Commit 9c26431c authored by Luca Pasa's avatar Luca Pasa
Browse files

restart GRID_100 dataset creation + result smallaer verison with different loss DAE_concat

parent 9c81ec46
#!/bin/bash -ex
HTK=/usr/local/htk/HTKTools/
HTK=~/htk/HTKTools/
MULTI_GRID_FOLDER=/DATA_NEW/lpasa/Data/MULTI_GRID/
MULTI_GRID_FOLDER=/home/storage/Data/MULTI_GRID_100/
for DIR in "$MULTI_GRID_FOLDER"/*
do
......
#!/usr/bin/env bash
#MULTI_GRID_FOLDER=/home/storage/Data/MULTI_GRID
MULTI_GRID_FOLDER=/home/storage/Data/GRID
#Comago
MULTI_GRID_FOLDER=/home/lpasa/Data/MULTI_GRID
#MULTI_GRID_FOLDER=/home/lpasa/Data/MULTI_GRID
for DIR in "$MULTI_GRID_FOLDER"/*
do
......
......@@ -4,7 +4,7 @@ from __future__ import division
import glob
import numpy as np
features_file_list = sorted(glob.glob('/home/storage/Data/MULTI_GRID/s*/base_audio/*.csv'))
features_file_list = sorted(glob.glob('/home/storage/Data/MULTI_GRID_100/s*/base_audio/*.csv'))
#features_file_list = sorted(glob.glob('/home/storage/Data/MULTI_GRID/s*/base_audio/*.csv'))
print('Total number of files = {}'.format(len(features_file_list)))
......
......@@ -4,7 +4,7 @@ from __future__ import division
import glob
import numpy as np
features_file_list = sorted(glob.glob('/home/storage/Data/MULTI_GRID/s*/multi_audio/*.csv'))
features_file_list = sorted(glob.glob('/home/storage/Data/MULTI_GRID_100/s*/multi_audio/*.csv'))
#features_file_list = sorted(glob.glob('/home/storage/Data/MULTI_GRID/s*/base_audio/*.csv'))
print('Total number of files = {}'.format(len(features_file_list)))
......
from __future__ import print_function
from __future__ import division
import glob
import numpy as np
features_file_list = sorted(glob.glob('/home/storage/Data/MULTI_GRID/s*/video/*.txt'))
features_file_list = sorted(glob.glob('/home/storage/Data/MULTI_GRID_100/s*/video/*.txt'))
#features_file_list = sorted(glob.glob('/home/lpasa/Data/MULTI_GRID/s*/video/*.txt'))
print('Total number of files = {}'.format(len(features_file_list)))
features=[]
f_len=[]
for file_index, txt_file in enumerate(features_file_list):
print(file_index,txt_file)
#print(file_index,txt_file)
data=np.loadtxt(txt_file)
if data.shape[0]<60:
#print data.shape
#print txt_file
last_frame=data[-1,:].reshape((1,data.shape[1]))
for _ in range(74-data.shape[0]):
data=np.concatenate((data,last_frame),axis=0)
f_len.append(data.shape[0])
features.append(data)
raw_input("STOP")
features = np.concatenate(features)
print(features.shape)
......
test_name: Overfitting_TEST_Concat_DAE_4_speech_Test_lr-0.001_batch_size-18_n_hidden_encode-750
2018-05-09 12:48:12.510559
#epoch distance time epoch_cost
1 0.06961785 6.78622699 2.45239592
10 0.36852735 202.38831592 0.96432685
20 0.49715054 199.80201793 0.73977233
30 0.57559055 197.90127516 0.57582273
40 0.62551898 199.26498199 0.48480341
50 0.65899760 198.82261491 0.42501304
60 0.68324602 198.74254608 0.37795419
70 0.70137244 199.85260105 0.34755723
80 0.71606129 200.70237017 0.32087798
90 0.72813118 201.01013494 0.28476567
100 0.73817694 201.61659694 0.26970364
110 0.74517608 193.44801497 0.24527414
120 0.75335252 192.78573298 0.22714136
130 0.75966150 199.74398994 0.21604389
140 0.76520383 197.73026490 0.19947750
150 0.77022362 200.70737815 0.18717629
160 0.77422708 200.47795987 0.18306259
170 0.77865803 199.03695011 0.16612206
180 0.78229952 198.00526094 0.16128634
190 0.78532273 200.05006003 0.16271033
200 0.78826910 200.08501291 0.15539532
210 0.79106683 197.72476006 0.14176094
220 0.79358387 201.68985105 0.13597901
230 0.79587132 197.36995101 0.13678846
240 0.79807884 197.92261100 0.13241834
250 0.80021232 200.92162514 0.12048453
260 0.80224603 199.34586096 0.11993909
270 0.80400079 201.94554710 0.11875744
280 0.80563962 198.09159398 0.12174742
290 0.80713826 199.52363086 0.11258183
300 0.80870026 199.48209810 0.10436552
310 0.80965555 197.64053416 0.10641402
320 0.81143409 202.05480194 0.10738569
330 0.81271958 202.53401399 0.10416521
340 0.81375283 201.31989789 0.09478336
350 0.81512487 203.11453819 0.09577895
360 0.81618583 198.01704812 0.09632075
370 0.81712580 203.86609983 0.09962768
380 0.81811357 187.92328596 0.09170707
390 0.81885076 210.57062817 0.08980663
400 0.81993061 223.20126700 0.08497685
410 0.82079691 219.47758484 0.08860879
420 0.82141316 196.60595608 0.08878033
430 0.82236439 195.96066904 0.08974486
440 0.82314223 196.48566103 0.07871293
450 0.82390541 195.61394286 0.08129330
460 0.82463837 193.12551689 0.08170252
470 0.82535899 195.58085799 0.07845773
480 0.82599562 199.96816206 0.07696115
490 0.82661468 195.36905408 0.07624938
500 0.82722825 202.62298918 0.07301893
test_name: Overfitting_TEST_Concat_DAE_4_speech_Test_lr-0.001_batch_size-18_n_hidden_encode-750
2018-05-09 12:48:12.510531
#epoch distance time epoch_cost
1 0.06797814 6.78622699 2.45239592
10 0.32849601 202.38831592 0.96432685
20 0.48554438 199.80201793 0.73977233
30 0.56913513 197.90127516 0.57582273
40 0.62107468 199.26498199 0.48480341
50 0.65798026 198.82261491 0.42501304
60 0.68296093 198.74254608 0.37795419
70 0.70143515 199.85260105 0.34755723
80 0.71654612 200.70237017 0.32087798
90 0.72866547 201.01013494 0.28476567
100 0.73904830 201.61659694 0.26970364
110 0.74737698 193.44801497 0.24527414
120 0.75365949 192.78573298 0.22714136
130 0.76062500 199.74398994 0.21604389
140 0.76610899 197.73026490 0.19947750
150 0.77121216 200.70737815 0.18717629
160 0.77564532 200.47795987 0.18306259
170 0.77964699 199.03695011 0.16612206
180 0.78328997 198.00526094 0.16128634
190 0.78632534 200.05006003 0.16271033
200 0.78884119 200.08501291 0.15539532
210 0.79199779 197.72476006 0.14176094
220 0.79456437 201.68985105 0.13597901
230 0.79671943 197.36995101 0.13678846
240 0.79897225 197.92261100 0.13241834
250 0.80104530 200.92162514 0.12048453
260 0.80305952 199.34586096 0.11993909
270 0.80482942 201.94554710 0.11875744
280 0.80647343 198.09159398 0.12174742
290 0.80790961 199.52363086 0.11258183
300 0.80901366 199.48209810 0.10436552
310 0.81087321 197.64053416 0.10641402
320 0.81216466 202.05480194 0.10738569
330 0.81346244 202.53401399 0.10416521
340 0.81468421 201.31989789 0.09478336
350 0.81581402 203.11453819 0.09577895
360 0.81693333 198.01704812 0.09632075
370 0.81780344 203.86609983 0.09962768
380 0.81878597 187.92328596 0.09170707
390 0.81969899 210.57062817 0.08980663
400 0.82057309 223.20126700 0.08497685
410 0.82143956 219.47758484 0.08860879
420 0.82222575 196.60595608 0.08878033
430 0.82299024 195.96066904 0.08974486
440 0.82373327 196.48566103 0.07871293
450 0.82448745 195.61394286 0.08129330
460 0.82521653 193.12551689 0.08170252
470 0.82594627 195.58085799 0.07845773
480 0.82656640 199.96816206 0.07696115
490 0.82716846 195.36905408 0.07624938
500 0.82778728 202.62298918 0.07301893
test_name: Overfitting_TEST_Concat_DAE_4_speech_Test_lr-0.001_batch_size-18_n_hidden_encode-750
2018-05-09 12:48:12.510566
#epoch distance time epoch_cost
1 0.06947280 6.78622699 2.45239592
10 0.39727661 202.38831592 0.96432685
20 0.50745064 199.80201793 0.73977233
30 0.58122700 197.90127516 0.57582273
40 0.62806565 199.26498199 0.48480341
50 0.65954196 198.82261491 0.42501304
60 0.68312228 198.74254608 0.37795419
70 0.70106179 199.85260105 0.34755723
80 0.71527600 200.70237017 0.32087798
90 0.72729397 201.01013494 0.28476567
100 0.73585492 201.61659694 0.26970364
110 0.74501365 193.44801497 0.24527414
120 0.75171161 192.78573298 0.22714136
130 0.75846016 199.74398994 0.21604389
140 0.76402360 197.73026490 0.19947750
150 0.76905310 200.70737815 0.18717629
160 0.77343142 200.47795987 0.18306259
170 0.77752745 199.03695011 0.16612206
180 0.78108603 198.00526094 0.16128634
190 0.78412163 200.05006003 0.16271033
200 0.78717732 200.08501291 0.15539532
210 0.78996402 197.72476006 0.14176094
220 0.79242933 201.68985105 0.13597901
230 0.79486233 197.36995101 0.13678846
240 0.79703587 197.92261100 0.13241834
250 0.79922611 200.92162514 0.12048453
260 0.80074751 199.34586096 0.11993909
270 0.80276924 201.94554710 0.11875744
280 0.80464470 198.09159398 0.12174742
290 0.80625010 199.52363086 0.11258183
300 0.80775940 199.48209810 0.10436552
310 0.80878305 197.64053416 0.10641402
320 0.81058580 202.05480194 0.10738569
330 0.81185991 202.53401399 0.10416521
340 0.81312072 201.31989789 0.09478336
350 0.81431752 203.11453819 0.09577895
360 0.81512940 198.01704812 0.09632075
370 0.81634104 203.86609983 0.09962768
380 0.81732684 187.92328596 0.09170707
390 0.81827152 210.57062817 0.08980663
400 0.81918168 223.20126700 0.08497685
410 0.82005292 219.47758484 0.08860879
420 0.82085574 196.60595608 0.08878033
430 0.82163954 195.96066904 0.08974486
440 0.82245547 196.48566103 0.07871293
450 0.82323289 195.61394286 0.08129330
460 0.82398242 193.12551689 0.08170252
470 0.82468325 195.58085799 0.07845773
480 0.82534397 199.96816206 0.07696115
490 0.82597613 195.36905408 0.07624938
500 0.82658684 202.62298918 0.07301893
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment