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The fastest way to get this model running locally is via Docker.

Please follow the instructions listed below to get started.

The setup auto-downloads all needed files (several GBs).

During setup, the script automatically determines and applies the best settings tailored to your machine.

🔧 Digest: c7665a4235ef9594138f7610e9155b37 • 🕒 Updated: 2026-06-23 Verify CPU: multi-threading optimized for fast prompt processing RAM: enough space for background apps and OS overhead Disk Space: 100 GB for multi-modal model vision components GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

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.

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