NVIDIA Nemotron-Labs-Audex-2B : LLM for all Audio Tasks

One Open-Source AI Model That Can Listen, Think, Speak, Translate, and Generate Audio

Photo by Pawel Czerwinski on Unsplash

The AI industry is slowly moving beyond text-only LLMs.

Today, developers need models that can understand voice, answer questions about audio, transcribe speech, translate conversations, generate natural speech, and even create sound effects, all while maintaining strong reasoning capabilities.

Most existing solutions combine multiple specialized models. You may use one model for speech recognition, another for text generation, another for text-to-speech, and yet another for audio generation. While this works, managing multiple models increases infrastructure complexity, latency, and deployment costs. NVIDIA is trying a different approach.

The company has introduced Nemotron-Labs-Audex-2B, a compact open-source unified audio-text Large Language Model that performs multiple audio tasks without sacrificing its text reasoning capabilities.

Let’s understand what makes this model interesting.

What is NVIDIA Nemotron-Labs-Audex-2B?

Nemotron-Labs-Audex-2B is a 2-billion parameter multimodal language model developed by NVIDIA.

Instead of focusing only on text, Audex-2B can process both audio and text as inputs while producing either text or audio as outputs. Think of it as a single AI model capable of acting like several AI systems combined.

It supports:

• Audio understanding
• Speech recognition
• Speech translation
• Text-to-speech
• Text-to-audio generation
• Speech-to-speech conversations
• Long-context reasoning
• Agentic workflows

The most impressive part is that NVIDIA claims these new audio capabilities come with little to no degradation in text intelligence, something that has been a challenge for many multimodal models.

Why Do We Need Unified Audio Models?

Most voice AI pipelines today look something like this:

Audio
   ↓
Speech Recognition
   ↓
LLM Reasoning
   ↓
Text Processing
   ↓
Text-to-Speech

Each stage requires a different model. Every model introduces:

• Additional latency
• More GPU memory
• More maintenance
• Separate optimization
• Higher inference costs

A unified model removes these intermediate steps. Instead of passing outputs between different models, one model performs the complete reasoning pipeline internally.

This simplifies production deployment considerably.

How Audex-2B Works

NVIDIA extends an existing text language model with additional components specifically designed for audio.

The architecture mainly consists of three parts.

1. Audio Encoder

The encoder converts raw audio into embeddings that the language model can understand. This enables the model to process:

• Spoken conversations
• Environmental sounds
• Music
• Mixed audio inputs

2. Extended Audio Vocabulary

Traditional LLMs only understand text tokens. Audex introduces a much larger vocabulary containing discrete audio tokens.

These tokens allow the model to generate:

• Human speech
• Audio effects
• Natural sounds

instead of only text.

3. Text LLM Backbone

At its core remains a powerful reasoning language model. This means Audex retains capabilities such as:

• Multi-step reasoning
• Instruction following
• Knowledge retrieval
• Long-context understanding
• Agent workflows

Rather than replacing the LLM, NVIDIA simply augments it with audio intelligence.

One Model, Multiple Audio Tasks

One of the biggest strengths of Audex-2B is the variety of supported tasks.

1. Audio Understanding

You can provide an audio recording and ask questions such as:

“Describe everything happening in this recording.”

The model understands both speech and non-speech sounds. For example, it can identify:

• Traffic noise
• Rain
• Dogs barking
• Musical instruments
• Human conversations

This makes it useful for surveillance, accessibility, multimedia search, and content analysis.

2. Speech Recognition

Audex also performs automatic speech recognition (ASR). Simply provide an audio clip with a transcription prompt, and it generates accurate text transcripts.

Unlike traditional ASR systems, transcription becomes just another instruction for the language model.

3. Speech Translation

The model can directly translate spoken language into another language. For example:

Spanish Audio
      ↓
English Text

or potentially integrate into multilingual conversational systems.

4. Text-to-Speech

Audex converts text into natural speech. Example:

Input:

"The weather is beautiful today."

↓

Natural spoken audio

NVIDIA recommends using its standalone Audex causal speech decoder for streaming speech synthesis.

5. Text-to-Audio

Instead of generating speech only, Audex can generate general audio from text descriptions. Example prompts include:

• Ocean waves during sunset
• Heavy rainfall
• Busy city traffic
• Birds singing in a forest

This opens opportunities for game development, media production, and content creation.

6. Speech-to-Speech Conversations

Perhaps the most interesting capability is speech-to-speech interaction.

The workflow becomes:

User speaks
      ↓
Model reasons internally
      ↓
Model replies using speech

The reasoning happens inside the model instead of requiring multiple independent systems.

Thinking Mode vs Instruct Mode

Audex supports two inference modes.

Thinking Mode

The model performs explicit reasoning before generating its final answer. Internally it produces reasoning enclosed between:

<think>
...
</think>

This improves performance on complex reasoning tasks.

Instruct Mode

For applications requiring lower latency, developers can disable explicit reasoning. This produces faster responses suitable for production assistants. This flexibility lets developers choose between speed and reasoning quality.

Large 128K Context Window

Audex supports an impressive 128K token context window. This allows developers to process:

• Long meetings
• Podcasts
• Customer support conversations
• Research discussions
• Multi-hour transcripts

without aggressive chunking.

For enterprise applications, this can significantly simplify long-document and long-audio processing.

Developer-Friendly Deployment

NVIDIA provides several deployment options. The recommended inference engine is vLLM 0.20, which supports:

• Audio understanding
• Text reasoning
• Text-to-speech
• Text-to-audio

Developers can also use:

• Hugging Face Transformers
• OpenAI-compatible API server
• Offline batch inference

This makes it easier to integrate Audex into existing AI infrastructure.

Benchmarks

NVIDIA evaluated Audex-2B across multiple categories, including:

• Text reasoning
• Text-to-speech
• Speech recognition
• Speech translation
• Audio understanding
• Text-to-audio
• Speech-to-speech generation

According to NVIDIA, the model maintains competitive text performance while adding strong multimodal audio capabilities. This is notable because many multimodal models experience noticeable drops in reasoning quality after incorporating audio or vision features.

Where Can Developers Use Audex-2B?

A single unified model can simplify many real-world applications. Some examples include:

• AI voice assistants
• Customer support bots
• Call center analytics
• Meeting transcription
• Podcast summarization
• Accessibility tools
• Voice translation systems
• Audio search engines
• Educational assistants
• Interactive gaming NPCs
• Smart home assistants
• Robotics

Instead of orchestrating multiple AI services, developers can build these applications around one model.

Current Limitations

Although promising, there are a few considerations before using Audex-2B.

The model is released under the NVIDIA Oneway Noncommercial License, meaning it is not intended for unrestricted commercial use.

Audio generation also requires additional components such as XCodec1, XCodec2, or the recommended Audex causal speech decoder, depending on the task. Setting up the full audio pipeline is therefore more involved than deploying a text-only language model.

Finally, while the model is relatively compact at 2B parameters, some audio generation workflows still benefit from GPU acceleration for efficient inference.

Final Thoughts

The AI industry is gradually moving toward models that can naturally handle multiple modalities instead of relying on separate systems for every task.

Nemotron-Labs-Audex-2B is an interesting step in that direction. Rather than specializing in a single capability, it combines speech recognition, audio understanding, speech translation, text-to-speech, text-to-audio generation, speech-to-speech interaction, and strong text reasoning within one compact model.

For researchers and developers building next-generation voice applications, this unified design can reduce pipeline complexity while enabling richer conversational experiences.

Although the current release is limited to non-commercial use, it provides an excellent reference for where multimodal AI is heading: models that can listen, understand, reason, and respond across text and audio without sacrificing intelligence in either modality.

Model weights are open-sourced below

[embed]nvidia/Nemotron-Labs-Audex-2B · Hugging Face We're on a journey to advance and democratize artificial intelligence through open source and open science.huggingface.co