mirror of
https://github.com/modelscope/FunASR
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2196844d1d
* multi tokenizer * support fsmn_kws, fsmn_kws_mt, sanm_kws, sanm_kws_streaming training * kws --------- Co-authored-by: pengteng.spt <pengteng.spt@alibaba-inc.com>
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
#!/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 copy
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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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# self.device = kwargs.get("device")
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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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if hasattr(model, "ctc"):
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model.encoder = encoder_class(
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model.encoder,
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onnx=is_onnx,
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feats_dim=kwargs.get("input_size", 560),
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ctc_linear=model.ctc.ctc_lo
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)
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else:
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assert False, print(model)
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model.encoder = encoder_class(model.encoder, onnx=is_onnx, feats_dim=kwargs.get("input_size", 560))
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# from funasr.utils.torch_function import sequence_mask
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# model.make_pad_mask = sequence_mask(max_seq_len=None, flip=False)
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encoder_model = copy.copy(model)
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# encoder
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encoder_model.forward = types.MethodType(export_encoder_forward, encoder_model)
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encoder_model.export_dummy_inputs = types.MethodType(export_encoder_dummy_inputs, encoder_model)
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encoder_model.export_input_names = types.MethodType(export_encoder_input_names, encoder_model)
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encoder_model.export_output_names = types.MethodType(export_encoder_output_names, encoder_model)
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encoder_model.export_dynamic_axes = types.MethodType(export_encoder_dynamic_axes, encoder_model)
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encoder_model.export_name = types.MethodType(export_encoder_name, encoder_model)
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return encoder_model
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def export_encoder_forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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):
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# a. To device
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batch = {
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"speech": speech,
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"speech_lengths": speech_lengths,
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"online": True
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}
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# batch = to_device(batch, device=self.device)
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encoder_out, encoder_out_len = self.encoder(**batch)
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# mask = self.make_pad_mask(encoder_out_len)[:, None, :]
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# alphas, _ = self.predictor.forward_cnn(enc, mask)
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# return encoder_out, encoder_out_len, alphas
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return encoder_out, encoder_out_len
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def export_encoder_dummy_inputs(self):
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speech = torch.randn(2, 30, 280)
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speech_lengths = torch.tensor([6, 30], dtype=torch.int32)
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return (speech, speech_lengths)
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def export_encoder_input_names(self):
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return ["speech", "speech_lengths"]
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def export_encoder_output_names(self):
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# return ["encoder_out", "encoder_out_len", "alphas"]
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return ["encoder_out", "encoder_out_len"]
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def export_encoder_dynamic_axes(self):
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return {
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"speech": {
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0: "batch_size", 1: "feats_length"
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},
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"speech_lengths": {
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0: "batch_size",
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},
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"encoder_out": {
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0: "batch_size", 1: "feats_length"
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},
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"encoder_out_len": {
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0: "batch_size",
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},
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}
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def export_encoder_name(self):
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return "encoder.onnx"
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