Integration

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, \
@@ -9,8 +10,13 @@ from LectureSummarizingApp.serializer import LectureAudioSerializer, LectureAudi
 from . import speech_to_text as stt
 from . import noiseRemove as nr
 
+import datetime
+
 
 # APIs used in Lecture Summarizing Component
+from .noise import noise_removal
+
+
 class LectureAudioAPI(APIView):
 
     def get(self, request):
@@ -22,9 +28,36 @@ class LectureAudioAPI(APIView):
 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(
@@ -44,11 +77,12 @@ class audioToTextList(APIView):
         serializer = LectureSpeechToTextSerializer(lecture_speech_to_text_id, many=True)
         # return Response(serializer.data)
 
-        video_name = request.query_params.get("video_name")
-
-        print('video name: ', video_name)
-
-        nr.noise_removal(video_name)
+        # video_name = request.query_params.get("video_name")
+        #
+        # print('video name: ', video_name)
+        #
+        # # nr.noise_removalll(video_name)
+        # noise_removal(video_name)
 
         # stt.speech_to_text(video_name)
 

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 +41,7 @@ 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 +58,49 @@ 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)
-
-    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 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')
+# lectures = ['Lecture01.wav', 'Lecture02.wav']
+
+# for s in lectures:
+#     filename = s
+#     a, sr = noise_removal(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

@@ -8,7 +8,7 @@ import os
 #get_ipython().magic('matplotlib inline')
 
 
-def noise_removal(video_name):
+def noise_removalll(video_name):
 
     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))

LectureSummarizingApp/templates/LectureSummarizingApp/summarization.html

@@ -48,18 +48,37 @@
         });
 
 <!--        background noise-->
-        $('.audio_process').click(function() {
-            alert('Processing');
+        $('.audio_process').click(function(e) {
+
+            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-to-text/?audio_name=')-->
+<!--            .then((res) => res.json())-->
+<!--            .then((out) => alert(out.response))-->
+<!--            .catch((err) => alert('error: ' + err))-->
+
+            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() {
             alert('Processing');
@@ -121,7 +140,7 @@
                                         <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"-->