seonglae/vls-10k
VLS 10K 9,987 MS-COCO images paired with a long written description, a one-sentence spoken summary of that description, the spoken audio, and that audio pre-encoded by two neural codecs. Images and audio are embedded in the parquet, so the viewer renders them and one call opens the set: from datasets import load_dataset ds = load_dataset("seonglae/vls-10k", split="train") ds[0]["image"] # PIL image ds[0]["audio"] # decoded waveform ds[0]["sst"] # the sentence that was… See the full description on the dataset page: https://huggingface.co/datasets/seonglae/vls-10k.
VLS 10K
9,987 MS-COCO images paired with a long written description, a one-sentence spoken summary of that description, the spoken audio, and that audio pre-encoded by two neural codecs. Images and audio are embedded in the parquet, so the viewer renders them and one call opens the set:
from datasets import load_dataset
ds = load_dataset("seonglae/vls-10k", split="train")
ds[0]["image"] # PIL image
ds[0]["audio"] # decoded waveform
ds[0]["sst"] # the sentence that was spokenColumns
Both token columns are [codebooks][frames], so their shape carries the frame and codebook counts directly.
Notes
Every row carries two independent tokenizations of the same waveform, which makes the set usable for comparing codecs with the text, image and audio held fixed.
The split ships as a column and as separate parquet files, so it never has to be re-derived and cannot drift between users.
Images come from MS-COCO and keep its terms. The descriptions, spoken summaries and audio are generated.
A larger companion set with 74,936 rows is at `seonglae/vls-100k`.
