Update app.py

app.py

@@ -29,7 +29,7 @@ from wienerFilter import wiener_filter
 from highPassFilter import high_pass_filter
 from waveletDenoise import wavelet_denoise
 
-transcriber_hindi_new = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-tamil_new")
+transcriber_taml_new = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-tamil_new")
 # transcriber_hindi_old = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-tamil_new")
 processor = AutoProcessor.from_pretrained("cdactvm/w2v-bert-tamil_new")
 vocab_dict = processor.tokenizer.get_vocab()
@@ -49,7 +49,7 @@ processor.feature_extractor._processor_class = "Wav2Vec2ProcessorWithLM"
 
 def transcribe_tamil_new(audio):
     # # Process the audio file
-    transcript = transcriber_hindi_new(audio)
+    transcript = transcriber_taml_new(audio)
     text_value = transcript['text']
     processd_doubles=process_doubles(text_value)
     replaced_words = replace_words(processd_doubles)
@@ -83,7 +83,7 @@ def Noise_cancellation_function(audio_file):
     write(temp_wav, sr, denoised_audio)
 
     # Perform speech recognition on the denoised audio
-    transcript = transcriber_hindi_lm(temp_wav)
+    transcript = transcriber_taml_new(temp_wav)
     text_value = transcript['text']
     cleaned_text=text_value.replace("<s>","")
     processd_doubles=process_doubles(cleaned_text)
@@ -102,13 +102,13 @@ def sel_lng(lng, mic=None, file=None):
         return "You must either provide a mic recording or a file"
     
     if lng == "model_1":
-        return transcribe_hindi_old(audio)
-    elif lng == "model_2":
-        return transcribe_hindi_new(audio)
-    elif lng== "model_3":
-        return transcribe_hindi_lm(audio)
-    elif lng== "model_4":
-        return Noise_cancellation_function(audio)
+        return transcriber_taml_new(audio)
+    # elif lng == "model_2":
+    #     return transcribe_hindi_new(audio)
+    # elif lng== "model_3":
+    #     return transcribe_hindi_lm(audio)
+    # elif lng== "model_4":
+    #     return Noise_cancellation_function(audio)
             
         
 demo=gr.Interface(