# load dataset
loader = DataLoader(dataset, sampleEach)
print("Decoding " + str(loader.getNumSamples()) + " samples now.")
print("")
# metrics calculates CER and WER for dataset
m = Metrics(loader.lm.getWordChars())
# write results to csv
csv = Utils.CSVWriter()
# decode each sample from dataset
for (idx, data) in enumerate(loader):
# decode matrix
res = wordBeamSearch(data.mat, 10, loader.lm, useNGrams)
print("Sample: " + str(idx + 1))
print("Filenames: " + data.fn)
print('Result: "' + res + '"')
print('Ground Truth: "' + data.gt + '"')
strEditDist = str(editdistance.eval(res, data.gt))
print("Editdistance: " + strEditDist)
# output CER and WER
m.addSample(data.gt, res)
print("Accumulated CER and WER so far:", "CER:", m.getCER(), "WER:",
m.getWER())
print("")
# output to csv
csv.write([res, data.gt, strEditDist])
loader=DataLoader(dataset, sampleEach)
print('Decoding '+str(loader.getNumSamples())+' samples now.')
print('')
# metrics calculates CER and WER for dataset
m=Metrics(loader.lm.getWordChars())
# write results to csv
csv=Utils.CSVWriter()
# decode each sample from dataset
for (idx,data) in enumerate(loader):
# decode matrix
res=wordBeamSearch(data.mat, 10, loader.lm, useNGrams)
print('Sample: '+str(idx+1))
print('Filenames: '+data.fn)
print('Result: "'+res+'"')
print('Ground Truth: "'+data.gt+'"')
strEditDist=str(editdistance.eval(res, data.gt))
print('Editdistance: '+strEditDist)
# output CER and WER
m.addSample(data.gt, res)
print('Accumulated CER and WER so far:','CER:', m.getCER(), 'WER:', m.getWER())
print('')
# output to csv
csv.write([res, data.gt, strEditDist])
