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import streamlit as st
from transformers import pipeline
# function part
# img2text
def img2text(url):
image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
text = image_to_text_model(url)[0]["generated_text"]
return text
# text2story
def text2story(text):
text_generation_model = pipeline("text-generation", model="openai-community/gpt2")
story_text = text_generation_model(text, min_length=50, max_length=100, do_sample=True, early_stopping=True, top_p=0.4)[0]["generated_text"]
return story_text
# text2audio
def text2audio(story_text):
text2audio_model = pipeline("text-to-speech", model="facebook/mms-tts-eng")
gen_audio = text2audio_model(story_text)
return gen_audio
def main():
st.set_page_config(page_title="Your Image to Audio Story",page_icon="🦜")
st.header("Turn Your Image to Audio Story")
uploaded_file = st.file_uploader("Select an Image...")
if uploaded_file is not None:
print(uploaded_file)
bytes_data = uploaded_file.getvalue()
with open(uploaded_file.name, "wb") as file:
file.write(bytes_data)
st.image(uploaded_file, caption="Uploaded Image",
use_container_width=True)
# #Stage 1: Image to Text
st.text('Processing img2text...')
scenario = img2text(uploaded_file.name)
st.write(scenario)
# #Stage 2: Text to Story
st.text('Generating a story...')
story = text2story(scenario)
st.write(story)
#Stage 3: Story to Audio data
st.text('Generating audio data...')
audio_data =text2audio(story)
# Play button
if st.button("Play Audio"):
# st.audio(audio_data['audio'], sample_rate = audio_data['sampling_rate'])
st.audio(audio_data['audio'],
format="audio/wav",
start_time=0,
sample_rate = audio_data['sampling_rate'])
# st.audio("kids_playing_audio.wav")
if __name__ == "__main__":
main() |