#!/usr/bin/python3

# ## {R2D2919B742E} ##
# ###########################################################################
# What if magic existed?
# What if a place existed where your every thought and dream come to life.
# There is only one catch: it has to be written down.
# Such a place exists, it is called programming.
#    - Scott Taylor Reph, RightThumb.com
# ###########################################################################
# ## {C3P0D40fAe8B} ##

##################################################
import sys, time
##################################################
import _rightThumb._construct as __
appDBA=__.clearFocus(__name__,__file__);__.appReg=appDBA;

def focus(parentApp='',childApp='',reg=True):
    global appDBA;f=__.appName(appDBA,parentApp,childApp);
    if reg:__.appReg=f;
    return f
import _rightThumb._base3 as _
fieldSet=_.l.vars(focus(),__name__,__file__,appDBA)
_.load()
##################################################
_v = __.imp('_rightThumb._vars')
_str = __.imp('_rightThumb._string')
##################################################

def sw():
    pass
    _.switches.register( 'Files', '-f,-fi,-file,-files','file.txt', isData='name', description='Files' )
# __.setting('require-list',['Files,Plus','File,Has']) # todo
# __.setting('require-list',['Pipe','Files'])
__.setting('receipt-log')
__.setting('receipt-file')
__.setting('myFileLocations-skip-validation',False)
__.setting('require-pipe',False)
__.setting('require-pipe||file',False)
__.setting('pre-error',False)
__.setting('switch-raw',[])

_.appInfo[focus()] = {
    # 'app': '8facG-jo0Cxk',
    'file': 'speech.py',
    'liveAppName': __.thisApp( __file__ ),
    'description': 'Changes the world',
        # _.ail(1,'subject')+
        # _.aib('one')+
    'categories': [
                        'DEFAULT',
                ],
    'usage': [
                        # 'epy another',
                        # 'e nmap',
                        # '',
    ],
    'relatedapps': [
                        # 'p another -file file.txt',
                        # '',
    ],
    'prerequisite': [
                        # 'p another -file file.txt',
                        # '',
    ],
    'examples': [
                        _.hp('p speech -file file.txt'),
                        _.linePrint(label='simple',p=0),
                        '',
    ],
    'columns': [
                       # { 'name': 'name', 'abbreviation': 'n' },
                       # { 'name': '{1}', 'abbreviation': '{0}', 'sort': '{2}' },
    ],
    'aliases': [
                       # 'this',
                       # 'app',
    ],
    'notes': [
                       # {},
    ],
}
_.appData[focus()] = {
        'start': __.startTime,
        'uuid': '',
        'audit': [],
        'pipe': False,
        'data': {
                    'field': {'sent': [], 'received': [] }, # { 'label': '', 'context': [],  }
                    'table': {'sent': [], 'received': [] },
        },
    }

def triggers():
    _.switches.trigger( 'Files', _.myFileLocations, vs=True )
    _.switches.trigger( 'Ago', _.timeAgo )
    _.switches.trigger( 'Folder', _.myFolderLocations )
    _.switches.trigger( 'URL', _.urlTrigger )
    _.switches.trigger( 'Duration', _.timeFuture )
_.l.conf('clean-pipe',True)
_.l.sw.register( triggers, sw )

########################################################################################
#n)--> start

def action():
    for path in _.isData():
        try: get_large_audio_transcription(path)
        except Exception as e: pass




# importing libraries 
import speech_recognition as sr 
import os 
from pydub import AudioSegment
from pydub.silence import split_on_silence

# create a speech recognition object
r = sr.Recognizer()

# a function that splits the audio file into chunks
# and applies speech recognition
def get_large_audio_transcription(path):
    """
    Splitting the large audio file into chunks
    and apply speech recognition on each of these chunks
    """
    # open the audio file using pydub
    sound = AudioSegment.from_wav(path)  
    # split audio sound where silence is 700 miliseconds or more and get chunks
    chunks = split_on_silence(sound,
        # experiment with this value for your target audio file
        min_silence_len = 500,
        # adjust this per requirement
        silence_thresh = sound.dBFS-14,
        # keep the silence for 1 second, adjustable as well
        keep_silence=500,
    )
    folder_name = "audio-chunks"
    # create a directory to store the audio chunks
    if not os.path.isdir(folder_name):
        os.mkdir(folder_name)
    whole_text = ""
    # process each chunk 
    for i, audio_chunk in enumerate(chunks, start=1):
        # export audio chunk and save it in
        # the `folder_name` directory.
        chunk_filename = os.path.join(folder_name, f"chunk{i}.wav")
        audio_chunk.export(chunk_filename, format="wav")
        # recognize the chunk
        with sr.AudioFile(chunk_filename) as source:
            audio_listened = r.record(source)
            # try converting it to text
            try:
                text = r.recognize_google(audio_listened)
            except sr.UnknownValueError as e:
                print("Error:", str(e))
            else:
                text = f"{text.capitalize()}. "
                print(chunk_filename, ":", text)
                whole_text += text
    # return the text for all chunks detected
    return whole_text




##################################################
######################################
if __name__ == '__main__':
    action()
    _.isExit(__file__)
