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Optimum Graphcore is a new open-source library and toolkit that enables developers to access IPU-optimized models certified by Hugging Face. It is an extension of Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on Graphcore’s IPUs - a completely new kind of massively parallel processor to accelerate machine intelligence. Learn more about how to take train Transformer models faster with IPUs at hf.co/hardware/graphcore.

Through HuggingFace Optimum, Graphcore released ready-to-use IPU-trained model checkpoints and IPU configuration files to make it easy to train models with maximum efficiency in the IPU. Optimum shortens the development lifecycle of your AI models by letting you plug-and-play any public dataset and allows a seamless integration to our State-of-the-art hardware giving you a quicker time-to-value for your AI project.

Model description

HUBERT (Hidden-Unit BERT) is a BERT-based model for self-supervised speech representation learning approach that relies on predicting K-means cluster assignments of masked segments of continuous output. This approach forces the model to learn a combined acoustic and language model over the continuous inputs by applying the prediction loss over the masked region.

Paper link : Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unit

Intended uses & limitations

This model contains just the IPUConfig files for running the HuBERT-base model (e.g. facebook/hubert-base-ls960) on Graphcore IPUs.

This model contains no model weights, only an IPUConfig.


from optimum.graphcore import IPUConfig
ipu_config = IPUConfig.from_pretrained("Graphcore/hubert-base-ipu")
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