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import streamlit as st
import whisper
import ffmpeg
import pandas as pd
import pickle
import os
import numpy as np
from sentence_transformers import SentenceTransformer
from chromadb import PersistentClient
# Initialize models
embed_model = SentenceTransformer('all-MiniLM-L6-v2')
# Function to extract audio
def extract_audio(uploaded_file):
audio_path = "temp_audio.wav"
temp_file = f"temp_{uploaded_file.name}"
with open(temp_file, "wb") as f:
f.write(uploaded_file.getvalue())
try:
if uploaded_file.name.endswith(('.mp4', '.mkv')):
ffmpeg.input(temp_file).output(audio_path).run(overwrite_output=True)
else:
audio_path = temp_file
return audio_path, temp_file
except Exception as e:
st.error(f"Error extracting audio: {str(e)}")
return None, None
# Function to transcribe audio
def transcribe_audio(audio_path):
try:
model = whisper.load_model("base")
result = model.transcribe(audio_path)
subtitles = []
for i, segment in enumerate(result['segments']):
start_time = format_timestamp(segment['start'])
end_time = format_timestamp(segment['end'])
text = segment['text']
subtitles.append(f"{i + 1}\n{start_time} --> {end_time}\n{text}\n")
return subtitles
except Exception as e:
st.error(f"Error during transcription: {str(e)}")
return []
# Timestamp formatting
def format_timestamp(seconds):
hours = int(seconds // 3600)
minutes = int((seconds % 3600) // 60)
secs = int(seconds % 60)
millis = int((seconds % 1) * 1000)
return f"{hours:02}:{minutes:02}:{secs:02},{millis:03}"
# Embed subtitles
def embed_subtitles(subtitles):
raw_texts = [line.split('\n')[2] for line in subtitles if line.strip()]
embeddings = embed_model.encode(raw_texts)
df = pd.DataFrame({
'subtitle': raw_texts,
'embedding': list(embeddings)
})
with open('subtitle_embeddings.pkl', 'wb') as f:
pickle.dump(df, f)
return df
# Save embeddings to ChromaDB
def save_to_chroma(embeddings):
client = PersistentClient(path="./chroma_db")
collection = client.create_collection(name="subtitles")
for idx, row in embeddings.iterrows():
collection.add(
documents=[row['subtitle']],
ids=[str(idx)],
embeddings=[row['embedding'].tolist()] # Convert to list
)
return collection
# Search subtitles
def search_subtitles(query, collection):
try:
query_embedding = embed_model.encode([query]).tolist()
results = collection.query(query_embeddings=query_embedding, n_results=5)
return results['documents']
except Exception as e:
st.error(f"Error searching subtitles: {str(e)}")
return []
# Main app
def main():
st.set_page_config(page_title="Video/Audio Subtitle Generator", layout="wide")
st.title("🎥🎵 Video/Audio Subtitle Generator")
with st.sidebar:
uploaded_file = st.file_uploader("Upload Video/Audio", type=["mp4", "mkv", "mp3", "wav"])
query = st.text_input("Search Subtitles")
download_btn = st.button("Download Subtitles")
if uploaded_file:
with st.spinner("Extracting audio..."):
audio_path, temp_file = extract_audio(uploaded_file)
if audio_path:
with st.spinner("Generating subtitles..."):
subtitles = transcribe_audio(audio_path)
st.success("Subtitles Generated!")
if uploaded_file.name.endswith(('.mp4', '.mkv')):
st.video(uploaded_file)
else:
st.audio(uploaded_file)
st.write("### Generated Subtitles:")
for sub in subtitles:
st.text(sub)
with st.spinner("Embedding and storing subtitles..."):
embeddings = embed_subtitles(subtitles)
if embeddings.empty:
st.warning("No subtitles generated.")
else:
collection = save_to_chroma(embeddings)
if query:
results = search_subtitles(query, collection)
st.write("### Matching Subtitles:")
if results:
for idx, sub in enumerate(results, start=1):
st.write(f"{idx}. {sub}")
else:
st.warning("No matching subtitles found.")
if download_btn:
with open("generated_subtitles.srt", "w") as f:
f.writelines(subtitles)
with open("generated_subtitles.srt", "rb") as f:
st.download_button("Download SRT", f, file_name="generated_subtitles.srt", mime="text/plain")
if __name__ == '__main__':
main()
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