From GPU Servers to Raspberry Pi: Benchmarking Open-Source Speech-to-Text for Edge Deployment
Open-source Speech-to-Text (STT) systems are increasingly deployed across a wide range of hardware, from GPU servers to resource-constrained edge devices. However, these systems are rarely evaluated jointly across heterogeneous platforms with respect to accuracy, latency, and resource usage, making deployment decisions difficult in practice.We present a cross-hardware benchmark of six open-source STT engines (Whisper, whisper.cpp, Sherpa-ONNX, Silero, Vosk, Pocketsphinx) across four platforms—Linux GPU server, macOS, Windows, and Raspberry Pi 5. Using a unified and reproducible evaluation harness, we measure accuracy (WER/CER), latency and throughput, and CPU/GPU/memory usage on clean, noisy, and multilingual datasets at two scales (1.5k and 10k audio clips).Our results show that STT performance is deployment-dependent rather than universal. Sherpa-ONNX yields the lowest error rates on desktop platforms, Silero provides the lowest CPU latency for interactive use, and Whisper delivers the highest throughput under GPU acceleration. On Raspberry Pi 5, only lightweight models achieve near-interactive performance, exposing a fundamental accuracy–latency trade-off in edge environments. We further observe that robustness to noise does not necessarily correlate with clean-speech accuracy, highlighting the importance of evaluation under realistic conditions.This work provides actionable deployment guidance and a reproducible benchmark that surfaces system-level trade-offs in open-source STT across the edge–cloud continuum. Scripts, model configurations, and environment manifests are provided to support replication and extension on new hardware.