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| | | from funasr.models.transformer.utils.add_sos_eos import add_sos_eos |
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| | | from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list |
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| | | from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank |
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| | | |
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| | | from funasr.train_utils.device_funcs import to_device |
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| | | |
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| | | @tables.register("model_classes", "Paraformer") |
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| | | class Paraformer(torch.nn.Module): |
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| | |
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| | | self.predictor_bias = predictor_bias |
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| | | self.sampling_ratio = sampling_ratio |
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| | | self.criterion_pre = mae_loss(normalize_length=length_normalized_loss) |
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| | | # self.step_cur = 0 |
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| | | # |
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| | | |
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| | | |
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| | | self.share_embedding = share_embedding |
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| | | if self.share_embedding: |
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| | | self.decoder.embed = None |
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| | |
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| | | |
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| | | return results, meta_data |
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| | | |
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| | | def export( |
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| | | self, |
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| | | max_seq_len=512, |
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| | | **kwargs, |
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| | | ): |
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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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| | | self.encoder = encoder_class(self.encoder, onnx=is_onnx) |
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| | | |
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| | | predictor_class = tables.predictor_classes.get(kwargs["predictor"]+"Export") |
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| | | self.predictor = predictor_class(self.predictor, onnx=is_onnx) |
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| | | |
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| | | |
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| | | decoder_class = tables.decoder_classes.get(kwargs["decoder"]+"Export") |
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| | | self.decoder = decoder_class(self.decoder, onnx=is_onnx) |
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| | | |
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| | | from funasr.utils.torch_function import MakePadMask |
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| | | from funasr.utils.torch_function import sequence_mask |
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| | | |
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| | | |
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| | | if is_onnx: |
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| | | self.make_pad_mask = MakePadMask(max_seq_len, flip=False) |
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| | | else: |
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| | | self.make_pad_mask = sequence_mask(max_seq_len, flip=False) |
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| | | |
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| | | self.forward = self._export_forward |
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| | | |
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| | | return self |
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| | | |
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| | | def export_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 = {"speech": speech, "speech_lengths": speech_lengths} |
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| | | batch = to_device(batch, device=self.device) |
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| | | |
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| | | enc, enc_len = self.encoder(**batch) |
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| | | mask = self.make_pad_mask(enc_len)[:, None, :] |
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| | | pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask) |
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| | | pre_token_length = pre_token_length.floor().type(torch.int32) |
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| | | |
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| | | decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length) |
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| | | decoder_out = torch.log_softmax(decoder_out, dim=-1) |
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| | | # sample_ids = decoder_out.argmax(dim=-1) |
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| | | |
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| | | return decoder_out, pre_token_length |
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| | | |
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| | | def export_dummy_inputs(self): |
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| | | speech = torch.randn(2, 30, 560) |
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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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| | | |
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| | | |
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| | | def export_input_names(self): |
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| | | return ['speech', 'speech_lengths'] |
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| | | |
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| | | def export_output_names(self): |
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| | | return ['logits', 'token_num'] |
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| | | |
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| | | def export_dynamic_axes(self): |
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| | | return { |
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| | | 'speech': { |
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| | | 0: 'batch_size', |
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| | | 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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| | | 'logits': { |
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| | | 0: 'batch_size', |
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| | | 1: 'logits_length' |
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| | | }, |
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| | | } |
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| | | |
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| | | def export_name(self, ): |
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| | | return "model.onnx" |
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