datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Audio2Tool
Audio2Tool: Speak, Call, Act — A Dataset for Benchmarking Speech Tool Use
Authors: Ramit Pahwa1,∗,∗∗, Apoorva Beedu1,∗, Parivesh Priye1, Rutu Gandhi†1, Saloni Takawale†1, Aruna Baijal1, Zengli Yang1
1 Rivian & Volkswagen Technologies · ∗ equal contribution · ∗∗ corresponding author · † equal contribution
📄 Project page / demo: https://audio2tool.github.io/
📦 Dataset: https://huggingface.co/datasets/RVtech/Audio2Tool
✉️ Contact (corresponding… See the full description on the dataset page: https://huggingface.co/datasets/RVtech/Audio2Tool.ark-asr-3b-open-asr-leaderboard-results
ARK-ASR-3B Open ASR Leaderboard Results
Raw JSONL manifests for AutoArk-AI/ARK-ASR-3B on the public English
short-form hf-audio/open-asr-leaderboard splits.
These manifests were generated on a local 8x RTX 4090 machine and scored with
the shared Open ASR Leaderboard scorer:
PYTHONPATH=. python - <<'PY'
from normalizer.eval_utils import score_results
score_results(
'ark_asr/results.AutoArk-AI-ARK-ASR-3B_20260622_official',
'AutoArk-AI/ARK-ASR-3B',
)
PY
Important:… See the full description on the dataset page: https://huggingface.co/datasets/Edge0/ark-asr-3b-open-asr-leaderboard-results.red_ace_asr_error_detection_and_correction
RED-ACE
Dataset Summary
This dataset can be used to train and evaluate ASR Error Detection or Correction models. It was introduced in the RED-ACE paper (Gekhman et al, 2022).
The dataset contains ASR outputs on the LibriSpeech corpus (Panayotov et al., 2015) with annotated transcription errors.
Dataset Details
The LibriSpeech corpus was decoded using Google Cloud Speech-to-Text API, with the default and video models.
The word-level confidence was enabled… See the full description on the dataset page: https://huggingface.co/datasets/google/red_ace_asr_error_detection_and_correction.verbalyze-stt-bench
Verbalyze: Indic Speech & ITN Benchmark (12 Languages)
Verbalyze is a scenario-weighted benchmark dataset designed to evaluate and train Speech-to-Text (ASR) and Inverse Text Normalization (ITN) models on real-world Indic speech phenomena.
It covers 12 major Indian languages with 172,800 balanced utterances categorized across 11 edge-case scenarios where standard speech models typically fail.
Languages Covered
Language
Code
Samples
Script
Assamese
as
14… See the full description on the dataset page: https://huggingface.co/datasets/ansh-rohilla/verbalyze-stt-bench.lunde_nor_nob_reading_optimisedTest only - not for training.
First version - 0.1 of lunde_nor_nob_reading_optimised
This dataset does not contain any audio data.
Export Details
Train samples: 10040932
Validation samples: 0
Test samples: 0
Dataset created using search datasets:lunde_nor_nob_reading_optimised.
uzbek-asr-train-manifests
Uzbek ASR Training Manifests
The exact training, validation and test splits behind
rustam1221/uzbek-asr-gigaam:
974 hours of Uzbek speech drawn from seven public corpora, filtered, text-normalized,
and split by speaker.
No audio is copied. Each row is a pointer — a parquet file plus a row
index in the upstream dataset — and the training dataloader decodes the audio
when the batch is built. That keeps the whole corpus definition at 200 MB
instead of roughly a terabyte of… See the full description on the dataset page: https://huggingface.co/datasets/rustam1221/uzbek-asr-train-manifests.bibletts-asante-twi-repaired
BibleTTS Asante Twi — Repaired Transcripts
The Asante Twi transcripts released with BibleTTS have had the
characters ɛ (U+025B) and ɔ (U+0254) stripped out. This dataset restores them.
Audio is not included. This is a drop-in replacement for the .txt files that ship with the
BibleTTS Asante Twi package, matched by clip ID.
The problem
Both are Twi vowels, and both are required by the orthography. Measured across the released
Asante Twi transcripts:
Character… See the full description on the dataset page: https://huggingface.co/datasets/danieldzikunuofmarvel/bibletts-asante-twi-repaired.arabic-english-code-switching-review-annotations
Review Annotations for Arabic-English Code-Switching Speech
This metadata-only dataset publishes review decisions and transcript-correction deltas for MohamedRashad/arabic-english-code-switching. It contains no human audio, no local file paths, no raw review notes, and no copies of unchanged upstream transcripts.
The annotations are pinned to upstream revision 4a3bffc45219c35949470de32b8d4cb328b0ce11 and join by upstream_row_index.
Coverage and outcomes
The… See the full description on the dataset page: https://huggingface.co/datasets/abdo1819/arabic-english-code-switching-review-annotations.quranic-asr-cloud-rawdata
Quranic ASR Provider Benchmark Results
Professional benchmark artifacts for comparing commercial and official ASR providers on the Quranic ASR benchmark hosted at Quran-Lab/quranic-asr-benchmark.
This repository contains metadata, normalized result tables, raw provider responses, unchanged run scripts, scoring outputs, Tarteel streaming probes, and reports. It does not duplicate the source audio.
What Is Included
Area
Path
Purpose
Benchmark split… See the full description on the dataset page: https://huggingface.co/datasets/Quran-Lab/quranic-asr-cloud-rawdata.common-voice-en-revoiceyogera_runyankore_ailab_4_0_1eval
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: jonathan harrison
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More Information… See the full description on the dataset page: https://huggingface.co/datasets/Raiff1982/eval.yogera_runyankore_ailabmedimind-r11-train
MediMind R11 — ASR training data
Unified manifest + packed audio for fine-tuning Whisper-large-v3 on Norwegian
clinical and conversational speech.
Training manifest: r11_manifest.jsonl — 11,022 packs
Held-out eval set: r11_heldout_eval.jsonl — 291 packs (NEVER train on these)
~see manifest audit packs total
11 sources: lege_*, podcasts (motiv/podk/stet), nb_samtale, nb_tale_m3, tts_drugs
Schema
See r11_manifest.jsonl (one JSON object per line) and… See the full description on the dataset page: https://huggingface.co/datasets/gallip0li/medimind-r11-train.