VAD_DNN.py 文件源码

def bdnn_prediction(bdnn_batch_size, logits, threshold=th):

    result = np.zeros((bdnn_batch_size, 1))
    indx = np.arange(bdnn_batch_size) + 1
    indx = indx.reshape((bdnn_batch_size, 1))
    indx = utils.bdnn_transform(indx, w, u)
    indx = indx[w:(bdnn_batch_size-w), :]
    indx_list = np.arange(w, bdnn_batch_size - w)
    for i in indx_list:
        indx_temp = np.where((indx-1) == i)
        pred = logits[indx_temp]
        pred = np.sum(pred)/pred.shape[0]
        result[i] = pred

    result = np.trim_zeros(result)
    result = result >= threshold

    return result.astype(int)