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funasr_local/export/models/CT_Transformer.py

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+from typing import Tuple
+
+import torch
+import torch.nn as nn
+
+from funasr_local.models.encoder.sanm_encoder import SANMEncoder
+from funasr_local.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
+from funasr_local.models.encoder.sanm_encoder import SANMVadEncoder
+from funasr_local.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export
+
+class CT_Transformer(nn.Module):
+    """
+    Author: Speech Lab of DAMO Academy, Alibaba Group
+    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
+    https://arxiv.org/pdf/2003.01309.pdf
+    """
+    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, inputs: 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(inputs)
+        # 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)).type(torch.int32)
+        text_lengths = torch.tensor([length-20, length], dtype=torch.int32)
+        return (text_indexes, text_lengths)
+
+    def get_input_names(self):
+        return ['inputs', 'text_lengths']
+
+    def get_output_names(self):
+        return ['logits']
+
+    def get_dynamic_axes(self):
+        return {
+            'inputs': {
+                0: 'batch_size',
+                1: 'feats_length'
+            },
+            'text_lengths': {
+                0: 'batch_size',
+            },
+            'logits': {
+                0: 'batch_size',
+                1: 'logits_length'
+            },
+        }
+
+
+class CT_Transformer_VadRealtime(nn.Module):
+    """
+    Author: Speech Lab of DAMO Academy, Alibaba Group
+    CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
+    https://arxiv.org/pdf/2003.01309.pdf
+    """
+    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
+        if isinstance(model.encoder, SANMVadEncoder):
+            self.encoder = SANMVadEncoder_export(model.encoder, onnx=onnx)
+        else:
+            assert False, "Only support samn encode."
+        self.decoder = model.decoder
+        self.model_name = model_name
+
+
+
+    def forward(self, inputs: torch.Tensor,
+                text_lengths: torch.Tensor,
+                vad_indexes: torch.Tensor,
+                sub_masks: 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(inputs)
+        # mask = self._target_mask(input)
+        h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
+        y = self.decoder(h)
+        return y
+
+    def with_vad(self):
+        return True
+
+    def get_dummy_inputs(self):
+        length = 120
+        text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)).type(torch.int32)
+        text_lengths = torch.tensor([length], dtype=torch.int32)
+        vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
+        sub_masks = torch.ones(length, length, dtype=torch.float32)
+        sub_masks = torch.tril(sub_masks).type(torch.float32)
+        return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])
+
+    def get_input_names(self):
+        return ['inputs', 'text_lengths', 'vad_masks', 'sub_masks']
+
+    def get_output_names(self):
+        return ['logits']
+
+    def get_dynamic_axes(self):
+        return {
+            'inputs': {
+                1: 'feats_length'
+            },
+            'vad_masks': {
+                2: 'feats_length1',
+                3: 'feats_length2'
+            },
+            'sub_masks': {
+                2: 'feats_length1',
+                3: 'feats_length2'
+            },
+            'logits': {
+                1: 'logits_length'
+            },
+        }