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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def load_books_of_speaker(corpus, path, speaker):
 """ Load all utterances for the speaker at the given path. """

        utt_ids = []

        for book_path in MailabsReader.get_folders(path):
            meta_path = os.path.join(book_path, 'metadata.csv')
            wavs_path = os.path.join(book_path, 'wavs')

            meta = textfile.read_separated_lines(meta_path,
                                                 separator='|',
                                                 max_columns=3)

            for entry in meta:
                file_basename = entry[0]
                transcription_raw = entry[1]
                transcription_clean = entry[2]

                if speaker is None:
                    idx = file_basename
                    utt_speaker = issuers.Speaker(idx)
                    speaker_idx = idx
                    corpus.import_issuers(utt_speaker)
                else:
                    idx = '{}-{}'.format(speaker.idx, file_basename)
                    speaker_idx = speaker.idx

                wav_name = '{}.wav'.format(file_basename)
                wav_path = os.path.join(wavs_path, wav_name)

                if os.path.isfile(wav_path):
                    corpus.new_file(wav_path, idx)

                    ll_raw = annotations.LabelList.create_single(
                        transcription_raw,
                        idx=audiomate.corpus.LL_WORD_TRANSCRIPT_RAW
                    )

                    ll_clean = annotations.LabelList.create_single(
                        transcription_clean,
                        idx=audiomate.corpus.LL_WORD_TRANSCRIPT
                    )

                    utterance = corpus.new_utterance(idx, idx, speaker_idx)
                    utterance.set_label_list(ll_raw)
                    utterance.set_label_list(ll_clean)

                    utt_ids.append(utterance.idx)

        return utt_ids