VibeVoice-ASR For Low VRAM (6GB/8GB) Easy Build

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

Please follow the instructions listed below to get started.

Then, run the build command to initialize the Docker container.

🧩 Hash sum → b634fc8f413e88ec6f676fdd1d5c803c — Update date: 2026-06-27



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading

The VibeVoice-ASR model delivers state‑of‑the‑art speech recognition with exceptional accuracy across a wide range of accents and domains. Built on a transformer‑based architecture, it supports over 30 languages and adapts seamlessly to both noisy and clean audio environments. Its low‑latency pipeline enables real‑time transcription with end‑to‑end processing times under 50 ms per utterance. Integrated with a proprietary language‑model fine‑tuning layer, the system maintains high contextual coherence while keeping computational requirements modest. Developers can easily integrate the model via a unified API that provides streaming support, confidence scores, and customizable vocabularies. The model has been benchmarked against leading open‑source alternatives, consistently achieving superior Word Error Rate (WER) scores in multilingual scenarios.

ParameterVibeVoice-ASRCompeting Model
Supported Languages30+15
Average WER (%)<812
Real‑time Latency (ms)<5070
API StreamingYesYes
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