Sort using duration time of the sound spectrogram.

Creates a speech-to-text nmtmodel with a custom opt function. Forwards a testbatch and checks output size. Args:

Pad and batch a sequence of images.

Transform from 3D to 2D, apply linear and return initial size

Transform from 3D to 2D, apply linear and return initial size

Returns a LossCompute subclass which wraps around an nn.Module subclass (such as nn.NLLLoss) which defines the loss criterion. The LossComput

Build the fields for all data sides. Args: train_dataset_files: a list of train dataset pt file. fields (dict[str, Field]): field

Returns zero as penalty

Our summary penalty.

GNMT coverage re-ranking score. See "Google's Neural Machine Translation System" :cite:`wu2016google`. ``cov`` is expected to be size

Decorator for critical section (mutually exclusive code)

Timeout function that frees GPU memory. Moves the model to CPU or unloads it; depending on attr`self.on_timemout` value

Embedding look-up table.

Tests if the embeddings works as expected args: opt: set of options source_l: Length of generated input sent

Tests if the encoder works as expected args: opt: set of options source_l: Length of generated input sentenc

error listener

A standard exponential decay, scaling the learning rate by :obj:`rate` every :obj:`decay_steps` steps.

Return whether an example is an acceptable length. If used with a dataset as ``filter_pred``, use :func:`partial()` for all keyword arguments

Keep a progress bar of frames finished.

Args: inputs (FloatTensor): ``(batch_size, input_len, model_dim)`` Returns: (FloatTensor, FloatTensor):

sparsemax: normalizing sparse transform (a la softmax) Parameters: input (Tensor): any shape dim: dimension along whi

Args: source (FloatTensor): query vectors ``(batch, tgt_len, dim)`` memory_bank (FloatTensor): source vectors ``(batch,

input (FloatTensor): ``(n, num_classes)``. target (LongTensor): ``(n,)``, the indices of the target classes

Args: base_target_emb: target emb tensor input_from_dec: output of decode conv encoder_out_top: the key m

Compute the context vector and the attention vectors. Args: key (FloatTensor): set of `key_len` key vector

Compute a distribution over the target dictionary extended by the dynamic dictionary implied by copying source words.

Args: scores (FloatTensor): ``(batch_size*tgt_len)`` x dynamic vocab size whose sum along dim 1 is less than or e

Embed inputs. Args: emb (FloatTensor): Sequence of word vectors ``(seq_len, batch_size, self.dim)`` s

Args: x (FloatTensor): input, ``(len, batch, 1, vec_feats)``. Returns: FloatTensor: embedded vecs ``(len, ba

Computes the embeddings for words and features. Args: source (LongTensor): index tensor ``(len, batch, nfeat)`` Returns:

Layer definition. Args: x: ``(batch_size, input_len, model_dim)`` Returns: (FloatTensor): Output ``(batch_si

output (FloatTensor): batch_size x n_classes target (LongTensor): batch_size

See :func:`EncoderBase.forward()`

See :func:`onmt.encoders.encoder.EncoderBase.forward()`

See :func:`onmt.encoders.encoder.EncoderBase.forward()`

Args: inputs (FloatTensor): ``(batch_size, src_len, model_dim)`` mask (LongTensor): ``(batch_size, 1, src_len)``

See :func:`EncoderBase.forward()`

Args: src (LongTensor): padded sequences of sparse indices ``(src_len, batch, nfeat)`` lengths (LongTe

See :func:`EncoderBase.forward()`

See :func:`EncoderBase.forward()`

See :class:`onmt.modules.EncoderBase.forward()`

Args: tgt (LongTensor): sequences of padded tokens ``(tgt_len, batch, nfeats)``. memory_bank (FloatT

See :obj:`onmt.modules.RNNDecoderBase.forward()`

Extend `_forward` for (possibly) multiple decoder pass: Always a default (future masked) decoder forward pass, Possibly a second futu

Decode, possibly stepwise.

See :func:`onmt.decoders.decoder.DecoderBase.forward()`.

Compute a distribution over the target dictionary by averaging distributions from models in the ensemble. All models in the e

Forward propagate a `src` and `tgt` pair for training. Possible initialized with a beginning decoder state. Args: src (Te

Alternate constructor.

Alternate constructor.

Alternate constructor.

Alternate constructor.

Alternate constructor.

Alternate constructor.

Alternate constructor.