Building Your Own Voice Assistant Locally: Whisper + Ollama + Bark
In this detailed guide, an easy-to-understand approach is provided for creating a personalized voice assistant, much like Jarvis from Iron Man movies, that operates offline on your computer. Unlike typical voice assistants that require an internet connection, this project allows users to maintain their privacy and create something truly their own using Python. The project utilizes various technologies to equip the assistant with listening, speaking, and conversation abilities.
Tech Stack Overview
To get started, set up a Python virtual environment using tools like pyenv, virtualenv, or Poetry, the latter being the preferred choice for this guide. The following libraries are essential for building your voice assistant:
- rich: Enhances the console output aesthetically.
- openai-whisper: A reliable tool for converting speech to text.
- suno-bark: Transforms text to high-quality audio speech.
- langchain: Facilitates interactions with large language models (LLMs).
- sounddevice, pyaudio, and speechrecognition: Handle audio recording and playback effectively.
The backbone of the assistant's conversational capabilities is driven by Ollama, a popular tool known for running LLMs offline on local machines.
Application Architecture
The voice assistant consists of three core components:
- Speech Recognition: Utilizes OpenAI's Whisper to transcribe spoken language into text, handling multiple languages and dialects.
- Conversational Chain: Leverages Langchain for the Llama-2 model's conversational capabilities, served through Ollama, providing engaging interactions.
- Speech Synthesizer: Employs Suno AI's Bark for natural-sounding text-to-speech synthesis.
This cycle starts with recording speech, transcribing it to text, generating a response using an LLM, and then vocalizing the reply.
Implementation Details
TextToSpeechService with Bark: The implementation begins by creating a service to synthesize speech using Bark, suitable for both short and long text inputs. It involves loading pre-trained models and utilizing methods for converting text to speech.
Setting Up Ollama for LLM: Download and serve the latest Llama-2 model using Ollama to power conversational responses.
Main Application Logic: The application consists of initializing:
- A rich interactive console for user engagement.
- A Whisper model for speech recognition.
- The Bark synthesizer for converting text to speech.
- A conversational chain managed by Langchain with the Llama-2 model.
Functions and Main Loop:
record_audio: Captures audio via the microphone.transcribe: Converts audio to text.get_llm_response: Retrieves responses from the Llama-2 model based on conversation context.play_audio: Plays synthesized audio back to the user.
The main loop involves recording the user's voice, transcribing it, obtaining a response from the LLM, and communicating the response back audibly.
Result and Features
A demo showcases the assistant's capabilities, highlighting real-time voice interaction and context-aware responses. The application is functional but may experience slower performance on less powerful computers unless optimized enhancements like CUDA support are used.
Suggestions for Improvement
For a production-level application, consider:
- Performance optimizations with lighter models.
- Customizable assistant prompts and personas.
- A graphical user interface (GUI) for better user experience.
- Multimodal interaction capabilities, including image or diagram generation.
In conclusion, building this voice assistant provides a practical example of integrating speech recognition, language modeling, and text-to-speech technologies, offering a unique opportunity to create a locally running intelligent assistant while protecting user privacy. Enjoy crafting your own Jarvis-like companion!