fix issues

.gitignore

 venv/
+backend/output.wav
+backend/backend/output.m4a
+ffmpeg-2022-08-03-git-d3f48e68b3-essentials_build/ffmpeg.exe
+ngrok.exe
+ffmpeg-2022-08-03-git-d3f48e68b3-essentials_build/ffplay.exe
+ffmpeg-2022-08-03-git-d3f48e68b3-essentials_build/ffprobe.exe

MobileApp/components/AudioRecorder.jsx

@@ -49,7 +49,7 @@ export const AudioRecorder = ({ setDetectedText }) => {
         }
 
         playSound(uri)
-        FileSystem.uploadAsync("https://1ec4-112-134-210-155.ap.ngrok.io/mlmodels/detect/", uri, { headers: headers, uploadType: FileSystem.FileSystemUploadType.MULTIPART })
+        FileSystem.uploadAsync("https://ebf4-2401-dd00-10-20-4ca2-920b-4150-8178.in.ngrok.io/mlmodels/detect/", uri, { headers: headers, uploadType: FileSystem.FileSystemUploadType.MULTIPART })
             .then(data => JSON.parse(data.body))
             .then(data => { console.log({ result: data }); setDetectedText(data.result); setTimeout(() => setDetectedText(''), 1000) })
             .catch(err => console.log({ err }))

backend/backend/cms/model/predict.py

@@ -20,17 +20,17 @@ def predict(samples):
     all_label = pickle.load(f1)
     print('loaded labels')
 
-    f2 = open('all_waves_file.txt', 'rb')
-    all_wave = pickle.load(f2)
-    print('loaded waves')
+    # f2 = open('all_waves_file.txt', 'rb')
+    # all_wave = pickle.load(f2)
+    # print('loaded waves')
 
     le = LabelEncoder()
     y = le.fit_transform(all_label)
     classes = list(le.classes_)
 
-    train_data_file = open("train_data_file.txt", 'rb')
-    [x_tr, x_val, y_tr, y_val] = np.load(train_data_file, allow_pickle=True)
-    train_data_file.close()
+    # train_data_file = open("train_data_file.txt", 'rb')
+    # [x_tr, x_val, y_tr, y_val] = np.load(train_data_file, allow_pickle=True)
+    # train_data_file.close()
 
     def predictSamples(audio):
         prob=model.predict(audio.reshape(1,8000,1))

backend/backend/cms/model/train.py

@@ -128,12 +128,15 @@ def train():
         pickle.dump(file=all_labels_file, obj=all_label)
         all_labels_file.close()
 
+        return False
+
         all_waves_file = open('all_waves_file.txt', 'wb+')
         pickle.dump(file=all_waves_file, obj=all_wave)
         all_waves_file.close()
 
         print('Done: creating labels and waves files')
 
+    return False
     le = LabelEncoder()
     y = le.fit_transform(all_label)
     classes = list(le.classes_)

backend/backend/cms/views.py

@@ -26,7 +26,6 @@ from .model.predict import predict
 from pydub import AudioSegment
 import numpy as np
 
-
 class UserViewSet(viewsets.ModelViewSet):
     """
     API endpoint that allows users to be viewed or edited.
@@ -80,7 +79,8 @@ class MlModelViewSet(viewsets.ViewSet):
         with open("output.m4a", "wb") as f:
             f.write(bytesio_object.getbuffer())
         
-
+        path = os.path.abspath("output.m4a")
+        print(path)
             # convert wav to mp3                                                            
         audSeg = AudioSegment.from_file("output.m4a")
         audSeg.export('output.wav', format="wav")
@@ -97,7 +97,7 @@ class MlModelViewSet(viewsets.ViewSet):
             print(samples.shape)
         else:
             len = 8000 - samples.shape[0]
-            new_arr = np.zeroes(len, )
+            new_arr = np.zeros(len, )
             samples = np.concatenate((samples, new_arr))
             print(samples.shape)
 

requirements.txt

 absl-py==1.2.0