code: def convert_speecht5_checkpoint(
      task,
      checkpoint_path,
      pytorch_dump_folder_path,
      config_path=None,
      vocab_path=None,
      repo_id=None,
  ):
      """
      Copy/paste/tweak model's weights to transformers design.
      """
      if config_path is not None:
          config = SpeechT5Config.from_pretrained(config_path)
      else:
          config = SpeechT5Config()
  
      if task == "s2t":
          config.max_length = config.max_text_positions
          model = SpeechT5ForSpeechToText(config)
      elif task == "t2s":
          config.max_speech_positions = 1876
          config.max_text_positions = 600
          config.max_length = config.max_speech_positions
          model = SpeechT5ForTextToSpeech(config)
      elif task == "s2s":
          config.max_speech_positions = 1876
          config.max_length = config.max_speech_positions
          model = SpeechT5ForSpeechToSpeech(config)
      else:
          raise ValueError(f"Unknown task name: {task}")
  
      if vocab_path:
          tokenizer = SpeechT5Tokenizer(
              vocab_path, model_max_length=config.max_text_positions
          )
  
          # Mask token behaves like a normal word, i.e. include the space before it
          mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
          tokenizer.mask_token = mask_token
          tokenizer.add_special_tokens({"mask_token": mask_token})
          tokenizer.add_tokens(["<ctc_blank>"])
  
      feature_extractor = SpeechT5FeatureExtractor()
      processor = SpeechT5Processor(
          tokenizer=tokenizer, feature_extractor=feature_extractor
      )
      processor.save_pretrained(pytorch_dump_folder_path)
  
      fairseq_checkpoint = torch.load(checkpoint_path)
      recursively_load_weights(fairseq_checkpoint["model"], model, task)
  
      model.save_pretrained(pytorch_dump_folder_path)
  
      if repo_id:
          print("Pushing to the hub...")
          processor.push_to_hub(repo_id)
          model.push_to_hub(repo_id)
  
file_path: transformers/src/transformers/models/speecht5/convert_speecht5_original_pytorch_checkpoint_to_pytorch.py
imports: import argparse
  import torch
  from transformers import SpeechT5Config, SpeechT5FeatureExtractor, SpeechT5ForSpeechToSpeech, SpeechT5ForSpeechToText, SpeechT5ForTextToSpeech, SpeechT5Processor, SpeechT5Tokenizer, logging
  from transformers.tokenization_utils import AddedToken
label_desc: Use image node_id 5 for calling a global function with example usage: convert_speecht5_checkpoint(task, checkpoint_path, pytorch_dump_folder_path, config_path, vocab_path, repo_id) without return types