Issue with yt helper? - Transcript not available. (transcript not found)

[exponentiallyio]

I've just installed fabric and the yt helper, but when I run:

yt --transcript https://www.youtube.com/watch?v=bhnfZhJWCWY

I get:
Transcript not available. (transcript not found)

it's the same with every video. I've inserted by Youtube API key

Thanks for any help!

[Krizzology]

I have had the same thing,,,,,,,Watching for a response on this channel. In addition I also get this error: Anybody that offer some help regarding the vendor?

(fabric) PS C:\Users\Christopher Jooste> yt https://youtu.be/w2gbOQOJ2BQ?si=s_fZ1d0ji5x5R_DB | fabric --pattern analyze_answers
could not find vendor.
Model =
DefaultModel = gpt-4
DefaultVendor = openai
(fabric) PS C:\Users\Christopher Jooste> fabric /vendor: openai
unknown flag `vendor'

[oort77]

Same here: "Transcript not available. (transcript not found)"

[drlakshan]

Hi
use the youtube link inside quotes. it works

[saxster]

It's not working even with the quotes like you suggest.
Regards

[alexxmugg]

https://github.com/danielmiessler/yt

[saxster]

Asked Claude to look at the code, here are the comments:

Potential errors:

The code assumes the presence of a .env file in a specific location (~/.config/fabric/.env). If this file doesn't exist or is inaccessible, the program will crash.
The transcript extraction method might be unreliable, as it depends on parsing the HTML of the YouTube page. YouTube's front-end structure can change, potentially breaking this functionality.
Error handling for API requests could be more robust. Currently, if comment fetching fails, it logs the error but continues execution.
The program doesn't handle rate limiting or quota exhaustion from the YouTube API, which could lead to unexpected behavior if limits are exceeded.

Suggestions for improvement:

Error handling: Implement more graceful error handling, especially for API calls and file operations.
Configuration: Use a more flexible configuration system, perhaps allowing users to specify the location of the .env file or use command-line arguments for the API key.
Modularization: Break down the large mainFunction into smaller, more focused functions to improve readability and maintainability.
Concurrency: Implement concurrent fetching of transcript, comments, and video details to improve performance for videos with many comments.
Testing: Add unit tests for critical functions like getVideoID, parseDuration, etc.
Input validation: Add more robust input validation, especially for the YouTube URL.
Pagination: Implement pagination for comment fetching to handle videos with more than 100 comments.
Caching: Consider implementing a caching mechanism for API responses to reduce API usage and improve performance for repeated queries.
Transcript reliability: Explore more reliable methods for transcript extraction, possibly using YouTube's official caption API if available.
Use context: Implement context for HTTP requests to allow for timeouts and cancellation.
Structured logging: Replace log.Fatal calls with a more structured logging approach that allows for better error reporting and doesn't abruptly terminate the program.
Implement custom errors: Define custom error types for different error scenarios to provide more meaningful error messages.

Overall, the code is functional but could benefit from improvements in robustness, performance, and maintainability. The suggestions above would help make the code more resilient and easier to maintain and extend in the future.