code: def __init__(self, output_dir="./wavs"):
      super(FastSpeech, self).__init__()
      fastspeech2_res_dir = os.path.join(
          MODULE_HOME,
          "fastspeech2_ljspeech",
          "assets/fastspeech2_nosil_ljspeech_ckpt_0.5",
      )
      pwg_res_dir = os.path.join(
          MODULE_HOME,
          "fastspeech2_ljspeech",
          "assets/pwg_ljspeech_ckpt_0.5",
      )
  
      phones_dict = os.path.join(
          fastspeech2_res_dir, "phone_id_map.txt"
      )
      with open(phones_dict, "r") as f:
          phn_id = [line.strip().split() for line in f.readlines()]
      vocab_size = len(phn_id)
      self.phone_id_map = {}
      for phn, _id in phn_id:
          self.phone_id_map[phn] = int(_id)
  
      # fastspeech2
      fastspeech2_config = os.path.join(
          fastspeech2_res_dir, "default.yaml"
      )
      with open(fastspeech2_config) as f:
          fastspeech2_config = CfgNode(yaml.safe_load(f))
      self.samplerate = fastspeech2_config.fs
  
      fastspeech2_checkpoint = os.path.join(
          fastspeech2_res_dir, "snapshot_iter_100000.pdz"
      )
      model = FastSpeech2(
          idim=vocab_size,
          odim=fastspeech2_config.n_mels,
          **fastspeech2_config["model"],
      )
      model.set_state_dict(
          paddle.load(fastspeech2_checkpoint)["main_params"]
      )
      logger.info(
          "Load fastspeech2 params from %s"
          % os.path.abspath(fastspeech2_checkpoint)
      )
      model.eval()
  
      # vocoder
      pwg_config = os.path.join(pwg_res_dir, "pwg_default.yaml")
      with open(pwg_config) as f:
          pwg_config = CfgNode(yaml.safe_load(f))
  
      pwg_checkpoint = os.path.join(
          pwg_res_dir, "pwg_snapshot_iter_400000.pdz"
      )
      vocoder = PWGGenerator(**pwg_config["generator_params"])
      vocoder.set_state_dict(
          paddle.load(pwg_checkpoint)["generator_params"]
      )
      logger.info(
          "Load vocoder params from %s"
          % os.path.abspath(pwg_checkpoint)
      )
      vocoder.remove_weight_norm()
      vocoder.eval()
  
      # frontend
      self.frontend = English()
      self.punc = "：，；。？！“”‘’':,;.?!"
  
      # stat
      fastspeech2_stat = os.path.join(
          fastspeech2_res_dir, "speech_stats.npy"
      )
      stat = np.load(fastspeech2_stat)
      mu, std = stat
      mu = paddle.to_tensor(mu)
      std = paddle.to_tensor(std)
      fastspeech2_normalizer = ZScore(mu, std)
  
      pwg_stat = os.path.join(pwg_res_dir, "pwg_stats.npy")
      stat = np.load(pwg_stat)
      mu, std = stat
      mu = paddle.to_tensor(mu)
      std = paddle.to_tensor(std)
      pwg_normalizer = ZScore(mu, std)
  
      # inference
      self.fastspeech2_inference = FastSpeech2Inference(
          fastspeech2_normalizer, model
      )
      self.pwg_inference = PWGInference(pwg_normalizer, vocoder)
  
      self.output_dir = Path(output_dir)
      self.output_dir.mkdir(parents=True, exist_ok=True)
  
imports: import os
  from pathlib import Path
  from typing import List
  import numpy
  import paddle
  from paddlehub.env import MODULE_HOME
  from paddlehub.module.module import moduleinfo, serving
  from paddlehub.utils.log import logger
  from parakeet.frontend import English
  from parakeet.models.fastspeech2 import FastSpeech2
  from parakeet.models.fastspeech2 import FastSpeech2Inference
  from parakeet.models.parallel_wavegan import PWGGenerator
  from parakeet.models.parallel_wavegan import PWGInference
  from parakeet.modules.normalizer import ZScore
  import soundfile
  from yacs.config import CfgNode
  import yaml
label_desc: Use image node_id 1 to create a new FastSpeech object from inherited base classes: paddle.nn with example: obj = FastSpeech(output_dir)