import torch
from transformers import AutoProcessor, AutoModelForCTC
filename = "demo.wav"  #change this line to the name of your audio file
sample_rate = 16_000   
processor = AutoProcessor.from_pretrained('ITG/wav2vec2-large-xlsr-gl')
model = AutoModelForSpeechSeq2Seq.from_pretrained('ITG/wav2vec2-large-xlsr-gl')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
speech_array, _ = librosa.load(filename, sr=sample_rate)
inputs = processor(speech_array, sampling_rate=sample_rate, return_tensors="pt", padding=True).to(device)
with torch.no_grad():
  logits = model(inputs.input_values, attention_mask=inputs.attention_mask.to(device)).logits
decode_output = processor.batch_decode(torch.argmax(logits, dim=-1))[0]
print(f"ASR Galician wav2vec2-large-xlsr output: {decode_output}")

Fine-tuning hyper-parameters

Hyper-parameter	Value
Training batch size	16
Evaluation batch size	8
Learning rate	3e-4
Gradient accumulation steps	2
Group by length	true
Evaluation strategy	steps
Max training epochs	50
Max steps	4000
Generate max length	225
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 wav2vec2 model, we suggest starting with the facebook/wav2vec2-large-xlsr-53 model. Additionally, you may find this fine-tuning on galician notebook by Diego Fustes to be a valuable resource. This guide served as a helpful reference during the training process of this Galician wav2vec2-large-xlsr model!