Post by Bhavishya Pandit

Meta went bonkers with this new open-source ASR that works for 1,600+ languages! 🤯 Now, businesses can reach customers in their native tongue, even in low-resource regions, without building ASR from scratch. → Fully open-source, supporting 500+ languages never covered by any ASR before → Trained on 4.3M hours of multilingual speech (1,600+ languages) → Best part: Works zero-shot on languages never seen during training How? Two breakthroughs: Dual-decoder architecture:  • CTC decoder for low-latency, real-time use  • LLM-ASR decoder (Transformer-based) for high-accuracy, context-aware transcription In-context learning: Just 5–10 speech-text examples at inference time, let it transcribe any new language even if the model was never trained on it. Even more surprising: → On FLEURS-81, Omnilingual ASR beats Whisper on 65/81 languages—including 24 of the world’s top 34 most spoken languages → Robust to noise: CER stays <10 even in the noisiest 5% of field recordings → Scales from edge to cloud: 300M (mobile) → 7B (max accuracy) But the real shift isn’t scale, it’s agency. Communities can now extend ASR to their own language with minimal data, compute, or expertise. Check out the carousel to know how it works in simple terms and what the challenges are in detail. Question for you: When building voice tech for underserved languages, do you prioritise zero-shot generalisation or lightweight fine-tuning and why? Follow me, Bhavishya Pandit, for honest takes on AI tools that actually work 🔥 P.S. Model card, inference code, and datasets in the first comment.