senselab.audio.tasks.speaker_embeddings
Speech to text evaluation
Overview
Speaker embeddings are fixed-dimensional vector representations that capture the unique characteristics of a speaker's voice, allowing for tasks such as speaker identification, verification, and diarization.
Speaker embedding extraction is a crucial task in speaker recognition systems. It involves transforming variable-length audio signals into fixed-size vector representations that encapsulate speaker-specific information while being robust to variations in speech content, background noise, and recording conditions.
Model Architecture:
The default model used in this module (speechbrain/spkrec-ecapa-voxceleb) is based on the ECAPA-TDNN architecture, which has shown strong performance across various speaker recognition tasks. Other supported models include ResNet TDNN (speechbrain/spkrec-resnet-voxceleb) and xvector (speechbrain/spkrec-xvect-voxceleb).
Note: Performance can vary significantly depending on the specific dataset, task, and evaluation protocol used. Always refer to the most recent literature for up-to-date benchmarks.