Esempio n. 1
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    # 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])
Esempio n. 2
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	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])