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
       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, 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
           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"
          )
  
      place = fluid.CUDAPlace(0) if use_gpu else 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.model.eval()
          monotonic_layers = [4]
          for text in predicted_data:
              # init input
              logger.info("Processing sentence: %s" % text)
              text = en.text_to_sequence(text, p=1.0)
              text = np.expand_dims(np.array(text, dtype="int64"), 0)
              lengths = np.array([text.size], dtype=np.int64)
              text_seqs = dg.to_variable(text)
              text_lengths = dg.to_variable(lengths)
  
              decoder_layers = self.tts_config["decoder_layers"]
              force_monotonic_attention = [False] * decoder_layers
              for i in monotonic_layers:
                  force_monotonic_attention[i] = True
  
              outputs = self.tts_model(
                  text_seqs,
                  text_lengths,
                  speakers=None,
                  force_monotonic_attention=force_monotonic_attention,
                  window=(
                      self.tts_config["backward_step"],
                      self.tts_config["forward_step"],
                  ),
              )
              decoded, refined, attentions = outputs
              if vocoder == "griffin-lim":
                  # synthesis use griffin-lim
                  wav = self.griffin(refined.numpy()[0].T)
              elif vocoder == "waveflow":
                  # synthesis use waveflow
                  wav = self.waveflow(
                      fluid.layers.transpose(refined, [0, 2, 1])
                  ).numpy()[0]
              else:
                  raise ValueError(
                      "vocoder error, we only support griffinlim and waveflow, but recevied %s."
                      % vocoder
                  )
              wavs.append(wav)
      return wavs, self.tts_config["sample_rate"]
  
imports: import os
  import argparse
  import ast
  import importlib.util
  import nltk
  import numpy
  import paddle.fluid
  import paddle.fluid.dygraph
  import paddlehub
  from paddlehub.common.logger import logger
  from paddlehub.module.module import moduleinfo, serving
  from paddlehub.common.dir import THIRD_PARTY_HOME
  from paddlehub.common.utils import mkdir
  from paddlehub.common.downloader import default_downloader
  from paddlehub.module.module import runnable
  from paddlehub.module.nlp_module import DataFormatError
label_desc: Use image node_id 2 for calling the DeepVoice3 obj's underlying member method code with example usage: obj.synthesize(texts, use_gpu, vocoder) and returns: wavs,  self