實現在諾諾

Question

中實施遞推等式（找到與HTK類似的德爾塔係數）我想實現下面的迴歸函數，其中用作的表達式以計算德爾塔係數。但是，我不知道如何將前面的輸入傳遞到當前步驟。

其中是在時間增量係數在相應的靜態係數到方面計算的。 的值使用配置參數DELTAWINDOW進行設置。除了在這種情況下窗口大小由ACCWINDOW設置之外，將相同公式應用於增量係數以獲得加速度係數。由於方程5.16依賴於過去和將來的語音參數值，所以在語音的開始和結束時需要一些修改。默認行爲是根據需要複製第一個或最後一個矢量以填充迴歸窗口。

我使用的DELTAWINDOW爲9，並且所述輸入是一個矩陣例如：

[[1,1,1,1,1,1,1,1,1], 
[2,2,2,2,2,2,2,2,2], 
[3,3,3,3,3,3,3,3,3], 
[4,4,4,4,4,4,4,4,4]]  

參考材料可在這個link

Answer 1

試圖找到在創建此一theano表達。

import numpy as np 
import theano 
import theano.tensor as T 


def delta_theta(theta, curr_delta, t, THETA, Y): 
    """ 
    compute a delta theta component at delta time step t 
    :param theta: current time step theta component 
    :param curr_delta: current accumulated delta_t 
    :param t: current delta_t to be computed 
    :param THETA: window size 
    :param Y: input sequence 
    :return: delta theta component for time step t 
    """ 
    # accumulator is shaped (1, no_features), transpose to perform column wise element operations 
    temp = curr_delta.T 
    d_theta = theta * (Y[:, THETA + t + theta] - Y[:, THETA + t - theta])/(2 * theta * theta) 
    temp += d_theta 
    temp = temp.astype('float32') 
    curr_delta = temp.T 
    return curr_delta 


def delta_t(t, THETA, Y): 
    """ 
    compute delta at time step t 
    :param t: time step 
    :param THETA: window size 
    :param Y: sequence in shape (number_of_features, time_step) 
    :return: delta coefficient at time step t 
    """ 
    theta = T.arange(1, THETA + 1, dtype='int32') 
    results, _ = theano.scan(delta_theta, outputs_info=T.zeros_like(Y), 
          sequences=theta, non_sequences=[t, THETA, Y]) 
    # only interested in the final results, discard the intermediate values 
    final_results = results[-1] 
    return final_results 


def delta_coeff(A, theta): 
    """ 
    compute delta coefficients given a sequence. 
    :param A: input sequence in shape (time_step, number_of_features) 
    :param theta: window size 
    :return: delta coefficients for the input sequence 
    """ 
    # transpose and repeat 
    X = A.T 
    Y = T.concatenate([T.extra_ops.repeat(X[:, 0], theta).reshape((X.shape[0], theta)), 
         X, T.extra_ops.repeat(X[:, -1], theta).reshape((X.shape[0], theta))], axis=1) 
    results, _ = theano.scan(delta_t, sequences=[T.arange(0, X.shape[1], dtype='int32')], non_sequences=[theta, Y]) 
    # transpose the results back to shape (time_step, number_of_features) 
    return results[:, :, -1].reshape(A.shape) 


def main(): 
    """ 
    test runner, computes delta for an array of sequences 
    :return: None 
    """ 
    A = T.tensor3('A', dtype='float32') 
    theta = T.iscalar('theta') 

    # compute delta coefficients for multiple sequences 
    results, updates = theano.scan(delta_coeff, sequences=A, non_sequences=theta) 
    compute_deltas = theano.function([A, theta], outputs=results, updates=updates) 

    seqs = np.array([[[1, 2, 3, 4, 5], 
         [10, 12, 13, 14, 15], 
         [300, 1, 23, 56, 22]], 
        [[1, 1, 1, 1, 1], 
         [1, 1, 100, 1, 1], 
         [1, 1, 1, 1, 1]]], dtype='float32') 
    res = compute_deltas(seqs, 1) 
    print(res) 

if __name__ == '__main__': 
    main() 

如果有任何錯誤，請將它們指出來，謝謝！