code: def get_speech_model():
      from transformers import (
          SpeechT5Processor,
          SpeechT5ForTextToSpeech,
          SpeechT5HifiGan,
      )
      import torch
      from datasets import load_dataset
  
      processor = SpeechT5Processor.from_pretrained(
          "microsoft/speecht5_tts"
      )  # .to("cuda:0")
      model = SpeechT5ForTextToSpeech.from_pretrained(
          "microsoft/speecht5_tts"
      ).to("cuda:0")
      vocoder = SpeechT5HifiGan.from_pretrained(
          "microsoft/speecht5_hifigan"
      ).to("cuda:0")
  
      # load xvector containing speaker's voice characteristics from a dataset
      embeddings_dataset = load_dataset(
          "Matthijs/cmu-arctic-xvectors", split="validation"
      )
      speaker_embedding = (
          torch.tensor(embeddings_dataset[7306]["xvector"])
          .unsqueeze(0)
          .to("cuda:0")
      )
      return processor, model, vocoder, speaker_embedding
  
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 1 for calling a global function with example usage: get_speech_model() and returns: processor,  model,  vocoder,  speaker_embedding