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---
language:
- am
license: mit
tags:
- automatic-speech-recognition
- speech
metrics:
- wer
- cer
---
# Amharic ASR using fine-tuned Wav2vec2 XLSR-53
This is a finetuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) trained on the [Amharic Speech Corpus](http://www.openslr.org/25/). This corpus was produced by [Abate et al. (2005)](https://www.isca-speech.org/archive/interspeech_2005/abate05_interspeech.html) (10.21437/Interspeech.2005-467).
## Usage
The model can be used as follows:
```python
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model = Wav2Vec2ForCTC.from_pretrained("agkphysics/wav2vec2-large-xlsr-53-amharic")
processor = Wav2Vec2Processor.from_pretrained("agkphysics/wav2vec2-large-xlsr-53-amharic")
audio, _ = librosa.load("/path/to/audio.wav", sr=16000)
input_values = processor(
audio.squeeze(),
sampling_rate=16000,
return_tensors="pt"
).input_values
model.eval()
with torch.no_grad():
logits = model(input_values).logits
preds = logits.argmax(-1)
texts = processor.batch_decode(preds)
print(texts[0])
```
## Training
The code to train this model is available at https://github.com/agkphysics/amharic-asr.
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