Jasper

Jasper Model

class openspeech.models.jasper.model.Jasper10x5Model(configs: omegaconf.dictconfig.DictConfig, tokenizer: openspeech.tokenizers.tokenizer.Tokenizer)[source]

Jasper: An End-to-End Convolutional Neural Acoustic Model Paper: https://arxiv.org/pdf/1904.03288.pdf

Parameters

  • configs (DictConfig) – configuration set.

  • tokenizer (Tokeizer) – 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 y_hats, logits, output_lengths

Return type

outputs (dict)

class openspeech.models.jasper.model.Jasper5x3Model(configs: omegaconf.dictconfig.DictConfig, tokenizer: openspeech.tokenizers.tokenizer.Tokenizer)[source]

Jasper: An End-to-End Convolutional Neural Acoustic Model Paper: https://arxiv.org/pdf/1904.03288.pdf

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 y_hats, logits, output_lengths

Return type

outputs (dict)

Jasper Configuration

class openspeech.models.jasper.configurations.Jasper10x5Config(model_name: str = 'jasper10x5', num_blocks: int = 10, num_sub_blocks: int = 5, in_channels: str = '(None, 256, 256, 256, 384, 384, 512, 512, 640, 640, 768, 768, 896, 1024)', out_channels: str = '(256, 256, 256, 384, 384, 512, 512, 640, 640, 768, 768, 768, 896, 1024, None)', kernel_size: str = '(11, 11, 11, 13, 13, 17, 17, 21, 21, 25, 25, 29, 1, 1)', dilation: str = '(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1)', dropout_p: str = '(0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.0)', optimizer: str = 'novograd')[source]

This is the configuration class to store the configuration of a Jasper10x5.

It is used to initiated an Jasper10x5 model.

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

Parameters

  • model_name (str) – Model name (default: jasper10x5)

  • num_blocks (int) – Number of jasper blocks (default: 10)

  • num_sub_blocks (int) – Number of jasper 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.

class openspeech.models.jasper.configurations.Jasper5x3Config(model_name: str = 'jasper5x3', num_blocks: int = 5, num_sub_blocks: int = 3, in_channels: str = '(None, 256, 256, 256, 384, 384, 512, 512, 640, 640, 768, 768, 896, 1024)', out_channels: str = '(256, 256, 256, 384, 384, 512, 512, 640, 640, 768, 768, 896, 1024, None)', kernel_size: str = '(11, 11, 11, 13, 13, 17, 17, 21, 21, 25, 25, 29, 1, 1)', dilation: str = '(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1)', dropout_p: str = '(0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.3, 0.3, 0.3, 0.3, 0.4, 0.4, 0.0)', optimizer: str = 'novograd')[source]

This is the configuration class to store the configuration of a Jasper5x3.

It is used to initiated an Jasper5x3 model.

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

Parameters

  • model_name (str) – Model name (default: jasper5x3)

  • num_blocks (int) – Number of jasper blocks (default: 5)

  • num_sub_blocks (int) – Number of jasper sub blocks (default: 3)

  • 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.