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.modules.attention import MultiHeadedAttentionSANM
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from funasr.export.models.modules.multihead_att import MultiHeadedAttentionSANM as MultiHeadedAttentionSANM_export
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from funasr.export.models.modules.encoder_layer import EncoderLayerSANM as EncoderLayerSANM_export
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from funasr.export.models.modules.encoder_layer import EncoderLayerConformer as EncoderLayerConformer_export
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from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward
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from funasr.export.models.modules.feedforward import PositionwiseFeedForward as PositionwiseFeedForward_export
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from funasr.export.models.encoder.sanm_encoder import SANMEncoder
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from funasr.modules.attention import RelPositionMultiHeadedAttention
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# from funasr.export.models.modules.multihead_att import RelPositionMultiHeadedAttention as RelPositionMultiHeadedAttention_export
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from funasr.export.models.modules.multihead_att import OnnxRelPosMultiHeadedAttention as RelPositionMultiHeadedAttention_export
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class ConformerEncoder(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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feats_dim=560,
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model_name='encoder',
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onnx: bool = True,
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):
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super().__init__()
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self.embed = model.embed
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self.model = model
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self.feats_dim = feats_dim
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self._output_size = model._output_size
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if onnx:
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self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
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else:
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self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
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for i, d in enumerate(self.model.encoders):
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if isinstance(d.self_attn, MultiHeadedAttentionSANM):
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d.self_attn = MultiHeadedAttentionSANM_export(d.self_attn)
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if isinstance(d.self_attn, RelPositionMultiHeadedAttention):
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d.self_attn = RelPositionMultiHeadedAttention_export(d.self_attn)
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if isinstance(d.feed_forward, PositionwiseFeedForward):
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d.feed_forward = PositionwiseFeedForward_export(d.feed_forward)
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self.model.encoders[i] = EncoderLayerConformer_export(d)
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self.model_name = model_name
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self.num_heads = model.encoders[0].self_attn.h
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self.hidden_size = model.encoders[0].self_attn.linear_out.out_features
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def prepare_mask(self, mask):
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if len(mask.shape) == 2:
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mask = 1 - mask[:, None, None, :]
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elif len(mask.shape) == 3:
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mask = 1 - mask[:, None, :]
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return mask * -10000.0
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def forward(self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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):
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speech = speech * self._output_size ** 0.5
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mask = self.make_pad_mask(speech_lengths)
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mask = self.prepare_mask(mask)
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if self.embed is None:
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xs_pad = speech
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else:
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xs_pad = self.embed(speech)
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encoder_outs = self.model.encoders(xs_pad, mask)
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xs_pad, masks = encoder_outs[0], encoder_outs[1]
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if isinstance(xs_pad, tuple):
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xs_pad = xs_pad[0]
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xs_pad = self.model.after_norm(xs_pad)
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return xs_pad, speech_lengths
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def get_output_size(self):
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return self.model.encoders[0].size
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def get_dummy_inputs(self):
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feats = torch.randn(1, 100, self.feats_dim)
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return (feats)
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def get_input_names(self):
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return ['feats']
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def get_output_names(self):
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return ['encoder_out', 'encoder_out_lens', 'predictor_weight']
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def get_dynamic_axes(self):
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return {
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'feats': {
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1: 'feats_length'
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},
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'encoder_out': {
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1: 'enc_out_length'
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},
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'predictor_weight':{
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1: 'pre_out_length'
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}
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}
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