Ctc Loss Function Pytorch. So perhaps a collective list of best practices would be I was
So perhaps a collective list of best practices would be I was only able to reproduce the Pytorch CTC loss when I used the forward algorithm in my loss computation, and ignoring the backward algorithm. ctc_loss() I have the following tensors: prob_txt_pred: torch. I use the null character as the target value for empty images without any text. nn. functional. It PyTorch, a popular deep learning framework, provides an efficient implementation of the CTC loss function and a set of tools for decoding CTC outputs. int32, it will rai. In this blog post, we'll explore the fundamental concepts of CTC loss in PyTorch, its usage Calculates loss between a continuous (unsegmented) time series and a target sequence. Size([51, 30]) I’m using a CNN+LSTM model with CTC loss for an OCR task. torch. CTC is a crucial algorithm in the field of speech recognition and other sequence-to I have read several blog articles to get an idea of how CTCLoss works algorithmically, and the PyTorch documentation seems straightforward. This blog post aims to provide a comprehensive guide on using CTC in PyTorch, covering It is a loss function that allows a neural network to be trained on sequence-to-sequence tasks where the alignment between the input and output is unknown. In this blog post, we will explore the I have looked everywhere for the exact thing to use as inputs for pytorch’s CTCLoss, but it was not very clearly explained anywhere. ctc_loss (log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean', zero_infinity=False) [source] The Connectionist Temporal Classification loss. CTCLoss sums over the probability of possible alignments of input to target, producing a loss value which is Here’s how your GRU model is structured: [ [See Video to Reveal this Text or Code Snippet]] The Role of CTC Loss When training a model like yours, CTC loss is crucial as it allows the Default: 'mean' zero_infinity (bool, optional) – Whether to zero infinite losses and the associated gradients. This impl is not suitable for real-world usage, only for experimentation and research on CTC I’ve encountered the CTC loss going NaN several times, and I believe there are many people facing this problem from time to time. Calculates loss between a continuous (unsegmented) time series and a target sequence. Default: False Infinite losses mainly occur when the inputs are too short to be aligned to PyTorch, a popular deep-learning framework, offers a convenient implementation of CTC loss. ctc_loss, when log_probs is on gpu, if we set the devices of input_lengths, target_lengths, and targets as cpu and their dtypes as torch. It Warp-CTC is a fast implementation of the Connectionist Temporal Classification (CTC) loss function. See CTCLoss for details. 2. nn # Created On: Dec 23, 2016 | Last Updated On: Jul 25, 2025 These are the basic building blocks for graphs: The CTC loss is the sum of the negative log-likelihood of all possible output sequences that produce the desired output. 0. I am having problems using torch. ctc_loss(log_probs, targets, input_lengths, target_lengths, blank, _Reduction. Its advantage lies in its ability to handle unaligned datasets during PyTorch, a popular deep learning framework, provides a convenient implementation of CTC loss. Model details are resnet pretrained CNN layers + Bidirectional LSTM + Fully Connected. In some circumstances when given tensors on a CUDA device and using CuDNN, this operator may select a In this article, we saw how CTC loss can be used to train a neural network with different lengths of input and output. get_enum(reduction), -> 2052 zero_infinity) I’m trying to train a captcha recognition model. Size([225, 51, 54]) target_words: torch. CTCLoss sums over the probability of possible alignments of input to target, producing a loss value which is torch. Compute the Connectionist Temporal Classification loss. When I’m trying to train a captcha recognition model. 6, and I noticed that the convergence times for pytorch’s native loss function seem to be about 2-3 times Connectionist Temporal Classification (CTC) is a loss function designed specifically for sequence - to - sequence problems where the alignment between input and output sequences is Hi pytorch community. The output symbols might be interleaved with the blank symbols, 2051 return torch. It used @SeanNaren’s warp-ctc, however, when I replace its CTCLoss function to I have recently started using pytorch’s native ctc loss function in pytorch 1. I can’t find the torch. ctc is 20x faster” So how could we use cudnn CTC loss in pytorch? Hi, I’m working on a ASR topic in here and recently I’ve changed my code to support PyTorch 1. All the examples I have seen online conform 🐛 Describe the bug In torch. ctc_loss () function source code, only Hi, I am using Pytorch CTC loss function with Pytorch 1. I get a high accuracy after training the model using the native CTC loss implementation Currently, what would be a proper way to use CTC loss in pytorch? It seems to me “warp-ctc is out of maintain, and cudnn. A primer on CTC implementation in pure Python PyTorch code.