567-labs--instructor/docs/examples/youtube_clips.md

title, description

title	description
Generating YouTube Clips from Transcripts Using Instructor	Learn to create concise YouTube clips from video transcripts with `instructor` and OpenAI, enhancing your content engagement.

Generating YouTube Clips from Transcripts

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.

First, install the necessary packages:

pip install youtube_transcript_api instructor rich

from youtube_transcript_api import YouTubeTranscriptApi
from pydantic import BaseModel, Field
from typing import List, Generator, Iterable
import instructor
import instructor

client = instructor.from_provider("openai/gpt-5-nano")


def extract_video_id(url: str) -> str | None:
    import re

    match = re.search(r"v=([a-zA-Z0-9_-]+)", url)
    if match:
        return match.group(1)


class TranscriptSegment(BaseModel):
    source_id: int
    start: float
    text: str


def get_transcript_with_timing(
    video_id: str,
) -> Generator[TranscriptSegment, None, None]:
    """
    Fetches the transcript of a YouTube video along with the start and end times
    for each text segment, and returns them as a list of Pydantic models.
    """
    transcript = YouTubeTranscriptApi.get_transcript(video_id)
    for ii, segment in enumerate(transcript):
        yield TranscriptSegment(
            source_id=ii, start=segment["start"], text=segment["text"]
        )


class YoutubeClip(BaseModel):
    title: str = Field(description="Specific and informative title for the clip.")
    description: str = Field(
        description="A detailed description of the clip, including notable quotes or phrases."
    )
    start: float
    end: float


class YoutubeClips(BaseModel):
    clips: List[YoutubeClip]


def yield_clips(segments: Iterable[TranscriptSegment]) -> Iterable[YoutubeClips]:
    return client.create(
        model="gpt-5.4-mini",
        stream=True,
        messages=[
            {
                "role": "system",
                "content": """You are given a sequence of YouTube transcripts and your job
                is to return notable clips that can be recut as smaller videos. Give very
                specific titles and descriptions. Make sure the length of clips is proportional
                to the length of the video. Note that this is a transcript and so there might
                be spelling errors. Note that and correct any spellings. Use the context to
                make sure you're spelling things correctly.""",
            },
            {
                "role": "user",
                "content": f"Let's use the following transcript segments.\n{segments}",
            },
        ],
        response_model=instructor.Partial[YoutubeClips],
        context={"segments": segments},
    )  # type: ignore


# Example usage
if __name__ == "__main__":
    from rich.table import Table
    from rich.console import Console
    from rich.prompt import Prompt

    console = Console()
    url = Prompt.ask("Enter a YouTube URL")

    with console.status("[bold green]Processing YouTube URL...") as status:
        video_id = extract_video_id(url)

        if video_id is None:
            raise ValueError("Invalid YouTube video URL")

        transcript = list(get_transcript_with_timing(video_id))
        status.update("[bold green]Generating clips...")

        for clip in yield_clips(transcript):
            console.clear()

            table = Table(title="Extracted YouTube Clips", padding=(0, 1))

            table.add_column("Title", style="cyan")
            table.add_column("Description", style="magenta")
            table.add_column("Start", justify="right", style="green")
            table.add_column("End", justify="right", style="green")
            for youtube_clip in clip.clips or []:
                table.add_row(
                    youtube_clip.title,
                    youtube_clip.description,
                    str(youtube_clip.start),
                    str(youtube_clip.end),
                )
            console.print(table)