docstr: Get the synthetic wavs from the texts.
  
  Args:
       texts(list): the input texts to be predicted.
       use_gpu(bool): whether use gpu to predict or not. Default False.
       speed(float): Controlling the voice speed. Default 1.0.
       vocoder(str): the vocoder name, "griffin-lim" or "waveflow".
  
  Returns:
       wavs(str): the audio wav with sample rate . You can use soundfile.write to save it.
       sample_rate(int): the audio sample rate.
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code: def synthesize(
      self, texts, use_gpu=False, speed=1.0, vocoder="griffin-lim"
  ):
      """
      Get the synthetic wavs from the texts.
  
      Args:
           texts(list): the input texts to be predicted.
           use_gpu(bool): whether use gpu to predict or not. Default False.
           speed(float): Controlling the voice speed. Default 1.0.
           vocoder(str): the vocoder name, "griffin-lim" or "waveflow".
  
      Returns:
           wavs(str): the audio wav with sample rate . You can use soundfile.write to save it.
           sample_rate(int): the audio sample rate.
      """
      if use_gpu and "CUDA_VISIBLE_DEVICES" not in os.environ:
          use_gpu = False
          logger.warning(
              "use_gpu has been set False as you didn't set the environment variable CUDA_VISIBLE_DEVICES while using use_gpu=True"
          )
      if use_gpu:
          place = fluid.CUDAPlace(0)
      else:
          place = fluid.CPUPlace()
  
      if texts and isinstance(texts, list):
          predicted_data = texts
      else:
          raise ValueError(
              "The input data is inconsistent with expectations."
          )
  
      wavs = []
      with fluid.dygraph.guard(place):
          self.tts_model.eval()
          self.waveflow.eval()
          for text in predicted_data:
              # init input
              logger.info("Processing sentence: %s" % text)
              text = np.asarray(text_to_sequence(text))
              text = np.expand_dims(text, axis=0)
              pos_text = np.arange(1, text.shape[1] + 1)
              pos_text = np.expand_dims(pos_text, axis=0)
  
              text = dg.to_variable(text).astype(np.int64)
              pos_text = dg.to_variable(pos_text).astype(np.int64)
  
              _, mel_output_postnet = self.tts_model(
                  text, pos_text, alpha=1 / speed
              )
  
              if vocoder == "griffin-lim":
                  # synthesis use griffin-lim
                  wav = self.synthesis_with_griffinlim(
                      mel_output_postnet, self.tts_config["audio"]
                  )
              elif vocoder == "waveflow":
                  wav = self.synthesis_with_waveflow(
                      mel_output_postnet, self.waveflow_config.sigma
                  )
              else:
                  raise ValueError(
                      "vocoder error, we only support griffinlim and waveflow, but recevied %s."
                      % vocoder
                  )
              wavs.append(wav)
      return wavs, self.tts_config["audio"]["sr"]
  
imports: import os
  import ast
  import argparse
  import importlib.util
  import nltk
  import paddle.fluid
  import paddle.fluid.dygraph
  import paddlehub
  from paddlehub.module.module import runnable
  from paddlehub.common.utils import mkdir
  from paddlehub.module.nlp_module import DataFormatError
  from paddlehub.common.logger import logger
  from paddlehub.module.module import moduleinfo, serving
  from paddlehub.common.dir import THIRD_PARTY_HOME
  from paddlehub.common.downloader import default_downloader
label_desc: Use image node_id 2 for calling the FastSpeech obj's underlying member method code with example usage: obj.synthesize(texts, use_gpu, speed, vocoder) and returns: wavs,  self