returnn.extern.WarpRna
¶
- Provides a RETURNN wrapper around warp-transducer:
Importing this module immediately compiles the library and TF module.
- returnn.extern.WarpRna.is_checked_out()[source]¶
Checks if the git submodule is checkout out.
- Return type:
bool
- returnn.extern.WarpRna.init_warprna(verbose=False)[source]¶
Initializes and compiles the library. Caches the TF module.
- Parameters:
verbose (bool)
- returnn.extern.WarpRna.rna_loss(log_probs, labels, input_lengths, label_lengths, blank_label=0)[source]¶
Computes the RNA loss between a sequence of activations and a ground truth labeling. Args:
- log_probs: A 4-D Tensor of floats. The dimensions
should be (B, T, U, V), where B is the minibatch index, T is the time index, U is the prediction network sequence length, and V indexes over activations for each symbol in the alphabet.
- labels: A 2-D Tensor of ints, shape (B,U-1) a padded label sequences to make sure
labels for the minibatch are same length.
- input_lengths: A 1-D Tensor of ints, shape (B,), the number of time steps
for each sequence in the minibatch.
- label_lengths: A 1-D Tensor of ints, shape (B,), the length of each label
for each example in the minibatch.
- blank_label: int, scalar, the label value/index that the RNA
calculation should use as the blank label
- Returns:
1-D float Tensor, the cost of each example in the minibatch (as negative log probabilities).