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import torch
import gradio as gr
from PIL import Image
import scipy.io.wavfile as wavfile
# Use a pipeline as a high-level helper
from transformers import pipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
# model_path = ("../Models/models--Salesforce--blip-image-captioning-large"
# "/snapshots/2227ac38c9f16105cb0412e7cab4759978a8fd90")
# tts_model_path = ("../Models/models--kakao-enterprise--vits-ljs/snapshots"
# "/3bcb8321394f671bd948ebf0d086d694dda95464")
# caption_image = pipeline("image-to-text",
# model=model_path, device=device)
# narrator = pipeline("text-to-speech",
# model=tts_model_path)
caption_image = pipeline("image-to-text",
model="Salesforce/blip-image-captioning-large", device=device)
narrator = pipeline("text-to-speech",
model="kakao-enterprise/vits-ljs")
def generate_audio(text):
# Generate the narrated text
narrated_text = narrator(text)
# Save the audio to a WAV file
wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
data=narrated_text["audio"][0])
# Return the path to the saved audio file
return "output.wav"
def caption_my_image(pil_image):
# Generate the caption
semantics = caption_image(images=pil_image)[0]['generated_text']
# Generate the audio for the caption
audio_path = generate_audio(semantics)
# Return both the caption and the audio
return semantics, audio_path
# Define the Gradio interface
demo = gr.Interface(
fn=caption_my_image,
inputs=[gr.Image(label="Select Image", type="pil")],
outputs=[
gr.Textbox(label="Generated Caption"),
gr.Audio(label="Image Caption")
],
title="Story Generation From Images",
description="THIS APPLICATION WILL BE USED TO GENERATE STORY OF THE IMAGE."
)
demo.launch() |