code: def get_tts_model(
      t5_model="microsoft/speecht5_tts",
      t5_gan_model="microsoft/speecht5_hifigan",
      use_gpu=True,
      gpu_id="auto",
  ):
      if gpu_id == "auto":
          gpu_id = 0
      if use_gpu:
          device = "cuda:%d" % gpu_id
      else:
          device = "cpu"
      from transformers import (
          SpeechT5Processor,
          SpeechT5ForTextToSpeech,
          SpeechT5HifiGan,
      )
  
      processor = SpeechT5Processor.from_pretrained(t5_model)
      model = SpeechT5ForTextToSpeech.from_pretrained(t5_model).to(
          device
      )
      vocoder = SpeechT5HifiGan.from_pretrained(t5_gan_model).to(
          model.device
      )
  
      return processor, model, vocoder
  
imports: from __future__ import annotations
  import base64
  import io
  import time
  from io import BytesIO
  import numpy
  import scipy
  import wavio
  import soundfile
  import torch
  import librosa
  from src.tts_sentence_parsing import init_sentence_state, get_sentence
  from src.tts_utils import prepare_speech, get_no_audio, chunk_speed_change
label_desc: Use image node_id 3 for calling a global function with example usage: get_tts_model(t5_model, t5_gan_model, use_gpu, gpu_id) and returns: processor,  model,  vocoder