class_docstr: torchaudio.models.RNNT()
  
  Recurrent neural network transducer (RNN-T) model.
  
  Note:
      To build the model, please use one of the factory functions.
  
  See Also:
      :class:`torchaudio.pipelines.RNNTBundle`: ASR pipeline with pre-trained models.
  
  Args:
      transcriber (torch.nn.Module): transcription network.
      predictor (torch.nn.Module): prediction network.
      joiner (torch.nn.Module): joint network.
class_docstr_tok: ["torchaudio.models.RNNT","(",")","Recurrent","neural","network","transducer","(","RNN-T",")","model",".","Note",":","To","build","the","model",",","please","use","one","of","the","factory","functions",".","See","Also",":",":","class",":","`","torchaudio.pipelines.RNNTBundle","`",":","ASR","pipeline","with","pre-trained","models",".","Args",":","transcriber","(","torch.nn.Module",")",":","transcription","network",".","predictor","(","torch.nn.Module",")",":","prediction","network",".","joiner","(","torch.nn.Module",")",":","joint","network","."]
docstr: Applies transcription network to sources in streaming mode.
  
  B: batch size;
  T: maximum source sequence segment length in batch;
  D: feature dimension of each source sequence frame.
  
  Args:
      sources (torch.Tensor): source frame sequence segments right-padded with right context, with
          shape `(B, T + right context length, D)`.
      source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
          number of valid frames for i-th batch element in ``sources``.
      state (List[List[torch.Tensor]] or None): list of lists of tensors
          representing transcription network internal state generated in preceding invocation
          of ``transcribe_streaming``.
  
  Returns:
      (torch.Tensor, torch.Tensor, List[List[torch.Tensor]]):
          torch.Tensor
              output frame sequences, with
              shape `(B, T // time_reduction_stride, output_dim)`.
          torch.Tensor
              output lengths, with shape `(B,)` and i-th element representing
              number of valid elements for i-th batch element in output.
          List[List[torch.Tensor]]
              output states; list of lists of tensors
              representing transcription network internal state generated in current invocation
              of ``transcribe_streaming``.
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code: def transcribe_streaming(
      self,
      sources: torch.Tensor,
      source_lengths: torch.Tensor,
      state: Optional[List[List[torch.Tensor]]],
  ) -> Tuple[torch.Tensor, torch.Tensor, List[List[torch.Tensor]]]:
      r"""Applies transcription network to sources in streaming mode.
  
      B: batch size;
      T: maximum source sequence segment length in batch;
      D: feature dimension of each source sequence frame.
  
      Args:
          sources (torch.Tensor): source frame sequence segments right-padded with right context, with
              shape `(B, T + right context length, D)`.
          source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
              number of valid frames for i-th batch element in ``sources``.
          state (List[List[torch.Tensor]] or None): list of lists of tensors
              representing transcription network internal state generated in preceding invocation
              of ``transcribe_streaming``.
  
      Returns:
          (torch.Tensor, torch.Tensor, List[List[torch.Tensor]]):
              torch.Tensor
                  output frame sequences, with
                  shape `(B, T // time_reduction_stride, output_dim)`.
              torch.Tensor
                  output lengths, with shape `(B,)` and i-th element representing
                  number of valid elements for i-th batch element in output.
              List[List[torch.Tensor]]
                  output states; list of lists of tensors
                  representing transcription network internal state generated in current invocation
                  of ``transcribe_streaming``.
      """
      return self.transcriber.infer(sources, source_lengths, state)
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models import Emformer
label_desc: Use image node_id 3 for calling the RNNT obj's underlying member method code with example usage: obj.transcribe_streaming(sources, source_lengths, state) and returns: self