---
description: Transcribe YouTube videos of any length using GPT-4o-mini-transcribe
---

# YouTube Transcriber - High-Quality Video Transcription

## Script Location
**Primary script**: `$YOUTUBE_TRANSCRIBER_DIR/transcribe.py`

## Key Features
- **Parallel processing**: Multiple videos can be transcribed simultaneously
- **Unlimited length**: Auto-chunks videos >10 minutes to prevent API limits
- **Organized output**:
  - Transcripts → `output/` directory
  - Temp files → `temp/` directory (auto-cleaned)
- **High quality**: Uses GPT-4o-mini-transcribe by default (OpenAI's recommended model)
- **Cost options**: Can use `-m gpt-4o-transcribe` for the full-size model at 2x cost

## Basic Usage

### Single Video
```bash
cd $YOUTUBE_TRANSCRIBER_DIR
uv run python transcribe.py "https://youtube.com/watch?v=VIDEO_ID"
```

**Output**: `output/Video_Title_2025-11-10.txt`

### Multiple Videos in Parallel
```bash
cd $YOUTUBE_TRANSCRIBER_DIR

# Launch all in background simultaneously
uv run python transcribe.py "URL1" &
uv run python transcribe.py "URL2" &
uv run python transcribe.py "URL3" &
wait
```

**Why parallel works**: Each transcription uses unique temp files (UUID-based) in `temp/` directory.

### Higher Quality Mode
```bash
cd $YOUTUBE_TRANSCRIBER_DIR
uv run python transcribe.py "URL" -m gpt-4o-transcribe
```

**When to use full model**: Noisy audio, critical accuracy requirements. Costs 2x more ($0.006/min vs $0.003/min).

## Command Options

```bash
uv run python transcribe.py [URL] [OPTIONS]

Options:
  -o, --output PATH           Custom output filename (default: auto-generated in output/)
  -m, --model MODEL          Transcription model (default: gpt-4o-mini-transcribe)
                             Options: gpt-4o-mini-transcribe, gpt-4o-transcribe, whisper-1
  -p, --prompt TEXT          Context prompt for better accuracy
  --chunk-duration MINUTES   Chunk size for long videos (default: 10 minutes)
  --keep-audio              Keep temp audio files (default: auto-delete)
```

## Workflow for User Requests

### Single Video Request
1. Change to transcriber directory
2. Run script with URL
3. Report output file location in `output/` directory

### Multiple Video Request
1. Change to transcriber directory
2. Launch all transcriptions in parallel using background processes
3. Wait for all to complete
4. Report all output files in `output/` directory

### Testing/Cost-Conscious Request
1. Default model (`gpt-4o-mini-transcribe`) is already the cheapest GPT-4o option
2. For even cheaper: suggest Groq's Whisper API as an alternative
3. Quality is excellent for most YouTube content

## Technical Details

**How it works**:
1. Downloads audio from YouTube (via yt-dlp)
2. Saves to unique temp file: `temp/download_{UUID}.mp3`
3. Splits long videos (>10 min) into chunks automatically
4. Transcribes with OpenAI API (GPT-4o-mini-transcribe)
5. Saves transcript: `output/Video_Title_YYYY-MM-DD.txt`
6. Cleans up temp files automatically

**Parallel safety**:
- Each process uses UUID-based temp files
- No file conflicts between parallel processes
- Temp files auto-cleaned after completion

**Auto-chunking**:
- Videos >10 minutes: Split into 10-minute chunks
- Context preserved between chunks
- Prevents API response truncation

## Requirements
- OpenAI API key: `$OPENAI_API_KEY` environment variable
- Python 3.10+ with uv package manager
- FFmpeg (for audio processing)
- yt-dlp (for YouTube downloads)

**Check requirements**:
```bash
echo $OPENAI_API_KEY  # Should show API key
which ffmpeg          # Should show path
```

## Cost Estimates

Default model (`gpt-4o-mini-transcribe` at $0.003/min):

- **5-minute video**: ~$0.015
- **25-minute video**: ~$0.075
- **60-minute video**: ~$0.18

Full model (`gpt-4o-transcribe` at $0.006/min):

- **5-minute video**: ~$0.03
- **25-minute video**: ~$0.15
- **60-minute video**: ~$0.36

## Quick Reference

```bash
# Single video (default quality)
uv run python transcribe.py "URL"

# Single video (higher quality, 2x cost)
uv run python transcribe.py "URL" -m gpt-4o-transcribe

# Multiple videos in parallel
for url in URL1 URL2 URL3; do
  uv run python transcribe.py "$url" &
done
wait

# With custom output
uv run python transcribe.py "URL" -o custom_name.txt

# With context prompt
uv run python transcribe.py "URL" -p "Context about video content"
```

## Integration

**With fabric**: Process transcripts after generation
```bash
cat output/Video_Title_2025-11-10.txt | fabric -p extract_wisdom
```

## Notes

- Script requires being in its directory to work correctly
- Always change to `$YOUTUBE_TRANSCRIBER_DIR` first
- Parallel execution is safe and recommended for multiple videos
- Default model (gpt-4o-mini-transcribe) is recommended for most content
- Output files automatically named with video title + date
- Temp files automatically cleaned after transcription