VoiceStreamAI
Near-Realtime audio transcription using self-hosted Whisper and WebSocket
GraphCanon updated 3w · GitHub synced 3w
Decision brief
VoiceStreamAI offers near-real-time transcriptions using WebSocket and self-hosted Whisper model, deployable via Docker or manual setup.
Good fit when
- Need high-quality real-time audio transcription
- Available for Python/JS-based projects
Avoid when
- Looking for a cloud-hosted solution without self-management
- Not prepared to handle GPU setup for Docker if leveraging CUDA
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (666d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- 38 low (38 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install VoiceStreamAISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
VoiceStreamAI provides near-real-time speech-to-text functionality leveraging WebSocket for real-time communication and Whisper for high-quality transcriptions. It operates with a server setup managed either by Docker containerization or through a manual Python installation.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Jul 31, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Jul 31, 2026
- Languages
- python
Source: github.language · Jul 31, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 31, 2026)
To set up the VoiceStreamAI server, you need Python 3.8 or later and theSource link
Tags
README
Running with Docker
This will not guide you in detail on how to use CUDA in docker, see for example here.
Still, these are the commands for Linux:
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
You can build the container image with:
sudo docker build -t voicestreamai .
After getting your VAD token (see next sections) run:
sudo docker volume create huggingface_models
sudo docker run --gpus all -p 8765:8765 -v huggingface_models:/root/.cache/huggingface -e PYANNOTE_AUTH_TOKEN='VAD_TOKEN_HERE' voicestreamai
The "volume" stuff will allow you not to re-download the huggingface models each time you re-run the container. If you don't need this, just use:
sudo docker run --gpus all -p 8765:8765 -e PYANNOTE_AUTH_TOKEN='VAD_TOKEN_HERE' voicestreamai
Normal, Manual Installation
To set up the VoiceStreamAI server, you need Python 3.8 or later and the following packages:
transformerspyannote.corepyannote.audiowebsocketsasynciosentence-transformersfaster-whisper
Install these packages using pip:
pip install -r requirements.txt
For the client-side, you need a modern web browser with JavaScript support.
For agents
This page has a .md twin and JSON over the API.