Speech Captions

Automatic speech-to-text and caption file generation. Use this skill when you need spoken content turned into subtitles — not when muxing or burning captions (see subtitles).

When to use

• This skill — transcribe dialogue, detect language, convert transcript text/segments to SRT or WebVTT.
• subtitles — convert/shift/extract/burn existing subtitle files after you have captions.
• audio — extract or normalize audio before transcription when the source is noisy or the wrong sample rate.
• inspect — confirm duration, audio codec, and that an audio stream exists before running whisper.

Gotchas

• First run downloads a model — faster-whisper caches models under ~/.cache/huggingface. tiny is fast but less accurate; use small or medium for production dialogue.
• CPU-only by default — scripts use device="cpu" and compute_type="int8". GPU setups can edit the script or run whisper elsewhere; these CLIs target portable agent use.
• Plain text → cues is approximate — to-srt.py / to-vtt.py split on sentence boundaries and spread cues evenly. For accurate timing, use transcribe.py or pass --segments-json from a real ASR pass.
• Music and silence — whisper may hallucinate text on instrumentals or long silence. Inspect output; trim leading/trailing silence with audio first if needed.
• Language auto-detection — detect-language.py samples the first 60 seconds. Short clips or multilingual content may misreport; pass an explicit language to whisper in a custom workflow if you know the locale.
• Video inputs — transcription extracts mono 16 kHz WAV via ffmpeg. Very long files take proportionally longer; there is no chunking in the default transcribe script.