from typing import Any
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from typing import List
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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.export.utils.torch_function import MakePadMask
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from funasr.export.utils.torch_function import sequence_mask
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#from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
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from funasr.punctuation.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.punctuation.abs_model import AbsPunctuation
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class TargetDelayTransformer(nn.Module):
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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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from typing import Any
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from typing import List
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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.export.utils.torch_function import MakePadMask
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from funasr.export.utils.torch_function import sequence_mask
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# from funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
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from funasr.punctuation.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.punctuation.abs_model import AbsPunctuation
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# class TargetDelayTransformer(nn.Module):
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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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#
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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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#
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# def forward(self, input: torch.Tensor, text_lengths: torch.Tensor) -> Tuple[torch.Tensor, None]:
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# """Compute loss value from buffer sequences.
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#
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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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# """
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# x = self.embed(input)
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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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#
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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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#
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# def get_input_names(self):
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# return ['input', 'text_lengths']
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#
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# def get_output_names(self):
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# return ['logits']
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#
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# def get_dynamic_axes(self):
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# return {
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# 'input': {
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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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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, input: 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(input)
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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 ['input', '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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'input': {
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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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