Developer Brings Offline Speech Recognition to ESP32-P4
The new open-source esp32-pie-asr project allows for private voice processing without needing an internet connection.
Updated on Oct. 28, 2026 in Artificial Intelligence
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Boumedine Billal has released the open-source project esp32-pie-asr, which enables offline speech recognition on the Espressif ESP32-P4 microcontroller. This implementation allows devices to process voice commands locally, ensuring user privacy and continuous functionality without network connectivity.
Why it matters
By enabling on-device audio processing, this project eliminates the need for cloud-based servers, effectively preserving user privacy while removing reliance on external internet connections. It provides a specialized solution for developers looking to integrate robust voice control into embedded hardware.
The system utilizes the 65.7-million-parameter Kroko Zipformer 2 neural network, compressed to 26.65 MB using 2-bit and 4-bit quantization. It requires 32 MB of PSRAM and achieves command recognition rates between 94% and 99% when using phrase lists.
The players
Boumedine Billal
The independent developer who authored the esp32-pie-asr project.
Espressif
The semiconductor company that designs the ESP32 series of microcontrollers.
The details
The project uses PIE vector instructions to handle audio processing on the dual-core RISC-V microcontroller without a dedicated neural accelerator. Users can flash the firmware via a web browser or the ESP-IDF v6.1 development framework, with the engine and firmware released under an Apache 2.0 license.
Timeline
- 2026-10-28
Article publication date.
The Tech Race
This development highlights the ongoing shift toward edge computing, where increasingly powerful microcontrollers replace the need for cloud-based AI processing. It positions the ESP32-P4 against more expensive or power-hungry processors by demonstrating sophisticated machine learning capabilities on standard hardware.
Users and developers can now implement voice-controlled systems that function instantly and privately without transmitting data to the cloud. The system provides rapid response times, with commands triggering within two seconds of completion, making it suitable for responsive local automation tasks.
The takeaway
This project demonstrates that high-performance AI tasks are increasingly feasible on low-power, localized hardware. It offers a practical model for hobbyists and professionals to build privacy-first voice interfaces that do not require an active internet connection to operate.
Further reading
For more developments in machine learning, explore our Artificial Intelligence section.
More information
View the source code and documentation at the esp32-pie-asr project GitHub repository.
Source note: This article includes information reported by Electronics For You - Official Site ElectronicsForU.com.
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