Merge remote-tracking branch 'origin/QA_RELEASE' into monitoring_student_behavior_IT17138000

AttendanceApp/camera.py

@@ -23,7 +23,7 @@ maskNet = load_model(os.path.join(settings.BASE_DIR,'face_detector/mask_detector
 
 class IPWebCam(object):
 	def __init__(self):
-		self.url = "http://192.168.8.103:8080/shot.jpg"
+		self.url = "http://192.168.8.100:8080/shot.jpg"
 
 	def __del__(self):
 		cv2.destroyAllWindows()

AttendanceApp/templates/AttendanceApp/Initiate_Lecture.html

@@ -55,6 +55,13 @@ function testAPI() {
     .then((out) => {})
     .catch((err) => alert('error: ' + err));
 
+    //audio
+    var audio_name = 'Lecture';
+    fetch('http://127.0.0.1:8000/summary/lecture-audio/?audio_name=' + audio_name)
+    then((res) => res.json())
+    .then((out) => {})
+    .catch((err) => alert('error: ' + err));
+
 }
 var time = 'time';
 function f() {

AttendanceApp/test.py

@@ -12,7 +12,7 @@ def IPWebcamTest():
 
     # Replace the URL with your own IPwebcam shot.jpg IP:port
     # url = 'http://192.168.2.35:8080/shot.jpg'
-    url = 'http://192.168.8.103:8080/shot.jpg'
+    url = 'http://192.168.8.100:8080/shot.jpg'
     # url = 'http://192.168.1.11:8080/startvideo?force=1&tag=rec'
     # url = 'http://192.168.1.11:8080/stopvideo?force=1'
 

FirstApp/templates/FirstApp/template.html

@@ -118,7 +118,7 @@
                 <div id="collapseThree" class="collapse" aria-labelledby="headingThree" data-parent="#accordionSidebar">
                     <div class="bg-white py-2 collapse-inner rounded">
                         <h6 class="collapse-header">Components:</h6>
-                        <a class="collapse-item" href="/summary/record">Record Lecture</a>
+<!--                        <a class="collapse-item" href="/summary/record">Record Lecture</a>-->
                         <a class="collapse-item" href="/summary/lecture">Summarization</a>
                     </div>
                 </div>
@@ -174,7 +174,7 @@
                     <div class="bg-white py-2 collapse-inner rounded">
                         <!--                    <h6 class="collapse-header">Login Screens:</h6>-->
                         <a class="collapse-item" href="/lecturer">Dashboard</a>
-                        <a class="collapse-item" href="/lecturer/lecture-video">Video Page</a>
+                        <a class="collapse-item" href="/lecturer/lecture-video">Audio analysis Key-words.</a>
 
                     </div>
                 </div>

LectureSummarizingApp/ExtractKeySentences.py

 import nltk
-read_lines = [line.rstrip('\n') for line in open("audioToText01.txt", "r")]
-sentences_list = []
-sentence_list = nltk.sent_tokenize(read_lines)
-word_search = "important"
-sentences_with_word = []
-for sentence in sentences_list:
-    if sentence.count(word_search)>0:
-        sentences_with_word.append(sentence)
-
-words_search = ["exam", "assignment"]
-word_sentence_dictionary = {"exam":[],"assignment":[]}
-
-for word in words_search:
+import os
+from fpdf import FPDF
+
+def LectureNotice(notice_name):
+
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+    FILE_PATH = os.path.join(BASE_DIR, "speechToText\\{}".format(notice_name))
+    DESTINATION_DIR = os.path.join(BASE_DIR, "notices\\Notice_{}".format(notice_name))
+    print('destination directory: ', DESTINATION_DIR)
+
+    text = ''
+    read_lines = [line.rstrip('\n') for line in open(FILE_PATH, "r")]
+    sentences_list = []
+    sentence_list = nltk.sent_tokenize(read_lines)
+    word_search = "important"
     sentences_with_word = []
-    for sentence in sentences_list:
-        if sentence.count(word)>0:
+    for sentence in sentence_list:
+        if sentence.count(word_search)>0:
             sentences_with_word.append(sentence)
-            word_sentence_dictionary[word] = sentences_with_word
+
\ No newline at end of file
+    words_search = ["exam", "assignment"]
+    word_sentence_dictionary = {"exam":[],"assignment":[]}
+
+    for word in words_search:
+        sentences_with_word = []
+        for sentence in sentences_list:
+            if sentence.count(word)>0:
+                sentences_with_word.append(sentence)
+                word_sentence_dictionary[word] = sentences_with_word
+
+    file = open('DESTINATION_DIR', 'w')
+    file.close()
+
+
+# def SaveNotices():
+
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+    # PDF_DESTINATION_DIR = os.path.dirname(os.path.join(BASE_DIR, "summaryPDF\\sample.txt"))
+    PDF_DESTINATION_DIR = os.path.join(BASE_DIR, "noticePDF\\Notice{}.pdf".format(notice_name))
+
+
+    pdf = FPDF()
+    # Add a page
+    pdf.add_page()
+    # set style and size of font
+    # that you want in the pdf
+    pdf.set_font("Arial", size=15)
+
+    # open the text file in read mode
+    f = open("DESTINATION_DIR", "r")
+
+    # insert the texts in pdf
+    for x in f:
+        pdf.cell(200, 10, txt=x, ln=1, align='C')
+
+    # save the pdf with name .pdf
+    pdf.output("PDF_DESTINATION_DIR")
+
+    return text
\ No newline at end of file

LectureSummarizingApp/Summary.py

 from spacy.lang.pt.stop_words import STOP_WORDS
 from sklearn.feature_extraction.text import CountVectorizer
 import pt_core_news_sm
+import os
+from fpdf import FPDF
 
+def LectureSummary(summary_name):
+
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+    FILE_PATH = os.path.join(BASE_DIR, "speechToText\\{}".format(summary_name))
+    DESTINATION_DIR = os.path.join(BASE_DIR, "summary\\Summary_{}".format(summary_name))
+    print('destination directory: ', DESTINATION_DIR)
 
 # Reading the file
-nlp = pt_core_news_sm.load()
-with open("audioToText01.txt", "r", encoding="utf-8") as f:
-    text = " ".join(f.readlines())
+    nlp = pt_core_news_sm.load()
+    # file = open(DESTINATION_DIR, 'w')
+    text = ''
+    with open(FILE_PATH, "r", encoding="utf-8") as f:
+        text = " ".join(f.readlines())
 
-doc = nlp(text)
+    doc = nlp(text)
 
 #calculating the word frequency
-corpus = [sent.text.lower() for sent in doc.sents ]
-cv = CountVectorizer(stop_words=list(STOP_WORDS))
-cv_fit=cv.fit_transform(corpus)
-word_list = cv.get_feature_names()
-count_list = cv_fit.toarray().sum(axis=0)
-word_frequency = dict(zip(word_list,count_list))
-
-
-val=sorted(word_frequency.values())
-higher_word_frequencies = [word for word,freq in word_frequency.items() if freq in val[-3:]]
-print("\nWords with higher frequencies: ", higher_word_frequencies)
-# gets relative frequency of words
-higher_frequency = val[-1]
-for word in word_frequency.keys():
-    word_frequency[word] = (word_frequency[word]/higher_frequency)
+    corpus = [sent.text.lower() for sent in doc.sents ]
+    cv = CountVectorizer(stop_words=list(STOP_WORDS))
+    cv_fit=cv.fit_transform(corpus)
+    word_list = cv.get_feature_names()
+    count_list = cv_fit.toarray().sum(axis=0)
+    word_frequency = dict(zip(word_list,count_list))
+
+
+    val=sorted(word_frequency.values())
+    higher_word_frequencies = [word for word,freq in word_frequency.items() if freq in val[-3:]]
+    print("\nWords with higher frequencies: ", higher_word_frequencies)
+    # gets relative frequency of words
+    higher_frequency = val[-1]
+    for word in word_frequency.keys():
+        word_frequency[word] = (word_frequency[word]/higher_frequency)
 
 #calculating sentence rank and taking top ranked sentences for the summary
-sentence_rank={}
-for sent in doc.sents:
-    for word in sent :
-        if word.text.lower() in word_frequency.keys():
-            if sent in sentence_rank.keys():
-                sentence_rank[sent]+=word_frequency[word.text.lower()]
-            else:
-                sentence_rank[sent]=word_frequency[word.text.lower()]
-top_sentences=(sorted(sentence_rank.values())[::-1])
-top_sent=top_sentences[:3]
-
-
-
-summary=[]
-for sent,strength in sentence_rank.items():
-    if strength in top_sent:
-        summary.append(sent)
-    else:
-        continue
-for i in summary:
-    file = open('Summary01.txt', 'w')
-    file.write(str(i))
-    file.close()
+    sentence_rank={}
+    for sent in doc.sents:
+        for word in sent :
+            if word.text.lower() in word_frequency.keys():
+                if sent in sentence_rank.keys():
+                    sentence_rank[sent]+=word_frequency[word.text.lower()]
+                else:
+                    sentence_rank[sent]=word_frequency[word.text.lower()]
+    top_sentences=(sorted(sentence_rank.values())[::-1])
+    top_sent=top_sentences[:3]
+
+    summary=[]
+    for sent,strength in sentence_rank.items():
+        if strength in top_sent:
+            summary.append(sent)
+        else:
+            continue
+
+
+    file = None
+    for i in summary:
+        file = open(DESTINATION_DIR, 'w')
+        # file = open('Summary01.txt', 'w')
\ No newline at end of file
+        file.write(str(i))
+        file.close()
+
+
+
+# def SaveSummary():
+
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+    # PDF_DESTINATION_DIR = os.path.dirname(os.path.join(BASE_DIR, "summaryPDF\\sample.txt"))
+    PDF_DESTINATION_DIR = os.path.join(BASE_DIR, "summaryPDF\\Summary_PDF_{}.pdf".format(summary_name))
+
+    pdf = FPDF()
+    # Add a page
+    pdf.add_page()
+    # set style and size of font
+    # that you want in the pdf
+    pdf.set_font("Arial", size=15)
+
+    # open the text file in read mode
+    f = open(DESTINATION_DIR, "r")
+
+    # insert the texts in pdf
+    for x in f:
+        pdf.cell(200, 10, txt=x, ln=1, align='C')
+
+    # save the pdf with name .pdf
+    pdf.output(PDF_DESTINATION_DIR)
+
+    # convert the summary list to a text
+    listToStr = ' '.join([str(elem) for elem in summary])
+
+    return text, listToStr
\ No newline at end of file

LectureSummarizingApp/Voice Recorder.py

 import sounddevice as sd
 from scipy.io.wavfile import write
 import wavio as wv
+import os
 
 
-# Sampling frequency
-freq = 44100
+def AudioRecorder(audio):
 
-# Recording duration
-duration = 10
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 
-# Start recorder with the given values of
-# duration and sample frequency
-recording = sd.rec(int(duration * freq),
-                   samplerate=freq, channels=2)
+    #for the array
+    DESTINATION_DIR = os.path.join(BASE_DIR, "audioArray\\{}".format(audio))
 
-# Record audio for the given number of seconds
-sd.wait()
+    #for the audio
+    LECTURE_AUDIO_DIR = os.path.join(BASE_DIR, "lectures\\Lecture_{}".format(audio))
 
-# This will convert the NumPy array to an audio
-# file with the given sampling frequency
-write("recording0.wav", freq, recording)
 
+    # Sampling frequency
+    freq = 44100
 
- #Convert the NumPy array to audio file
-wv.write("recording1.wav", recording, freq, sampwidth=2)
+    # Recording duration
+    duration = 20
\ No newline at end of file
+
+    # Start recorder with the given values of
+    # duration and sample frequency
+    recording = sd.rec(int(duration * freq),samplerate=freq, channels=2)
+
+    # Record audio for the given number of seconds
+    sd.wait()
+
+    # This will convert the NumPy array to an audio
+    # file with the given sampling frequency
+    write(DESTINATION_DIR, freq, recording)
+
+
+    #Convert the NumPy array to audio file
+    wv.write(LECTURE_AUDIO_DIR, recording, freq, sampwidth=2)
\ No newline at end of file

LectureSummarizingApp/api.py

 from rest_framework.views import APIView
 from rest_framework.response import Response
 
+from FirstApp.logic.id_generator import generate_new_id
 from LectureSummarizingApp.models import LectureAudio, LectureAudioNoiseRemoved, LectureSpeechToText, \
     LectureAudioSummary, LectureNotices
 from LectureSummarizingApp.serializer import LectureAudioSerializer, LectureAudioNoiseRemovedSerializer, \
     LectureSpeechToTextSerializer, LectureAudioSummarySerializer, LectureNoticesSerializer
 
 from . import speech_to_text as stt
+from . import noiseRemove as nr
+
+import datetime
 
 
 # APIs used in Lecture Summarizing Component
+from .Summary import LectureSummary
+from .noise import noise_removal
+from .speech_to_text import speech_to_text
+
+
 class LectureAudioAPI(APIView):
 
     def get(self, request):
@@ -18,12 +27,40 @@ class LectureAudioAPI(APIView):
         return Response(lecture_audio_serializer.data)
 
 
+
 class audioNoiseRemovedList(APIView):
 
     def get(self, request):
-        lecture_audio_noise_removed = LectureAudioNoiseRemoved.objects.all()
-        serializer = LectureAudioNoiseRemovedSerializer(lecture_audio_noise_removed, many=True)
-        return Response(serializer.data)
+        # lecture_audio_noise_removed = LectureAudioNoiseRemoved.objects.all()
+        # serializer = LectureAudioNoiseRemovedSerializer(lecture_audio_noise_removed, many=True)
+        audio_noise_removed_list = LectureAudioNoiseRemoved.objects.order_by('lecture_audio_noise_removed_id').last()
+
+        audio_name = request.query_params.get("audio_name")
+        id = int(request.query_params.get("id"))
+
+
+        current_date = datetime.datetime.now().date()
+
+        fake_duration = datetime.timedelta(minutes=2, seconds=10, milliseconds=00)
+
+        # generate new id for audio noise removed
+        new_audio_noise_removed_id = generate_new_id(audio_noise_removed_list.lecture_audio_noise_removed_id)
+
+
+        # nr.noise_removalll(video_name)
+        noise_removal(audio_name)
+
+        LectureAudioNoiseRemoved(
+            lecture_audio_noise_removed_id=new_audio_noise_removed_id,
+            lecture_audio_id_id=id,
+            lecturer_date=current_date,
+            lecture_audio_name=audio_name,
+            lecture_audio_length=fake_duration
+        ).save()
+
+        return Response({
+            "response": Response.status_code
+        })
 
     def post(self, request):
         LectureAudioNoiseRemoved(
@@ -39,47 +76,68 @@ class audioNoiseRemovedList(APIView):
 class audioToTextList(APIView):
 
     def get(self, request):
-        lecture_speech_to_text_id = LectureSpeechToText.objects.all()
-        serializer = LectureSpeechToTextSerializer(lecture_speech_to_text_id, many=True)
+        #lecture_speech_to_text_id = LectureSpeechToText.objects.all()
+        #serializer = LectureSpeechToTextSerializer(lecture_speech_to_text_id, many=True)
+        audio_to_text_list = LectureSpeechToText.objects.order_by('lecture_speech_to_text_id').last()
         # return Response(serializer.data)
 
-        video_name = request.query_params.get("video_name")
+        speech_to_text_name = request.query_params.get("speech_to_text_name")
 
-        print('video name: ', video_name)
+        print('file name: ', speech_to_text_name)
+        id = int(request.query_params.get("id"))
+        # id = request.query_params.get("id")
 
-        stt.speech_to_text(video_name)
+
+        # generate new id for speech to text file
+        new_speech_to_text_id = generate_new_id(audio_to_text_list.lecture_speech_to_text_id)
+
+        speech_to_text(speech_to_text_name)
+
+        LectureSpeechToText(
+            lecture_speech_to_text_id=new_speech_to_text_id,
+            lecture_audio_id_id=id,
+            audio_original_text=speech_to_text_name
+        ).save()
 
         return Response({
-            "response": "successful"
+            "response": Response.status_code
         })
 
     def post(self, request):
-
-        # video_name = request.data["video_name"]
-        #
-        # print('video name: ', video_name)
-        #
-        # stt.speech_to_text(video_name)
-
         LectureSpeechToText(
             lecture_speech_to_text_id=request.data["lecture_speech_to_text_id"],
             lecture_audio_id=request.data["lecture_audio_id"],
-            audio_original_text=request.data["audio_original_text"]
+            audio_original_text=request.data["audio_original_text"],
         ).save()
         return Response({"response": request.data})
 
 
-class lectureSummaryList(APIView):
+class LectureSummaryList(APIView):
 
     def get(self, request):
         lecture_audio_summary_id = LectureAudioSummary.objects.all()
-        serializer = LectureAudioSummarySerializer(lecture_audio_summary_id, many=True)
-        return Response(serializer.data)
+        # serializer = LectureAudioSummarySerializer(lecture_audio_summary_id, many=True)
+        # return Response(serializer.data)
 
-    def post(self, request):
+        lecture_summary_list = LectureAudioSummary.objects.order_by('lecture_audio_summary_id').last()
+
+        lecture_summary_name = request.query_params.get("lecture_summary_name")
+        id = int(request.query_params.get("id"))
 
+        # generate new id for summary
+        lecture_summary_id = "LSUM0001" if lecture_summary_list is None else generate_new_id(lecture_summary_list.lecture_audio_summary_id)
 
+        text, summary = LectureSummary(lecture_summary_name)
 
+        LectureAudioSummary(
+            lecture_audio_summary_id=lecture_summary_id,
+            lecture_audio_id_id=id,
+            audio_original_text=text,
+            audio_summary=summary
+        ).save()
+        return Response({"response": request.data})
+
+    def post(self, request):
         LectureAudioSummary(
             lecture_speech_to_text_id=request.data["lecture_speech_to_text_id"],
             lecture_audio_id=request.data["lecture_audio_id"],
@@ -90,12 +148,31 @@ class lectureSummaryList(APIView):
 
 
 
-class lectureNoticeList(APIView):
+class LectureNoticeList(APIView):
 
     def get(self, request):
         lecture_notice_id = LectureNotices.objects.all()
-        serializer = LectureNoticesSerializer(lecture_notice_id, many=True)
-        return Response(serializer.data)
+        # serializer = LectureNoticesSerializer(lecture_notice_id, many=True)
+        # return Response(serializer.data)
+
+        lecture_notice_list = LectureNotices.objects.order_by('lecture_notice_id').last()
+
+        lecture_notice_name = request.query_params.get("lecture_notice_name")
+        id = int(request.query_params.get("id"))
+
+        # generate new id for notice
+        notice_id = "LN0001" if lecture_notice_list is None else generate_new_id(lecture_notice_list.lecture_notice_id)
+
+        text = LectureNotices(lecture_notice_name)
+
+        LectureNotices(
+            lecture_notice_id=notice_id,
+            lecture_audio_id=id,
+            notice_text=text
+        ).save()
+        return Response({"response": request.data})
+
+
 
     def post(self, request):
         LectureNotices(

LectureSummarizingApp/noise.py

 import librosa
 from pysndfx import AudioEffectsChain
 import python_speech_features
+import os
 
 
+def noise_removal(video_name):
 
-def read_file(file_name):
-    sample_file = file_name
-    sample_directory = 'lectures/'
-    sample_path = sample_directory + sample_file
+    # sample_file = file_name
+    # sample_directory = 'lectures/'
+    # sample_path = sample_directory + sample_file
 
+
+    BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    LECTURE_VIDEO_DIR = os.path.join(BASE_DIR, "LectureSummarizingApp\\lectures\\{}".format(video_name))
+    print('lecture audio directory: ', LECTURE_VIDEO_DIR)
+    # DESTINATION_DIR = os.path.join(BASE_DIR, "LectureSummarizingApp\\noise_removed_lectures")
+    DESTINATION_DIR = os.path.dirname(os.path.join(BASE_DIR, "LectureSummarizingApp\\noise_removed_lectures\\sample.txt"))
+    print('destination directory: ', DESTINATION_DIR)
     # generating audio time series and a sampling rate (int)
-    a, sr = librosa.load(sample_path)
+    a, sr = librosa.load(path=LECTURE_VIDEO_DIR)
+
+    print('a: ', a)
+    print('sr: ', sr)
+
+    # speech_boosted = mffc_highshelf(a, sr)
+
+    output_file(destination=DESTINATION_DIR, filename=video_name, a=a, sr=sr)
 
     return a, sr
 
@@ -24,6 +39,7 @@ def mffc_highshelf(a, sr):
     mfcc = python_speech_features.base.logfbank(a)
     mfcc = python_speech_features.base.lifter(mfcc)
 
+
     sum_of_squares = []
     index = -1
     for r in mfcc:
@@ -40,73 +56,25 @@ def mffc_highshelf(a, sr):
 
     speech_booster = AudioEffectsChain().highshelf(frequency=min_hz*(-1)*1.2, gain=-12.0, slope=0.6).limiter(gain=8.0)
     a_speach_boosted = speech_booster(a)
+    # a_speach_boosted = speech_booster.
 
     return (a_speach_boosted)
 
-def mfcc_lowshelf(a, sr):
 
-    mfcc = python_speech_features.base.mfcc(a)
-    mfcc = python_speech_features.base.logfbank(a)
-    mfcc = python_speech_features.base.lifter(mfcc)
+# def trim_silence(y):
+#     a_trimmed, index = librosa.effects.trim(y, top_db=20, frame_length=2, hop_length=500)
+#     trimmed_length = librosa.get_duration(y) - librosa.get_duration(a_trimmed)
+#
+#     return a_trimmed, trimmed_length
 
-    sum_of_squares = []
-    index = -1
-    for x in mfcc:
-        sum_of_squares.append(0)
-        index = index + 1
-        for n in x:
-            sum_of_squares[index] = sum_of_squares[index] + n**2
 
-    strongest_frame = sum_of_squares.index(max(sum_of_squares))
-    hz = python_speech_features.base.mel2hz(mfcc[strongest_frame])
-
-    max_hz = max(hz)
-    min_hz = min(hz)
-
-    speech_booster = AudioEffectsChain().lowshelf(frequency=min_hz*(-1), gain=12.0, slope=0.5)
-    a_speach_boosted = speech_booster(a)
-
-    return (a_speach_boosted)
-
-
-def trim_silence(y):
-    a_trimmed, index = librosa.effects.trim(y, top_db=20, frame_length=2, hop_length=500)
-    trimmed_length = librosa.get_duration(y) - librosa.get_duration(a_trimmed)
-
-    return a_trimmed, trimmed_length
-
-
-def enhance(y):
-    apply_audio_effects = AudioEffectsChain().lowshelf(gain=10.0, frequency=260, slope=0.1).reverb(reverberance=25, hf_damping=5, room_scale=5, stereo_depth=50, pre_delay=20, wet_gain=0, wet_only=False)#.normalize()
-    a_enhanced = apply_audio_effects(y)
-
-    return a_enhanced
+# def enhance(y):
+#     apply_audio_effects = AudioEffectsChain().lowshelf(gain=10.0, frequency=260, slope=0.1).reverb(reverberance=25, hf_damping=5, room_scale=5, stereo_depth=50, pre_delay=20, wet_gain=0, wet_only=False)#.normalize()
+#     a_enhanced = apply_audio_effects(y)
+#
+#     return a_enhanced
 
 def output_file(destination ,filename, a, sr, ext=""):
     destination = destination + filename[:-4] + ext + '.wav'
     librosa.output.write_wav(destination, a, sr)
 
-
-lectures = ['Lecture01.wav']
-
-for s in lectures:
-    filename = s
-    a, sr = read_file(filename)
-
-
-    # a_reduced_centroid_s = reduce_noise_centroid_s(a, sr)
-    a_reduced_mfcc_lowshelf = mfcc_lowshelf(a, sr)
-    a_reduced_mfcc_highshelf = mffc_highshelf(a, sr)
-
-
-    # trimming silences
-    # a_reduced_centroid_s, time_trimmed = trim_silence(a_reduced_centroid_s)
-    a_reduced_mfcc_up, time_trimmed = trim_silence(mfcc_lowshelf)
-    a_reduced_mfcc_down, time_trimmed = trim_silence(mffc_highshelf)
-
-
-
-    # output_file('lectures_trimmed_noise_reduced/' ,filename, y_reduced_centroid_s, sr, '_ctr_s')
-    output_file('lectures_trimmed_noise_reduced/' ,filename, a_reduced_mfcc_up, sr, '_mfcc_up')
-    # output_file('lectures_trimmed_noise_reduced/' ,filename, a_reduced_mfcc_down, sr, '_mfcc_down')
-    # output_file('lectures_trimmed_noise_reduced/' ,filename, a, sr, '_org')

LectureSummarizingApp/noiseRemove.py

@@ -4,54 +4,63 @@ from scipy.io.wavfile import read
 from scipy.io.wavfile import write
 from scipy import signal
 import matplotlib.pyplot as mplt
+import os
 #get_ipython().magic('matplotlib inline')
 
-(Frequency, array) = read('lectures/Lecture01.wav')
 
-len(array)
+def noise_removalll(video_name):
 
-mplt.plot(array)
-mplt.title('Original Signal Spectrum')
-mplt.xlabel('Frequency(Hz)')
-mplt.ylabel('Amplitude')
+    BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    LECTURE_VIDEO_DIR = os.path.join(BASE_DIR, "LectureSummarizingApp\\lectures\\{}".format(video_name))
 
-fourierTransformation = sip.fft(array)
+    # (Frequency, array) = read('lectures/Lecture01.wav')
+    # (Frequency, array) = read('lectures/{}'.format(video_name))
+    (Frequency, array) = read(LECTURE_VIDEO_DIR)
 
-scale = sip.linspace(0, Frequency, len(array))
+    len(array)
 
-mplt.stem(scale[0:5000], nump.abs(fourierTransformation[0:5000]), 'r')
-mplt.title('Signal spectrum after FFT')
-mplt.xlabel('Frequency(Hz)')
-mplt.ylabel('Amplitude')
+    mplt.plot(array)
+    mplt.title('Original Signal Spectrum')
+    mplt.xlabel('Frequency(Hz)')
+    mplt.ylabel('Amplitude')
 
+    fourierTransformation = sip.fft(array)
 
-guassianNoise = nump.random.rand(len(fourierTransformation))
+    scale = sip.linspace(0, Frequency, len(array))
 
+    mplt.stem(scale[0:5000], nump.abs(fourierTransformation[0:5000]), 'r')
+    mplt.title('Signal spectrum after FFT')
+    mplt.xlabel('Frequency(Hz)')
+    mplt.ylabel('Amplitude')
 
-NewSound = guassianNoise + array
 
-write("New-Sound-Added-With-Guassian-Noise.wav", Frequency, NewSound)
+    guassianNoise = nump.random.rand(len(fourierTransformation))
 
-u,v = signal.butter(5, 1000/(Frequency/2), btype='highpass')
 
-filteredSignal = signal.lfilter(u,v,NewSound)
+    NewSound = guassianNoise + array
 
-# plotting the signal.
-mplt.plot(filteredSignal)
-mplt.title('Highpass Filter')
-mplt.xlabel('Frequency(Hz)')
-mplt.ylabel('Amplitude')
+    write("New-Sound-Added-With-Guassian-Noise.wav", Frequency, NewSound)
 
-# ButterWorth low-filter
-x,y = signal.butter(5, 380/(Frequency/2), btype='lowpass')
+    u,v = signal.butter(5, 1000/(Frequency/2), btype='highpass')
 
-# Applying the filter to the signal
-newFilteredSignal = signal.lfilter(x,y,filteredSignal)
+    filteredSignal = signal.lfilter(u,v,NewSound)
 
-# plotting the signal.
-mplt.plot(newFilteredSignal)
-mplt.title('Lowpass Filter')
-mplt.xlabel('Frequency(Hz)')
-mplt.ylabel('Amplitude')
+    # plotting the signal.
+    mplt.plot(filteredSignal)
+    mplt.title('Highpass Filter')
+    mplt.xlabel('Frequency(Hz)')
+    mplt.ylabel('Amplitude')
 
-write("removed.wav", Frequency, nump.int16(newFilteredSignal/nump.max(nump.abs(newFilteredSignal)) * 32767))
+    # ButterWorth low-filter
\ No newline at end of file
+    x,y = signal.butter(5, 380/(Frequency/2), btype='lowpass')
+
+    # Applying the filter to the signal
+    newFilteredSignal = signal.lfilter(x,y,filteredSignal)
+
+    # plotting the signal.
+    mplt.plot(newFilteredSignal)
+    mplt.title('Lowpass Filter')
+    mplt.xlabel('Frequency(Hz)')
+    mplt.ylabel('Amplitude')
+
+    write("removed.wav", Frequency, nump.int16(newFilteredSignal/nump.max(nump.abs(newFilteredSignal)) * 32767))
\ No newline at end of file

LectureSummarizingApp/speech_to_text.py

@@ -2,17 +2,23 @@ import speech_recognition as sr
 import os
 
 
-def speech_to_text(video_name):
+def speech_to_text(speech_to_text_name):
 
 #calling the Recognizer()
     r = sr.Recognizer()
 
     BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-    VIDEO_PATH = os.path.join(BASE_DIR, "lectures\\{}".format(video_name))
+    # FILE_PATH = os.path.join(BASE_DIR, "noise_removed_lectures\\noise_removed_lectures_{}".format(speech_to_text_name))
+    FILE_PATH = os.path.join(BASE_DIR, "noise_removed_lectures\\{}".format(speech_to_text_name))
+    print('file path: ', FILE_PATH)
+    # DESTINATION_DIR = os.path.dirname(os.path.join(BASE_DIR, "LectureSummarizingApp\\speechToText\\{}.txt".format(speech_to_text_name)))
+    DESTINATION_DIR = os.path.join(BASE_DIR, "speechToText\\{}.txt".format(speech_to_text_name))
+    print('destination directory: ', DESTINATION_DIR)
 
-    with sr.AudioFile(VIDEO_PATH) as source:
+    with sr.AudioFile(FILE_PATH) as source:
         audio = r.listen(source)
-        file = open('audioToText01.txt', 'w') #open file
+        # file = open('audioToText01.txt', 'w') #open file
+        file = open(DESTINATION_DIR, 'w') #open file
         try:
             text = r.recognize_google(audio) #Convert using google recognizer
             file.write(text)

LectureSummarizingApp/templates/LectureSummarizingApp/RecordLecture.html

-{% extends 'FirstApp/template.html' %}
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <title>Lecture Recording</title>
-</head>
-<body>
-% block javascript %}
-{% load static %}
-
-<!-- Bootstrap core JavaScript-->
-<script src="{% static 'FirstApp/vendor/jquery/jquery.min.js' %}"></script>
-<script src="{% static 'FirstApp/vendor/bootstrap/js/bootstrap.bundle.min.js' %}"></script>
-
-<!-- Page level plugins -->
-<script src="{% static 'FirstApp/vendor/datatables/jquery.dataTables.min.js' %}"></script>
-<script src="{% static 'FirstApp/vendor/datatables/dataTables.bootstrap4.min.js' %}"></script>
-
-<!-- Page level custom scripts -->
-<script src="{% static 'FirstApp/js/demo/datatables-demo.js' %}"></script>
-
-<!-- Core plugin JavaScript-->
-<script src="{% static 'FirstApp/vendor/jquery-easing/jquery.easing.min.js' %}"></script>
-
-<!-- Load TensorFlow.js -->
-<script src="https://unpkg.com/@tensorflow/tfjs"></script>
-<!-- Load Posenet -->
-<script src="https://unpkg.com/@tensorflow-models/posenet">
-</script>
-
-
-
-{% endblock %}
-
-<div id="wrapper">
-    <div id="content-wrapper" class="d-flex flex-column">
-        <div id="content">
-            {% block 'container-fluid' %}
-            <div class="container-fluid">
-                {% load static %}
-                <div class="d-sm-flex align-items-center justify-content-between mb-4">
-                    <h1 class="h3 mb-0 text-gray-800">Lecture Record</h1>
-                </div>
-                <div>
-                     <button TYPE="button" class="btn btn-success audio_process">Start Recording</button>
-                </div>
-            </div>
-        </div>
-    </div>
-</div>
-
-</body>
-</html>
+<!--{% extends 'FirstApp/template.html' %}-->
+<!--<!DOCTYPE html>-->
+<!--<html lang="en">-->
+<!--<head>-->
+<!--    <meta charset="UTF-8">-->
+<!--    <title>Lecture Recording</title>-->
+<!--</head>-->
+<!--<body>-->
+<!--% block javascript %}-->
+<!--{% load static %}-->
+
+<!--&lt;!&ndash; Bootstrap core JavaScript&ndash;&gt;-->
+<!--<script src="{% static 'FirstApp/vendor/jquery/jquery.min.js' %}"></script>-->
+<!--<script src="{% static 'FirstApp/vendor/bootstrap/js/bootstrap.bundle.min.js' %}"></script>-->
+
+<!--&lt;!&ndash; Page level plugins &ndash;&gt;-->
+<!--<script src="{% static 'FirstApp/vendor/datatables/jquery.dataTables.min.js' %}"></script>-->
+<!--<script src="{% static 'FirstApp/vendor/datatables/dataTables.bootstrap4.min.js' %}"></script>-->
+
+<!--&lt;!&ndash; Page level custom scripts &ndash;&gt;-->
+<!--<script src="{% static 'FirstApp/js/demo/datatables-demo.js' %}"></script>-->
+
+<!--&lt;!&ndash; Core plugin JavaScript&ndash;&gt;-->
+<!--<script src="{% static 'FirstApp/vendor/jquery-easing/jquery.easing.min.js' %}"></script>-->
+
+<!--&lt;!&ndash; Load TensorFlow.js &ndash;&gt;-->
+<!--<script src="https://unpkg.com/@tensorflow/tfjs"></script>-->
+<!--&lt;!&ndash; Load Posenet &ndash;&gt;-->
+<!--<script src="https://unpkg.com/@tensorflow-models/posenet">-->
+<!--</script>-->
+
+
+
+<!--{% endblock %}-->
+
+<!--<div id="wrapper">-->
+<!--    <div id="content-wrapper" class="d-flex flex-column">-->
+<!--        <div id="content">-->
+<!--            {% block 'container-fluid' %}-->
+<!--            <div class="container-fluid">-->
+<!--                {% load static %}-->
+<!--                <div class="d-sm-flex align-items-center justify-content-between mb-4">-->
+<!--                    <h1 class="h3 mb-0 text-gray-800">Lecture Record</h1>-->
+<!--                </div>-->
+<!--                <div>-->
+<!--                     <button TYPE="button" class="btn btn-success audio_process">Start Recording</button>-->
+<!--                </div>-->
+<!--            </div>-->
+<!--        </div>-->
+<!--    </div>-->
+<!--</div>-->
+
+<!--</body>-->
+<!--</html>-->
\ No newline at end of file

LectureSummarizingApp/templates/LectureSummarizingApp/summarization.html

@@ -34,43 +34,102 @@
 
     $(document).ready(function() {
 
-
 <!--        speech to text-->
-        $('.audio_to_text_process').click(function() {
-            alert('Processing');
+        $('.audio_to_text_process').click(function(e) {
+            alert('Converting');
+            let id = e.target.parentNode.parentNode.getAttribute('id');
+            let speech_to_text_name = e.target.parentNode.parentNode.getAttribute('data-noiseless-audio-name');
+<!--            speech_to_text_name = speech_to_text_name + ".txt";-->
+
+            alert('id: ' + id);
+            alert('speech to text file name: ' + speech_to_text_name);
 
             //call the fetch API
-            fetch('http://127.0.0.1:8000/summary/lecture-audio-to-text/?video_name=Lecture01.wav')
+            fetch('http://127.0.0.1:8000/summary/lecture-audio-to-text/?speech_to_text_name=' + speech_to_text_name + '&id=' + id)
             .then((res) => res.json())
-            .then((out) => alert(out.response))
+            .then((out) => handleSpeechToText(out.response))
             .catch((err) => alert('error: ' + err))
 
         });
+          //this function will handle the success response for speech-to-text
+        function handleSpeechToText(response) {
+            if (response === 200) {
+                document.location.reload();
+            }
+        }
 
 <!--        background noise-->
-        $('.audio_process').click(function() {
+        $('.audio_process').click(function(e) {
             alert('Processing');
-
+            let id = e.target.parentNode.parentNode.getAttribute('id');
+            let audio_name = e.target.parentNode.parentNode.getAttribute('data-audio-name');
+            audio_name = audio_name + ".wav";
+            alert('id: ' + id);
+            alert('audio name: ' + audio_name);
             //call the fetch API
-            fetch('http://127.0.0.1:8000/summary/lecture-audio-to-text/?video_name=Lecture01.wav')
+
+            fetch('http://127.0.0.1:8000/summary/lecture-audio-noise-removed/?audio_name=' + audio_name + '&id=' + id)
             .then((res) => res.json())
-            .then((out) => alert(out.response))
+            .then((out) => handleNoiseRemoved(out.response))
             .catch((err) => alert('error: ' + err))
 
         });
+        //this function will handle the success respopnse for noise removed
+        function handleNoiseRemoved(response) {
 
+            if (response === 200) {
+                document.location.reload();
+            }
+        }
 
         <!--        To summary-->
-        $('.to_summary').click(function() {
+        $('.to_summary').click(function(e) {
             alert('Processing');
+            let id = e.target.parentNode.parentNode.getAttribute('id');
+<!--            let lecture_summary_name = e.target.parentNode.parentNode.getAttribute('data-summary-name');-->
+            let lecture_summary_name = e.target.parentNode.parentNode.getAttribute('data-notice-name');
+<!--            lecture_summary_name = lecture_summary_name + ".txt";-->
+            lecture_summary_name = lecture_summary_name + ".wav.txt";
+            alert('id: ' + id);
+            alert('lecture_summary_name: ' + lecture_summary_name);
 
-            //call the fetch API
-            fetch('http://127.0.0.1:8000/summary/lecture-audio-to-text/?video_name=Lecture01.wav')
+        //call the fetch API
+
+            fetch('http://127.0.0.1:8000/summary/lecture-summary/?lecture_summary_name=' + lecture_summary_name + '&id=' + id)
             .then((res) => res.json())
-            .then((out) => alert(out.response))
+            .then((out) => handleLectureRemoved(out.response))
             .catch((err) => alert('error: ' + err))
+        });
+         //this function will handle the success response for summary
+        function handleLectureRemoved(response) {
+
+            if (response === 200) {
+                document.location.reload();
+            }
+        }
+
+        <!--        To Notice-->
+        $('.get_notices').click(function(e) {
+            alert('Processing');
+            let id = e.target.parentNode.parentNode.getAttribute('id');
+            let lecture_notice_name = e.target.parentNode.parentNode.getAttribute('data-summary-name');
+            lecture_notice_name = lecture_notice_name + ".wav.txt";
+            alert('id: ' + id);
+            alert('lecture_notice_name: ' + lecture_notice_name);
 
+        //call the fetch API
+
+            fetch('http://127.0.0.1:8000/summary/lecture-notices/?lecture_notice_name=' + lecture_notice_name + '&id=' + id)
+            .then((res) => res.json())
+            .then((out) => handleNoticeRemoved(out.response))
+            .catch((err) => alert('error: ' + err))
         });
+         //this function will handle the success response for notices
+        function handleNoticeRemoved(response) {
+            if (response === 200) {
+                document.location.reload();
+            }
+        }
 
     });
 
@@ -108,6 +167,7 @@
                                     <span class="font-italic">No Recordings</span>
                                 </div>
                                 {% else %}
+                                #lecture list
                                 <div class="table-responsive">
                                     <table class="table table-bordered" id="datatable">
                                         <thead>
@@ -121,7 +181,7 @@
                                         <tbody>
 
                                         {% for lec_audio in lec_audio_data %}
-                                        <tr class="recordings not_clicked" id="{{ lec_audio.lecture_audio_id }}">
+                                        <tr class="recordings not_clicked" id="{{ lec_audio.id }}" data-audio-name="{{ lec_audio.lecture_audio_name }}">
 <!--                                            <td>-->
 <!--                                                <div class="radio">-->
 <!--                                                    <label><input type="radio"-->
@@ -168,6 +228,7 @@
                                     <span class="font-italic">No Recordings</span>
                                 </div>
                                 {% else %}
+                                #noise removes list
                                 <div class="table-responsive">
                                     <table class="table table-bordered" id="datatable">
                                         <thead>
@@ -182,7 +243,7 @@
                                         <tbody>
 
                                         {% for noiseless_audio in noiseless_data %}
-                                        <tr class="recordings not_clicked" id="{{ noiseless_audio.lecture_audio_id }}">
+                                        <tr class="recordings not_clicked" id="{{ noiseless_audio.lecture_audio_id.id }}" data-noiseless-audio-name="{{ noiseless_audio.lecture_audio_name }}">
 <!--                                            <td>-->
 <!--                                                <div class="radio">-->
 <!--                                                    <label><input type="radio"-->
@@ -250,7 +311,7 @@
                                         <tbody>
 
                                         {% for lec_text in lecture_text_data %}
-                                        <tr class="recordings not_clicked" id="{{ lec_text.lecture_audio_id }}">
+                                        <tr class="recordings not_clicked" id="{{ lec_text.lecture_audio_id.id }}" data-summary-name="{{lec_text.lecture_audio_id.lecture_audio_name}}" data-notice-name="{{lec_text.lecture_audio_id}}" >
 <!--                                            <td>-->
 <!--                                                <div class="radio">-->
 <!--                                                    <label><input type="radio"-->
@@ -268,7 +329,7 @@
                                                 </button>
                                             </td>
                                             <td>
-                                                <button TYPE="button" class="btn btn-danger get_notices">Notices
+                                                <button TYPE="button" class="btn btn-success get_notices">Notices
                                                 </button>
                                             </td>
                                         </tr>

LectureSummarizingApp/urls.py

@@ -8,7 +8,7 @@ router = routers.DefaultRouter()
 
 urlpatterns = [
     path('lecture', views.summarization),
-    path('record', views.lectureRecord),
+    # path('record', views.lectureRecord),
 
 
     # API to retrieve lecture summarizing details
@@ -18,9 +18,9 @@ urlpatterns = [
 
     url(r'^lecture-audio-to-text/', api.audioToTextList.as_view()),
 
-    url(r'^lecture-summary/$', api.lectureSummaryList.as_view()),
+    url(r'^lecture-summary/$', api.LectureSummaryList.as_view()),
 
-    url(r'^lecture-notices/$', api.lectureNoticeList.as_view()),
+    url(r'^lecture-notices/$', api.LectureNoticeList.as_view()),
 
     path('api-auth/', include('rest_framework.urls', namespace='rest_framework'))
 

LectureSummarizingApp/views.py

@@ -13,7 +13,7 @@ def lectureRecord(request):
 
     print('lecture record data: ', lecture_audio_ser.data)
 
-    return render(request, "LectureSummarizationApp/RecordLecture.html")
+    return render(request, "LectureSummarizingApp/RecordLecture.html")
 
 # Views used in Lecture Summarization