{ "@context": "https://schema.org", "@id": "https://aggr.aymericbeaumet.com/items/huggingface-co/2025-11-21-open-asr-leaderboard-trends-and-insights-with-new/#webpage", "@type": [ "WebPage", "ArchiveComponent" ], "articleBody": "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](https://huggingface.co/models?pipeline_tag=audio-text-to-text&sort=trending)** and **27K [ASR models](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=trending)** on the Hub 🤯\n\nMost 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.\n\nOver the past two years, the [**Open ASR Leaderboard**](https://huggingface.co/spaces/hf-audio/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 🎉\n\n### TL;DR - [Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)\n\n- 📝 **New preprint** on ASR trends from the leaderboard: [https://hf.co/papers/2510.06961](https://hf.co/papers/2510.06961)\n- 🧠 **Best accuracy:** Conformer encoder + LLM decoders (open-source ftw 🥳)\n- ⚡ **Fastest:** CTC / TDT decoders\n- 🌍 **Multilingual:** Comes at the cost of single-language performance\n- ⌛ **Long-form:** Closed-source systems still lead (for now 😉)\n- 🧑💻 **Fine-tuning guides** ([Parakeet](https://github.com/Deep-unlearning/Finetune-Parakeet), [Voxtral](https://github.com/Deep-unlearning/Finetune-Voxtral-ASR), [Whisper](https://huggingface.co/learn/audio-course/chapter5/fine-tuning)): to continue pushing performance\n\n## Takeaways from 60+ models\n\nAs of 21 Nov 2025, the *Open ASR Leaderboard* compares **60+ open and closed-source models** from **18 organizations**, across **11 datasets**.\n\nIn a recent [preprint](https://hf.co/papers/2510.06961), we dive into the technical setup and highlight some key trends in modern ASR. Here are the big takeaways 👇\n\n## 1. Conformer encoder 🤝 LLM decoder tops the charts 📈\n\n![thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open_asr_leaderboard/leaderboard_WER.png)\n\nModels combining [**Conformer encoders**](https://huggingface.co/papers/2005.08100) with **large language model (LLM) decoders** currently lead in English transcription accuracy. For example, **NVIDIA’s [Canary-Qwen-2.5B](https://huggingface.co/nvidia/canary-qwen-2.5b)**, **IBM’s [Granite-Speech-3.3-8B](https://huggingface.co/ibm-granite/granite-speech-3.3-8b)**, and **Microsoft’s [Phi-4-Multimodal-Instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct)** achieve the lowest word error rates ([WER](https://huggingface.co/learn/audio-course/en/chapter5/evaluation#word-error-rate)), showing that integrating LLM reasoning can significantly boost ASR accuracy.\n\n💡 *Pro-tip: NVIDIA introduced [Fast Conformer](https://huggingface.co/papers/2305.05084), a 2x faster variant of the Conformer, that is used in their Canary and Parakeet suite of models.*\n\n## 2. Speed–accuracy tradeoffs ⚖️\n\n![thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open_asr_leaderboard/leaderboard_RTX.png)\n\nWhile highly accurate, these LLM decoders tend to be **slower** than simpler approaches. On the *Open ASR Leaderboard*, efficiency is measured using *inverse real-time factor* (RTFx), where higher is better.\n\nFor even faster inference, [**CTC**](https://huggingface.co/learn/audio-course/en/chapter3/ctc#ctc-architectures) and [**TDT**](https://huggingface.co/papers/2304.06795) decoders deliver **10–100× faster throughput**, albeit with slightly higher error rates. This makes them ideal for **real-time**, **offline**, or **batch transcription** tasks (such as meetings, lectures, or podcasts).\n\n## 3. Multilingual 🌍\n\n![thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open_asr_leaderboard/multilingual.png)\n\nOpenAI’s [**Whisper Large v3**](https://huggingface.co/openai/whisper-large-v3) remains a strong multilingual baseline, supporting **99 languages**. However, **fine-tuned or distilled variants** like [**Distil-Whisper**](https://huggingface.co/distil-whisper/distil-large-v3.5) and [**CrisperWhisper**](https://huggingface.co/nyrahealth/CrisperWhisper) often outperform the original on **English-only** tasks, showing how targeted fine-tuning can improve specialization (*how to fine-tune? Check out guides for [Whisper](https://huggingface.co/learn/audio-course/chapter5/fine-tuning), [Parakeet](https://github.com/Deep-unlearning/Finetune-Parakeet), and [Voxtral](https://github.com/Deep-unlearning/Finetune-Voxtral-ASR)*).\n\nThat 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)**](https://huggingface.co/facebook/mms-1b-all) and [**Omnilingual ASR**](https://github.com/facebookresearch/omnilingual-asr) can support 1K+ languages, they trail behind language-specific encoders in accuracy.\n\n⭐ *While just five languages are currently benchmarked, we’re planning to expand to more languages and are excited for new dataset and models contributions to multilingual ASR through GitHub [pull requests](https://github.com/huggingface/open_asr_leaderboard).*\n\n🎯 Alongside multilingual benchmarks, several **community-driven leaderboards** focus on individual languages. For example, the [**Open Universal Arabic ASR Leaderboard**](https://huggingface.co/spaces/elmresearchcenter/open_universal_arabic_asr_leaderboard) compares models across **Modern Standard Arabic and regional dialects**, highlighting how speech variation and diglossia challenge current systems. Similarly. the [**Russian ASR Leaderboard**](https://huggingface.co/spaces/Vikhrmodels/Russian_ASR_Leaderboard) provides a growing hub for evaluating encoder-decoder and CTC models on **Russian-specific phonology and morphology**. These localized efforts mirror the broader multilingual leaderboard’s mission to encourage **dataset sharing, fine-tuned checkpoints, and transparent model comparisons**, especially in languages with fewer established ASR resources.\n\n## 4. Long-form transcription is a different game ⏳\n\n![thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open_asr_leaderboard/long_form.png)\n\nFor **long-form audio** (e.g., podcasts, lectures, meetings), **closed-source systems** still edge out open ones. It could be due to domain tuning, custom chunking, or production-grade optimization.\n\nAmong open models, **OpenAI’s Whisper Large v3** performs the best. But for throughput, **CTC-based Conformers** shine 👉 for example, **NVIDIA’s [Parakeet CTC 1.1B](https://huggingface.co/nvidia/parakeet-ctc-1.1b)** achieves an **RTFx of 2793.75**, compared to **68.56** for Whisper Large v3, with only a moderate WER degradation (**6.68** and **6.43** respectively).\n\nThe tradeoff? Parakeet is **English-only,** again reminding us of that multilingual and specialization tradeoff 🫠.\n\n⭐ *While closed systems still lead, there’s huge potential for open-source innovation here. Long-form ASR remains one of the most exciting frontiers for the community to tackle next!*\n\n## 🎤 The Show Must Go On\n\nGiven how fast ASR is evolving, we’re excited to see what new architectures push performance and efficiency, and how the *Open ASR Leaderboard* continues to serve as a **transparent, community-driven benchmark** for the field, and as a reference for other leaderboards ([Russian](https://huggingface.co/spaces/Vikhrmodels/Russian_ASR_Leaderboard), [Arabic](https://huggingface.co/spaces/elmresearchcenter/open_universal_arabic_asr_leaderboard), and [Speech DeepFake Detection](https://huggingface.co/spaces/Speech-Arena-2025/Speech-DF-Arena)).\n\nWe’ll keep expanding the *Open ASR LeaderBoard* with **more models, more languages, and more datasets** so stay tuned 👀\n\n👉 **Want to contribute?** Head on over to the [GitHub repo](https://github.com/huggingface/open_asr_leaderboard) to open a *pull request* 🚀\n", "authors": [], "captured_at": "2026-10-04T12:50:30.804479567+00:00", "category": "ai", "content_html": "

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 🤯

\n

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.

\n

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 🎉

\n

TL;DR - Open ASR Leaderboard

\n\n

Takeaways from 60+ models

\n

As of 21 Nov 2025, the Open ASR Leaderboard compares 60+ open and closed-source models from 18 organizations, across 11 datasets.

\n

In a recent preprint, we dive into the technical setup and highlight some key trends in modern ASR. Here are the big takeaways 👇

\n

1. Conformer encoder 🤝 LLM decoder tops the charts 📈

\n

\"thumbnail\"

\n

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.

\n

💡 Pro-tip: NVIDIA introduced Fast Conformer, a 2x faster variant of the Conformer, that is used in their Canary and Parakeet suite of models.

\n

2. Speed–accuracy tradeoffs ⚖️

\n

\"thumbnail\"

\n

While highly accurate, these LLM decoders tend to be slower than simpler approaches. On the Open ASR Leaderboard, efficiency is measured using inverse real-time factor (RTFx), where higher is better.

\n

For even faster inference, CTC and TDT decoders deliver 10–100× faster throughput, albeit with slightly higher error rates. This makes them ideal for real-time, offline, or batch transcription tasks (such as meetings, lectures, or podcasts).

\n

3. Multilingual 🌍

\n

\"thumbnail\"

\n

OpenAI’s Whisper Large v3 remains a strong multilingual baseline, supporting 99 languages. However, fine-tuned or distilled variants like Distil-Whisper and CrisperWhisper often outperform the original on English-only tasks, showing how targeted fine-tuning can improve specialization (how to fine-tune? Check out guides for Whisper, Parakeet, and Voxtral).

\n

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.

\n

⭐ While just five languages are currently benchmarked, we’re planning to expand to more languages and are excited for new dataset and models contributions to multilingual ASR through GitHub pull requests.

\n

🎯 Alongside multilingual benchmarks, several community-driven leaderboards focus on individual languages. For example, the Open Universal Arabic ASR Leaderboard compares models across Modern Standard Arabic and regional dialects, highlighting how speech variation and diglossia challenge current systems. Similarly. the Russian ASR Leaderboard provides a growing hub for evaluating encoder-decoder and CTC models on Russian-specific phonology and morphology. These localized efforts mirror the broader multilingual leaderboard’s mission to encourage dataset sharing, fine-tuned checkpoints, and transparent model comparisons, especially in languages with fewer established ASR resources.

\n

4. Long-form transcription is a different game ⏳

\n

\"thumbnail\"

\n

For long-form audio (e.g., podcasts, lectures, meetings), closed-source systems still edge out open ones. It could be due to domain tuning, custom chunking, or production-grade optimization.

\n

Among open models, OpenAI’s Whisper Large v3 performs the best. But for throughput, CTC-based Conformers shine 👉 for example, NVIDIA’s Parakeet CTC 1.1B achieves an RTFx of 2793.75, compared to 68.56 for Whisper Large v3, with only a moderate WER degradation (6.68 and 6.43 respectively).

\n

The tradeoff? Parakeet is English-only, again reminding us of that multilingual and specialization tradeoff 🫠.

\n

⭐ While closed systems still lead, there’s huge potential for open-source innovation here. Long-form ASR remains one of the most exciting frontiers for the community to tackle next!

\n

🎤 The Show Must Go On

\n

Given how fast ASR is evolving, we’re excited to see what new architectures push performance and efficiency, and how the Open ASR Leaderboard continues to serve as a transparent, community-driven benchmark for the field, and as a reference for other leaderboards (Russian, Arabic, and Speech DeepFake Detection).

\n

We’ll keep expanding the Open ASR LeaderBoard with more models, more languages, and more datasets so stay tuned 👀

\n

👉 Want to contribute? Head on over to the GitHub repo to open a pull request 🚀

\n", "content_markdown": "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](https://huggingface.co/models?pipeline_tag=audio-text-to-text&sort=trending)** and **27K [ASR models](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=trending)** on the Hub 🤯\n\nMost 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.\n\nOver the past two years, the [**Open ASR Leaderboard**](https://huggingface.co/spaces/hf-audio/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 🎉\n\n### TL;DR - [Open ASR Leaderboard](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard)\n\n- 📝 **New preprint** on ASR trends from the leaderboard: [https://hf.co/papers/2510.06961](https://hf.co/papers/2510.06961)\n- 🧠 **Best accuracy:** Conformer encoder + LLM decoders (open-source ftw 🥳)\n- ⚡ **Fastest:** CTC / TDT decoders\n- 🌍 **Multilingual:** Comes at the cost of single-language performance\n- ⌛ **Long-form:** Closed-source systems still lead (for now 😉)\n- 🧑💻 **Fine-tuning guides** ([Parakeet](https://github.com/Deep-unlearning/Finetune-Parakeet), [Voxtral](https://github.com/Deep-unlearning/Finetune-Voxtral-ASR), [Whisper](https://huggingface.co/learn/audio-course/chapter5/fine-tuning)): to continue pushing performance\n\n## Takeaways from 60+ models\n\nAs of 21 Nov 2025, the *Open ASR Leaderboard* compares **60+ open and closed-source models** from **18 organizations**, across **11 datasets**.\n\nIn a recent [preprint](https://hf.co/papers/2510.06961), we dive into the technical setup and highlight some key trends in modern ASR. Here are the big takeaways 👇\n\n## 1. Conformer encoder 🤝 LLM decoder tops the charts 📈\n\n![thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open_asr_leaderboard/leaderboard_WER.png)\n\nModels combining [**Conformer encoders**](https://huggingface.co/papers/2005.08100) with **large language model (LLM) decoders** currently lead in English transcription accuracy. For example, **NVIDIA’s [Canary-Qwen-2.5B](https://huggingface.co/nvidia/canary-qwen-2.5b)**, **IBM’s [Granite-Speech-3.3-8B](https://huggingface.co/ibm-granite/granite-speech-3.3-8b)**, and **Microsoft’s [Phi-4-Multimodal-Instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct)** achieve the lowest word error rates ([WER](https://huggingface.co/learn/audio-course/en/chapter5/evaluation#word-error-rate)), showing that integrating LLM reasoning can significantly boost ASR accuracy.\n\n💡 *Pro-tip: NVIDIA introduced [Fast Conformer](https://huggingface.co/papers/2305.05084), a 2x faster variant of the Conformer, that is used in their Canary and Parakeet suite of models.*\n\n## 2. Speed–accuracy tradeoffs ⚖️\n\n![thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open_asr_leaderboard/leaderboard_RTX.png)\n\nWhile highly accurate, these LLM decoders tend to be **slower** than simpler approaches. On the *Open ASR Leaderboard*, efficiency is measured using *inverse real-time factor* (RTFx), where higher is better.\n\nFor even faster inference, [**CTC**](https://huggingface.co/learn/audio-course/en/chapter3/ctc#ctc-architectures) and [**TDT**](https://huggingface.co/papers/2304.06795) decoders deliver **10–100× faster throughput**, albeit with slightly higher error rates. This makes them ideal for **real-time**, **offline**, or **batch transcription** tasks (such as meetings, lectures, or podcasts).\n\n## 3. Multilingual 🌍\n\n![thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open_asr_leaderboard/multilingual.png)\n\nOpenAI’s [**Whisper Large v3**](https://huggingface.co/openai/whisper-large-v3) remains a strong multilingual baseline, supporting **99 languages**. However, **fine-tuned or distilled variants** like [**Distil-Whisper**](https://huggingface.co/distil-whisper/distil-large-v3.5) and [**CrisperWhisper**](https://huggingface.co/nyrahealth/CrisperWhisper) often outperform the original on **English-only** tasks, showing how targeted fine-tuning can improve specialization (*how to fine-tune? Check out guides for [Whisper](https://huggingface.co/learn/audio-course/chapter5/fine-tuning), [Parakeet](https://github.com/Deep-unlearning/Finetune-Parakeet), and [Voxtral](https://github.com/Deep-unlearning/Finetune-Voxtral-ASR)*).\n\nThat 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)**](https://huggingface.co/facebook/mms-1b-all) and [**Omnilingual ASR**](https://github.com/facebookresearch/omnilingual-asr) can support 1K+ languages, they trail behind language-specific encoders in accuracy.\n\n⭐ *While just five languages are currently benchmarked, we’re planning to expand to more languages and are excited for new dataset and models contributions to multilingual ASR through GitHub [pull requests](https://github.com/huggingface/open_asr_leaderboard).*\n\n🎯 Alongside multilingual benchmarks, several **community-driven leaderboards** focus on individual languages. For example, the [**Open Universal Arabic ASR Leaderboard**](https://huggingface.co/spaces/elmresearchcenter/open_universal_arabic_asr_leaderboard) compares models across **Modern Standard Arabic and regional dialects**, highlighting how speech variation and diglossia challenge current systems. Similarly. the [**Russian ASR Leaderboard**](https://huggingface.co/spaces/Vikhrmodels/Russian_ASR_Leaderboard) provides a growing hub for evaluating encoder-decoder and CTC models on **Russian-specific phonology and morphology**. These localized efforts mirror the broader multilingual leaderboard’s mission to encourage **dataset sharing, fine-tuned checkpoints, and transparent model comparisons**, especially in languages with fewer established ASR resources.\n\n## 4. Long-form transcription is a different game ⏳\n\n![thumbnail](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open_asr_leaderboard/long_form.png)\n\nFor **long-form audio** (e.g., podcasts, lectures, meetings), **closed-source systems** still edge out open ones. It could be due to domain tuning, custom chunking, or production-grade optimization.\n\nAmong open models, **OpenAI’s Whisper Large v3** performs the best. But for throughput, **CTC-based Conformers** shine 👉 for example, **NVIDIA’s [Parakeet CTC 1.1B](https://huggingface.co/nvidia/parakeet-ctc-1.1b)** achieves an **RTFx of 2793.75**, compared to **68.56** for Whisper Large v3, with only a moderate WER degradation (**6.68** and **6.43** respectively).\n\nThe tradeoff? Parakeet is **English-only,** again reminding us of that multilingual and specialization tradeoff 🫠.\n\n⭐ *While closed systems still lead, there’s huge potential for open-source innovation here. Long-form ASR remains one of the most exciting frontiers for the community to tackle next!*\n\n## 🎤 The Show Must Go On\n\nGiven how fast ASR is evolving, we’re excited to see what new architectures push performance and efficiency, and how the *Open ASR Leaderboard* continues to serve as a **transparent, community-driven benchmark** for the field, and as a reference for other leaderboards ([Russian](https://huggingface.co/spaces/Vikhrmodels/Russian_ASR_Leaderboard), [Arabic](https://huggingface.co/spaces/elmresearchcenter/open_universal_arabic_asr_leaderboard), and [Speech DeepFake Detection](https://huggingface.co/spaces/Speech-Arena-2025/Speech-DF-Arena)).\n\nWe’ll keep expanding the *Open ASR LeaderBoard* with **more models, more languages, and more datasets** so stay tuned 👀\n\n👉 **Want to contribute?** Head on over to the [GitHub repo](https://github.com/huggingface/open_asr_leaderboard) to open a *pull request* 🚀\n", "dateCreated": "2026-10-04T12:50:30.804479567+00:00", "date_created": "2025-11-21T00:00:00+00:00", "date_modified": "2025-11-21T00:00:00+00:00", "date_published": "2025-11-21T00:00:00+00:00", "description": "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…", "external_url": "https://huggingface.co/blog/open-asr-leaderboard", "git": { "commit": "ebb0941c5c06a2ab2377342d607535262a3e969e", "path": "items/huggingface-co/2025/11/2025-11-21-open-asr-leaderboard-trends-and-insights-with-new.md", "permalink": "https://github.com/aymericbeaumet/aggr-instance/blob/ebb0941c5c06a2ab2377342d607535262a3e969e/items/huggingface-co/2025/11/2025-11-21-open-asr-leaderboard-trends-and-insights-with-new.md" }, "id": "urn:aggr:item:81cf5c75df6fffdada1927ccac0cb2d2003bc073", "isBasedOn": { "@id": "https://huggingface.co/blog/open-asr-leaderboard" }, "mainEntity": { "@id": "https://huggingface.co/blog/open-asr-leaderboard", "@type": "CreativeWork", "archivedAt": { "@id": "https://aggr.aymericbeaumet.com/items/huggingface-co/2025-11-21-open-asr-leaderboard-trends-and-insights-with-new/#webpage" }, "dateModified": "2025-11-21T00:00:00+00:00", "datePublished": "2025-11-21T00:00:00+00:00", "headline": "Open ASR Leaderboard: Trends and Insights with New Multilingual & Long-Form Tracks", "timeRequired": "PT4M", "url": "https://huggingface.co/blog/open-asr-leaderboard", "wordCount": 812 }, "name": "Archived snapshot: Open ASR Leaderboard: Trends and Insights with New Multilingual & Long-Form Tracks", "preview": null, "reading_minutes": 4, "replicated_at": null, "source": "huggingface-co", "source_name": "Hugging Face - Blog", "summary": null, "tags": [], "temporalCoverage": "2026-10-04T12:50:30.804479567+00:00", "title": "Open ASR Leaderboard: Trends and Insights with New Multilingual & Long-Form Tracks", "url": "https://aggr.aymericbeaumet.com/items/huggingface-co/2025-11-21-open-asr-leaderboard-trends-and-insights-with-new/", "word_count": 812 }