Option to continue training from the official checkpoint

README.md

@@ -73,6 +73,30 @@ with fused residual and skip connections.
    python3 inference.py -f mel_files.txt -w checkpoints/waveglow_10000 -o . --is_fp16 -s 0.6
    ```
 
+## Finetuning the official checkpoint with your own data
+
+The "official" checkpoint above was trained using an older version of the code.
+Therefore, you need to use `glow_old.py` to continue training from the official
+checkpoint:
+
+1. Download our [published model]
+2. Update the checkpoint to comply with recent code modifications:
+
+   `python convert_model.py waveglow_old.pt waveglow_old_updated.pt`
+
+3. Perform steps 1 and 2 from the section above
+
+4. Set `"checkpoint_path": "./waveglow_old_updated.pt"` in `config.json`
+
+5. Train your WaveGlow networks with `OLD_GLOW=1` (not yet tested with
+   `distributed.py`) 
+
+   ```command
+   mkdir checkpoints
+   OLD_GLOW=1 python train.py -c config.json
+   ```
+
+
 [//]: # (TODO)
 [//]: # (PROVIDE INSTRUCTIONS FOR DOWNLOADING LJS)
 [pytorch 1.0]: https://github.com/pytorch/pytorch#installation

glow.py

@@ -97,7 +97,7 @@ def forward(self, z, reverse=False):
             return z
         else:
             # Forward computation
-            log_det_W = batch_size * n_of_groups * torch.logdet(W)
+            log_det_W = batch_size * n_of_groups * torch.slogdet(W)[1]
             z = self.conv(z)
             return z, log_det_W
 

glow_old.py

@@ -116,11 +116,9 @@ def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
         self.n_remaining_channels = n_remaining_channels  # Useful during inference
 
     def forward(self, forward_input):
-        return None
-        """
-        forward_input[0] = audio: batch x time
-        forward_input[1] = upsamp_spectrogram:  batch x n_cond_channels x time
         """
+        forward_input[0] = mel_spectrogram:  batch x n_mel_channels x frames
+        forward_input[1] = audio: batch x time
         """
         spect, audio = forward_input
 
@@ -135,39 +133,40 @@ def forward(self, forward_input):
 
         audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
         output_audio = []
-        s_list = []
-        s_conv_list = []
+        log_s_list = []
+        log_det_W_list = []
 
         for k in range(self.n_flows):
-            if k%4 == 0 and k > 0:
-                output_audio.append(audio[:,:self.n_multi,:])
-                audio = audio[:,self.n_multi:,:]
+            if k % self.n_early_every == 0 and k > 0:
+                output_audio.append(audio[:,:self.n_early_size,:])
+                audio = audio[:,self.n_early_size:,:]
 
-            # project to new basis
-            audio, s = self.convinv[k](audio)
-            s_conv_list.append(s)
+            audio, log_det_W = self.convinv[k](audio)
+            log_det_W_list.append(log_det_W)
 
             n_half = int(audio.size(1)/2)
+
             if k%2 == 0:
                 audio_0 = audio[:,:n_half,:]
                 audio_1 = audio[:,n_half:,:]
             else:
                 audio_1 = audio[:,:n_half,:]
                 audio_0 = audio[:,n_half:,:]
 
-            output = self.nn[k]((audio_0, spect))
-            s = output[:, n_half:, :]
+            output = self.WN[k]((audio_0, spect))
+            log_s = output[:, n_half:, :]
             b = output[:, :n_half, :]
-            audio_1 = torch.exp(s)*audio_1 + b
-            s_list.append(s)
+            audio_1 = torch.exp(log_s)*audio_1 + b
+            log_s_list.append(log_s)
+
+            if k%2 != 0:
+                audio_0, audio_1 = audio_1, audio_0
+
+            audio = torch.cat([audio_0, audio_1],1)
 
-            if k%2 == 0:
-                audio = torch.cat([audio[:,:n_half,:], audio_1],1)
-            else:
-                audio = torch.cat([audio_1, audio[:,n_half:,:]], 1)
         output_audio.append(audio)
-        return torch.cat(output_audio,1), s_list, s_conv_list
-        """
+        return torch.cat(output_audio,1), log_s_list, log_det_W_list
+
 
     def infer(self, spect, sigma=1.0):
         spect = self.upsample(spect)

train.py

@@ -35,14 +35,27 @@
 #=====END:   ADDED FOR DISTRIBUTED======
 
 from torch.utils.data import DataLoader
-from glow import WaveGlow, WaveGlowLoss
+if os.getenv('OLD_GLOW') == '1':
+    print("Warning! Using old_glow.py instead of glow.py for training")
+    from glow_old import WaveGlow
+else:
+    from glow import WaveGlow
+
+from glow import WaveGlowLoss
 from mel2samp import Mel2Samp
 
 def load_checkpoint(checkpoint_path, model, optimizer):
     assert os.path.isfile(checkpoint_path)
     checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-    iteration = checkpoint_dict['iteration']
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
+
+    if 'iteration' in checkpoint_dict:
+        iteration = checkpoint_dict['iteration']
+    else:
+        iteration = 0
+
+    if 'optimizer' in checkpoint_dict:
+        optimizer.load_state_dict(checkpoint_dict['optimizer'])
+
     model_for_loading = checkpoint_dict['model']
     model.load_state_dict(model_for_loading.state_dict())
     print("Loaded checkpoint '{}' (iteration {})" .format(