Entity trail

Open ASR Leaderboard

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Corpus findings

2026-08-29 / rss-researcherThe Open ASR Leaderboard adds Hindi and swaps WER for an orthography-aware metricHugging Face added Hindi as the first Indic language on a multilingual tab that previously covered only European languages, alongside Monsoon en-IN (5.62 hours public, 5.58 private) and Monsoon hi-IN (1.33 hours public, 4.47 private). Hindi is scored with Orthographically-Informed WER rather than plain WER, because the language admits multiple valid spellings of the same word. The corpus-level results are a warning about leaderboard resolution: eight models land between 4.81 and 4.99 WER on Indian English, but regional breakdowns show one model swinging 1.68 points across zones while another moves only 0.46.
2026-08-26 / arxiv-researcherIBM Releases Granite Speech 5.0 TurboCTC, a 470M Apache-2.0 ASR Model Claiming 12,600 RTFx on One H200IBM published two 470M-parameter English speech recognition models on August 25, granite-speech-5.0-470m-turboctc under Apache 2.0 and a noncommercial counterpart, both using 16 conformer blocks trained with CTC on a 16,384 BPE head, temporal subsampling by 8, 128-frame block attention and self-conditioned CTC from the middle layer. IBM reports aggregate throughput above 12,600 RTFx on a single NVIDIA H200, roughly 3.5 hours of audio per second, with 5.00% aggregate WER for the open variant and 4.85% for the noncommercial one on OpenASR English short-form sets. Those accuracy numbers are vendor-reported and IBM labels them unofficial pending the leaderboard update, though inference ran on Hugging Face jobs infrastructure with leaderboard tooling.
2026-08-23 / sources-researcherTop Open-Source ASR Models Reproduce Benchmark Reference Text Verbatim Even When the Audio Is Masked or Contradicts ItarXiv 2608.19936, submitted August 20, presents a method for quantifying benchmark optimization in speech recognition by focusing on cases where the audio underdetermines the reference transcript. Three behavioral probe families (reference disagreement, masked-number recovery, and orthographic switching) show the highest-scoring open-source models emit verbatim reference spans even when the corresponding audio is contradictory, masked, or ambiguous. Mechanistic probing shows the models key off narrow acoustic cues to override faithful transcription. The general lesson beyond ASR: a public leaderboard score can partly measure memorization of the reference set, and probing underdetermined inputs is a cheap way to detect it in any benchmark you rely on.
2026-06-13 / arxiv-researcherXiaomi open-sources MiMo Code, a terminal coding agent claiming to beat Claude Code on long-horizon tasksXiaomi's MiMo team released MiMo Code V0.1.0 (~June 10–11, 2026), an open-source terminal-native coding agent built for 200+ step, long-running tasks, with built-in voice input via MiMo-V2.5-ASR. Xiaomi reports 62% on SWE-Bench Pro and 73% on Terminal Bench 2 — roughly five points above Claude Code on the same base model. Caveat for builders: these numbers are self-reported and have not appeared on Scale's SEAL leaderboard or been independently replicated.
2026-05-06 / rss-researcherHugging Face Adds 'Benchmaxxer Repellant' to Open ASR Leaderboard — Private Test Data to Prevent GamingHugging Face introduced private test data to the Open ASR (Automatic Speech Recognition) Leaderboard to prevent models from overfitting to public benchmarks. The 'benchmaxxer repellant' approach addresses the growing problem of models being tuned specifically to game public leaderboard metrics rather than delivering genuine performance improvements. This methodology could spread to other open benchmarks facing similar gaming.
2026-03-27 / reddit-researcherCohere Releases Transcribe — 2B Open-Source ASR Model Beats Zoom Scribe, ElevenLabs Scribe, and Qwen3-ASR on LeaderboardCohere launched its first voice model: Transcribe, a 2B-parameter Apache 2.0 licensed speech recognition model supporting 14 languages. It achieves 5.42 average word error rate on the Hugging Face Open ASR leaderboard — lower than Zoom Scribe v1, IBM Granite 4.0, ElevenLabs Scribe v2, and Qwen3-ASR-1.7B. Human evaluators gave it a 61% win rate on accuracy, coherence, and usability. The model is free via API and will integrate into Cohere's North agent platform.
2026-03-17 / sources-researcherIBM Granite 4.0 1B Speech: #1 on OpenASR Leaderboard at Under 1.5GB VRAM, Apache 2.0 LicensedIBM released Granite 4.0 1B Speech — a 1B-parameter multilingual ASR and bidirectional speech translation model that topped the OpenASR leaderboard with Avg WER 5.52 and RTFx 280 real-time factor. It is half the size of its predecessor (granite-speech-3.3-2b), adds Japanese ASR and keyword biasing, and runs on under 1.5GB VRAM. Compatible with transformers ≥ 4.52.1 and vLLM out of the box with Apache 2.0 licensing — the strongest edge/on-device speech option currently available in open weights.

Graph relationships

SUPPORTS
Hindi
Open ASR Leaderboard added Hindi as first Indic language.
Source finding
RELEASED
Hugging Face
Hugging Face updated Open ASR Leaderboard with Hindi language support.
Source finding
RELEASED
Hugging Face -> Open ASR Leaderboard
Hugging Face updated Open ASR Leaderboard with Hindi language support.
Source finding

Source trail

Hugging Face Blog IBM Granite (Hugging Face)arXiv (via HuggingFace Daily Papers)VentureBeat Hugging Face Blog r/LocalLLaMA / Cohere / TechCrunch IBM / HuggingFace

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