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import streamlit as st
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from transformers import pipeline
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import time
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@st.cache_resource
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def load_pipeline(model_name):
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with st.spinner(f"Loading model {model_name}..."):
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return pipeline("text-generation", model=model_name)
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MODEL = "teamapocalypseml/regben2ipa-byt5small"
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MAPPER = {
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"byt5 small regional transcriber": "teamapocalypseml/regben2ipa-byt5small",
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"umt5 base regional transcriber": "teamapocalypseml/regben2ipa-umt5base",
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"mt5 base regional transcriber": "teamapocalypseml/regben2ipa-mt5-base",
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}
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st.title("Model configurations")
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MODEL = st.selectbox("Select Model", [
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"byt5 small regional transcriber",
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"umt5 base regional transcriber",
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"mt5 base regional transcriber"
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])
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st.link_button(
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f"{MAPPER[MODEL]}", f"https://huggingface.co/{MAPPER[MODEL]}",
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type="tertiary",
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icon="🔗"
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)
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model = load_pipeline(MAPPER[MODEL])
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model = pipeline("text2text-generation", model=MAPPER[MODEL])
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prompt = st.text_input("Enter your regional bengali text:")
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district = st.selectbox("Select District", [
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"Kishoreganj", "Narail", "Narsingdi", "Chittagong", "Rangpur", "Tangail"
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])
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if st.button("Generate Text"):
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if prompt != "" and district != "":
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ipa_transcription = model(f"<{district}> {prompt}",
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max_length=512)[0]["generated_text"]
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st.write(f"IPA Transcription:\n{ipa_transcription}")
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