Update app.py

app.py

@@ -6,7 +6,6 @@ import re
 import pywt
 import librosa
 import webrtcvad
-import nbimporter
 import torchaudio
 import numpy as np
 import gradio as gr
@@ -52,104 +51,7 @@ def wavelet_denoise(audio, wavelet='db1', level=1):
 # Function to apply a Wiener filter for noise reduction
 def apply_wiener_filter(audio):
     return wiener(audio)
-
-
-
-# def createlex(filename):
-# # Initialize an empty dictionary
-#     data_dict = {}
-
-# # Open the file and read it line by line
-#     with open(filename, "r", encoding="utf-8") as f:
-#         for line in f:
-#         # Strip newline characters and split by tab
-#             key, value = line.strip().split("\t")
-#         # Add to dictionary
-#             data_dict[key] = value
-#     return data_dict
     
-# lex=createlex("num_words_ta.txt")
-
-# def addnum(inlist):
-#     sum=0
-#     for num in inlist:
-#         sum+=int(num)
-   
-#     return sum
-    
-# from rapidfuzz import process
-# def get_val(word, lexicon):
-#     threshold = 80  # Minimum similarity score
-#     length_difference = 4
-#     #length_range = (4, 6)  # Acceptable character length range (min, max)
-
-#     # Find the best match above the similarity threshold
-#     result = process.extractOne(word, lexicon.keys(), score_cutoff=threshold)
-#     #print (result)
-#     if result:
-#         match, score, _ = result
-#         #print(lexicon[match])
-#         #return lexicon[match]
-#         if abs(len(match) - len(word)) <= length_difference:
-#         #if length_range[0] <= len(match) <= length_range[1]:
-#             return lexicon[match]
-#         else:
-#             return None
-#     else:
-#         return None
-
-# def convert2num(input, lex):
-#     input += " #"  # Add a period for termination
-#     words = input.split()
-#     i = 0
-#     num = 0
-#     outstr = ""
-#     digit_end = True
-#     numlist = []
-#     addflag = False
-
-#     # Process the words
-#     while i < len(words):
-#         #checkwordlist = handleSpecialnum(words[i])
-        
-#         # Handle special numbers
-#         #if len(checkwordlist) == 2:
-#         #    words[i] = checkwordlist[0]
-#         #    words.insert(i + 1, checkwordlist[1])  # Collect new word for later processing
-
-#         ## Get numerical value of the word
-#         numval = get_val(words[i], lex)
-#         if numval is not None:
-#             if words[i][-4:] in ('த்து', 'ற்று'):
-#                 addflag = True
-#                 numlist.append(numval)
-#             else:
-#                 if addflag:
-#                     numlist.append(numval)
-#                     num = addnum(numlist)
-#                     outstr += str(num) + " "
-#                     addflag = False
-#                     numlist = []
-#                 else:
-#                     outstr += " " + str(numval) + " "
-#             digit_end = False
-#         else:
-#             if addflag:
-#                 num = addnum(numlist)
-#                 outstr += str(num) + " " + words[i] + " "
-#                 addflag = False
-#                 numlist = []
-#             else:
-#                 outstr += words[i] + " "
-#             if not digit_end:
-#                 digit_end = True
-
-#         # Move to the next word
-#         i += 1
-
-#     # Final processing
-#     outstr = outstr.replace('#','')  # Remove trailing spaces
-#     return outstr
     
 # # Function to handle speech recognition
 def recognize_speech(audio_file):
@@ -176,13 +78,6 @@ def sel_lng(lng, mic=None, file=None):
     
     if lng == "model_1":
         return recognize_speech(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(
     fn=sel_lng,