app.py

import spaces
from transformers import pipeline
import gradio as gr
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
asr = pipeline(model="asif00/whisper-bangla")
# asr.to(device=device)
ser = pipeline("text2text-generation", model="asif00/mbart_bn_error_correction")
ser.to(device=device)

@spaces.GPU
def transcribe(audio):
    text = asr(audio)["text"]
    print(text)
    return text


@spaces.GPU
def correction(text):
    corrected_text = ser(text)["generated_text"]
    print(corrected_text)
    return corrected_text


def transcribe_and_correct(audio):
    text = transcribe(audio)
    corrected_text = correction(text)
    return corrected_text


iface = gr.Interface(
    fn=transcribe_and_correct,
    inputs=gr.Audio(sources="microphone", type="filepath"),
    outputs="text",
    title="Whisper Bangla",
    description="Realtime demo for Bengali speech recognition using a fine-tuned Whisper small model.",
)

iface.launch()