How to Launch VibeVoice-ASR-HF Locally (No Cloud)

Deploying this model locally is quickest when done via Docker.

Please follow the instructions listed below to get started. The setup auto-downloads all needed files (several GBs).

The smart installation system will instantly find the perfect configuration for your specific hardware.

📄 Hash Value: 86074eaceceea5dc878a26fd8b978f8e | 📆 Update: 2026-06-22



  • CPU: multi-threading optimized for fast prompt processing
  • RAM: fast 5600MHz+ required to avoid memory bottlenecks
  • Disk Space: at least 100 GB for multiple local LLM variants
  • GPU: modern architecture (Ada Lovelace / Ampere minimum)

The VibeVoice-ASR-HF leverages a transformer-based architecture optimized for low‑latency speech recognition in edge environments. It supports over 100 languages and dialects, delivering real-time transcription with an average word error rate below 5 %. The model achieves sub‑200 ms inference time on standard CPUs, making it suitable for live captioning and voice‑controlled applications. Integrated with popular frameworks through a lightweight API, developers can deploy the model without extensive hardware resources. A comparison of key metrics is provided below.

Parameter Value
Model size ≈ 150 M parameters
Supported languages 100+ languages & dialects
Average latency <200 ms on CPU
Word error rate <5 %
API compatibility REST & gRPC
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