v1.0.0 – Initial Release

v1.0.0 – Initial Release

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:

  pip install wavlmmsdd