Update onnx_export.py

onnx_export.py

@@ -1,51 +1,9 @@
 import torch
-from torchaudio.models.wav2vec2.utils import import_fairseq_model
-from fairseq import checkpoint_utils
 from onnxexport.model_onnx import SynthesizerTrn
 import utils
 
-def get_hubert_model():
-    vec_path = "hubert/checkpoint_best_legacy_500.pt"
-    print("load model(s) from {}".format(vec_path))
-    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-        [vec_path],
-        suffix="",
-    )
-    model = models[0]
-    model.eval()
-    return model
-
-
-def main(HubertExport, NetExport):
+def main(NetExport):
     path = "SoVits4.0"
-
-    '''if HubertExport:
-        device = torch.device("cpu")
-        vec_path = "hubert/checkpoint_best_legacy_500.pt"
-        models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
-            [vec_path],
-            suffix="",
-        )
-        original = models[0]
-        original.eval()
-        model = original
-        test_input = torch.rand(1, 1, 16000)
-        model(test_input)
-        torch.onnx.export(model,
-                          test_input,
-                          "hubert4.0.onnx",
-                          export_params=True,
-                          opset_version=16,
-                          do_constant_folding=True,
-                          input_names=['source'],
-                          output_names=['embed'],
-                          dynamic_axes={
-                              'source':
-                                  {
-                                      2: "sample_length"
-                                  },
-                          }
-                          )'''
     if NetExport:
         device = torch.device("cpu")
         hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
@@ -57,6 +15,7 @@ def main(HubertExport, NetExport):
         _ = SVCVITS.eval().to(device)
         for i in SVCVITS.parameters():
             i.requires_grad = False
+        
         test_hidden_unit = torch.rand(1, 10, 256)
         test_pitch = torch.rand(1, 10)
         test_mel2ph = torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0)
@@ -65,7 +24,7 @@ def main(HubertExport, NetExport):
         test_sid = torch.LongTensor([0])
         input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"]
         output_names = ["audio", ]
-        SVCVITS.eval()
+        
         torch.onnx.export(SVCVITS,
                           (
                               test_hidden_unit.to(device),
@@ -91,4 +50,4 @@ def main(HubertExport, NetExport):
 
 
 if __name__ == '__main__':
-    main(False, True)
+    main(True)