text: # Copyright (c) 2021 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 librosa
  import numpy as np
  import paddle
  import torch
  from parallel_wavegan.losses import stft_loss as sl
  from scipy import signal
  
  from paddlespeech.t2s.modules.stft_loss import MultiResolutionSTFTLoss
  from paddlespeech.t2s.modules.stft_loss import STFT
  
  
  def test_stft():
      stft = STFT(n_fft=1024, hop_length=256, win_length=1024)
      x = paddle.uniform([4, 46080])
      S = stft.magnitude(x)
      window = signal.get_window('hann', 1024, fftbins=True)
      D2 = torch.stft(
          torch.as_tensor(x.numpy()),
          n_fft=1024,
          hop_length=256,
          win_length=1024,
          window=torch.as_tensor(window))
      S2 = (D2**2).sum(-1).sqrt()
      S3 = np.abs(
          librosa.stft(x.numpy()[0], n_fft=1024, hop_length=256, win_length=1024))
      print(S2.shape)
      print(S.numpy()[0])
      print(S2.data.cpu().numpy()[0])
      print(S3)
  
  
  def test_torch_stft():
      # NOTE: torch.stft use no window by default
      x = np.random.uniform(-1.0, 1.0, size=(46080, ))
      window = signal.get_window('hann', 1024, fftbins=True)
      D2 = torch.stft(
          torch.as_tensor(x),
          n_fft=1024,
          hop_length=256,
          win_length=1024,
          window=torch.as_tensor(window))
      D3 = librosa.stft(
          x, n_fft=1024, hop_length=256, win_length=1024, window='hann')
      print(D2[:, :, 0].data.cpu().numpy()[:, 30:60])
      print(D3.real[:, 30:60])
      # print(D3.imag[:, 30:60])
  
  
  def test_multi_resolution_stft_loss():
      net = MultiResolutionSTFTLoss()
      net2 = sl.MultiResolutionSTFTLoss()
  
      x = paddle.uniform([4, 46080])
      y = paddle.uniform([4, 46080])
      sc, m = net(x, y)
      sc2, m2 = net2(torch.as_tensor(x.numpy()), torch.as_tensor(y.numpy()))
      print(sc.numpy())
      print(sc2.data.cpu().numpy())
      print(m.numpy())
      print(m2.data.cpu().numpy())
  