In this problem, you will implement a Wav2Vec model for speech recognition. The Wav2Vec model is a self-supervised model that learns to predict the input audio signal given a context window. This is a challenging problem that requires a good understanding of deep learning and audio processing.
**Example:** Suppose you have an input audio signal with a length of 10 seconds. You need to predict the next 1 second of the audio signal given a context window of 5 seconds.
**Constraints:** You can only use the Wav2Vec model architecture and you must use the PyTorch library to implement the model.
**Note:** You can assume that the input audio signal is a 1D NumPy array with a shape of (n_samples,).
Test Cases
Test Case 1
Input:
[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]Expected:
[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]]Test Case 2
Input:
[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]]Expected:
[[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0]]+ 3 hidden test cases