| | |
|---|
| | | results.append(result_i) |
|---|
| | | |
|---|
| | | return results, 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) |
|---|
| | | |
|---|
| | | 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 |
|---|
| | | |
|---|
| | | return self |
|---|
| | | |
|---|
| | | def _export_forward( |
|---|
| | | self, |
|---|
| | | speech: torch.Tensor, |
|---|
| | | speech_lengths: torch.Tensor, |
|---|
| | | ): |
|---|
| | | # a. To device |
|---|
| | | batch = {"speech": speech, "speech_lengths": speech_lengths} |
|---|
| | | # batch = to_device(batch, device=self.device) |
|---|
| | | |
|---|
| | | enc, enc_len = self.encoder(**batch) |
|---|
| | | mask = self.make_pad_mask(enc_len)[:, None, :] |
|---|
| | | pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask) |
|---|
| | | pre_token_length = pre_token_length.round().type(torch.int32) |
|---|
| | | |
|---|
| | | decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length) |
|---|
| | | decoder_out = torch.log_softmax(decoder_out, dim=-1) |
|---|
| | | |
|---|
| | | # get predicted timestamps |
|---|
| | | us_alphas, us_cif_peak = self.predictor.get_upsample_timestmap(enc, mask, pre_token_length) |
|---|
| | | |
|---|
| | | return decoder_out, pre_token_length, us_alphas, us_cif_peak |
|---|
| | | |
|---|
| | | def export_dummy_inputs(self): |
|---|
| | | speech = torch.randn(2, 30, 560) |
|---|
| | | speech_lengths = torch.tensor([6, 30], dtype=torch.int32) |
|---|
| | | return (speech, speech_lengths) |
|---|
| | | |
|---|
| | | def export_input_names(self): |
|---|
| | | return ['speech', 'speech_lengths'] |
|---|
| | | |
|---|
| | | def export_output_names(self): |
|---|
| | | return ['logits', 'token_num', 'us_alphas', 'us_cif_peak'] |
|---|
| | | |
|---|
| | | def export_dynamic_axes(self): |
|---|
| | | return { |
|---|
| | | 'speech': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | 1: 'feats_length' |
|---|
| | | }, |
|---|
| | | 'speech_lengths': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | }, |
|---|
| | | 'logits': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | 1: 'logits_length' |
|---|
| | | }, |
|---|
| | | 'us_alphas': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | 1: 'alphas_length' |
|---|
| | | }, |
|---|
| | | 'us_cif_peak': { |
|---|
| | | 0: 'batch_size', |
|---|
| | | 1: 'alphas_length' |
|---|
| | | }, |
|---|
| | | } |
|---|
| | | |
|---|
| | | def export_name(self, ): |
|---|
| | | return "model.onnx" |
|---|
