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An API to transcribe audio with OpenAI's Whisper Large v3!
| Date | Stars |
|---|---|
| 2026-07-24 | 355 |
| 2026-07-25 | 355 |
| 2026-07-28 | 355 |
| 2026-07-30 | 355 |
| 2026-08-06 | 355 |
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# Insanely Fast Whisper API An API to transcribe audio with [OpenAI's Whisper Large v3](https://huggingface.co/openai/whisper-large-v3)! Powered by 🤗 Transformers, Optimum & flash-attn Features: * 🎤 Transcribe audio to text at blazing fast speeds * 📖 Fully open source and deployable on any GPU cloud provider * 🗣️ Built-in speaker diarization * ⚡ Easy to use and Fast API layer * 📃 Async background tasks and webhooks * 🔥 Optimized for concurrency and parallel processing * ✅ Task management, cancel and status endpoints * 🔒 Admin authentication for secure API access * 🧩 Fully managed API available on [JigsawStack](https://jigsawstack.com/speech-to-text) Based on [Insanely Fast Whisper CLI](https://github.com/Vaibhavs10/insanely-fast-whisper) project. Check it out if you like to set up this project locally or understand the background of insanely-fast-whisper. This project is focused on providing a deployable blazing fast whisper API with docker on cloud infrastructure with GPUs for scalable production use cases. With [Fly.io recent GPU service launch](https://fly.io/docs/gpus/gpu-quickstart/), I've set up the fly config file to easily deploy on fly machines! However, you can deploy this on any other VM environment that supports GPUs and docker. Here are some benchmarks we ran on Nvidia A100 - 80GB and fly.io GPU infra👇 | Optimization type | Time to Transcribe (150 mins of Audio) | |------------------|------------------| | **large-v3 (Transformers) (`fp16` + `batching [24]` + `Flash Attention 2`)** | **~2 (*1 min 38 sec*)** | | **large-v3 (Transformers) (`fp16` + `batching [24]` + `Flash Attention 2` + `diarization`)** | **~2 (*3 min 16 sec*)** | | **large-v3 (Transformers) (`fp16` + `batching [24]` + `Flash Attention 2` + `fly machine startup`)** | **~2 (*1 min 58 sec*)** | | **large-v3 (Transformers) (`fp16` + `batching [24]` + `Flash Attention 2` + `diarization + fly machine startup`)** | **~2 (*3 min 36 sec*)**| The estimated startup time for the Fly machine with GPU and loading up the model is around ~20 seconds. The rest of the time is spent on the actual computation. ## Docker image ``` yoeven/insanely-fast-whisper-api:latest ``` Docker hub: [yoeven/insanely-fast-whisper-api](https://hub.docker.com/r/yoeven/insanely-fast-whisper-api) ## Deploying to Fly - Make sure you already have access to Fly GPUs. - Clone the project locally and open a terminal in the root - Rename the `app` name in the `fly.toml` if you like - Remove `image = 'yoeven/insanely-fast-whisper-api:latest'` in `fly.toml` only if you want to rebuild the image from the `Dockerfile` [Install fly cli](https://fly.io/docs/hands-on/install-flyctl/) if don't already have it Only need to run this the first time you launch a new fly app ```bash fly launch ``` - Fly will prompt: `Would you like to copy its configuration to the new app? (y/N)`. Yes (`y`) to copy configuration from the repo. - Fly will prompt: `Do you want to tweak these settings before proceeding` if you have nothing to adjust. Most of the required settings are already configured in the `fly.toml` file. No `n` to proceed and deploy. The first time you deploy it will take some time since the image is huge. Subsequent deploys will be a lot faster. Run the following if you want to set up speaker diarization or an auth token to secure your API: ```bash fly secrets set ADMIN_KEY=<your_token> HF_TOKEN=<your_hf_key> ``` Run `fly secrets list` to check if the secrets exist. To get the Hugging face token for speaker diarization you need to do the following: 1. Accept [`pyannote/segmentation-3.0`](https://hf.co/pyannote/segmentation-3.0) user conditions 2. Accept [`pyannote/speaker-diarization-3.1`](https://hf.co/pyannote/speaker-diarization-3.1) user conditions 3. Create an access token at [`hf.co/settings/tokens`](https://hf.co/settings/tokens). Your API should look something like this: ``` https://insanely-fast-whisper-api.fly.dev ``` Run `fly logs -a in
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e01ed978c13b33de, topic:whisper, topic:speech-to-text, readme:speech-to-text