Whisper Tiny is OpenAI's 39M-parameter multilingual Whisper checkpoint, the smallest member of the Whisper family. It uses the standard Whisper encoder-decoder architecture for automatic speech recognition and speech translation, trained with large-scale weak supervision on 680k hours of labelled speech. The tiny model trades some accuracy for the lowest footprint and fastest inference, which suits low-resource devices and latency-sensitive use. This OpenASR repo repackages the original openai/whisper-tiny weights as .oasr packs that run natively in the OpenASR runtime with no Python at inference time. For most users the q8_0 build is the recommended default; q4_k is for the tightest memory budgets and fp16 is for verification or maximum fidelity.