datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
thai-aligner-bench
Thai Aligner Bench
🚧 Development in progress.
How accurately can a forced aligner place Thai token and word boundaries in
speech? This is a self-contained benchmark: one Python file
(aligner_bench.py) plus 1,572 clips of Thai speech with frame-exact timing
ground truth. No Thai NLP stack or other code is needed — just
numpy soundfile torch torchaudio transformers.
The ground truth is what makes the dataset useful: the audio was rendered by a
TTS model whose duration predictor… See the full description on the dataset page: https://huggingface.co/datasets/wayu-ai/thai-aligner-bench.voice-code-bench
VoiceCodeBench
VoiceCodeBench is a test-only benchmark for evaluating whether automatic
speech recognition (ASR) systems preserve exact structured values in English
workplace speech.
Paper: VoiceCodeBench: Evaluating Exact Structured-Token Recovery in Automatic Speech Recognition
The benchmark targets cases where a transcript is software input: callback
numbers, email addresses, command-line flags, file paths, URLs, account
identifiers, dates, measurements, and similar values… See the full description on the dataset page: https://huggingface.co/datasets/besimple-ai/voice-code-bench.kimiDatasetvocal-affect-bench
VocalAffectBench
VocalAffectBench is a test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw audio.
Paper: VocalAffectBench: Evaluating Vocal Emotion Recognition in AI Audio Models
The benchmark targets the expressed emotion — what the speaker conveys through vocal tone, prosody, pace, intensity, and pauses — not inferred internal state.
Contents
280 human-recorded English WAV clips, totalling 2.32 hours.
7… See the full description on the dataset page: https://huggingface.co/datasets/besimple-ai/vocal-affect-bench.aijockey-public-corpusuzbek-asr-curated-701h
Uzbek ASR Curated Dataset (701 hours)
A curated multi-source Uzbek speech dataset for automatic speech recognition (ASR) training and evaluation.
Dataset Description
Language
Uzbek (Latin script with okina ʻ)
Total utterances
337,920
Total duration
~701 hours
Audio format
16 kHz mono WAV (PCM_16)
Manifest format
NeMo JSONL
Splits
train (94%) / val (3%) / test (3%)
Splits
Split
Utterances
Hours
Train
317,655… See the full description on the dataset page: https://huggingface.co/datasets/uzinfocom-edu-ai/uzbek-asr-curated-701h.darija-tts-8400
Darija TTS 8400
Synthetic Moroccan Darija speech for TTS fine-tuning: 8,400 single-speaker clips (20.73 hours), 24 kHz mono PCM16 WAV.
All audio is generated with Gemini 3.1 Flash TTS (gemini-3.1-flash-tts-preview, voice Kore). Clips are unreviewed; there are no human recordings.
Write-up of how this data was used: Training a Voice.
At a glance
Clips / hours
8,400 / 20.73
Unique texts
4,800
Voice
Kore (1 speaker)
Sample rate
24 kHz mono PCM16… See the full description on the dataset page: https://huggingface.co/datasets/ai-ssam/darija-tts-8400.aishell1mix-ver2-n100-per-mix
AISHELL-1 Mix ver2 — 100 clips per mix
This is AISHELL-1 Mix ver2, not ver1. 8 kHz mono test subset: 100 mixtures per speaker count (N=1\ldots5) (50 mix_clean + 50 mix_both each) → 500 clips.
Derived from the local aishell1mix_ver2 test SCPs (data/scp/scp_aishell1mix_ver2). Includes mixture + oracle speaker stems and transcripts.
Split
Count
1mix / 2mix / 3mix / 4mix / 5mix
100 each
clean / both
250 each
Files
manifests/test.jsonl —… See the full description on the dataset page: https://huggingface.co/datasets/playwithmino/aishell1mix-ver2-n100-per-mix.multimodal-ai-taxonomy
Multimodal AI Taxonomy
A comprehensive, structured taxonomy for mapping multimodal AI model capabilities across input and output modalities.
Dataset Description
This dataset provides a systematic categorization of multimodal AI capabilities, enabling users to:
Navigate the complex landscape of multimodal AI models
Filter models by specific input/output modality combinations
Understand the nuanced differences between similar models (e.g., image-to-video with/without audio… See the full description on the dataset page: https://huggingface.co/datasets/danielrosehill/multimodal-ai-taxonomy.IndicST
IndicST: Indian Multilingual Translation Corpus For Evaluating Speech Large Language Models
Introduction
IndicST, a new dataset tailored for training and evaluating Speech LLMs for AST tasks (including ASR and TTS), featuring meticulously curated, automatically, and manually verified synthetic data. The dataset offers 10.8k hrs of training data and 1.13k hrs of evaluation data.
Use-Cases
ASR (Speech-to-Text)
Transcribing Indic languages
Handling… See the full description on the dataset page: https://huggingface.co/datasets/krutrim-ai-labs/IndicST.kimiPlayAI-VoiceExcited to share Play AI Voice Profile. We release 267 unique voice profiles including Israeli, Arabic, Russian, Filipino and many other exclusive voice profiles. Play AI was recently acquired by Meta which sparked our interest in releasing this dataset.
mascarade-dsp-dataset
Mascarade — DSP & Signal Processing Q&A
✅ ATTRIBUTION AUDIT COMPLETED (2026-05-11)
Per-sample Stack Exchange Electronics attribution recovered via the SE
/search/advanced + /questions/{id} API search :
169 samples (~5.35 %) confirmed as Stack Exchange Electronics
(CC-BY-SA-4.0) — fully attributed in metadata.stack_exchange_attribution
(URL + author display name + author user_id + post_id + creation_date_unix + match_confidence ≥ 0.60).
535 samples (~16.93 %) marked… See the full description on the dataset page: https://huggingface.co/datasets/Ailiance-fr/mascarade-dsp-dataset.aitf-dfk3-synthetic-audio-datasetworld-ai-db