datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
script-fidelity-benchmark
Script fidelity benchmark
Anonymous supplement for the paper "Script collapse in multilingual ASR:
A reference-free metric and 100-pair benchmark."
Script Fidelity Rate (SFR) measures the fraction of ASR hypothesis characters
that belong to the expected target script. WER measures word edits, while SFR
checks whether the output is written in the target orthography.
Related resources:
PyPI package: https://pypi.org/project/script-fidelity/
Hugging Face Evaluate metric:… See the full description on the dataset page: https://huggingface.co/datasets/themechanism/script-fidelity-benchmark.clipquill-asr-benchmark
Measuring whisper-tiny vs whisper-base in a browser tab
Word error rate, wall-clock timing, transfer size and peak memory for two
quantised Whisper tiers running entirely client-side in a real Chrome window,
with the scripts that produced every number.
If you are building an in-browser transcription page, the two results worth
knowing before you pick a model tier:
On clean synthetic audio the two tiers tie. If that is all you test, you
will conclude the tier does not matter… See the full description on the dataset page: https://huggingface.co/datasets/sophia8888/clipquill-asr-benchmark.French-Medical-Transcription-Benchmark
🩺 French Medical Transcription Evaluation Dataset
Ce dataset a été créé et ouvert à la communauté dans le cadre du développement R&D de LucioleScribe, la plateforme souveraine de transcription IA 100% locale, spécifiquement conçue pour les milieux médicaux et juridiques (compatibilité RGPD, HDS, et architectures Air-Gapped).
🔗 Découvrir LucioleScribe Édition Santé | ⚙️ Voir le Pipeline Technologique Local
📊 Présentation du Dataset
L'évaluation des modèles de… See the full description on the dataset page: https://huggingface.co/datasets/AWANNABY/French-Medical-Transcription-Benchmark.Agri_STT_Benchmarking_DatasetThis is a domain-specific, multilingual agricultural speech dataset with a primary focus on Hindi, Telugu, and Odia, designed for speech-to-text and automatic speech recognition (ASR) tasks. It features human-annotated transcriptions and is intended for benchmarking ASR model performance in real-world agricultural scenarios.
This paper presents a comprehensive benchmark of 10 ASR models for agricultural advisory use across Hindi, Telugu, and Odia, using 10,934 real-world Farmer.Chat audio… See the full description on the dataset page: https://huggingface.co/datasets/bullseye-4/Agri_STT_Benchmarking_Dataset.