def METHOD_NAME(args, logger):
    # Acoustic Model
    logger.info(f"Loading acoustic model from {args.tts_model_path}...")
    feature_reader_cls = get_feature_reader(args.feature_type)
    reader = feature_reader_cls(
        checkpoint_path=args.acoustic_model_path, layer=args.layer
    )
    # K-means Model
    logger.info(f"Loading K-means model from {args.kmeans_model_path} ...")
    kmeans_model = joblib.load(open(args.kmeans_model_path, "rb"))
    kmeans_model.verbose = False
    # TTS Model
    logger.info(f"Loading TTS model from {args.tts_model_path}...")
    tacotron_model, sample_rate, hparams = load_tacotron(
        tacotron_model_path=args.tts_model_path,
        max_decoder_steps=args.max_decoder_steps,
    )
    # Waveglow Model
    logger.info(f"Loading Waveglow model from {args.waveglow_path}...")
    waveglow, denoiser = load_waveglow(waveglow_path=args.waveglow_path)
    # Dataset
    if not os.path.exists(hparams.code_dict):
        hparams.code_dict = args.code_dict_path
    tts_dataset = TacotronInputDataset(hparams)
    iters = 0
    while True:
        in_file_path = input("Input: Enter the full file path of audio file...\n")
        out_file_path = input("Output: Enter the full file path of audio file...\n")
        feats = reader.get_feats(in_file_path).cpu().numpy()
        iters += 1
        if iters == 1000:
            gc.collect()
            torch.cuda.empty_cache()
        quantized_units = kmeans_model.predict(feats)
        quantized_units_str = " ".join(map(str, quantized_units))
        tts_input = tts_dataset.get_tensor(quantized_units_str)
        mel, aud, aud_dn, has_eos = synthesize_audio(
            tacotron_model,
            waveglow,
            denoiser,
            tts_input.unsqueeze(0),
            strength=args.denoiser_strength,
        )
        sf.write(f"{out_file_path}", aud_dn[0].cpu().float().numpy(), sample_rate)
        logger.info("Resynthesis done!\n")