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| import streamlit as st | |
| from transformers import pipeline | |
| import numpy as np | |
| import torch | |
| from scipy.io.wavfile import write | |
| # 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") | |
| generator = pipeline("text-generation", model="openai-community/gpt2") | |
| story_text = text_generation_model(text, max_length=60, truncation=True) | |
| story = story_text[0]["generated_text"] | |
| return story | |
| # # text2audio | |
| def text2audio(story_text, output_path="kids_playing_audio.wav"): | |
| text_to_speech_model = pipeline("text-to-speech", model="facebook/mms-tts-eng", device=0) # Use GPU if available | |
| audio_data = text_to_speech_model(story_text) | |
| ## Save the audio to kids_playing_audio.wav, so that it can play | |
| # Extract audio waveform and sample rate | |
| waveform = np.array(audio_data["audio"], dtype=np.float32) # Ensure correct format | |
| sample_rate = int(audio_data["sampling_rate"]) # Convert sample rate to integer | |
| # 🔥 Ensure sample rate is within a valid range (between 1 and 65535 Hz) | |
| if sample_rate <= 0 or sample_rate > 96000: # 96000 is a high-res audio limit | |
| print(f"⚠️ Warning: Invalid sample rate detected ({sample_rate} Hz). Setting to 44100 Hz.") | |
| sample_rate = 44100 # Use standard sample rate | |
| # Ensure waveform is a 1D array (some models return 2D arrays) | |
| if waveform.ndim > 1: | |
| waveform = waveform.mean(axis=0) # Convert to mono by averaging channels | |
| # Convert float waveform (-1.0 to 1.0) to 16-bit PCM format | |
| waveform_int16 = np.int16(waveform * 32767) | |
| # Save the audio file | |
| write(output_path, sample_rate, waveform_int16) | |
| print(f"✅ Audio saved as {output_path} with sample rate {sample_rate}") | |
| 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...') | |
| text2audio(story) | |
| # # Play button | |
| if st.button("Play Audio"): | |
| st.audio("kids_playing_audio.wav") | |