def METHOD_NAME(clean_speech, denoised_speech):
    """
    computes the word error rate(WER) score for 1 single data point
    """
    def _transcription(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]
    transcript = _transcription(clean_speech, denoised_speech)
    try:  # if no words are predicted
        wer_val = jiwer.METHOD_NAME(transcript[0], transcript[1])
    except ValueError:
        wer_val = None
    return wer_val