datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
a5sv2-asr-benchmark-dataset
A5Sv2 ASR Benchmark Dataset
Public references, saved predictions, scores, and provenance for the
A5Sv2 ASR benchmark. The benchmark evaluates
streaming English ASR on four fixed public corpora with approximately equal normalized reference
word counts.
Corpus
Fixed selection
Reference words
Audio in this repository
Mega-ASR / Voices-in-the-Wild-2M
1,250 utterances, 250 per acoustic condition
32,928
Yes
AMI
7 scenario-only unseen-evaluation meetings
32,928
Yes
DiPCo… See the full description on the dataset page: https://huggingface.co/datasets/AirCaps/a5sv2-asr-benchmark-dataset.entity-transcription-benchmark
Entity Transcription Benchmark
Measures whether a speech recognition system transcribes named entities
correctly — as distinct from word error rate.
WER weights every token equally. The tokens that matter for redaction, lookup,
routing and search are proper nouns, and they are a small fraction of any
transcript. A system can improve WER while getting worse at exactly the words a
downstream consumer needs, and nothing in the standard evaluation will show it.
2,151 clips, 6.0… See the full description on the dataset page: https://huggingface.co/datasets/modulate/entity-transcription-benchmark.multilingual-tts-benchmark
Multilingual Speech Benchmark for Zero-Shot TTS
A voice-cloning and intelligibility benchmark for six language variants, built
from Common Voice 17.0 by coverage-driven selection rather than random sampling.
Every example pairs a reference clip of one speaker with a target text that
speaker never read, so a system is asked to clone a voice and produce new
speech, which is what zero-shot TTS is actually for.
Pipeline source code:… See the full description on the dataset page: https://huggingface.co/datasets/nineninesix/multilingual-tts-benchmark.quran-alignment-benchmark
Quran Recitation Alignment Benchmark
Audio recordings of Quran recitation with a reviewed word-level ground truth: every recited word, in the order it was recited, with its start and end time, plus the reviewed segmentation and non-Quran regions. This is the corpus behind the Quran Recitation Alignment Benchmark; the task, scoring rules, leaderboard and submission format are documented there, not here.
16 recordings · 357 minutes · 18,421 recited words · Hafs ʿan ʿĀṣim ·… See the full description on the dataset page: https://huggingface.co/datasets/QUD-Technologies/quran-alignment-benchmark.Agri_STT_Benchmarking_Dataset
Agri STT Benchmarking Dataset
10,808 farmer voice queries in Hindi, Telugu and Odia, with reference transcripts, for benchmarking automatic speech recognition in agricultural contexts. The audio is included in this repository.
Every recording is a smallholder farmer speaking a question to Farmer.Chat, an AI advisory service run by Digital Green. Reference transcripts were produced by human annotators. Nothing here is read from a script or recorded in a studio, so the audio… See the full description on the dataset page: https://huggingface.co/datasets/DigiGreen/Agri_STT_Benchmarking_Dataset.Persian-ASR-BenchmarkThis dataset consists of 3 hours of 16kHz audio collected from diverse environments to better represent real-world scenarios. The recordings were sourced from audiobooks, YouTube, and other public sources, ensuring a wide variety of speech styles and acoustic conditions.
One key advantage of this dataset is that it was collected from recent sources within the last few months, ensuring no overlap with training data and fairness for evaluating other STT models.
To enable a robust and fair… See the full description on the dataset page: https://huggingface.co/datasets/C1Tech/Persian-ASR-Benchmark.Vaani-Benchmark-V1.0
Vaani-Benchmark-V1.0
A curated ASR evaluation set drawn from the Vaani project. This benchmark contains 5,050 audio segments from 1,103 speakers across 104 Indian districts, each with three independent human transcriptions.
Evaluation Toolkit
A standalone toolkit implementing this benchmark's scoring methodology, plus
Latin-script normalization for code-switched predictions and one-command
publishing of results to a model's HF card, is available at… See the full description on the dataset page: https://huggingface.co/datasets/ARTPARK-IISc/Vaani-Benchmark-V1.0.humans-benchmark
HUMANS Benchmark Dataset
Authors: Woody Haosheng Gan¹, William Held²'³, Diyi Yang²
¹University of Southern California, ²Stanford University, ³OpenAthena
This dataset is part of the Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment paper.
HUMANS (HUman-aligned Minimal Audio evaluatioN Subsets for Large Audio Models) Benchmark is designed to efficiently evaluate Large Audio Models using minimal subsets while predicting human preferences through learned… See the full description on the dataset page: https://huggingface.co/datasets/woodygan/humans-benchmark.gametime
Gametime Benchmark
The Gametime dataset provides lightweight, streaming-friendly splits for TTS/ASR/SpokenLM prototyping.For full details, please refer to the paper:👉 Game-Time: Evaluating Temporal Dynamics in Spoken Language Models
📦 Download Options
1️⃣ Recommended — Full ZIP Download
If you prefer the original folder layout you can download one of the ZIPs packaged in gametime/download/. There are two kinds available in this repository:… See the full description on the dataset page: https://huggingface.co/datasets/gametime-benchmark/gametime.ASR-Benchmark-Public
Revolab ASR Benchmark (Public Split)
The Revolab ASR Benchmark is a human-annotated evaluation dataset for Malaysian Malay speech recognition. This is the public split - a downloadable subset that anyone can use to run their own evaluation. A larger private split is used to maintain the official leaderboard.
Dataset Details
Audio samples: 820
Duration: ~2.2 hours
Languages: Bahasa Malaysia and English (including code-switching)
Sample rate: 16kHz
License:… See the full description on the dataset page: https://huggingface.co/datasets/Revolab/ASR-Benchmark-Public.kurdish-multidialect-asr-benchmark
Kurdish Dialect Speech Corpus
This project aims to provide a multi-dialect speech recognition benchmark for the Kurdish language. The Central Kurdish portion is the same as the Asosoft benchmark. The sentences were originally written in Central Kurdish (CKB), translated into other Kurdish dialects, and then recorded by native speakers.
The current version includes three Kurdish dialects: Central Kurdish, Northern Kurdish, and Southern Kurdish. A Hawrami version and the Badini… See the full description on the dataset page: https://huggingface.co/datasets/aranemini/kurdish-multidialect-asr-benchmark.Hypa-Voices-ASR-Benchmark
Hypa-Voices-ASR-Benchmark
AfroVoices, a subsidiary of Hypa AI, is dedicated to amplifying African voices, languages, and cultures in the intelligence age. Focused on bridging the digital representation gap, AfroVoices curates datasets and resources for African languages, promoting inclusivity and cultural appreciation in AI technologies.
Dataset Summary
Hypa-Voices-ASR-Benchmark is a compact, 20-sample benchmark drawn from the larger hypaai/Hypa-Voices corpus. It… See the full description on the dataset page: https://huggingface.co/datasets/hypaai/Hypa-Voices-ASR-Benchmark.humans-benchmark
HUMANS Benchmark Dataset (Anonymous, Under Review)
This dataset is part of the HUMANS (HUman-aligned Minimal Audio evaluatioN Subsets for Large Audio Models) Benchmark, designed to efficiently evaluate Large Audio Models using minimal subsets while predicting human preferences through learned regression weights.
Installation
Install the HUMANS evaluation package from GitHub (our anonymous repo):
# Option 1: Install via pip
pip install… See the full description on the dataset page: https://huggingface.co/datasets/HUMANSBenchmark/humans-benchmark.bambara-asr-benchmark
Bambara ASR Benchmark
The first standardized evaluation set for Automatic Speech Recognition in Bambara (Bamanankan). One hour of studio-quality constitutional text, transcribed and validated by linguists from Mali's Direction Nationale de l'Éducation Non Formelle et des Langues Nationales (DNENF-LN).
This benchmark accompanies the paper "Where Are We at with Automatic Speech Recognition for the Bambara Language?" and the public leaderboard at MALIBA-AI/bambara-asr-leaderboard.… See the full description on the dataset page: https://huggingface.co/datasets/MALIBA-AI/bambara-asr-benchmark.bam-asr-benchmark
Bambara ASR Benchmark
The first standardized evaluation set for Automatic Speech Recognition in Bambara (Bamanankan). One hour of studio-quality constitutional text, transcribed and validated by linguists from Mali's Direction Nationale de l'Éducation Non Formelle et des Langues Nationales (DNENF-LN).
This benchmark accompanies the paper "Where Are We at with Automatic Speech Recognition for the Bambara Language?" and the public leaderboard at MALIBA-AI/bambara-asr-leaderboard.… See the full description on the dataset page: https://huggingface.co/datasets/sudoping01/bam-asr-benchmark.ASR-Benchmarking-Dataset
Hindi STT Benchmarking Eval
Overview
This dataset packages the Hindi eval split used for STT benchmarking across six Vistaar-derived parts: IndicTTS, FLEURS, CommonVoice, Kathbath, Kathbath noisy, and MUCS. Each row contains the audio, original reference transcript, and raw plus normalized transcripts from Ringg, ElevenLabs, Deepgram, and Sarvam.
The dataset contains 10,000 utterances and about 15.5 hours of 16 kHz mono WAV audio.
The dataset is published as part-specific… See the full description on the dataset page: https://huggingface.co/datasets/RinggAI/ASR-Benchmarking-Dataset.hindi-asr-benchmark
SkunkWorks Hindi STT Benchmark
Hindi ASR benchmark evaluating SkunkWorks alongside major commercial Hindi STT providers
(ElevenLabs, Deepgram, Sarvam) across 6 held-out evaluation subsets.
Subsets
config
source
n
kathbath
AI4Bharat Kathbath
1,929
kathbath_noisy
Kathbath noisy mic conditions
1,929
commonvoice
Mozilla Common Voice Hindi
1,727
mucs
MUCS 2021 Hindi subtask
3,897
fleurs
Google FLEURS hi_in test
418
indictts
AI4Bharat IndicTTS
100… See the full description on the dataset page: https://huggingface.co/datasets/SkunkWorkLabs/hindi-asr-benchmark.visualears-benchmark-269-gold
🗂️ visualears-benchmark-269-gold
English + فارسی · Part of Shenava 1.0 · Project hub · SLT paper submission
🌟 At a glance | معرفی سریع
English
فارسی
🎯 Purpose
269-record gold/noisy benchmark dataset.
معیار طلایی ۲۶۹ نمونهای برای بررسی سریع خطاهای گفتار نویزی و مقایسهٔ نسخههای مدل.
🧩 Role
evaluation and benchmarking asset
مصنوع ارزیابی و بنچمارک
📦 Snapshot
276 files; approximately 44.35 MB
276 فایل؛ حدود 44.35 MB
🧱 Packaging
1 Parquet… See the full description on the dataset page: https://huggingface.co/datasets/Reza2kn/visualears-benchmark-269-gold.nepali-asr-benchmark
Nepali ASR Benchmark
Per-utterance reference, hypothesis, WER, and CER for the six released Nepali ASR
checkpoints evaluated on three independent test sets. Released alongside the paper
Comparative Analysis of Multilingual Pre-trained Models for Nepali Automatic Speech
Recognition.
Contents
Field
Type
Description
utterance_id
string
stable identifier {test_set}-{index}
reference
string
NFC-normalised gold transcription (Devanagari)
hypothesis
string… See the full description on the dataset page: https://huggingface.co/datasets/sumanpaudel1997/nepali-asr-benchmark.meddies-asr-benchmark
Meddies ASR Benchmark
Vietnamese and English ASR benchmark slices for Meddies evaluation, packaged as Parquet with
embedded audio in a datasets.Audio column.
This repo currently exposes four subsets:
vi_general: deterministic Vietnamese general-domain slice from google/fleurs test
en_general: deterministic English general-domain slice from google/fleurs test
vi_medical: Vietnamese medical audio set from Dr Cao Huu Thinh Official, with references pending later curation… See the full description on the dataset page: https://huggingface.co/datasets/Meddies/meddies-asr-benchmark.