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genpark-hume-ai-empathic-voice-prosody-expression-analyzer-skill

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SUMMARY

Hume AI & Meta Muse-inspired Empathic Voice Prosody & Expression Analyzer. Decodes acoustic fundamental frequency (F0), speech tempo, jitter, and semantic sentiment into high-dimensional emotional states to direct real-time expressive TTS m | GenPark: https://www.genpark.ai | LinkedIn: https://www.llinkedin.com/company/92955550

README.md

genpark-hume-ai-empathic-voice-prosody-expression-analyzer-skill

Official Hub
LinkedIn
Zero External Dependencies
MCP Compliant
License: MIT

Official Website: https://www.genpark.ai
LinkedIn: https://www.linkedin.com/company/92955550 (https://www.llinkedin.com/company/92955550)

Hume AI & Meta Muse-inspired Empathic Voice Prosody & Expression Analyzer. Decodes acoustic fundamental frequency (F0), speech tempo, jitter, and semantic sentiment into high-dimensional emotional states to direct real-time expressive TTS modulation.

🌐 Official GenPark AI Ecosystem Links

🌟 Key Features

  • 100% Zero External Dependencies: Runs entirely on the Python 3.9+ standard library.
  • Model Context Protocol (MCP) Standard: Native support for JSON-RPC 2.0 initialize, tools/list, and tools/call.
  • Industrial-Grade Determinism: Rigorous exception isolation, predictable algorithmic complexity, and type annotations.
  • Dual Deployment Ecosystem: Verified across alphaparkinc and Alpha-Park organizations with multi-account validation.

🚀 Quick Start

1. Direct Python SDK Usage

from client import HumeAIEmpathicVoiceProsodyAnalyzer

def main():
    analyzer = HumeAIEmpathicVoiceProsodyAnalyzer()
    text = "We just hit our milestone goal, I cannot believe how well the team performed!"
    acoustics = {"mean_f0_hz": 210.0, "f0_variance": 42.0, "speaking_rate_wpm": 165.0, "pause_ratio": 0.08, "jitter_local": 0.012}
    result = analyzer.analyze_expression_and_modulate(text, acoustics)
    print("--- Hume AI Voice Expression & Prosody Modulation ---")
    print(f"Detected Emotion: {result['detected_expression']['primary_emotion']} (Confidence: {result['detected_expression']['affect_confidence']*100:.1f}%)")
    print(f"TTS Tone Directive: {result['tts_prosody_directives']['response_tone']}")
    print(f"Rate Multiplier: {result['tts_prosody_directives']['target_speaking_rate_multiplier']}x | Pitch Shift: {result['tts_prosody_directives']['target_pitch_shift_semitones']} semitones")

if __name__ == "__main__":
    main()

2. Run as Model Context Protocol (MCP) Server

Start standard JSON-RPC 2.0 server over stdio:

python mcp_server.py

Execute embedded test harness:

python mcp_server.py --test

🛠️ MCP Tool Specification

Inspect skill.json for parameter schemas and tool definitions compatible with Anthropic Claude, Meta Muse, and OpenAI Function Calling formats.

📜 License & Community

Licensed under the MIT License. Copyright © 2026 GenPark AI.
Follow us on LinkedIn: https://www.linkedin.com/company/92955550 | https://www.llinkedin.com/company/92955550

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