def METHOD_NAME(clean_speech, denoised_speech):
    # transcribe clean audio
    input_values = wer_tokenizer(clean_speech, return_tensors="pt").input_values
    logits = wer_model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcript_clean = wer_tokenizer.batch_decode(predicted_ids)[0]
    # transcribe
    input_values = wer_tokenizer(denoised_speech, return_tensors="pt").input_values
    logits = wer_model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcript_estimate = wer_tokenizer.batch_decode(predicted_ids)[0]
    return [transcript_clean, transcript_estimate]