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