#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import types
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import torch
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from funasr.register import tables
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def export_rebuild_model(model, **kwargs):
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
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model.encoder = encoder_class(model.encoder, onnx=is_onnx)
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model.forward = types.MethodType(export_forward, model)
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model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
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model.export_input_names = types.MethodType(export_input_names, model)
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model.export_output_names = types.MethodType(export_output_names, model)
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model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
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model.export_name = types.MethodType(export_name, model)
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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):
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if data_in is None:
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speech = torch.randn(1, frame, self.encoder_conf.get("input_dim"))
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else:
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speech = None # Undo
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cache_frames = self.encoder_conf.get("lorder") + self.encoder_conf.get("rorder") - 1
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in_cache0 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
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in_cache1 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
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in_cache2 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
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in_cache3 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
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return (speech, in_cache0, in_cache1, in_cache2, in_cache3)
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def export_input_names(self):
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return ["speech", "in_cache0", "in_cache1", "in_cache2", "in_cache3"]
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def export_output_names(self):
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return ["logits", "out_cache0", "out_cache1", "out_cache2", "out_cache3"]
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def export_dynamic_axes(self):
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return {
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"speech": {1: "feats_length"},
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}
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def export_name(
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self,
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):
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return "model.onnx"
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