text: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import argparse
  from pathlib import Path
  
  import numpy as np
  import paddle
  from matplotlib import pyplot as plt
  
  from parakeet.exps.tacotron2.config import get_cfg_defaults
  from parakeet.frontend import EnglishCharacter
  from parakeet.models.tacotron2 import Tacotron2
  from parakeet.utils import display
  
  
  def main(config, args):
      paddle.set_device(args.device)
  
      # model
      frontend = EnglishCharacter()
      model = Tacotron2.from_pretrained(config, args.checkpoint_path)
      model.eval()
  
      # inputs
      input_path = Path(args.input).expanduser()
      sentences = []
      with open(input_path, "rt") as f:
          for line in f:
              line_list = line.strip().split()
              utt_id = line_list[0]
              sentence = " ".join(line_list[1:])
              sentences.append((utt_id, sentence))
  
      if args.output is None:
          output_dir = input_path.parent / "synthesis"
      else:
          output_dir = Path(args.output).expanduser()
      output_dir.mkdir(exist_ok=True)
  
      for i, sentence in enumerate(sentences):
          sentence = paddle.to_tensor(frontend(sentence)).unsqueeze(0)
          outputs = model.infer(sentence)
          mel_output = outputs["mel_outputs_postnet"][0].numpy().T
          alignment = outputs["alignments"][0].numpy().T
  
          np.save(str(output_dir / f"sentence_{i}"), mel_output)
          display.plot_alignment(alignment)
          plt.savefig(str(output_dir / f"sentence_{i}.png"))
          if args.verbose:
              print("spectrogram saved at {}".format(output_dir /
                                                     f"sentence_{i}.npy"))
  
  
  if __name__ == "__main__":
      config = get_cfg_defaults()
  
      parser = argparse.ArgumentParser(
          description="generate mel spectrogram with TransformerTTS.")
      parser.add_argument(
          "--config",
          type=str,
          metavar="FILE",
          help="extra config to overwrite the default config")
      parser.add_argument(
          "--checkpoint_path", type=str, help="path of the checkpoint to load.")
      parser.add_argument("--input", type=str, help="path of the text sentences")
      parser.add_argument("--output", type=str, help="path to save outputs")
      parser.add_argument(
          "--device", type=str, default="cpu", help="device type to use.")
      parser.add_argument(
          "--opts",
          nargs=argparse.REMAINDER,
          help="options to overwrite --config file and the default config, passing in KEY VALUE pairs"
      )
      parser.add_argument(
          "-v", "--verbose", action="store_true", help="print msg")
  
      args = parser.parse_args()
      if args.config:
          config.merge_from_file(args.config)
      if args.opts:
          config.merge_from_list(args.opts)
      config.freeze()
      print(config)
      print(args)
  
      main(config, args)
  