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| # This script will transcribe an audio file (mp3, wav, etc.) to text and then clean the text using a local LLM model via Ollama. Technically, this script will work with any LLM that supports the standard OpenAI bindings with minor adjustments. | |
| # GETTING STARTED: | |
| # 1. Install required python packages (pip install openai python-dotenv) | |
| # 2. Git clone a copy of ggerganov/whisper (https://github.com/ggerganov/whisper.cpp) | |
| # 3. Build the whisper binary (see the whisper.cpp README for instructions) | |
| # 4. Download one of the whisper models (largev2 is the most accurate for all languages, though the base model works reasonably well for English). | |
| # 5. Install ffmpeg (brew install ffmpeg on macOS, apt-get install ffmpeg) | |
| # 6. Install ollama (https://ollama.com/download) | |
| # 7. Download an LLM model (https://ollama.com/library) |
| import os | |
| import arel | |
| from fastapi import FastAPI, Request | |
| from fastapi.templating import Jinja2Templates | |
| app = FastAPI() | |
| templates = Jinja2Templates("templates") | |
| if _debug := os.getenv("DEBUG"): |
