The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →voicechat2 is an open-source, WebSocket-based project for running a local AI voice-chat pipeline: speech recognition turns your voice into text, an LLM generates a reply, and a text-to-speech (TTS) engine speaks it back. You can swap the back ends for each stage, but the documented setup targets Ubuntu LTS and assumes CUDA or ROCm is already installed. It does not specify a universal minimum GPU or a current hardware compatibility matrix.
How voicechat2 works
The project connects three services in a chain. Its browser interface captures speech and uses voice activity detection (VAD); a speech-recognition service transcribes the audio; an LLM produces a response; and a TTS service returns spoken audio. The README describes a WebSocket server, browser UI, and Opus audio support. The recognition, LLM, and synthesis components can be selected separately, so the project is not tied to one model family or a single hosted AI provider.
Which speech, LLM, and TTS back ends can you use?
The project README documents these options. Their inclusion establishes that the components are supported choices in the project documentation, not that they have equivalent quality, speed, or ease of installation.
| Pipeline stage | Documented choices | What to consider |
|---|---|---|
| Speech recognition (called SRT in the repository) | whisper.cpp; faster-whisper; Hugging Face Transformers Whisper | Check model availability, fit with your software and GPU stack, and responsiveness for your audio. |
| LLM | llama.cpp; any OpenAI API-compatible server | “OpenAI API-compatible” refers to a server interface. It does not require the rest of the stack to use OpenAI’s hosted service. |
| Text-to-speech | Coqui TTS; StyleTTS2; Piper; MeloTTS | Choose according to speech quality, responsiveness, model availability, and setup complexity. |
The README does not provide controlled comparative scores for these alternatives. Expect to evaluate the combination that fits your hardware and priorities rather than assume one listed back end is best for everyone. (voicechat2 project README)
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How to run voicechat2 locally
The repository’s documented installation path targets Ubuntu LTS. It assumes the relevant GPU software stack—CUDA or ROCm—is already configured. The README suggests conda or mamba and shows a Python 3.11 environment example, Python dependency installation, and system audio prerequisites. Treat those as the project’s instructions, not as a current compatibility guarantee: the README does not give a clear, up-to-date matrix of tested operating systems, drivers, Python versions, GPUs, and back ends.
Prepare the environment
Follow the project README for its conda or mamba environment-creation example and Python requirements. The example uses Python 3.11; confirm the repository’s current instructions before choosing versions, since dependency and backend compatibility can change.
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Install audio prerequisites
The README names espeak-ng, ffmpeg, libopus0, and libopus-dev among the system audio packages. Install the packages required by the chosen back ends using the repository’s Ubuntu instructions.
Build and configure the model back ends
If using llama.cpp, the README shows separate HIPBLAS and CUDA build examples, along with model-download commands. Choose the path that matches your installed GPU stack, then configure the recognition and TTS services as well as the LLM. The project’s modular design means a working setup depends on the compatibility of the full combination, not just the LLM.
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Because the repository’s page and dependencies may change, use its current instructions for exact commands rather than treating any example as a guaranteed, current recipe. (voicechat2 project README)
What GPU do you need?
The project does not prescribe a minimum GPU. Its documented setup assumes CUDA or ROCm, but that is not the same as a published minimum requirement or a guarantee that a particular card will work with every backend. Before selecting hardware, verify the current compatibility of the GPU, drivers, CUDA or ROCm stack, Python dependencies, and chosen models.
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The README describes examples on an AMD 7900-class RDNA3 GPU and an RTX 4090; these demonstrate particular configurations, not required hardware. Your practical choice depends on which recognition, LLM, and TTS models you intend to run and on the software stack they support.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much latency should you expect?
There is no established universal latency figure for voicechat2. The project author’s README describes “voice-to-voice latency in the 1 second range” for an AMD 7900-class RDNA3 configuration using distil-whisper/distil-large-v2, a quantized Llama 3.1 8B model, and Coqui VITS. It also reports “as low as 300ms” for an RTX 4090 configuration using Faster Whisper and faster-distil-whisper-large-v2. These are author-reported examples, not independent benchmarks or promises for other setups. (voicechat2 project README)
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A 2024 Hackster.io write-up gives different estimates: about 1–1.5 seconds on a W7900 and as low as 500ms on an RTX 4090 for the configurations it discusses. Because the configurations and reporting contexts differ, these figures are not a controlled comparison and should not be combined into one expected result. (Hackster.io write-up)
For a useful comparison of your own setup, hold the spoken task and component settings constant. Record end-to-end voice-to-voice response time, and note the GPU software stack, recognition model, LLM and model size, and TTS engine. The sources provide no independent statistical study or controlled cross-hardware benchmark for voicechat2.
Can you access it remotely?
The README describes a WebSocket server for remote access and includes tunnel helper scripts described as connecting GPU and jump machines. That establishes documented remote-access and tunnelling support, not a secure deployment configuration. Do not assume that exposing the service or using a tunnel makes it safe by default; secure network access requires configuration appropriate to your environment.
Quick Recap
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