Open ASR Leaderboard: Trends and Insights with New Multilingual & Long-Form Tracks

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 🎉

TL;DR - Open ASR Leaderboard

• 📝 New preprint on ASR trends from the leaderboard: https://hf.co/papers/2510.06961
• 🧠 Best accuracy: Conformer encoder + LLM decoders (open-source ftw 🥳)
• ⚡ Fastest: CTC / TDT decoders
• 🌍 Multilingual: Comes at the cost of single-language performance
• ⌛ Long-form: Closed-source systems still lead (for now 😉)
• 🧑‍💻 Fine-tuning guides (Parakeet, Voxtral, Whisper): to continue pushing performance

Takeaways from 60+ models

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 👇

1. Conformer encoder 🤝 LLM decoder tops the charts 📈

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.