speechbrain.nnet.transducer.transducer_joint module
Library implementing transducer_joint.
- Author
Abdelwahab HEBA 2020
Summary
Classes:
Computes joint tensor between Transcription network (TN) & Prediction network (PN) |
Reference
- class speechbrain.nnet.transducer.transducer_joint.Transducer_joint(joint_network=None, joint='sum', nonlinearity=<class 'torch.nn.modules.activation.LeakyReLU'>)[source]
Bases:
Module
Computes joint tensor between Transcription network (TN) & Prediction network (PN)
- Parameters
joint_network (torch.class (neural network modules)) – if joint == “concat”, we call this network after the concatenation of TN and PN if None, we don’t use this network.
joint (joint the two tensors by ("sum",or "concat") option.) –
nonlinearity (torch class) –
- Activation function used after the joint between TN and PN
Type of nonlinearity (tanh, relu).
Example
>>> from speechbrain.nnet.transducer.transducer_joint import Transducer_joint >>> from speechbrain.nnet.linear import Linear >>> input_TN = torch.rand(8, 200, 1, 40) >>> input_PN = torch.rand(8, 1, 12, 40) >>> joint_network = Linear(input_size=80, n_neurons=80) >>> TJoint = Transducer_joint(joint_network, joint="concat") >>> output = TJoint(input_TN, input_PN) >>> output.shape torch.Size([8, 200, 12, 80])
- init_params(first_input)[source]
- Parameters
first_input (tensor) – A first input used for initializing the parameters.
- forward(input_TN, input_PN)[source]
Returns the fusion of inputs tensors.
- Parameters
input_TN (torch.Tensor) – Input from Transcription Network.
input_PN (torch.Tensor) – Input from Prediction Network.