(w10) Offline ASR (Automatic Speech Recognition) system

 

pocketsphinx

• Offline speech recognition in real-time on mobile devices, ported from the CMUSphinx project

How to use pocketsphinx:

pip install pocketsphinx

 

# Pocketsphinx on live input
from pocketsphinx import LiveSpeech
for phrase in LiveSpeech(): print(phrase)

# Pocketsphinx for keywords
from pocketsphinx import LiveSpeech
speech = LiveSpeech(lm=False, keyphrase='move forward', kws_threshold=le-20)
for phrase in speech: print(phrase.segments(detailed=True))

# Specify phrases in an external file
from pocketsphinx improt LiveSpeech
speech = LiveSpeech(lm=False, kws='./kws.text')
for phrase in speech: print(phrase.segments(detailed=True))
# File contents:
#	move forward /le-40/
#	go backwards /le-40/
#	turn left /le-20/
#	turn right /le-20/

# Pocketsphinx and audio files
from pocketsphinx import Pocketsphinx
ps = Pocketsphinx()
ps.decode(audio_file='nines.wav')
ps.hypothesis()
ps.confidence()
ps.best(count=4)

from pocketsphinx import Pocketsphinx
ps = Pocketsphinx(l=False, kws='./kws.txt')
ps.decode(audio_file='nines.wav')
ps.hypothesis()

 

vosk

• An easy to install API which is able to run efficient offline Kaldi models
• A neat wrapper around kaldi models

kaldi

• Large, open source collection of components for constructing ASR system based on finite-state transducers
  • Finite-state transducer
    : Intuitively - a simplified version of an HMM (Hidden Markov Model)
    → The tagging speed when using transducers is up to five times higher than when using the underlying HMMs. The main advantage of transforming an HMM is that the resulting transducer can be handled by finite state calculus.

Mozilla DeepSpeech

• An open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry pi 4 to high power GPU servers