README.md

---
license: cc-by-2.0
datasets:
- openslr/librispeech_asr
language:
- en
metrics:
- wer
base_model:
- facebook/wav2vec2-base-960h
pipeline_tag: automatic-speech-recognition
library_name: transformers
---

<img src="./EE.gif" align="center" width="70%">

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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### Results

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#### Summary



## Citation [optional]

## Citation

```
@inproceedings{wright2024training,
  title={Training early-exit architectures for automatic speech recognition: Fine-tuning pre-trained models or training from scratch},
  author={Wright, George August and Cappellazzo, Umberto and Zaiem, Salah and Raj, Desh and Yang, Lucas Ondel and Falavigna, Daniele and Ali, Mohamed Nabih and Brutti, Alessio},
  booktitle={2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
  pages={685--689},
  year={2024},
  organization={IEEE}
}

```