A new speech-to-text model of only 1.06MB can transcribe audio ~436× faster than real time on ONE CPU thread! No GPU. AuroraASR-1M is a super super small local speech-recognition model with these stats 🧠 949K parameters 💾 FP32 → 3.81MB 💾 FP16 → 1.91MB 💾 INT8 → 1.06MB 🔓 Apache 2.0 🖥️ CPU or GPU The developer measured the full end-to-end pipeline — feature extraction + speech recognition + CTC decoding at ... 🖥️ 1 CPU thread → ~436× real time 🔥 RTX PRO 6000 Blackwell → ~1,362× real time 🎯So 10 minutes of audio could be processed in about 1.4 seconds at the reported single-thread CPU rate. So to underscore, this isn't using an external language model! It's a tiny Conformer-CTC model trained on LibriSpeech and can run through Transformers locally. There's a tradeoff, accuracy AuroraASR-1M test-clean WER → 11.43% Whisper Tiny English → 5.6% So Whisper Tiny is substantially more accurate. Aurora is also 🇺🇸 English only 🔡 lowercase output ❌ no punctuation A 1MB model can now turn speech into text locally at hundreds of times real time on a single CPU core. That makes completely offline transcription, voice commands and embedded voice features almost absurdly cheap. 🔗 Hugging Face model in ALT.