Openspeech Model

Openspeech Model

class openspeech.models.openspeech_model.OpenspeechModel(configs: omegaconf.dictconfig.DictConfig, tokenizer: openspeech.tokenizers.tokenizer.Tokenizer)[source]

Super class of openspeech models.

Note

Do not use this class directly, use one of the sub classes.

Parameters
  • configs (DictConfig) – configuration set.

  • tokenizer (Tokenizer) – tokenizer is in charge of preparing the inputs for a model.

Inputs:

inputs (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded FloatTensor of size (batch, seq_length, dimension). input_lengths (torch.LongTensor): The length of input tensor. (batch)

Returns

Result of model predictions.

Return type

outputs (dict)

configure_criterion(criterion_name: str) → torch.nn.modules.module.Module[source]

Configure criterion for training.

Parameters

criterion_name (str) – name of criterion

Returns

criterion for training

Return type

criterion (nn.Module)

configure_optimizers()[source]

Choose what optimizers and learning-rate schedulers to use in your optimization.

Returns

  • Dictionary - The first item has multiple optimizers, and the second has multiple LR schedulers (or multiple lr_dict).

forward(inputs: torch.FloatTensor, input_lengths: torch.LongTensor) → Dict[str, torch.Tensor][source]

Forward propagate a inputs and targets pair for inference.

Inputs:

inputs (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded FloatTensor of size (batch, seq_length, dimension). input_lengths (torch.LongTensor): The length of input tensor. (batch)

Returns

Result of model predictions.

Return type

outputs (dict)

info(dictionary: dict)None[source]

Logging information from dictionary.

Parameters

dictionary (dict) – dictionary contains information.

test_step(batch: tuple, batch_idx: int)[source]

Forward propagate a inputs and targets pair for test.

Inputs:

batch (tuple): A train batch contains inputs, targets, input_lengths, target_lengths batch_idx (int): The index of batch

Returns

loss for training

Return type

loss (torch.Tensor)

training_step(batch: tuple, batch_idx: int)[source]

Forward propagate a inputs and targets pair for training.

Inputs:

batch (tuple): A train batch contains inputs, targets, input_lengths, target_lengths batch_idx (int): The index of batch

Returns

loss for training

Return type

loss (torch.Tensor)

validation_step(batch: tuple, batch_idx: int)[source]

Forward propagate a inputs and targets pair for validation.

Inputs:

batch (tuple): A train batch contains inputs, targets, input_lengths, target_lengths batch_idx (int): The index of batch

Returns

loss for training

Return type

loss (torch.Tensor)