import numpy as np


def slice_songs(X, Y, S,
                sr=22050,
                hop_length=512,
                length_in_seconds=30,
                overlap=0.5):
    """
    Slice spectrograms into smaller splits with overlap.

    Parameters:
        X: Array of spectrograms
        Y: Array of labels
        S: Array of song names
        sr: Sample rate (default: 22050)
        hop_length: Hop length used in spectrogram creation (default: 512)
        length_in_seconds: Length of each slice in seconds (default: 30)
        overlap: Overlap ratio between consecutive slices (default: 0.5 for 50% overlap)
    """
    # Compute the number of frames for the desired slice length
    frames_per_second = sr / hop_length
    slice_length_frames = int(length_in_seconds * frames_per_second)

    # Calculate hop size for overlapping (stride)
    stride = int(slice_length_frames * (1 - overlap))

    # Initialize lists for sliced data
    X_slices = []
    Y_slices = []
    S_slices = []

    # Slice each spectrogram
    for i, spectrogram in enumerate(X):
        num_frames = spectrogram.shape[1]

        # Calculate start positions for all slices
        start_positions = range(0, num_frames - slice_length_frames + 1, stride)

        for start_frame in start_positions:
            end_frame = start_frame + slice_length_frames

            # Extract the slice
            slice_ = spectrogram[:, start_frame:end_frame]

            # Only add if the slice is the expected length
            if slice_.shape[1] == slice_length_frames:
                X_slices.append(slice_)
                Y_slices.append(Y[i])
                S_slices.append(S[i])

    # Convert lists to numpy arrays
    X_slices = np.array(X_slices)
    Y_slices = np.array(Y_slices)
    S_slices = np.array(S_slices)

    return X_slices, Y_slices, S_slices