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---
license: cc-by-nc-nd-4.0
datasets:
- openslr
- mozilla-foundation/common_voice_13_0
language:
- gl
pipeline_tag: automatic-speech-recognition
tags:
- ITG
- PyTorch
- Transformers
- whisper
- whisper-small
---
# Whisper Small Galician
## Description
This is a fine-tuned version of the [openai/whisper-small](https://huggingface.co/openai/whisper-small) pre-trained model for ASR in galician.
---
## Dataset
We used two datasets combined:
1. The [OpenSLR galician](https://huggingface.co/datasets/openslr/viewer/SLR77) dataset, available in the openslr repository.
2. The [Common Voice 13 galician](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0/viewer/gl) dataset, available in the Common Voice repository.
---
## Example inference script
### Check this example script to run our model in inference mode
```python
import torch
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
filename = "demo.wav" #change this line to the name of your audio file
sample_rate = 16_000
processor = AutoProcessor.from_pretrained('ITG/whisper-small-gl')
model = AutoModelForSpeechSeq2Seq.from_pretrained('ITG/whisper-small-gl')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
with torch.no_grad():
speech_array, _ = librosa.load(filename, sr=sample_rate)
inputs = processor(speech_array, sampling_rate=sample_rate, return_tensors="pt").to(device)
input_features = inputs.input_features
generated_ids = model.generate(inputs=input_features, max_length=225)
decode_output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(f"ASR Galician whisper-small output: {decode_output}")
```
---
## Fine-tuning hyper-parameters
| **Hyper-parameter** | **Value** |
|:----------------------------------------:|:---------------------------:|
| Training batch size | 16 |
| Evaluation batch size | 8 |
| Learning rate | 1e-5 |
| Gradient checkpointing | true |
| Gradient accumulation steps | 1 |
| Max training epochs | 100 |
| Max steps | 4000 |
| Generate max length | 225 |
| Warmup training steps (%) | 12,5% |
| FP16 | true |
| Metric for best model | wer |
| Greater is better | false |
## Fine-tuning in a different dataset or style
If you're interested in fine-tuning your own whisper model, we suggest starting with the [openai/whisper-small model](https://huggingface.co/openai/whisper-small). Additionally, you may find the Transformers
step-by-step guide for [fine-tuning whisper on multilingual ASR datasets](https://huggingface.co/blog/fine-tune-whisper) to be a valuable resource. This guide served as a helpful reference during the training
process of this Galician whisper-small model! |