-
Notifications
You must be signed in to change notification settings - Fork 11
/
regress_ALUCs.py
36 lines (25 loc) · 1.1 KB
/
regress_ALUCs.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
# regress_ALUCS.py maps signal-level and SylNet features to phoneme, syllable,
# and word counts using a pre-trained set of linear model coefficients.
import sys
import numpy as np
from numpy import genfromtxt
curdir = sys.argv[1]
F = genfromtxt(curdir + "/tmp_data/features/final_feats.txt", delimiter='\t')
# Old diarizer (Dec 2019)
#X_phones = [1.1092, 16.1548, -0.317, -0.0014, 48.7033]
#X_syls = [0.6267, 8.8197, -0.1579, -0.0010, 23.0796]
#X_words = [0.3029, 5.9524, -0.0977, -0.0004, 14.2853]
# New diarizer (March 2020)
X_phones = [0.6675, 10.0683, -0.2763, -0.0009, 42.7907]
X_syls = [0.3094, 5.5473, -0.1538, -0.0006, 23.2143]
X_words = [0.1697, 3.5229, -0.0920, -0.0003, 13.9089]
F = np.column_stack((F,np.ones((F.shape[0],1))))
words_est = np.dot(F,X_words)
words_est = words_est.clip(min = 0)
syls_est = np.dot(F,X_syls)
syls_est = syls_est.clip(min = 0)
phones_est = np.dot(F,X_phones)
phones_est = phones_est.clip(min = 0)
Y = np.column_stack((phones_est,syls_est))
Y = np.column_stack((Y,words_est))
np.savetxt(curdir + "/tmp_data/features/ALUCs_out_individual_tmp.txt", Y, delimiter="\t",fmt='%0.2f')