| import torch | |
| from datasets import load_dataset | |
| from transformers import AutoModelForCTC, AutoProcessor | |
| import torchaudio.functional as F | |
| model_id = "cahya/wav2vec2-base-turkish" | |
| sample_iter = iter(load_dataset("common_voice", "tr", split="test", streaming=True)) | |
| sample = next(sample_iter) | |
| resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() | |
| model = AutoModelForCTC.from_pretrained(model_id) | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| input_values = processor(resampled_audio, return_tensors="pt").input_values | |
| with torch.no_grad(): | |
| logits = model(input_values).logits | |
| transcription = processor.batch_decode(logits.numpy()).text | |
| print(transcription) |