Updata stft complex

README.md

@@ -265,7 +265,7 @@ After enabling loudness embedding, the trained model will match the loudness of
 
 * `timesteps`: The total number of steps in the diffusion model, which defaults to 1000.
 
-* `k_step_max`: Training can only train 'k_step_max' step diffusion to save training time, note that the value must be less than 'timesteps', 0 is to train the entire diffusion model, **Note: if you do not train the entire diffusion model will not be able to use only_diffusion!**
+* `k_step_max`: Training can only train `k_step_max` step diffusion to save training time, note that the value must be less than `timesteps`, 0 is to train the entire diffusion model, **Note: if you do not train the entire diffusion model will not be able to use only_diffusion!**
 
 ##### **List of Vocoders**
 

README_zh_CN.md

@@ -265,7 +265,7 @@ python preprocess_flist_config.py --speech_encoder vec768l12 --vol_aug
 
 * `timesteps` : 扩散模型总步数，默认为1000.
 
-* `k_step_max` : 训练时可仅训练`k_step_max`步扩散以节约训练时间，注意，该值必须小于`timesteps`，0为训练全部整个扩散模型，**注意，如果不训练整个扩散模型将无法使用仅扩散推理!**
+* `k_step_max` : 训练时可仅训练`k_step_max`步扩散以节约训练时间，注意，该值必须小于`timesteps`，0为训练整个扩散模型，**注意，如果不训练整个扩散模型将无法使用仅扩散模型推理!**
   
 ##### **声码器列表**
 

modules/mel_processing.py

@@ -64,8 +64,8 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     y = y.squeeze(1)
 
     spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
+    spec = torch.view_as_real(spec)
     spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
     return spec