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Today I tried to get voice recognition working using hardware acceleration on an ARM64 system with an Nvidia GPU.  I eventually found the easiest way was to make my own Dockerfile based on their own, but using this base image:-

FROM nvidia/cuda:12.3.2-cudnn9-runtime-ubuntu22.04

This comes with all the necessary Cuda support libraries pre-loaded and allowed me to quickly compile, build and publish a Grace Blackwell compatible docker image of faster-whisper.  Once I've tested it a bit and set up an automated pipeline I'll publish it.

The difference between CPU and GPU processing was only a second or so, but it does make the recognition feel snappy and you can use the large model easily instead of the tiny one.

So I was trying to give my current personal AI project access to Gitea, and had to read the code to get it working.  If you want to start it up on docker and use http as the transport, then your docker-compose is:-

version: "3.8"

services:
  gitea-mcp:
    image: gitea/gitea-mcp-server:latest
    container_name: gitea-mcp
    restart: no
    ports:
      - "8080:8080"
    environment:
      - MCP_MODE=http
      - GITEA_URL=https://yourgiteainstance
      - GITEA_TOKEN=yoursecrettoken