Transcribe Audio

Transcribe Audio Skill

Transcribe an audio file with speaker diarisation, then identify the speakers by name where the evidence supports it.

Transcribe

bash ~/.claude/skills/transcribe-audio/scripts/transcribe.sh \
  [--output-dir /path/to/output] \
  "/absolute/path/to/audio.mp3"

The script handles duration checks, chunking for long files, diarisation, alignment and filler-word cleanup. It prints transcript_path: and srt_path:, and defaults the output directory to the audio file's own directory.

Backends:

• Parakeet + FluidAudio (default) — local, fast, English only. Requires parakeet-mlx and fluidaudio in ~/.local/bin; if either is missing the script says how to install it.
• AssemblyAI — add --backend assemblyai. Use only when the user explicitly asks for AssemblyAI or cloud transcription, or when the audio is not in English. Costs about $0.01/minute and needs ASSEMBLYAI_API_KEY in the skill's .env.

Set TRANSCRIBE_REMOVE_FILLERS=0 to keep "um" and "uh" in the markdown transcript.

Identify speaker names

The pipeline outputs generic Speaker 1 / Speaker 2 labels. Before presenting the transcript, try to replace them with real names. Gather hints from several sources and cross-reference them:

1. Transcript content — the most reliable source. Scan the opening paragraphs for self-introductions ("This is…", "I'm…", "My name is…"), host introductions ("Our guest is…", "…is NAME. NAME is a…"), and direct address ("NAME, welcome to the show").
2. Audio filename — kebab- or snake-case names often carry both guest and show: david-sloan-wilson-trajectory-podcast.mp3.
3. Conversation context — the user may already have named the participants ("transcribe this interview with…").
4. YouTube metadata, when invoked via youtube-transcribe — ~/.claude/skills/youtube-transcribe/data/metadata/<audio-basename>.json, whose title, channel and description usually name the guest and host.

Apply a name only where the sources agree and you are confident. Keep the generic label when uncertain — a wrong name is worse than Speaker 2.

Output

Return to the calling skill or user:

• transcript_path — markdown with bold speaker labels
• srt_path — timestamped subtitles (Parakeet backend only)
• transcript_text — the transcript content

**David Sloan Wilson:** Hello, how are you?

**Daniel Faggella:** I'm doing well, thanks for asking.

Reference

REFERENCE.md documents the pipeline's mechanics: the long-audio chunking thresholds, the speaker-separation threshold, and the exact filler-word cleanup rules.