def main():
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Holddir = SysPath + 'HoldDir/'
ByCount = 4000
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
print(f'Total count of samples:{totalcount}')
files = os.listdir(Traindir)
maxN_frames = 0
lengths = []
seqofn = 0
seq_size = 16
for file in files:
npzfile = np.load(Traindir + file)
y_train = npzfile['labels']
y_train = np.asarray(y_train)
n_frames = len(y_train)
seqofn += int(np.floor(n_frames / seq_size))
lengths.append(n_frames)
if maxN_frames < n_frames:
maxN_frames = n_frames
lengths = np.asarray(lengths)
u = np.mean(lengths)
sd = np.std(lengths)
print(f'Max number of frames:{maxN_frames}')
print(f'Mean: {u} StandDev: {sd}')
print(f'{seqofn} sequences of {seq_size} length')
if False:
plt.hist(lengths)
plt.show()
print(nseqsofnsize(Traindir, seq_size))
def main(testing=False):
# Config Values[DNN params]
Frame_length = 0.025
Frame_step = 0.01
Dbdir = './speakers_db_correct/'
overwrite_MFCCs = False
TrainAll = False
#testing = True
FramelevelORword = True
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Wavdir = SysPath + 'corpus/dat/speakers/'
Dbdir = SysPath + 'GOP-LSTM/PhoneInfo/speakers_db_correct/'
Holddir = SysPath + 'HoldDir/'
Traindir = Holddir + 'Train/'
Testdir = Holddir + 'Test/'
N_context = 2
N_ceps = 26
wordcount = 30
# Training & Test Data
if FramelevelORword:
speakers_trainNtest(db_corpus=Dbdir,
wav_corpus=Wavdir,
n_ceps=N_ceps,
n_context=N_context,
frame_length=Frame_length,
frame_step=Frame_step,
inmat=True,
holddir=Holddir,
overwrite=overwrite_MFCCs)
cdnn_dict_name = 'dnn_gridsearch_records_ALL.pk'
if not TrainAll:
ByCount = 8000
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
cdnn_dict_name = f'dnn_gridsearch_records_{ByCount}.pk'
else:
selected_phones,totalcount = createNcount_trainNtest(frame_length=Frame_length,
frame_step=Frame_step,
n_ceps=N_ceps,
n_context=N_context,
dbdir=Dbdir,
datdir=Wavdir,
holddir=Holddir,
wordcount=wordcount)
N_classes = len(selected_phones)
Traindir = Holddir + f'FLP_Train_{wordcount}/'
Testdir = Holddir + f'FLP_Test_{wordcount}/'
cdnn_dict_name = f'dnn_gridsearch_records_wl{wordcount}.pk'
if testing:
cdnn_dict_name = f'testing_records.pk'
cdnn_address = SysPath + 'GOP-LSTM/Results/CDNN_phones/'
print(f'total counts: {totalcount}')
# Iterate over gridsearch
N_epochs = 50
Input_tuple = (5, 26, 1)
layers1 = [[2048,'relu'] for _ in range(0,6)]
layers2 = [[2048,'relu'] for _ in range(0,4)]
layers3 = [[2048,'relu'] for _ in range(0,2)]
layers4 = [[1024,'relu'] for _ in range(0,4)]
layers5 = [[512,'relu'] for _ in range(0,3)]
DenseLayerList = [layers1,layers2,layers3,layers4,layers5]
DropoutList = [0.2,0.8]
for do in DropoutList:
for dl in DenseLayerList:
# Model Creation or Loading
if not os.path.isdir('Models'):
os.mkdir('Models')
# Compile Params
dname = '_'.join(str(x) for X in dl for x in X)
Model_name = f'DND{dname}_DO{do}'
# Check if model accounts
if not cdnn_records_check(Model_name,cdnn_dict_name,cdnn_address):
print(f'Creating & training: {Model_name}')
model = make_CDNN_classifier(input_tuple=Input_tuple,
dense_layers=dl,
n_classes=N_classes,
dropout_rate=do)
model, Model_name = train_model(n_epochs=N_epochs,
model=model,
traindir=Traindir,
model_name=Model_name,
n_classes=N_classes,
totalsamples=totalcount[0],
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
testing=testing)
evaluate_model(model=model,
testdir=Testdir,
n_classes=N_classes,
totalsamples=totalcount[1],
model_name=Model_name,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
testing=testing)
del model
k.clear_session()
else:
print(f'{Model_name} has already been trained, skipping...')
cdnn_records_rankNprint(nn_record_name=cdnn_dict_name,results_address=cdnn_address)
def main(testing=False):
# Config Values[DNN params]
Frame_length = 0.025
Frame_step = 0.01
Dbdir = './speakers_db_correct/'
overwrite_MFCCs = False
TrainAll = False
testing = True
FramelevelORword = False
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Wavdir = SysPath + 'corpus/dat/speakers/'
Dbdir = SysPath +'GOP-LSTM/PhoneInfo/speakers_db_correct/'
Holddir = SysPath + 'HoldDir/'
Traindir = Holddir + 'Train/'
Testdir = Holddir + 'Test/'
PhoInfDir = SysPath + 'GOP-LSTM/PhoneInfo/'
N_context = 2
N_ceps = 26
wordcount = 10
# Training & Test Data
if FramelevelORword:
speakers_trainNtest(db_corpus=Dbdir,
wav_corpus=Wavdir,
n_ceps=N_ceps,
n_context=N_context,
frame_length=Frame_length,
frame_step=Frame_step,
inmat=True,
holddir=Holddir,
overwrite=overwrite_MFCCs)
cdnn_dict_name = 'crnn_gridsearch_records_ALL.pk'
if not TrainAll:
ByCount = 4000
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
cdnn_dict_name = f'crnn_gridsearch_records_{ByCount}.pk'
else:
selected_phones, totalcount, w2pdict = createNcount_trainNtest(frame_length=Frame_length,
frame_step=Frame_step,
n_ceps=N_ceps,
n_context=N_context,
dbdir=Dbdir,
datdir=Wavdir,
holddir=Holddir,
wordcount=wordcount,
phoinfdir=PhoInfDir)
N_classes = len(selected_phones)
Traindir = Holddir + f'FLP_Train_{wordcount}/'
Testdir = Holddir + f'FLP_Test_{wordcount}/'
cdnn_dict_name = f'ddcp_blstm_gridsearch_records_wl_{wordcount}.pk'
print(f'Selected phones: {selected_phones}')
print(f'Train count & test count: {totalcount}')
if testing:
cdnn_dict_name = f'testing_records.pk'
cdnn_address = SysPath + 'GOP-LSTM/Results/CDNN_phones/'
# Iterate over gridsearch
N_epochs = 70
Input_tuple = (5, 26, 1)
ConvLayerList = [[32 for _ in range(15)]]
DropoutList =[0.8]
# add one for sil
N_classes += 1
selected_phones.append('_')
seq_sizelist = [64]
for seq_size in seq_sizelist:
totaltrain = nseqsofnsize(Traindir,seq_size=seq_size)
totaltest = nseqsofnsize(Testdir,seq_size=seq_size)
for cl in ConvLayerList:
for dl in DropoutList:
# Compile Params
#cname = '_'.join(str(x) for x in cl)
cname = f'{cl[0]}_x{len(cl)}'
Model_name = f'BLSTM_CP{cname}_FBN_SS{seq_size}_DL{dl}_V3'
model = make_CNNLSTM_classifier(input_tuple=Input_tuple,
conv_layers=cl,
n_classes=N_classes,
seq_size=seq_size,
dropout_rate=dl,
channel_order='channels_last')
model, Model_name = train_model(n_epochs=N_epochs,
model=model,
traindir=Traindir,
model_name=Model_name,
n_classes=N_classes,
totalsamples=totaltrain,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
batch_size=seq_size,
testing=testing)
print('...Evaluating...')
evaluate_model(model=model,
testdir=Testdir,
n_classes=N_classes,
totalsamples=totaltest,
model_name=Model_name,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
batch_size=seq_size,
testing=testing)
# Forced Accuracy
print('...Predicting...')
if testing:
totaltest = 30
gen = generator_test_bufferedseq_wfname(train_dir=Testdir,
batch_size=seq_size,
n_classes=N_classes,
wfname=True)
''' Return Word Accuracy (by Softmax & ForcedMax), Max Seg Accuracy (from goldstandard-gst)'''
that = True
if that:
selected_phones.append('_')
diagnose = False
s_correct = 0
f_correct = 0
total = 0
s_IDS = 0
f_IDS = 0
maxsegtotal = 0
s_seg = 0
f_seg = 0
print(f'Total Test size:{totaltest}\n')
x, y, file = next(gen)
cfile = file
for _ in range(totaltest): # amount of words to be judged
if diagnose:
print(f'Current file:{file}')
print(f'Word\'s phones{potphones}')
print(file)
segcount = 0
gwordphones = [] # gold standard word segments
swordphones = [] # softmax word segments
fwordphones = [] # forced word segments
fname = file.split('.')[0]
potphones = w2pdict[fname]
potphones.append('_')
pind = [selected_phones.index(sp) for sp in potphones]
predictions = model.predict(x=x)
segcount += 1
gstd = [selected_phones[sp] for sp in np.argmax(y, axis=2)[0]]
softmax = [selected_phones[sp] for sp in np.argmax(predictions, axis=2)[0]]
forceda = [selected_phones[pind[sp]] for sp in
np.argmax(predictions[:, :, pind][0], axis=1)]
gwordphones += gstd
swordphones += softmax
fwordphones += forceda
x, y, file = next(gen)
predictions = model.predict(x=x)
if cfile == file: # break out of word while loop
samefile = True
cfile = file
else:
samefile = False
cfile = file
while samefile: # track error for same file
segcount += 1
gstd = [selected_phones[sp] for sp in np.argmax(y, axis=2)[0]]
softmax = [selected_phones[sp] for sp in np.argmax(predictions, axis=2)[0]]
forceda = [selected_phones[pind[sp]] for sp in
np.argmax(predictions[:, :, pind][0], axis=1)]
gwordphones += gstd
swordphones += softmax
fwordphones += forceda
x, y, file = next(gen)
predictions = model.predict(x=x)
if cfile != file: # break out of word while loop
samefile = False
cfile = file
# got word segs, process them
gseg = segmentphonelist(gwordphones)
sseg = segmentphonelist(swordphones)
fseg = segmentphonelist(fwordphones)
sLD = uttLD(gseg, sseg)
fLD = uttLD(gseg, fseg)
s_IDS += sLD
f_IDS += fLD
if diagnose:
print('\n')
print(gseg)
print(sseg)
print(fseg)
print('\n')
print(sLD)
print(fLD)
print('\n')
# accuracy
startsil = gseg[-1][1] # Index of Silence
g_len = len(gwordphones[:startsil])
s_correct += segCorrect(gwordphones[:startsil], swordphones[:startsil])
f_correct += segCorrect(gwordphones[:startsil], fwordphones[:startsil])
total += g_len
# max-seg-score with known boundaries
# per word, then test set score
wordweight = 0
for seg in gseg[:-1]: # last phone is silence '_'
maxsegtotal += 1
cphone = seg[0]
sboundedlist = swordphones[seg[1]:seg[2]]
fboundedlist = fwordphones[seg[1]:seg[2]]
smaxphone = max(sboundedlist, key=sboundedlist.count)
fmaxphone = max(fboundedlist, key=fboundedlist.count)
if smaxphone == cphone:
s_seg += 1
if fmaxphone == cphone:
f_seg += 1
if diagnose:
print(seg)
print(smaxphone, fmaxphone, cphone)
sLDpercent = sLD / total * 100
fLDpercent = fLD / total * 100
print(f'Insertions, Deletions, Substitions (SM):{sLD} out of {total}: {sLDpercent}%')
print(f'Insertions, Deletions, Substitions (FM):{fLD} out of {total}: {fLDpercent}%')
Spercent = s_correct / total * 100
Fpercent = f_correct / total * 100
print('\n')
print(f'Softmax: {s_correct} out of {total}, {Spercent}%')
print(f'Forced: {f_correct} out of {total}, {Fpercent}%')
Spercent = s_seg / maxsegtotal * 100
Fpercent = f_seg / maxsegtotal * 100
print(f'Softmax (seg): {s_seg} out of {maxsegtotal}, {Spercent}%')
print(f'Forced (seg): {f_seg} out of {maxsegtotal}, {Fpercent}%')
cdnn_records_add(loss=Spercent,
accuracy=Fpercent,
model_name=Model_name,
nn_records_name=cdnn_dict_name,
results_address=cdnn_address)
del gen
del model
k.clear_session()
def main(testing=False):
# Config Values, Feature Assembly, focused on WordCount/FrameCount
Frame_length = 0.025
Frame_step = 0.01
overwrite_MFCCs = False
TrainAll = False
#testing = True
FramelevelORword = False
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Dbdir = SysPath + 'GOP-LSTM/PhoneInfo/speakers_db_correct/'
'''Based on your computer setup. (I have both folders on same level as GOP-LSTM)'''
Wavdir = SysPath + 'corpus/dat/speakers/' # On SysPath level
Holddir = SysPath + 'HoldDir/'
Traindir = Holddir + 'Train/'
Testdir = Holddir + 'Test/'
PhoInfDir = SysPath + 'GOP-LSTM/PhoneInfo/'
N_context = 2
N_ceps = 26
wordcount = 30
# Training & Test Data
if FramelevelORword:
speakers_trainNtest(db_corpus=Dbdir,
wav_corpus=Wavdir,
n_ceps=N_ceps,
n_context=N_context,
frame_length=Frame_length,
frame_step=Frame_step,
inmat=True,
holddir=Holddir,
overwrite=overwrite_MFCCs)
cdnn_dict_name = 'crnn_gridsearch_records_ALL.pk'
if not TrainAll:
ByCount = 4000
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(
bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
cdnn_dict_name = f'segmentation_records_{ByCount}.pk'
else:
selected_phones, totalcount, w2pdict = createNcount_trainNtest(
frame_length=Frame_length,
frame_step=Frame_step,
n_ceps=N_ceps,
n_context=N_context,
dbdir=Dbdir,
datdir=Wavdir,
holddir=Holddir,
phoinfdir=PhoInfDir,
wordcount=wordcount)
N_classes = len(selected_phones)
Traindir = Holddir + f'FLP_Train_{wordcount}/'
Testdir = Holddir + f'FLP_Test_{wordcount}/'
cdnn_dict_name = f'segment_blstm_records_wl_{wordcount}.pk'
print(f'Selected phones: {selected_phones}')
print(f'Train count & test count: {totalcount}')
if testing:
cdnn_dict_name = f'testing_records.pk'
print(f' Using:{cdnn_dict_name}')
cdnn_address = SysPath + 'GOP-LSTM/Results/CDNN_segments/'
# Iterate over gridsearch
N_epochs = 70
Input_tuple = (5, 26, 1)
# ,[32 for _ in range(10)]
ConvLayerList = [[32 for _ in range(5)]]
DropoutList = [0.8]
# add one for sil
N_classes += 1
seq_sizelist = [64]
for seq_size in seq_sizelist:
totaltrain = nseqsofnsize(Traindir, seq_size=seq_size)
totaltest = nseqsofnsize(Testdir, seq_size=seq_size)
for cl in ConvLayerList:
for dl in DropoutList:
# Compile Params
#cname = '_'.join(str(x) for x in cl)
cname = f'{cl[0]}_x{len(cl)}'
Model_name = f'BLSTM_CP{cname}_FBN_SS{seq_size}_DL{dl}_V2'
# check if model exist with said name
Model_address = f'{Model_name}model.hdf5'
# if manual entry
Model_address = 'BLSTM_CP32_x5_FBN_SS64_DL0.8_V2model.hdf5'
if os.path.exists(Model_address):
print(f'loading model: {Model_address}')
model = load_model(Model_address)
print(model.inputs, model.outputs)
firsttime = False
print(f'Loaded')
else:
print(f'No such model as :{Model_address}')
# Forced Accuracy
print('...Predicting...')
if testing:
totaltest = 30
gen = generator_test_bufferedseq_wfname(train_dir=Testdir,
batch_size=seq_size,
n_classes=N_classes,
wfname=True)
''' Need to add dictionary with similar sounding word
however it seems there are too few words to do this with a small test set.
also, might be having problems with switching to 25 wc from 30wc'''
this = True
if this:
sameworddiffprondict = defaultdict(list)
for keys, v in w2pdict.items():
wordnum = keys.split('_')[-1]
#check if already in dict list
if v not in sameworddiffprondict[wordnum]:
sameworddiffprondict[wordnum].append(v)
sameworddiffprondict.pop('17')
sameworddiffprondict.pop('18')
for key, v in sameworddiffprondict.items():
v[0] += '_'
print(key, v)
similardict = {'13': '48', '48': '13', '25': '5', '5': '25'}
dolist = ['13', '48', '25', '5']
''' Return Word Accuracy (by Softmax & ForcedMax), Max Seg Accuracy (from goldstandard-gst)'''
that = True
# import the whole list for testing each possible word, instead of softmax
totaltest = 15 # for testing purpose shorten
if that:
selected_phones.append('_')
print(f'Total Test size:{totaltest}\n')
x, y, file = next(gen)
cfile = file
cfname = cfile.split('.')[0].split('_')[-1]
n = 0
for _ in range(totaltest): # amount of words to be judged
# Two versions, Right Word and Random Wrong Word
print(f'starting file:{file}, {cfname}')
cfile = file
cfname = cfile.split('.')[0].split('_')[-1]
if cfname not in dolist:
notinlist = True
while notinlist:
n += 1
x, y, file = next(gen)
cfile = file
cfname = cfile.split('.')[0].split('_')[-1]
if cfname in dolist:
print(f'in')
notinlist = False
print(f'N:{n}')
elif n > 100:
notinlist = False
print(f'N:{n}')
predictions = []
samefile = True
while samefile:
tp = model.predict(x=x)[0].tolist()
predictions += tp
# get all values then roll through loops
x, y, file = next(gen)
if cfile != file: # break out of word while loop
samefile = False
print(f'Next file:{file}')
print(f'Going in: {cfile},{cfname}')
for turnN in range(2):
sscores = []
fscores = []
fwordphones = []
if turnN == 1:
fname = similardict[cfname]
print(f'Wrong: {fname}')
potphones = sameworddiffprondict[fname][0]
else:
print(f'Right: {cfname}')
potphones = sameworddiffprondict[cfname][0]
print(potphones)
pind = [
selected_phones.index(sp) for sp in potphones
]
print(f'Expected word: {potphones}')
predictions = np.asarray(predictions)
smargs = np.max(predictions, axis=1)
fargs = np.max(predictions[:, pind], axis=1)
fwordphones += [
selected_phones[pind[sp]]
for sp in np.argmax(predictions[:, pind],
axis=1)
]
sscores += smargs.tolist()
fscores += fargs.tolist()
# got word segs, process them
fseg = segmentphonelist(fwordphones)
# use fseg to separate phones and then show their prob
fscores = np.asarray(fscores)
sscores = np.asarray(sscores)
gop_per_frame = np.round(
np.abs(np.log(fscores / sscores)), 4)
gop_list = []
avg_phone_error = []
for seg in fseg[:-1]: #skip space
phone_chuck = gop_per_frame[seg[1] - 1:seg[2] -
1]
phonescore = np.round(
np.sum(phone_chuck) / seg[3], 3)
gop_list.append((seg[0], phonescore, seg[3]))
avg_phone_error.append(phonescore)
print(gop_list)
print(np.round(np.average(avg_phone_error)))
print('\n')
# accuracy
# GOP, create ratios between RNN-WF/RNN-O and RNN-WF/(0.5*WF + 0.5*O)
# for correct words, for similar words, then for opposite words
del gen
del model
k.clear_session()
def main(testing=False):
# Config Values, Feature Assembly, focused on WordCount/FrameCount
Frame_length = 0.025
Frame_step = 0.01
overwrite_MFCCs = False
TrainAll = False
#testing = True
FramelevelORword = False
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Dbdir = SysPath + 'GOP-LSTM/PhoneInfo/speakers_db_correct/'
'''Based on your computer setup. (I have both folders on same level as GOP-LSTM)'''
Wavdir = SysPath + 'corpus/dat/speakers/' # On SysPath level
Holddir = SysPath + 'HoldDir/'
Traindir = Holddir + 'Train/'
Testdir = Holddir + 'Test/'
PhoInfDir = SysPath + 'GOP-LSTM/PhoneInfo/'
N_context = 2
N_ceps = 26
wordcount = 10
# Training & Test Data
if FramelevelORword:
speakers_trainNtest(db_corpus=Dbdir,
wav_corpus=Wavdir,
n_ceps=N_ceps,
n_context=N_context,
frame_length=Frame_length,
frame_step=Frame_step,
inmat=True,
holddir=Holddir,
overwrite=overwrite_MFCCs)
cdnn_dict_name = 'crnn_gridsearch_records_ALL.pk'
if not TrainAll:
ByCount = 4000
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(
bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
cdnn_dict_name = f'segmentation_records_{ByCount}.pk'
else:
selected_phones, totalcount, w2pdict = createNcount_trainNtest(
frame_length=Frame_length,
frame_step=Frame_step,
n_ceps=N_ceps,
n_context=N_context,
dbdir=Dbdir,
datdir=Wavdir,
holddir=Holddir,
phoinfdir=PhoInfDir,
wordcount=wordcount)
N_classes = len(selected_phones)
Traindir = Holddir + f'FLP_Train_{wordcount}/'
Testdir = Holddir + f'FLP_Test_{wordcount}/'
cdnn_dict_name = f'segment_blstm_records_wl_{wordcount}.pk'
print(f'Selected phones: {selected_phones}')
print(f'Train count & test count: {totalcount}')
if testing:
cdnn_dict_name = f'testing_records.pk'
print(f' Using:{cdnn_dict_name}')
cdnn_address = SysPath + 'GOP-LSTM/Results/CDNN_segments/'
# Iterate over gridsearch
N_epochs = 70
Input_tuple = (5, 26, 1)
# ,[32 for _ in range(10)]
ConvLayerList = [[32 for _ in range(10)]]
DropoutList = [0.8]
# add one for sil
N_classes += 1
seq_sizelist = [64]
for seq_size in seq_sizelist:
totaltrain = nseqsofnsize(Traindir, seq_size=seq_size)
totaltest = nseqsofnsize(Testdir, seq_size=seq_size)
for cl in ConvLayerList:
for dl in DropoutList:
# Compile Params
#cname = '_'.join(str(x) for x in cl)
cname = f'{cl[0]}_x{len(cl)}'
Model_name = f'BLSTM_CP{cname}_FBN_SS{seq_size}_DL{dl}_V2'
# check if model exist with said name
Model_address = f'{Model_name}model.hdf5'
if os.path.exists(Model_address):
print(f'loading model: {Model_address}')
model = load_model(Model_address)
firsttime = False
print(f'Loaded')
else:
print(f'No such model as :{Model_address}')
# Forced Accuracy
print('...Predicting...')
if testing:
totaltest = 30
gen = generator_test_bufferedseq_wfname(train_dir=Testdir,
batch_size=seq_size,
n_classes=N_classes,
wfname=True)
''' Need to add dictionary with similar sounding word
however it seems there are too few words to do this with a small test set.
also, might be having problems with switching to 25 wc from 30wc'''
this = True
if this:
sameworddiffprondict = defaultdict(list)
for keys, v in w2pdict.items():
wordnum = keys.split('_')[-1]
#check if already in dict list
if v not in sameworddiffprondict[wordnum]:
sameworddiffprondict[wordnum].append(v)
for keys, v in sameworddiffprondict.items():
print(keys, v)
''' Return Word Accuracy (by Softmax & ForcedMax), Max Seg Accuracy (from goldstandard-gst)'''
that = True
# possibly create function to address, which words are possibly useful forced-word-max
#sameworddiffname
swdname = ['9', '44']
swdforms = [[['b', 'ɔ', 'l', '_'], ['b', 'a', 'l', '_'],
['b', 'ɔ', '_']],
[['d', 'ɹ', 'ʌ', 'm', '_'],
['dʒ', 'ɹ', 'ʌ', 'm', '_']]]
# import the whole list for testing each possible word, instead of softmax
#totaltest = 3 # for testing purpose shorten
if that:
perphoneaccdict = defaultdict(
list) # Word-key,list-accuracy, post-process
selected_phones.append('_')
diagnose = False
s_correct = 0
f_correct = 0
fw_correct = 0
total = 0
s_IDS = 0
f_IDS = 0
maxsegtotal = 0
s_seg = 0
f_seg = 0
print(f'Total Test size:{totaltest}\n')
x, y, file = next(gen)
cfile = file
for _ in range(totaltest): # amount of words to be judged
if diagnose:
print(f'Current file:{file}')
print(f'Word\'s phones{potphones}')
segcount = 0
gwordphones = [] # gold standard word segments
swordphones = [] # softmax word segments
fwordphones = [] # forced word segments
fname = file.split('.')[0]
potphones = w2pdict[fname]
fnamelast = fname.split('_')[-1]
potphones.append('_')
pind = [selected_phones.index(sp) for sp in potphones]
predictions = model.predict(x=x)
segcount += 1
gstd = argmaxpredicts2phones(y[0], selected_phones)
softmax = argmaxpredicts2phones(
predictions[0], selected_phones)
forceda = argmaxpredicts2forcedphones(
predictions[0], selected_phones, pind)
wordmaxforce = []
if fnamelast in swdname:
wordmaxforce = swdforms[swdname.index(fnamelast)]
swdlist = []
swdlistscore = [0 for _ in wordmaxforce]
for index, wsp in enumerate(wordmaxforce):
wspind = [
selected_phones.index(sp) for sp in wsp
]
tv, tswd = argmaxpredicts2forcedphones(
predictions[0],
selected_phones,
wspind,
fwords=True)
swdlist.append(tswd)
swdlistscore[index] += tv
gwordphones += gstd
swordphones += softmax
fwordphones += forceda
x, y, file = next(gen)
predictions = model.predict(x=x)
if cfile == file: # break out of word while loop
samefile = True
cfile = file
else:
samefile = False
cfile = file
while samefile: # track error for same file
segcount += 1
gstd = argmaxpredicts2phones(y[0], selected_phones)
softmax = argmaxpredicts2phones(
predictions[0], selected_phones)
forceda = argmaxpredicts2forcedphones(
predictions[0], selected_phones, pind)
if wordmaxforce:
for index, wsp in enumerate(wordmaxforce):
wspind = [
selected_phones.index(sp) for sp in wsp
]
tv, tswd = argmaxpredicts2forcedphones(
predictions[0],
selected_phones,
wspind,
fwords=True)
swdlist[index] += tswd
swdlistscore[index] += tv
gwordphones += gstd
swordphones += softmax
fwordphones += forceda
x, y, file = next(gen)
predictions = model.predict(x=x)
if cfile != file: # break out of word while loop
samefile = False
cfile = file
# got word segs, process them
gseg = segmentphonelist(gwordphones)
sseg = segmentphonelist(swordphones)
fseg = segmentphonelist(fwordphones)
sLD = uttLD(gseg, sseg)
fLD = uttLD(gseg, fseg)
s_IDS += sLD
f_IDS += fLD
if diagnose:
print('\n')
print(gseg)
print(sseg)
print(fseg)
print('\n')
print(sLD)
print(fLD)
print('\n')
# accuracy
startsil = gseg[-1][1] #Index of Silence
g_len = len(gwordphones[:startsil])
s_correct += segCorrect(gwordphones[:startsil],
swordphones[:startsil])
f_correct += segCorrect(gwordphones[:startsil],
fwordphones[:startsil])
total += g_len
if wordmaxforce:
print(f'actual:{potphones}')
print(f'swd {wordmaxforce}'
) # list for individual comparision
print(f'scores{swdlistscore}')
for wlist in swdlist:
print(
segCorrect(gwordphones[:startsil],
wlist[:startsil]) / g_len)
print('\n')
# max-seg-score with known boundaries
# per word, then test set score
wordweight = 0
for seg in gseg[:-1]: # last phone is silence '_'
maxsegtotal += 1
cphone = seg[0]
sboundedlist = swordphones[seg[1]:seg[2]]
fboundedlist = fwordphones[seg[1]:seg[2]]
smaxphone = max(sboundedlist,
key=sboundedlist.count)
fmaxphone = max(fboundedlist,
key=fboundedlist.count)
if smaxphone == cphone:
s_seg += 1
if fmaxphone == cphone:
f_seg += 1
#if cphone in perphoneaccdict.keys():
# perphoneaccdict[cphone].append()
if diagnose:
print(seg)
print(smaxphone, fmaxphone, cphone)
sLDpercent = sLD / total * 100
fLDpercent = fLD / total * 100
print(
f'Insertions, Deletions, Substitions (SM):{sLD} out of {total}: {sLDpercent}%'
)
print(
f'Insertions, Deletions, Substitions (FM):{fLD} out of {total}: {fLDpercent}%'
)
Spercent = s_correct / total * 100
Fpercent = f_correct / total * 100
print('\n')
print(f'Softmax: {s_correct} out of {total}, {Spercent}%')
print(f'Forced: {f_correct} out of {total}, {Fpercent}%')
Spercent = s_seg / maxsegtotal * 100
Fpercent = f_seg / maxsegtotal * 100
print(
f'Softmax (seg): {s_seg} out of {maxsegtotal}, {Spercent}%'
)
print(
f'Forced (seg): {f_seg} out of {maxsegtotal}, {Fpercent}%'
)
del gen
del model
k.clear_session()
def main(testing=False):
# Config Values, Feature Assembly, focused on WordCount/FrameCount
Frame_length = 0.025
Frame_step = 0.01
overwrite_MFCCs = False
TrainAll = False
#testing = True
FramelevelORword = False
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Dbdir = SysPath + 'GOP-LSTM/PhoneInfo/speakers_db_correct/'
'''Based on your computer setup. (I have both folders on same level as GOP-LSTM)'''
Wavdir = SysPath + 'corpus/dat/speakers/' # On SysPath level
Holddir = SysPath + 'HoldDir/'
Traindir = Holddir + 'Train/'
Testdir = Holddir + 'Test/'
PhoInfDir = SysPath + 'GOP-LSTM/PhoneInfo/'
N_context = 2
N_ceps = 26
wordcount = 25
# Training & Test Data
if FramelevelORword:
speakers_trainNtest(db_corpus=Dbdir,
wav_corpus=Wavdir,
n_ceps=N_ceps,
n_context=N_context,
frame_length=Frame_length,
frame_step=Frame_step,
inmat=True,
holddir=Holddir,
overwrite=overwrite_MFCCs)
cdnn_dict_name = 'crnn_gridsearch_records_ALL.pk'
if not TrainAll:
ByCount = 4000
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(
bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
cdnn_dict_name = f'segmentation_records_{ByCount}.pk'
else:
selected_phones, totalcount, w2pdict = createNcount_trainNtest(
frame_length=Frame_length,
frame_step=Frame_step,
n_ceps=N_ceps,
n_context=N_context,
dbdir=Dbdir,
datdir=Wavdir,
holddir=Holddir,
phoinfdir=PhoInfDir,
wordcount=wordcount)
N_classes = len(selected_phones)
Traindir = Holddir + f'FLP_Train_{wordcount}/'
Testdir = Holddir + f'FLP_Test_{wordcount}/'
cdnn_dict_name = f'segment_blstm_records_wl_{wordcount}.pk'
print(f'Selected phones: {selected_phones}')
print(f'Train count & test count: {totalcount}')
if testing:
cdnn_dict_name = f'testing_records.pk'
print(f' Using:{cdnn_dict_name}')
cdnn_address = SysPath + 'GOP-LSTM/Results/CDNN_segments/'
# Iterate over gridsearch
N_epochs = 70
Input_tuple = (5, 26, 1)
# ,[32 for _ in range(10)]
ConvLayerList = [[32 for _ in range(10)]]
DropoutList = [0.8]
# add one for sil
print(f'Number of phones:{N_classes}')
N_classes += 1
seq_sizelist = [64]
for seq_size in seq_sizelist:
totaltrain = nseqsofnsize(Traindir, seq_size=seq_size)
totaltest = nseqsofnsize(Testdir, seq_size=seq_size)
for cl in ConvLayerList:
for dl in DropoutList:
# Compile Params
#cname = '_'.join(str(x) for x in cl)
cname = f'{cl[0]}_x{len(cl)}'
Model_name = f'BLSTM_CP{cname}_FBN_SS{seq_size}_DL{dl}_V2'
# check if model exist with said name
Model_address = f'{Model_name}model.hdf5'
if os.path.exists(Model_address):
print(f'loading model: {Model_address}')
model = load_model(Model_address)
firsttime = False
print(f'Loaded')
else:
print(f'No such model as :{Model_address}')
# Forced Accuracy
print('...Predicting...')
if testing:
totaltest = 30
gen = generator_test_bufferedseq_wfname(train_dir=Testdir,
batch_size=seq_size,
n_classes=N_classes,
wfname=True)
''' Need to add dictionary with similar sounding word
however it seems there are too few words to do this with a small test set.
also, might be having problems with switching to 25 wc from 30wc'''
this = True
if this:
swddict = defaultdict(list)
for keys, v in w2pdict.items():
wordnum = keys.split('_')[-1]
if v not in swddict[wordnum]:
swddict[wordnum].append(v)
for keys, v in swddict.items():
v[0].append('_')
#print(keys,v)
that = True
if that:
perwordaccdict = defaultdict(
list) # Word-key,list-accuracy, post-process
perwordmaxdict = defaultdict(
list) # Word-key,list-accuracy, post-process
skiplist = [17, 18]
selected_phones.append('_')
diagnose = False
print(f'Total Test size:{totaltest}\n')
x, y, file = next(gen)
cfile = file
for _ in range(totaltest): # amount of words to be judged
if diagnose:
print(f'Word\'s phones{potphones}')
#print(f'Current file:{file}')
trueword = file.split('.')[0].split('_')[1]
#print(trueword)
segcount = 0
gwordphones = [] # gold standard word segments
# we need to do this for all words, not just expected
predictions = model.predict(x=x)
segcount += 1
gstd = argmaxpredicts2phones(y[0], selected_phones)
twordlistdict = defaultdict(list)
twordscoredict = defaultdict(list)
twordaccdict = defaultdict(list)
for index, plist in swddict.items():
twordlistdict[index] = []
twordscoredict[index] = 0
twordaccdict[index] = 0
if index not in skiplist:
for iindex, wsp in enumerate(
plist
): # should only be one, two later
wspind = [
selected_phones.index(sp) for sp in wsp
]
tv, tswd = argmaxpredicts2forcedphones(
predictions[0],
selected_phones,
wspind,
fwords=True)
twordlistdict[index].append(tswd)
twordscoredict[index] += tv
gwordphones += gstd
x, y, file = next(gen)
predictions = model.predict(x=x)
if cfile == file: # break out of word while loop
samefile = True
cfile = file
else:
samefile = False
cfile = file
while samefile: # track error for same file
segcount += 1
gstd = argmaxpredicts2phones(y[0], selected_phones)
for index, plist in swddict.items():
if index not in skiplist:
for iindex, wsp in enumerate(
plist
): # should only be one, two later
wspind = [
selected_phones.index(sp)
for sp in wsp
]
tv, tswd = argmaxpredicts2forcedphones(
predictions[0],
selected_phones,
wspind,
fwords=True)
twordlistdict[index] += (tswd)
twordscoredict[index] += tv
gwordphones += gstd
x, y, file = next(gen)
predictions = model.predict(x=x)
if cfile != file: # break out of word while loop
samefile = False
cfile = file
gseg = segmentphonelist(gwordphones)
startsil = gseg[-1][1] #Index of Silence
g_len = len(gwordphones[:startsil])
for index, _ in swddict.items():
if index not in skiplist:
tacc = segCorrect(
gwordphones[:startsil],
twordlistdict[index][0][:startsil]) / g_len
twordaccdict[index] = tacc
sortedacc = sorted(twordaccdict.items(),
key=operator.itemgetter(1),
reverse=True)
sortedmax = sorted(twordscoredict.items(),
key=operator.itemgetter(1),
reverse=True)
for index, tuple in enumerate(sortedacc):
if tuple[0] == trueword:
perwordaccdict[trueword].append(index)
for index, tuple in enumerate(sortedmax):
if tuple[0] == trueword:
perwordmaxdict[trueword].append(index)
#print(sortedmax)
#print(sortedacc)
# Number of correct, binary score, then a relative score, the greater the worse
averageaccuracy = 0
numberoftrials = 0
averagelengthaway = 0
rankingsacc = []
rankingsmax = []
for _, alist in perwordaccdict.items():
for score in alist:
numberoftrials += 1
averagelengthaway += score
if score == 0:
averageaccuracy += 1
rankingsacc.append(score)
print(
f'Avg num of distance from 0, {averagelengthaway/numberoftrials} using max accuracy'
)
print(f'Avg accuracy {averageaccuracy/numberoftrials}')
averageaccuracy = 0
averagelengthaway = 0
for _, mlist in perwordmaxdict.items():
for score in mlist:
numberoftrials += 1
averagelengthaway += score
if score == 0:
averageaccuracy += 1
rankingsmax.append(score)
print(
f'Avg num of distance from 0, {averagelengthaway/numberoftrials} using sum of max'
)
print(
f'Avg sum phone maxs {averageaccuracy/numberoftrials}')
print(f'Out {len(perwordaccdict.keys())} words')
plt.subplot(1, 2, 1)
plt.title('Rankings by Accuracy')
plt.hist(rankingsacc, bins=38)
plt.xlabel('Occurrences')
plt.xlabel('Distance')
plt.subplot(1, 2, 2)
plt.title('Rankings by Sum of Softmax')
plt.hist(rankingsmax, bins=38)
plt.xlabel('Distance')
plt.tight_layout()
plt.show()
del gen
del model
k.clear_session()
def main(testing=False):
# Config Values[DNN params]
Frame_length = 0.025
Frame_step = 0.01
Dbdir = './speakers_db_correct/'
overwrite_MFCCs = False
TrainAll = False
#testing = True
FramelevelORword = False
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Wavdir = SysPath + 'corpus/dat/speakers/'
Dbdir = SysPath + 'GOP-LSTM/PhoneInfo/speakers_db_correct/'
Holddir = SysPath + 'HoldDir/'
Traindir = Holddir + 'Train/'
Testdir = Holddir + 'Test/'
N_context = 2
N_ceps = 26
wordcount = 40
# Training & Test Data
if FramelevelORword:
speakers_trainNtest(db_corpus=Dbdir,
wav_corpus=Wavdir,
n_ceps=N_ceps,
n_context=N_context,
frame_length=Frame_length,
frame_step=Frame_step,
inmat=True,
holddir=Holddir,
overwrite=overwrite_MFCCs)
cdnn_dict_name = 'crnn_gridsearch_records_ALL.pk'
if not TrainAll:
ByCount = 4000
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(
bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
cdnn_dict_name = f'crnn_gridsearch_records_{ByCount}.pk'
else:
selected_phones, totalcount = createNcount_trainNtest(
frame_length=Frame_length,
frame_step=Frame_step,
n_ceps=N_ceps,
n_context=N_context,
dbdir=Dbdir,
datdir=Wavdir,
holddir=Holddir,
wordcount=wordcount,
overwrite=True)
N_classes = len(selected_phones)
Traindir = Holddir + f'FLP_Train_{wordcount}/'
Testdir = Holddir + f'FLP_Test_{wordcount}/'
cdnn_dict_name = f'crnn_gridsearch_records_wl_{wordcount}.pk'
if testing:
cdnn_dict_name = f'testing_records.pk'
cdnn_address = SysPath + 'GOP-LSTM/Results/CDNN_phones/'
print(totalcount)
# Iterate over gridsearch
N_epochs = 70
Input_tuple = (5, 26, 1)
ConvLayerList = [[8], [32]]
DropoutList = [0.8, 0.9]
seq_sizelist = [64, 128]
for seq_size in seq_sizelist:
totalcount = nseqsofnsize(Traindir, seq_size=seq_size)
for cl in ConvLayerList:
for dl in DropoutList:
# Compile Params
cname = '_'.join(str(x) for x in cl)
Model_name = f'CP{cname}_FBN_SS{seq_size}_DL{dl}'
model = make_CNNLSTM_classifier(input_tuple=Input_tuple,
conv_layers=cl,
n_classes=N_classes,
seq_size=seq_size,
dropout_rate=dl,
channel_order='channels_last')
model, Model_name = train_model(n_epochs=N_epochs,
model=model,
traindir=Traindir,
model_name=Model_name,
n_classes=N_classes,
totalsamples=totalcount,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
batch_size=seq_size,
testing=testing)
evaluate_model(model=model,
testdir=Testdir,
n_classes=N_classes,
totalsamples=totalcount,
model_name=Model_name,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
batch_size=seq_size,
testing=testing)
del model
cdnn_records_rankNprint(nn_record_name=cdnn_dict_name,
results_address=cdnn_address)
def main(testing=False):
# Config Values[DNN params]
Frame_length = 0.025
Frame_step = 0.01
Dbdir = './speakers_db_correct/'
overwrite_MFCCs = False
TrainAll = False
#testing = True
FramelevelORword = False
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Wavdir = SysPath + 'corpus/dat/speakers/'
Dbdir = SysPath + 'GOP-LSTM/PhoneInfo/speakers_db_correct/'
Holddir = SysPath + 'HoldDir/'
Traindir = Holddir + 'Train/'
Testdir = Holddir + 'Test/'
N_context = 2
N_ceps = 26
wordcount = 30
ByCount = 4000
# Training & Test Data
if FramelevelORword:
speakers_trainNtest(db_corpus=Dbdir,
wav_corpus=Wavdir,
n_ceps=N_ceps,
n_context=N_context,
frame_length=Frame_length,
frame_step=Frame_step,
inmat=True,
holddir=Holddir,
overwrite=overwrite_MFCCs)
cdnn_dict_name = 'crnn_gridsearch_records_ALL.pk'
if not TrainAll:
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(
bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
cdnn_dict_name = f'crnn_gridsearch_records_{ByCount}.pk'
else:
selected_phones, totalcount, w2pdict = createNcount_trainNtest(
frame_length=Frame_length,
frame_step=Frame_step,
n_ceps=N_ceps,
n_context=N_context,
dbdir=Dbdir,
datdir=Wavdir,
holddir=Holddir,
wordcount=wordcount)
N_classes = len(selected_phones)
Traindir = Holddir + f'FLP_Train_{wordcount}/'
Testdir = Holddir + f'FLP_Test_{wordcount}/'
print(f'Selected phones: {selected_phones}')
cdnn_dict_name = f'cp_blstm_gridsearch_records_wl_{wordcount}.pk'
if testing:
cdnn_dict_name = f'testing_records.pk'
cdnn_address = SysPath + 'GOP-LSTM/Results/CDNN_phones/'
print(f'Train count \& test count: {totalcount}')
# Iterate over gridsearch
N_epochs = 70
Input_tuple = (5, 26, 1)
ConvLayerList = [[64, 64, 64, 64], [32, 32, 32, 32]]
DropoutList = [0.8]
seq_sizelist = [16, 32]
for seq_size in seq_sizelist:
totaltrain = nseqsofnsize(Traindir, seq_size=seq_size)
totaltest = nseqsofnsize(Testdir, seq_size=seq_size)
for cl in ConvLayerList:
for dl in DropoutList:
# Compile Params
cname = '_'.join(str(x) for x in cl)
Model_name = f'BLSTM_CP{cname}_FBN_SS{seq_size}_DL{dl}'
model = make_CNNLSTM_classifier(input_tuple=Input_tuple,
conv_layers=cl,
n_classes=N_classes,
seq_size=seq_size,
dropout_rate=dl,
channel_order='channels_last')
model, Model_name = train_model(n_epochs=N_epochs,
model=model,
traindir=Traindir,
model_name=Model_name,
n_classes=N_classes,
totalsamples=totaltrain,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
batch_size=seq_size,
testing=testing)
evaluate_model(model=model,
testdir=Testdir,
n_classes=N_classes,
totalsamples=totaltest,
model_name=Model_name,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
batch_size=seq_size,
testing=testing)
print('...Predicting...')
if testing:
totaltest = 30
gen = generator_train_bufferedseq_wfname(train_dir=Testdir,
batch_size=seq_size,
n_classes=N_classes)
p_correct = 0
f_correct = 0
total = 0
for s in range(totaltest):
x, y, file = next(gen)
fname = file.split('.')[0]
potphones = w2pdict[fname]
pind = [selected_phones.index(sp) for sp in potphones]
predictions = model.predict_proba(x=x)
TrueY = [
selected_phones[sp] for sp in np.argmax(y, axis=2)[0]
]
PredY = [
selected_phones[sp]
for sp in np.argmax(predictions, axis=2)[0]
]
ForcY = [
selected_phones[pind[sp]]
for sp in np.argmax(predictions[:, :, pind][0], axis=1)
]
p_correct += len(
[1 for x, y in zip(TrueY, PredY) if x == y])
f_correct += len(
[1 for x, y in zip(TrueY, ForcY) if x == y])
total += len(TrueY)
p_percent = p_correct / total * 100
f_percent = f_correct / total * 100
cdnn_records_add(loss=p_percent,
accuracy=f_percent,
model_name=Model_name,
nn_records_name=cdnn_dict_name,
results_address=cdnn_address)
print(
f'Predicted correct:{p_correct} out of {total}, {p_percent}'
)
print(
f'Forced correct:{f_correct} out of {total}, {f_percent}')
diagnosis = False
if diagnosis:
print(potphones)
print(file)
print(
f'Goldstd:{[selected_phones[sp] for sp in np.argmax(y, axis=2)[0]]}'
)
print(
f'Max All:{[selected_phones[sp] for sp in np.argmax(predictions,axis=2)[0]]}'
)
print(
f'ForcedA:{[selected_phones[pind[sp]] for sp in np.argmax(predictions[:, :, pind][0], axis=1)]}'
)
del gen
del model
k.clear_session()
cdnn_records_rankNprint(nn_record_name=cdnn_dict_name,
results_address=cdnn_address)
def main(testing=False):
# Config Values[DNN params]
Frame_length = 0.025
Frame_step = 0.01
Dbdir = './speakers_db_correct/'
overwrite_MFCCs = False
TrainAll = False
#testing = True
FramelevelORword = False
cwd = os.getcwd()
SysPath = cwd.split('GOP-LSTM')[0]
Wavdir = SysPath + 'corpus/dat/speakers/'
Dbdir = SysPath + 'GOP-LSTM/PhoneInfo/speakers_db_correct/'
Holddir = SysPath + 'HoldDir/'
Traindir = Holddir + 'Train/'
Testdir = Holddir + 'Test/'
PhoInfDir = SysPath + 'GOP-LSTM/PhoneInfo/'
N_context = 2
N_ceps = 26
wordcount = 10
# Training & Test Data
if FramelevelORword:
speakers_trainNtest(db_corpus=Dbdir,
wav_corpus=Wavdir,
n_ceps=N_ceps,
n_context=N_context,
frame_length=Frame_length,
frame_step=Frame_step,
inmat=True,
holddir=Holddir,
overwrite=overwrite_MFCCs)
cdnn_dict_name = 'crnn_gridsearch_records_ALL.pk'
if not TrainAll:
ByCount = 4000
Traindir = Holddir + 'Train_Correct/'
Testdir = Holddir + 'Test_Correct/'
selected_phones, totalcount = select_trainNtest(
bycount=ByCount,
holddir=Holddir,
train_corpus=Traindir,
test_corpus=Testdir,
overwrite=False)
Traindir = Holddir + f'Train_Select_{ByCount}/'
Testdir = Holddir + f'Test_Select_{ByCount}/'
N_classes = len(selected_phones)
print(f'N selected classes: {N_classes}')
cdnn_dict_name = f'crnn_gridsearch_records_{ByCount}.pk'
else:
selected_phones, totalcount, w2pdict = createNcount_trainNtest(
frame_length=Frame_length,
frame_step=Frame_step,
n_ceps=N_ceps,
n_context=N_context,
dbdir=Dbdir,
datdir=Wavdir,
holddir=Holddir,
phoinfdir=PhoInfDir,
wordcount=wordcount)
N_classes = len(selected_phones)
Traindir = Holddir + f'FLP_Train_{wordcount}/'
Testdir = Holddir + f'FLP_Test_{wordcount}/'
cdnn_dict_name = f'drescp_blstm_records_wl_{wordcount}.pk'
print(f'Selected phones (amount: {selected_phones}')
print(f'Train count & test count: {totalcount}')
if testing:
cdnn_dict_name = f'testing_records.pk'
cdnn_address = SysPath + 'GOP-LSTM/Results/CDNN_phones/'
# Iterate over gridsearch
N_epochs = 80
Input_tuple = (5, 26, 1)
ConvLayerList = [[32, 7, 1024]]
DropoutList = [0.8]
# add one for sil
N_classes += 1
selected_phones.append('_')
seq_sizelist = [64]
for seq_size in seq_sizelist:
totaltrain = nseqsofnsize(Traindir, seq_size=seq_size)
totaltest = nseqsofnsize(Testdir, seq_size=seq_size)
for cl in ConvLayerList:
for dl in DropoutList:
# Compile Params
cname = '_'.join(str(x) for x in cl)
Model_name = f'ResBLSTM_C{cname}_FBN_SS{seq_size}_DL{dl}_V3'
Model_address = f'{Model_name}model.hdf5'
if os.path.exists(Model_address):
print(f'Loading Model:{Model_address}')
model = load_model(Model_address)
firsttime = False
print('Loaded')
else:
model = make_CNNLSTM_classifier(
input_tuple=Input_tuple,
conv_layers=cl,
n_classes=N_classes,
seq_size=seq_size,
dropout_rate=dl,
channel_order='channels_last')
model, Model_name2 = train_model(n_epochs=N_epochs,
model=model,
traindir=Traindir,
model_name=Model_name,
n_classes=N_classes,
totalsamples=totaltrain,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
batch_size=seq_size,
testing=testing)
print('...Evaluating...')
evaluate_model(model=model,
testdir=Testdir,
n_classes=N_classes,
totalsamples=totaltest,
model_name=Model_name2,
dict_name=cdnn_dict_name,
results_dir=cdnn_address,
batch_size=seq_size,
testing=testing)
firsttime = True
if firsttime:
model.save(Model_address)
# Forced Accuracy
print('...Predicting...')
if testing:
totaltest = 30
gen = generator_test_bufferedseq_wfname(train_dir=Testdir,
batch_size=seq_size,
n_classes=N_classes,
wfname=True)
that = True
# possibly create function to address, which words are possibly useful forced-word-max
#sameworddiffname
swdname = ['9', '44']
swdforms = [[['b', 'ɔ', 'l', '_'], ['b', 'a', 'l', '_'],
['b', 'ɔ', '_']],
[['d', 'ɹ', 'ʌ', 'm', '_'],
['dʒ', 'ɹ', 'ʌ', 'm', '_']]]
# import the whole list for testing each possible word, instead of softmax
#totaltest = 3 # for testing purpose shorten
if that:
perphoneaccdict = defaultdict(list)
selected_phones.append('_')
diagnose = False
s_correct = 0
f_correct = 0
fw_correct = 0
total = 0
s_IDS = 0
f_IDS = 0
maxsegtotal = 0
s_seg = 0
f_seg = 0
print(f'Total Test size:{totaltest}\n')
x, y, file = next(gen)
cfile = file
for _ in range(totaltest): # amount of words to be judged
if diagnose:
print(f'Current file:{file}')
print(f'Word\'s phones{potphones}')
segcount = 0
gwordphones = [] # gold standard word segments
swordphones = [] # softmax word segments
fwordphones = [] # forced word segments
fname = file.split('.')[0]
potphones = w2pdict[fname]
fnamelast = fname.split('_')[-1]
potphones.append('_')
pind = [selected_phones.index(sp) for sp in potphones]
predictions = model.predict(x=x)
segcount += 1
gstd = argmaxpredicts2phones(y[0], selected_phones)
softmax = argmaxpredicts2phones(
predictions[0], selected_phones)
forceda = argmaxpredicts2forcedphones(
predictions[0], selected_phones, pind)
wordmaxforce = []
if fnamelast in swdname:
wordmaxforce = swdforms[swdname.index(fnamelast)]
swdlist = []
swdlistscore = [0 for _ in wordmaxforce]
for index, wsp in enumerate(wordmaxforce):
wspind = [
selected_phones.index(sp) for sp in wsp
]
tv, tswd = argmaxpredicts2forcedphones(
predictions[0],
selected_phones,
wspind,
fwords=True)
swdlist.append(tswd)
swdlistscore[index] += tv
gwordphones += gstd
swordphones += softmax
fwordphones += forceda
x, y, file = next(gen)
predictions = model.predict(x=x)
if cfile == file: # break out of word while loop
samefile = True
cfile = file
else:
samefile = False
cfile = file
while samefile: # track error for same file
segcount += 1
gstd = argmaxpredicts2phones(y[0], selected_phones)
softmax = argmaxpredicts2phones(
predictions[0], selected_phones)
forceda = argmaxpredicts2forcedphones(
predictions[0], selected_phones, pind)
if wordmaxforce:
for index, wsp in enumerate(wordmaxforce):
wspind = [
selected_phones.index(sp) for sp in wsp
]
tv, tswd = argmaxpredicts2forcedphones(
predictions[0],
selected_phones,
wspind,
fwords=True)
swdlist[index] += tswd
swdlistscore[index] += tv
gwordphones += gstd
swordphones += softmax
fwordphones += forceda
x, y, file = next(gen)
predictions = model.predict(x=x)
if cfile != file: # break out of word while loop
samefile = False
cfile = file
# got word segs, process them
gseg = segmentphonelist(gwordphones)
sseg = segmentphonelist(swordphones)
fseg = segmentphonelist(fwordphones)
sLD = uttLD(gseg, sseg)
fLD = uttLD(gseg, fseg)
s_IDS += sLD
f_IDS += fLD
if diagnose:
print('\n')
print(gseg)
print(sseg)
print(fseg)
print('\n')
print(sLD)
print(fLD)
print('\n')
# accuracy
startsil = gseg[-1][1] #Index of Silence
g_len = len(gwordphones[:startsil])
s_correct += segCorrect(gwordphones[:startsil],
swordphones[:startsil])
f_correct += segCorrect(gwordphones[:startsil],
fwordphones[:startsil])
total += g_len
if wordmaxforce:
print(f'actual:{potphones}')
print(f'swd {wordmaxforce}'
) # list for individual comparision
print(f'scores{swdlistscore}')
for wlist in swdlist:
print(
segCorrect(gwordphones[:startsil],
wlist[:startsil]) / g_len)
print('\n')
# max-seg-score with known boundaries
# per word, then test set score
wordweight = 0
for seg in gseg[:-1]: # last phone is silence '_'
maxsegtotal += 1
cphone = seg[0]
sboundedlist = swordphones[seg[1]:seg[2]]
fboundedlist = fwordphones[seg[1]:seg[2]]
smaxphone = max(sboundedlist,
key=sboundedlist.count)
fmaxphone = max(fboundedlist,
key=fboundedlist.count)
if smaxphone == cphone:
s_seg += 1
if fmaxphone == cphone:
f_seg += 1
#if cphone in perphoneaccdict.keys():
# perphoneaccdict[cphone].append()
if diagnose:
print(seg)
print(smaxphone, fmaxphone, cphone)
sLDpercent = sLD / total * 100
fLDpercent = fLD / total * 100
print(
f'Insertions, Deletions, Substitions (SM):{sLD} out of {total}: {sLDpercent}%'
)
print(
f'Insertions, Deletions, Substitions (FM):{fLD} out of {total}: {fLDpercent}%'
)
Spercent = s_correct / total * 100
Fpercent = f_correct / total * 100
print('\n')
print(f'Softmax: {s_correct} out of {total}, {Spercent}%')
print(f'Forced: {f_correct} out of {total}, {Fpercent}%')
Spercent = s_seg / maxsegtotal * 100
Fpercent = f_seg / maxsegtotal * 100
print(
f'Softmax (seg): {s_seg} out of {maxsegtotal}, {Spercent}%'
)
print(
f'Forced (seg): {f_seg} out of {maxsegtotal}, {Fpercent}%'
)
del gen
del model
k.clear_session()