def METHOD_NAME(audio, model):
    """
    audio: n_sounds x n_samples of mono audio in the range [-1, 1]. All sounds in a batch will be padded/trimmed to the same length.
    model: Loaded Model.
    Returns:
    embedding: A float32 Tensor with shape (n_sounds, n_timestamps, model.timestamp_embedding_size).
    timestamps: A float32 Tensor with shape (`n_sounds, n_timestamps). Centered timestamps in milliseconds corresponding to each embedding in the output.
    """
    model.eval()
    with torch.no_grad():
        embedmel, tmel = model.get_timestamp_mels(audio, window_size=1920)
        # print(embedmel.shape)
        embed1, t1 = model.get_timestamp_embeddings(audio)
        embed2, t2 = model.get_timestamp_embeddings(
            audio, window_size=model.timestamp_window * 4
        )  # larger window
        embed = torch.cat((embed1, embed2, embedmel), dim=-1)
        # print(t1==t2)
        return embed, t1