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license: apache-2.0 |
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# Graphcore/whisper-small-ipu |
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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](https://huggingface.co/hardware/graphcore). |
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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. |
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## Model description |
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Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours |
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of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need |
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for fine-tuning. |
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Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) |
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by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper). |
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## Intended uses & limitations |
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This model contains just the `IPUConfig` files for running the Whisper Small model (e.g. [openai/whisper-small](https://huggingface.co/openai/whisper-small)) on Graphcore IPUs. |
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**This model contains no model weights, only an IPUConfig.** |
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## Usage |
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``` |
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from optimum.graphcore import IPUConfig |
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ipu_config = IPUConfig.from_pretrained("Graphcore/whisper-small-ipu") |
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``` |
