#4203 Speech-to-Text-to-LLM-to-Report
#4203 Speech-to-Text-to-LLM-to-Report #4203 Provide a Python app to perform real-time speech-to-text and provide intermittent LLM analysis and reporting of the conversation To build a real-time speech-to-text (STT) app with intermittent LLM analysis, the most efficient modern approach is to use a streaming SDK (like AssemblyAI or Deepgram) for low-latency transcription and a threading/buffer system to send chunks of that text to an LLM (like OpenAI's GPT-4o) every N seconds. ............................ This must be locally, on-machine To move this entirely on-machine , we shift from cloud APIs to local inference. This requires more hardware "oomph" (specifically a GPU with 8GB+ VRAM is ideal, though CPUs work slower), but it ensures total privacy and zero API costs. We will use: Faster-Whisper : A highly optimized version of OpenAI’s Whisper for real-time STT. Ollama : The industry standard for running LLMs (like Llama 3 or Mistral) locally. 1. Prerequ...