Metadata-Version: 2.1
Name: warp-rnnt
Version: 0.0.2
Summary: PyTorch bindings for CUDA-Warp RNN-Transducer
Home-page: https://github.com/1ytic/warp-rnnt/pytorch_binding
Author: Ivan Sorokin
Author-email: sorokin.ivan@inbox.ru
License: MIT
Description: # PyTorch bindings for CUDA-Warp RNN-Transducer
        
        
        ```python
        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.
        
            Args:
              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
        ```
        
        ## Requirements
        
        - C++11 compiler (tested with GCC 5.4).
        - Python: 3.5, 3.6, 3.7 (tested with version 3.6).
        - [PyTorch](http://pytorch.org/) >= 1.0.0 (tested with version 1.1.0).
        - [CUDA Toolkit](https://developer.nvidia.com/cuda-zone) (tested with version 10.0).
        
        
        
        ## Install
        
        Currently, there is no compiled version of the package. The following setup instructions compile the package from the source code locally.
        
        ### From Pypi
        
        ```bash
        pip install warp_rnnt
        ```
        
        ### From GitHub
        
        ```bash
        git clone https://github.com/1ytic/warp-rnnt
        cd warp-rnnt/pytorch_binding
        python setup.py install
        ```
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
