Update README.md

README.md

@@ -1798,10 +1798,24 @@ The <a href="#example-use-in-python">Example use in Python</a> section contains
 <!-- * <u>Citation</u>: No paper found. -->
 
 #### YouTube
-* <u>Source</u>: Corpus contributed by LINAGORA Labs (OpenLLM-France) and [LeVoiceLab](https://www.levoicelab.org/).
+* <u>Source</u>: Corpus contributed by LINAGORA Labs and [LeVoiceLab](https://www.levoicelab.org/).
 * <u>Extracted from</u>: [YouTube](https://www.youtube.com/). <!-- License: TODO? -->
 * <u>Description</u>: French subtitles from videos published with permissive licenses on YouTube. <!-- TODO -->
-
+* <u>Extraction pipeline description</u>:
+  * **Searching for YouTube videos likely in French:** Based on searches generated automatically from random sequences of words extracted from a corpus of French journalistic articles (initially obtained through a web-crawling tool applied to publicly accessible news and media sites such as Huffington Post, 20 Minutes, Le Parisien, Actu, Numerama, Slate, etc.).  
+  Selection of videos with subtitles labeled as "French," excluding those marked as "automatically generated."  
+  *At this stage: 52,778 videos selected, corresponding to 10,654 hours of audio.*  
+  * **Selection of videos whose subtitle language classification confirms French with a certain confidence index:**  
+  *At this stage: 51,934 videos selected, corresponding to 10,425 hours of audio.*  
+  * **Selection of videos whose subtitles contain uppercase, lowercase, and punctuation marks:**  
+  This step filters out automatically generated subtitles created with speech recognition tools.  
+  *At this stage: 45,488 videos selected, corresponding to 8,904 hours of audio.*  
+  * **Extraction of audio tracks from the selected videos.**  
+  * **Automatic formatting of transcripts obtained from subtitles:** Removal of emojis, sound event annotations in brackets (like "[Music]") and extra text such as "subtitled by XXX."  (on last seconds of the video).
+  * **Selection of videos where an automatic speech recognition tool correctly transcribes the first 30 seconds with a minimum recall and precision rate:**  
+  *At this stage: 37,513 videos selected, corresponding to 7,541 hours of audio.*  
+  * **Realignment of the transcript:** Ensuring accurate timestamps in the transcriptions based on the subtitles and excluding audios where alignment fails.  
+  *At this stage: 36,618 videos selected, corresponding to 6,729 hours of audio.* 
 
 
 ## Example use in Python