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PyTorch bindings for CUDA-Warp RNN-Transducer

Project description

PyTorch bindings for CUDA-Warp RNN-Transducer

def rnnt_loss(
        log_probs,  # type: torch.FloatTensor
        labels,  # type: torch.IntTensor
        frames_lengths,  # type: torch.IntTensor
        labels_lengths,  # type: torch.IntTensor
        average_frames=False,  # type: bool
        reduction=None,  # type: Optional[AnyStr]
        blank=0,  # type: int
    """The CUDA-Warp RNN-Transducer loss.

      log_probs (torch.Tensor): Input tensor (float) with shape
        (T, N, U, V) where T is the maximum number of input frames, N is the
        minibatch size, U is the maximum number of output labels and V is
        the vocabulary of labels (including the blank).
      labels (torch.IntTensor): Tensor with shape (N, U-1) representing the
        reference labels for all samples in the minibatch.
      frames_lengths (torch.IntTensor): Tensor with shape (N,) representing the
        number of frames for each sample in the minibatch.
      labels_lengths (torch.IntTensor): Tensor with shape (N,) representing the
        length of the transcription for each sample in the minibatch.
      average_frames (bool, optional): Specifies whether the loss of each
        sample should be divided by its number of frames. Default: ``False''.
      reduction (string, optional): Specifies the type of reduction.
        Default: None.
      blank (int, optional): label used to represent the blank symbol.
        Default: 0.
    # type: (...) -> torch.Tensor


  • C++11 compiler (tested with GCC 5.4).
  • Python: 3.5, 3.6, 3.7 (tested with version 3.6).
  • PyTorch >= 1.0.0 (tested with version 1.1.0).
  • CUDA Toolkit (tested with version 10.0).


Currently, there is no compiled version of the package. The following setup instructions compile the package from the source code locally.

From Pypi

pip install warp_rnnt

From GitHub

git clone
cd warp-rnnt/pytorch_binding
python install

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