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. Additionally, you may find the Transformers step-by-step guide for fine-tuning whisper on multilingual ASR datasets to be a valuable resource. This guide served as a helpful reference during the training process of this Galician whisper-small model!