README.md

---
dataset_info:
  features:
  - name: video_id
    dtype: string
  - name: video_link
    dtype: string
  - name: title
    dtype: string
  - name: text
    dtype: string
  - name: channel
    dtype: string
  - name: channel_id
    dtype: string
  - name: date
    dtype: string
  - name: license
    dtype: string
  - name: original_language
    dtype: string
  - name: language_id_method
    dtype: string
  - name: transcription_language
    dtype: string
  - name: word_count
    dtype: int64
  - name: character_count
    dtype: int64
  - name: source_language
    dtype: string
  splits:
  - name: train
    num_bytes: 298197594003
    num_examples: 22684737
  download_size: 162573072184
  dataset_size: 298197594003
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- text-generation
tags:
- conversational
language:
- en
- fr
- es
- pt
- de
- ru
- nl
- tr
- it
pretty_name: YouTube Commons Re-upload
---

## YouTube Commons Re-upload

This is a re-upload of [PleIAs' YouTube Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons), a valuable open dataset:
> YouTube-Commons is a collection of audio transcripts of 2,063,066 videos shared on YouTube under a CC BY 4.0 license.
>
> **Content**
>
> The collection comprises 22,709,724 original and automatically translated transcripts from 3,156,703 videos (721,136 individual channels).

Unfortunately, there are [problems](https://huggingface.co/datasets/PleIAs/YouTube-Commons/discussions/10) with loading YouTube Commons with Hugging Face Datasets.
In order to alleviate those and to further process the dataset, I took the source parquet-files and reuploaded this fixed version to HuggingFace.

## Code
The code used for this reupload. It makes use of a git clone of the [PleIAs/YouTube-Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons) dataset.

```python
from pathlib import Path

from datasets import load_dataset, Dataset
from tqdm import tqdm

columns = set('''video_link
video_id
title
text
channel
channel_id
date
license
original_language
language_id_method
transcription_language
source_language
word_count
character_count'''.split('\n'))

def generate():
    for filepath in tqdm(sorted(Path('/Path/To/PleIAs/YouTube-Commons').rglob('*.parquet'))):
        print(filepath)
        dataset = load_dataset("parquet",
                               data_files={'train': str(filepath)})
        for row in dataset['train']:
            keys = set(row)
            # Some of the files are missing one of these two columns.
            # Setting them to None results in an Arrow error, so we use '' instead
            if 'language_id_method' not in keys:
                row['language_id_method'] = ''
            if 'source_language' not in keys:
                row['source_language'] = ''
            if '__index_level_0__' in keys:
                del row['__index_level_0__']

            if not set(row) == columns:
                raise ValueError(f'Error in columns: {set(row)}')
            yield row

youtube = Dataset.from_generator(generate)
youtube.push_to_hub('Rijgersberg/YouTube-Commons')

```