app.py

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

device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model_id = "openai/whisper-medium"

print("\n\nReading Languages...\n\n")

with open("languages.txt", "r") as file:
    languages = file.read().strip().split(",")

languages = [language.strip().lower() for language in languages]

print("\n\nInitializing model...\n\n")

transcriber = pipeline(
    "automatic-speech-recognition",
    model=model_id,
    torch_dtype=torch_dtype,
    device=device,
)

print("\n\nModel Ready!!\n\nLaunching Interface...\n\n")

def transcribe(audio, language: str):
    sr, y = audio
    
    # Convert to mono if stereo
    if y.ndim > 1:
        y = y.mean(axis=1)
        
    y = y.astype(np.float32)
    y /= np.max(np.abs(y))
    
    language = language.lower()
    if(language not in languages):
        return "Error!! Not a valid language!!"

    args = {"task":"transcribe", "language":language}

    return transcriber({"sampling_rate": sr, "raw": y}, generate_kwargs=args)["text"]  

demo = gr.Interface(
    transcribe,
    inputs=[gr.Audio(sources="microphone"), gr.Textbox(label="Language", placeholder="Enter the language")],
    outputs=["text"],
    title="Whisper Model Interface",
    description=model_id
)

demo.launch()