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_to_story = pipeline("text-generation", model="isarth/distill_gpt2_story_generator", max_length=300, # 增加最大生成长度 min_length=100, # 设置最小生成长度 do_sample=True, # 启用随机采样 temperature=0.9, # 控制随机性(0-1,越大越随机) top_k=50, # 限制候选词数量 top_p=0.95, # 核采样参数 repetition_penalty=1.2) # story_text = "" # to be completed story_text = text_to_story(text)[0]["generated_text"] return story_text # text2audio def text2audio(story_text): text_to_audio = pipeline("text-to-speech", model="facebook/mms-tts-eng") audio_data = text_to_audio(story_text) return audio_data 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_column_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'], format="audio/wav", start_time=0, sample_rate = audio_data['sampling_rate']) # # st.audio("kids_playing_audio.wav")