Enable CPU training for vocoder (CorentinJ#397)

vocoder/models/fatchord_version.py

@@ -118,8 +118,12 @@ def __init__(self, rnn_dims, fc_dims, bits, pad, upsample_factors,
     def forward(self, x, mels):
         self.step += 1
         bsize = x.size(0)
-        h1 = torch.zeros(1, bsize, self.rnn_dims).cuda()
-        h2 = torch.zeros(1, bsize, self.rnn_dims).cuda()
+        if torch.cuda.is_available():
+            h1 = torch.zeros(1, bsize, self.rnn_dims).cuda()
+            h2 = torch.zeros(1, bsize, self.rnn_dims).cuda()
+        else:
+            h1 = torch.zeros(1, bsize, self.rnn_dims).cpu()
+            h2 = torch.zeros(1, bsize, self.rnn_dims).cpu()
         mels, aux = self.upsample(mels)
 
         aux_idx = [self.aux_dims * i for i in range(5)]
@@ -209,8 +213,11 @@ def generate(self, mels, batched, target, overlap, mu_law, progress_callback=Non
                 if self.mode == 'MOL':
                     sample = sample_from_discretized_mix_logistic(logits.unsqueeze(0).transpose(1, 2))
                     output.append(sample.view(-1))
-                    # x = torch.FloatTensor([[sample]]).cuda()
-                    x = sample.transpose(0, 1).cuda()
+                    if torch.cuda.is_available():
+                        # x = torch.FloatTensor([[sample]]).cuda()
+                        x = sample.transpose(0, 1).cuda()
+                    else:
+                        x = sample.transpose(0, 1)
 
                 elif self.mode == 'RAW' :
                     posterior = F.softmax(logits, dim=1)

vocoder/train.py

@@ -10,6 +10,7 @@
 import vocoder.hparams as hp
 import numpy as np
 import time
+import torch
 
 
 def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path, ground_truth: bool,
@@ -32,8 +33,14 @@ def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path, ground_tr
         hop_length=hp.hop_length,
         sample_rate=hp.sample_rate,
         mode=hp.voc_mode
-    ).cuda()
-
+    )
+
+    if torch.cuda.is_available():
+        model = model.cuda()
+        device = torch.device('cuda')
+    else:
+        device = torch.device('cpu')   
+
     # Initialize the optimizer
     optimizer = optim.Adam(model.parameters())
     for p in optimizer.param_groups: 
@@ -79,7 +86,8 @@ def train(run_id: str, syn_dir: Path, voc_dir: Path, models_dir: Path, ground_tr
         running_loss = 0.
 
         for i, (x, y, m) in enumerate(data_loader, 1):
-            x, m, y = x.cuda(), m.cuda(), y.cuda()
+            if torch.cuda.is_available():
+                x, m, y = x.cuda(), m.cuda(), y.cuda()
 
             # Forward pass
             y_hat = model(x, m)