Combining with old files

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:
+    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
\ No newline at end of file

LectureSummarizingApp/Summary.py

+
+import spacy
+
+from spacy.lang.pt.stop_words import STOP_WORDS
+from sklearn.feature_extraction.text import CountVectorizer
+import pt_core_news_sm
+
+nlp = pt_core_news_sm.load()
+with open("audioToText01.txt", "r", encoding="utf-8") as f:
+    text = " ".join(f.readlines())
+
+doc = nlp(text)
+
+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)
+
+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:
+    print(i,end=" ")
\ No newline at end of file

LectureSummarizingApp/api.py

 from rest_framework.views import APIView
 from rest_framework.response import Response
 
-from LectureSummarizingApp.models import LectureAudio
-from LectureSummarizingApp.serializer import LectureAudioSerializer
+from LectureSummarizingApp.models import LectureAudio, LectureAudioNoiseRemoved, LectureSpeechToText, \
+    LectureAudioSummary, LectureNotices
+from LectureSummarizingApp.serializer import LectureAudioSerializer, LectureAudioNoiseRemovedSerializer, \
+    LectureSpeechToTextSerializer, LectureAudioSummarySerializer
+
 
 # this API will retrieve lecture audio details
 class LectureAudioAPI(APIView):
 
     def get(self, request):
-        lecture_audio = LectureAudio.objects.all()
+        lecture_audio = LectureAudio.objects.all().order_by('lecturer_date')
         lecture_audio_serializer = LectureAudioSerializer(lecture_audio, many=True)
+        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)
+
+    def post(self, request):
+        LectureAudioNoiseRemoved(
+            lecture_audio_noise_removed_id=request.data["lecture_audio_noise_removed_id"],
+            lecture_audio_id=request.data["lecture_audio_id"],
+            lecturer_date=request.data["lecturer_date"],
+            lecture_audio_name=request.data["lecture_audio_name"],
+            lecture_audio_length=request.data["lecture_audio_length"]
+        ).save()
+        return Response({"response": request.data})
+
+
+class audioToTextList(APIView):
+
+    def get(self, request):
+        lecture_speech_to_text_id = LectureSpeechToText.objects.all()
+        serializer = LectureSpeechToTextSerializer(lecture_speech_to_text_id, many=True)
+        return Response(serializer.data)
+
+    def post(self, request):
+        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"]
+        ).save()
+        return Response({"response": request.data})
+
+
+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)
+
+    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"],
+            audio_original_text=request.data["audio_original_text"],
+            audio_summary=request.data["audio_summary"]
+        ).save()
+        return Response({"response": request.data})
+
+    class lectureNoticeList(APIView):
 
-        data = lecture_audio_serializer.data
+        def get(self, request):
+            lecture_notice_id = LectureNotices.objects.all()
+            serializer = LectureSpeechToTextSerializer(lecture_notice_id, many=True)
+            return Response(serializer.data)
 
-        return Response({
-            "response": data
-        })
\ No newline at end of file
+        def post(self, request):
+            LectureSpeechToText(
+                lecture_notice_id=request.data["lecture_notice_id"],
+                lecture_audio_id=request.data["lecture_audio_id"],
+                notice_text=request.data["notice_text"]
+            ).save()
+            return Response({"response": request.data})
\ No newline at end of file

LectureSummarizingApp/audioToText01.txt

+perhaps this is what is PR agency is are their dignity schedule III was much is 50 feet then the choreographer missed arbitrated never go back into acquiescence with things as they work finds it in misery and isolation around us in this instance such personal purchase for a luxury leather cases of severe and advisement say he is a horse days Ranjan or he may have a point that contains between fuel prices straight line which symbolises uniqueness the circuit universality of small hole in wall with client has more subtle implications in passport expiry marketing program manufacturers taking initiative of the costs involved cricket overlapping twisted widely spaced to you always navigate like this
\ No newline at end of file

LectureSummarizingApp/models.py

@@ -23,8 +23,6 @@ class LectureAudioNoiseRemoved (models.Model):
     lecturer_date = models.DateField()
     lecture_audio_name = models.CharField(max_length=50)
     lecture_audio_length = models.DurationField()
-    lecturer = models.ForeignKey(Lecturer, on_delete=models.CASCADE, default=0)
-    subject = models.ForeignKey(Subject, on_delete=models.CASCADE, default=0)
 
     def __str__(self):
         return self.lecture_audio_noise_removed_id
@@ -48,3 +46,11 @@ class LectureAudioSummary (models.Model):
     def __str__(self):
         return self.lecture_audio_summary_id
 
+class LectureNotices (models.Model):
+    lecture_notice_id = models.CharField(max_length=10)
+    lecture_audio_id = models.ForeignKey(LectureAudio, on_delete=models.CASCADE)
+    notice_text = models.TextField()
+
+    def __str__(self):
+        return self.lecture_notice_id
+

LectureSummarizingApp/noise.py

+import librosa
+from pysndfx import AudioEffectsChain
+import numpy as np
+import math
+import python_speech_features
+import scipy as sp
+from scipy import signal
+import soundfile
+
+
+def read_file(file_name):
+    sample_file = file_name
+    sample_directory = 'lectures/'
+    sample_path = sample_directory + sample_file
+
+    # generating audio time series and a sampling rate (int)
+    y, sr = librosa.load(sample_path)
+
+    return y, sr
+
+
+'''CENTROID'''
+
+def reduce_noise_centroid_s(y, sr):
+
+    cent = librosa.feature.spectral_centroid(y=y, sr=sr)
+    threshold_h = np.max(cent)
+    threshold_l = np.min(cent)
+    less_noise = AudioEffectsChain().lowshelf(gain=-12.0, frequency=threshold_l, slope=0.5).highshelf(gain=-12.0, frequency=threshold_h, slope=0.5).limiter(gain=6.0)
+    y_cleaned = less_noise(y)
+    return y_cleaned
+
+
+
+'''MFCC'''
+
+def mffc_highshelf(y, sr):
+
+
+    mfcc = python_speech_features.base.mfcc(y)
+    mfcc = python_speech_features.base.logfbank(y)
+    mfcc = python_speech_features.base.lifter(mfcc)
+
+    sum_of_squares = []
+    index = -1
+    for r in mfcc:
+        sum_of_squares.append(0)
+        index = index + 1
+        for n in r:
+            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().highshelf(frequency=min_hz*(-1)*1.2, gain=-12.0, slope=0.6).limiter(gain=8.0)
+    y_speach_boosted = speech_booster(y)
+
+    return (y_speach_boosted)
+
+def mfcc_lowshelf(y, sr):
+
+    mfcc = python_speech_features.base.mfcc(y)
+    mfcc = python_speech_features.base.logfbank(y)
+    mfcc = python_speech_features.base.lifter(mfcc)
+
+    sum_of_squares = []
+    index = -1
+    for r in mfcc:
+        sum_of_squares.append(0)
+        index = index + 1
+        for n in r:
+            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)
+    y_speach_boosted = speech_booster(y)
+
+    return (y_speach_boosted)
+
+
+def trim_silence(y):
+    y_trimmed, index = librosa.effects.trim(y, top_db=20, frame_length=2, hop_length=500)
+    trimmed_length = librosa.get_duration(y) - librosa.get_duration(y_trimmed)
+
+    return y_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()
+    y_enhanced = apply_audio_effects(y)
+
+    return y_enhanced
+
+def output_file(destination ,filename, y, sr, ext=""):
+    destination = destination + filename[:-4] + ext + '.wav'
+    librosa.output.write_wav(destination, y, sr)
+
+
+lectures = ['Lecture01.wav']
+
+for s in lectures:
+    filename = s
+    y, sr = read_file(filename)
+
+
+    y_reduced_centroid_s = reduce_noise_centroid_s(y, sr)
+    y_reduced_mfcc_lowshelf = mfcc_lowshelf(y, sr)
+    y_reduced_mfcc_highshelf = mffc_highshelf(y, sr)
+
+
+    # trimming silences
+    y_reduced_centroid_s, time_trimmed = trim_silence(y_reduced_centroid_s)
+    y_reduced_mfcc_up, time_trimmed = trim_silence(mfcc_lowshelf)
+    y_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, y_reduced_mfcc_up, sr, '_mfcc_up')
+    output_file('lectures_trimmed_noise_reduced/' ,filename, y_reduced_mfcc_down, sr, '_mfcc_down')
+    output_file('lectures_trimmed_noise_reduced/' ,filename, y, sr, '_org')

LectureSummarizingApp/serializer.py

@@ -33,6 +33,14 @@ class LectureSpeechToTextSerializer(serializers.ModelSerializer):
 class LectureAudioSummarySerializer(serializers.ModelSerializer):
     lecture_audio_noise_removed_id = LectureSpeechToTextSerializer()
 
+    class Meta:
+        model = LectureAudioSummary
+        fields = '__all__'
+
+
+class LectureNoticesSerializer(serializers.ModelSerializer):
+    lecture_audio_noise_removed_id = LectureSpeechToTextSerializer()
+
     class Meta:
         model = LectureAudioSummary
         fields = '__all__'
\ No newline at end of file

LectureSummarizingApp/speech-to-text.py

+import speech_recognition as sr
+
+r = sr.Recognizer()
+
+with sr.AudioFile('female.wav') as source:
+    audio = r.listen(source)
+    file = open('audioToText01.txt', 'w')
+    try:
+        text = r.recognize_google(audio)
+        file.write(text)
+    except:
+        file.write('error')
+
+    file.close()
\ No newline at end of file

LectureSummarizingApp/urls.py

@@ -26,7 +26,15 @@ urlpatterns = [
     # # path('Video', views.hello)
 
     # API to retrieve activity recognition
-    url(r'^get-lecture-audio/$', api.LectureAudioAPI.as_view()),
+    url(r'^lecture-audio/$', api.LectureAudioAPI.as_view()),
+
+    url(r'^lecture-audio-noise-removed/$', api.audioNoiseRemovedList.as_view()),
+
+    url(r'^lecture-audio-to-text/$', api.audioToTextList.as_view()),
+
+    url(r'^lecture-summary/$', api.lectureSummaryList.as_view()),
+
+    url(r'^lecture-notices/$', api.lectureNoticeList.as_view()),
 
     # # API to retrieve audio analysis
     # url(r'^get-audio-analysis', api.GetLectureAudioAnalysis.as_view()),

LectureSummarizingApp/views.py

-from django.shortcuts import render
+from django.contrib.auth.decorators import login_required
+from django.http import HttpResponse
+from django.shortcuts import get_object_or_404, render
+from rest_framework.views import APIView
+from rest_framework.response import Response
+from rest_framework import viewsets
+from .models import LectureAudio, LectureAudioNoiseRemoved, LectureSpeechToText, LectureAudioSummary, LectureNotices
+from .serializer import LectureAudioSerializer, LectureAudioNoiseRemovedSerializer, LectureAudioSummarySerializer, \
+    LectureSpeechToTextSerializer
+
 
 # Create your views here.
 
 def summarization(request):
+    return render(request, "LectureSummarizingApp/summarization.html")
+
+
+class audioList(APIView):
+
+    def get(self, request):
+        lecture_audio = LectureAudio.objects.all()
+        serializer = LectureAudioSerializer(lecture_audio, many=True)
+        return Response(serializer.data)
+
+    def post(self):
+        pass
+
+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)
+
+    def post(self, request):
+        LectureAudioNoiseRemoved(
+            lecture_audio_noise_removed_id=request.data["lecture_audio_noise_removed_id"],
+            lecture_audio_id=request.data["lecture_audio_id"],
+            lecturer_date=request.data["lecturer_date"],
+            lecture_audio_name=request.data["lecture_audio_name"],
+            lecture_audio_length=request.data["lecture_audio_length"]
+        ).save()
+        return Response({"response": request.data})
+
+class audioToTextList(APIView):
+
+    def get(self, request):
+        lecture_speech_to_text_id = LectureSpeechToText.objects.all()
+        serializer = LectureSpeechToTextSerializer(lecture_speech_to_text_id, many=True)
+        return Response(serializer.data)
+
+    def post(self, request):
+        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"]
+        ).save()
+        return Response({"response": request.data})
+
+
+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)
+
+    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"],
+            audio_original_text=request.data["audio_original_text"],
+            audio_summary=request.data["audio_summary"]
+        ).save()
+        return Response({"response": request.data})
+
+
+    class lectureNoticeList(APIView):
+
+        def get(self, request):
+            lecture_notice_id = LectureNotices.objects.all()
+            serializer = LectureSpeechToTextSerializer(lecture_notice_id, many=True)
+            return Response(serializer.data)
 
-    return render(request, "LectureSummarizingApp/summarization.html")
\ No newline at end of file
+        def post(self, request):
+            LectureSpeechToText(
+                lecture_notice_id=request.data["lecture_notice_id"],
+                lecture_audio_id=request.data["lecture_audio_id"],
+                notice_text=request.data["notice_text"]
+            ).save()
+            return Response({"response": request.data})
\ No newline at end of file