datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tlott-digital-products
T. Lott Digital Products
Digital product files for T. Lott's online store.
Products
Audiobooks (MP3)
eBooks (PDF)
Software (ZIP)
Cover images (PNG)
Download URLs
Files can be downloaded directly:
https://huggingface.co/datasets/ziggylott/tlott-digital-products/resolve/main/{filepath}
uyghur-common-voice-tts
Uyghur Common Voice TTS Dataset
A cleaned and processed Text-to-Speech (TTS) dataset for the Uyghur language, derived from Mozilla Common Voice.
Dataset Summary
Property
Value
Language
Uyghur (ug)
Total Samples
43,054
Train Samples
40,901
Validation Samples
2,153
Audio Format
WAV
Source
Mozilla Common Voice
License
CC0-1.0
Dataset Structure
/
├── train.jsonl # Training data (40,901 samples)
├── val.jsonl #… See the full description on the dataset page: https://huggingface.co/datasets/anke01/uyghur-common-voice-tts.VietSuperSpeech
VietSuperSpeech
Vietnamese Speech Recognition Dataset
Dataset Information
Total samples: 32,267
Train samples: 29,041
Dev samples: 3,226
Total duration: 103.18 hours
Sample rate: 16000 Hz
Average segment length: ~12 seconds
Source Datasets
asr_dataset_nguoivietdailynews
asr_dataset_nguyenkhangofficial
asr_dataset_trinhlieu
Format
The dataset follows Icefall format:
train.json: Training samples
dev.json: Development samples
manifest.json:… See the full description on the dataset page: https://huggingface.co/datasets/thanhnew2001/VietSuperSpeech.TalkVid
TalkVid Dataset
This repository hosts the TalkVid dataset.
Paper: TalkVid: A Large-Scale Diversified Dataset for Audio-Driven Talking Head Synthesis
Arxiv paper: https://arxiv.org/abs/2508.13618
Project Page: https://freedomintelligence.github.io/talk-vid
GitHub: https://github.com/FreedomIntelligence/TalkVid
Abstract
Audio-driven talking head synthesis has achieved remarkable photorealism, yet state-of-the-art (SOTA) models exhibit a critical failure: they lack… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/TalkVid.UltraVoice
UltraVoice: Scaling Fine-Grained Style-Controlled Speech Conversations for Spoken Dialogue Models
📝 Abstract
Spoken dialogue models currently lack the ability for fine-grained speech style control, a critical capability for human-like interaction that is often overlooked in favor of purely functional capabilities like reasoning and question answering. To address this limitation, we introduce UltraVoice, the first large-scale speech dialogue dataset… See the full description on the dataset page: https://huggingface.co/datasets/tutu0604/UltraVoice.dahih-tts2-demucs-cleaneddaily-bio-newsyoutube-transcriptionsThe YouTube transcriptions dataset contains technical tutorials (currently from James Briggs, Daniel Bourke, and AI Coffee Break) transcribed using OpenAI's Whisper (large). Each row represents roughly a sentence-length chunk of text alongside the video URL and timestamp.
Note that each item in the dataset contains just a short chunk of text. For most use cases you will likely need to merge multiple rows to create more substantial chunks of text, if you need to do that, this code snippet will… See the full description on the dataset page: https://huggingface.co/datasets/jamescalam/youtube-transcriptions.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.speech-translation-and-summarization
English-Centric Multilingual Audio Dataset
This dataset contains generated article and summary audio for English-centric multilingual directions.
Each direction folder contains metadata JSONL files and corresponding audio files for few_shot and test splits.
Included directions
amharic_english / english_amharic
arabic_english / english_arabic
bengali_english / english_bengali
chinese_simplified_english / english_chinese_simplified
english_english
french_english /… See the full description on the dataset page: https://huggingface.co/datasets/McGill-NLP/speech-translation-and-summarization.DCASE2026-Task5-DevSet
DCASE 2026 Task 5 Audio-Dependent Question Answering (ADQA) Development Set
This is the official Development Set for DCASE 2026 Challenge Task 5: Audio-Dependent Question Answering (ADQA).
The ADQA task focuses on addressing "Textual Hallucination" in Large Audio-Language Models (LALMs) — where models pass audio understanding benchmarks by relying on text prompts and internal linguistic priors rather than actual audio perception. ADQA introduces a rigorous evaluation… See the full description on the dataset page: https://huggingface.co/datasets/Harland/DCASE2026-Task5-DevSet.AgentChat-Test
Test Set Description
This directory contains the test set used for tool-use evaluation. The JSON files under Test-JSON/ are organized by task type:
SingleTaskProcessing/tool-select_test.json: single-tool selection tasks.
ParallelProcessing/parallel-call_test.json: parallel tool-call tasks.
ProactiveSeeking/searchTools_test_predictions_kept.json: proactive tool-search tasks.
TaskDecomposition/muti-tool-select_test.json: multi-tool task decomposition tasks.… See the full description on the dataset page: https://huggingface.co/datasets/leungtianle/AgentChat-Test.asr-reference-set-eval-temp
Temporary ASR evaluation audio
Temporary public audio files used for hosted ASR evaluation.
bengali-talkshow-audio
Bengali Talkshow Audio Dataset
A large-scale collection of 1,180 Bengali talk show audio recordings totaling 789+ hours of multi-speaker speech, sourced from Bangladeshi television talk shows and political debate programs.
Dataset Description
This dataset contains audio from Bengali-language TV talk shows, political debates, and news discussion programs from major Bangladeshi television channels. Each recording features multiple speakers engaged in discussion, making it… See the full description on the dataset page: https://huggingface.co/datasets/nymtheescobar/bengali-talkshow-audio.multichannel-meetings-10h
GroundTruth Multi-Channel Meeting Audio Dataset (10h)
Summary
This dataset contains approximately 10 hours of co-located, multi-speaker meeting recordings, each captured simultaneously via a room (built-in) microphone and individual close-talk lapel microphones worn by each participant.
Each meeting includes:
One full meeting recording (room microphone)
Individual close-talk recordings for each participant (one file per speaker)
Structured metadata describing speakers… See the full description on the dataset page: https://huggingface.co/datasets/ground-truth/multichannel-meetings-10h.orig-plus-asr-tamil-clean
orig-plus-asr-tamil-clean
Combined ASR dataset built from:
albagon/til26-asr-split (orig rows)
whyismydininghallonfire/asr-tamil-clean (asr_tamil_clean rows)
Audio paths are namespaced under each split to avoid filename collisions:
audio/orig/...
audio/asr_tamil_clean/...
Each row keeps key, audio, transcript, and language, with an added source_dataset field.
Counts:
train: 3595 orig + 891 asr_tamil_clean = 4486
validation: 899 orig + 224 asr_tamil_clean = 1123
jam-actions-v1
jam-actions-v1
Schema: jam-actions-v1/1.0.0 · Version: 1.1.0 · Records: 213 (154 train / 59 test, split by song) ·
Songs: 11 · Families: 9 · Licence: CC-BY-SA-3.0-DE ·
Source repo: mcp-tool-shop-org/ai-jam-sessions
The successor to jam-actions-v0.
Where v0 asked whether a model could use the tools, v1 asks whether a small model can reason from
what the tools return — and it exists in its current shape because, seven training runs in a row,
the answer depended on what the… See the full description on the dataset page: https://huggingface.co/datasets/mcp-tool-shop/jam-actions-v1.music2chords_v2teochew_wild
Teochew-Wild：首个正字标注的野外潮州话数据集
本数据集（Teochew-Wild）是从网络上发音清晰、噪声较少的音视频内容中获取的，原始音视频的数据来源为：民生新闻、潮汕讲古、地方电视节目、故事书、抖音自媒体口播等，我借鉴了Emilla提出的数据集自动处理流水线，对原始数据进行归一化、降噪和剪切（部分自动剪切效果差的使用手工修正）；
Teochew-Wild总共包括20个发音标准、念错率低的潮汕母语说话人、共12500条音频片段，包含潮州市区、汕头市区、澄海、榕江音、潮安南部等多个区域的口音，语料内容覆盖书面用语与口头用语，并同时提供正字和拼音标注，是首个公开可用、标注准确率高的潮州话数据集，主要面向语音识别和语音合成任务。
文件说明 （File Structure Explanation）
├── label_for_qwen_asr/ # 预处理标签文件夹，完全适配Qwen-ASR模型读取格式
├── README.md # 项目说明文档（本文档）… See the full description on the dataset page: https://huggingface.co/datasets/panlr/teochew_wild.MutiEmo-Test
MultiEmo-Test
MultiEmo-Test is an English evaluation set for instruction-following multi-emotion text-to-speech synthesis. It accompanies HybridEmo, a system for modeling sequential emotion trajectories and simultaneous emotion blending within an utterance.
The dataset is intended for evaluation only. It contains synthesis text, natural-language emotion instructions, emotion annotations, and prompt audio for speaker-timbre conditioning. It does not contain target synthesized… See the full description on the dataset page: https://huggingface.co/datasets/ICTNLP/MutiEmo-Test.instructtts-three-model-gemini-zh
InstructTTSEval 三模型 Gemini 评测数据
本目录整理了 InstructTTSEval 中文集上三个 TTS 模型的生成音频和 Gemini 一致性评测结果：Qwen3-TTS-12Hz-1.7B-VoiceDesign、Seed-Audio-1.0、VoxCPM2。
字段
records.jsonl 每行对应一个模型和一种控制格式（APS、DSD 或 RP）：
id：InstructTTSEval 样本 ID
mode：控制格式
model、model_name：模型标识
text：合成文本
instruction：历史评测记录中的输入控制指令，按本行 APS/DSD/RP 格式保留；不等同于各模型 API 的完整请求封装
generated_audio：该模型生成音频的相对路径
reference_audio：原始参考音频的相对路径
gemini_consistent：Gemini judge 的一致性判断
inconsistency_reason：判断为不一致时的原因… See the full description on the dataset page: https://huggingface.co/datasets/zsy814/instructtts-three-model-gemini-zh.jam-actions-v1-probe
jam-actions-v1-probe
Schema: jam-actions-v1-probe/1.0.0 · Records: 24, all split: test · Evaluation only ·
Companion to: jam-actions-v1
Why it exists
An adapter trained on an earlier version of the corpus scored 47/54 on held-out acoustic takes.
Its completions, which state the comparison before the label, showed that it wrote against a 50-cent gate whenever it saw a minus sign — and negative cents occurred in exactly one class of that
corpus. The main split could… See the full description on the dataset page: https://huggingface.co/datasets/mcp-tool-shop/jam-actions-v1-probe.jam-actions-acoustic-v0
Dataset Card for jam-actions-acoustic-v0
Version: 1.0.2
Published at mcp-tool-shop/jam-actions-acoustic-v0. No DOI.
Summary
108 constructible gold records of grounded MCP tool use over monophonic audio analysis. Each record pairs a 4-note right-hand reduction of a public-domain library phrase with a seeded synthetic take and a gold verdict (match, pitch fail/warn, timing fail/pass, missed, extra, in-tune vibrato, or nothing-to-grade silence).
This is not a musical… See the full description on the dataset page: https://huggingface.co/datasets/mcp-tool-shop/jam-actions-acoustic-v0.Tech-Sentences-For-ASR-Training
TechVoice Dataset
Work in Progress – This dataset is actively being expanded with new recordings.
Dataset Statistics
Metric
Current
Target
Progress
Duration
38m 43s
5h 0m 0s
██░░░░░░░░░░░░░░░░░░ 12.9%
Words
10,412
50,000
████░░░░░░░░░░░░░░░░ 20.8%
Total Recordings: 205 samples
Total Characters: 74,312
A specialized speech dataset for fine-tuning Automatic Speech Recognition (ASR) models on technical and developer vocabulary. Contains human-recorded… See the full description on the dataset page: https://huggingface.co/datasets/danielrosehill/Tech-Sentences-For-ASR-Training.tr-full-dataset
TR-Full_dataset
This is a merged speech dataset containing 41427 audio segments from 88 source datasets.
Dataset Information
Total Segments: 41427
Speakers: 222
Languages: tr
Emotions: neutral, angry, sad, happy
Original Datasets: 88
Dataset Structure
Each example contains:
audio: Audio file (WAV format, original sampling rate preserved)
text: Transcription of the audio
speaker_id: Unique speaker identifier (made unique across all merged… See the full description on the dataset page: https://huggingface.co/datasets/Codyfederer/tr-full-dataset.agentic-asr
Agentic ASR
Public consolidated audio and ASR result dataset for the OSWorld and
WildClawBench benchmark families.
Layout
osworld/: synthetic raw/colloquial speech, human recordings, DNS-noise
pairs, task images, ASR results, and reports.
wildclawbench/: 60 formal colloquialized prompts, synthetic speech,
20 synthetic ASR condition tables, and ten-participant human recordings.
task0_template derivatives are excluded.
metadata/conditions.jsonl: model, variant… See the full description on the dataset page: https://huggingface.co/datasets/tterumiimurett1/agentic-asr.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.Kyrg-TTSopen-vi-dialog-synthetic-100h
OpenDialog Vietnamese Synthetic Dialogue 100h
Synthetic Vietnamese two-speaker dialogue for ZipVoice-Dialog experiments.
12,000 chunks
30 seconds per chunk
100.0 hours total
Each item contains S1/S2 speaker labels, turn timings, target text,
relationship, pronouns, environment, topic, mood, and source reference IDs.
Audio renderer: vLLM-Omni VoxCPM2
Audio format: mono WAV, 48 kHz, 30 seconds per chunk
This is a research dataset. Review the source/reference licensing and the… See the full description on the dataset page: https://huggingface.co/datasets/tsdocode/open-vi-dialog-synthetic-100h.Aren
ARen — Arabic/English ASR Robustness Set
Curated and published by TNSA AI.
A small, deliberately hard evaluation set for Arabic and English speech
recognition. Every clip exists in three acoustic conditions so you can measure
not just how a model scores, but how fast it falls apart as the channel
degrades.
Built because clean read-speech benchmarks stop discriminating between modern
ASR systems long before real deployments stop breaking.
Why it exists
On clean… See the full description on the dataset page: https://huggingface.co/datasets/TNSA/Aren.