| | |
| | | model.bias_encoder.batch_first = False |
| | | self.bias_encoder = model.bias_encoder |
| | | |
| | | def export_dummy_inputs(self): |
| | | hotword = torch.tensor( |
| | | [ |
| | | [10, 11, 12, 13, 14, 10, 11, 12, 13, 14], |
| | | [100, 101, 0, 0, 0, 0, 0, 0, 0, 0], |
| | | [1, 0, 0, 0, 0, 0, 0, 0, 0, 0], |
| | | [10, 11, 12, 13, 14, 10, 11, 12, 13, 14], |
| | | [100, 101, 0, 0, 0, 0, 0, 0, 0, 0], |
| | | [1, 0, 0, 0, 0, 0, 0, 0, 0, 0], |
| | | ], |
| | | dtype=torch.int32, |
| | | ) |
| | | # hotword_length = torch.tensor([10, 2, 1], dtype=torch.int32) |
| | | return (hotword) |
| | | |
| | | |
| | | def export_rebuild_model(model, **kwargs): |
| | | is_onnx = kwargs.get("type", "onnx") == "onnx" |
| | |
| | | backbone_model.export_dynamic_axes = types.MethodType( |
| | | export_backbone_dynamic_axes, backbone_model |
| | | ) |
| | | backbone_model.export_name = types.MethodType(export_backbone_name, backbone_model) |
| | | |
| | | embedder_model.export_name = "model_eb" |
| | | backbone_model.export_name = "model" |
| | | |
| | | return backbone_model, embedder_model |
| | | |