app.py

import time
from transformers import pipeline
import gradio as gr
import numpy as np
import librosa

transcriber_hindi = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec-hindi")
transcriber_bengali = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_bengali")
transcriber_odia = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec-odia")
transcriber_gujarati = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_gujarati")
transcriber_telugu = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_telugu")
transcriber_sinhala = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_sinhala")
transcriber_tamil = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_tamil")
transcriber_nepali = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_nepali")
transcriber_marathi = pipeline("automatic-speech-recognition", model="ai4bharat/indicwav2vec_v1_marathi")


def resample_to_16k(audio, orig_sr):
    y_resampled = librosa.resample(y=audio, orig_sr=orig_sr, target_sr=16000)
    return y_resampled

def transcribe(audio):
    sr,y = audio
    y = y.astype(np.float32)
    y/= np.max(np.abs(y))
    y_resampled = resample_to_16k(y,sr)

    pipe= eval(f'transcriber_{lang}')
    trans = pipe(y_resampled)
    
    return trans["text"]

demo = gr.Interface(
            transcribe,
            inputs=["microphone",gr.Radio(["hindi","bangali","odia","gujarati","telugu","sinhala","tamil","nepali","marathi"],value="hindi")],
            outputs="text",
            examples=[["./Samples/Hindi_1.mp3","hindi"],["./Samples/Hindi_2.mp3","hindi"],["./Samples/Tamil_1.mp3","tamil"],["./Samples/Tamil_2.mp3","hindi"],["./Samples/Nepal_1.mp3","nepali"],["./Samples/Nepal_2.mp3","nepali"],["./Samples/Marathi_1.mp3","marathi"],["./Samples/Marathi_2.mp3","marathi"],["./Samples/climate ex short.wav","hindi"]])
demo.launch()