AI processing pipeline building program
Three modes of audio and video AI processing via LiveKit:
- Client-side processing: Running VAD models in the browser via WebAssembly
- service middleware: Receive an audio stream and call the ASR API with Webhook
- Native plug-ins: By
livekit-egressDirect interface to AI services
Specific integration steps (in Python)
- Install the voice processing SDK:
pip install livekit-api whisper - Create a speech recognition pipeline:
room = Room()
room.on('track_subscribed', transcribe_audio) - Realize real-time transcription logic:
model = whisper.load_model('tiny')
result = model.transcribe(audio_buffer)
Performance Optimization Recommendations
- utilization
opus_dtxReduced data transmission during quiet hours - set up
audio_level_thresholdFiltering of ambient noise - Synchronize timestamps using DataChannel for AI results
This answer comes from the articleLiveKit: an open source tool for building real-time audio and video applicationsThe
May not be reproduced without permission:AI productivity tools " How to achieve deep integration of real-time audio and video with AI speech recognition?
