Ultravox V0 6 Gemma 3 27b

🚀 Model Card for Ultravox

Ultravox is a multimodal Speech LLM that combines a pretrained LLM and a speech encoder, enabling it to handle both speech and text inputs.

🚀 Quick Start

Think of the model as an LLM that can also hear and understand speech. As such, it can be used as a voice agent, and also to do speech-to-speech translation, analysis of spoken audio, etc.

To use the model, try the following:

# pip install transformers peft librosa

import transformers
import numpy as np
import librosa

pipe = transformers.pipeline(model='fixie-ai/ultravox-v0_6-llama-3_1-8b', 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)

✨ Features

• Multimodal input: Accepts both speech and text as input.
• Improved Hindi performance: Significantly enhanced speech understanding on Hindi in v0.6 series.
• Noise robustness: Trained on noise datasets to handle noisy input.
• Future voice output: Plan to expand token vocabulary for voice output.

📦 Installation

No specific installation steps provided in the original document.

📚 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 v0.6 series, ultravox models are trained on expanded Hindi speech data, resulting in significantly improved speech understanding performance on Hindi and modest degradation on other languages. Additionally, the v0.6 models are also trained on noise datasets for improved noise robustness and the ability to output a special string ((noise)) if the input audio is too noisy or doesn't contain clear speech.

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

The model uses a pre-trained LLM (Llama, Gemma, Qwen, etc) backbone as well as the encoder part of whisper-large-v3-turbo.

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

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

Training Data

The training dataset is a mix of ASR datasets, extended with continuations generated by Llama 3.1 8B, speech translation datasets, and noise datasets.

Training Procedure

Supervised speech instruction finetuning via knowledge-distillation. For more info, see training code in Ultravox repo.

Training Hyperparameters

• Training regime: BF16 mixed precision training
• Hardware 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 text-based LLM (Llama, Gemma, or Qwen) backbone.

Check out the audio tab on TheFastest.ai for daily benchmarks and a comparison with other existing models.

Evaluation

Evaluations are conducted on covost2 (speech translation measured in BLEU), fleurs and ultravox_calls (speech recognition measured in WER), big bench audio (audio reasoning measured in accuracy), as well as musan and ultravox_unintelligible (noise/unintelligible speech detection measured in recall).

📄 License

This model is released under the MIT license.