53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
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#!/usr/bin/python
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from os import environ, path
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from pocketsphinx.pocketsphinx import *
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from sphinxbase.sphinxbase import *
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MODELDIR = "../../../model"
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DATADIR = "../../../test/data"
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# Create a decoder with certain model
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config = Decoder.default_config()
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config.set_string('-hmm', path.join(MODELDIR, 'en-us/en-us'))
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config.set_string('-lm', path.join(MODELDIR, 'en-us/en-us.lm.bin'))
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config.set_string('-dict', path.join(MODELDIR, 'en-us/cmudict-en-us.dict'))
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# Decode streaming data.
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decoder = Decoder(config)
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print ("Pronunciation for word 'hello' is ", decoder.lookup_word("hello"))
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print ("Pronunciation for word 'abcdf' is ", decoder.lookup_word("abcdf"))
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decoder.start_utt()
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stream = open(path.join(DATADIR, 'goforward.raw'), 'rb')
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while True:
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buf = stream.read(1024)
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if buf:
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decoder.process_raw(buf, False, False)
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else:
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break
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decoder.end_utt()
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hypothesis = decoder.hyp()
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logmath = decoder.get_logmath()
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print ('Best hypothesis: ', hypothesis.hypstr, " model score: ", hypothesis.best_score, " confidence: ", logmath.exp(hypothesis.prob))
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print ('Best hypothesis segments: ', [seg.word for seg in decoder.seg()])
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# Access N best decodings.
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print ('Best 10 hypothesis: ')
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for best, i in zip(decoder.nbest(), range(10)):
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print (best.hypstr, best.score)
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stream = open(path.join(DATADIR, 'goforward.mfc'), 'rb')
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stream.read(4)
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buf = stream.read(13780)
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decoder.start_utt()
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decoder.process_cep(buf, False, True)
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decoder.end_utt()
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hypothesis = decoder.hyp()
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print ('Best hypothesis: ', hypothesis.hypstr, " model score: ", hypothesis.best_score, " confidence: ", hypothesis.prob)
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