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Building stuff with machine learning and natural language processing.
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A Timeline of Large Transformer Models for Speech
Since the arrival of the Transformer architecture in 2017, transformer-based models have made their way into all domains of machine learning. As Andrej Karpathy of Tesla noted, they are quickly becoming the go-to architecture as either a strong baseline o
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2022 Trends for Speech Processing
It's an exciting time to be working in speech processing. The field has seen a lot of transformations and changes just over the past few years. In this post, I'd like to share my perspective on how I see the field moving forward in 2022, based on what we
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HuBERT: How to Apply BERT to Speech, Visually Explained
Research in self-supervised learning for speech has been accelerating since the original wav2vec model released by Facebook AI in 2019. HuBERT is one of the latest of such models, with an open-source implementation already available in HuggingFace's Trans
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An Illustrated Tour of Wav2vec 2.0
Transformer-based neural networks have been revolutionizing the natural language processing field, but are only starting to become popular in the speech processing community. Wav2vec 2.0 is set to change that. Its architecture is based on the Transformer'
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Simple Audio Augmentation with PyTorch
No matter if you are training a model for automatic speech recognition or something more esoteric like recognizing birds from sound, you could benefit a lot from audio data augmentation. The idea is simple: by applying random transformations to your train
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