wav2vec2-large-xlsr-korean

[Google colab notebook(Korean)](https://colab.research.google.com/github/indra622/tutorials/blob/master/wav2vec2_korean_tutorial.ipynb) from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset import soundfile as sf import torch from jiwer import wer processor = Wav2Vec2Processor.from_pretrained("kresnik/wav2vec2-large-xlsr-korean") model = Wav2Vec2ForCTC.from_pretrained("kresnik/wav2vec2-large-xlsr-korean").to('cuda') ds = load_dataset("kresnik/zeroth_korean", "clean") test_ds = ds['test'] def map_to_array(batch): speech, _ = sf.read(batch["file"]) batch["speech"] = speech return batch test_ds = test_ds.map(map_to_array) def map_to_pred(batch): inputs = processor(batch["speech"], sampling_rate=16000, return_tensors="pt", padding="longest") input_values = inputs.input_values.to("cuda") with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) batch["transcription"] = transcription return batch result = test_ds.map(map_to_pred, batched=True, batch_size=16, remove_columns=["speech"]) print("WER:", wer(result["text"], result["transcription"]))