├── img
    └── multitarget_baseline_v2.png
├── README.md
├── mce2018_baseline_dev.py
├── mce2018_baseline_test.py
└── data
    └── bl_matching_dev.csv


/img/multitarget_baseline_v2.png:
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https://raw.githubusercontent.com/swshon/multi-speakerID/HEAD/img/multitarget_baseline_v2.png


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/README.md:
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 1 | # multi-speakerID baseline
 2 | This is python implementation of multi-target speaker recognition based on i-vector feature. This is also baseline system of the first Multi-target speaker detection and identification Challenge Evaluation (MCE 2018, http://www.mce2018.org )
 3 | 
 4 | 
 5 | # Dataset
 6 | You can download i-vector dataset here
 7 | 
 8 |     https://www.kaggle.com/kagglesre/blacklist-speakers-dataset
 9 | 
10 | After download, extract into data folder
11 | 
12 | # System flow
13 | <img align="center" width="600" src="https://github.com/swshon/multi-speakerID/blob/master/img/multitarget_baseline_v2.png ">
14 | 
15 | # Performance
16 | If you run the code like
17 | 
18 |     python mce2018_baseline_test.py
19 |     
20 | you will see the performance on top-S and top-1 detector as below :
21 | 
22 |     Dev set score using train set :
23 |     Top S detector EER is 2.01%
24 |     Top 1 detector EER is 12.26% (Total confusion error is 444)
25 | 
26 |     Test set score using train set:
27 |     Top S detector EER is 7.52%
28 |     Top 1 detector EER is 16.61% (Total confusion error is 579)
29 | 
30 |     Test set score using train + dev set:
31 |     Top S detector EER is 6.24%
32 |     Top 1 detector EER is 11.24% (Total confusion error is 369)
33 | 
34 | And the code also generate example submission file with name "teamname_fixed_primary.csv" and the format are [test utterance ID],[score],[Closest blacklist speaker ID] per each files. For example
35 | 
36 |     pnah_431154,0.36613864,33762391
37 |     qtxw_470243,0.39585015,60587769
38 |     ....
39 | 
40 | # Reference
41 | If you want to check detailed information, please read below
42 | 
43 | Suwon Shon, Najim Dehak, Douglas Reynolds, James Glass,<br>
44 | "MCE 2018: The 1st Multi-target speaker detection and identification Challenge Evaluation (MCE) Plan, Dataset and Baseline System”<br>
45 | https://arxiv.org/abs/1807.06663
46 | 
47 | 
48 | 
49 | # Question
50 | Please email to mce organizer if you have question.
51 | mce@lists.csail.mit.edu or swshon@mit.edu
52 | 
53 | 


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/mce2018_baseline_dev.py:
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  1 | import numpy as np
  2 | from sklearn.metrics import roc_curve
  3 | 
  4 | ## making dictionary to find blacklist pair between train and test dataset
  5 | bl_match = np.loadtxt('data/bl_matching_dev.csv',dtype='string')
  6 | dev2train={}
  7 | dev2id={}
  8 | train2dev={}
  9 | train2id={}
 10 | for iter, line in enumerate(bl_match):
 11 |     line_s = line.split(',')
 12 |     dev2train[line_s[1].split('_')[-1]]= line_s[2].split('_')[-1]
 13 |     dev2id[line_s[1].split('_')[-1]]= line_s[0].split('_')[-1]
 14 |     train2dev[line_s[2].split('_')[-1]]= line_s[1].split('_')[-1]
 15 |     train2id[line_s[2].split('_')[-1]]= line_s[0].split('_')[-1]
 16 |     
 17 | def load_ivector(filename):
 18 |     utt = np.loadtxt(filename,dtype='string',delimiter=',',skiprows=1,usecols=[0])
 19 |     ivector = np.loadtxt(filename,dtype='float32',delimiter=',',skiprows=1,usecols=range(1,601))
 20 |     spk_id = []
 21 |     for iter in range(len(utt)):
 22 |         spk_id = np.append(spk_id,utt[iter].split('_')[0])
 23 | 
 24 |     return spk_id, utt, ivector
 25 | 
 26 | def length_norm(mat):
 27 | # length normalization (l2 norm)
 28 | # input: mat = [utterances X vector dimension] ex) (float) 8631 X 600
 29 | 
 30 |     norm_mat = []
 31 |     for line in mat:
 32 |         temp = line/np.math.sqrt(sum(np.power(line,2)))
 33 |         norm_mat.append(temp)
 34 |     norm_mat = np.array(norm_mat)
 35 |     return norm_mat
 36 | 
 37 | def make_spkvec(mat, spk_label):
 38 | # calculating speaker mean vector
 39 | # input: mat = [utterances X vector dimension] ex) (float) 8631 X 600
 40 | #        spk_label = string vector ex) ['abce','cdgd']
 41 | 
 42 | #     for iter in range(len(spk_label)):
 43 | #         spk_label[iter] = spk_label[iter].split('_')[0]
 44 | 
 45 |     spk_label, spk_index  = np.unique(spk_label,return_inverse=True)
 46 |     spk_mean=[]
 47 |     mat = np.array(mat)
 48 | 
 49 |     # calculating speaker mean i-vector
 50 |     for i, spk in enumerate(spk_label):
 51 |         spk_mean.append(np.mean(mat[np.nonzero(spk_index==i)],axis=0))
 52 |     spk_mean = length_norm(spk_mean)
 53 |     return spk_mean, spk_label
 54 | 
 55 | def calculate_EER(trials, scores):
 56 | # calculating EER of Top-S detector
 57 | # input: trials = boolean(or int) vector, 1: postive(blacklist) 0: negative(background)
 58 | #        scores = float vector
 59 | 
 60 |     # Calculating EER
 61 |     fpr,tpr,threshold = roc_curve(trials,scores,pos_label=1)
 62 |     fnr = 1-tpr
 63 |     EER_threshold = threshold[np.argmin(abs(fnr-fpr))]
 64 |     
 65 |     # print EER_threshold
 66 |     EER_fpr = fpr[np.argmin(np.absolute((fnr-fpr)))]
 67 |     EER_fnr = fnr[np.argmin(np.absolute((fnr-fpr)))]
 68 |     EER = 0.5 * (EER_fpr+EER_fnr)
 69 |     
 70 |     print "Top S detector EER is %0.2f%%"% (EER*100)
 71 |     return EER
 72 | 
 73 | def get_trials_label_with_confusion(identified_label, groundtruth_label,dict4spk,is_trial ):
 74 | # determine if the test utterance would make confusion error
 75 | # input: identified_label = string vector, identified result of test utterance among multi-target from the detection system 
 76 | #        groundtruth_label = string vector, ground truth speaker labels of test utterances
 77 | #        dict4spk = dictionary, convert label to target set, ex) train2dev convert train id to dev id
 78 | 
 79 |     trials = np.zeros(len(identified_label))
 80 |     for iter in range(0,len(groundtruth_label)):
 81 |         enroll = identified_label[iter].split('_')[0]
 82 |         test = groundtruth_label[iter].split('_')[0]
 83 |         if is_trial[iter]:
 84 |             if enroll == dict4spk[test]:
 85 |                 trials[iter]=1 # for Target trial (blacklist speaker)
 86 |             else:
 87 |                 trials[iter]=-1 # for Target trial (backlist speaker), but fail on blacklist classifier
 88 |                 
 89 |         else :
 90 |             trials[iter]=0 # for non-target (non-blacklist speaker)
 91 |     return trials
 92 | 
 93 | def calculate_EER_with_confusion(scores,trials):
 94 | # calculating EER of Top-1 detector
 95 | # input: trials = boolean(or int) vector, 1: postive(blacklist) 0: negative(background) -1: confusion(blacklist)
 96 | #        scores = float vector
 97 | 
 98 |     # exclude confusion error (trials==-1)
 99 |     scores_wo_confusion = scores[np.nonzero(trials!=-1)[0]]
100 |     trials_wo_confusion = trials[np.nonzero(trials!=-1)[0]]
101 | 
102 |     # dev_trials contain labels of target. (target=1, non-target=0)
103 |     fpr,tpr,threshold = roc_curve(trials_wo_confusion,scores_wo_confusion,pos_label=1, drop_intermediate=False)
104 |     fnr = 1-tpr
105 |     EER_threshold = threshold[np.argmin(abs(fnr-fpr))]
106 |     
107 |     # EER withouth confusion error
108 |     EER = fpr[np.argmin(np.absolute((fnr-fpr)))]
109 |     
110 |     # Add confusion error to false negative rate(Miss rate)
111 |     total_negative = len(np.nonzero(np.array(trials_wo_confusion)==0)[0])
112 |     total_positive = len(np.nonzero(np.array(trials_wo_confusion)==1)[0])
113 |     fp= fpr*np.float(total_negative)  
114 |     fn= fnr*np.float(total_positive) 
115 |     fn += len(np.nonzero(trials==-1)[0])
116 |     total_positive += len(np.nonzero(trials==-1)[0])
117 |     fpr= fp/total_negative
118 |     fnr= fn/total_positive
119 | 
120 |     # EER with confusion Error
121 |     EER_threshold = threshold[np.argmin(abs(fnr-fpr))]
122 |     EER_fpr = fpr[np.argmin(np.absolute((fnr-fpr)))]
123 |     EER_fnr = fnr[np.argmin(np.absolute((fnr-fpr)))]
124 |     EER = 0.5 * (EER_fpr+EER_fnr)
125 |     
126 |     print "Top 1 detector EER is %0.2f%% (Total confusion error is %d)"% ((EER*100), len(np.nonzero(trials==-1)[0]))
127 |     return EER
128 | 
129 | # Loading i-vector
130 | trn_bl_id, trn_bl_utt, trn_bl_ivector = load_ivector('data/trn_blacklist.csv')
131 | trn_bg_id, trn_bg_utt, trn_bg_ivector = load_ivector('data/trn_background.csv')
132 | dev_bl_id, dev_bl_utt, dev_bl_ivector = load_ivector('data/dev_blacklist.csv')
133 | dev_bg_id, dev_bg_utt, dev_bg_ivector = load_ivector('data/dev_background.csv')
134 | 
135 | # Calculating speaker mean vector
136 | spk_mean, spk_mean_label = make_spkvec(trn_bl_ivector,trn_bl_id)
137 | 
138 | #length normalization
139 | trn_bl_ivector = length_norm(trn_bl_ivector)
140 | trn_bg_ivector = length_norm(trn_bg_ivector)
141 | dev_bl_ivector = length_norm(dev_bl_ivector)
142 | dev_bg_ivector = length_norm(dev_bg_ivector)
143 | 
144 | 
145 | print '\nDev set score using train set :'
146 | # making trials of Dev set
147 | dev_ivector = np.append(dev_bl_ivector, dev_bg_ivector,axis=0)
148 | dev_trials = np.append( np.ones([len(dev_bl_id), 1]), np.zeros([len(dev_bg_id), 1]))
149 | 
150 | # Cosine distance scoring
151 | scores = spk_mean.dot(dev_ivector.transpose())
152 | 
153 | # Multi-target normalization
154 | blscores = spk_mean.dot(trn_bl_ivector.transpose())
155 | mnorm_mu = np.mean(blscores,axis=1)
156 | mnorm_std = np.std(blscores,axis=1)
157 | for iter in range(np.shape(scores)[1]):
158 |     scores[:,iter]= (scores[:,iter] - mnorm_mu) / mnorm_std
159 | dev_scores = np.max(scores,axis=0)
160 | 
161 | # Top-S detector EER
162 | dev_EER = calculate_EER(dev_trials, dev_scores)
163 | 
164 | #divide trial label into target and non-target, plus confusion error(blacklist, fail at blacklist detector)
165 | dev_identified_label = spk_mean_label[np.argmax(scores,axis=0)]
166 | dev_trials_label = np.append( dev_bl_id,dev_bg_id)
167 | dev_trials_utt_label = np.append( dev_bl_utt,dev_bg_utt)
168 | 
169 | # Top-1 detector EER
170 | dev_trials_confusion = get_trials_label_with_confusion(dev_identified_label, dev_trials_label, dev2train, dev_trials )
171 | dev_EER_confusion = calculate_EER_with_confusion(dev_scores,dev_trials_confusion)
172 | 
173 | # Generating submission file on Dev set for example
174 | filename = 'teamname_fixed_primary.csv'
175 | # filename = 'teamname_fixed_contrastive1.csv'
176 | with open(filename, "w") as text_file:
177 |     for iter,score in enumerate(dev_scores):
178 |         id_in_trainset = dev_identified_label[iter].split('_')[0]
179 |         input_file = dev_trials_utt_label[iter]
180 |         text_file.write('%s,%s,%s\n' % (input_file,score,train2id[id_in_trainset]))
181 | 
182 |         
183 |         


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/mce2018_baseline_test.py:
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  1 | import numpy as np
  2 | from sklearn.metrics import roc_curve
  3 | 
  4 | ## making dictionary to find blacklist pair between train and test dataset
  5 | # bl_match = np.loadtxt('data/bl_matching_dev.csv',dtype='string')
  6 | bl_match = np.loadtxt('data/bl_matching.csv',dtype='string')
  7 | dev2train={}
  8 | dev2id={}
  9 | train2dev={}
 10 | train2id={}
 11 | test2train={}
 12 | train2test={}
 13 | for iter, line in enumerate(bl_match):
 14 |     line_s = line.split(',')
 15 |     dev2train[line_s[1].split('_')[-1]]= line_s[3].split('_')[-1]
 16 |     dev2id[line_s[1].split('_')[-1]]= line_s[0].split('_')[-1]
 17 |     train2dev[line_s[3].split('_')[-1]]= line_s[1].split('_')[-1]
 18 |     train2id[line_s[3].split('_')[-1]]= line_s[0].split('_')[-1]
 19 |     test2train[line_s[2].split('_')[-1]]= line_s[3].split('_')[-1]
 20 |     train2test[line_s[3].split('_')[-1]]= line_s[2].split('_')[-1]
 21 |     
 22 | def load_ivector(filename):
 23 |     utt = np.loadtxt(filename,dtype='string',delimiter=',',skiprows=1,usecols=[0])
 24 |     ivector = np.loadtxt(filename,dtype='float32',delimiter=',',skiprows=1,usecols=range(1,601))
 25 |     spk_id = []
 26 |     for iter in range(len(utt)):
 27 |         spk_id = np.append(spk_id,utt[iter].split('_')[0])
 28 | 
 29 |     return spk_id, utt, ivector
 30 | 
 31 | def length_norm(mat):
 32 | # length normalization (l2 norm)
 33 | # input: mat = [utterances X vector dimension] ex) (float) 8631 X 600
 34 | 
 35 |     norm_mat = []
 36 |     for line in mat:
 37 |         temp = line/np.math.sqrt(sum(np.power(line,2)))
 38 |         norm_mat.append(temp)
 39 |     norm_mat = np.array(norm_mat)
 40 |     return norm_mat
 41 | 
 42 | def make_spkvec(mat, spk_label):
 43 | # calculating speaker mean vector
 44 | # input: mat = [utterances X vector dimension] ex) (float) 8631 X 600
 45 | #        spk_label = string vector ex) ['abce','cdgd']
 46 | 
 47 | #     for iter in range(len(spk_label)):
 48 | #         spk_label[iter] = spk_label[iter].split('_')[0]
 49 | 
 50 |     spk_label, spk_index  = np.unique(spk_label,return_inverse=True)
 51 |     spk_mean=[]
 52 |     mat = np.array(mat)
 53 | 
 54 |     # calculating speaker mean i-vector
 55 |     for i, spk in enumerate(spk_label):
 56 |         spk_mean.append(np.mean(mat[np.nonzero(spk_index==i)],axis=0))
 57 |     spk_mean = length_norm(spk_mean)
 58 |     return spk_mean, spk_label
 59 | 
 60 | def calculate_EER(trials, scores):
 61 | # calculating EER of Top-S detector
 62 | # input: trials = boolean(or int) vector, 1: postive(blacklist) 0: negative(background)
 63 | #        scores = float vector
 64 | 
 65 |     # Calculating EER
 66 |     fpr,tpr,threshold = roc_curve(trials,scores,pos_label=1)
 67 |     fnr = 1-tpr
 68 |     EER_threshold = threshold[np.argmin(abs(fnr-fpr))]
 69 |     
 70 |     # print EER_threshold
 71 |     EER_fpr = fpr[np.argmin(np.absolute((fnr-fpr)))]
 72 |     EER_fnr = fnr[np.argmin(np.absolute((fnr-fpr)))]
 73 |     EER = 0.5 * (EER_fpr+EER_fnr)
 74 |     
 75 |     print "Top S detector EER is %0.2f%%"% (EER*100)
 76 |     return EER
 77 | 
 78 | def get_trials_label_with_confusion(identified_label, groundtruth_label,dict4spk,is_trial ):
 79 | # determine if the test utterance would make confusion error
 80 | # input: identified_label = string vector, identified result of test utterance among multi-target from the detection system 
 81 | #        groundtruth_label = string vector, ground truth speaker labels of test utterances
 82 | #        dict4spk = dictionary, convert label to target set, ex) train2dev convert train id to dev id
 83 | 
 84 |     trials = np.zeros(len(identified_label))
 85 |     for iter in range(0,len(groundtruth_label)):
 86 |         enroll = identified_label[iter].split('_')[0]
 87 |         test = groundtruth_label[iter].split('_')[0]
 88 |         if is_trial[iter]:
 89 |             if enroll == dict4spk[test]:
 90 |                 trials[iter]=1 # for Target trial (blacklist speaker)
 91 |             else:
 92 |                 trials[iter]=-1 # for Target trial (backlist speaker), but fail on blacklist classifier
 93 |                 
 94 |         else :
 95 |             trials[iter]=0 # for non-target (non-blacklist speaker)
 96 |     return trials
 97 | 
 98 | 
 99 | def calculate_EER_with_confusion(scores,trials):
100 | # calculating EER of Top-1 detector
101 | # input: trials = boolean(or int) vector, 1: postive(blacklist) 0: negative(background) -1: confusion(blacklist)
102 | #        scores = float vector
103 | 
104 |     # exclude confusion error (trials==-1)
105 |     scores_wo_confusion = scores[np.nonzero(trials!=-1)[0]]
106 |     trials_wo_confusion = trials[np.nonzero(trials!=-1)[0]]
107 | 
108 |     # dev_trials contain labels of target. (target=1, non-target=0)
109 |     fpr,tpr,threshold = roc_curve(trials_wo_confusion,scores_wo_confusion,pos_label=1, drop_intermediate=False)
110 |     fnr = 1-tpr
111 |     EER_threshold = threshold[np.argmin(abs(fnr-fpr))]
112 |     
113 |     # EER withouth confusion error
114 |     EER = fpr[np.argmin(np.absolute((fnr-fpr)))]
115 |     
116 |     # Add confusion error to false negative rate(Miss rate)
117 |     total_negative = len(np.nonzero(np.array(trials_wo_confusion)==0)[0])
118 |     total_positive = len(np.nonzero(np.array(trials_wo_confusion)==1)[0])
119 |     fp= fpr*np.float(total_negative)  
120 |     fn= fnr*np.float(total_positive) 
121 |     fn += len(np.nonzero(trials==-1)[0])
122 |     total_positive += len(np.nonzero(trials==-1)[0])
123 |     fpr= fp/total_negative
124 |     fnr= fn/total_positive
125 | 
126 |     # EER with confusion Error
127 |     EER_threshold = threshold[np.argmin(abs(fnr-fpr))]
128 |     EER_fpr = fpr[np.argmin(np.absolute((fnr-fpr)))]
129 |     EER_fnr = fnr[np.argmin(np.absolute((fnr-fpr)))]
130 |     EER = 0.5 * (EER_fpr+EER_fnr)
131 |     
132 |     print "Top 1 detector EER is %0.2f%% (Total confusion error is %d)"% ((EER*100), len(np.nonzero(trials==-1)[0]))
133 |     return EER
134 | 
135 | 
136 | # Loading i-vector
137 | trn_bl_id, trn_bl_utt, trn_bl_ivector = load_ivector('data/trn_blacklist.csv')
138 | trn_bg_id, trn_bg_utt, trn_bg_ivector = load_ivector('data/trn_background.csv')
139 | dev_bl_id, dev_bl_utt, dev_bl_ivector = load_ivector('data/dev_blacklist.csv')
140 | dev_bg_id, dev_bg_utt, dev_bg_ivector = load_ivector('data/dev_background.csv')
141 | tst_id, test_utt, tst_ivector = load_ivector('data/tst_evaluation.csv')
142 | 
143 | # Calculating speaker mean vector
144 | spk_mean, spk_mean_label = make_spkvec(trn_bl_ivector,trn_bl_id)
145 | 
146 | #length normalization
147 | trn_bl_ivector = length_norm(trn_bl_ivector)
148 | trn_bg_ivector = length_norm(trn_bg_ivector)
149 | dev_bl_ivector = length_norm(dev_bl_ivector)
150 | dev_bg_ivector = length_norm(dev_bg_ivector)
151 | tst_ivector = length_norm(tst_ivector)
152 | 
153 | # load test set information
154 | filename = 'data/tst_evaluation_keys.csv'
155 | tst_info = np.loadtxt(filename,dtype='string',delimiter=',',skiprows=1,usecols=range(0,3))
156 | tst_trials = []
157 | tst_trials_label = []
158 | tst_ground_truth =[]
159 | for iter in range(len(tst_info)):
160 |     tst_trials_label.extend([tst_info[iter,0]])
161 |     if tst_info[iter,1]=='background':
162 |         tst_trials = np.append(tst_trials,0)
163 |         
164 |     else:
165 |         tst_trials = np.append(tst_trials,1)
166 | 
167 |         
168 | print '\nDev set score using train set :'
169 | # making trials of Dev set
170 | dev_ivector = np.append(dev_bl_ivector, dev_bg_ivector,axis=0)
171 | dev_trials = np.append( np.ones([len(dev_bl_id), 1]), np.zeros([len(dev_bg_id), 1]))
172 | 
173 | # Cosine distance scoring
174 | scores = spk_mean.dot(dev_ivector.transpose())
175 | 
176 | # Multi-target normalization
177 | blscores = spk_mean.dot(trn_bl_ivector.transpose())
178 | mnorm_mu = np.mean(blscores,axis=1)
179 | mnorm_std = np.std(blscores,axis=1)
180 | for iter in range(np.shape(scores)[1]):
181 |     scores[:,iter]= (scores[:,iter] - mnorm_mu) / mnorm_std
182 | dev_scores = np.max(scores,axis=0)
183 | 
184 | # Top-S detector EER
185 | dev_EER = calculate_EER(dev_trials, dev_scores)
186 | 
187 | #divide trial label into target and non-target, plus confusion error(blacklist, fail at blacklist detector)
188 | dev_identified_label = spk_mean_label[np.argmax(scores,axis=0)]
189 | dev_trials_label = np.append( dev_bl_id,dev_bg_id)
190 | 
191 | # Top-1 detector EER
192 | dev_trials_confusion = get_trials_label_with_confusion(dev_identified_label, dev_trials_label, dev2train, dev_trials )
193 | dev_EER_confusion = calculate_EER_with_confusion(dev_scores,dev_trials_confusion)
194 | 
195 | print '\nTest set score using train set:'
196 | #Cosine distance scoring on Test set
197 | scores = spk_mean.dot(tst_ivector.transpose())
198 | 
199 | # Multi-target normalization
200 | blscores = spk_mean.dot(trn_bl_ivector.transpose())
201 | mnorm_mu = np.mean(blscores,axis=1)
202 | mnorm_std = np.std(blscores,axis=1)
203 | for iter in range(np.shape(scores)[1]):
204 |     scores[:,iter]= (scores[:,iter] - mnorm_mu) / mnorm_std
205 | tst_scores = np.max(scores,axis=0)
206 | 
207 | # top-S detector EER
208 | tst_EER = calculate_EER(tst_trials, tst_scores)
209 | 
210 | #divide trial label into target and non-target, plus confusion error(blacklist, fail at blacklist detector)
211 | tst_identified_label = spk_mean_label[np.argmax(scores,axis=0)]
212 | 
213 | # Top-1 detector EER
214 | tst_trials_confusion = get_trials_label_with_confusion(tst_identified_label, tst_trials_label, test2train, tst_trials )
215 | tst_EER_confusion = calculate_EER_with_confusion(tst_scores,tst_trials_confusion)
216 | 
217 | 
218 | print '\nTest set score using train + dev set:'
219 | # get dev set id consistent with Train set
220 | dev_bl_id_along_trnset = []
221 | for iter in range(len(dev_bl_id)):
222 |     dev_bl_id_along_trnset.extend([dev2train[dev_bl_id[iter]]])
223 | 
224 | # Calculating speaker mean vector
225 | spk_mean, spk_mean_label = make_spkvec(np.append(trn_bl_ivector,dev_bl_ivector,0),np.append(trn_bl_id,dev_bl_id_along_trnset))
226 | 
227 | #Cosine distance scoring on Test set
228 | scores = spk_mean.dot(tst_ivector.transpose())
229 | # tst_scores = np.max(scores,axis=0)
230 | 
231 | 
232 | # Multi-target normalization
233 | blscores = spk_mean.dot(np.append(trn_bl_ivector.transpose(),dev_bl_ivector.transpose(),axis=1))
234 | mnorm_mu = np.mean(blscores,axis=1)
235 | mnorm_std = np.std(blscores,axis=1)
236 | for iter in range(np.shape(scores)[1]):
237 |     scores[:,iter]= (scores[:,iter] - mnorm_mu) / mnorm_std
238 | tst_scores = np.max(scores,axis=0)
239 | 
240 | # top-S detector EER
241 | tst_EER = calculate_EER(tst_trials, tst_scores)
242 | 
243 | #divide trial label into target and non-target, plus confusion error(blacklist, fail at blacklist detector)
244 | tst_identified_label = spk_mean_label[np.argmax(scores,axis=0)]
245 | 
246 | # Top-1 detector EER
247 | tst_trials_confusion = get_trials_label_with_confusion(tst_identified_label, tst_trials_label, test2train,tst_trials )
248 | tst_EER_confusion = calculate_EER_with_confusion(tst_scores,tst_trials_confusion)
249 | 
250 | 
251 | # Generating submission file on TST set for example
252 | filename = 'teamname_fixed_primary_tst.csv'
253 | # filename = 'teamname_fixed_contrastive1.csv'
254 | with open(filename, "w") as text_file:
255 |     for iter,score in enumerate(tst_scores):
256 |         id_in_trainset = tst_identified_label[iter].split('_')[0]
257 |         input_file = tst_trials_label[iter]
258 |         text_file.write('%s,%s,%s\n' % (input_file,score,train2id[id_in_trainset]))
259 | 


--------------------------------------------------------------------------------
/data/bl_matching_dev.csv:
--------------------------------------------------------------------------------
   1 | Speakerid,dev_id,train_id
   2 | 50399530,dev_fvth,train_phee
   3 | 22217263,dev_civu,train_yhao
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   6 | 61914144,dev_dryq,train_pjni
   7 | 59559261,dev_vffe,train_jzgm
   8 | 76172450,dev_bszj,train_ywme
   9 | 29819502,dev_cjai,train_gmju
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  12 | 54322337,dev_xcfo,train_jjod
  13 | 43039865,dev_tzbd,train_kxyz
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1978 | 36868702,dev_ogly,train_ewch
1979 | 19120631,dev_jjcj,train_zhha
1980 | 29534953,dev_hvaq,train_rpaf
1981 | 63358940,dev_kosq,train_gxet
1982 | 25508397,dev_aqtp,train_ovev
1983 | 40523196,dev_cbhl,train_jobd
1984 | 52437060,dev_bxkx,train_esym
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1987 | 83428474,dev_octn,train_pkad
1988 | 22592342,dev_jwwm,train_pxqr
1989 | 28082712,dev_vzqu,train_acdr
1990 | 63650505,dev_okil,train_xleq
1991 | 22251403,dev_ziel,train_boxb
1992 | 48442489,dev_eojl,train_wega
1993 | 87502728,dev_jmas,train_pdml
1994 | 41613666,dev_yqju,train_hcox
1995 | 03548897,dev_hbrw,train_frow
1996 | 38923263,dev_txxd,train_tfcw
1997 | 86431690,dev_hehw,train_oalj
1998 | 78943685,dev_eopy,train_gnuq
1999 | 58853082,dev_bigh,train_lktc
2000 | 83742741,dev_lles,train_uqex
2001 | 47947741,dev_hyzg,train_bvzk
2002 | 24830166,dev_mlue,train_kfaz
2003 | 57711303,dev_ohpe,train_qesw
2004 | 54149833,dev_ztca,train_tdpe
2005 | 40661646,dev_epyq,train_fsat
2006 | 53943243,dev_fvtb,train_rybz
2007 | 54737624,dev_gzch,train_rnsl
2008 | 18661171,dev_ymro,train_koss
2009 | 79694815,dev_hpvk,train_awap
2010 | 76474164,dev_rois,train_imss
2011 | 20912037,dev_lpql,train_gbeu
2012 | 82979271,dev_fhpe,train_aote
2013 | 36609278,dev_gbzt,train_rwns
2014 | 40587932,dev_oggj,train_rxuk
2015 | 64780847,dev_nsdj,train_feaa
2016 | 65488325,dev_xcjf,train_nbcf
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3633 | 


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