Open Source Text Processing Project: pocketsphinx-python

Deep Learning Specialization on Coursera

pocketsphinx-python: Python interface to CMU SphinxBase and PocketSphinx libraries

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Python interface to CMU SphinxBase and PocketSphinx libraries created with SWIG. Pocketsphinx packages include python support, however, it is based on Automake and not well supported on Windows.

This package provides module created with Python distutils setup and can be more portable.

#!/usr/bin/env python
from os import environ, path

from pocketsphinx.pocketsphinx import *
from sphinxbase.sphinxbase import *

MODELDIR = “pocketsphinx/model”
DATADIR = “pocketsphinx/test/data”

# Create a decoder with certain model
config = Decoder.default_config()
config.set_string(‘-hmm’, path.join(MODELDIR, ‘en-us/en-us’))
config.set_string(‘-lm’, path.join(MODELDIR, ‘en-us/en-us.lm.bin’))
config.set_string(‘-dict’, path.join(MODELDIR, ‘en-us/cmudict-en-us.dict’))
decoder = Decoder(config)

# Decode streaming data.
decoder = Decoder(config)
stream = open(path.join(DATADIR, ‘goforward.raw’), ‘rb’)
while True:
buf =
if buf:
decoder.process_raw(buf, False, False)
print (‘Best hypothesis segments: ‘, [seg.word for seg in decoder.seg()])

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