| # Pyannote | |
| Run **Pyannote** optimized for **Qualcomm SnapDragon device's NPU** with [nexaSDK](https://sdk.nexa.ai). | |
| ## Quickstart | |
| 1. **Install NexaSDK** and create a free account at [sdk.nexa.ai](https://sdk.nexa.ai) | |
| 2. **Activate your device** with your access token: | |
| ```bash | |
| nexa config set license '<access_token>' | |
| ``` | |
| 3. Run the model on Qualcomm NPU in one line: | |
| ```bash | |
| nexa infer NexaAI/Pyannote-NPU | |
| ``` | |
| - Input: Enter input audio path, | |
| - Output: Returns speech diarization results, or report error if any required input cannot be found | |
| ## Model Description | |
| **pyannote-audio (Community Version)** is an open-source **speech diarization** model designed for accurate speaker segmentation and labeling in audio streams. | |
| Developed by the **Pyannote community**, it combines **audio processing**, **speaker embedding**, and **clustering** into a unified framework, enabling robust speech segmentation on local machines without cloud dependency. | |
| ## Features | |
| - 🔊 **End-to-End Diarization Pipeline** — Automatically detects and labels who spoke when in an audio file. | |
| - ⚡ **Lightweight & Efficient** — Optimized for real-time or batch processing on consumer hardware and GPUs. | |
| - 🧠 **Speaker Embedding & Clustering** — Extracts rich speaker representations and groups them for identity separation. | |
| - 🔧 **Customizable & Modular** — Easily integrates with PyTorch pipelines or modified components for research and prototyping. | |
| - 🌍 **Community-Driven & Transparent** — Fully open and maintained by an active community of speech researchers and developers. | |
| ## Use Cases | |
| - **Meeting Transcription**: Segment conversations by speaker for clearer transcripts. | |
| - **Broadcast and Podcast Analysis**: Attribute voices and structure long-form audio content. | |
| - **Call Center Analytics**: Separate agent and customer segments for interaction insights. | |
| - **Research**: Test diarization algorithms or contribute new speaker models. | |
| - **Voice Dataset Preparation**: Preprocess large audio datasets for training ASR or emotion recognition systems. | |
| ## Inputs and Outputs | |
| **Input** | |
| - Audio file or stream | |
| **Output** | |
| - Speaker-labeled time segments | |
| ## License | |
| This repo is licensed under the **Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0)** license, which allows use, sharing, and modification only for non-commercial purposes with proper attribution. | |
| All NPU-related models, runtimes, and code in this project are protected under this non-commercial license and cannot be used in any commercial or revenue-generating applications. | |
| Commercial licensing or enterprise usage requires a separate agreement. | |
| For inquiries, please contact `dev@nexa.ai`. |