# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# SOFTWARE.
from dataclasses import dataclass, field
from openspeech.dataclass.configurations import OpenspeechDataclass
[docs]@dataclass
class ContextNetConfigs(OpenspeechDataclass):
r"""
This is the configuration class to store the configuration of
a :class:`~openspeech.models.ContextNet`.
It is used to initiated an `ContextNet` model.
Configuration objects inherit from :class: `~openspeech.dataclass.configs.OpenspeechDataclass`.
Args:
model_name (str): Model name (default: contextnet)
model_size (str, optional): Size of the model['small', 'medium', 'large'] (default : 'medium')
input_dim (int, optional): Dimension of input vector (default : 80)
num_encoder_layers (int, optional): The number of convolution layers (default : 5)
kernel_size (int, optional): Value of convolution kernel size (default : 5)
num_channels (int, optional): The number of channels in the convolution filter (default: 256)
encoder_dim (int, optional): Dimension of encoder output vector (default: 640)
optimizer (str): Optimizer for training. (default: adam)
"""
model_name: str = field(
default="contextnet", metadata={"help": "Model name"}
)
model_size: str = field(
default="medium", metadata={"help": "Model size"}
)
input_dim: int = field(
default=80, metadata={"help": "Dimension of input vector"}
)
num_encoder_layers: int = field(
default=5, metadata={"help": "The number of convolution layers"}
)
kernel_size: int = field(
default=5, metadata={"help": "Value of convolution kernel size"}
)
num_channels: int = field(
default=256, metadata={"help": "The number of channels in the convolution filter"}
)
encoder_dim: int = field(
default=640, metadata={"help": "Dimension of encoder output vector"}
)
optimizer: str = field(
default="adam", metadata={"help": "Optimizer for training"}
)
[docs]@dataclass
class ContextNetLSTMConfigs(OpenspeechDataclass):
r"""
This is the configuration class to store the configuration of
a :class:`~openspeech.models.ContextNetLSTM`.
It is used to initiated an `ContextNetLSTM` model.
Configuration objects inherit from :class: `~openspeech.dataclass.configs.OpenspeechDataclass`.
Args:
model_name (str): Model name (default: contextnet_lstm)
model_size (str, optional): Size of the model['small', 'medium', 'large'] (default : 'medium')
input_dim (int, optional): Dimension of input vector (default : 80)
num_encoder_layers (int, optional): The number of convolution layers (default : 5)
num_decoder_layers (int): The number of decoder layers. (default: 2)
kernel_size (int, optional): Value of convolution kernel size (default : 5)
num_channels (int, optional): The number of channels in the convolution filter (default: 256)
encoder_dim (int, optional): Dimension of encoder output vector (default: 640)
num_attention_heads (int): The number of attention heads. (default: 8)
attention_dropout_p (float): The dropout probability of attention module. (default: 0.1)
decoder_dropout_p (float): The dropout probability of decoder. (default: 0.1)
max_length (int): Max decoding length. (default: 128)
teacher_forcing_ratio (float): The ratio of teacher forcing. (default: 1.0)
rnn_type (str): Type of rnn cell (rnn, lstm, gru) (default: lstm)
decoder_attn_mechanism (str): The attention mechanism for decoder. (default: loc)
optimizer (str): Optimizer for training. (default: adam)
"""
model_name: str = field(
default="contextnet_lstm", metadata={"help": "Model name"}
)
model_size: str = field(
default="medium", metadata={"help": "Model size"}
)
input_dim: int = field(
default=80, metadata={"help": "Dimension of input vector"}
)
num_encoder_layers: int = field(
default=5, metadata={"help": "The number of convolution layers"}
)
num_decoder_layers: int = field(
default=2, metadata={"help": "The number of decoder layers."}
)
kernel_size: int = field(
default=5, metadata={"help": "Value of convolution kernel size"}
)
num_channels: int = field(
default=256, metadata={"help": "The number of channels in the convolution filter"}
)
encoder_dim: int = field(
default=640, metadata={"help": "Dimension of encoder output vector"}
)
num_attention_heads: int = field(
default=8, metadata={"help": "The number of attention heads."}
)
attention_dropout_p: float = field(
default=0.1, metadata={"help": "The dropout probability of attention module."}
)
decoder_dropout_p: float = field(
default=0.1, metadata={"help": "The dropout probability of decoder."}
)
max_length: int = field(
default=128, metadata={"help": "Max decoding length."}
)
teacher_forcing_ratio: float = field(
default=1.0, metadata={"help": "The ratio of teacher forcing. "}
)
rnn_type: str = field(
default="lstm", metadata={"help": "Type of rnn cell (rnn, lstm, gru)"}
)
decoder_attn_mechanism: str = field(
default="loc", metadata={"help": "The attention mechanism for decoder."}
)
optimizer: str = field(
default="adam", metadata={"help": "Optimizer for training."}
)
[docs]@dataclass
class ContextNetTransducerConfigs(OpenspeechDataclass):
r"""
This is the configuration class to store the configuration of
a :class:`~openspeech.models.ContextNetTransducer`.
It is used to initiated an `ContextNetTransducer` model.
Configuration objects inherit from :class: `~openspeech.dataclass.configs.OpenspeechDataclass`.
Args:
model_name (str): Model name (default: contextnet_transducer)
model_size (str, optional): Size of the model['small', 'medium', 'large'] (default : 'medium')
input_dim (int, optional): Dimension of input vector (default : 80)
num_encoder_layers (int, optional): The number of convolution layers (default : 5)
num_decoder_layers (int, optional): The number of rnn layers (default : 1)
kernel_size (int, optional): Value of convolution kernel size (default : 5)
num_channels (int, optional): The number of channels in the convolution filter (default: 256)
hidden_dim (int, optional): The number of features in the decoder hidden state (default : 2048)
encoder_dim (int, optional): Dimension of encoder output vector (default: 640)
decoder_output_dim (int, optional): Dimension of decoder output vector (default: 640)
dropout (float, optional): Dropout probability of decoder (default: 0.1)
rnn_type (str, optional): Type of rnn cell (rnn, lstm, gru) (default: lstm)
optimizer (str): Optimizer for training. (default: adam)
"""
model_name: str = field(
default="contextnet_transducer", metadata={"help": "Model name"}
)
model_size: str = field(
default="medium", metadata={"help": "Model size"}
)
input_dim: int = field(
default=80, metadata={"help": "Dimension of input vector"}
)
num_encoder_layers: int = field(
default=5, metadata={"help": "The number of convolution layers"}
)
num_decoder_layers: int = field(
default=1, metadata={"help": "The number of rnn layers"}
)
kernel_size: int = field(
default=5, metadata={"help": "Value of convolution kernel size"}
)
num_channels: int = field(
default=256, metadata={"help": "The number of channels in the convolution filter"}
)
decoder_hidden_state_dim: int = field(
default=2048, metadata={"help": "The number of features in the decoder hidden state"}
)
encoder_dim: int = field(
default=640, metadata={"help": "Dimension of encoder output vector"}
)
decoder_output_dim: int = field(
default=640, metadata={"help": "Dimension of decoder output vector"}
)
decoder_dropout_p: float = field(
default=0.1, metadata={"help": "Dropout probability of decoder"}
)
rnn_type: str = field(
default='lstm', metadata={"help": "Type of rnn cell"}
)
optimizer: str = field(
default="adam", metadata={"help": "Optimizer for training"}
)