app.py

#import gradio as gr
#from transformers import pipeline
#p1= pipeline(task="automatic-speech-recognition", model="cdactvm/w2v-bert-2.0-odia_v1")

#def transcribe_odiya(speech):
#    #print (p1(speech))
#    text = p1(speech)["text"]
    #text=cleanhtml(text)
#    return text
#def sel_lng(lng,mic=None, file=None):
#    if mic is not None:
#        audio = mic
#    elif file is not None:
#        audio = file
#    else:
#        return "You must either provide a mic recording or a file"
#    if (lng=="Odiya"):
#        return transcribe_odiya(audio)
    
        
#demo=gr.Interface(
#    fn=sel_lng, 
      
#    inputs=[
        
#        gr.Dropdown(["Odiya"],value="Odiya",label="Select Language"),
#        gr.Audio(sources="microphone", type="filepath"),
#        gr.Audio(sources="upload", type="filepath"),
        #"state"
#    ],
#    outputs=[
#        "textbox"
#        #"state"
#    ],
#    title="Automatic Speech Recognition",
#    description = "Demo for Automatic Speech Recognition. Use microphone to record speech. Please press Record button. Initially it will take some time to load the model. The recognized text will appear in the output textbox",
#      ).launch()



#demo.launch()
from huggingface_hub import Repository
repo = Repository(local_dir="indic-trans", clone_from="https://github.com/libindic/indic-trans.gi")
#import os
#os.system('git clone https://github.com/libindic/indic-trans.gi')