aipieces/RWTH-PHOENIX-Weather-2014T

Information

• Language: German
• The dataset contains only frontal-view RGB data.
• Gloss is available.
• Storage
  • RGB
    • Train: 632 MB
    • Development: 42.8 MB
    • Test: 49.8 MB

Structure

Each sample will have a structure as follows:

{
  'name': Value(dtype='string', id=None),
  'video': Value(dtype='large_binary', id=None),
  'start': Value(dtype='int8', id=None),
  'end': Value(dtype='int8', id=None),
  'speaker': Value(dtype='string', id=None),
  'orth': Value(dtype='string', id=None),
  'translation': Value(dtype='string', id=None)
}

{
  'name': '06October_2012_Saturday_tagesschau-8730',
  'video': <video-bytes>,
  'start': -1,
  'end': -1,
  'speaker': 'Signer08',
  'orth': 'MORGEN DEUTSCH LAND IX TIEF KOMMEN KUEHL KOMMEN',
  'translation': 'ein tiefausläufer bringt morgen vor allem dem süden deutschlands noch regen',
}

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}")