|
|
--- |
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|
configs: |
|
|
- config_name: all |
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|
data_files: "*/*.tar" |
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|
default: true |
|
|
- config_name: ahju |
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|
data_files: ahju/*.tar |
|
|
- config_name: bboblackboxoffice |
|
|
data_files: bboblackboxoffice/*.tar |
|
|
- config_name: dangerousperson2.0 |
|
|
data_files: dangerousperson2.0/*.tar |
|
|
- config_name: greenbeanmediaofficial |
|
|
data_files: greenbeanmediaofficial/*.tar |
|
|
- config_name: hieggo1001 |
|
|
data_files: hieggo1001/*.tar |
|
|
- config_name: hkcrime |
|
|
data_files: hkcrime/*.tar |
|
|
- config_name: jerson8964 |
|
|
data_files: jerson8964/*.tar |
|
|
- config_name: maviskuku |
|
|
data_files: maviskuku/*.tar |
|
|
- config_name: mingjai14 |
|
|
data_files: mingjai14/*.tar |
|
|
- config_name: mm.millmilk |
|
|
data_files: mm.millmilk/*.tar |
|
|
- config_name: mpweekly |
|
|
data_files: mpweekly/*.tar |
|
|
- config_name: pinkytalks |
|
|
data_files: pinkytalks/*.tar |
|
|
- config_name: pricehongkongofficial |
|
|
data_files: pricehongkongofficial/*.tar |
|
|
- config_name: sunchannelhk |
|
|
data_files: sunchannelhk/*.tar |
|
|
- config_name: thedoshow0909 |
|
|
data_files: thedoshow0909/*.tar |
|
|
- config_name: unwire |
|
|
data_files: unwire/*.tar |
|
|
license: mit |
|
|
task_categories: |
|
|
- automatic-speech-recognition |
|
|
language: |
|
|
- zh |
|
|
- yue |
|
|
size_categories: |
|
|
- 100K<n<1M |
|
|
--- |
|
|
|
|
|
# Cantonese Audio Dataset from YouTube |
|
|
|
|
|
This dataset contains Cantonese 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 Cantonese subtitles (`.srt` for `zh-TW`, `zh-HK`, `zh-Hant`) 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 |
|
|
---------------------- | ----------- | ------------ | ------- |
|
|
AhJu | 132 videos | 28.81 hours | 1.56% |
|
|
BBOBlackboxoffice | 122 videos | 32.66 hours | 1.76% |
|
|
DangerousPerson2.0 | 114 videos | 70.53 hours | 3.81% |
|
|
greenbeanmediaofficial | 594 videos | 179.97 hours | 9.71% |
|
|
hieggo1001 | 1251 videos | 279.30 hours | 15.07% |
|
|
hkcrime | 99 videos | 35.06 hours | 1.89% |
|
|
JERSON8964 | 500 videos | 97.60 hours | 5.27% |
|
|
maviskuku | 165 videos | 29.21 hours | 1.58% |
|
|
mingjai14 | 158 videos | 43.85 hours | 2.37% |
|
|
mm.millmilk | 958 videos | 271.25 hours | 14.64% |
|
|
MPWeekly | 1119 videos | 156.45 hours | 8.44% |
|
|
pinkytalks | 125 videos | 20.72 hours | 1.12% |
|
|
pricehongkongofficial | 959 videos | 131.94 hours | 7.12% |
|
|
SunChannelHK | 1160 videos | 409.18 hours | 22.08% |
|
|
TheDoShow0909 | 23 videos | 17.78 hours | 0.96% |
|
|
unwire | 345 videos | 48.53 hours | 2.62% |
|
|
---------------------- | ----------- | ------------ | ------- |
|
|
Total | 7824 videos | 1852.83 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-Cantonese", |
|
|
"all", # or channel_id as config |
|
|
split="train", |
|
|
streaming=False, # or True |
|
|
num_proc=os.cpu_count(), |
|
|
) |
|
|
``` |
|
|
|
