Oído: Open-Source Speech Recognition on a $5 Chip

说明与用途

Oído 在 $5 ESP32‑S3 上实现完整英文句子语音识别，LibriSpeech test‑clean WER 3.7%，优于现有芯片识别器和同等性能的笔记本模型，且完全开源。

问题

On microcontrollers, speech recognition is limited to wake words or a short command list, preventing open-ended English speech.

结果

Oído achieves 3.7 % WER on LibriSpeech test‑clean and 8.4 % WER in noisy conditions on a $5 ESP32‑S3, outperforming existing on‑chip recognizers and comparable laptop models, while remaining fully open source.

工作流

• 1. Clone repository and build host demo: git clone https://github.com/lokutor-ai/oido && cd oido/esp32/host && make python live_demo.py
• 2. Flash firmware to ESP32‑S3‑DevKitC‑1 N16R8 with INMP441 microphone using one‑line script in README
• 3. Run on‑device inference; model weights stored in flash, processed in int8 or int4 arithmetic
• 4. Optionally enable on‑chip language model for lower WER

证据

Oído, int8 – ESP32‑S3 – test‑clean 3.7%

来源

Oído makes 2.3 to 2.6 times fewer errors than Espressif's own recognizer

来源

The int8 version is within about a tenth of a point of the original full‑precision model (3.70% vs 3.68% on test‑clean).

来源

Everything is on GitHub at github.com/lokutor-ai/oido

来源