CTC (Connectionist Temporal Classification) loss is a widely used loss function in speech recognition systems. It is used to optimize the model's parameters to minimize the difference between the predicted and true labels. In this problem, you will implement a CTC loss function using NumPy.
**Example:** Compute the CTC loss for a sample speech recognition model.
**Constraints:** Use the numpy library to perform element-wise operations on the model's output and the true labels.
**Note:** You can use the torch library to compute the CTC loss, but you should implement the CTC loss function from scratch.
Test Cases
Test Case 1
Input:
[[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]Expected:
-0.191Test Case 2
Input:
[[0.7, 0.8, 0.9], [0.1, 0.2, 0.3]]Expected:
-0.191+ 3 hidden test cases