---
license: apache-2.0
---

# Graphcore/wav2vec2-ctc-base-ipu

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](https://huggingface.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
From [wav2vec 2.0: A Framework for Self-Supervised
Learning of Speech Representations](https://arxiv.org/pdf/2006.11477v3.pdf),  

“Wave2vec2 is a framework for self-supervised learning of speech representations. It masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned.” 


## Intended uses & limitations

This model contains just the `IPUConfig` files for running the Wav2Vec2ForCTC base model  (e.g. [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base)) on Graphcore IPUs.

**This model contains no model weights, only an IPUConfig.** 

## Usage

```
from optimum.graphcore import IPUConfig
ipu_config = IPUConfig.from_pretrained("Graphcore/wav2vec2-ctc-base-ipu")
```