isolated_word (isolated word MFCC module)

Class (class)

isolated_word

The isolated_word construction parameters are as follows:

-dmac: DMA channel used for recording. [Channel 2] is used by default. -i2s: Recording device, I2S.DEVICE_0 is used by default. -size: The capacity of vocabulary templates, which means the total number of templates that can be loaded. The default is 10. -shift: Channel selection. Maix series hardware recording devices usually use mono input. Set 0 as the left channel, so 1 is the right channel.

from speech_recognizer import isolated_word
sr = isolated_word(dmac=2, i2s=I2S.DEVICE_0, size=10, shift=0)

Method (function)

size

Returns the total number of current word templates.

from speech_recognizer import isolated_word
sr = isolated_word()
sr.size()

set_threshold

Set the working parameters of the isolated word module.

-Parameter①: Noise threshold, used for short-time zero-crossing rate calculation -Parameter ②: Short-time zero-crossing rate threshold, exceeding this threshold is regarded as entering the transition section. -Parameter ③: Short-term accumulation and threshold value. If the threshold is exceeded, it is regarded as entering the transition section.

from speech_recognizer import isolated_word
sr = isolated_word()
sr.set_threshold(0, 0, 10000)

record

Enter [Vocabulary Template].

-Parameter ①: Save the entered template to the specified index location.

from speech_recognizer import isolated_word
sr = isolated_word()
while True:
  if sr.Done == sr.record(0):
    pass

state

You can return to the following working status.

Function Description
Init The module has been initialized.
Idle The module is idling and is not working.
Ready Module recording is processing.
MaybeNoise The module determines whether it is a noise environment.
Speak The module waits for voice recording.
Restrain The data entered by the module is illegal, and the module returns to the Speak state.
Done The module voice recognition is successful, and the result can be obtained through result.

recognize

Recognize [word template].

from speech_recognizer import isolated_word
sr = isolated_word()
while True:
  if sr.Done == sr.recognize():
    print(sr.result())

result

Get [Vocabulary Template] and return an array of (matched template number, matched dtw value, current frame length, matched frame length).

from speech_recognizer import isolated_word
sr = isolated_word()
print(sr.result())

get

Get 【Vocabulary Template】, and return (data frame length, data frame) array.

return value

  • frm_len: data frame length
  • frm_data: data frame
from speech_recognizer import isolated_word
sr = isolated_word()
print(sr.get(0))

set

Load [Vocabulary Template] into the module.

from speech_recognizer import isolated_word
sr = isolated_word()
print(sr.set(1, sr.get(0)))

run

Run the isolated word module (recording).

from speech_recognizer import isolated_word
sr = isolated_word()
sr.run()

reset

Reset the isolated word module.

from speech_recognizer import isolated_word
sr = isolated_word()
sr.reset()

dtw

Back to Dynamic Time Warping (DTW) algorithm Calculate the optimal matching value, the smaller the value, the better.

return value

  • dis: Cumulative matching distance (int)
from speech_recognizer import isolated_word
sr = isolated_word()
print(sr.dtw(sr.get(0)))

del

To release the orphan word module, it can be called actively, or it can be automatically collected by gc.collect().

from speech_recognizer import isolated_word
sr = isolated_word()
sr.__del__()
del sr