Datasets:
File size: 3,922 Bytes
21b3dab bb08075 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | ---
license: cc-by-4.0
task_categories:
- text-to-video
language:
- en
tags:
- text-to-video
- Video Generative Model Training
- Text-to-Video Diffusion Model Training
- prompts
pretty_name: OpenVid-1M
size_categories:
- 10K<n<100K
dataset_info:
features:
- name: video
dtype: video
- name: caption
dtype: string
- name: aesthetic_score
dtype: float32
- name: motion_score
dtype: float32
- name: temporal_consistency_score
dtype: float32
- name: camera_motion
dtype: string
- name: frame
dtype: int32
- name: fps
dtype: float32
- name: seconds
dtype: float32
- name: part_id
dtype: int32
---
<p align="center">
<img src="https://huggingface.co/datasets/nkp37/OpenVid-1M/resolve/main/OpenVid-1M.png">
</p>
Combination of part_id's from [bigdata-pw/OpenVid-1M](https://huggingface.co/datasets/bigdata-pw/OpenVid-1M) and video data from [nkp37/OpenVid-1M](https://huggingface.co/datasets/nkp37/OpenVid-1M).
This is a 10k video split of the original dataset for faster iteration during testing. The split was obtained by filtering on aesthetic and motion scores by iteratively increasing their values until there were at most 1000 videos. Only videos containing between 80 and 240 frames were considered.
```python
from datasets import load_dataset, disable_caching, DownloadMode
from torchcodec.decoders import VideoDecoder
# disable_caching()
def decode_float(sample):
return float(sample.decode("utf-8"))
def decode_int(sample):
return int(sample.decode("utf-8"))
def decode_str(sample):
return sample.decode("utf-8")
def decode_video(sample):
decoder = VideoDecoder(sample)
return decoder[:1024]
def decode_batch(batch):
decoded_sample = {
"__key__": batch["__key__"],
"__url__": batch["__url__"],
"video": list(map(decode_video, batch["video"])),
"caption": list(map(decode_str, batch["caption"])),
"aesthetic_score": list(map(decode_float, batch["aesthetic_score"])),
"motion_score": list(map(decode_float, batch["motion_score"])),
"temporal_consistency_score": list(map(decode_float, batch["temporal_consistency_score"])),
"camera_motion": list(map(decode_str, batch["camera_motion"])),
"frame": list(map(decode_int, batch["frame"])),
"fps": list(map(decode_float, batch["fps"])),
"seconds": list(map(decode_float, batch["seconds"])),
"part_id": list(map(decode_int, batch["part_id"])),
}
return decoded_sample
ds = load_dataset("finetrainers/OpenVid-10k-split", split="train", download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)
ds.set_transform(decode_batch)
iterator = iter(ds)
for i in range(10):
data = next(iterator)
breakpoint()
```
Environment tested:
```
- huggingface_hub version: 0.25.2
- Platform: macOS-15.3.1-arm64-arm-64bit
- Python version: 3.11.10
- Running in iPython ?: No
- Running in notebook ?: No
- Running in Google Colab ?: No
- Running in Google Colab Enterprise ?: No
- Token path ?: /Users/aryanvs/Desktop/huggingface/token
- Has saved token ?: True
- Who am I ?: a-r-r-o-w
- Configured git credential helpers: osxkeychain
- FastAI: N/A
- Tensorflow: N/A
- Torch: 2.6.0
- Jinja2: 3.1.4
- Graphviz: N/A
- keras: N/A
- Pydot: N/A
- Pillow: 10.4.0
- hf_transfer: 0.1.8
- gradio: 5.6.0
- tensorboard: N/A
- numpy: 1.26.4
- pydantic: 2.10.1
- aiohttp: 3.10.10
- ENDPOINT: https://huggingface.co
- HF_HUB_CACHE: /Users/aryanvs/Desktop/huggingface/hub
- HF_ASSETS_CACHE: /Users/aryanvs/Desktop/huggingface/assets
- HF_TOKEN_PATH: /Users/aryanvs/Desktop/huggingface/token
- HF_HUB_OFFLINE: False
- HF_HUB_DISABLE_TELEMETRY: False
- HF_HUB_DISABLE_PROGRESS_BARS: None
- HF_HUB_DISABLE_SYMLINKS_WARNING: False
- HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False
- HF_HUB_DISABLE_IMPLICIT_TOKEN: False
- HF_HUB_ENABLE_HF_TRANSFER: True
- HF_HUB_ETAG_TIMEOUT: 10
- HF_HUB_DOWNLOAD_TIMEOUT: 10
``` |