---
license: apache-2.0
language:
- en
metrics:
- cer
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---

# Model
This model is [Wav2Vec2-Large-XLSR-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
fine-tuned on the manually annotated subset of
CMU's [L2-Arctic dataset](https://psi.engr.tamu.edu/l2-arctic-corpus/). It was fine-tuned
to perform automatic phonetic transcriptions in IPA.
It was tuned following a similar procedure as described
by [vitouphy](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-timit-phoneme)
with the TIMIT dataset.

# Usage
To use the model, create a pipeline and invoke it with
the path to your WAV, which must be sampled at 16KHz.

```python
from transformers import pipeline

pipe = pipeline(model="mrrubino/wav2vec2-large-xlsr-53-l2-arctic-phoneme")
transcription = pipe("file.wav")["text"]
```

# Results
The manually annotated subset of L2-Arctic was divided
into training and testing datasets with a 90/10 split.
The performance metrics for the testing dataset are
included below.


WER - 0.425

CER - 0.128

# Citation

If you find our model helpful, please feel free to cite us.

```
@article{Bo_Rubino_Xu_2024,
  title={A Mispronunciation-Based Voice-Omics Representation Framework for Screening Specific Language Impairments in Children},
  DOI={10.1109/ichi61247.2024.00045},
  journal={2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)},
  author={Bo, Wei and Rubino, Matthew and Xu, Wenyao},
  year={2024},
  month={Jun},
  pages={294–304}
} 
```