from typing import Tuple
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import torch
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import torch.nn as nn
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from funasr.models.encoder.sanm_encoder import SANMEncoder
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from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export
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from funasr.models.encoder.sanm_encoder import SANMVadEncoder
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from funasr.export.models.encoder.sanm_encoder import SANMVadEncoder as SANMVadEncoder_export
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class CT_Transformer(nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
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https://arxiv.org/pdf/2003.01309.pdf
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"""
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def __init__(
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self,
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model,
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max_seq_len=512,
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model_name='punc_model',
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**kwargs,
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):
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super().__init__()
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onnx = False
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if "onnx" in kwargs:
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onnx = kwargs["onnx"]
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self.embed = model.embed
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self.decoder = model.decoder
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# self.model = model
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self.feats_dim = self.embed.embedding_dim
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self.num_embeddings = self.embed.num_embeddings
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self.model_name = model_name
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if isinstance(model.encoder, SANMEncoder):
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self.encoder = SANMEncoder_export(model.encoder, onnx=onnx)
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else:
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assert False, "Only support samn encode."
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def forward(self, inputs: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
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"""Compute loss value from buffer sequences.
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Args:
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input (torch.Tensor): Input ids. (batch, len)
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hidden (torch.Tensor): Target ids. (batch, len)
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"""
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x = self.embed(inputs)
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# mask = self._target_mask(input)
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h, _ = self.encoder(x, text_lengths)
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y = self.decoder(h)
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return y
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def get_dummy_inputs(self):
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length = 120
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text_indexes = torch.randint(0, self.embed.num_embeddings, (2, length))
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text_lengths = torch.tensor([length-20, length], dtype=torch.int32)
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return (text_indexes, text_lengths)
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def get_input_names(self):
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return ['inputs', 'text_lengths']
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def get_output_names(self):
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return ['logits']
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def get_dynamic_axes(self):
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return {
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'inputs': {
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0: 'batch_size',
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1: 'feats_length'
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},
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'text_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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class CT_Transformer_VadRealtime(nn.Module):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
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https://arxiv.org/pdf/2003.01309.pdf
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"""
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def __init__(
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self,
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model,
|
max_seq_len=512,
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model_name='punc_model',
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**kwargs,
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):
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super().__init__()
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onnx = False
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if "onnx" in kwargs:
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onnx = kwargs["onnx"]
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self.embed = model.embed
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if isinstance(model.encoder, SANMVadEncoder):
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self.encoder = SANMVadEncoder_export(model.encoder, onnx=onnx)
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else:
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assert False, "Only support samn encode."
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self.decoder = model.decoder
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self.model_name = model_name
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def forward(self, inputs: torch.Tensor,
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text_lengths: torch.Tensor,
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vad_indexes: torch.Tensor,
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sub_masks: torch.Tensor,
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) -> Tuple[torch.Tensor, None]:
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"""Compute loss value from buffer sequences.
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Args:
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input (torch.Tensor): Input ids. (batch, len)
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hidden (torch.Tensor): Target ids. (batch, len)
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"""
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x = self.embed(inputs)
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# mask = self._target_mask(input)
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h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
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y = self.decoder(h)
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return y
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def with_vad(self):
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return True
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def get_dummy_inputs(self):
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length = 120
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text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length))
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text_lengths = torch.tensor([length], dtype=torch.int32)
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vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
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sub_masks = torch.ones(length, length, dtype=torch.float32)
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sub_masks = torch.tril(sub_masks).type(torch.float32)
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return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])
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def get_input_names(self):
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return ['inputs', 'text_lengths', 'vad_masks', 'sub_masks']
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def get_output_names(self):
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return ['logits']
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def get_dynamic_axes(self):
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return {
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'inputs': {
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1: 'feats_length'
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},
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'vad_masks': {
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2: 'feats_length1',
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3: 'feats_length2'
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},
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'sub_masks': {
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2: 'feats_length1',
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3: 'feats_length2'
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},
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'logits': {
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1: 'logits_length'
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},
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}
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