fixes

codes/trainer/eval/audio_diffusion_fid.py

@@ -53,6 +53,7 @@ class AudioDiffusionFid(evaluator.Evaluator):
             self.local_modules['dvae'] = load_speech_dvae().cpu()
             self.diffusion_fn = self.perform_diffusion_vocoder
         elif mode == 'tts9_mel':
+            mel_means, self.mel_max, self.mel_min, mel_stds, mel_vars = torch.load('../experiments/univnet_mel_norms.pth')
             self.local_modules['dvae'] = load_speech_dvae().cpu()
             self.local_modules['vocoder'] = load_univnet_vocoder().cpu()
             self.diffusion_fn = self.perform_diffusion_tts9_mel_from_codes
@@ -136,6 +137,8 @@ class AudioDiffusionFid(evaluator.Evaluator):
         gen_mel = self.diffuser.p_sample_loop(self.model, output_shape,
                                     model_kwargs={'aligned_conditioning': mel_codes,
                                                   'conditioning_input': univnet_mel})
+        # denormalize mel
+        gen_mel = ((gen_mel+1)/2)*(self.mel_max-self.mel_min)+self.mel_min
 
         gen_wav = self.local_modules['vocoder'].inference(gen_mel)
         real_dec = self.local_modules['vocoder'].inference(univnet_mel)

codes/trainer/injectors/audio_injectors.py

@@ -28,7 +28,7 @@ class MelSpectrogramInjector(Injector):
             self.mel_max = mel_max
             self.mel_min = mel_min
         else:
-            self.mel_norms = None
+            self.mel_max = None
 
     def forward(self, state):
         inp = state[self.input]
@@ -37,7 +37,7 @@ class MelSpectrogramInjector(Injector):
         assert len(inp.shape) == 2
         self.stft = self.stft.to(inp.device)
         mel = self.stft.mel_spectrogram(inp)
-        if self.mel_norms is not None:
+        if self.mel_max is not None:
             mel = 2 * ((mel - self.mel_min) / (self.mel_max - self.mel_min)) - 1
         return {self.output: mel}