cvqluu/Factorized-TDNN
PyTorch implementation of the Factorized TDNN (TDNN-F) from "Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks" and Kaldi
This project offers an implementation of Factorized Time Delay Neural Network (TDNN-F) layers and a full FTDNN x-vector architecture, crucial for developing advanced speech and speaker recognition systems. It takes sequences of audio features as input and outputs processed feature representations, which are then used for tasks like identifying speakers. This is designed for machine learning engineers and researchers building robust speech technologies.
149 stars. No commits in the last 6 months.
Use this if you are developing deep learning models for speaker recognition or speech processing and need highly efficient, state-of-the-art neural network components that manage model complexity effectively.
Not ideal if you are looking for a complete, out-of-the-box speaker recognition solution or if your primary interest is in general natural language processing tasks not focused on audio.
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149
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34
Language
Python
License
MIT
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Last pushed
Jan 06, 2020
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