VGG Transformer Model

VGG Transformer Model

class openspeech.models.vgg_transformer.model.VGGTransformerModel(configs: omegaconf.dictconfig.DictConfig, vocab: openspeech.vocabs.vocab.Vocabulary)[source]

A Speech Transformer model. User is able to modify the attributes as needed. The model is based on the paper “Attention Is All You Need”.

Parameters
  • configs (DictConfig) – configuration set.

  • vocab (Vocabulary) – the class of vocabulary

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)

forward(inputs: torch.Tensor, input_lengths: torch.Tensor) → 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)

set_beam_decoder(batch_size: int = None, beam_size: int = 3, n_best: int = 1)[source]

Setting beam search decoder

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

VGG Transformer Model Configuration

class openspeech.models.vgg_transformer.configurations.VGGTransformerConfigs(model_name: str = 'vgg_transformer', extractor: str = 'vgg', d_model: int = 512, d_ff: int = 2048, num_attention_heads: int = 8, num_encoder_layers: int = 12, num_decoder_layers: int = 6, encoder_dropout_p: float = 0.3, decoder_dropout_p: float = 0.3, ffnet_style: str = 'ff', max_length: int = 128, teacher_forcing_ratio: float = 1.0, joint_ctc_attention: bool = False, optimizer: str = 'adam')[source]

This is the configuration class to store the configuration of a VGGTransformer.

It is used to initiated an VGGTransformer model.

Configuration objects inherit from :class: ~openspeech.dataclass.configs.OpenspeechDataclass.

Configurations:

model_name (str): Model name (default: vgg_transformer) extractor (str): The CNN feature extractor. (default: vgg) d_model (int): Dimension of model. (default: 512) d_ff (int): Dimenstion of feed forward network. (default: 2048) num_attention_heads (int): The number of attention heads. (default: 8) num_encoder_layers (int): The number of encoder layers. (default: 12) num_decoder_layers (int): The number of decoder layers. (default: 6) encoder_dropout_p (float): The dropout probability of encoder. (default: 0.3) decoder_dropout_p (float): The dropout probability of decoder. (default: 0.3) ffnet_style (str): Style of feed forward network. (ff, conv) (default: ff) max_length (int): Max decoding length. (default: 128) teacher_forcing_ratio (float): The ratio of teacher forcing. (default: 1.0) joint_ctc_attention (bool): Flag indication joint ctc attention or not (default: False) optimizer (str): Optimizer for training. (default: adam)