QuartzNet 10x5 Model¶
QuartzNet 10x5 Model¶
-
class
openspeech.models.quartznet10x5.model.
QuartzNet10x5Model
(configs: omegaconf.dictconfig.DictConfig, vocab: openspeech.vocabs.vocab.Vocabulary)[source]¶ QUARTZNET: DEEP AUTOMATIC SPEECH RECOGNITION WITH 1D TIME-CHANNEL SEPARABLE CONVOLUTIONS Paper: https://arxiv.org/abs/1910.10261.pdf
- 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)
QuartzNet 10x5 Model Configuration¶
-
class
openspeech.models.quartznet10x5.configurations.
QuartzNet10x5Configs
(model_name: str = 'quartznet10x5', num_blocks: int = 10, num_sub_blocks: int = 5, in_channels: str = '(None, 256, 256, 256, 256, 256, 512, 512, 512, 512, 512, 512, 512, 1024)', out_channels: str = '(256, 256, 256, 256, 256, 512, 512, 512, 512, 512, 512, 512, 1024, None)', kernel_size: str = '(33, 33, 33, 39, 39, 51, 51, 63, 63, 75, 75, 87, 1, 1)', dilation: str = '(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2)', dropout_p: str = '(0.2, None, None, None, None, None, None, None, None, None, None, 0.2, 0.2, 0.2)', optimizer: str = 'novograd')[source]¶ This is the configuration class to store the configuration of a
QuartzNet10x5
.It is used to initiated an QuartzNet10x5 model.
Configuration objects inherit from :class: ~openspeech.dataclass.configs.OpenspeechDataclass.
- Configurations:
model_name (str): Model name (default: quartznet5x5) num_blocks (int): Number of quartznet blocks (default: 10) num_sub_blocks (int): Number of quartznet sub blocks (default: 5) in_channels (str): Output channels of jasper block’s convolution out_channels (str): Output channels of jasper block’s convolution kernel_size (str): Kernel size of jasper block’s convolution dilation (str): Dilation of jasper block’s convolution dropout_p (str): Dropout probability optimizer (str): Optimizer for training.