Ultravox V0 5 Llama 3 3 70b Tempfix

🚀 Model Card for Ultravox

Ultravox is a multimodal Speech LLM that combines speech and text processing capabilities, leveraging pre - trained backbones for enhanced performance.

🚀 Quick Start

Ultravox is a multimodal Speech LLM built around a pretrained [Llama3.3 - 70B - Instruct](https://huggingface.co/meta - llama/Llama - 3.3 - 70B - Instruct) and [whisper - large - v3 - turbo](https://huggingface.co/openai/whisper - large - v3 - turbo) backbone.

See https://ultravox.ai for the GitHub repo and more information.

✨ Features

• Multimodal input: Can consume both speech and text as input.
• Potential for voice output in future revisions.
• Applicable in various speech - related tasks such as voice agents and speech - to - speech translation.

📦 Installation

# pip install transformers peft librosa

💻 Usage Examples

Basic Usage

import transformers
import numpy as np
import librosa

pipe = transformers.pipeline(model='fixie - ai/ultravox - v0_5 - llama - 3_3 - 70b', trust_remote_code=True)

path = "<path - to - input - audio>"  # TODO: pass the audio here
audio, sr = librosa.load(path, sr = 16000)

turns = [
  {
    "role": "system",
    "content": "You are a friendly and helpful character. You love to answer questions for people."
  },
]
pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens = 30)

📚 Documentation

Model Details

Model Description

Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message). The input to the model is given as a text prompt with a special <|audio|> pseudo - token, and the model processor will replace this magic token with embeddings derived from the input audio. Using the merged embeddings as input, the model will then generate output text as usual.

In a future revision of Ultravox, we plan to expand the token vocabulary to support generation of semantic and acoustic audio tokens, which can then be fed to a vocoder to produce voice output. No preference tuning has been applied to this revision of the model.

• Developed by: Fixie.ai
• License: MIT

Model Sources

• Repository: https://ultravox.ai
• Demo: See repo

Training Details

Training Data

The training dataset is a mix of ASR datasets, extended with continuations generated by Llama 3.1 8B, and speech translation datasets, which yield a modest improvement in translation evaluations.

Training Procedure

Supervised speech instruction finetuning via knowledge - distillation. For more info, see [training code in Ultravox repo](https://github.com/fixie - ai/ultravox/blob/main/ultravox/training/train.py).

Training Hyperparameters

• Training regime: BF16 mixed precision training
• Hardward used: 8x H100 GPUs

Speeds, Sizes, Times

The current version of Ultravox, when invoked with audio content, has a time - to - first - token (TTFT) of approximately 150ms, and a tokens - per - second rate of ~50 - 100 when using an A100 - 40GB GPU, all using a Llama 3.3 70B backbone.

Check out the audio tab on [TheFastest.ai](https://thefastest.ai/?m = audio) for daily benchmarks and a comparison with other existing models.

🔧 Technical Details

The model uses a pre - trained [Llama3.3 - 70B - Instruct](https://huggingface.co/meta - llama/Llama - 3.3 - 70B - Instruct) backbone as well as the encoder part of [whisper - large - v3 - turbo](https://huggingface.co/openai/whisper - large - v3 - turbo).

The multi - modal adapter is trained, the Whisper encoder is fine - tuned, and the Llama model is kept frozen.

We use a knowledge - distillation loss where Ultravox is trying to match the logits of the text - based Llama backbone.

📄 License

The model is licensed under the MIT license.

Evaluation