Workers AI - Deepgram and Leonardo partner models now available on Workers AI

New state-of-the-art models have landed on Workers AI! This time, we're introducing new partner models trained by our friends at Deepgram and Leonardo , hosted on Workers AI infrastructure. As well, we're introuding a new turn detection model that enables you to detect when someone is done speaking — useful for building voice agents! Read the blog for more details and check out some of the new models on our platform: @cf/deepgram/aura-1 is a text-to-speech model that allows you to input text and have it come to life in a customizable voice @cf/deepgram/nova-3 is speech-to-text model that transcribes multilingual audio at a blazingly fast speed @cf/pipecat-ai/smart-turn-v2 helps you detect when someone is done speaking @cf/leonardo/lucid-origin is a text-to-image model that generates images with sharp graphic design, stunning full-HD renders, or highly specific creative direction @cf/leonardo/phoenix-1.0 is a text-to-image model with exceptional prompt adherence and coherent text You can filter out new partner models with the Partner capability on our Models page. As well, we're introducing WebSocket support for some of our audio models, which you can filter though the Realtime capability on our Models page. WebSockets allows you to create a bi-directional connection to our inference server with low latency — perfect for those that are building voice agents. An example python snippet on how to use WebSockets with our new Aura model: import json import os import asyncio import websockets uri = f"wss://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/deepgram/aura-1" input = [ "Line one, out of three lines that will be provided to the aura model.", "Line two, out of three lines that will be provided to the aura model.", "Line three, out of three lines that will be provided to the aura model. This is a last line.", ] async def text_to_speech(): async with websockets.connect(uri, additional_headers={"Authorization": os.getenv("CF_TOKEN")}) as websocket: print("connection established") for line in input: print(f"sending `{line}`") await websocket.send(json.dumps({"type": "Speak", "text": line})) print("line was sent, flushing") await websocket.send(json.dumps({"type": "Flush"})) print("flushed, recving") resp = await websocket.recv() print(f"response received {resp}") if __name__ == "__main__": asyncio.run(text_to_speech())