How to build a free Whisper API with GPU backend
Blog post from AssemblyAI
Developers are increasingly integrating Speech AI into their applications for modern user experiences. Whisper is an open-source model that offers Speech-to-Text capabilities, making it a popular choice among developers. However, using large Whisper models on CPU can be slow, and many developers lack the necessary GPU resources at home. This article provides a tutorial on building a free, GPU-powered Whisper API to overcome these issues. The technique involves leveraging Google Colab's free GPUs and creating a Flask API that serves an endpoint for transcription. By using ngrok as a proxy, developers can access the API from various sources such as Python scripts or frontend applications.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Real-time | 3 | 4,539 | 1,016 | 242 | +4% |
| Vector Search | 2 | 4,713 | 314 | 102 | +27% |
| Voice AI | 2 | 470 | 58 | 26 | +3% |
| LLM | 1 | 3,988 | 514 | 165 | -1% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.