|
|
--- |
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|
configs: |
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|
- config_name: all |
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|
data_files: "*/*.tar" |
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|
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 |
|
|
--- |
|
|
|
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|
# English Audio Dataset from YouTube |
|
|
|
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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. |
|
|
|
|
|
## 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(), |
|
|
) |
|
|
``` |
|
|
|
