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4fe11eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | import streamlit as st
import soundfile as sf
from transformers import pipeline
import sentence_transformers
import chromadb
# Load the Hugging Face speech-to-text model
speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-small")
# Load the subtitle search model (Assuming embeddings are stored in ChromaDB)
chroma_client = chromadb.PersistentClient(path="./dataset")
collection = chroma_client.get_collection("subtitles")
# Function to convert audio to text
def convert_audio_to_text(audio_file):
audio_data, samplerate = sf.read(audio_file)
result = speech_to_text(audio_data)
return result["text"]
# Function to search for relevant subtitles
def search_subtitles(query):
results = collection.query(query_texts=[query], n_results=5)
return results["documents"][0] if results["documents"] else ["No matching subtitles found."]
# Streamlit UI
st.title("🎬 Video Subtitle Search Engine")
st.write("Upload an audio file to search for matching subtitles.")
# File uploader for audio
uploaded_file = st.file_uploader("Upload Audio File", type=["wav", "mp3", "ogg"])
if uploaded_file is not None:
st.audio(uploaded_file, format="audio/wav")
with st.spinner("Converting speech to text... 🎙"):
query_text = convert_audio_to_text(uploaded_file)
st.write("*Detected Text:*", query_text)
with st.spinner("Searching subtitles... 🔎"):
subtitles = search_subtitles(query_text)
st.write("*Matching Subtitles:*")
for subtitle in subtitles:
st.write("- ", subtitle) |