class_docstr: NaiveRNNDP-SVS module.
  
  This is an implementation of naive RNN with duration prediction
  for singing voice synthesis
  The features are processed directly over time-domain from music score and
  predict the singing voice features
class_docstr_tok: ["NaiveRNNDP-SVS","module",".","This","is","an","implementation","of","naive","RNN","with","duration","prediction","for","singing","voice","synthesis","The","features","are","processed","directly","over","time-domain","from","music","score","and","predict","the","singing","voice","features"]
docstr: Integrate speaker embedding with hidden states.
  
  Args:
      hs (Tensor): Batch of hidden state sequences (B, Tmax, adim).
      spembs (Tensor): Batch of speaker embeddings (B, spk_embed_dim).
  
  Returns:
      Tensor: Batch of integrated hidden state sequences (B, Tmax, adim).
docstr_tok: ["Integrate","speaker","embedding","with","hidden","states",".","Args",":","hs","(","Tensor",")",":","Batch","of","hidden","state","sequences","(","B",",","Tmax",",","adim",")",".","spembs","(","Tensor",")",":","Batch","of","speaker","embeddings","(","B",",","spk_embed_dim",")",".","Returns",":","Tensor",":","Batch","of","integrated","hidden","state","sequences","(","B",",","Tmax",",","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, Tmax, adim).
          spembs (Tensor): Batch of speaker embeddings (B, spk_embed_dim).
  
      Returns:
          Tensor: Batch of integrated hidden state sequences (B, Tmax, 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
  
file_path: espnet/espnet2/svs/naive_rnn/naive_rnn_dp.py
imports: from typing import Dict, Optional, Tuple
  import torch
  import torch.nn.functional
  from typeguard import check_argument_types
  from espnet2.svs.abs_svs import AbsSVS
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.torch_utils.initialize import initialize
  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_pad_mask
  from espnet.nets.pytorch_backend.tacotron2.decoder import Postnet
  from espnet.nets.pytorch_backend.tacotron2.encoder import Encoder
label_desc: Use image node_id 5 for calling the NaiveRNNDP obj's underlying member method code with example usage: obj._integrate_with_spk_embed(hs, spembs) and returns: hs