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Table of Contents

• Architecture
• Features
• Reports
• Installation
• Usage
• File Structure
• Version Control System
• Upcoming
• Documentations
• License
• Links
• Team
• Contact
• Citation

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Architecture

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Features

Key Capabilities

• WavLM-Based Embeddings: Leverages WavLM to generate high-quality speech representations, improving speaker identification.
• Multi-Scale Diarization (MSDD): Applies multi-scale inference for precise speaker segmentation, even with overlapping speech.
• Scalable Pipeline: Modular design allows easy integration and customization for various diarization tasks or research experiments.

Models

• WavLM-Base-Plus for Speaker Verification
• WavLM-Base for Speaker Verification
• Nvidia NeMo Diarization MSDD Telephonic

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Reports

Benchmark

Below is an example benchmark comparing metrics for different models on a The Ami Corpus dataset. These models use Multi-Scale Diarization Decoder (MSDD) with different embedding backbones: TitaNet and WavLM.

INFO

• Experiments were conducted on an NVIDIA GeForce RTX 3060 using CUDA 12.6 (Driver Version 560.35.03).
• We randomly selected 10 samples from the AMI Corpus (Array1-01.tar.gz), each 60 seconds long.
• MSDD (Titanet) and MSDD (WavLMMSDD) refer to using TitaNet vs. WavLM as the speaker-embedding model.
• For a detailed Jupyter notebook demonstrating how this benchmark was performed, see:
  > notebook/benchmark.ipynb

Model	DER	FA	MISS	CER	Duration(sec)
MSDD + TitaNet	0.9963	0.0010	0.9946	0.0015	644
MSDD + WavLMBasePlus	0.9961	0.0010	0.9946	0.0016	18

• DER: Diarization Error Rate
• FA: False Alarm Rate
• MISS: Missed Detection Rate
• CER: Confusion Error Rate

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Installation

The Python Package Index (PyPI)

pip insall wavlmmsdd

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Usage

# Standard library imports
from typing import Annotated

# Local imports
from wavlmmsdd.audio.diarization.diarize import Diarizer
from wavlmmsdd.audio.feature.embedding import WavLMSV
from wavlmmsdd.audio.preprocess.resample import Resample
from wavlmmsdd.audio.preprocess.convert import Convert
from wavlmmsdd.audio.utils.utils import Build

def main() -> Annotated[None, "No return value"]:
    """
    Demonstrate the audio processing workflow from a WAV file
    to a diarization result.

    This function performs the following steps:
    1. Resamples the audio to 16 kHz.
    2. Converts the audio to mono.
    3. Builds a manifest file.
    4. Obtains embeddings.
    5. Runs diarization.

    Returns
    -------
    None

    Examples
    --------
    >>> main()
    No direct output is produced, but the specified audio file is
    processed and the results are saved or printed as logs.
    """
    
    # Audio Path
    audio_path = "audio.wav"

    # Resample to 16000 Khz
    resampler = Resample(audio_file=audio_path)
    wave_16k, sr_16k = resampler.to_16k()

    # Convert to Mono
    converter = Convert(waveform=wave_16k, sample_rate=sr_16k)
    converter.to_mono()
    saved_path = converter.save()

    # Build Manifest File
    builder = Build(saved_path)
    manifest_path = builder.manifest()

    # Embedding
    embedder = WavLMSV()

    # Diarization
    diarizer = Diarizer(embedding=embedder, manifest_path=manifest_path)
    diarizer.run()

if __name__ == "__main__":
    main()

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File Structure

.
├── .data
│   └── example
│       └── ae.wav
├── .docs
│   ├── documentation
│   │   ├── CONTRIBUTING.md
│   │   └── RESOURCES.md
│   └── img
│       └── architecture
│           ├── WavLMMSDDArchitecture.drawio
│           └── WavLMMSDDArchitecture.gif
├── environment.yaml
├── .github
│   ├── CODEOWNERS
│   └── workflows
│       └── pypi.yaml
├── .gitignore
├── LICENSE
├── MANIFEST.in
├── notebook
│   └── benchmark.ipynb
├── pyproject.toml
├── README.md
├── requirements.txt
└── src
    └── wavlmmsdd
        ├── audio
        │   ├── config
        │   │   ├── config.yaml
        │   │   ├── diar_infer_telephonic.yaml
        │   │   └── schema.py
        │   ├── diarization
        │   │   └── diarize.py
        │   ├── feature
        │   │   └── embedding.py
        │   ├── preprocess
        │   │   ├── convert.py
        │   │   └── resample.py
        │   └── utils
        │       └── utils.py
        └── main.py

18 directories, 24 files

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Version Control System

Releases

• v0.1.0 .zip
• v0.1.0 .tar.gz

Branches

• main
• develop

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Upcoming

• WavLM Large: Integrate the WavLM Large model.

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Documentations

• RESOURCES
• CONTRIBUTING

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Licence

• LICENSE

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Links

• Github
• Website
• Linkedin

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Team

• Bunyamin Ergen

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Contact

• Mail

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Citation

@software{       WavLMMSDD,
  author       = {Bunyamin Ergen},
  title        = {{WavLMMSDD}},
  year         = {2025},
  month        = {02},
  url          = {https://github.com/bunyaminergen/WavLMMSDD},
  version      = {v0.1.0},
}

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