Vertex AI is a comprehensive platform for deploying, fine-tuning, and integrating large language models (LLMs), designed to make these processes straightforward and accessible. This article provides a step-by-step guide to deploying one such model: Whisper Large, a voice recognition LLM developed by OpenAI. The guide covers the deployment process and demonstrates how to interact with the model using Python and curl commands.
There are many ways to integrate large language models (LLMs) into your product, but not all approaches are equally effective. Self-deploying an LLM can be complex and costly, while using a generic API may lack the specific features required to meet your unique needs. Additionally, some models come with licensing limitations that can introduce further complications. The key advantage of Vertex AI lies in its straightforward and user-friendly approach to deploying and fine-tuning models for specific use cases. Its flexibility allows you to tailor the model to your requirements with ease, making it an ideal choice for businesses looking to leverage LLMs effectively.
Go to the model’s page, deploy the model on Vertex AI. It takes 20-25 minutes to deploy.
Got to this page and create a GCP bucket, I called it whisper-sound-bucket
I used this archive to download some arbitrary file with speech sounds
