Three Options for Optimizing Speech Response Rate
Core Strategy: Reducing latency through computational resource allocation and model optimization by:
- Hardware level::
- Increase CPU limit to 4+ cores in Docker settings
- Allocate at least 8GB of memory for the container (modify the resources configuration of docker-compose.yml)
- Model Selection::
- Prioritize the use of locally deployed Ollama quantitative models (e.g., q4 versions with 7B parameters)
- If you have to use OpenAI then choose gpt-3.5-turbo instead of gpt-4
- Switch to Bert-VITS2 speech synthesis (300-500ms latency savings over Edge TTS)
- network optimization::
- Configure B Live API reverse generation for deployment on domestic servers
- Open Docker's
network_mode: hostReduced NAT conversion loss
Advanced Tips:
exist.envAddSTREAMING_INTERVAL=0.3Parameters to achieve streaming response, the audience can see the effect of sentence-by-sentence generation, the actual delay reduction of more than 40%.
This answer comes from the articleVirtualWife: A secondary digital person that supports B-station live streaming and voice interactionThe
May not be reproduced without permission:AI productivity tools " How to improve the voice response speed of VirtualWife in B Live?
