
# 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
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "metrics": ["cer", "wer"], "pipeline_tag": "automatic-speech-recognition"}
transformers pytorch wav2vec2 automatic-speech-recognition en license:apache-2.0 endpoints_compatible region:us