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kmeans1_withkdtree.py
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import os
import numpy as np
from tqdm import tqdm
import time
from sklearn.cluster import KMeans
from sklearn.neighbors import KDTree
eps = 1e-6
cn_raw_list_path = '/datapool/home/hujk17/chenxueyuan/DataBaker_Bilingual_CN/meta_good.txt'
cn_raw_ppg_path = '/datapool/home/hujk17/chenxueyuan/DataBaker_Bilingual_CN/ppg_from_generate_batch'
cn_raw_linear_dir ='/datapool/home/hujk17/chenxueyuan/DataBaker_Bilingual_CN/spec_5ms_by_audio_2'
en_raw_list_path = '/datapool/home/hujk17/chenxueyuan/LJSpeech-1.1/meta_good.txt'
en_raw_ppg_path = '/datapool/home/hujk17/chenxueyuan/LJSpeech-1.1/ppg_from_generate_batch'
en_raw_linear_dir = '/datapool/home/hujk17/chenxueyuan/LJSpeech-1.1/spec_5ms_by_audio_2'
en_final_cn_log_path = '/datapool/home/hujk17/chenxueyuan/en_final_cn_log_withkdtree'
if os.path.exists(en_final_cn_log_path) is False:
os.makedirs(en_final_cn_log_path)
en_final_cn_idx_path = os.path.join(en_final_cn_log_path, 'en_final_cn_idx_withkdtree.npy')
# f = open(en_final_cn_idx, 'w')
# f......
# np.save(en_final_cn_idx, ...)
Linear_DIM = 201
PPG_DIM = 345 #每一帧ppg的维度
# !!!!!!
K_small = 1 #类
K = 20000 #类
en_all_cnt = 1
cn_all_cnt = 5000
def en_text2list(file): #封装读出每一句英文ppg文件名的函数，输入文本，得到每一句ppg文件名序列的列表
en_file_list = []
global en_all_cnt
with open(file, 'r') as f:
for i, line in enumerate(f.readlines()):
# !!!!!!!!!!!!!!!!
en_file_list.append(line.strip())
if i >= en_all_cnt - 1:
break
print('en len:', len(en_file_list), 'en:', en_file_list[:min(3, en_all_cnt)])
return en_file_list
# 000001 那些#1庄稼#1田园#2在#1果果#1眼里#2感觉#1太亲切了#4
# na4 xie1 zhuang1 jia5 tian2 yuan2
def cn_text2list(file): #封装读出每一句中文ppg文件名的函数，输入文本，得到每一句ppg文件名序列的列表
cn_file_list = []
global cn_all_cnt
with open(file, 'r') as f:
a = [i.strip() for i in f.readlines()]
# print(a[0])
# print(a[1])
i = 0
while i < len(a):
fname = a[i]
cn_file_list.append(fname)
i += 1
if i >= cn_all_cnt:
break
print('cn len:', len(cn_file_list), 'cn:', cn_file_list[:min(3, cn_all_cnt)])
return cn_file_list
def get_single_data_pair(fname, ppgs_dir, linears_dir): #输入每一句的文件名，ppg的地址，线性谱的地址，得到每一句的ppg和linear
assert os.path.isdir(ppgs_dir) and os.path.isdir(linears_dir)
# mfcc_f = os.path.join(os.path.join(os.path.join(mfcc_dir, fname.split('-')[0]),fname.split('-')[1]),fname+'.npy')#fname+'.npy')
ppg_f = os.path.join(ppgs_dir, fname+'.npy')#os.path.join(ppg_dir, fname+'.npy')
linear_f = os.path.join(linears_dir, fname+'.npy')#os.path.join(ppg_dir, fname+'.npy')
ppg = np.load(ppg_f)
linear = np.load(linear_f)
# ppg = onehot(ppg, depth=PPG_DIM)
assert ppg.shape[0] == linear.shape[0],fname+' 维度不相等'
assert ppg.shape[1] == PPG_DIM and linear.shape[1] == Linear_DIM
return ppg, linear
def for_loop_en(): #得到每一帧的英文ppg列表
en_file_list = en_text2list(file=en_raw_list_path)
en_ppgs_ls = []
for f in tqdm(en_file_list):
wav_ppgs, linears = get_single_data_pair(f, ppgs_dir=en_raw_ppg_path, linears_dir=en_raw_linear_dir)
# 需要确认下
en_ppgs_ls.extend(list(wav_ppgs))
# 或者
# for i in range(wav_ppgs.shape[0]):
# # ppg[i]
# en_ppgs_ls.append(wav_ppgs[i])
# # find_jin(ppg[i])
# 只考虑第一句话
print('en now only sentence:', f)
# break
# shuffule
# wav_id, frame_id
return en_ppgs_ls
def for_loop_cn(): #得到每一帧的中文ppg列表
cn_file_list = cn_text2list(file=cn_raw_list_path)
cn_ppgs_ls = []
for f in tqdm(cn_file_list):
wav_ppgs, linears = get_single_data_pair(f, ppgs_dir=cn_raw_ppg_path, linears_dir=cn_raw_linear_dir)
# 需要确认下
cn_ppgs_ls.extend(list(wav_ppgs))
# 或者
# for i in range(wav_ppgs.shape[0]):
# # ppg[i]
# cn_ppgs_ls.append(wav_ppgs[i])
# # find_jin(ppg[i])
# shuffule
# wav_id, frame_id
return cn_ppgs_ls
def dist(ppg_e, ppg_c): #分别输入一帧中英文ppg，返回二者距离
# # array, 345 dim
# assert ppg_c.shape[0] == 345
# ans = 0
# for i in range(345):
# ans += (ppg_e[i] - ppg_c[i]) ** 2
# ans = ans ** 0.5
# print(ppg_e.shape)
ans = np.linalg.norm(ppg_e - ppg_c)
return ans
def cluster_kmeans(all, K): #聚类，输入是所有帧的ppg列表all,K类,输出是每一类的ppg列表
# a = [a1, a2, ...], y = [label, label,...]
#kmeans = KMeans(n_clusters=K, random_state=0).fit(all)
class_as_index = KMeans(n_clusters=K, random_state=0).fit_predict(all)
#class_as_index = k_means(a, K)
return class_as_index
def bruce_find_closest(i, now_class, en_l, cn_l, class_cn_ppgs):
ans = 1e100 #科学计数法 1*10的100次方
ans_id = -1
# ans_id_etc = -1
for j in class_cn_ppgs[now_class]: #class_cn_ppgs[now_class] = [2,8,19,...]或[3,48,79,...]或[4,5,36,...]或... eg,[2,8,19,...]
e = en_l[i] #e = en_l[0], en_l[1], en_l[2], ...
c = cn_l[j] #c = cn_l[2], cn_l[8], ...
dist_e_c = dist(e, c)
if dist_e_c < ans:
ans = dist_e_c
ans_id = j #cn_id 距离每一个en_ppg最近的cn_ppg的帧id
# ans_id_etc = c #cn_l[j]
# 已经找到最接近的了，记下来
return ans, ans_id
def kdtree_find_closest(i, en_l, now_class_cn_ppgs_value_kdtree, now_class_cn_ppgs):
e = en_l[i]
e_2d = np.expand_dims(e, axis=0)
# print(e_2d.shape)
dist, ind = now_class_cn_ppgs_value_kdtree.query(e_2d)
# print(dist, ind)
dist = dist[0][0]
ind = ind[0][0]
real_ind = now_class_cn_ppgs[ind]
return dist, real_ind
'''
def hjk_main1(): #
# in cn & in en
en_l = []
cn_l = []
en_final_cn_idx = np.zeros((len(en_l))) # a[1000000]
for i, e in enumerate(en_l):
ans = 1e100
ans_id = -1
ans_id_etc = -1
for j, c in enumerate(cn_l):
if dist(e, c) < ans:
ans = dist(e, c)
ans_id = c
ans_id_etc = c
en_final_cn_idx[i] = ans_id
np.save('en_final_cn_idx', en_final_cn_idx)
'''
def main():
print('start program')
en_l = for_loop_en() #英文每一帧ppg的列表 en_l = [en_ppg1,en_ppg2,...]
cn_l = for_loop_cn() #中文每一帧ppg的列表 cn_l = [cn_ppg1,cn_ppg2,...]
all_l = en_l + cn_l #中英文混合后的ppg的列表
print('start cluster...')
# 需要快速的聚类 #all_l=[en_ppg1,en_ppg2,...,cn_ppg1,cn_ppg2,...]
all_class = cluster_kmeans(all_l, K_small) #all_class=[en_label,en_label,...,cn_label,cn_label,...]
print('end cluster...')
#... a[100], a[0].1, 2, 3,...
class_cn_ppgs = list() #建立一个列表class_cn_ppgs，列表中包含K个空列表class_cn_ppg = [[],[],[],...]
class_cn_ppgs_value = list()
class_cn_ppgs_value_kdtree = list()
for i in range(K_small):
l = list()
class_cn_ppgs.append(l) #append()在列表后面添加元素
l_value = list()
class_cn_ppgs_value.append(l_value)
# int a[10][10];
# a[0][0] = 888
# a[0][1] = 999
# a[0][2] = -1
# a[1][0] = 777
# 构造类的信息, 筛选出每个类里都有哪些中文的ppg; 并且平均每个类有100个中文ppg
for i in range(len(cn_l)):
idx = i + len(en_l)
now_class = all_class[idx] #now_class = cn_label 可能是0-1999
class_cn_ppgs[now_class].append(i) #class_cn_ppg = [[2,8,19,...],[3,48,79,...],[4,5,36,...],...] 2000个类，每个类中含有cn_l中对应帧ppg的序列号
class_cn_ppgs_value[now_class].append(cn_l[i])
# 看下哪些类中没有中文的PPG
print('start kdtree')
have_cnt = 0
for i in tqdm(range(K_small)):
l = len(class_cn_ppgs[i])
if l > 0:
have_cnt += 1
print('-----:', i, l)
class_cn_ppgs_value[i] = np.asarray(class_cn_ppgs_value[i])
class_cn_ppgs_value_kdtree.append(KDTree(class_cn_ppgs_value[i], leaf_size=40) )
print('have class:', have_cnt)
print('end kdtree')
# 开始寻找en对应的类内所有中文ppg离他最近的
en_final_cn_idx = np.zeros((len(en_l))) # a[1000000] np.zeros()返回来一个给定形状和类型的用0填充的数组；
for i in tqdm(range(len(en_l))): #遍历英文ppg列表，
now_class = all_class[i] #now_class = en_label 可能是0-1999
# 暴力寻找
# ans1, ans_id1 = bruce_find_closest(i, now_class, en_l, cn_l, class_cn_ppgs)
ans, ans_id = kdtree_find_closest(i, en_l, class_cn_ppgs_value_kdtree[now_class], class_cn_ppgs[now_class])
# assert np.absolute(ans1 - ans) < eps and ans_id1 == ans_id
en_final_cn_idx[i] = ans_id
np.save(en_final_cn_idx_path, en_final_cn_idx)
if __name__ == '__main__':
main()