clean_keyword.py

'''
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              A:::A              l:::::l l:::::l  i::::i                     
             A:::::A             l:::::l l:::::l   iiii                      
            A:::::::A            l:::::l l:::::l                             
           A:::::::::A            l::::l  l::::l iiiiiii     eeeeeeeeeeee    
          A:::::A:::::A           l::::l  l::::l i:::::i   ee::::::::::::ee  
         A:::::A A:::::A          l::::l  l::::l  i::::i  e::::::eeeee:::::ee
        A:::::A   A:::::A         l::::l  l::::l  i::::i e::::::e     e:::::e
       A:::::A     A:::::A        l::::l  l::::l  i::::i e:::::::eeeee::::::e
      A:::::AAAAAAAAA:::::A       l::::l  l::::l  i::::i e:::::::::::::::::e 
     A:::::::::::::::::::::A      l::::l  l::::l  i::::i e::::::eeeeeeeeeee  
    A:::::AAAAAAAAAAAAA:::::A     l::::l  l::::l  i::::i e:::::::e           
   A:::::A             A:::::A   l::::::ll::::::li::::::ie::::::::e          
  A:::::A               A:::::A  l::::::ll::::::li::::::i e::::::::eeeeeeee  
 A:::::A                 A:::::A l::::::ll::::::li::::::i  ee:::::::::::::e  
AAAAAAA                   AAAAAAAlllllllllllllllliiiiiiii    eeeeeeeeeeeeee  


/  __ \ |                (_)              / _ \ | ___ \_   _|  _ 
| /  \/ | ___  __ _ _ __  _ _ __   __ _  / /_\ \| |_/ / | |   (_)
| |   | |/ _ \/ _` | '_ \| | '_ \ / _` | |  _  ||  __/  | |      
| \__/\ |  __/ (_| | | | | | | | | (_| | | | | || |    _| |_   _ 
 \____/_|\___|\__,_|_| |_|_|_| |_|\__, | \_| |_/\_|    \___/  (_)
                                   __/ |                         
                                  |___/                          
  ___            _ _       
 / _ \          | (_)      
/ /_\ \_   _  __| |_  ___  
|  _  | | | |/ _` | |/ _ \ 
| | | | |_| | (_| | | (_) |
\_| |_/\__,_|\__,_|_|\___/ 
                           

This cleaning script breaks up the source audio file into utterances and keeps 
utterances with a certain keyword. Therefore, there can be multiple or no 
audio files resulting after the cleaning process dependent on whether multiple
keywords were used.

Note that any utterance under 20000 bytes (20KB) is deleted.

This cleaning script is enabled if default_audio_cleaners=['clean_keyword']
'''
import sys, os, shutil, librosa, uuid
from pyvad import vad, trim, split
import matplotlib.pyplot as plt
import numpy as np

# make sure the right version of numba is installed
os.system('pip3 install numba==0.48')

def transcribe_audiofile(file):

    curdir=os.getcwd()
    listdir=os.listdir()
    deepspeech_dir=os.getcwd()

    # download models if not in helper directory
    if 'deepspeech-0.7.0-models.pbmm' not in listdir:
        os.system('wget https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.pbmm')
    if 'deepspeech-0.7.0-models.scorer' not in listdir:
        os.system('wget https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.scorer')

    # initialize filenames
    textfile=file[0:-4]+'.txt'
    newaudio=file[0:-4]+'_newaudio.wav'

    if deepspeech_dir.endswith('/'):
        deepspeech_dir=deepspeech_dir[0:-1]

    # go back to main directory
    os.chdir(curdir)

    # try:
    # convert audio file to 16000 Hz mono audio 
    os.system('ffmpeg -i "%s" -acodec pcm_s16le -ac 1 -ar 16000 "%s" -y'%(file, newaudio))
    command='deepspeech --model %s/deepspeech-0.7.0-models.pbmm --scorer %s/deepspeech-0.7.0-models.scorer --audio "%s" >> "%s"'%(deepspeech_dir, deepspeech_dir, newaudio, textfile)
    print(command)
    os.system(command)

    # get transcript
    transcript=open(textfile).read().replace('\n','')
    # remove temporary files
    os.remove(textfile)
    os.remove(newaudio)
    # except:
    #     try:
    #         # remove temporary files
    #         os.remove(textfile)
    #     except:
    #         pass
    #     try:
    #         os.remove(newaudio)
    #     except:
    #         pass
    #     transcript=''

    return transcript 


def clean_keyword(audiofile,keyword):
    '''
    taken from https://github.com/F-Tag/python-vad/blob/master/example.ipynb
    '''
    show=False
    curdir=os.getcwd()
    data, fs = librosa.core.load(audiofile)
    time = np.linspace(0, len(data)/fs, len(data))
    try:
        vact = vad(data, fs, fs_vad = 16000, hop_length = 30, vad_mode=3)
        vact = list(vact)
        while len(time) > len(vact):
            vact.append(0.0)
        utterances=list()

        for i in range(len(vact)):
            try:
                if vact[i] != vact[i-1]:
                    # voice shift 
                    if vact[i] == 1:
                        start = i
                    else:
                        # this means it is end 
                        end = i
                        utterances.append([start,end])
            except:
                pass
        
        print(utterances)
        vact=np.array(vact)

        tempfiles=list()
        keptfiles=list()
        for i in range(len(utterances)):
            trimmed = data[utterances[i][0]:utterances[i][1]]
            tempfile = str(uuid.uuid4())+'.wav'
            librosa.output.write_wav(tempfile, trimmed, fs)
            tempfiles.append(tempfile)

        for i in range(len(tempfiles)):
            if os.path.getsize(tempfiles[i]) > 20000:
                pass
                transcript=transcribe_audiofile(tempfiles[i])
                print('TRANSCRIPT --> %s'%(transcript))
                if transcript == keyword:
                   keptfiles.append(tempfiles[i])
                else:
                    os.remove(tempfiles[i])
            else:
                os.remove(tempfiles[i])
    except:
        print('ERROR - ValueError: When data.type is float, data must be -1.0 <= data <= 1.0.')

    os.remove(audiofile)
    return keptfiles