o ei @s2dZddlmZddlmZGdddejZdS)zM Assorted reusable neural network modules. Authors * Artem Ploujnikov 2023 )nn)length_to_maskcs*eZdZdZfddZdddZZS) DoneDetectoraA wrapper for the done detector using a model (e.g. a CRDNN) and an output layer. The goal of using a wrapper is to apply masking before the output layer (e.g. Softmax) so that the model can't "cheat" by outputting probabilities in the masked area Arguments --------- model: torch.nn.Module the model used to make the prediction out: torch.nn.Module the output function Example ------- >>> import torch >>> from torch import nn >>> from speechbrain.nnet.activations import Softmax >>> from speechbrain.nnet.containers import Sequential >>> from speechbrain.nnet.linear import Linear >>> from speechbrain.lobes.models.CRDNN import CRDNN >>> crdnn = CRDNN( ... input_size=80, ... cnn_blocks=1, ... cnn_kernelsize=3, ... rnn_layers=1, ... rnn_neurons=16, ... dnn_blocks=1, ... dnn_neurons=16 ... ) >>> model_out = Linear(n_neurons=1, input_size=16) >>> model_act = nn.Sigmoid() >>> model = Sequential( ... crdnn, ... model_out, ... model_act ... ) >>> out = Softmax( ... apply_log=False, ... ) >>> done_detector = DoneDetector( ... model=model, ... out=out, ... ) >>> preds = torch.randn(4, 10, 80) # Batch x Length x Feats >>> length = torch.tensor([1., .8, .5, 1.]) >>> preds_len = done_detector(preds, length) >>> preds_len.shape torch.Size([4, 10, 1]) cst||_||_dSN)super__init__modelout)selfrr  __class__T/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/speechbrain/nnet/utils.pyrBs  zDoneDetector.__init__NcCsH||}|dur|d}t|||d}||d}||}|S)a>Computes the forward pass Arguments --------- feats: torch.Tensor the features used for the model (e.g. spectrograms) length: torch.Tensor a tensor of relative lengths Returns ------- preds: torch.Tensor predictions N)lengthmax_len)rsizer unsqueezer )r featsrr rmaskr r rforwardGs   zDoneDetector.forwardr)__name__ __module__ __qualname____doc__rr __classcell__r r r rr s 4rN)rtorchrspeechbrain.dataio.dataiorModulerr r r rs