Instructions to use lemuriandezapada/VibeVoice-ASR-Streaming-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lemuriandezapada/VibeVoice-ASR-Streaming-AWQ-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lemuriandezapada/VibeVoice-ASR-Streaming-AWQ-4bit")# Load model directly from transformers import AutoProcessor, VibeVoiceForASRStreamingTraining processor = AutoProcessor.from_pretrained("lemuriandezapada/VibeVoice-ASR-Streaming-AWQ-4bit") model = VibeVoiceForASRStreamingTraining.from_pretrained("lemuriandezapada/VibeVoice-ASR-Streaming-AWQ-4bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array
VibeVoice-ASR-Streaming-7B
AWQ 4-bit quantization
This repository is a Gaia-produced 4-bit AWQ quantization of
microsoft/VibeVoice-ASR-Streaming-7B.
The Qwen2 language-model decoder is quantized with AutoAWQ using 4-bit,
group-size 128, GEMM-compatible weights. The VibeVoice acoustic and semantic
tokenizers/connectors remain BF16 to preserve multimodal streaming ASR
behavior. The original model is a BF16 checkpoint reported as ~9B parameters
despite the repository's 7B name.
The model requires the VibeVoice vLLM plugin from Microsoft's
VibeVoice repository and a vLLM
build with AWQ support. The quantized decoder is in decoder-awq/; the root
configuration preserves the streaming architecture and points the plugin to it.
Quantization used 128 ASR/transcription-oriented calibration prompts. This is
weight-only quantization; speech-tokenizer weights were not quantized.
VibeVoice-ASR-Streaming is a unified streaming ASR model that transcribes Who (Speaker) said What (Content), with support for Customized Hotwords and 10 languages.
➡️ Code: microsoft/VibeVoice
➡️ Demo: VibeVoice-ASR-Streaming
🔥 Key Features
📝 Streaming Speaker-Attributed Transcription: Continuously transcribes who said what as speech arrives.
👤 Customized Hotwords: Users can provide customized hotwords, such as names and technical terms, to improve recognition of domain-specific content.
🌍 Multilingual Support: It supports Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish.
Technical Report
📄 VibeVoice-ASR-Streaming Technical Report
Evaluation
Installation and Usage
Please refer to the GitHub repository.
License
This project is licensed under the MIT License.
Contact
This project was conducted by members of Microsoft Research. We welcome feedback and collaboration from our audience. If you have suggestions, questions, or observe unexpected/offensive behavior in our technology, please contact us at VibeVoice@microsoft.com. If the team receives reports of undesired behavior or identifies issues independently, we will update this repository with appropriate mitigations.
- Downloads last month
- -