Ultravox V0 4 1 Mistral Nemo
Meet Ultravox V0 4 1 Mistral Nemo, a cutting-edge multimodal Speech LLM that can understand both speech and text. How does it work? It takes a text prompt with a special audio token, replaces it with audio embeddings, and generates output text. Think of it as an LLM that can also hear and understand speech. With its unique capabilities, it can be used for voice agents, speech-to-speech translation, and analysis of spoken audio. What sets it apart? It's built on a pre-trained Mistral-Nemo-Instruct-2407 backbone and the encoder part of whisper-large-v3-turbo, and its multi-modal adapter is trained using knowledge-distillation loss. The result? A model that can process audio content with a time-to-first-token of approximately 150ms and a tokens-per-second rate of ~50-100 on an A100-40GB GPU. Want to try it out? Simply install the necessary libraries, load your audio file, and use the model to generate output text.
Table of Contents
Model Overview
Imagine having a conversation with a model that can hear and respond to your voice, just like a human. That’s what Ultravox is all about. This multimodal Speech LLM can understand both speech and text, making it a powerful tool for various applications.
Key Features
- Can consume both speech and text as input
- Uses a special
<|audio|>pseudo-token to merge audio and text embeddings - Can generate output text based on the merged embeddings
- Plans to expand token vocabulary to support generation of semantic and acoustic audio tokens in the future
Capabilities
Ultravox is a game-changer when it comes to multimodal communication. It can process both text and speech inputs, enabling more natural and intuitive interactions.
What can it do?
- Speech-to-Text: Transcribe spoken audio into text, allowing for speech-to-text translation, analysis of spoken audio, and more.
- Voice Agent: Think of Ultravox as a voice assistant that can understand and respond to voice commands.
- Multimodal Interaction: Process both text and speech inputs, enabling more natural and intuitive interactions.
How does it work?
Uses a pre-trained Mistral-Nemo-Instruct-2407 backbone and the encoder part of whisper-large-v3-turbo. The model processor replaces a special <|audio|> pseudo-token with embeddings derived from the input audio, allowing the model to generate output text.
Performance
Ultravox is fast! When processing audio content, it can generate its first token in approximately 150ms. That’s quick! Additionally, it can process around 50-100 tokens per second using an A100-40GB GPU.
Accuracy
But how accurate is Ultravox? Let’s look at some numbers:
| Language Pair | Score |
|---|---|
| en_ar | 10.36 |
| en_de | 28.39 |
| es_en | 37.49 |
| ru_en | 41.64 |
| en_ca | 26.85 |
| zh_en | 12.65 |
These numbers show that Ultravox performs well in various language translation tasks. But how does it compare to other models?
Limitations
Ultravox is a powerful multimodal model, but it’s not perfect. Let’s talk about some of its limitations.
Speech-to-Text Challenges
While Ultravox can understand speech, it’s not always accurate. Speech-to-text technology is still a challenging task, and Ultravox may struggle with:
- Noisy audio inputs
- Different accents or dialects
- Fast or slow speech
Limited Vocabulary
Ultravox’s token vocabulary is currently limited, which means it may not be able to generate certain words or phrases.
What’s Next?
While Ultravox has its limitations, it’s still a powerful tool for multimodal tasks. As the model continues to evolve, we can expect to see improvements in its performance, vocabulary, and overall capabilities.
