Datasets:
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---
configs:
- config_name: all
data_files: "*/*.tar"
default: true
- config_name: 3blue1brown
data_files: 3blue1brown/*.tar
- config_name: boxofficemoviesscenes
data_files: boxofficemoviesscenes/*.tar
- config_name: business
data_files: business/*.tar
- config_name: markrober
data_files: markrober/*.tar
- config_name: marvel
data_files: marvel/*.tar
- config_name: mitocw
data_files: mitocw/*.tar
- config_name: mkbhd
data_files: mkbhd/*.tar
- config_name: msftmechanics
data_files: msftmechanics/*.tar
- config_name: neoexplains
data_files: neoexplains/*.tar
- config_name: nvidia
data_files: nvidia/*.tar
- config_name: quantasciencechannel
data_files: quantasciencechannel/*.tar
- config_name: teded
data_files: teded/*.tar
- config_name: theinfographicsshow
data_files: theinfographicsshow/*.tar
- config_name: twominutepapers
data_files: twominutepapers/*.tar
- config_name: veritasium
data_files: veritasium/*.tar
license: mit
task_categories:
- automatic-speech-recognition
language:
- en
size_categories:
- 100K<n<1M
---
# English Audio Dataset from YouTube
This dataset contains English audio segments and **creator uploaded** transcripts (likely higher quality) extracted from various YouTube channels, along with corresponding transcript metadata. The data is intended for training automatic speech recognition (ASR) models.
## Data Source and Processing
The data was obtained through the following process:
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.
2. **Segmentation:** The raw audio files were segmented based on the timing information in the `.srt` files.
- 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.
- The corresponding audio portions for each group are extracted using `ffmpeg` and saved as `.mp3` files at a 16000 Hz sample rate.
- Metadata for each segment, including channel/video info and the text/timing of subtitles within the segment, is saved in a corresponding `.json` file.
## Intermediate Dataset Structure (`dataset` directory)
Before being packaged into TAR archives for Hugging Face, the segmented data resides in the `dataset` directory with the following structure:
```
dataset/
└── {channel_id}/ # Directory named after the YouTube channel ID
└── {video_id}/ # Directory named after the YouTube video ID
├── {video_id}_{group_name}.mp3 # Segmented audio file
├── {video_id}_{group_name}.json # Corresponding metadata file
└── ...
```
- **`{channel_id}`:** The ID of the YouTube channel (e.g., `greenbeanmediaofficial`).
- **`{video_id}`:** The unique identifier for the YouTube video.
- **`{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.
## Dataset Summary
The dataset comprises audio from the following channels:
```
Channel | Videos | Duration | Percent
--------------------- | ------------ | ------------- | -------
3blue1brown | 136 videos | 37.82 hours | 1.08%
boxofficemoviesscenes | 1626 videos | 153.06 hours | 4.38%
business | 887 videos | 187.80 hours | 5.38%
markrober | 97 videos | 21.77 hours | 0.62%
marvel | 763 videos | 35.17 hours | 1.01%
mitocw | 2844 videos | 1738.07 hours | 49.79%
mkbhd | 114 videos | 27.61 hours | 0.79%
msftmechanics | 732 videos | 131.52 hours | 3.77%
neoexplains | 35 videos | 8.06 hours | 0.23%
nvidia | 134 videos | 19.42 hours | 0.56%
quantasciencechannel | 93 videos | 13.60 hours | 0.39%
teded | 1768 videos | 145.53 hours | 4.17%
theinfographicsshow | 3402 videos | 827.06 hours | 23.69%
twominutepapers | 871 videos | 79.34 hours | 2.27%
veritasium | 291 videos | 64.96 hours | 1.86%
--------------------- | ------------ | ------------- | -------
Total | 13793 videos | 3490.79 hours | 100.00%
```
## Loading the Data
You can load the data using the Hugging Face `datasets` library:
```python
import os
from datasets import load_dataset
ds = load_dataset(
"OrcinusOrca/YouTube-English",
"all", # or channel_id as config
split="train",
streaming=False, # or True
num_proc=os.cpu_count(),
)
```
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