Files
ten31-transcripts/Ten31Transcripts/Visual/TimelineBuilder.swift
T
Grant Gilliam 863136aeec Phases 2-6: detection, visual timeline, backend hand-off, voiceprints
Phase 2 (call detection): CallDetector using CoreAudio per-process mic
attribution (anarlog technique) — robust start+stop for Zoom/Teams/Signal/Meet,
ignoring our own recording; auto-record toggle. Built; pending live multi-app
confirmation by the user.

Phase 3 (visual timeline foundation): AppAdapter protocol + SpeakerObservation,
TimelineBuilder (hysteresis/overlap/self-merge/aliases), VisualTimeline (schema
1.1), TextRecognizer (Vision OCR), FrameSampler + GridCallAnalyzer (name OCR +
saturated-highlight active-speaker attribution), SignalAdapter, VisualObserver
(window capture; frames released, never saved; minimized->visual_gap, idle != gap).
Synthetic-frame tested; adapter geometry pending real Signal fixtures + live
VisualObserver validation.

Phase 5 (backend hand-off): SparkControlClient (multipart label-merge, sequential,
TLS-skip, 503 Retry-After/413), SessionPackager (chunk plan + WAV slice + timeline
slice/rebase), TranscriptAssembler + SpeakersFile, TranscriptPipeline. Validated
END-TO-END against the live backend (chunk -> label-merge -> speakers.json).

Phase 6 (voiceprints): VoiceprintStore (known_voiceprints, persist named
fingerprints, skip Unknown). Wired: 'Send to backend' button + transcript status,
auto-send toggle (default off) + self-name setting.

All adversarial-review findings fixed. App + XCTest suite build; tests pass.
2026-06-06 00:15:49 -05:00

128 lines
4.7 KiB
Swift

import Foundation
/// Turns noisy per-frame `SpeakerObservation`s into clean
/// `(start, end, name, confidence)` segments.
///
/// - Hysteresis: open a segment after `openFrames` consecutive speaking frames,
/// close after `closeFrames` quiet frames rides out UI-cue lag/flicker.
/// - Overlaps allowed: each name is tracked independently (crosstalk).
/// - mic-VAD "self" spans are merged in as high-confidence segments.
/// - OCR name variants are normalized via an alias table.
///
/// Pure logic, no UI/capture deps fully unit-testable offline.
final class TimelineBuilder {
private let openFrames: Int
private let closeFrames: Int
private var aliases: [String: String] = [:] // normalized variant -> canonical
private var states: [String: NameState] = [:]
private var lastFrameT: Double = 0
private(set) var segments: [VisualTimeline.Segment] = []
init(openFrames: Int = 2, closeFrames: Int = 2) {
self.openFrames = max(1, openFrames)
self.closeFrames = max(1, closeFrames)
}
/// Register that `variant` (e.g. "Sarah J") should map to `canonical`
/// (e.g. "Sarah Jones").
func addAlias(_ variant: String, canonical: String) {
aliases[Self.normalize(variant)] = canonical
}
/// Ingest one frame's observations (all sharing time `t`). Names not present
/// (or present but not speaking) count as a quiet frame for any open segment.
func ingest(_ observations: [SpeakerObservation], at t: TimeInterval) {
lastFrameT = t
// Best confidence per canonical name that is speaking this frame.
var speaking: [String: Double] = [:]
for obs in observations where obs.speaking && !obs.name.isEmpty {
let name = canonical(obs.name)
speaking[name] = max(speaking[name] ?? 0, obs.confidence)
}
let names = Set(states.keys).union(speaking.keys)
for name in names {
var st = states[name] ?? NameState()
if let conf = speaking[name] {
if st.voiced == 0 { st.runStart = t }
st.voiced += 1
st.silent = 0
st.lastVoicedT = t
if !st.open && st.voiced >= openFrames {
st.open = true
st.segStart = st.runStart
st.confSum = 0
st.confN = 0
}
if st.open { st.confSum += conf; st.confN += 1 }
} else {
st.silent += 1
st.voiced = 0
if st.open && st.silent >= closeFrames {
closeSegment(name: name, state: st)
st.open = false
}
}
states[name] = st
}
}
/// Merge mic-VAD self spans (the user) as high-confidence segments.
func mergeSelfSpans(_ spans: [VADSpan], selfName: String) {
for span in spans where span.end > span.start {
segments.append(.init(start: span.start, end: span.end,
name: selfName, confidence: span.confidence, source: "mic_vad"))
}
}
/// Force-close any open segments at `t` (used when a visual gap begins, so a
/// segment isn't carried across the gap).
func closeOpenSegments(at t: TimeInterval) {
for (name, st) in states where st.open {
closeSegment(name: name, state: st)
states[name]?.open = false
states[name]?.voiced = 0
states[name]?.silent = 0
}
}
/// Close any still-open segments at end of capture.
func finish() {
for (name, st) in states where st.open {
closeSegment(name: name, state: st)
states[name]?.open = false
}
segments.sort { $0.start < $1.start }
}
// MARK: - Internal
private struct NameState {
var voiced = 0
var silent = 0
var open = false
var runStart: Double = 0
var segStart: Double = 0
var lastVoicedT: Double = 0
var confSum: Double = 0
var confN = 0
}
private func closeSegment(name: String, state st: NameState) {
guard st.lastVoicedT > st.segStart else { return }
let confidence = st.confN > 0 ? st.confSum / Double(st.confN) : 0.8
segments.append(.init(start: st.segStart, end: st.lastVoicedT,
name: name, confidence: confidence, source: "vision"))
}
private func canonical(_ raw: String) -> String {
let key = Self.normalize(raw)
return aliases[key] ?? raw.trimmingCharacters(in: .whitespacesAndNewlines)
}
private static func normalize(_ s: String) -> String {
s.lowercased().trimmingCharacters(in: .whitespacesAndNewlines)
}
}