docs: add README

README.md

@@ -0,0 +1,58 @@
+---
+task_categories:
+- translation
+language:
+- en
+---
+# Structure
+Each sample will have a structure as follows:
+```
+{
+  "id": <id-of-sample>,
+  "type" <rgb-or-keypoints-data>,
+  "view": <frontal-or-side-view>,
+  "text": <translation-of-sample-in-spoken-language>,
+  "video": <video-in-bytes>,
+}
+```
+
+# How To Use
+Because the returned video will be in bytes, here is a way to extract frames and fps:
+```
+# pip install av
+
+import av
+import io
+import numpy as np
+import os
+from datasets import load_dataset
+
+
+def extract_frames(video_bytes):
+    # Create a memory-mapped file from the bytes
+    container = av.open(io.BytesIO(video_bytes))
+
+    # Find the video stream
+    visual_stream = next(iter(container.streams.video), None)
+
+    # Extract video properties
+    video_fps = visual_stream.average_rate
+
+    # Initialize arrays to store frames and audio
+    frames_array = []
+
+    # Extract frames
+    for packet in container.demux([visual_stream]):
+        for frame in packet.decode():
+            img_array = np.array(frame.to_image())
+            frames_array.append(img_array)
+
+    return frames_array, video_fps
+
+
+dataset = load_dataset("VieSignLang/phoenix14-t", split="test", streaming=True)
+sample = next(iter(dataset))["video"]
+frames, video_fps = extract_frames(sample)
+print(f"Number of frames: {frames.shape[0]}")
+print(f"Video FPS: {video_fps}")
+```