misc

codes/models/diffusion/unet_diffusion.py

@@ -646,22 +646,6 @@ class UNetModel(nn.Module):
             zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
         )
 
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
     def forward(self, x, timesteps, y=None):
         """
         Apply the model to an input batch.
@@ -891,20 +875,6 @@ class EncoderUNetModel(nn.Module):
         else:
             raise NotImplementedError(f"Unexpected {pool} pooling")
 
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-
     def forward(self, x, timesteps):
         """
         Apply the model to an input batch.

codes/models/gpt_voice/unet_diffusion_vocoder_with_ref.py

@@ -281,22 +281,6 @@ class DiffusionVocoderWithRef(nn.Module):
                     del p.DO_NOT_TRAIN
                     p.requires_grad = True
 
-    def convert_to_fp16(self):
-        """
-        Convert the torso of the model to float16.
-        """
-        self.input_blocks.apply(convert_module_to_f16)
-        self.middle_block.apply(convert_module_to_f16)
-        self.output_blocks.apply(convert_module_to_f16)
-
-    def convert_to_fp32(self):
-        """
-        Convert the torso of the model to float32.
-        """
-        self.input_blocks.apply(convert_module_to_f32)
-        self.middle_block.apply(convert_module_to_f32)
-        self.output_blocks.apply(convert_module_to_f32)
-
     def forward(self, x, timesteps, spectrogram, conditioning_input=None):
         """
         Apply the model to an input batch.

codes/scripts/audio/speech_synthesis_utils.py

@@ -15,7 +15,7 @@ def wav_to_mel(wav):
     """
     Converts an audio clip into a MEL tensor that the vocoder, DVAE and GptTts models use whenever a MEL is called for.
     """
-    return TorchMelSpectrogramInjector({'in': 'wav', 'out': 'mel', 'normalize': True},{})({'wav': wav})['mel']
+    return TorchMelSpectrogramInjector({'in': 'wav', 'out': 'mel'},{})({'wav': wav})['mel']
 
 
 def convert_mel_to_codes(dvae_model, mel):

codes/scripts/audio/use_discrete_vocoder.py

@@ -26,7 +26,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae.yml')
     parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator')
-    parser.add_argument('-diffusion_model_path', type=str, help='Name of the diffusion model in opt.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae\\models\\6200_generator_ema.pth')
+    parser.add_argument('-diffusion_model_path', type=str, help='Name of the diffusion model in opt.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\200_generator_ema.pth')
     parser.add_argument('-dvae_model_name', type=str, help='Name of the DVAE model in opt.', default='dvae')
     parser.add_argument('-input_file', type=str, help='Path to the input audio file.', default='Z:\\clips\\books1\\3_dchha04 Romancing The Tribes\\00036.wav')
     parser.add_argument('-cond', type=str, help='Path to the conditioning input audio file.', default=None)

codes/scripts/diffusion/diffusion_noise_surfer.py

@@ -49,10 +49,7 @@ def forward_pass(model, data, output_dir, spacing, audio_mode):
 def load_image(path, audio_mode):
     # Load test image
     if audio_mode:
-        im = load_audio(path, 22050)
+        im = load_audio(path, 22050).unsqueeze(0)
-        padding_needed = ((im.shape[1]//8192)+1)*8192-im.shape[1]
-        im = torch.nn.functional.pad(im, (0, padding_needed))
-        im = im[:, :(im.shape[1]//8192)*8192].unsqueeze(0)
     else:
         im = ToTensor()(Image.open(path)) * 2 - 1
         _, h, w = im.shape

codes/train.py

@@ -286,7 +286,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/validate_lrdvae_proper.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_bench.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/trainer/injectors/base_injectors.py

@@ -627,6 +627,9 @@ def test_torch_mel_injector():
     inj = TorchMelSpectrogramInjector({'in': 'in', 'out': 'out'}, {})
     f = inj({'in': a.unsqueeze(0)})['out']
     plot_spectrogram(f[0])
+    inj = MelSpectrogramInjector({'in': 'in', 'out': 'out'}, {})
+    t = inj({'in': a.unsqueeze(0)})['out']
+    plot_spectrogram(t[0])
     print('Pause')