datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
common_voice_17_0voicehub-arena-seed-tts-eval
VoiceHub Arena — native TTS evaluations
Incrementally published generated audio and WER, CER, DNSMOS, WavLM-large ECAPA
speaker SIM and UTMOS22 measurements. The full campaign is still running.
Each generation method is evaluated separately using its publisher's native API.
Full evaluations contain all 1,088 English Seed-TTS-Eval targets; eight-target
diagnostic pilots are stored separately and must not be treated as full scores.
Interactive demo ·
Source code
Layout… See the full description on the dataset page: https://huggingface.co/datasets/VoiceHub/voicehub-arena-seed-tts-eval.genshin-voice
Genshin Voice
Genshin Voice is a dataset of voice lines from the popular game Genshin Impact.
Hugging Face 🤗 Genshin-Voice
ModelScope Genshin-Voice
Per-speaker downloads are grouped by language and ZIP size. Browse every archive in the ZIP index.
Last update at 2026-08-13
654252 wavs
7291 without speaker (1%)
52693 without transcription (8%)
1088 without inGameFilename (0%)
Dataset Details
Dataset Description
The dataset contains voice lines… See the full description on the dataset page: https://huggingface.co/datasets/simon3000/genshin-voice.voicebench
License
The dataset is available under the Apache 2.0 license.
Citation
If you use the VoiceBench dataset in your research, please cite the following paper:
@article{chen2024voicebench,
title={VoiceBench: Benchmarking LLM-Based Voice Assistants},
author={Chen, Yiming and Yue, Xianghu and Zhang, Chen and Gao, Xiaoxue and Tan, Robby T. and Li, Haizhou},
journal={arXiv preprint arXiv:2410.17196},
year={2024}
}
zenless-voice
Zenless Voice
Zenless Voice is a dataset of voice lines from the popular game Zenless Zone Zero.
Hugging Face 🤗 Zenless-Voice
ModelScope Zenless-Voice
Per-speaker downloads are grouped by language and WAV count. Browse every archive in the ZIP index.
Last update at 2026-09-17, game version 3.2.0
406720 wavs
78785 without speaker (19%)
123429 without transcription (30%)
83509 without inGameFilename (21%)
Speaker archives contain 327,935 WAVs in 4,322 ZIPs. The 78,785 rows… See the full description on the dataset page: https://huggingface.co/datasets/simon3000/zenless-voice.fallout-4-voicesgenshin-voice-v3.3-mandarin
Dataset Card for Genshin Voice
Dataset Description
Dataset Summary
The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game.
Languages
The text in the dataset is in Mandarin.
Dataset Creation
Source Data
Initial Data Collection and Normalization
The data was obtained by unpacking the Genshin Impact game.
Who are the source language producers?
The… See the full description on the dataset page: https://huggingface.co/datasets/hanamizuki-ai/genshin-voice-v3.3-mandarin.portuguese-male-voice-A-datasetVoiceAgentBench
VoiceAgentBench
This repository contains dataset for VoiceAgentBench, a large-scale speech benchmark introduced in “VoiceAgentBench: Are Voice Assistants Ready for Agentic Tasks?” (arXiv:2510.07978).
VoiceAgentBench is designed to evaluate end-to-end speech-based agents in realistic, tool-driven settings. Unlike prior speech benchmarks that focus on transcription, intent detection, and speech question answering, this benchmark targets agentic reasoning from speech input, requiring… See the full description on the dataset page: https://huggingface.co/datasets/krutrim-ai-labs/VoiceAgentBench.numberblocks-one-voice-datasetX-Voice-Dataset-Train
X-Voice Training Dataset
Overview
The X-Voice training dataset is a large-scale multilingual speech corpus curated for high-performance speech models. It provides a robust foundation for cross-lingual phonetic and prosodic modeling.
Also the train set of X-Voice Model.
Core Statistics
Total Speech Duration: 420K hours
30 languages
European: bg (Bulgarian), cs (Czech), da (Danish), de (German), el (Greek), en (English), es (Spanish), et (Estonian), fi… See the full description on the dataset page: https://huggingface.co/datasets/XRXRX/X-Voice-Dataset-Train.emolia-thinking
Emolia-Thinking — a VoiceNet-annotated, balanced subset of Emolia
Emolia-Thinking is a richly annotated speech dataset created for the VoiceNet project. It takes a balanced subset of the Emolia corpus — balanced across speaker-embedding clusters and emotion-embedding clusters so that speakers, voices and emotional states are evenly represented rather than dominated by the most common cases — and annotates every clip along the full VoiceNet Extended voice-performance taxonomy… See the full description on the dataset page: https://huggingface.co/datasets/VoiceNet/emolia-thinking.VoiceAssistant-400KReal_Voiceuyghur-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.common_voice_16_0Japanese-Materials
仓库信息
电报地址：https://t.me/vomebook ，有问题请在：https://huggingface.co/datasets/VoiceOfML/Japanese-Materials/discussions 提出。
此仓库存储日共资料：https://huggingface.co/datasets/VoiceOfML/Japanese-Materials/tree/main 。
请使用：https://voiceofml-search.hf.space/Japanese-Materials 进行文件检索（备用搜索站：https://vomebook.github.io/search/#/Japanese-Materials ）。
可使用：https://voiceofml-search.hf.space/Japanese-Materials?wide=1 进行仓库内容查看（备用站：https://voiceofml-search.hf.space/Japanese-Materials?wide=1 ）。… See the full description on the dataset page: https://huggingface.co/datasets/VoiceOfML/Japanese-Materials.common-voice-13-faThe Persian portion of the original CommonVoice 13 dataset at https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0
Load
# Using HF Datasets
from datasets import load_dataset
dataset = load_dataset("hezarai/common-voice-13-fa", split="train")
# Using Hezar
from hezar.data import Dataset
dataset = Dataset.load("hezarai/common-voice-13-fa", split="train")
Voices-in-the-Wild-2M
Voices in the Wild
Project Page | Paper | GitHub
Voices in the Wild (Voices-in-the-Wild-2M) is a large-scale automatic speech recognition (ASR) dataset designed for robustness training and evaluation under diverse, real-world acoustic conditions. It covers 7 classic acoustic phenomena (including noise, far-field speech, obstruction, echo/reverberation, recording artifacts, electronic distortion, and transmission dropout) and 54 physically plausible compound scenarios.
The… See the full description on the dataset page: https://huggingface.co/datasets/zhifeixie/Voices-in-the-Wild-2M.voicehub-arena-seed-tts-eval
VoiceHub Arena — full English Seed-TTS-Eval
35,904 synthesized WAV files: 33 model families × the same 1,088 target texts.
The campaign completed on 15 September 2026 on one NVIDIA A100-SXM4 40 GB.
All 198 shards and every WAV SHA256 were verified after backup.
Interactive leaderboard and all audio samples
· Source repository (access required).
Contents
audio_shards/<model>.tar: 33 WebDataset shards, each containing 1,088 original WAVs and matching JSON metadata.… See the full description on the dataset page: https://huggingface.co/datasets/kadirnar/voicehub-arena-seed-tts-eval.starrail-voice
StarRail Voice
StarRail Voice is a dataset of voice lines from the popular game Honkai: Star Rail.
Hugging Face 🤗 StarRail-Voice
ModelScope StarRail-Voice
Last update at 2026-07-16, game version 4.4.0
403437 wavs
60164 without speaker (15%)
61375 without transcription (15%)
57869 without inGameFilename (14%)
Dataset Details
Dataset Description
The dataset contains voice lines from the game's characters in multiple languages, including Chinese… See the full description on the dataset page: https://huggingface.co/datasets/simon3000/starrail-voice.voicedaolylaions_got_talent_with_voice_emotion_speed_tags_for_orpheus_tuningLAION's Got Talent: Generated Voice Acting Dataset
Overview
"LAION's Got Talent" is a synthetic voice acting dataset designed to offer a broad range of emotional expressions, vocal bursts, and multi-language utterances. This dataset is a component of the BUD-E project, led by LAION with support from Intel, and aims to drive forward research in context-aware and empathetic AI voice assistants.
Updated Composition
Voices and Languages
English: 11 OpenAI voices, each… See the full description on the dataset page: https://huggingface.co/datasets/laion/laions_got_talent_with_voice_emotion_speed_tags_for_orpheus_tuning.common_voice_21_0_miniVoiceBank-DEMAND-16kjapanese-listening-voicevox-backuparc-voicesamples-generatedOmnibus
仓库信息
电报地址：https://t.me/vomebook ，有问题请在：https://huggingface.co/datasets/VoiceOfML/Omnibus/discussions 提出。
此仓库存储书纳百川专题：https://huggingface.co/datasets/VoiceOfML/Omnibus/tree/main 。
请使用：https://voiceofml-search.hf.space/Omnibus 进行文件检索（备用搜索站：https://vomebook.github.io/search/#/Omnibus ）。
可使用：https://voiceofml-search.hf.space/Omnibus?wide=1 进行仓库内容查看（备用站：https://voiceofml-search.hf.space/Omnibus?wide=1 ）。
你可以仅下载指针（只有文件名的信息）
If you want to clone without large files - just their… See the full description on the dataset page: https://huggingface.co/datasets/VoiceOfML/Omnibus.voice-light-synthetic-audio
Voice-Light Synthetic Audio
English-only synthetic conversational speech for training and evaluating streaming
turn-taking models. The corpus focuses on end-of-turn prediction, continuation holds,
short backchannels, interruptions, and response timing.
The dataset contains user-side FLAC speech units plus typed conversation plans,
rendering provenance, quality ledgers, and deterministic reconstruction metadata.
Assistant speech is represented as a time-varying… See the full description on the dataset page: https://huggingface.co/datasets/BertilBraun/voice-light-synthetic-audio.voicebench
License
The dataset is available under the Apache 2.0 license.
Citation
If you use the VoiceBench dataset in your research, please cite the following paper:
@article{chen2024voicebench,
title={VoiceBench: Benchmarking LLM-Based Voice Assistants},
author={Chen, Yiming and Yue, Xianghu and Zhang, Chen and Gao, Xiaoxue and Tan, Robby T. and Li, Haizhou},
journal={arXiv preprint arXiv:2410.17196},
year={2024}
}