Add VibeVoice-ASR
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# VibeVoice-ASR: Long-Form Rich Transcription with User Prompts
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**VibeVoice-ASR** is the latest addition to the **VibeVoice** family. While the original VibeVoice / VibeVoice-Realtime focused on expressive TTS, **VibeVoice-ASR** focuses on understanding long-form speech with high precision and rich metadata.
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It is a unified speech-to-text model designed to handle **1-hour long-form audio** in a single pass, generating structured transcriptions containing **Who (Speaker), When (Timestamps), and What (Content)**, with support for **User-Customized Context**.
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## 🔥 Key Features
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- **🕒 60-min Single-Pass Processing**:
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Unlike conventional ASR models that slice audio into short chunks (often losing global context), VibeVoice ASR accepts up to **60 minutes** of continuous audio input within 64K length. This ensures consistent speaker tracking and semantic coherence across the entire hour.
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- **👤 Optional Context Injection**:
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Users can provide customized context (e.g., specific names, technical terms, or background info) to guide the recognition process, significantly improving accuracy on domain-specific content.
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- **📝 Rich Transcription (Who, When, What)**:
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The model performs ASR, Diarization, and Timestamping simultaneously. The output is a structured sequence indicating *who* said *what* at *which time*.
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## 🏗️ Model Architecture
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<p align="center">
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<img src="../Figures/VibeVoice_ASR_archi.png" alt="VibeVoice ASR Architecture" width="80%">
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</p>
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## Installation
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We recommend to use NVIDIA Deep Learning Container to manage the CUDA environment.
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1. Launch docker
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```bash
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# NVIDIA PyTorch Container 24.07 ~ 25.12 verified.
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# Previous versions are also compatible.
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sudo docker run --privileged --net=host --ipc=host --ulimit memlock=-1:-1 --ulimit stack=-1:-1 --gpus all --rm -it nvcr.io/nvidia/pytorch:25.12-py3
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## If flash attention is not included in your docker environment, you need to install it manually
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## Refer to https://github.com/Dao-AILab/flash-attention for installation instructions
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# pip install flash-attn --no-build-isolation
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```
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2. Install from github
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```bash
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git clone https://github.com/microsoft/VibeVoice.git
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cd VibeVoice
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pip install -e .[asr]
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```
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## Usages
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### Usage 1: Launch Gradio demo
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```bash
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apt update && apt install ffmpeg -y # for demo
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python demo/vibevoice_asr_gradio_demo.py --model_path microsoft/VibeVoice-ASR --share
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```
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### Usage 2: Inference from files directly
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```bash
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python demo/vibevoice_asr_inference_from_file.py --model_path microsoft/VibeVoice-ASR --audio_files [add a audio path here]
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```
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## 📄 License
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This project is licensed under the [MIT License](../LICENSE).
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