added try except in test

server.py

@@ -39,44 +39,41 @@ def delFiles():
 
 
 async def predictor(names, file_uploads, usersNum, recordingsNum):
-    try:
-        speaker_embed_list = []
-        encoder = VoiceEncoder()
-        # Iterating over list of files corresponding to each user
-        speaker_wavs_list = []
-        fileInd = 0
-        names.pop()  # to remove key named "test"
-        print("file_uploads ", file_uploads, "recordingNums ", recordingsNum)
-        for name in names:
-            wav_fpaths = []
-            for ind in range(int(recordingsNum)):
-                print("inside yo")
-                file_upload = file_uploads[fileInd]
-                data = await file_upload.read()
-                # appending person's name to the his/her recordings
-                filename = name+"¬"+file_upload.filename
-                file_path = UPLOAD_DIR / filename
-                with open(file_path, "wb") as file_object:
-                    file_object.write(data)
-                wav_fpaths.append(Path(file_path))
-                fileInd += 1
-                print("wav_fpaths len", len(wav_fpaths), "name", name)
-            try:
-                speaker_wavs = {speaker: list(map(preprocess_wav, wav_fpaths)) for speaker, wav_fpaths in
-                                groupby(tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit="wavs"),
-                                        lambda wav_fpath: os.path.basename(wav_fpath).split("¬")[0])}  # extracting person's name from file name
-                speaker_wavs_list.append(speaker_wavs)
-            except Exception as e:
-                print("error ", e)
-    except Exception as e:
-        print("function error ", e)
+    speaker_embed_list = []
+    encoder = VoiceEncoder()
+    # Iterating over list of files corresponding to each user
+    speaker_wavs_list = []
+    fileInd = 0
+    names.pop()  # to remove key named "test"
+    print("file_uploads ", file_uploads, "recordingNums ", recordingsNum)
+    for name in names:
+        wav_fpaths = []
+        for ind in range(int(recordingsNum)):
+            print("inside yo")
+            file_upload = file_uploads[fileInd]
+            data = await file_upload.read()
+            # appending person's name to the his/her recordings
+            filename = name+"¬"+file_upload.filename
+            file_path = UPLOAD_DIR / filename
+            with open(file_path, "wb") as file_object:
+                file_object.write(data)
+            wav_fpaths.append(Path(file_path))
+            fileInd += 1
+            print("wav_fpaths len", len(wav_fpaths), "name", name)
+        try:
+            speaker_wavs = {speaker: list(map(preprocess_wav, wav_fpaths)) for speaker, wav_fpaths in
+                            groupby(tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit="wavs"),
+                                    lambda wav_fpath: os.path.basename(wav_fpath).split("¬")[0])}  # extracting person's name from file name
+            speaker_wavs_list.append(speaker_wavs)
+        except Exception as e:
+            print("error ", e)
 
     # make a list of the pre-processed audios ki arrays
     for sp_wvs in speaker_wavs_list:
         speaker_embed_list.append(
             np.array([encoder.embed_speaker(wavs) for wavs in sp_wvs.values()]))
 
-
+    print("preprocessed audio ki array ", speaker_embed_list)
     # making preprocessed test audio
     wav_fpaths = []
     file_upload = file_uploads[-1]
@@ -87,11 +84,14 @@ async def predictor(names, file_uploads, usersNum, recordingsNum):
         file_object.write(data)
     wav_fpaths.append(Path(file_path))
     print("about to test\n")
-    test_pos_wavs = {speaker: list(map(preprocess_wav, wav_fpaths)) for speaker, wav_fpaths in
-                     groupby(tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit="wavs"),
-                             lambda wav_fpath: "test")}
-    test_pos_emb = np.array([encoder.embed_speaker(wavs)
-                            for wavs in test_pos_wavs.values()])
+    try:
+        test_pos_wavs = {speaker: list(map(preprocess_wav, wav_fpaths)) for speaker, wav_fpaths in
+                        groupby(tqdm(wav_fpaths, "Preprocessing wavs", len(wav_fpaths), unit="wavs"),
+                                lambda wav_fpath: "test")}
+        test_pos_emb = np.array([encoder.embed_speaker(wavs)
+                                for wavs in test_pos_wavs.values()])
+    except Exception as error:
+        print("test error ", error)
 
     # calculates cosine similarity between the ground truth (test file) and registered audios
     speakers = {}