datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
acestep-v15-turbo-synthetic-60smidi-audio-abc_60smidi, synthesized audio, ABC code triples
(this dataset contains those with audio duration in 5-60s, sampled from the full set with max 300s duration)
(token_length_abc field represents the token count of the abc text w.r.t. Qwen3's tokenizer)
midi files are from bread-midi-dataset
synthesized audio: use Don Allen's Timbres of Heaven as soundfont and FluidSynth as synthesizer
abc notation: mid2abc by EasyABC (midi2abc.py)
Citation
@misc{jiang2025advancingfoundationmodelmusic… See the full description on the dataset page: https://huggingface.co/datasets/Yi3852/midi-audio-abc_60s.solo-leveling-60s-assetsjetson-zed-20260319t220901-lerobot-debug-60s-rich
Lerobot Debug 60S Rich
This dataset is a lightweight LeRobot export derived from a real Jetson + ZED recording session.
Dataset Information
Format: LeRobot v3.0
Purpose: lightweight browse/share validation
Source session: 20260319T220901.363Z
Episodes: 1
Frames: 1800
Cameras:
observation.images.head
observation.images.left_wrist
observation.images.right_wrist
observation.images.depth_preview
Dataset fps: 30
Included Telemetry
Sync metrics
IMU accel /… See the full description on the dataset page: https://huggingface.co/datasets/HapticaAI/jetson-zed-20260319t220901-lerobot-debug-60s-rich.bi_so101_cam_60s_10fps_01This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "bi_so_follower",
"total_episodes": 4,
"total_frames": 675,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 10,
"splits": {
"train": "0:4"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Moncif/bi_so101_cam_60s_10fps_01.solo_cli_training_20eps_60sec_June19This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so_follower",
"total_episodes": 21,
"total_frames": 16636,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:21"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/CatGoesMeow/solo_cli_training_20eps_60sec_June19.bi_so101_cam_60s_12fps_01This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "bi_so_follower",
"total_episodes": 0,
"total_frames": 0,
"total_tasks": 0,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 12,
"splits": {},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Moncif/bi_so101_cam_60s_12fps_01.so101_60s_taskThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "so_follower",
"total_episodes": 3,
"total_frames": 1765,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 30,
"splits": {
"train": "0:3"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/ryanxuee/so101_60s_task.Ditto_videos_hdtf_400_audio_60s_chunks_checkpointsmovie_gen_60s_real_refine_step4_nvfp4_KVautocorrelation-spend-time-60s-diversetrain_data_60sttm_validation_dataset_60secbrowseragent-rl-hotpot7693-nq7693-2000-60stp3_grpo_train_data_60s3_grpo_val_data_60smovie_gen_60s_real_refine_step4_nvfp4hdtf_400_audio_60s_chunksDitto_videos_hdtf_400_audio_60s_chunks_all_preprocessDitto_videos_hdtf_400_audio_60s_chunksTokenizedPodcastDataset_60s-320M_TOKENA dataset with about 500 podcasts chopped into 60s segments, then tokenized into discrete tokens. This is meant for autoregressive training.
There is also a variant with 30s segments instead. In total there is 320 million tokens in this dataset or 0.32B tokens. Hopefully enough to train a medium sized model.
By using this dataset you agree:
You cant use this commerically
You have to reference me
You have to use this lisence
If you want to work out something please contact me.