This groundbreaking project revolutionizes the way we interact with audio content. By seamlessly processing audio files and transforming them into searchable, interactive experiences, it opens up a world of possibilities. With the ability to convert podcast MP3 files into text using Whisper and offering question-answering through embeddings stored in Pinecone, this system empowers users to effortlessly engage with and extract valuable insights from podcast content. It's time to experience audio in a whole new way.

• Audio Chunking: Podcast MP3 files are split into smaller audio chunks for better handling and processing
• Speech-to-Text: Using Whisper, each audio chunk is transcribed into text, ensuring high-quality transcription
• Q&A System: Users can ask questions based on the podcast transcript. The system uses the transcript's embeddings to return accurate answers
• Embeddings with Pinecone: Text chunks are embedded into vector format and stored in Pinecone for quick retrieval during the question-answering process

1. Upload Podcast MP3: The user uploads a podcast MP3 file
2. Chunking: The MP3 file is split into smaller chunks
3. Speech-to-Text: Each chunk is transcribed into text using the Whisper model
4. Embeddings Generation: The transcribed text is split into smaller chunks and converted into embeddings using sentence-transformers or other embedding models
5. Storing Embeddings: The embeddings are stored in Pinecone, a vector database for fast retrieval
6. Question Answering: Users can ask questions based on the podcast, and the system retrieves relevant chunks using Pinecone embeddings to provide accurate answers

1. Podcast MP3 → MP3 Chunks: The MP3 is split into smaller audio chunks
2. Speech-to-Text Conversion: Whisper converts these audio chunks into text
3. Embeddings Generation: Text chunks are converted into vector embeddings for storage
4. Embeddings Storage (Pinecone): Vector embeddings are stored and managed in Pinecone
5. Interactive Q&A: Users can ask questions and receive answers from the embedded text data

• Python 3.8 or higher
• Virtual environment management tool (venv, conda, etc.)
• All dependencies listed in requirements.txt

git clone <repository-url>
cd <repository-folder>

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows
venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate

# Create conda environment
conda create -n venv python=3.12

# Activate conda environment
conda activate venv

# Make sure your virtual environment is activated
pip install -r requirements.txt

streamlit run app.py

Upload a podcast MP3 and interact with the transcription through the Q&A interface.

• Groq: For optimizing the audio processing pipeline
• Langchain: For creating modular and scalable components for question-answering
• Pinecone: A vector database for managing embeddings and facilitating the Q&A process
• Streamlit: A lightweight web app framework for creating interactive user interfaces
• Whisper: A powerful model for converting speech to text
• Pydub: Used for handling and chunking audio files
• Sentence-Transformers: For generating embeddings from text chunks
• Tiktoken: For tokenizing text

If you encounter any dependency-related issues:

1. Make sure your virtual environment is activated
2. Verify Python version compatibility: python --version
3. Try upgrading pip: pip install --upgrade pip
4. If using conda, ensure conda-forge channel is added: conda config --add channels conda-forge

The source code for the project is licensed under the MIT license, which you can find in the LICENSE.md file.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request