base on WhisperFusion builds upon the capabilities of WhisperLive and WhisperSpeech to provide a seamless conversations with an AI. # WhisperFusion <h2 align="center"> <a href="https://www.youtube.com/watch?v=_PnaP0AQJnk"><img src="https://img.youtube.com/vi/_PnaP0AQJnk/0.jpg" style="background-color:rgba(0,0,0,0);" height=300 alt="WhisperFusion"></a> <br><br>Seamless conversations with AI (with ultra-low latency)<br><br> </h2> Welcome to WhisperFusion. WhisperFusion builds upon the capabilities of the [WhisperLive](https://github.com/collabora/WhisperLive) and [WhisperSpeech](https://github.com/collabora/WhisperSpeech) by integrating Mistral, a Large Language Model (LLM), on top of the real-time speech-to-text pipeline. Both LLM and Whisper are optimized to run efficiently as TensorRT engines, maximizing performance and real-time processing capabilities. While WhiperSpeech is optimized with torch.compile. ## Features - **Real-Time Speech-to-Text**: Utilizes OpenAI WhisperLive to convert spoken language into text in real-time. - **Large Language Model Integration**: Adds Mistral, a Large Language Model, to enhance the understanding and context of the transcribed text. - **TensorRT Optimization**: Both LLM and Whisper are optimized to run as TensorRT engines, ensuring high-performance and low-latency processing. - **torch.compile**: WhisperSpeech uses torch.compile to speed up inference which makes PyTorch code run faster by JIT-compiling PyTorch code into optimized kernels. ## Hardware Requirements - A GPU with at least 24GB of RAM - For optimal latency, the GPU should have a similar FP16 (half) TFLOPS as the RTX 4090. Here are the [hardware specifications](https://www.techpowerup.com/gpu-specs/geforce-rtx-4090.c3889) for the RTX 4090. The demo was run on a single RTX 4090 GPU. WhisperFusion uses the Nvidia TensorRT-LLM library for CUDA optimized versions of popular LLM models. TensorRT-LLM supports multiple GPUs, so it should be possible to run WhisperFusion for even better performance on multiple GPUs. ## Getting Started We provide a Docker Compose setup to streamline the deployment of the pre-built TensorRT-LLM docker container. This setup includes both Whisper and Phi converted to TensorRT engines, and the WhisperSpeech model is pre-downloaded to quickly start interacting with WhisperFusion. Additionally, we include a simple web server for the Web GUI. - Build and Run with docker compose ```bash mkdir docker/scratch-space cp docker/scripts/build-* docker/scripts/run-whisperfusion.sh docker/scratch-space/ docker compose build export MODEL=Phi-3-mini-4k-instruct #Phi-3-mini-128k-instruct or phi-2, By default WhisperFusion uses phi-2 docker compose up ``` - Start Web GUI on `http://localhost:8000` **NOTE** ## Contact Us For questions or issues, please open an issue. Contact us at: [email protected], [email protected], [email protected] ", Assign "at most 3 tags" to the expected json: {"id":"7409","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"