Update README.md

README.md

@@ -36,4 +36,130 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
+task_categories:
+- automatic-speech-recognition
+- audio-to-audio
+- audio-classification
+- text-to-speech
+- text-to-audio
+language:
+- nl
+pretty_name: Youtube Commons NL Audio
 ---
+
+# YouTube Commons NL Audio
+This dataset has audio files for the Dutch-language videos in [Rijgersberg/YouTube-Commons-nl-transcriptions](https://huggingface.co/datasets/Rijgersberg/YouTube-Commons-nl-transcriptions),
+all under a CC BY 4.0 license.
+
+It contains 11,669 files for a total runtime of 2493h 43m 5s, coming in at approximately 130 GB.
+
+## Source
+The original source of the dataset (minus the titles, descriptions and audio files) is [YouTube Commons](https://huggingface.co/datasets/PleIAs/YouTube-Commons):
+> 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).
+
+
+## Usage
+The datasets into actually two datasets wrapped into one.
+Firstly it is a HuggingFace Dataset of [Rijgersberg/Rijgersberg/YouTube-Commons-nl-transcriptions](https://huggingface.co/datasets/Rijgersberg/YouTube-Commons-nl-transcriptions).
+Secondly all the audio files are present in the `audiofiles` subfolder of the dataset.
+
+The use it, it is recommended to use Git LFS to clone the dataset locally as follows:
+
+```bash
+$ git lfs install
+$ git clone https://huggingface.co/datasets/Rijgersberg/YouTube-Commons-nl-audio
+```
+
+You can then load the HuggingFace Dataset with the video info and transcriptions from disk just as you normally would,
+and access the audio files directly with a bit of path mapping:
+
+```python
+from pathlib import Path
+from pprint import pprint
+
+import datasets
+
+
+folder = Path('/path/to/where/you/cloned/YouTube-Commons-nl-audio')
+
+# load the dataset containing everything but the audio files
+dataset = datasets.load_dataset('parquet', data_dir=folder, split='train')
+
+# build up an index of audio file paths
+paths = {
+    path.stem: path  # the stem of the file is the video_id
+    for path in (folder / 'audiofiles').rglob('*')
+    if path.suffix in {".m4a", ".webm", '.mp4'}
+}
+
+# example: get the path to the audio file of the first video in the data set
+video = dataset[0]
+pprint(video)
+print(f"The file can be found at {paths[video['video_id']]}")
+```
+
+## Acquisition
+
+The audio files were acquired using Youtube-DLP and the following code:
+
+```python
+import random
+import time
+from pathlib import Path
+
+from datasets import load_dataset
+from yt_dlp import YoutubeDL, DownloadError
+
+
+def main():
+    dataset = load_dataset('Rijgersberg/YouTube-Commons-descriptions', split='train')
+    dataset = dataset.filter(lambda row: row['language'] == 'nl')
+
+    output_dir = Path('/path/to/output/dir/youtube-audio')
+    output_dir.mkdir(exist_ok=True, parents=True)
+
+    # clean up old partial downloads
+    parts = list(output_dir.glob('*.part'))
+    for f in parts:
+        f.unlink()
+
+    # skip ids that have already been downloaded
+    existing = set(p.stem for p in output_dir.glob('*.*'))
+    ids = set(dataset['id'])
+    to_get = ids - existing
+
+    urls = [f'https://www.youtube.com/watch?v={video_id}' for video_id in to_get]
+    random.shuffle(urls)
+
+    t = 5
+    while urls:
+        print(len(urls))
+        url = urls.pop()
+
+        try:
+            with YoutubeDL({'format': 'bestaudio/best',
+                            'extractaudio': True,
+                            'outtmpl': str(output_dir / '%(id)s.%(ext)s'),
+                            'quiet': True}) as ydl:
+                ydl.download([url])
+            t = 5
+        except DownloadError as e:
+            print(e)
+            if 'try again later' in str(e):
+                urls.append(url)
+                random.shuffle(urls)
+
+                time.sleep(t)
+                t = min(2*t, 10*60)
+            else:
+                continue
+
+
+if __name__ == "__main__":
+    main()
+```