New Tiny Model Updata

compress_model.py

@@ -18,7 +18,7 @@ def copyStateDict(state_dict):
     return new_state_dict
 
 
-def removeOptimizer(config: str, input_model: str, output_model: str):
+def removeOptimizer(config: str, input_model: str, ishalf: bool, output_model: str):
     hps = utils.get_hparams_from_file(config)
 
     net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1,
@@ -35,8 +35,8 @@ def removeOptimizer(config: str, input_model: str, output_model: str):
     keys = []
     for k, v in new_dict_g['model'].items():
         keys.append(k)
-
-    new_dict_g = {k: new_dict_g['model'][k] for k in keys}
+    
+    new_dict_g = {k: new_dict_g['model'][k].half() for k in keys} if ishalf else {k: new_dict_g['model'][k] for k in keys}
 
     torch.save(
         {
@@ -56,7 +56,8 @@ if __name__ == "__main__":
                         default='configs/config.json')
     parser.add_argument("-i", "--input", type=str)
     parser.add_argument("-o", "--output", type=str, default=None)
-
+    parser.add_argument('-hf', '--half', action='store_true', default=False, help='Save as FP16')
+    
     args = parser.parse_args()
 
     output = args.output
@@ -64,6 +65,7 @@ if __name__ == "__main__":
     if output is None:
         import os.path
         filename, ext = os.path.splitext(args.input)
-        output = filename + "_release" + ext
+        half = "_half" if args.half else ""
+        output = filename + "_release" + half + ext
 
-    removeOptimizer(args.config, args.input, output)
+    removeOptimizer(args.config, args.input, args.half, output)

configs_template/config_template.json

@@ -64,6 +64,7 @@
     "speaker_embedding":false,
     "vol_embedding":false,
     "use_depthwise_conv":false,
+    "flow_share_parameter": false,
     "use_automatic_f0_prediction": true
   },
   "spk": {

models.py

@@ -20,7 +20,9 @@ class ResidualCouplingBlock(nn.Module):
                  dilation_rate,
                  n_layers,
                  n_flows=4,
-                 gin_channels=0):
+                 gin_channels=0,
+                 share_parameter=False
+                 ):
         super().__init__()
         self.channels = channels
         self.hidden_channels = hidden_channels
@@ -31,10 +33,13 @@ class ResidualCouplingBlock(nn.Module):
         self.gin_channels = gin_channels
 
         self.flows = nn.ModuleList()
+
+        self.wn = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=0, gin_channels=gin_channels) if share_parameter else None
+
         for i in range(n_flows):
             self.flows.append(
                 modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
-                                              gin_channels=gin_channels, mean_only=True))
+                                              gin_channels=gin_channels, mean_only=True, wn_sharing_parameter=self.wn))
             self.flows.append(modules.Flip())
 
     def forward(self, x, x_mask, g=None, reverse=False):
@@ -320,6 +325,7 @@ class SynthesizerTrn(nn.Module):
                  vocoder_name = "nsf-hifigan",
                  use_depthwise_conv = False,
                  use_automatic_f0_prediction = True,
+                 flow_share_parameter = False,
                  n_flow_layer = 4,
                  **kwargs):
 
@@ -386,7 +392,7 @@ class SynthesizerTrn(nn.Module):
             self.dec = Generator(h=hps)
 
         self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-        self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels)
+        self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels, share_parameter= flow_share_parameter)
         if self.use_automatic_f0_prediction:
             self.f0_decoder = F0Decoder(
                 1,

modules/modules.py

@@ -263,7 +263,9 @@ class ResidualCouplingLayer(nn.Module):
       n_layers,
       p_dropout=0,
       gin_channels=0,
-      mean_only=False):
+      mean_only=False,
+      wn_sharing_parameter=None
+      ):
     assert channels % 2 == 0, "channels should be divisible by 2"
     super().__init__()
     self.channels = channels
@@ -275,7 +277,7 @@ class ResidualCouplingLayer(nn.Module):
     self.mean_only = mean_only
 
     self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) if wn_sharing_parameter is None else wn_sharing_parameter
     self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
     self.post.weight.data.zero_()
     self.post.bias.data.zero_()

preprocess_flist_config.py

@@ -85,7 +85,7 @@ if __name__ == "__main__":
         config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 768
         d_config_template["data"]["encoder_out_channels"] = 768
     elif args.speech_encoder == "vec256l9" or args.speech_encoder == 'hubertsoft':
-        config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 256
+        config_template["model"]["ssl_dim"] = config_template["model"]["gin_channels"] = 256
         d_config_template["data"]["encoder_out_channels"] = 256
     elif args.speech_encoder == "whisper-ppg" or args.speech_encoder == 'cnhubertlarge':
         config_template["model"]["ssl_dim"] = config_template["model"]["filter_channels"] = config_template["model"]["gin_channels"] = 1024