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Datasets:
OrcinusOrca
/
YouTube-English

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
webdataset
Languages:
English
Size:
100K - 1M
Libraries:
Datasets
WebDataset
License:
Dataset card Data Studio Files
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  1. 3blue1brown/3blue1brown_000.tar +3 -0
  2. README.md +118 -0
  3. boxofficemoviesscenes/boxofficemoviesscenes_000.tar +3 -0
  4. boxofficemoviesscenes/boxofficemoviesscenes_001.tar +3 -0
  5. business/business_000.tar +3 -0
  6. business/business_001.tar +3 -0
  7. business/business_002.tar +3 -0
  8. business/business_003.tar +3 -0
  9. markrober/markrober_000.tar +3 -0
  10. marvel/marvel_000.tar +3 -0
  11. mitocw/mitocw_000.tar +3 -0
  12. mitocw/mitocw_001.tar +3 -0
  13. mitocw/mitocw_002.tar +3 -0
  14. mitocw/mitocw_003.tar +3 -0
  15. mitocw/mitocw_004.tar +3 -0
  16. mitocw/mitocw_005.tar +3 -0
  17. mitocw/mitocw_006.tar +3 -0
  18. mitocw/mitocw_007.tar +3 -0
  19. mitocw/mitocw_008.tar +3 -0
  20. mitocw/mitocw_009.tar +3 -0
  21. mitocw/mitocw_010.tar +3 -0
  22. mitocw/mitocw_011.tar +3 -0
  23. mitocw/mitocw_012.tar +3 -0
  24. mitocw/mitocw_013.tar +3 -0
  25. mitocw/mitocw_014.tar +3 -0
  26. mitocw/mitocw_015.tar +3 -0
  27. mitocw/mitocw_016.tar +3 -0
  28. mitocw/mitocw_017.tar +3 -0
  29. mitocw/mitocw_018.tar +3 -0
  30. mitocw/mitocw_019.tar +3 -0
  31. mitocw/mitocw_020.tar +3 -0
  32. mitocw/mitocw_021.tar +3 -0
  33. mitocw/mitocw_022.tar +3 -0
  34. mitocw/mitocw_023.tar +3 -0
  35. mitocw/mitocw_024.tar +3 -0
  36. mitocw/mitocw_025.tar +3 -0
  37. mitocw/mitocw_026.tar +3 -0
  38. mitocw/mitocw_027.tar +3 -0
  39. mitocw/mitocw_028.tar +3 -0
  40. mkbhd/mkbhd_000.tar +3 -0
  41. msftmechanics/msftmechanics_000.tar +3 -0
  42. msftmechanics/msftmechanics_001.tar +3 -0
  43. msftmechanics/msftmechanics_002.tar +3 -0
  44. neoexplains/neoexplains_000.tar +3 -0
  45. nvidia/nvidia_000.tar +3 -0
  46. quantasciencechannel/quantasciencechannel_000.tar +3 -0
  47. teded/teded_000.tar +3 -0
  48. teded/teded_001.tar +3 -0
  49. teded/teded_002.tar +3 -0
  50. teded/teded_003.tar +3 -0
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README.md ADDED
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+ ---
2
+ configs:
3
+ - config_name: all
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+ data_files: "*/*.tar"
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+ default: true
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+ - config_name: 3blue1brown
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+ data_files: 3blue1brown/*.tar
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+ - config_name: boxofficemoviesscenes
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+ data_files: boxofficemoviesscenes/*.tar
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+ - config_name: business
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+ data_files: business/*.tar
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+ - config_name: markrober
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+ data_files: markrober/*.tar
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+ - config_name: marvel
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+ data_files: marvel/*.tar
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+ - config_name: mitocw
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+ data_files: mitocw/*.tar
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+ - config_name: mkbhd
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+ data_files: mkbhd/*.tar
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+ - config_name: msftmechanics
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+ data_files: msftmechanics/*.tar
22
+ - config_name: neoexplains
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+ data_files: neoexplains/*.tar
24
+ - config_name: nvidia
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+ data_files: nvidia/*.tar
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+ - config_name: quantasciencechannel
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+ data_files: quantasciencechannel/*.tar
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+ - config_name: teded
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+ data_files: teded/*.tar
30
+ - config_name: theinfographicsshow
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+ data_files: theinfographicsshow/*.tar
32
+ - config_name: twominutepapers
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+ data_files: twominutepapers/*.tar
34
+ - config_name: veritasium
35
+ data_files: veritasium/*.tar
36
+ license: mit
37
+ task_categories:
38
+ - automatic-speech-recognition
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+ language:
40
+ - en
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+ size_categories:
42
+ - 100K<n<1M
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+ ---
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+
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+ # English Audio Dataset from YouTube
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+
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+ This dataset contains English audio segments extracted from various YouTube channels, along with corresponding transcription metadata. The data is intended for training automatic speech recognition (ASR) models.
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+
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+ ## Data Source and Processing
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+
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+ The data was obtained through the following process:
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+
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+ 1. **Download:** Audio (`.m4a`) and available English subtitles (`.srt` for `en`, `en.j3PyPqV-e1s`) were downloaded from selected YouTube channels. This raw data, along with video metadata (`metadata.csv`), is stored initially in a `data/{channel_id}/` directory structure.
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+ 2. **Segmentation:** The raw audio files were segmented based on the timing information in the `.srt` files.
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+ - Audio files are splitted by SRT segments and then combined to a maximum duration less than but close to 30 seconds per group for Whisper.
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+ - The corresponding audio portions for each group are extracted using `ffmpeg` and saved as `.mp3` files at a 16000 Hz sample rate.
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+ - Metadata for each segment, including channel/video info and the text/timing of subtitles within the segment, is saved in a corresponding `.json` file.
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+
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+ ## Intermediate Dataset Structure (`dataset` directory)
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+
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+ Before being packaged into TAR archives for Hugging Face, the segmented data resides in the `dataset` directory with the following structure:
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+
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+ ```
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+ dataset/
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+ └── {channel_id}/ # Directory named after the YouTube channel ID
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+ └── {video_id}/ # Directory named after the YouTube video ID
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+ ├── {video_id}_{group_name}.mp3 # Segmented audio file
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+ ├── {video_id}_{group_name}.json # Corresponding metadata file
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+ └── ...
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+ ```
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+
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+ - **`{channel_id}`:** The ID of the YouTube channel (e.g., `greenbeanmediaofficial`).
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+ - **`{video_id}`:** The unique identifier for the YouTube video.
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+ - **`{group_name}`:** Represents the subtitles included in the segment. It's either the index of the first subtitle (e.g., `1`) if the group contains only one, or a range indicating the first and last subtitle indices (e.g., `1-5`) if the group contains multiple subtitles.
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+
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+ ## Dataset Summary
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+
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+ The dataset comprises audio from the following channels:
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+
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+ ```
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+ Channel | Videos | Duration | Percent
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+ --------------------- | ------------ | ------------- | -------
83
+ 3blue1brown | 136 videos | 37.82 hours | 1.08%
84
+ boxofficemoviesscenes | 1626 videos | 153.06 hours | 4.38%
85
+ business | 887 videos | 187.80 hours | 5.38%
86
+ markrober | 97 videos | 21.77 hours | 0.62%
87
+ marvel | 763 videos | 35.17 hours | 1.01%
88
+ mitocw | 2844 videos | 1738.07 hours | 49.79%
89
+ mkbhd | 114 videos | 27.61 hours | 0.79%
90
+ msftmechanics | 732 videos | 131.52 hours | 3.77%
91
+ neoexplains | 35 videos | 8.06 hours | 0.23%
92
+ nvidia | 134 videos | 19.42 hours | 0.56%
93
+ quantasciencechannel | 93 videos | 13.60 hours | 0.39%
94
+ teded | 1768 videos | 145.53 hours | 4.17%
95
+ theinfographicsshow | 3402 videos | 827.06 hours | 23.69%
96
+ twominutepapers | 871 videos | 79.34 hours | 2.27%
97
+ veritasium | 291 videos | 64.96 hours | 1.86%
98
+ --------------------- | ------------ | ------------- | -------
99
+ Total | 13793 videos | 3490.79 hours | 100.00%
100
+ ```
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+
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+ ## Loading the Data
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+
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+ You can load the data using the Hugging Face `datasets` library:
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+
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+ ```python
107
+ import os
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+
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+ from datasets import load_dataset
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+
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+ ds = load_dataset(
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+ "OrcinusOrca/YouTube-English",
113
+ "all", # or channel_id as config
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+ split="train",
115
+ streaming=False, # or True
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+ num_proc=os.cpu_count(),
117
+ )
118
+ ```
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