This content originally appeared on DEV Community and was authored by ZedIoT
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 |
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()
4. Deployment to MCU
Convert .tflite → C array:
xxd -i model.tflite > model_data.cc
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");
}
}
Performance on ESP32-S3:
| Metric | Value |
| ----------------- | -------- |
| Inference latency | 50–60 ms |
| FPS | 15–20 |
| Model size | ~240 KB |
| RAM usage | ~350 KB |
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
.tflitemodels
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/
This content originally appeared on DEV Community and was authored by ZedIoT
ZedIoT | Sciencx (2025-11-25T05:56:28+00:00) ESP32-S3 + TensorFlow Lite Micro: A Practical Guide to Local Wake Word & Edge AI Inference. Retrieved from https://www.scien.cx/2025/11/25/esp32-s3-tensorflow-lite-micro-a-practical-guide-to-local-wake-word-edge-ai-inference/
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