Oído: Conformer-CTC Small, int8, for the ESP32-S3

¡Oído! is Spanish kitchen slang for heard, got it.

This is open-vocabulary English speech recognition that runs entirely on an ESP32-S3 microcontroller (240 MHz dual-core, 8 MB PSRAM, 16 MB flash), with no cloud and no neural accelerator. The file nemo8.tnm is NVIDIA's stt_en_conformer_ctc_small (13 M parameters), quantized to int8 and packed for Lokutor's on-chip engine.

Code, firmware and tools: github.com/lokutor-ai/oido (GPLv3, commercial licenses available).

Status (30 September 2026). The WERs are computed with the host build of the firmware engine, whose arithmetic is bit-identical to the chip's. Firmware transcripts under Espressif's QEMU emulator match it word for word. Real-time speed is estimated at 0.7–0.95× real time from exact instruction counts; measurements on physical boards are coming.

Use

git clone https://github.com/lokutor-ai/oido && cd oido
esp32/host/tasr_cli models/nemo8.tnm recording.wav          # laptop, chip-exact arithmetic (after `make` in esp32/host)
esp32/tools/flash.sh /dev/ttyUSB0 models/nemo8.tnm          # ESP32-S3-DevKitC-1 N16R8 + INMP441 microphone

Files

• nemo8.tnm: int8 weights in the TNM1 format (14.0 MB), produced by train/export_nemo.py in the GitHub repository.
• tokenizer.model: the original SentencePiece tokenizer (1024 BPE pieces).

License and attribution

Derived from NVIDIA's stt_en_conformer_ctc_small, licensed CC-BY-4.0. Changes: re-implemented in PyTorch, quantized to int8 and repacked. This model is also released under CC-BY-4.0. The engine and firmware are GPLv3, with commercial licenses from Lokutor.