code: def init_vits(
      model_to_use: str,
      emotion_path: Path,
      speaker_id: int,
      seed: int,
  ):
      model_config = VITS_MODELS[model_to_use]
  
      # Load the hyperparameters from the config file.
      hps = get_hparams_from_file(fetch(model_config[0]))
  
      # If model has multiple speakers, validate speaker id and retrieve name if available.
      model_has_multiple_speakers = hps.data.n_speakers > 0
      if model_has_multiple_speakers:
          if speaker_id >= hps.data.n_speakers:
              raise ValueError(
                  f"Speaker ID {speaker_id} is invalid for this model."
              )
          if hps.__contains__("speakers"):  # maps speaker ids to names
              speakers = hps.speakers
              if isinstance(speakers, list):
                  speakers = {
                      speaker: i for i, speaker in enumerate(speakers)
                  }
  
      # Load emotions if any. TODO: find an english model with emotions, this is untested atm.
      emotion_embedding = None
      if emotion_path is not None:
          if emotion_path.endswith(".npy"):
              emotion_embedding = Tensor(
                  np.load(emotion_path), dtype=dtypes.int64
              ).unsqueeze(0)
          else:
              raise ValueError("Emotion path must be a .npy file.")
  
      # Load symbols, instantiate TextMapper and clean the text.
      if hps.__contains__("symbols"):
          symbols = hps.symbols
      elif model_to_use == "mmts-tts":
          symbols = [
              x.replace("\n", "")
              for x in fetch(
                  "https://huggingface.co/facebook/mms-tts/raw/main/full_models/eng/vocab.txt"
              )
              .open(encoding="utf-8")
              .readlines()
          ]
      else:
          symbols = (
              ["_"]
              + list(';:,.!?¡¿—…"«»“” ')
              + list(
                  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
              )
              + list(
                  "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
              )
          )
      text_mapper = TextMapper(apply_cleaners=True, symbols=symbols)
  
      # Load the model.
      Tensor.no_grad = True
      if seed is not None:
          Tensor.manual_seed(seed)
          np.random.seed(seed)
      net_g = load_model(text_mapper.symbols, hps, model_config)
  
      return (
          net_g,
          emotion_embedding,
          text_mapper,
          hps,
          model_has_multiple_speakers,
      )
  
imports: import argparse
  import multiprocessing
  import os
  import re
  import sys
  import time
  from contextlib import contextmanager
  from pathlib import Path
  import numpy
  import pyaudio
  import yaml
  from llama import LLaMa
  from vits import MODELS
  from vits import Y_LENGTH_ESTIMATE_SCALARS, HParams, Synthesizer, TextMapper, get_hparams_from_file, load_model
  from whisper import init_whisper, transcribe_waveform
  from sentencepiece import SentencePieceProcessor
  from tinygrad.helpers import Timing, fetch
  from tinygrad import Tensor, dtypes
label_desc: Use image node_id 8 for calling a global function with example usage: init_vits(model_to_use, emotion_path, speaker_id, seed) and returns: net_g,  emotion_embedding,  text_mapper,  hps,  model_has_multiple_speakers