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from datasets import load_dataset from transformers import WhisperForConditionalGeneration, WhisperProcessor import torch from evaluate import load librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en").to("cuda") def map_to_pred(batch): audio = batch["audio"] input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features batch["reference"] = processor.tokenizer._normalize(batch['text']) with torch.no_grad(): predicted_ids = model.generate(input_features.to("cuda"))[0] transcription = processor.decode(predicted_ids) batch["prediction"] = processor.tokenizer._normalize(transcription) return batch result = librispeech_test_clean.map(map_to_pred) wer = load("wer") print(100 * wer.compute(references=result["reference"], predictions=result["prediction"]))
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