Whisper Extract

• Audio/video file path — absolute or relative path to the file (mp3, mp4, wav, m4a, ogg, flac, webm, mkv, mov, mpeg, mpga, oga, wma).
• Language — spoken language in the recording. Examples: Spanish, English, Portuguese. If unsure, say "auto-detect" and Whisper will figure it out (slower).
• Recording context — a short description of what this is (e.g., "team meeting about Q3 roadmap", "interview with a candidate", "product demo call", "lecture on clean architecture"). This is used to write a better summary.

Optional (ask only if not obvious):

• Whisper model — default is medium. Options: tiny (fastest, less accurate), base, small, medium (recommended balance), large-v3 (most accurate, ~3 GB download). Ask if the user cares about speed vs. accuracy.
• Output directory — where to save the .md file. Default: same directory as the audio file.
• Output language for summary — language for the summary and headings. Default: same as the recording language. If the user wants the summary in a different language, note it.

Wait for the user's answers before proceeding to Step 2.

Step 2: Transcribe with Whisper

Run Whisper on the provided file. Use the --output_format json flag to capture word-level timing and text cleanly, and --output_dir to control where the raw output goes.

whisper "<file_path>" --model <model> --language <language_code_or_auto> --output_format json --output_dir /tmp/whisper-extract-temp

Important: Always emit this as a single line — never split with \ continuations. A trailing space after \ is not a line continuation in zsh; it becomes an escaped space that Whisper receives as a second (empty) file path, causing ffmpeg to fail with Error opening input file .

Language codes: es for Spanish, en for English, pt for Portuguese, fr for French, etc. For auto-detect, omit --language entirely.

If the file is large (> 1 hour): Whisper will take several minutes. Tell the user:

"Starting transcription — this may take a few minutes depending on file length and model."

After the command completes, read the JSON output from /tmp/whisper-extract-temp/ and extract the text field. This is the full raw transcript.

If Whisper fails (file not found, unsupported format, ffmpeg missing), report the exact error and suggest a fix before continuing.

Step 3: Generate the summary

Given the full transcript text and the recording context provided by the user, produce a structured summary. Write it in the output language chosen in Step 1.

The summary must cover:

1. What this recording is about — one or two sentences.
2. Key topics discussed — bulleted list of the main themes or agenda items covered.
3. Key decisions or conclusions — if any were reached (skip this section if the recording is a lecture or monologue with no decisions).
4. Action items — concrete next steps mentioned, with owner if stated (skip if none mentioned).
5. Notable quotes or moments — 1-3 verbatim fragments that best capture the essence of the conversation (optional but highly recommended for interviews and meetings).

Keep the summary concise: aim for 150-300 words. Do not pad it.

Step 4: Write the .md file

Construct and save the output Markdown file.

File naming

Use this pattern: YYYY-MM-DD-<slugified-context>.md

Examples:

• 2026-04-14-team-meeting-q3-roadmap.md
• 2026-04-14-candidate-interview-backend.md
• 2026-04-14-lecture-clean-architecture.md

If today's date is available in context, use it. Otherwise, use the file's modification date via stat or just omit the date prefix and use the slugified context alone.

File structure

Use this exact template:

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