$0.15/hr audio transcription API based on OpenAI Whisper

Hi HN, OpenAI recently released a model for automatic speech recognition called Whisper [0]. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies. The Github project is here: https://github.com/ggerganov/whisper.cpp With this implementation I can very easily build and run the model - “make…

I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.

I built Whispering because I believe transcription is too fundamental a tool to be locked behind paywalls. It's a cross-platform desktop and web transcription app that turns speech into text with a keyboard shortcut, among other things. The app lets you bring your own API key (OpenAI, Groq, etc.) and make direct calls. If you want complete privacy, it also supports local transcription. Either way, your audio never goes through any middleman servers. It's super lightweight (~22MB), built with Svelte 5 and Tauri, and works on Mac, Windows, and Linux. I've been using it daily for the past few…

Hey HN. I’m Ivan, hacker from Ukraine. For about a year, I was working on Listenly — an app to listen to text content with OpenAI's natural-sounding text-to-speech model. At some moment, I realized that it would be cool to take all the public domain e-books and create audio versions for them. So I did it... kind-of. It would cost an immense amount of money to generate all the audio right away (OpenAI TTS costs approximately $0.84/hour of audio; 11labs, for comparison, is 10 times more expensive). So, I took a more gradual approach. I took all the metadata from the Project Gutenberg…

Hello all, I made a small transcription app for your Mac based on OpenAI’s Whisper. Would love some feedback. My plan is to make it easy to load weights from any fine-tuned whisper model to enable specialized dictation for any subfield. It’s still early in development. Thanks!