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Prompt

Are there any LLM tools which have been designed primarily (or from the outset) for voice interaction? I'm looking for an LLM that supports seamless voice integration so that I can prompt with speech to text recognition

Output

You are looking for large language models (LLMs) that are designed primarily for voice interaction, enabling seamless integration of speech-to-text recognition. Several LLM tools and frameworks have been developed with this capability in mind, either through intrinsic design or by integrating advanced speech recognition systems. Here are some notable options:

1. IntrinsicVoice

IntrinsicVoice is an LLM specifically designed for real-time voice interaction. Unlike traditional models that rely on cascading systems (separate ASR and TTS modules), IntrinsicVoice integrates voice interaction directly into the model, reducing latency and computational overhead in multi-turn dialogues. This model bridges the gap between text and speech modalities, allowing for high-quality speech responses with low latency (under 100ms), making it ideal for real-time applications[6][8].

2. Whisper + LLaMA Integration

Whisper, an open-source speech-to-text model developed by OpenAI, can be integrated with various LLMs like LLaMA to enable voice-driven interactions. For example, a combination of Whisper for speech recognition and LLaMA 3 as the core LLM has been used to create applications that allow near real-time voice communication with LLMs. This setup allows users to speak their prompts, which are then transcribed by Whisper and processed by the LLM to generate responses[4]. The system can be extended with frameworks like NiceGUI to provide a user-friendly interface[4].

3. SpeechGPT

SpeechGPT is another model designed for end-to-end voice interaction. It uses a Chain-of-Modality (CoM) prompting technique to handle intermediate text generation before producing speech responses. This model improves the quality of generated content but may introduce some latency due to the intermediate text generation step[6].

4. AudioPaLM

AudioPaLM is a multimodal model from Google that supports both audio and text inputs and outputs. It integrates ASR and TTS capabilities into a single framework, allowing seamless interaction through speech without needing separate models for transcription and generation[6].

5. Whisper (OpenAI)

Whisper is widely considered one of the best open-source speech-to-text models available today. It can handle various accents, background noise, and technical language, making it a robust choice for integrating with LLMs for voice interaction[3]. While Whisper itself is not an LLM, it is frequently used in conjunction with models like GPT-4 or LLaMA to create voice-interactive applications.

6. DeepSpeech

DeepSpeech is another open-source ASR system that can be integrated with LLMs to enable voice interactions. While it may not be as advanced as Whisper in terms of handling long transcriptions or complex audio environments, it remains a viable option for simpler applications[3].

7. GPT-4o (GPT-4 Omni)

GPT-4o is OpenAI's successor to GPT-4 and includes multimodal capabilities, accepting inputs in various formats including audio. It supports real-time interactivity with low latency (around 232ms), making it suitable for natural conversational applications where users can interact via voice[2].

Summary

Several tools and models exist that support seamless voice integration with LLMs:

  • IntrinsicVoice offers real-time voice interaction capabilities designed from the ground up.
  • Whisper + LLaMA provides a flexible setup using open-source models.
  • SpeechGPT and AudioPaLM offer more specialized solutions for voice-based interactions.
  • GPT-4o provides multimodal capabilities including audio input.

These solutions enable you to build or use applications where you can prompt an LLM using speech seamlessly.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.