Merge pull request #279 from svc-develop-team/4.1-Latest

To Latest

inference/infer_tool.py

@@ -192,6 +192,7 @@ class Svc(object):
             self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
             **self.hps_ms.model)
         _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
+        self.dtype = list(self.net_g_ms.parameters())[0].dtype
         if "half" in self.net_g_path and torch.cuda.is_available():
             _ = self.net_g_ms.half().eval().to(self.dev)
         else:
@@ -276,8 +277,9 @@ class Svc(object):
             sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
             c, f0, uv = self.get_unit_f0(wav, tran, cluster_infer_ratio, speaker, f0_filter,f0_predictor,cr_threshold=cr_threshold)
             n_frames = f0.size(1)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            c = c.half()
+        c = c.to(self.dtype)
+        f0 = f0.to(self.dtype)
+        uv = uv.to(self.dtype)
         with torch.no_grad():
             start = time.time()
             vol = None
@@ -289,6 +291,10 @@ class Svc(object):
             else:
                 audio = torch.FloatTensor(wav).to(self.dev)
                 audio_mel = None
+            if self.dtype != torch.float32:
+                c = c.to(torch.float32)
+                f0 = f0.to(torch.float32)
+                uv = uv.to(torch.float32)
             if self.only_diffusion or self.shallow_diffusion:
                 vol = self.volume_extractor.extract(audio[None,:])[None,:,None].to(self.dev) if vol is None else vol[:,:,None]
                 if self.shallow_diffusion and second_encoding:

utils.py

@@ -153,6 +153,7 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
     if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
         optimizer.load_state_dict(checkpoint_dict['optimizer'])
     saved_state_dict = checkpoint_dict['model']
+    model = model.to(list(saved_state_dict.values())[0].dtype)
     if hasattr(model, 'module'):
         state_dict = model.module.state_dict()
     else:
@@ -165,9 +166,10 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False
             new_state_dict[k] = saved_state_dict[k]
             assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
         except Exception:
-            print("error, %s is not in the checkpoint" % k)
-            logger.info("%s is not in the checkpoint" % k)
-            new_state_dict[k] = v
+            if "enc_q" not in k or "emb_g" not in k:
+              print("error, %s is not in the checkpoint" % k)
+              logger.info("%s is not in the checkpoint" % k)
+              new_state_dict[k] = v
     if hasattr(model, 'module'):
         model.module.load_state_dict(new_state_dict)
     else:

vdecoder/hifigan/models.py

@@ -266,7 +266,7 @@ class SourceModuleHnNSF(torch.nn.Module):
         """
         # source for harmonic branch
         sine_wavs, uv, _ = self.l_sin_gen(x)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs.to(self.l_linear.weight.dtype)))
 
         # source for noise branch, in the same shape as uv
         noise = torch.randn_like(uv) * self.sine_amp / 3

vdecoder/hifiganwithsnake/models.py

@@ -279,7 +279,7 @@ class SourceModuleHnNSF(torch.nn.Module):
         """
         # source for harmonic branch
         sine_wavs, uv, _ = self.l_sin_gen(x)
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs.to(self.l_linear.weight.dtype)))
 
         # source for noise branch, in the same shape as uv
         noise = torch.randn_like(uv) * self.sine_amp / 3