| | |
|---|
| | | |
|---|
| | | return result, meta_data |
|---|
| | | |
|---|
| | | def export( |
|---|
| | | self, |
|---|
| | | max_seq_len=512, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | |
|---|
| | | is_onnx = kwargs.get("type", "onnx") == "onnx" |
|---|
| | | encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export") |
|---|
| | | self.encoder = encoder_class(self.encoder, onnx=is_onnx) |
|---|
| | | |
|---|
| | | predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export") |
|---|
| | | self.predictor = predictor_class(self.predictor, onnx=is_onnx) |
|---|
| | | |
|---|
| | | if kwargs["decoder"] == "ParaformerSANMDecoder": |
|---|
| | | kwargs["decoder"] = "ParaformerSANMDecoderOnline" |
|---|
| | | decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export") |
|---|
| | | self.decoder = decoder_class(self.decoder, onnx=is_onnx) |
|---|
| | | |
|---|
| | | from funasr.utils.torch_function import MakePadMask |
|---|
| | | from funasr.utils.torch_function import sequence_mask |
|---|
| | | |
|---|
| | | if is_onnx: |
|---|
| | | self.make_pad_mask = MakePadMask(max_seq_len, flip=False) |
|---|
| | | else: |
|---|
| | | self.make_pad_mask = sequence_mask(max_seq_len, flip=False) |
|---|
| | | |
|---|
| | | self.forward = self._export_forward |
|---|
| | | |
|---|
| | | import copy |
|---|
| | | import types |
|---|
| | | encoder_model = copy.copy(self) |
|---|
| | | decoder_model = copy.copy(self) |
|---|
| | | |
|---|
| | | # encoder |
|---|
| | | encoder_model.forward = types.MethodType(ParaformerStreaming._export_encoder_forward, encoder_model) |
|---|
| | | encoder_model.export_dummy_inputs = types.MethodType(ParaformerStreaming.export_encoder_dummy_inputs, encoder_model) |
|---|
| | | encoder_model.export_input_names = types.MethodType(ParaformerStreaming.export_encoder_input_names, encoder_model) |
|---|
| | | encoder_model.export_output_names = types.MethodType(ParaformerStreaming.export_encoder_output_names, encoder_model) |
|---|
| | | encoder_model.export_dynamic_axes = types.MethodType(ParaformerStreaming.export_encoder_dynamic_axes, encoder_model) |
|---|
| | | encoder_model.export_name = types.MethodType(ParaformerStreaming.export_encoder_name, encoder_model) |
|---|
| | | |
|---|
| | | # decoder |
|---|
| | | decoder_model.forward = types.MethodType(ParaformerStreaming._export_decoder_forward, decoder_model) |
|---|
| | | decoder_model.export_dummy_inputs = types.MethodType(ParaformerStreaming.export_decoder_dummy_inputs, decoder_model) |
|---|
| | | decoder_model.export_input_names = types.MethodType(ParaformerStreaming.export_decoder_input_names, decoder_model) |
|---|
| | | decoder_model.export_output_names = types.MethodType(ParaformerStreaming.export_decoder_output_names, decoder_model) |
|---|
| | | decoder_model.export_dynamic_axes = types.MethodType(ParaformerStreaming.export_decoder_dynamic_axes, decoder_model) |
|---|
| | | decoder_model.export_name = types.MethodType(ParaformerStreaming.export_decoder_name, decoder_model) |
|---|
| | | |
|---|
| | | return encoder_model, decoder_model |
|---|
| | | |
|---|
| | | def _export_encoder_forward( |
|---|
| | | self, |
|---|
| | | speech: torch.Tensor, |
|---|
| | | speech_lengths: torch.Tensor, |
|---|
| | | ): |
|---|
| | | # a. To device |
|---|
| | | batch = {"speech": speech, "speech_lengths": speech_lengths, "online": True} |
|---|
| | | # batch = to_device(batch, device=self.device) |
|---|
| | | |
|---|
| | | enc, enc_len = self.encoder(**batch) |
|---|
| | | mask = self.make_pad_mask(enc_len)[:, None, :] |
|---|
| | | alphas, _ = self.predictor.forward_cnn(enc, mask) |
|---|
| | | |
|---|
| | | return enc, enc_len, alphas |
|---|
| | | |
|---|
| | | def export_encoder_dummy_inputs(self): |
|---|
| | | speech = torch.randn(2, 30, 560) |
|---|
| | | speech_lengths = torch.tensor([6, 30], dtype=torch.int32) |
|---|
| | | return (speech, speech_lengths) |
|---|
| | | |
|---|
| | | def export_encoder_input_names(self): |
|---|
| | | return ['speech', 'speech_lengths'] |
|---|
| | | |
|---|
| | | def export_encoder_output_names(self): |
|---|
| | | return ['enc', 'enc_len', 'alphas'] |
|---|
| | | |
|---|
| | | def export_encoder_dynamic_axes(self): |
|---|
| | | return { |
|---|
| | | 'speech': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | 1: 'feats_length' |
|---|
| | | }, |
|---|
| | | 'speech_lengths': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | }, |
|---|
| | | 'enc': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | 1: 'feats_length' |
|---|
| | | }, |
|---|
| | | 'enc_len': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | }, |
|---|
| | | 'alphas': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | 1: 'feats_length' |
|---|
| | | }, |
|---|
| | | } |
|---|
| | | |
|---|
| | | def export_encoder_name(self): |
|---|
| | | return "model.onnx" |
|---|
| | | |
|---|
| | | def _export_decoder_forward( |
|---|
| | | self, |
|---|
| | | enc: torch.Tensor, |
|---|
| | | enc_len: torch.Tensor, |
|---|
| | | acoustic_embeds: torch.Tensor, |
|---|
| | | acoustic_embeds_len: torch.Tensor, |
|---|
| | | *args, |
|---|
| | | ): |
|---|
| | | decoder_out, out_caches = self.decoder(enc, enc_len, acoustic_embeds, acoustic_embeds_len, *args) |
|---|
| | | sample_ids = decoder_out.argmax(dim=-1) |
|---|
| | | |
|---|
| | | return decoder_out, sample_ids, out_caches |
|---|
| | | |
|---|
| | | def export_decoder_dummy_inputs(self): |
|---|
| | | dummy_inputs = self.decoder.get_dummy_inputs(enc_size=self.encoder._output_size) |
|---|
| | | return dummy_inputs |
|---|
| | | |
|---|
| | | def export_decoder_input_names(self): |
|---|
| | | |
|---|
| | | return self.decoder.get_input_names() |
|---|
| | | |
|---|
| | | def export_decoder_output_names(self): |
|---|
| | | |
|---|
| | | return self.decoder.get_output_names() |
|---|
| | | |
|---|
| | | def export_decoder_dynamic_axes(self): |
|---|
| | | return self.decoder.get_dynamic_axes() |
|---|
| | | def export_decoder_name(self): |
|---|
| | | return "decoder.onnx" |
|---|
