live-audio-intelligence-mcp 0.1.2

Institutional-grade MCP server for live financial webcast transcription and vocal stress analysis

live-audio-intelligence-mcp

MCP server for live financial webcast transcription and heuristic vocal stress analysis.

Turns any live webcast URL (earnings calls, CNBC, investor days) into a real-time pipeline that feeds an LLM two things simultaneously:

1. A rolling transcript via faster-whisper (CPU, int8).
2. A heuristic vocal stress score (0–100) derived from F0 pitch jitter, hesitation ratio, and voiced-frame fraction. These prosodic features are well-established correlates of speaker arousal in the vocal-analysis literature; their composition into the score below is heuristic and has not been empirically validated against market outcomes. Treat it as a coarse signal, not an oracle.

Built on the Model Context Protocol. Exposes 4 tools over stdio; drop it into Claude Desktop, Claude Code, or any MCP client.

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Why this exists

Sell-side analysts and hedge-fund PMs don't just want to read the earnings transcript after the fact — they want a real-time signal about how confident the CFO sounds when asked about Q4 guidance. This server wires a Whisper pipeline and a pYIN-based prosody analyzer directly into an LLM's tool loop, so the model can ask "what did the CEO just say about China?" and "how stressed did they sound saying it?" in the same conversation.

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Install

Requires Python ≥ 3.10 and ffmpeg on your PATH.

pip install live-audio-intelligence-mcp

Verify ffmpeg:

ffmpeg -version

The first run will download the faster-whisper base.en model (~140 MB).

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Run it

Stdio MCP server:

live-audio-intelligence-mcp

Or equivalently:

python -m live_audio_intelligence_mcp

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "live-audio-intelligence": {
      "command": "live-audio-intelligence-mcp"
    }
  }
}

Claude Code

claude mcp add live-audio-intelligence -- live-audio-intelligence-mcp

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Tools

Tool	Purpose
monitor_live_stream(url, disable_vad=False)	Resolve the audio URL, spawn ffmpeg, start chunking + transcription. Returns a stream_id.
get_rolling_transcript(stream_id, minutes_back=10)	Get the last N minutes of concatenated transcript text.
analyze_speaker_stress(stream_id, time_window_seconds=60)	Run prosody analysis over the last N seconds of audio. Returns stress score, pitch jitter, hesitation ratio, pause stats, and a human-readable interpretation.
stop_monitor(stream_id)	Kill ffmpeg, clean up temp files, drop the transcript buffer.

The stress score

Score	Interpretation
0–20	Confident, fluent delivery
20–45	Normal variation
45–75	Elevated stress — worth monitoring
75–100	High stress — potential market-moving signal

Composite of:

• Pitch jitter (coefficient of variation of F0) — 50% weight, saturating at jitter = 0.12
• Hesitation ratio (fraction of audio in pauses > 400 ms) — 35% weight, saturating at 0.30
• Unvoiced fraction (speaker trailing off) — 15% weight

The three features are literature-backed correlates of speaker arousal (see pYIN for F0 tracking, and the broad "disfluency is a correlate of cognitive load" line of work). The weights and saturation points are hand-picked defaults, chosen so that a calm speaker scores in the 0–20 band on clean studio audio and visibly stressed speech scores ≥ 45 — they are not fit to any labeled dataset. Consumers who care about absolute numbers should recalibrate thresholds against their own recordings.

Low-SNR mode

For speakerphone audio (most earnings Q&A), pass disable_vad=true to monitor_live_stream. Silero VAD tends to aggressively classify muddy conference-call speech as silence; disabling it preserves more of the speech at the cost of transcribing a bit more ambient noise.

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Architecture

                 ┌──────────────────┐
    URL  ─────▶  │  yt-dlp resolve  │
                 └────────┬─────────┘
                          │ audio URL
                          ▼
                 ┌──────────────────┐      ┌────────────────┐
                 │  ffmpeg (bg)     │ ───▶ │  15s WAV chunk │
                 │  16kHz mono PCM  │      │  queue         │
                 └──────────────────┘      └───────┬────────┘
                                                   │
                                ┌──────────────────┴────────────────┐
                                ▼                                   ▼
                       ┌──────────────────┐              ┌──────────────────┐
                       │ faster-whisper   │              │  librosa.pyin    │
                       │ (int8 / CPU)     │              │  + pause detect  │
                       └────────┬─────────┘              └────────┬─────────┘
                                │ rolling transcript              │ stress score
                                ▼                                 ▼
                            ┌────────────── MCP stdio ───────────────┐
                            │    LLM (Claude) — calls tools freely   │
                            └────────────────────────────────────────┘

All blocking work (Whisper inference, ffmpeg I/O, librosa DSP) is dispatched to threads via asyncio.to_thread so the MCP event loop stays responsive.

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Development

git clone https://github.com/ykshah1309/live-audio-intelligence-mcp
cd live-audio-intelligence-mcp
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -e .
live-audio-intelligence-mcp

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License

MIT — see LICENSE.