👾 V/ALect

ViaLect streamlines your media intake by transforming audio into workable text and generated summaries!

$\textcolor{gray}{\textit{Toward words unheard,}}$
$\textcolor{gray}{\textit{traverse digital oceans}}$
$\textcolor{gray}{\textit{for treasures unseen.}}$

Features:

Tip

📡 Audio Extraction → Pull from various media platforms or uploads
🛸 ASR & Diarization → Identify and align speakers to timestamped dialogues
🌎 Translation → Detect languages and translate to English
🤖 Speech-to-Text → Accurately transcribe text from extracted audio
💬 Summarization → Focus on key concepts with transcript-based summaries
🔊 Text-to-Speech → Have your generated summaries read back to you
📚 Media Collection → Locally store and navigate your transformed data
🚀 Intuitive UI → Seamless frontend layout via Streamlit

more

Setup:

Important

Key Packages: OpenAI Whisper, PyTorch (CUDA v11.8), pyannote.audio, yt-dlp, Streamlit

1. Git clone this repository:

git clone https://github.com/imgta/vialect.git

2. Install ffmpeg and requirements:

sudo apt install ffmpeg
pip install -r requirements.txt

3. Obtain Hugging Face token/access, obtain OpenAI API Key
4. Create and update .streamlit/secrets.toml' (Optional: input keys in Secret Keys Drawer after launch)
5. Launch streamlit app:

streamlit run app/Home.py

Usage:

Note

1. Select whisper model and options
2. Input or upload video/audio file
3. Submit for transcription

Roadmap:

• Generate summary based on transcript text
• Create and store text embeddings in vector DB for RAG querying
• ASR/Diarization for timestamps => reduced sliding window
• Partition overlap + translation stress test (Anime subbing?)
• Realtime ASR