live-audio-intelligence-mcp
<!-- mcp-name: io.github.ykshah1309/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:
- A rolling transcript via
faster-whisper(CPU, int8). - 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.
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.
Install
1. System prerequisite — FFmpeg
FFmpeg is a system binary, not a Python package. The ffmpeg-python
wrapper is not a dependency here — we drive the binary directly via
subprocess. You must install it yourself.
macOS (Homebrew):
brew install ffmpeg
Linux (Debian / Ubuntu):
sudo apt-get update && sudo apt-get install -y ffmpeg
Linux (Fedora / RHEL):
sudo dnf install -y ffmpeg
Windows — choose one:
# Option A — winget (Windows 10/11)
winget install --id=Gyan.FFmpeg -e
# Option B — Chocolatey
choco install ffmpeg
# Option C — Scoop
scoop install ffmpeg
Confirm it's on your PATH:
ffmpeg -version
If the command errors with "not found", reopen the terminal (PATH changes
don't propagate to already-open shells) or add the ffmpeg bin/ directory
to your PATH manually.
2. Python package
Requires Python ≥ 3.10.
pip install live-audio-intelligence-mcp
Or run directly without installing with uv:
uvx live-audio-intelligence-mcp
The first run will download the faster-whisper base.en model (~140 MB) from
Hugging Face and cache it under ~/.cache/huggingface/.
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
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.
A synthetic-audio calibration harness lives at scripts/validate_stress_score.py. It generates controlled audio (smooth sine, jittered pitch, silence-padded speech) and asserts that the score responds in the expected direction. This is calibration evidence, not market-outcome validation.
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.
Concurrency limits
By default the server caps concurrent streams at 4 (each stream holds an ffmpeg subprocess, a yt-dlp subprocess, a thread, and a temp directory). Override via env var for high-throughput deployments:
LAI_MAX_CONCURRENT_STREAMS=16 live-audio-intelligence-mcp
Exceeding the cap raises StreamLimitExceededError rather than silently
queuing.
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.
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 ".[dev]"
pytest
live-audio-intelligence-mcp
Running the tests
The pytest suite in tests/ covers the pure-Python logic that doesn't require network or ffmpeg:
- URL syntactic validation (scheme allow-list, host presence)
- Concurrency-cap enforcement in
StreamManager - Custom exception hierarchy (backward-compat with
ValueError/RuntimeError) - Prosody analyzer on synthetic audio (sine tone, silence, jittered pitch)
pytest -q
Calibration benchmark
python scripts/validate_stress_score.py
This generates synthetic audio with known acoustic properties and verifies the stress score responds in the expected direction. It's a sanity check for the weighting heuristics — not a replacement for empirical validation against real earnings-call outcomes.
Troubleshooting
ffmpeg: command not found — ffmpeg isn't on PATH. See the install
section above. On Windows, reopen your terminal after installing.
yt-dlp could not resolve URL — The site isn't supported by yt-dlp
or the URL is malformed. Test with yt-dlp -F <url> from the command
line; if that fails, the server will too.
Whisper downloads hang on first run — The ~140 MB model download goes
to ~/.cache/huggingface/. Check your network and Hugging Face access.
"Insufficient voiced frames" in stress output — The audio window is
mostly silence or noise. Usually means the stream is still buffering;
wait 30s and retry. For speakerphone Q&A, start the monitor with
disable_vad=true.
Contributing
See CONTRIBUTING.md.
Changelog
See CHANGELOG.md.
License
MIT — see LICENSE.