//  [python]
def __getitem__(self, index):
        sample = self.data[index]
        rel_audio_path = Path(sample["audio_filepath"]).relative_to(self.base_data_dir).with_suffix("")
        rel_audio_path_as_text_id = str(rel_audio_path).replace("/", "_")
        speaker = torch.tensor(sample["speaker"]).long()
        dataset_id = torch.tensor(sample["dataset_id"]).long()

        audio, audio_length = self._get_wav_from_filepath(sample["audio_filepath"])

        pitch_contour = None
        if self.pitch_conditioning:
            pitch_contour = self.get_pitch_contour(rel_audio_path_as_text_id)

        content_embedding, speaker_embedding, encoded_len, duration = self.get_ssl_features(rel_audio_path_as_text_id)

        if self.speaker_conditioning_type == "mean":
            assert sample["speaker"] in self.mean_speaker_embeddings, "{} not in speaker emb".format(sample['speaker'])
            speaker_embedding = self.mean_speaker_embeddings[sample["speaker"]]

        elif self.speaker_conditioning_type == "interpolate":
            assert sample["speaker"] in self.mean_speaker_embeddings, "{} not in speaker emb".format(sample['speaker'])
            e1 = self.mean_speaker_embeddings[sample["speaker"]]
            e2 = speaker_embedding
            interpolate_factor = np.random.uniform(0, 1)
            speaker_embedding = e1 * (1 - interpolate_factor) + e2 * interpolate_factor
            l2_norm = torch.linalg.norm(speaker_embedding, p=2)
            speaker_embedding = speaker_embedding / l2_norm

        mel_spectrogram = None
        mel_len = None

        mel_spectrogram = self.get_mel_spectrogram(rel_audio_path_as_text_id)
        mel_len = torch.tensor(mel_spectrogram.shape[1]).long()

        if pitch_contour is not None:
            if self.pitch_normalization in ["speaker_wise", "global"]:
                mean, std = self.pitch_mean, self.pitch_std
                if self.pitch_normalization == "speaker_wise":
                    mean = self.speaker_stats[sample["speaker"]]["pitch_mean"]
                    std = self.speaker_stats[sample["speaker"]]["pitch_std"]
                    if np.isnan(mean) or np.isnan(std) or mean == 0 or std == 0:
                        logging.warning("NaN found in pitch mean/std for speaker {}".format(sample["speaker"]))
                        mean = self.pitch_mean
                        std = self.pitch_std
                elif self.pitch_normalization == "global":
                    mean = self.pitch_mean
                    std = self.pitch_std

                pitch_contour = pitch_contour - mean
                pitch_contour[pitch_contour == -mean] = 0.0
                pitch_contour = pitch_contour / std

            if pitch_contour.dtype != torch.float32:
                logging.warning("invalid pitch contour for {}".format(sample["audio_filepath"]))
                logging.warning("Setting pitch contour to 0")
                pitch_contour = torch.zeros(mel_spectrogram.shape[1])

        item = {
            'audio': audio,
            'audio_len': audio_length,
            'content_embedding': content_embedding,
            'speaker_embedding': speaker_embedding,
            'encoded_len': encoded_len,
            'pitch_contour': pitch_contour,
            'speaker': speaker,
            'mel_spectrogram': mel_spectrogram,
            'mel_len': mel_len,
            'dataset_id': dataset_id,
            'duration': duration,
        }

        return item