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doc: Extracting YouTube clips
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Jason Liu
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# 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 youtube-clips > 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, Dict, 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(
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description="Specific and informative title for the clip."
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)
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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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@@ -0,0 +1,124 @@
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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, Dict, 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 for each text segment,
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and returns them as a list of Pydantic models.
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Parameters:
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- video_id (str): The YouTube video ID for which the transcript is to be fetched.
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Returns:
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- A generator that yields TranscriptSegment models, each containing 'index', 'start', and 'text' keys.
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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(
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description="Specific and informative title for the individual clip."
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)
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description: str = Field(
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description="A detailed description of the clip, including any notable quotes or phrases. should be a summary of sorts."
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)
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start: float
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end: float
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source_ids: List[int] = Field(exclude=True)
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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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"""
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Extracts a list of YouTube clips from a list of transcript segments.
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Parameters:
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- segments (Iterable[TranscriptSegment]): A list of TranscriptSegment models, each containing 'index', 'start', and 'text' keys.
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Returns:
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- A generator that yields YoutubeClipw models, each containing 'title', 'description', 'start', 'end', and 'source_ids' keys.
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"""
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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 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. ",
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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="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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@@ -180,6 +180,7 @@ nav:
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- Extract Contact Info: 'hub/extract_contact_info.md'
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- Using Mistral Large: 'hub/mistral.md'
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- Knowledge Graphs: 'hub/knowledge_graph.md'
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- Extract Youtube Clips: "hub/youtube_clips.md"
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- Tutorials:
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- Tutorials (Notebooks): 'tutorials/1-introduction.ipynb'
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- Tips and Tricks: 'tutorials/2-tips.ipynb'
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