The Audiopedia AI Framework

Empowering underserved communities through culturally adaptive, lightweight AI solutions designed for inclusivity and impact.

Technical Requirements and Design

The Audiopedia.AI system integrates lightweight Keyword Spotting (KWS) technology with culturally localized Audiopedia content, prioritizing offline functionality, scalability, and adaptability for Indigenous communities.

The core workflow includes:

User Speech Input: Users interact via mobile devices, solar-powered audio players, or Audiopedia Access Points (QR-code-based or Wi-Fi-enabled).

KWS Engine: Detects critical keywords or phrases from user speech to identify relevant topics and match associated content.

Content Retrieval: Searches a repository of localized Audiopedia audio files based on recognized keywords.

Audio Playback: Delivers the selected content in the Indigenous language via offline-ready devices, Access Points, or community loudspeakers.

Components

Audiopedia AI phases

Deployment Channels

  1. Offline-First Devices:
    • Solar-Powered Audio Players and SD Cards: Preloaded with localized content and KWS capabilities for personal or household use.
    • Preloaded Smartphones/Tablets: Distributed to community facilitators for content access and voice-based query functionality.
  2. Audiopedia Access Points:
    • QR Code-Based: Posters with QR codes directing users to preloaded content via mobile devices.
    • Wi-Fi-Enabled Access Points: Solar-powered devices hosting Audiopedia content locally for browsing or downloading without an internet connection.

  3. Community Radio Integration:

    Pre-recorded content broadcast on local stations, customized for health campaigns or regular programming.

  4. Central Loudspeakers:

    Audiopedia content broadcasted in villages using existing loudspeaker systems, effective for group learning or urgent messaging.

Technical Components

  1. KWS Engine:
    • Frameworks: Lightweight, offline-compatible frameworks like TensorFlow Lite or PyTorch.
    • Training Data: 10–20 hours of localized recordings focused on health-related keywords.
    • Model Size: Compact (<10MB) for low-resource devices.
    • Vocabulary: 100–200 keywords optimized for health and livelihood topics.
  2. Audiopedia Content Repository:
    • Format: Compressed MP3/OGG files (~100KB per 2–3 minute file).
    • Storage: 500MB for 400 files.
    • Indexing: Metadata tags for efficient keyword mapping.
  3. Playback Interface:
    • Features: Voice-triggered keyword detection and offline playback.
    • Platforms: Android Go devices, solar-powered audio players, and IVR systems.
  4. Hardware Requirements:
    • Devices:
      • Smartphones/Tablets: Minimum 512MB–1GB RAM with lightweight OS.
      • Solar-Powered Players: Preloaded with KWS and audio content.
    • Training and Processing:
      • Lightweight servers or low-cost cloud setups for model training.

Additional Notes on Access Points

  • QR Code-Based Audiopedia Audiopedia Access Points are cost-effective, require no hardware beyond printed posters, and allow usage monitoring through web analytics.
  • Wi-Fi-Enabled Access Points expand functionality in semi-connected regions and can serve as local hubs for community-wide content access.