Overview
This is the first official release of **WavLMMSDD**, combining Microsoft’s **WavLM** (a robust speech representation model) with Nvidia’s **MSDD** (Multi-Scale Diarization Decoder) to deliver accurate multi-speaker diarization. By leveraging WavLM’s feature extraction and MSDD’s advanced segmentation and clustering, this project aims to handle even noisy or overlapping speech scenarios with greater precision.
Key Features
- **WavLM-Based Embeddings**: High-quality, robust embeddings that enhance speaker identification.
- **MSDD Integration**: Utilizes multi-scale inference for precise speaker diarization, including overlapping speech segments.
- **Telephony Model Support**: Incorporates `diar_msdd_telephonic` (Nvidia NeMo), making it ideal for call-center and telephonic environments.
Use Cases
- **Call Centers**: Efficiently track speakers in busy or noisy conversations.
- **Meeting Transcripts**: Clearly segment overlapping voices in multi-participant discussions.
- **Voice Applications**: Provides a strong foundation for any application that requires accurate speaker segmentation in diverse audio conditions.
Getting Started
- **Installation**: You can install via PyPI using:
bash
pip install wavlmmsdd