Opening article
The Hugging Face Open ASR Leaderboard now includes multilingual and long‑form tracks, comparing over 60 models from 18 organizations across 11 datasets. Recent findings show that Conformer encoders with large‑language‑model decoders lead in English accuracy, while CTC and TDT decoders offer 10–100× faster throughput for real‑time tasks. OpenAI’s Whisper Large v3 remains a strong multilingual baseline, yet open models like NVIDIA’s Parakeet CTC achieve higher throughput with modest error trade‑offs.
While everyone (and their grandma 👵) is spinning up new ASR models, picking the right one for your use case can feel more overwhelming than choosing your next Netflix show. As of 21 Nov 2025, there are 150 Audio-Text-to-Text and 27K ASR models on the Hub 🤯
Most benchmarks focus on short-form English transcription (<30s), and overlook other important tasks, such as (1) multilingual performance and (2) model throughput, which can a be deciding factor for long-form audio like meetings and podcasts.
Over the past two years, the Open ASR Leaderboard has become a standard for comparing open and closed-source models on both accuracy and efficiency . Recently, multilingual and long-form transcription tracks have been added to the leaderboard 🎉
That said, focusing on English tends to reduce multilingual coverage 👉 a classic case of the tradeoff between specialization and generalization. Similarly, while self-supervised systems like Meta’s Massively Multilingual Speech (MMS) and Omnilingual ASR can support 1K+ languages, they trail behind language-specific encoders in accuracy.
As of 21 Nov 2025, the Open ASR Leaderboard compares 60+ open and closed-source models from 18 organizations , across 11 datasets .
In a recent preprint , we dive into the technical setup and highlight some key trends in modern ASR. Here are the big takeaways 👇
Models combining Conformer encoders with large language model (LLM) decoders currently lead in English transcription accuracy. For example, NVIDIA’s Canary-Qwen-2.5B , IBM’s Granite-Speech-3.3-8B , and Microsoft’s Phi-4-Multimodal-Instruct achieve the lowest word error rates ( WER ), showing that integrating LLM reasoning can significantly boost ASR accuracy.
The leaderboard highlights the trade‑off between accuracy and efficiency, guiding developers toward models that fit specific deployment needs. It also encourages open‑source innovation in multilingual and long‑form ASR, a critical area for global AI adoption.