01Public
Common Voice 26 ↗
Mozilla Common Voice Scripted Speech 26.0 for Armenian, using the full hy-AM test split released in June 2026.
🇦🇲 ArmBench-ASR: Benchmarking Speech-to-Text Models on Armenian
v0.1
Overall leaderboard
Rank is based on WER. Scores are percentages; lower is better.
No matching models
Select Open or Closed in the Availability column to show models.
Model comparison
Compare model error rates for one dataset and metric. Lower is better.
No models match the current filters.
Cite ArmBench-ASR
If you use ArmBench-ASR in your research, please cite it as follows:
@misc{armbench-asr,
title={ArmBench-ASR: Benchmarking Speech-to-Text Models on Armenian},
author={Metric-AI-Lab},
year={2026},
howpublished={\url{https://huggingface.co/spaces/Metric-AI/ArmBench-ASR}},
note={Benchmark for Evaluating Speech-to-Text Models on Armenian}
}Five datasets, one Armenian benchmark
10,113 clips and 20.7 hours of speech spanning read, literary, cinematic, and conversational Armenian. Every model is evaluated on the same fixed test material.
02Public
FLEURS ↗
Google FLEURS Armenian (hy_am) test split. Raw transcriptions retain punctuation and capitalization for strict scoring, with some manual corrections applied.
03Private
Poems
Read Armenian poetry with background music, literary vocabulary, expressive phrasing, and text structures underrepresented in general ASR sets.
04Private
Movies
Armenian movie dialogue with conversational delivery, produced-media acoustics, and occasional background noise.
05Private
News
A compact Armenian news dataset recited by a speaker, designed to evaluate clear narrated speech beyond standard public test sets.
Cite ArmBench-ASR
If you use ArmBench-ASR in your research, please cite it as follows:
@misc{armbench-asr,
title={ArmBench-ASR: Benchmarking Speech-to-Text Models on Armenian},
author={Metric-AI-Lab},
year={2026},
howpublished={\url{https://huggingface.co/spaces/Metric-AI/ArmBench-ASR}},
note={Benchmark for Evaluating Speech-to-Text Models on Armenian}
}Reading the benchmark
Armenian ASR Benchmark compares automatic speech recognition systems on a varied Armenian evaluation suite. Every score is an error rate, so lower values are better.
The benchmark aims to bring clarity to the Armenian ASR landscape, make model comparisons easier, and help model creators identify strengths, gaps, and opportunities for meaningful improvement.
Availability
Open means model weights are publicly available. Closed means access is provided through a hosted product or API.
API settings
Hosted systems use fixed model identifiers and transcription-only prompts. Evaluated Gemini runs use temperature 0, a 1,000-token output cap, and audio limited to 40 seconds. Thinking is disabled or set to the lowest applicable level: budget 0 for Gemini 2.5 Flash, budget 128 for Gemini 2.5 Pro, and MINIMAL for Gemini 3.1 Flash Lite, 3.5 Flash Lite, and 3.6 Flash.
Text cleaning
References and predictions are processed with the same deterministic pipeline. Strict scoring standardizes punctuation, spacing, capitalization, and Unicode; normalized scoring additionally reduces orthographic and formatting differences while preserving Armenian, English, Russian letters, digits, and recognized clock times. Both WER / CER and WER-norm / CER-norm are reported.
About Us
Armenian ASR Benchmark is a Metric AI Lab project. The applied AI research lab builds production systems across custom language and vision-language models, multilingual voice agents, Physical AI, retrieval, and analytics—while publishing frontier research and open benchmarks.
Acknowledgments
We gratefully acknowledge HiSpeech, Talk2Edit, TBB ASR, and Wav.am for making their systems available for evaluation at no cost, and Infocom for providing Armenian news data used in this benchmark.