general punc model conversion onnx

funasr/export/export_model.py

@@ -174,7 +174,10 @@ class ModelExport:
             json_file = os.path.join(model_dir, 'configuration.json')
             with open(json_file, 'r') as f:
                 config_data = json.load(f)
-                mode = config_data['model']['model_config']['mode']
+                if config_data['task'] == "punctuation":
+                    mode = config_data['model']['punc_model_config']['mode']
+                else:
+                    mode = config_data['model']['model_config']['mode']
         if mode.startswith('paraformer'):
             from funasr.tasks.asr import ASRTaskParaformer as ASRTask
             config = os.path.join(model_dir, 'config.yaml')
@@ -195,6 +198,13 @@ class ModelExport:
             )
             self.export_config["feats_dim"] = 400
             self.frontend = model.frontend
+        elif mode.startswith('punc'):
+            from funasr.tasks.punctuation import PunctuationTask as PUNCTask
+            punc_train_config = os.path.join(model_dir, 'config.yaml')
+            punc_model_file = os.path.join(model_dir, 'punc.pb')
+            model, punc_train_args = PUNCTask.build_model_from_file(
+                punc_train_config, punc_model_file, 'cpu'
+            )
         self._export(model, tag_name)
             
 

funasr/export/models/target_delay_transformer.py

@@ -0,0 +1,160 @@
+from typing import Any
+from typing import List
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+
+from funasr.export.utils.torch_function import MakePadMask
+from funasr.export.utils.torch_function import sequence_mask
+#from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
+from funasr.punctuation.sanm_encoder import SANMEncoder
+from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
+from funasr.punctuation.abs_model import AbsPunctuation
+
+
+class TargetDelayTransformer(nn.Module):
+
+    def __init__(
+            self,
+            model,
+            max_seq_len=512,
+            model_name='punc_model',
+            **kwargs,
+    ):
+        super().__init__()
+        onnx = False
+        if "onnx" in kwargs:
+            onnx = kwargs["onnx"]
+        self.embed = model.embed
+        self.decoder = model.decoder
+        self.model = model
+        self.feats_dim = self.embed.embedding_dim
+        self.num_embeddings = self.embed.num_embeddings
+        self.model_name = model_name
+        from typing import Any
+        from typing import List
+        from typing import Tuple
+
+        import torch
+        import torch.nn as nn
+
+        from funasr.export.utils.torch_function import MakePadMask
+        from funasr.export.utils.torch_function import sequence_mask
+        # from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
+        from funasr.punctuation.sanm_encoder import SANMEncoder
+        from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
+        from funasr.punctuation.abs_model import AbsPunctuation
+
+        class TargetDelayTransformer(nn.Module):
+
+            def __init__(
+                    self,
+                    model,
+                    max_seq_len=512,
+                    model_name='punc_model',
+                    **kwargs,
+            ):
+                super().__init__()
+                onnx = False
+                if "onnx" in kwargs:
+                    onnx = kwargs["onnx"]
+                self.embed = model.embed
+                self.decoder = model.decoder
+                self.model = model
+                self.feats_dim = self.embed.embedding_dim
+                self.num_embeddings = self.embed.num_embeddings
+                self.model_name = model_name
+
+                if isinstance(model.encoder, SANMEncoder):
+                    self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
+                else:
+                    assert False, "Only support samn encode."
+
+            def forward(self, input: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
+                """Compute loss value from buffer sequences.
+
+                Args:
+                    input (torch.Tensor): Input ids. (batch, len)
+                    hidden (torch.Tensor): Target ids. (batch, len)
+
+                """
+                x = self.embed(input)
+                # mask = self._target_mask(input)
+                h, _ = self.encoder(x, text_lengths)
+                y = self.decoder(h)
+                return y
+
+            def get_dummy_inputs(self):
+                length = 120
+                text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length))
+                text_lengths = torch.tensor([length - 20, length], dtype=torch.int32)
+                return (text_indexes, text_lengths)
+
+            def get_input_names(self):
+                return ['input', 'text_lengths']
+
+            def get_output_names(self):
+                return ['logits']
+
+            def get_dynamic_axes(self):
+                return {
+                    'input': {
+                        0: 'batch_size',
+                        1: 'feats_length'
+                    },
+                    'text_lengths': {
+                        0: 'batch_size',
+                    },
+                    'logits': {
+                        0: 'batch_size',
+                        1: 'logits_length'
+                    },
+                }
+
+        if isinstance(model.encoder, SANMEncoder):
+            self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
+        else:
+            assert False, "Only support samn encode."
+
+    def forward(self, input: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
+        """Compute loss value from buffer sequences.
+
+        Args:
+            input (torch.Tensor): Input ids. (batch, len)
+            hidden (torch.Tensor): Target ids. (batch, len)
+
+        """
+        x = self.embed(input)
+        # mask = self._target_mask(input)
+        h, _ = self.encoder(x, text_lengths)
+        y = self.decoder(h)
+        return y
+
+    def get_dummy_inputs(self):
+        length = 120
+        text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length))
+        text_lengths = torch.tensor([length-20, length], dtype=torch.int32)
+        return (text_indexes, text_lengths)
+
+    def get_input_names(self):
+        return ['input', 'text_lengths']
+
+    def get_output_names(self):
+        return ['logits']
+
+    def get_dynamic_axes(self):
+        return {
+            'input': {
+                0: 'batch_size',
+                1: 'feats_length'
+            },
+            'text_lengths': {
+                0: 'batch_size',
+            },
+            'logits': {
+                0: 'batch_size',
+                1: 'logits_length'
+            },
+        }
+