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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
# from phonemizer.backend.espeak.wrapper import EspeakWrapper
# _ESPEAK_LIBRARY = '/opt/homebrew/Cellar/espeak/1.48.04_1/lib/libespeak.1.1.48.dylib' #use the Path to the library.
# EspeakWrapper.set_library(_ESPEAK_LIBRARY)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
# tts_model_path = "./Model/models--kakao-enterprise--vits-ljs/snapshots/3bcb8321394f671bd948ebf0d086d694dda95464"
# narrator = pipeline("text-to-speech", model=tts_model_path)
# Load the pretrained weights
caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
# model_path = "./Model/models--Salesforce--blip-image-captioning-large/snapshots/2227ac38c9f16105cb0412e7cab4759978a8fd90"
# Load the pretrained weights
# caption_image = pipeline("image-to-text", model=model_path, 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"
def caption_my_image(pil_image):
semantics = caption_image(pil_image)[0]["generated_text"]
audio = generate_audio(semantics)
return audio
gr.close_all()
demo = gr.Interface(fn=caption_my_image,
inputs=[gr.Image(label="Select Image", type="pil")],
outputs=[gr.Audio(label="Generated Audio")],
title="@IT AI Enthusiast (https://www.youtube.com/@itaienthusiast/) - Project 8: Image Captioning with AI",
description="THIS APPLICATION WILL BE USED TO CAPTION IMAGES WITH THE HELP OF AI")
demo.launch()
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