DeepSpeech2 Model¶
DeepSpeech2 Model¶
-
class
openspeech.models.deepspeech2.model.
DeepSpeech2Model
(configs: omegaconf.dictconfig.DictConfig, vocab: openspeech.vocabs.vocab.Vocabulary)[source]¶ Deep Speech2 model with configurable encoders and decoders. Paper: https://arxiv.org/abs/1512.02595
- 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)
DeepSpeech2 Model Configuration¶
-
class
openspeech.models.deepspeech2.configurations.
DeepSpeech2Configs
(model_name: str = 'deepspeech2', rnn_type: str = 'gru', num_rnn_layers: int = 5, rnn_hidden_dim: int = 1024, dropout_p: float = 0.3, bidirectional: bool = True, activation: str = 'hardtanh', optimizer: str = 'adam')[source]¶ This is the configuration class to store the configuration of a
DeepSpeech2
.It is used to initiated an DeepSpeech2 model.
Configuration objects inherit from :class: ~openspeech.dataclass.configs.OpenspeechDataclass.
- Configurations:
model_name (str): Model name (default: deepspeech2) num_rnn_layers (int): The number of rnn layers. (default: 5) rnn_hidden_dim (int): The hidden state dimension of rnn. (default: 1024) dropout_p (float): The dropout probability of model. (default: 0.3) bidirectional (bool): If True, becomes a bidirectional encoders (default: True) rnn_type (str): Type of rnn cell (rnn, lstm, gru) (default: gru) activation (str): Type of activation function (default: str) optimizer (str): Optimizer for training. (default: adam)