Openspeech Transducer Model

Openspeech Transducer Model

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

Base class for OpenSpeech’s transducer models.

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 that contains predictions, logits, encoder_outputs, encoder_output_lengths

Return type

dict (dict)

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

Decode encoder_outputs.

Parameters

  • 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 that contains predictions,

encoder_outputs, encoder_output_lengths

Return type

dict (dict)

greedy_decode(encoder_outputs: torch.Tensor, max_length: int) → torch.Tensor[source]

Decode encoder_outputs.

Parameters

  • encoder_outputs (torch.FloatTensor) – A output sequence of encoders. FloatTensor of size (batch, seq_length, dimension)

  • max_length (int) – max decoding time step

Returns

Log probability of model predictions.

Return type

  • logits (torch.FloatTensor)

joint(encoder_outputs: torch.Tensor, decoder_outputs: torch.Tensor) → torch.Tensor[source]

Joint encoder_outputs and decoder_outputs.

Parameters

  • encoder_outputs (torch.FloatTensor) – A output sequence of encoders. FloatTensor of size (batch, seq_length, dimension)

  • decoder_outputs (torch.FloatTensor) – A output sequence of decoders. FloatTensor of size (batch, seq_length, dimension)

Returns

outputs of joint encoder_outputs and decoder_outputs..

Return type

outputs (torch.FloatTensor)

set_beam_decode(beam_size: int = 3, expand_beam: float = 2.3, state_beam: float = 4.6)[source]

Setting beam search decode

test_step(batch: tuple, batch_idx: int) → collections.OrderedDict[source]

Forward propagate a inputs and targets pair for test.

Inputs:

train_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) → collections.OrderedDict[source]

Forward propagate a inputs and targets pair for training.

Inputs:

train_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) → collections.OrderedDict[source]

Forward propagate a inputs and targets pair for validation.

Inputs:

train_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)