Whisper Speaker Identification (WSI)

Whisper Speaker Identification (WSI) is a state-of-the-art speaker identification model designed for multilingual scenarios.The WSI model adapts OpenAI's Whisper encoder and fine-tunes it with a projection head using triplet loss-based metric learning. This approach enhances its ability to generate discriminative, language-agnostic speaker embeddings.WSI demonstrates state-of-the-art performance on multilingual datasets, achieving lower Equal Error Rates (EER) and higher F1 Scores compared to models such as pyannote/wespeaker-voxceleb-resnet34-LM and speechbrain/spkrec-ecapa-voxceleb.

Installation

Install the whisper_speaker_id library via pip:

pip install whisper_speaker_id 

Usage

The whisper_speaker_id library provides a simple interface to use the WSI model for embedding generation and speaker similarity tasks.

Download the model from Huggingface

WSI Model on Hugging Face

Generate Speaker Embeddings

from whisper-speaker-id import load_model, process_single_audio
model, feature_extractor = load_model(
    model_path_or_repo_id="emon-j/WSI",
    filename="wsi.pth"
)
# Process an audio file
embedding = process_single_audio(model, feature_extractor, "path/to/audio.wav")
print("Speaker Embedding:", embedding)

Calculate Similarity Between Two Audio Files

from whisper_speaker_id import load_model, process_audio_pair

model, feature_extractor = load_model(
    model_path_or_repo_id="emon-j/WSI",
    filename="wsi.pth"
)

# Compute similarity between two audio files
similarity = process_audio_pair(
    model, feature_extractor, "path/to/audio1.wav", "path/to/audio2.wav"
)
print("Similarity Score:", similarity)

Cite This Work

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License

This project is licensed under the CC BY-NC-SA 4.0 License.