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Datasets:
lukasbraach
/
bundestag_slr

Languages:
German
Size:
100K<n<1M
Tags:
sign_language
License:
Dataset card Files
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 Community
Lukas Braach commited on
Commit
aed0607
·
1 Parent(s): 6acb43e

move to merged files

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  1. .gitattributes +2 -0
  2. bundestag_slr.py +134 -0
  3. metadata.csv +3 -0
  4. video_ids.txt +0 -433
  5. videos/7337902.mp4 +3 -0
  6. videos/7337902/0.mp4 +0 -3
  7. videos/7337902/1.mp4 +0 -3
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.gitattributes CHANGED
@@ -53,5 +53,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
53
  *.jpg filter=lfs diff=lfs merge=lfs -text
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  *.jpeg filter=lfs diff=lfs merge=lfs -text
55
  *.webp filter=lfs diff=lfs merge=lfs -text
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
 
 
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  *.jpg filter=lfs diff=lfs merge=lfs -text
54
  *.jpeg filter=lfs diff=lfs merge=lfs -text
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  *.webp filter=lfs diff=lfs merge=lfs -text
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+
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ *.csv filter=lfs diff=lfs merge=lfs -text
bundestag_slr.py ADDED
@@ -0,0 +1,134 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List, Dict
2
+
3
+ import cv2
4
+ import datasets
5
+ import numpy as np
6
+ import pandas as pd
7
+ from datasets import Sequence, Image, Value
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+
9
+ base_url = "."
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+
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+
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+ def read_non_empty_lines(file_path):
13
+ with open(file_path) as file:
14
+ lines = [line.strip() for line in file if line.strip()]
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+
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+ return lines
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+
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+
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+ def load_video(video_path):
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+ """Load video and return frames as a list of arrays."""
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+ cap = cv2.VideoCapture(video_path)
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+ frames = []
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+ while cap.isOpened():
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+ ret, frame = cap.read()
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+ if not ret:
26
+ break
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+ frames.append(frame)
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+ cap.release()
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+ return np.array(frames)
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+
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+
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+ class BundestagSLR(datasets.GeneratorBasedBuilder):
33
+ """BUNDESTAG SLR: Continuous Sign Language Recognition Dataset."""
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+
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+ VERSION = datasets.Version("1.0.0")
36
+ DEFAULT_WRITER_BATCH_SIZE = 25
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+
38
+ def _info(self):
39
+ features_dict = {
40
+ "id": Value("string"),
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+ "subtitle": Value("string"),
42
+ "frames": Sequence(Image()),
43
+ }
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+
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+ return datasets.DatasetInfo(
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+ features=datasets.Features(features_dict),
47
+ # No default supervised_keys (as we have to pass both question
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+ # and context as input).
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+ supervised_keys=None,
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+ )
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+
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+ def _split_generators(self, dl_manager: datasets.DownloadManager):
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+ frames = {}
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+ other_data = {}
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+
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+ data_csv = dl_manager.download(f"{base_url}/video_ids.txt")
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+ df = pd.read_csv(data_csv, header=None, names=['id'])
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+
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+ video_ids_all = df['id']
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+
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+ video_ids = {
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+ datasets.Split.TRAIN: video_ids_all[:int(len(video_ids_all) * 0.8)],
63
+ datasets.Split.VALIDATION: video_ids_all[int(len(video_ids_all) * 0.8):int(len(video_ids_all) * 0.9)],
64
+ datasets.Split.TEST: video_ids_all[int(len(video_ids_all) * 0.9):],
65
+ }
66
+
67
+ for split in [
68
+ datasets.Split.TRAIN,
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+ datasets.Split.VALIDATION,
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+ datasets.Split.TEST,
71
+ ]:
72
+ subtitle_files_split = dl_manager.download([
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+ f"{base_url}/videos/{id}/subtitles.txt"
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+ for id in video_ids[split]
75
+ ])
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+
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+ video_file_names_split = []
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+ video_subtitles_split = []
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+ video_frames_split = []
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+
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+ for idx, subtitle_file in zip(video_ids[split], subtitle_files_split):
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+ lines = read_non_empty_lines(subtitle_file)
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+
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+ video_file_names = [f"{base_url}/videos/{idx}/{i}.mp4" for i in range(len(lines))]
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+ video_file_names_split.extend(video_file_names)
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+ video_subtitles_split.extend(lines)
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+
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+ video_frames_split.extend(dl_manager.download(video_file_names))
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+
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+ other_data_split = {}
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+
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+ for video, file_name, subtitle, in zip(video_frames_split, video_file_names_split, video_subtitles_split):
93
+ other_data_split[video] = {
94
+ "id": file_name,
95
+ "subtitle": subtitle,
96
+ }
97
+
98
+ other_data[split] = other_data_split
99
+ frames[split] = video_frames_split
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+
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+ return [
102
+ datasets.SplitGenerator(
103
+ name=split,
104
+ gen_kwargs={
105
+ "videos": frames[split],
106
+ "other_data": other_data[split],
107
+ },
108
+ )
109
+ for split in [
110
+ datasets.Split.TRAIN,
111
+ datasets.Split.VALIDATION,
112
+ datasets.Split.TEST,
113
+ ]
114
+ ]
115
+
116
+ def _generate_examples(self, videos: List[dict], other_data: Dict[dict, dict]):
117
+ """
118
+ _generate_examples generates examples for the HuggingFace dataset.
119
+ It takes a list of frame_archives and the corresponding dict of other data.
120
+ Each frame_archive acts as a key for the further data.
121
+
122
+ :param frame_archives: list of ArchiveIterables
123
+ :param other_data: Dict from ArchiveIterables to other data
124
+ """
125
+ for key, frames in enumerate(videos):
126
+ ex = other_data[frames]
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+
128
+ result = {
129
+ "id": ex['id'],
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+ "subtitle": ex['subtitle'],
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+ "frames": load_video(frames),
132
+ }
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+
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+ yield key, result
metadata.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:e8bae608b4b67168ca76d291a9c7607b7eb0760d13afd2e5c6fc4242b4b56b92
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+ size 37376385
video_ids.txt DELETED
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videos/7337902.mp4 ADDED
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+ oid sha256:557c56831209fa47ce042835c55184c0acd2e3767578f1262af5365debe03d2d
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+ size 120060889
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videos/7337902/1.mp4 DELETED
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