app.py

import gradio as gr
import numpy as np
from transformers import pipeline
import torch

device = "cuda:0" if torch.cuda.is_available() else "cpu"

transcriber = pipeline("automatic-speech-recognition", model="mahimairaja/whisper-base-tamil", \
                        chunk_length_s=15, device=device)
transcriber.model.config.forced_decoder_ids = transcriber.tokenizer.get_decoder_prompt_ids(language="ta", task="transcribe")

def transcribe(audio):
    return transcriber(audio)["text"]

TITLE = "ASR for ALL - Democratizing Tamil"

demo = gr.Blocks()

mic_transcribe = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs="text",
    title=TITLE,
)

file_transcribe = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(source="upload", type="filepath"),
    outputs="text",
    examples=[
        "assets/tamil-audio-01.mp3",
        "assets/tamil-audio-02.mp3",
        "assets/tamil-audio-03.mp3",
        "assets/tamil-audio-04.mp3",
    ],
    title=TITLE,
)


with demo:
    gr.TabbedInterface(
        [mic_transcribe, file_transcribe],
        ["Real Time Transcription", "Audio File", ]
        )

demo.launch(share=True)