Whisper Beat Finder

When to Use

Use this skill in Stage 03, immediately after elevenlabs-narration produces an mp3. It outputs two things:

1. transcript.json -- Whisper's full output with word-level timestamps
2. beat-timings.md -- absolute timestamps for each beat boundary and key sub-callout

Stage 04 reads beat-timings.md to populate timing.ts and re-time per-scene T constants.

What You Need

• Python with openai-whisper installed
• The mp3 produced by Stage 03 (Whisper takes the file path)
• A list of phrases to find (these come from the script, one per beat boundary)

No GPU is required. The default configuration runs medium.en on CPU, which finishes ~1 minute per minute of audio on a modern laptop and is reliable across drivers.

CPU vs GPU

Configured model: {{WHISPER_MODEL}}

We default to medium.en on CPU because large-v3 on GPU has been observed to segfault on some CUDA driver combinations (RTX 5080 + driver 591.86 was one). medium.en is accurate enough for beat extraction and never crashes. See rules/cpu-fallback.md.

If you have a stable GPU setup and need faster turnaround, switch to large-v3 with device="cuda". If anything segfaults, fall back to medium.en on CPU.

How It Works

1. Load the model: whisper.load_model("medium.en", device="cpu")
2. Transcribe with word_timestamps=True, language="en", fp16=False
3. Save the full result to transcript.json
4. For each beat in the script, search the word stream for that beat's opening phrase (case-insensitive substring match across N consecutive words). Record the absolute start time.
5. Optionally search for sub-callout phrases (mid-beat moments worth animating against) and record their times too.
6. Write beat-timings.md with the structured table.

See rules/phrase-matching.md for the matching contract.

Scripts

• scripts/transcribe.py -- transcribe an mp3, save transcript.json
• scripts/find-beats.py -- given transcript.json and a list of beat phrases, emit beat-timings.md

Copy both into the project root and edit the audio path / beat list at the top.

Output Format

beat-timings.md contains a markdown table that Stage 04 reads directly:

| Beat | Name | Start (abs) | End (abs) | Sub-callouts |
|------|------|-------------|-----------|--------------|
| 1 | Open on the topic | 0.00 | 21.02 | nine years @ 9.16, keeps emailing @ 13.04 |
| 2 | The backlog | 21.02 | 46.04 | directors by hand @ 31.10, quitting @ 36.86 |

The last beat's end is the total audio length (from transcript.json's last word end). Plus a small tail if there is an end card.