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import streamlit as st |
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import tempfile |
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import torch |
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from transformers import pipeline |
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from diffusers import StableDiffusionPipeline |
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from pydub import AudioSegment |
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import base64 |
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st.set_page_config(page_title="Tamil Audio to Story & Image", layout="centered") |
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@st.cache_resource |
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def load_models(): |
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whisper = pipeline("automatic-speech-recognition", model="openai/whisper-tiny") |
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ta-en") |
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text_gen = pipeline("text-generation", model="sshleifer/tiny-gpt2") |
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image_gen = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") |
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image_gen.to("cuda" if torch.cuda.is_available() else "cpu") |
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return whisper, translator, text_gen, image_gen |
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whisper, translator, text_gen, image_gen = load_models() |
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st.title("🎙️ Tamil Audio to Story & Image") |
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st.write("Upload or record Tamil audio to generate English story and image.") |
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input_mode = st.radio("Choose Input Mode", ["Upload Audio", "Record Live Audio"]) |
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audio_bytes = None |
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if input_mode == "Upload Audio": |
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uploaded_file = st.file_uploader("Upload Tamil Audio (.wav, .mp3)", type=["wav", "mp3"], key="upload") |
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if uploaded_file: |
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audio_bytes = uploaded_file.read() |
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else: |
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audio_recorder = st.audio_recorder("Record your audio", format="audio/wav", key="recorder") |
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if audio_recorder: |
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audio_bytes = audio_recorder |
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if audio_bytes: |
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st.audio(audio_bytes, format="audio/wav") |
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with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: |
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tmp.write(audio_bytes) |
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tmp_path = tmp.name |
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if tmp_path.endswith(".mp3"): |
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sound = AudioSegment.from_mp3(tmp_path) |
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tmp_path = tmp_path.replace(".mp3", ".wav") |
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sound.export(tmp_path, format="wav") |
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with st.spinner("Transcribing..."): |
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transcription = whisper(tmp_path)["text"] |
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st.text_area("Transcribed Tamil Text", transcription) |
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with st.spinner("Translating..."): |
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translation = translator(transcription)[0]['translation_text'] |
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st.text_area("Translated English Text", translation) |
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with st.spinner("Generating Story..."): |
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story = text_gen(translation, max_length=100)[0]['generated_text'] |
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st.text_area("Generated Story", story) |
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with st.spinner("Generating Image..."): |
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image = image_gen(prompt=translation).images[0] |
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st.image(image, caption="Generated Image") |
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else: |
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st.warning("Please upload or record an audio to proceed.") |
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