def METHOD_NAME(self, file_path, ref_len=None):
    x = self.read_audio(file_path, ref_len)
    # Inspired from CPC_audio feature_loader.py
    with torch.no_grad():
        x = torch.from_numpy(x).float().cuda()
        x = x.view(1, 1, -1)
        size = x.size(2)
        feat = []
        start = 0
        while start < size:
            if start + self.max_chunk > size:
                break
            x_chunk = x[..., start : start + self.max_chunk]
            feat_chunk = self.model.extract_features(
                source=x_chunk,
                get_encoded=self.use_encoder_layer,
                norm_output=self.norm_features,
            )
            feat.append(feat_chunk)
            start += self.max_chunk
        if start < size:
            x_chunk = x[:, -self.max_chunk :]
            feat_chunk = self.model.extract_features(
                source=x_chunk,
                get_encoded=self.use_encoder_layer,
                norm_output=self.norm_features,
            )
            df = x_chunk.size(2) // feat_chunk.size(1)
            delta = (size - start) // df
            feat.append(feat_chunk[:, -delta:])
    return torch.cat(feat, 1).squeeze(0)