Production-oriented system architecture design
SpeechGPT 2.0-preview adopts a split architecture design, where the speech codec (Codec) and language model (7B parameters) are deployed independently. This architecture has three major advantages: 1) the Codec model focuses on speech feature extraction and synthesis, and the model size is controlled within 500MB; 2) the language model supports quantized deployment and can run on consumer-grade GPUs; and 3) the modular design facilitates feature expansion.
The deployment process reflects engineering thinking: 1) manage large model weights via git-lfs; 2) use flash-attn to optimize computational efficiency; 3) gradio provides a lightweight demo interface. The system resource consumption is controlled within 16GB of video memory, and the single response energy consumption is 30% lower than similar systems.
Empirical tests show that the architecture supports 200+ concurrent requests and still maintains a latency of <200ms, with an error rate of less than 0.5%, which fully meets the standards for industrial-grade applications.
This answer comes from the articleSpeechGPT 2.0-preview: an end-to-end anthropomorphic speech dialog grand model for real-time interactionThe
