Update app.py

app.py

@@ -30,32 +30,39 @@ def resampler(input_file_path, output_file_path):
 def parse_transcription(logits):
     predicted_ids = torch.argmax(logits, dim=-1)
     transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+    del(logits)
     return transcription
 
 
-# def parse(wav_file):
-#     input_values = read_file_and_process(wav_file)
-#     with torch.no_grad():
-#         logits = model(**input_values).logits
-#     sentence = parse_transcription(logits)
-#     check_spell = spell_checker.check(sentence)
-#     if check_spell[0] is False:
-#       corrected = check_spell[1]
-#     else:
-#       corrected = sentence
-#     return corrected
-
+def corrector(sentence):
+  check_spell = spell_checker.check(sentence)
+  if check_spell[0] is False:
+    corrected = check_spell[1]
+    return corrected
+  else:
+    return sentence
+      
 def parse(wav_file):
     input_values = read_file_and_process(wav_file)
     with torch.no_grad():
         logits = model(**input_values).logits
-    # sentence = parse_transcription(logits)
-    check_spell = spell_checker.check(parse_transcription(logits))
-    # if check_spell[0] is False:
-    #   corrected = check_spell[1]
-    # else:
-    #   corrected = sentence
-    return spell_checker.check(parse_transcription(logits))[1] if spell_checker.check(parse_transcription(logits))[0] is False else parse_transcription(logits)
+    sentence = parse_transcription(logits)
+    corrected_sent = corrector(sentence)
+    return corrected_sent
+    
+
+# def parse(wav_file):
+#     check_spell = ''
+#     input_values = read_file_and_process(wav_file)
+#     with torch.no_grad():
+#         logits = model(**input_values).logits
+#     # sentence = parse_transcription(logits)
+#     check_spell = spell_checker.check(parse_transcription(logits))
+#     # if check_spell[0] is False:
+#     #   corrected = check_spell[1]
+#     # else:
+#     #   corrected = sentence
+#     return spell_checker.check(parse_transcription(logits))[1] if spell_checker.check(parse_transcription(logits))[0] is False else parse_transcription(logits)
 
 model_id = "jonatasgrosman/wav2vec2-large-xlsr-53-persian"
 processor = Wav2Vec2Processor.from_pretrained(model_id)