
# 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).

The model achieves a WER of 26% and a CER of 7% on the validation set of the Amharic Readspeech data.

## 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.
{"language": ["am"], "license": "mit", "tags": ["automatic-speech-recognition", "speech"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"}
transformers pytorch tensorboard safetensors wav2vec2 automatic-speech-recognition speech am license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #speech #am #license-mit #endpoints_compatible #region-us 


# Amharic ASR using fine-tuned Wav2vec2 XLSR-53
This is a finetuned version of facebook/wav2vec2-large-xlsr-53 trained on the Amharic Speech Corpus. This corpus was produced by Abate et al. (2005) (10.21437/Interspeech.2005-467).

The model achieves a WER of 26% and a CER of 7% on the validation set of the Amharic Readspeech data.

## Usage
The model can be used as follows:


## Training
The code to train this model is available at URL
# Amharic ASR using fine-tuned Wav2vec2 XLSR-53
This is a finetuned version of facebook/wav2vec2-large-xlsr-53 trained on the Amharic Speech Corpus. This corpus was produced by Abate et al. (2005) (10.21437/Interspeech.2005-467).

The model achieves a WER of 26% and a CER of 7% on the validation set of the Amharic Readspeech data. ## Usage
The model can be used as follows: ## Training
The code to train this model is available at URL
TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #speech #am #license-mit #endpoints_compatible #region-us 
 # Amharic ASR using fine-tuned Wav2vec2 XLSR-53
This is a finetuned version of facebook/wav2vec2-large-xlsr-53 trained on the Amharic Speech Corpus. This corpus was produced by Abate et al. (2005) (10.21437/Interspeech.2005-467).

The model achieves a WER of 26% and a CER of 7% on the validation set of the Amharic Readspeech data. ## Usage
The model can be used as follows: ## Training
The code to train this model is available at URL