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|
|
| import types |
| import torch |
| from funasr.register import tables |
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|
| def export_rebuild_model(model, **kwargs): |
| is_onnx = kwargs.get("type", "onnx") == "onnx" |
| encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export") |
| model.encoder = encoder_class(model.encoder, onnx=is_onnx) |
|
|
| model.forward = types.MethodType(export_forward, model) |
| model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model) |
| model.export_input_names = types.MethodType(export_input_names, model) |
| model.export_output_names = types.MethodType(export_output_names, model) |
| model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model) |
| model.export_name = types.MethodType(export_name, model) |
|
|
| return model |
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|
|
| def export_forward(self, feats: torch.Tensor, *args, **kwargs): |
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| scores, out_caches = self.encoder(feats, *args) |
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| return scores, out_caches |
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|
| def export_dummy_inputs(self, data_in=None, frame=30): |
| if data_in is None: |
| speech = torch.randn(1, frame, self.encoder_conf.get("input_dim")) |
| else: |
| speech = None |
|
|
| cache_frames = self.encoder_conf.get("lorder") + self.encoder_conf.get("rorder") - 1 |
| in_cache0 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1) |
| in_cache1 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1) |
| in_cache2 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1) |
| in_cache3 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1) |
|
|
| return (speech, in_cache0, in_cache1, in_cache2, in_cache3) |
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|
|
| def export_input_names(self): |
| return ["speech", "in_cache0", "in_cache1", "in_cache2", "in_cache3"] |
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|
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| def export_output_names(self): |
| return ["logits", "out_cache0", "out_cache1", "out_cache2", "out_cache3"] |
|
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|
|
| def export_dynamic_axes(self): |
| return { |
| "speech": {1: "feats_length"}, |
| } |
|
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|
|
| def export_name( |
| self, |
| ): |
| return "model.onnx" |
|
|
