for embedded inference?

ESP32-S3 brings a useful combination of:

  • Xtensa LX7 dual-core @ 240 MHz
  • Vector acceleration for DSP/NN ops
  • 512 KB SRAM + PSRAM options
  • I2S, SPI, ADC, UART
  • Wi-Fi + BLE

It’s powerful enough to run quantized CNNs for audio, IMU, and multimodal workloads while staying power-efficient.

Pipeline: From microphone to inference

1. Audio front-end

  • I2S MEMS microphones (INMP441 / SPH0645 / MSM261S4030)
  • 16 kHz / 16-bit / mono
  • 40 ms frames (~640 samples)

Preprocessing steps:

  • High-pass filter
  • Pre-emphasis
  • Windowing (Hamming)
  • VAD (optional)

ESP-DSP supports optimized FFT, DCT, and filtering primitives.

2. Feature extraction (MFCC)
MFCC remains the standard for low-power speech workloads:

  • FFT
  • Mel filter banks
  • Log scaling
  • DCT → 10–13 coefficients

On ESP32-S3, MFCC extraction typically takes 2–3 ms per frame.

3. Compact CNN model

Typical architecture for wake-word detection:
| Layer           | Output Example |
| --------------- | -------------- |
| Conv2D + ReLU   | 20×10×16       |
| DepthwiseConv2D | 10×5×32        |
| Flatten         | 1600           |
| Dense + Softmax | 2 classes      |
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Model size after int8 quantization: 100–300 KB.
Convert & quantize:

converter = tf.lite.TFLiteConverter.from_saved_model("model_path")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.int8]
tflite_quant_model = converter.convert()
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4. Deployment to MCU
Convert .tflite → C array:

xxd -i model.tflite > model_data.cc
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Load + run with TensorFlow Lite Micro:

const tflite::Model* model = tflite::GetModel(model_data);
static tflite::MicroInterpreter interpreter(...);
interpreter.AllocateTensors();

while (true) {
    GetAudioFeature(input->data.int8);
    interpreter.Invoke();
    if (output->data.uint8[0] > 200) {
        printf("Wake word detected!\n");
    }
}
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Performance on ESP32-S3:

| Metric            | Value    |
| ----------------- | -------- |
| Inference latency | 50–60 ms |
| FPS               | 15–20    |
| Model size        | ~240 KB  |
| RAM usage         | ~350 KB  |
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Beyond wake words: What else runs well on TFLM?

Because the workflow is generalizable, simply swapping the model unlocks new tasks:

Environmental sound classification
Glass break, alarm, pet sound detection
(8–12 FPS depending on model)

Vibration & anomaly detection
Predictive maintenance for pumps, motors, or fans.

IMU-based gesture recognition
Hand-wave, wrist-raise, walking/running classification.

Multimodal environmental semantics
Fuse sound + IMU + temperature/light for context-aware devices.

OTA updates = evolving intelligence

A major advantage of MCU-based AI:

  • Cloud trains models
  • Device runs inference locally
  • OTA delivers updated .tflite models

This keeps devices adaptable across noise changes, accents, or new product features.

Use cases we see in real deployments

  • Offline voice interfaces
  • Industrial sound/vibration monitoring
  • Wearable gesture recognition
  • Smart home acoustics
  • Retail terminals with local AI

ESP32-S3 provides a good balance of cost, flexibility, and inference performance.

Full article with diagrams / extended explanation

This Dev.to post is the short version.
Full technical deep-dive is here:
👉 https://zediot.com/blog/esp32-s3-tensorflow-lite-micro/

Need help building an ESP32-S3 or embedded AI system?

We design:

  • Wake-word engines
  • TensorFlow Lite Micro model deployment
  • Embedded AI prototypes
  • IoT + Edge AI solutions

Contact: https://zediot.com/contact/

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