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

Please follow the instructions listed below to get started.

The setup auto-streams the model assets (expect a multi-GB download).

Once launched, the setup wizard will detect your specs to configure the model for maximum efficiency.

🧮 Hash-code: 5c7ae5b158ab6f8aa79a2773c8673ffe • 📆 2026-06-24 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: enough space for background apps and OS overhead Disk Space: 100 GB for multi-modal model vision components Graphics: CUDA Compute Capability 8.0+ required for flash-attention

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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