move all experiments to trials

trials/youtube_scraping.py

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+import json
+import yt_dlp as youtube_dl
+from whisper_jax import FlaxWhisperPipline
+import jax.numpy as jnp
+
+# Function to extract chapter information from a YouTube video URL
+def get_youtube_chapters(video_id):
+    video_url = "https://www.youtube.com/watch?v=" + video_id
+    ydl_opts = {
+        'extract_flat': 'in_playlist',
+        'skip_download': True,
+        'quiet': True,
+    }
+
+    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+        video_info = ydl.extract_info(video_url, download=False)
+
+    chapters = []
+
+    if 'chapters' in video_info:
+        for chapter in video_info['chapters']:
+            start_time = chapter['start_time']
+            end_time = chapter['end_time']
+            title = chapter['title']
+
+            chapters.append({
+                'start': start_time,
+                'end': end_time,
+                'title': title
+            })
+
+    return chapters
+
+
+# Function to extract video transcription using yt_dlp
+def get_youtube_transcription(video_id):
+    ydl_opts = {
+            'format': 'bestaudio/best',
+            'postprocessors': [{
+                    'key': 'FFmpegExtractAudio',
+                    'preferredcodec': 'mp3',
+                    'preferredquality': '192',
+            }],
+            'outtmpl': './artefacts/audio',  # Specify output file path and name
+    }
+
+    # Download the audio
+    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+        ydl.download(["https://www.youtube.com/watch?v=" + video_id])
+    media_file = "./artefacts/audio.mp3"
+
+    pipeline = FlaxWhisperPipline("openai/whisper-" + "tiny",
+                                  dtype=jnp.float16,
+                                  batch_size=16)
+    whisper_result = pipeline(media_file, return_timestamps=True)
+    return whisper_result["chunks"]
+
+
+
+# Function to scrape YouTube video transcripts and chapter information
+def scrape_youtube_data(video_id):
+    transcript_text = get_youtube_transcription(video_id)
+    chapters = get_youtube_chapters(video_id)
+    print("transcript_text", transcript_text)
+    print("chapters", chapters)
+    return transcript_text, chapters
+
+
+# Function to generate fine-tuning dataset from YouTube data
+def generate_finetuning_dataset(video_ids):
+    prompt_completion_pairs = []
+    for video_id in video_ids:
+        transcript_text, chapters = scrape_youtube_data(video_id)
+        if transcript_text is not None and chapters is not None:
+            for chapter in chapters:
+                start_time = chapter["start"]
+                end_time = chapter["end"]
+                chapter_text = chapter["title"]
+
+                prompt = ""
+                for transcript in transcript_text:
+                    if transcript["timestamp"][0] >= start_time and transcript["timestamp"][1] < end_time:
+                        prompt += transcript["text"]
+
+                if prompt is not None:
+                    completion = chapter_text
+                    prompt_completion_pairs.append({"prompt": prompt, "completion": completion})
+
+    return prompt_completion_pairs
+
+
+# Add all the video ids here, the videos must have captions [chapters]
+video_ids = ["yTnSEZIwnkU"]
+dataset = generate_finetuning_dataset(video_ids)
+
+with open("finetuning_dataset.jsonl", "w") as f:
+    for example in dataset:
+        f.write(json.dumps(example) + "\n")