Getting Every ReviewBrah Rating Using LLMs

“Hello Everyone!”

A phrase that echoes throughout my house on weekly basis from my favorite YouTuber, ReviewBrah.

368 ratings tracked | Last updated: February 2026

2025 Year in Review

Chick-fil-A dominated with the year’s highest score, a 9.9 for the Pretzel Cheddar Club Sandwich, while Burger King hit rock bottom with the SpongeBob Crabby Whopper at 0.2. The McRib returned to a 9.2, Taco Bell’s Birthday Cake Churros surprised at 9.1, and Chili’s Big QP Burger made the case that sit-down restaurants are now the better value play. On the other end, Chick-fil-A’s reformulated fries earned a devastating 1.0, Domino’s Hot Buffalo Loaded Chicken got compared to dog food, and every Mike’s Hot Honey collaboration was terrible. Recurring themes: rising prices, missing ingredients, and an industry chasing brand deals over quality.

How I did it

The original approach used YouTube’s auto-generated captions via youtube-transcript, but YouTube started aggressively rate-limiting the transcript API endpoint. The updated approach bypasses this entirely by downloading audio and transcribing locally with OpenAI’s Whisper model.

Step 1: Listing All Videos in a Channel

Use yt-dlp to get video URLs and upload dates from the channel:

yt-dlp -s --flat-playlist 
  --print-to-file "%(upload_date)s %(id)s %(title)s" videos.txt 
  "https://www.youtube.com/@TheReportOfTheWeek/videos"

This gives you a file with one line per video containing the date, video ID, and title.

Step 2: Downloading Audio

YouTube’s transcript/subtitle endpoints are heavily rate-limited, but the audio stream endpoint is not. Download audio for each video:

mkdir -p audio
while IFS=' ' read -r date id rest; do
  yt-dlp --extract-audio --audio-format mp3 
    -o "audio/${id}.mp3" 
    "https://www.youtube.com/watch?v=${id}"
done < videos.txt

For ~57 videos this produces around 850MB of MP3 files.

Step 3: Transcribing with Whisper

Install mlx-whisper for fast transcription on Apple Silicon (about 20 seconds per video vs 30+ minutes with CPU-only Whisper):

pip install mlx-whisper

Then transcribe each audio file:

import mlx_whisper, os, glob

for mp3 in sorted(glob.glob("audio/*.mp3")):
    vid_id = os.path.basename(mp3).replace(".mp3", "")
    result = mlx_whisper.transcribe(
        mp3,
        path_or_hf_repo="mlx-community/whisper-large-v3-turbo",
        condition_on_previous_text=False
    )
    with open(f"transcripts/{vid_id}.txt", "w") as f:
        f.write(result["text"])

The condition_on_previous_text=False flag is important — without it, Whisper occasionally gets stuck in hallucination loops where it repeats the same phrase for minutes.

Step 4: Extracting Ratings with an LLM

Combine all transcripts into a single file with their dates prepended:

while IFS=' ' read -r date id rest; do
  echo "=== ${rest} (${date}) ==="
  cat "transcripts/${id}.txt"
  echo
done < videos.txt > combined.txt

Then feed the combined transcripts to an LLM. I used Claude’s CLI in batches of about 20 videos at a time to avoid output truncation:

claude --print -p "Extract all food ratings from these ReviewBrah transcripts.
Output a markdown table with columns: Item, Restaurant, Cost, Rating (out of 10),
Date (YYYY-MM-DD). Only output the table rows, no headers." < batch1.txt

Step 5: Deduplication and Cleanup

The LLM output across batches may have some inconsistencies. Merge the batch outputs and do a final cleanup pass:

cat batch*_output.txt | claude --print -p "Deduplicate these table rows,
normalize dates to YYYY-MM-DD, and output a clean markdown table."

Conclusion

By combining:

• yt-dlp to download audio (bypassing YouTube’s transcript rate limits)
• mlx-whisper for fast local transcription on Apple Silicon
• An LLM to extract structured ratings from free-form transcripts
• A final deduplication/cleanup pass

…you get a comprehensive, structured dataset from hundreds of YouTube videos without hitting any API rate limits.