class_docstr: FastSpeech module for end-to-end text-to-speech.
  
  This is a module of FastSpeech, feed-forward Transformer with duration predictor
  described in `FastSpeech: Fast, Robust and Controllable Text to Speech`_, which
  does not require any auto-regressive processing during inference, resulting in
  fast decoding compared with auto-regressive Transformer.
  
  .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
      https://arxiv.org/pdf/1905.09263.pdf
class_docstr_tok: ["FastSpeech","module","for","end-to-end","text-to-speech",".","This","is","a","module","of","FastSpeech",",","feed-forward","Transformer","with","duration","predictor","described","in","`","FastSpeech",":","Fast",",","Robust","and","Controllable","Text","to","Speech","`","_",",","which","does","not","require","any","auto-regressive","processing","during","inference",",","resulting","in","fast","decoding","compared","with","auto-regressive","Transformer",".","..","_","`","FastSpeech",":","Fast",",","Robust","and","Controllable","Text","to","Speech","`",":","https",":","\/\/arxiv.org\/pdf\/1905.09263.pdf"]
docstr: Integrate speaker embedding with hidden states.
  
  Args:
      hs (Tensor): Batch of hidden state sequences (B, T_text, adim).
      spembs (Tensor): Batch of speaker embeddings (B, spk_embed_dim).
  
  Returns:
      Tensor: Batch of integrated hidden state sequences (B, T_text, adim).
docstr_tok: ["Integrate","speaker","embedding","with","hidden","states",".","Args",":","hs","(","Tensor",")",":","Batch","of","hidden","state","sequences","(","B",",","T_text",",","adim",")",".","spembs","(","Tensor",")",":","Batch","of","speaker","embeddings","(","B",",","spk_embed_dim",")",".","Returns",":","Tensor",":","Batch","of","integrated","hidden","state","sequences","(","B",",","T_text",",","adim",")","."]
code: def _integrate_with_spk_embed(
      self, hs: torch.Tensor, spembs: torch.Tensor
  ) -> torch.Tensor:
      """Integrate speaker embedding with hidden states.
  
      Args:
          hs (Tensor): Batch of hidden state sequences (B, T_text, adim).
          spembs (Tensor): Batch of speaker embeddings (B, spk_embed_dim).
  
      Returns:
          Tensor: Batch of integrated hidden state sequences (B, T_text, adim).
  
      """
      if self.spk_embed_integration_type == "add":
          # apply projection and then add to hidden states
          spembs = self.projection(F.normalize(spembs))
          hs = hs + spembs.unsqueeze(1)
      elif self.spk_embed_integration_type == "concat":
          # concat hidden states with spk embeds and then apply projection
          spembs = (
              F.normalize(spembs)
              .unsqueeze(1)
              .expand(-1, hs.size(1), -1)
          )
          hs = self.projection(torch.cat([hs, spembs], dim=-1))
      else:
          raise NotImplementedError("support only add or concat.")
  
      return hs
  
imports: import logging
  from typing import Dict, Optional, Sequence, Tuple
  import torch
  import torch.nn.functional
  from typeguard import check_argument_types
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.torch_utils.initialize import initialize
  from espnet2.tts.abs_tts import AbsTTS
  from espnet2.tts.gst.style_encoder import StyleEncoder
  from espnet.nets.pytorch_backend.conformer.encoder import Encoder
  from espnet.nets.pytorch_backend.e2e_tts_fastspeech import FeedForwardTransformerLoss
  from espnet.nets.pytorch_backend.fastspeech.duration_predictor import DurationPredictor
  from espnet.nets.pytorch_backend.fastspeech.length_regulator import LengthRegulator
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask, make_pad_mask
  from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding, ScaledPositionalEncoding
  from espnet.nets.pytorch_backend.transformer.encoder import Encoder
label_desc: Use image node_id 5 for calling the FastSpeech obj's underlying member method code with example usage: obj._integrate_with_spk_embed(hs, spembs) and returns: hs