---
license: mit
datasets:
- openslr/librispeech_asr
language:
- en
pipeline_tag: automatic-speech-recognition
---

# Splitformer

<div align="center" style="line-height: 1;">
  <a href="https://github.com/augustgw/early-exit-transformer" target="_blank" style="margin: 2px;">
    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-Splitformer-181717?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://www.arxiv.org/abs/2506.18035" target="_blank" style="margin: 2px;">
    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-2506.18035-B31B1B?logo=arxiv&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>


## 1. Overview

**Splitformer** is a 36.7M parameters Conformer-based ASR model trained from scratch on 1000 hours of the **LibriSpeech dataset** with an **early‐exit objective**. 

This architecture introduces **parallel downsampling layers** before the first and last exits to improve performance with minimal extra overhead, while retaining inference speed.

Our code for training and inference is available on our [GitHub](https://github.com/augustgw/early-exit-transformer) repository.

### 2. Results on LibriSpeech

<table>
  <thead>
    <tr>
      <th rowspan="2">Layer</th>
      <th colspan="2">EE-baseline (31.5M)</th>
      <th colspan="2">Splitformer (36.7M)</th>
      <th colspan="2">Wav2Vec2 (94.0M)</th>
      <th colspan="2">WavLM (94.7M)</th>
    </tr>
    <tr>
      <th>test-clean</th>
      <th>test-other</th>
      <th>test-clean</th>
      <th>test-other</th>
      <th>test-clean</th>
      <th>test-other</th>
      <th>test-clean</th>
      <th>test-other</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>2</td>
      <td>31.0</td>
      <td>51.0</td>
      <td>28.1</td>
      <td>48.3</td>
      <td>33.7</td>
      <td>56.0</td>
      <td>28.0</td>
      <td>48.5</td>
    </tr>
    <tr>
      <td>4</td>
      <td>11.7</td>
      <td>27.8</td>
      <td>10.8</td>
      <td>26.4</td>
      <td>17.4</td>
      <td>36.7</td>
      <td>13.9</td>
      <td>27.3</td>
    </tr>
    <tr>
      <td>6</td>
      <td>7.1</td>
      <td>19.8</td>
      <td>6.7</td>
      <td>19.2</td>
      <td>9.6</td>
      <td>23.7</td>
      <td>8.7</td>
      <td>18.4</td>
    </tr>
    <tr>
      <td>8</td>
      <td>5.8</td>
      <td>16.6</td>
      <td>5.5</td>
      <td>16.3</td>
      <td>5.8</td>
      <td>15.9</td>
      <td>4.8</td>
      <td>12.4</td>
    </tr>
    <tr>
      <td>10</td>
      <td>5.3</td>
      <td>15.3</td>
      <td>5.1</td>
      <td>15.1</td>
      <td>4.5</td>
      <td>12.6</td>
      <td>4.0</td>
      <td>9.5</td>
    </tr>
    <tr>
      <td>12</td>
      <td>5.1</td>
      <td>14.8</td>
      <td>4.8</td>
      <td>14.7</td>
      <td>4.3</td>
      <td>12.2</td>
      <td>3.6</td>
      <td>8.8</td>
    </tr>
  </tbody>
</table>

## 3. Citation

```bibtex
@misc{lasbordes2025splitformer,
      title={Splitformer: An improved early-exit architecture for automatic speech recognition on edge devices}, 
      author={Maxence Lasbordes, Daniele Falavigna and Alessio Brutti},
      year={2025},
      note={Proc. of EUSIPCO 2025},
}