Normalize a transcript into speaker turns and compute structural metrics: turn counts, airtime share per speaker, airtime concentration, and question rate. Run this when a transcript's format is uncertain, to confirm speakers were detected correctly before scoring.

Transcribe a single audio or video recording to WebVTT using a local speech model. Audio stays on this machine. A mixed track cannot be reliably split by speaker, so the result omits speaker labels and cannot produce per-speaker scores. For those, record one file per participant and use transcribe_session.

Transcribe a folder of per-participant recordings and merge them into one speaker-labeled transcript ordered by timestamp. Each file contains exactly one person, so speaker attribution is exact. This is the recommended path for anything that needs per-speaker scores. In Zoom, enable Settings > Recording > 'Record a separate audio file for each participant'. Speaker names come from the filenames.

Report which local speech-to-text backends and media tools are installed, with install instructions if none are. Use this before attempting transcription so a missing dependency surfaces as a clear answer.

Score a conversation for bridging quality. Returns a 0-4 score per speaker per indicator (receptiveness, perspective-taking, contempt, curiosity, concession, personal disclosure), each with quoted evidence anchored to turn numbers, a confidence value, and deterministic airtime metrics. Set arc_segments to 3 to see how the conversation changed from opening to close.

Return every pattern match the instrument found, with the quote and the rule that fired, plus the turns where nothing matched. Use this when the reading matters more than the number: you confirm or reject each candidate yourself, catching sarcasm and quoted speech that a word list scores wrong, and catching behavior in the unmatched turns that no pattern covers. This is the tool for producing a defensible qualitative summary.

Score two transcripts and lay them side by side with the difference on each indicator. Use for before/after comparisons: the same group early and late in a program, one workshop format against another, or a facilitator's first session against their tenth.

Combine scores from multiple conversations into one report: per-indicator means, spread, and optional group comparison with effect size. This is the tool that produces something a funder can read. Pass the structured score objects returned by score_conversation.

Return the full rubric for one indicator: what it measures, what counts as evidence, what does not count, the 0-4 anchors, the thresholds, what the score cannot tell you, and the research behind it. Use whenever someone asks what a score means or challenges one.