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README.md

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+# 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`.