v0.13.0:4 - redaction gateway, embeddings proxy, expanded audio API
- Add redaction gateway (redaction_gateway.py, redaction/ scrub + tests) - Add embeddings proxy and spark_embed service (Dockerfile + main.py) - Expand audio_proxy with speaker-aware handling; deep_health/health/server updates - Package: configureSparks action + sparkConfig model updates, manifest/main wiring - Docs: AUDIO_API, EMBEDDINGS, REDACTION_GATEWAY; HANDOFF and runbook/known-issues refresh
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Keysat
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#!/bin/bash
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# End-to-end test of the v0.10 + v0.11 audio pipeline:
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# audio file → spark-control /api/audio/transcribe-with-speakers
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# (Parakeet + Sortformer merged)
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# → Qwen3.6 via vLLM with long-form prompt + speaker name
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# resolution
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# → ~/Desktop/<filename>-analysis.md
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#
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# Usage:
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# bash scripts/test-audio-with-speakers.sh <audio-file> [--people "Name1, Name2"]
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#
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# Examples:
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# # No participants list (LLM will only resolve speakers it can verify from audio cues)
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# bash scripts/test-audio-with-speakers.sh ~/Library/Application\ Support/hyprnote/sessions/*/audio.mp3
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#
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# # With known participants (LLM constrained to these names)
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# bash scripts/test-audio-with-speakers.sh ~/Downloads/podcast.mp3 --people "Dax, Will"
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#
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# Designed to mirror exactly what recap-relay's spark-control backend will do
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# once the PR lands. If the output looks good here, the recap-relay version
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# will look the same.
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set -e
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AUDIO="${1:?Usage: $0 <audio-file> [--people \"Name1, Name2\"]}"
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PEOPLE=""
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if [ "$2" = "--people" ] && [ -n "$3" ]; then
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PEOPLE="$3"
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fi
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if [ ! -f "$AUDIO" ]; then
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echo "ERROR: audio file not found: $AUDIO" >&2
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exit 1
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fi
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SPARK_CONTROL="${SPARK_CONTROL:-https://spark.satsflows.com}"
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VLLM="${VLLM:-http://<spark-1-ip>:8888/v1}"
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echo "════════════════════════════════════════════════════════════════"
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echo "Audio: $AUDIO ($(du -h "$AUDIO" | cut -f1))"
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echo "Spark Control: $SPARK_CONTROL"
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echo "vLLM: $VLLM"
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echo "Participants: ${PEOPLE:-<none — LLM will only resolve speakers from audio cues>}"
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echo "════════════════════════════════════════════════════════════════"
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echo
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# ───────── Stage 1: transcribe + diarize ─────────
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echo "▶ Stage 1: transcribe + diarize (Parakeet + Sortformer in parallel)..."
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START=$(date +%s)
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HTTP=$(curl -sSk -X POST "$SPARK_CONTROL/api/audio/transcribe-with-speakers" \
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-F "file=@$AUDIO" \
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-o /tmp/diarized.json \
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-w "%{http_code}")
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END=$(date +%s)
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echo " HTTP $HTTP, $((END - START))s wall time"
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if [ "$HTTP" != "200" ]; then
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echo "ERROR — non-200 response. Full body:"
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cat /tmp/diarized.json
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exit 1
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fi
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python3 -c "
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import json
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d = json.load(open('/tmp/diarized.json'))
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print(f\" Duration: {d['duration']}s Speakers: {d['speakers_detected']} Segments: {len(d['segments'])}\")"
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# ───────── Stage 2: format transcript ─────────
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echo
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echo "▶ Stage 2: format diarized transcript as [MM:SS] Speaker_N: text..."
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python3 > /tmp/transcript-formatted.txt <<'PY'
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import json
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d = json.load(open('/tmp/diarized.json'))
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out = []
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for s in d['segments']:
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ms = s['start_ms'] // 1000
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h, m, sec = ms // 3600, (ms % 3600) // 60, ms % 60
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ts = f"{h}:{m:02d}:{sec:02d}" if h else f"{m:02d}:{sec:02d}"
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out.append(f"[{ts}] {s['speaker']}: {s['text']}")
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print("\n".join(out))
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PY
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echo " $(wc -l < /tmp/transcript-formatted.txt) formatted lines"
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echo " Sample (first 3):"
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head -3 /tmp/transcript-formatted.txt | sed 's/^/ /'
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# ───────── Stage 3: discover current LLM ─────────
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echo
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echo "▶ Stage 3: discover current vLLM model..."
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MODEL=$(curl -sS $VLLM/models | python3 -c "import json,sys; print(json.load(sys.stdin)['data'][0]['id'])")
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echo " Model: $MODEL"
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# ───────── Stage 4: build LLM request ─────────
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echo
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echo "▶ Stage 4: build LLM request with speaker-name-resolution prompt..."
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python3 - "$MODEL" /tmp/transcript-formatted.txt "$PEOPLE" > /tmp/request.json <<'PY'
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import json, sys
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model, transcript_path, people = sys.argv[1], sys.argv[2], sys.argv[3]
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transcript = open(transcript_path).read()
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participants_block = ""
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if people.strip():
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participants_block = f"""
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Known participants in this conversation: {people}
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Constrain your speaker→name mappings to this list. Still only assign a
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name when the audio cues unambiguously identify which participant is
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which — do not guess based on topic or role."""
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system = (
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"You are a meeting analyst producing comprehensive long-form notes. "
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"Preserve specific quotes, numbers, dates, names, and decisions verbatim. "
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"Quote speakers directly when they said something memorable. "
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"Generate as many sections as the meeting naturally has. "
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"Do not summarize aggressively — aim for 3000-6000 words for a 60-90 min conversation."
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)
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user_prompt = f"""You will analyze a transcript with anonymous speaker labels (Speaker_0, Speaker_1, ...).
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CRITICAL — speaker name resolution rules:
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Map a speaker label to a real name ONLY when you have direct, unambiguous evidence:
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- The speaker explicitly identifies themselves ("I'm X", "this is X", "my name is X")
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- Another speaker addresses them by name as a vocative ("thanks X", "X, what do you think?")
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If you have ANY doubt, leave the mapping as null. False mappings are worse than no mapping.
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Do NOT infer names from topic context, role descriptions, or weak associations.{participants_block}
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OUTPUT FORMAT — produce exactly two parts:
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PART 1: A JSON block at the very top of your response with this shape:
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```json
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{{
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"speaker_mapping": {{
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"Speaker_0": {{"name": "Real Name", "confidence": "high", "evidence": "quoted line + [MM:SS]"}},
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"Speaker_1": {{"name": null, "confidence": null, "evidence": null}}
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}}
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}}
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```
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PART 2: Below the JSON, a structured long-form report with these sections:
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# Detailed Discussion Log
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Chronological account of every topic discussed, with verbatim quotes from speakers for important points. Aim for 8+ bullets per major topic. Use sub-bullets for examples or supporting detail.
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# Decisions Made
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Every decision, with who proposed it, who agreed, any dissent, and rationale.
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# Action Items
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Every action item, with owner, deadline, and any context. Include even minor "I'll think about it" commitments.
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# Open Questions
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Things raised that weren't resolved, with who raised them.
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# Key Quotes
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Direct quotes worth preserving, with speaker attribution.
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In the report body: use REAL NAMES where you mapped them, and Speaker_N where you couldn't.
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---
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TRANSCRIPT:
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{transcript}"""
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print(json.dumps({
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"model": model,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": user_prompt},
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],
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"max_tokens": 16000,
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"temperature": 0.3,
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"chat_template_kwargs": {"enable_thinking": False},
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}))
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PY
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REQ_BYTES=$(wc -c < /tmp/request.json)
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echo " Request size: $REQ_BYTES bytes"
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# ───────── Stage 5: LLM call ─────────
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echo
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echo "▶ Stage 5: send to Qwen3.6 (this is the slow part — 30-90s typical)..."
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START=$(date +%s)
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curl -sS $VLLM/chat/completions \
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-H "Content-Type: application/json" \
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-d @/tmp/request.json \
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> /tmp/llm-raw.json
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END=$(date +%s)
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echo " Wall time: $((END - START))s"
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# Extract content
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python3 -c "
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import json
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r = json.load(open('/tmp/llm-raw.json'))
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if 'choices' in r:
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print(r['choices'][0]['message']['content'])
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else:
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print('ERROR — unexpected response:')
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print(json.dumps(r, indent=2))
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" > /tmp/analysis.md
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# ───────── Stage 6: save + display ─────────
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BASENAME=$(basename "$AUDIO" | sed 's/\.[^.]*$//')
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DEST="$HOME/Desktop/${BASENAME}-analysis.md"
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cp /tmp/analysis.md "$DEST"
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echo
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echo "════════════════════════════════════════════════════════════════"
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echo "✔ Saved: $DEST"
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echo " ($(wc -l < "$DEST") lines, $(wc -w < "$DEST") words)"
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echo "════════════════════════════════════════════════════════════════"
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echo
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echo "─── Top of the report (speaker mapping JSON, if produced) ───"
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head -30 "$DEST"
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echo "..."
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echo
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open -a "TextEdit" "$DEST"
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