# VibeVoice ASR BitNet

URL: https://interfaze.ai/models/microsoftvibevoice-asr-bitnet

VibeVoice ASR BitNet by microsoft, a automatic-speech-recognition model with speech-to-text, multimodal capabilities. Understand and compare speech-to-text, multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | VibeVoice ASR BitNet | Interfaze |
| --- | --- | --- |
| Input Modalities | audio, text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 51 partial | 162+ |
| Native Speech-to-Text | Yes | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 64K | 1M |
| Tool Calling | No | Tool calling supported + built in browser, code execution and web search |

### Speech-to-Text Capabilities

| Feature | VibeVoice ASR BitNet | Interfaze |
| --- | --- | --- |
| Time Stamping | No | Yes |
| Speaker Diarization | No | Yes |
| Long Audio Processing | Partial | Yes |
| Audio Processing Speed | 1hr of audio more than 5mins (varies depending on provider and quality output) | 1hr of audio under 30 seconds |
| Intent Recognition | No | Yes |
| Sentiment Analysis | No | Yes |
| Prompting Correction | No | Yes |

### Scaling

| Feature | VibeVoice ASR BitNet | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/microsoft/VibeVoice-ASR-BitNet)

## VibeVoice-ASR-BitNet

[![GitHub](https://img.shields.io/badge/GitHub-Repo-black?logo=github)](https://github.com/microsoft/VibeASR.cpp) [![Technical Report](https://img.shields.io/badge/arXiv-2607.21075-b31b1b?logo=arxiv)](https://arxiv.org/abs/2607.21075) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

**VibeVoice-ASR-BitNet** is a compressed variant of [VibeVoice-ASR](https://huggingface.co/microsoft/VibeVoice-ASR) optimized for **real-time inference on edge CPUs** — no GPU required. Through heterogeneous quantization, the model is compressed from 4.62 GB to **1.58 GB** while achieving **1.6–2.3× faster** inference than Whisper.cpp with real-time capability (RTF < 1) on as few as 3 CPU threads.

➡️ **Code:** [microsoft/VibeASR.cpp](https://github.com/microsoft/VibeASR.cpp) ➡️ **Report:** [VibeVoice-ASR-BitNet Technical Report](https://arxiv.org/abs/2607.21075) ➡️ **Base Model:** [microsoft/VibeVoice-ASR](https://huggingface.co/microsoft/VibeVoice-ASR)

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## 🔥 Key Features

-   **⚡ Real-time on CPU** — RTF < 1 with 3+ threads on commodity x86 (AVX2) and ARM (NEON) hardware
-   **📦 Compact** — 1.58 GB total (2.9× compression from FP16), fits in edge device memory
-   **🌍 Multilingual** — English, Chinese, French, Italian, Korean, Portuguese, Vietnamese, and more
-   **🔧 Custom SIMD Kernels** — Fused operators within the ggml framework for both ARM and x86 platforms

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## Quantization Strategy

| Component | FP16 | Quantized | Method | Compression |
| --- | --- | --- | --- | --- |
| VAE Tokenizer | 1.31 GB | 0.65 GB | I8\_S | 2.0× |
| LM Decoder | 3.32 GB | 0.92 GB | I2\_S + Q6\_K | 3.6× |
| Total | 4.62 GB | 1.58 GB | — | 2.9× |

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## Evaluation

### Inference Speed

| Threads | 1 | 2 | 3 | 4 | 6 | 8 |
| --- | --- | --- | --- | --- | --- | --- |
| RTF | 1.98 | 1.08 | 0.77 | 0.63 | 0.49 | 0.42 |
| vs. Whisper.cpp | 2.28× | 2.12× | 1.86× | 1.86× | 1.71× | 1.55× |

> Benchmarked on AMD EPYC 7V13 (AVX2+FMA) with 20s audio. **Bold** = RTF < 1 (real-time).

### Accuracy (WER%)

| Benchmark | VibeVoice-ASR-7B | VibeVoice-ASR-BitNet | Parakeet | Whisper | SenseVoice | FunASR |
| --- | --- | --- | --- | --- | --- | --- |
| MLC-EN | 7.82 | 8.25 | 8.40 | 13.57 | 12.39 | 11.36 |
| MLC-FR | 16.03 | 17.41 | — | — | — | — |
| MLC-IT | 15.67 | 17.23 | — | — | — | — |
| MLC-KO | 9.83 | 11.15 | — | — | — | — |
| MLC-PT | 22.41 | 24.87 | — | — | — | — |
| MLC-VI | 20.15 | 22.38 | — | — | — | — |
| AISHELL4 | 19.83 | 27.45 | — | — | 22.52 | 20.41 |
| AMI-ihm | 17.42 | 21.36 | 21.92 | 27.07 | 30.81 | 32.07 |
| AMI-sdm | 24.18 | 25.87 | 26.33 | 36.92 | 48.11 | 40.17 |
| AliMeeting | 36.21 | 40.58 | — | — | 38.75 | 39.27 |
| Fleurs-en | 4.73 | 5.21 | 4.09 | 3.99 | 6.84 | 4.93 |
| Fleurs-zh | 7.92 | 8.35 | — | — | 5.56 | 7.00 |
| Libri-clean | 2.17 | 2.41 | 1.49 | 1.98 | 2.78 | 1.58 |
| Libri-other | 5.84 | 6.27 | 3.13 | 3.60 | 6.81 | 4.01 |
| VoxPopuli | 4.92 | 5.18 | 5.26 | 7.19 | 8.63 | 6.46 |

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## Model Files

| File | Size | Description |
| --- | --- | --- |
| vibeasr-vae-encoder-i8\_s.gguf | 0.65 GB | VAE tokenizer, I8\_S quantized (ready to use) |
| vibeasr-lm-i2\_s-embed-q6\_k.gguf | 0.92 GB | LM decoder, I2\_S quantized (ready to use) |
| model-\*.safetensors | 10.7 GB | Original SafeTensors (for conversion) |

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## License

This project is licensed under the MIT License.

## Contact

This project was conducted by members of Microsoft Research. If you have suggestions, questions, or observe unexpected behavior, please contact us at [VibeVoice@microsoft.com](mailto:VibeVoice@microsoft.com).

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)