Transformer with CTC Model¶
Transformer with CTC Model¶
-
class
openspeech.models.transformer_with_ctc.model.
TransformerWithCTCModel
(configs: omegaconf.dictconfig.DictConfig, vocab: openspeech.vocabs.vocab.Vocabulary)[source]¶ Transformer Encoder Only Model.
- 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 that contains y_hats, logits, output_lengths
- Return type
dict (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 that contains y_hats, logits, output_lengths
- Return type
dict (dict)
-
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)
Transformer with CTC Model Configuration¶
-
class
openspeech.models.transformer_with_ctc.configurations.
TransformerWithCTCConfigs
(model_name: str = 'transformer_with_ctc', d_model: int = 512, d_ff: int = 2048, num_attention_heads: int = 8, num_encoder_layers: int = 12, encoder_dropout_p: float = 0.3, ffnet_style: str = 'ff', optimizer: str = 'adam')[source]¶ This is the configuration class to store the configuration of a
TransformerWithCTC
.It is used to initiated an TransformerWithCTC model.
Configuration objects inherit from :class: ~openspeech.dataclass.configs.OpenspeechDataclass.
- Configurations:
model_name (str): Model name (default: transformer_with_ctc) 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) encoder_dropout_p (float): The dropout probability of encoder. (default: 0.3) ffnet_style (str): Style of feed forward network. (ff, conv) (default: ff) optimizer (str): Optimizer for training. (default: adam)