Automatic Speech Recognition
Safetensors
GGUF
VibeVoice
ggml
ASR
quantization
cpu-inference
bitnet
multilingual
conversational
Instructions to use microsoft/VibeVoice-ASR-BitNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use microsoft/VibeVoice-ASR-BitNet with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="microsoft/VibeVoice-ASR-BitNet", filename="vibeasr-lm-i2_s-embed-q6_k.gguf", )
llm.create_chat_completion( messages = "\"sample1.flac\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use microsoft/VibeVoice-ASR-BitNet with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf microsoft/VibeVoice-ASR-BitNet:Q6_K # Run inference directly in the terminal: llama cli -hf microsoft/VibeVoice-ASR-BitNet:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf microsoft/VibeVoice-ASR-BitNet:Q6_K # Run inference directly in the terminal: llama cli -hf microsoft/VibeVoice-ASR-BitNet:Q6_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf microsoft/VibeVoice-ASR-BitNet:Q6_K # Run inference directly in the terminal: ./llama-cli -hf microsoft/VibeVoice-ASR-BitNet:Q6_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf microsoft/VibeVoice-ASR-BitNet:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf microsoft/VibeVoice-ASR-BitNet:Q6_K
Use Docker
docker model run hf.co/microsoft/VibeVoice-ASR-BitNet:Q6_K
- LM Studio
- Jan
- Ollama
How to use microsoft/VibeVoice-ASR-BitNet with Ollama:
ollama run hf.co/microsoft/VibeVoice-ASR-BitNet:Q6_K
- Unsloth Studio
How to use microsoft/VibeVoice-ASR-BitNet with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for microsoft/VibeVoice-ASR-BitNet to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for microsoft/VibeVoice-ASR-BitNet to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for microsoft/VibeVoice-ASR-BitNet to start chatting
- Atomic Chat new
- Docker Model Runner
How to use microsoft/VibeVoice-ASR-BitNet with Docker Model Runner:
docker model run hf.co/microsoft/VibeVoice-ASR-BitNet:Q6_K
- Lemonade
How to use microsoft/VibeVoice-ASR-BitNet with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull microsoft/VibeVoice-ASR-BitNet:Q6_K
Run and chat with the model
lemonade run user.VibeVoice-ASR-BitNet-Q6_K
List all available models
lemonade list
Initial model release
Browse files- .gitattributes +5 -35
- README.md +124 -0
- config.json +148 -0
- figures/report_overview.png +3 -0
- generation_config.json +7 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +0 -0
- tokenizer.json +0 -0
- tokenizer_config.json +40 -0
- vibeasr-lm-i2_s-embed-q6_k.gguf +3 -0
- vibeasr-vae-encoder-i8_s.gguf +3 -0
- vocab.json +0 -0
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README.md
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---
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| 2 |
+
language:
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- en
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- zh
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- fr
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- it
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- ko
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- pt
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- vi
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license: mit
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pipeline_tag: automatic-speech-recognition
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tags:
|
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- ASR
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| 14 |
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- quantization
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| 15 |
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- cpu-inference
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- gguf
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- bitnet
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- multilingual
|
| 19 |
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library_name: ggml
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| 20 |
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---
|
| 21 |
+
|
| 22 |
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## VibeVoice-ASR-BitNet
|
| 23 |
+
|
| 24 |
+
[](https://github.com/microsoft/VibeASR.cpp)
|
| 25 |
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[](https://arxiv.org/abs/2607.21075)
|
| 26 |
+
[](https://opensource.org/licenses/MIT)
|
| 27 |
+
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+
**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.
|
| 29 |
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|
| 30 |
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➡️ **Code:** [microsoft/VibeASR.cpp](https://github.com/microsoft/VibeASR.cpp)<br>
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| 31 |
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➡️ **Report:** [VibeVoice-ASR-BitNet Technical Report](https://arxiv.org/abs/2607.21075)<br>
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| 32 |
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➡️ **Base Model:** [microsoft/VibeVoice-ASR](https://huggingface.co/microsoft/VibeVoice-ASR)<br>
|
| 33 |
+
|
| 34 |
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<p align="center">
|
| 35 |
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<img src="figures/report_overview.png" width="90%"/>
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| 36 |
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</p>
|
| 37 |
+
|
| 38 |
+
---
|
| 39 |
+
|
| 40 |
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## 🔥 Key Features
|
| 41 |
+
|
| 42 |
+
- **⚡ Real-time on CPU** — RTF < 1 with 3+ threads on commodity x86 (AVX2) and ARM (NEON) hardware
|
| 43 |
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- **📦 Compact** — 1.58 GB total (2.9× compression from FP16), fits in edge device memory
|
| 44 |
+
- **🌍 Multilingual** — English, Chinese, French, Italian, Korean, Portuguese, Vietnamese, and more
|
| 45 |
+
- **🔧 Custom SIMD Kernels** — Fused operators within the ggml framework for both ARM and x86 platforms
|
| 46 |
+
|
| 47 |
+
---
|
| 48 |
+
|
| 49 |
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## Quantization Strategy
|
| 50 |
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| 51 |
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<div align="center">
|
| 52 |
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| 53 |
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| Component | FP16 | Quantized | Method | Compression |
|
| 54 |
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|:-:|:-:|:-:|:-:|:-:|
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| 55 |
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| VAE Tokenizer | 1.31 GB | 0.65 GB | I8\_S | 2.0× |
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| 56 |
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| LM Decoder | 3.32 GB | 0.92 GB | I2\_S + Q6\_K | 3.6× |
|
| 57 |
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| **Total** | **4.62 GB** | **1.58 GB** | — | **2.9×** |
|
| 58 |
+
|
| 59 |
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</div>
|
| 60 |
+
|
| 61 |
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---
|
| 62 |
+
|
| 63 |
+
## Evaluation
|
| 64 |
+
|
| 65 |
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### Inference Speed
|
| 66 |
+
|
| 67 |
+
<div align="center">
|
| 68 |
+
|
| 69 |
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| Threads | 1 | 2 | 3 | 4 | 6 | 8 |
|
| 70 |
+
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
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| 71 |
+
| RTF | 1.98 | 1.08 | **0.77** | **0.63** | **0.49** | **0.42** |
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| 72 |
+
| vs. Whisper.cpp | 2.28× | 2.12× | 1.86× | 1.86× | 1.71× | 1.55× |
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| 73 |
+
|
| 74 |
+
</div>
|
| 75 |
+
|
| 76 |
+
> Benchmarked on AMD EPYC 7V13 (AVX2+FMA) with 20s audio. **Bold** = RTF < 1 (real-time).
|
| 77 |
+
|
| 78 |
+
### Accuracy (WER%)
|
| 79 |
+
|
| 80 |
+
<div align="center">
|
| 81 |
+
|
| 82 |
+
| Benchmark | VibeVoice-ASR-7B | VibeVoice-ASR-BitNet | Parakeet | Whisper | SenseVoice | FunASR |
|
| 83 |
+
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
|
| 84 |
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| MLC-EN | 7.82 | **8.25** | 8.40 | 13.57 | 12.39 | 11.36 |
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| 85 |
+
| MLC-FR | 16.03 | 17.41 | — | — | — | — |
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| 86 |
+
| MLC-IT | 15.67 | 17.23 | — | — | — | — |
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| 87 |
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| MLC-KO | 9.83 | 11.15 | — | — | — | — |
|
| 88 |
+
| MLC-PT | 22.41 | 24.87 | — | — | — | — |
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| 89 |
+
| MLC-VI | 20.15 | 22.38 | — | — | — | — |
|
| 90 |
+
| AISHELL4 | 19.83 | 27.45 | — | — | 22.52 | **20.41** |
|
| 91 |
+
| AMI-ihm | 17.42 | **21.36** | 21.92 | 27.07 | 30.81 | 32.07 |
|
| 92 |
+
| AMI-sdm | 24.18 | **25.87** | 26.33 | 36.92 | 48.11 | 40.17 |
|
| 93 |
+
| AliMeeting | 36.21 | 40.58 | — | — | **38.75** | 39.27 |
|
| 94 |
+
| Fleurs-en | 4.73 | 5.21 | 4.09 | **3.99** | 6.84 | 4.93 |
|
| 95 |
+
| Fleurs-zh | 7.92 | 8.35 | — | — | **5.56** | 7.00 |
|
| 96 |
+
| Libri-clean | 2.17 | 2.41 | **1.49** | 1.98 | 2.78 | 1.58 |
|
| 97 |
+
| Libri-other | 5.84 | 6.27 | **3.13** | 3.60 | 6.81 | 4.01 |
|
| 98 |
+
| VoxPopuli | 4.92 | **5.18** | 5.26 | 7.19 | 8.63 | 6.46 |
|
| 99 |
+
|
| 100 |
+
</div>
|
| 101 |
+
|
| 102 |
+
---
|
| 103 |
+
|
| 104 |
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## Model Files
|
| 105 |
+
|
| 106 |
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<div align="center">
|
| 107 |
+
|
| 108 |
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| File | Size | Description |
|
| 109 |
+
|:-:|:-:|:-:|
|
| 110 |
+
| `vibeasr-vae-encoder-i8_s.gguf` | 0.65 GB | VAE tokenizer, I8\_S quantized (ready to use) |
|
| 111 |
+
| `vibeasr-lm-i2_s-embed-q6_k.gguf` | 0.92 GB | LM decoder, I2\_S quantized (ready to use) |
|
| 112 |
+
| `model-*.safetensors` | 10.7 GB | Original SafeTensors (for conversion) |
|
| 113 |
+
|
| 114 |
+
</div>
|
| 115 |
+
|
| 116 |
+
---
|
| 117 |
+
|
| 118 |
+
## License
|
| 119 |
+
|
| 120 |
+
This project is licensed under the MIT License.
|
| 121 |
+
|
| 122 |
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## Contact
|
| 123 |
+
|
| 124 |
+
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.
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