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# to create nueral network
import torch
# for interface
import gradio as gr
# to open images
from PIL import Image
# used for audio
import scipy.io.wavfile as wavfile
# Use a pipeline as a high-level helper
from transformers import pipeline
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
# Load the pretrained weights
caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
# Define the function to generate audio from text
def generate_audio(text):
# Generate the narrated text
narrated_text = narrator(text)
# Save the audio to WAV file
wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
data=narrated_text["audio"][0])
# Return the path to the saved output WAV file
return "output.wav" # return audio
def caption_my_image(pil_image):
semantics = caption_image(images=pil_image)[0]['generated_text']
audio = generate_audio(semantics)
return semantics,audio # returns both text and audio output
gr.close_all()
demo = gr.Interface(fn=caption_my_image,
inputs=[gr.Image(label="Select Image",type="pil")],
outputs=[
gr.Textbox(label="Image Caption"),
gr.Audio(label="Image Caption Audio")],
title="IMAGE CAPTIONING WITH AUDIO OUTPUT",
description="THIS APPLICATION WILL BE USED TO CAPTION IMAGES WITH THE HELP OF AI")
demo.launch() |