FunASR/funasr/export/models/target_delay_transformer.py

from typing import Tuple

import torch
import torch.nn as nn

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 funasr.models.encoder.sanm_encoder import SANMEncoder as Encoder
        from funasr.models.encoder.sanm_encoder import SANMEncoder
        from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export

        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'
            },
        }