I'm doing and application that do the fallowing:
1:
If some noise is detected by the microphone, its starts to record audio, until no noise is detected. After it, the audio is recorded to a wav file.
2:
I have to detect some words on it. There are only, 5 to 10 words to detect.
So far, my code only does the first part (detect noise and record audio). Now, I have a list with the following words: help, please, yes, no, could, you, after, tomorrow
. I need an offline way to detect if my sound contains these words. Is this possible? How can I do that? I'm using linux and there is no way to change my operational system to windows or use virtual machine.
I'm thinking to use the sound's spectrogram, create a train database and use some classifier to predict. For example, this is a spectrogram of a word. Is this a good technique to use?
Thanks.
You can use pocketsphinx from python, install with pip install pocketsphinx
. Code looks like this:
import sys, os
from pocketsphinx.pocketsphinx import *
from sphinxbase.sphinxbase import *
modeldir = "../../../model"
datadir = "../../../test/data"
# Create a decoder with certain model
config = Decoder.default_config()
config.set_string('-hmm', os.path.join(modeldir, 'en-us/en-us'))
config.set_string('-dict', os.path.join(modeldir, 'en-us/cmudict-en-us.dict'))
config.set_string('-kws', 'command.list')
# Open file to read the data
stream = open(os.path.join(datadir, "goforward.raw"), "rb")
# Alternatively you can read from microphone
# import pyaudio
#
# p = pyaudio.PyAudio()
# stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=1024)
# stream.start_stream()
# Process audio chunk by chunk. On keyword detected perform action and restart search
decoder = Decoder(config)
decoder.start_utt()
while True:
buf = stream.read(1024)
if buf:
decoder.process_raw(buf, False, False)
else:
break
if decoder.hyp() != None:
print ([(seg.word, seg.prob, seg.start_frame, seg.end_frame) for seg in decoder.seg()])
print ("Detected keyword, restarting search")
decoder.end_utt()
decoder.start_utt()
The list of keywords should look like this:
forward /1e-1/
down /1e-1/
other phrase /1e-20/
The numbers are thresholds for detection
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