Update app.py

app.py

@@ -0,0 +1,56 @@
+import warnings
+warnings.filterwarnings("ignore")
+import os
+import re
+import gradio as gr
+import numpy as np
+import torchaudio
+import nbimporter
+from transformers import pipeline
+from transformers import AutoProcessor
+from pyctcdecode import build_ctcdecoder
+from transformers import Wav2Vec2ProcessorWithLM
+from text2int import text_to_int
+from isNumber import is_number
+from Text2List import text_to_list
+from convert2list import convert_to_list
+from processDoubles import process_doubles
+from replaceWords import replace_words
+
+transcriber = pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-hindi_v1")
+processor = AutoProcessor.from_pretrained("cdactvm/w2v-bert-2.0-hindi_v1")
+
+vocab_dict = processor.tokenizer.get_vocab()
+
+sorted_vocab_dict = {k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])}
+decoder = build_ctcdecoder(
+    labels=list(sorted_vocab_dict.keys()),
+    kenlm_model_path="lm.binary",
+    )
+processor_with_lm = Wav2Vec2ProcessorWithLM(
+    feature_extractor=processor.feature_extractor,
+    tokenizer=processor.tokenizer,
+    decoder=decoder
+    )
+processor.feature_extractor._processor_class = "Wav2Vec2ProcessorWithLM"
+
+ 
+def transcribe(audio):
+    # # Process the audio file
+    transcript = transcriber(audio)
+    text_value = transcript['text']
+    print(text_value)
+    processd_doubles=process_doubles(text_value)
+    converted_to_list=convert_to_list(processd_doubles,text_to_list())
+    replaced_words = replace_words(converted_to_list)
+    converted_text=text_to_int(replaced_words)
+    return converted_text
+
+
+demo = gr.Interface(
+    transcribe,
+    gr.Audio(sources="microphone", type="filepath"),
+    "text",
+)
+
+demo.launch()