Transcribe Video

Extract transcript text from a local video file. The skill checks for embedded subtitles first (faster and more accurate), and only falls back to API-based speech recognition if none are found.

Step 1: Identify the video file

Confirm the video file path with the user. Supported formats: mp4, mkv, mov, avi, webm, and any format ffmpeg can handle.

Step 2: Check for embedded subtitles

ffprobe -v quiet -select_streams s -show_entries stream=index,codec_name:stream_tags=language,title -of json "<video_path>"

• If subtitle streams exist → go to Step 3a (extract embedded subtitles)
• If no subtitle streams → go to Step 3b (API transcription)

Step 3a: Extract embedded subtitles

If multiple subtitle tracks exist, prefer the one matching the video's primary language or ask the user which track to use.

# Extract as SRT (stream index 0 for first subtitle track; adjust if needed)
ffmpeg -i "<video_path>" -map 0:s:0 -c:s srt "<output_path>.srt" -y

After extraction, convert SRT to clean text:

• Remove sequence numbers
• Remove timestamp lines (lines matching \d{2}:\d{2}:\d{2})
• Remove HTML-like tags (<i>, </i>, etc.)
• Join remaining non-empty lines

Save the clean transcript to <video_name>.txt next to the video file. Done — skip Step 3b.

Step 3b: API-based transcription

Use the bundled transcription script. It reads credentials from ~/.transcribe_video.env.

Prerequisites check

1. Verify the env file exists:

   test -f ~/.transcribe_video.env && echo "OK" || echo "MISSING"
   

2. If MISSING, tell the user to create ~/.transcribe_video.env with:

   OPENAI_API_KEY=your-key-here
   # Optional Base URL:
   # OPENAI_API_BASE=https://<base-url>/v1/
   # Optional Model Name:
   # TRANSCRIBE_MODEL=gpt-4o-transcribe
   

   Wait for the user to confirm before proceeding.

3. Verify dependencies:

   python3 -c "from openai import OpenAI; from dotenv import load_dotenv; print('OK')" 2>&1
   

   If missing: pip install openai python-dotenv

Run transcription

python3 <skill_directory>/scripts/transcribe.py "<video_path>"

The script extracts audio (WAV, 16kHz mono), sends it to the API, and saves the transcript to <video_name>.txt next to the video file.

Step 4: Report results

Tell the user:

• Where the transcript file was saved
• How many lines / approximate word count
• Whether it came from embedded subtitles or API transcription
• Display the first few lines as a preview