Update README.md

README.md

@@ -42,4 +42,92 @@ dataset_info:
 
 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.
+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
+```