The ONLY Real Time Speech AI that can run locally!!!

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AI Summary

Summary of Video Transcript

• Introduction to a real-time speech-to-speech model called Moshi, developed by a research lab named Cotai (unsure of pronunciation).
• The video covers three main points:
  1. Information about the Moshi model.
  2. Instructions on running Moshi locally on a MacBook.
  3. Experimentation with the Moshi model.

Details about Moshi

• Moshi V 0.1 is the release version.
• It includes machine learning weights, a Rust library called Candle, and PyTorch support.
• The model supports different quantizations for ease of use.
• The team behind Moshi is commended for their release approach.

Components of Moshi

1. Helium: A 7 billion parameter language model trained on 2.1 trillion tokens.
2. Mimi: A neural audio codec that models semantic and acoustic information.
3. New Multistream Architecture: Models audio from the user and Moshi on separate channels.

Demonstration and Setup

• The presenter has already installed Moshi on their local computer.
• They demonstrate running the model with quantization 4.
• Moshi is described as an experimental conversational AI with conversations limited to 5 minutes.
• The AI can perform tasks like role-playing, discussing topics, and answering questions.
• Chrome is recommended for the best browser support.

Installation and Commands

• The presenter creates a virtual environment to avoid conflicts with existing Python packages.
• The installation command provided is pip install mosior mlx.
• To run Moshi, the command is python -m mlx.mosior mlx --web -q4.
• The model is downloaded from a source like Hugging Face on the first run.

Models and Performance

• Different versions of Moshi are available, such as Moshi Car and Moshi Co, each with unique capabilities.
• The model is praised for its real-time interaction capabilities.
• The presenter plans to test Moshi on different machines and possibly with a better GPU.

Licensing and Usage

• Moshi comes with a commercially permissive license (CC BY), allowing for commercial use with proper attribution.
• The model is considered low-latency and suitable for local use or at scale.

Conclusion

• The presenter believes Moshi is one of the best speech-to-speech models available for real-time interaction.
• They express interest in seeing how companies might use Moshi to compete with other labs.
• The video ends with an encouragement for feedback and a prompt to subscribe for more content.