datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
VoiceAssistant-400K-SLAM-Omni
VoiceAssistant-400K (Modified)
This dataset is prepared for the reproduction of SLAM-Omni.
This is a single-round English spoken dialogue training dataset. For code and usage examples, please refer to the related GitHub repository: X-LANCE/SLAM-LLM (examples/s2s)
🔧 Modifications
Data Filtering: We removed samples with excessively long data.
Speech Response Tokens: We used CosyVoice to synthesize corresponding semantic speech tokens for the speech response. These… See the full description on the dataset page: https://huggingface.co/datasets/worstchan/VoiceAssistant-400K-SLAM-Omni.VoiceAssistant-Eval
🔥 VoiceAssistant-Eval: Benchmarking AI Assistants across Listening, Speaking, and Viewing
[🌐 Homepage]
[🔮 Visualization]
[💻 Github]
[📖 Paper]
[📊 Leaderboard ]
[📊 Detailed Leaderboard ]
[📊 Roleplay Leaderboard ]
🚀 Data Usage
from datasets import load_dataset
for split in ['listening_general', 'listening_music', 'listening_sound', 'listening_speech',
'speaking_assistant', 'speaking_emotion', 'speaking_instruction_following'… See the full description on the dataset page: https://huggingface.co/datasets/MathLLMs/VoiceAssistant-Eval.VoiceAssistant-400K-SLAM-Omni
VoiceAssistant-400K (Modified)
This dataset is prepared for the reproduction of SLAM-Omni.
This is a single-round English spoken dialogue training dataset. For code and usage examples, please refer to the related GitHub repository: X-LANCE/SLAM-LLM (examples/s2s)
🔧 Modifications
Data Filtering: We removed samples with excessively long data.
Speech Response Tokens: We used CosyVoice to synthesize corresponding semantic speech tokens for the speech response. These… See the full description on the dataset page: https://huggingface.co/datasets/mwei/VoiceAssistant-400K-SLAM-Omni.voices-of-civilizations
Voices of Civilizations (VoC)
Voices of Civilizations (VoC) is the first multilingual QA benchmark designed to assess audio LLMs’ cultural comprehension using full-length music recordings. VoC spans:
38 languages 🇸🇦 Arabic (ar), 🇧🇩 Bengali (bn), 🇧🇬 Bulgarian (bg), 🇨🇳 Chinese (zh), 🇭🇷 Croatian (hr), 🇨🇿 Czech (cs), 🇩🇰 Danish (da), 🇳🇱 Dutch (nl), 🇬🇧 English (en), 🇪🇪 Estonian (et), 🇫🇮 Finnish (fi), 🇫🇷 French (fr), 🇩🇪 German (de), 🇬🇷 Greek (el), 🇮🇱 Hebrew… See the full description on the dataset page: https://huggingface.co/datasets/sander-wood/voices-of-civilizations.VoiceGiraffe
VoiceGiraffe (Benchmark)
VoiceGiraffe is a benchmark for evaluating large audio language models (LALMs) on hour-level, long-context audio understanding. It contains 1,500 curated question-answer triplets over real-world recordings central to real-world long-form audio understanding — broadcast, sports/esports commentary, news, and TV drama — organized into a dual-level taxonomy of single-hop perception and multi-hop reasoning.
This repo is public and holds the annotations… See the full description on the dataset page: https://huggingface.co/datasets/Jashin-Yeah/VoiceGiraffe.VoiceAssistant-Eval
🔥 VoiceAssistant-Eval: Benchmarking AI Assistants across Listening, Speaking, and Viewing
[🌐 Homepage]
[🔮 Visualization]
[💻 Github]
[📖 Paper]
[📊 Leaderboard ]
[📊 Detailed Leaderboard ]
[📊 Roleplay Leaderboard ]
🚀 Data Usage
from datasets import load_dataset
for split in ['listening_general', 'listening_music', 'listening_sound', 'listening_speech',
'speaking_assistant', 'speaking_emotion', 'speaking_instruction_following'… See the full description on the dataset page: https://huggingface.co/datasets/SamSoko83/VoiceAssistant-Eval.