mirror of
https://github.com/kennethreitz/instructor.git
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Jason Liu
126 lines
4.3 KiB
Markdown
126 lines
4.3 KiB
Markdown
# Generating YouTube Clips from Transcripts
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This guide demonstrates how to generate concise, informative clips from YouTube video transcripts using the `instructor` library. By leveraging the power of OpenAI's models, we can extract meaningful segments from a video's transcript, which can then be recut into smaller, standalone videos. This process involves identifying key moments within a transcript and summarizing them into clips with specific titles and descriptions.
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If you're interested in trying this example using `instructor hub`, you can pull it by running:
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```bash
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pip install youtube_transcript_api instructor rich
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instructor hub pull --slug youtube-clips --py > youtube_clips.py
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```
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```python
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from youtube_transcript_api import YouTubeTranscriptApi
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from pydantic import BaseModel, Field
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from typing import List, Generator, Iterable
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import instructor
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import openai
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client = instructor.patch(openai.OpenAI())
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def extract_video_id(url: str) -> str | None:
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import re
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match = re.search(r"v=([a-zA-Z0-9_-]+)", url)
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if match:
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return match.group(1)
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class TranscriptSegment(BaseModel):
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source_id: int
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start: float
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text: str
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def get_transcript_with_timing(
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video_id: str,
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) -> Generator[TranscriptSegment, None, None]:
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"""
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Fetches the transcript of a YouTube video along with the start and end times
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for each text segment, and returns them as a list of Pydantic models.
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"""
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transcript = YouTubeTranscriptApi.get_transcript(video_id)
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for ii, segment in enumerate(transcript):
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yield TranscriptSegment(
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source_id=ii, start=segment["start"], text=segment["text"]
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)
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class YoutubeClip(BaseModel):
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title: str = Field(description="Specific and informative title for the clip.")
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description: str = Field(
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description="A detailed description of the clip, including notable quotes or phrases."
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)
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start: float
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end: float
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class YoutubeClips(BaseModel):
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clips: List[YoutubeClip]
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def yield_clips(segments: Iterable[TranscriptSegment]) -> Iterable[YoutubeClips]:
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return client.chat.completions.create(
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model="gpt-4-turbo-preview",
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stream=True,
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messages=[
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{
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"role": "system",
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"content": """You are given a sequence of YouTube transcripts and your job
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is to return notable clips that can be recut as smaller videos. Give very
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specific titles and descriptions. Make sure the length of clips is proportional
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to the length of the video. Note that this is a transcript and so there might
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be spelling errors. Note that and correct any spellings. Use the context to
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make sure you're spelling things correctly.""",
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},
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{
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"role": "user",
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"content": f"Let's use the following transcript segments.\n{segments}",
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},
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],
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response_model=instructor.Partial[YoutubeClips],
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validation_context={"segments": segments},
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) # type: ignore
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# Example usage
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if __name__ == "__main__":
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from rich.table import Table
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from rich.console import Console
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from rich.prompt import Prompt
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console = Console()
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url = Prompt.ask("Enter a YouTube URL")
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with console.status("[bold green]Processing YouTube URL...") as status:
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video_id = extract_video_id(url)
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if video_id is None:
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raise ValueError("Invalid YouTube video URL")
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transcript = list(get_transcript_with_timing(video_id))
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status.update("[bold green]Generating clips...")
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for clip in yield_clips(transcript):
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console.clear()
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table = Table(title="Extracted YouTube Clips", padding=(0, 1))
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table.add_column("Title", style="cyan")
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table.add_column("Description", style="magenta")
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table.add_column("Start", justify="right", style="green")
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table.add_column("End", justify="right", style="green")
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for youtube_clip in clip.clips or []:
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table.add_row(
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youtube_clip.title,
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youtube_clip.description,
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str(youtube_clip.start),
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str(youtube_clip.end),
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)
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console.print(table)
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```
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