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library_name: transformers
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# Model Card for
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## Model Details
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### Model Description
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- **Developed by:**
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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### Downstream Use [optional]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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### Training Data
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[More Information Needed]
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### Training Procedure
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:**
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#### Speeds, Sizes, Times
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## Evaluation
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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language:
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- ar
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- de
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- en
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- es
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- fr
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- hi
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- it
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- ja
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- nl
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- pt
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- ru
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- sv
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- tr
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- uk
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- zh
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license: mit
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library_name: transformers
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datasets:
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- fixie-ai/librispeech_asr
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- fixie-ai/common_voice_17_0
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- fixie-ai/peoples_speech
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- fixie-ai/gigaspeech
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- fixie-ai/multilingual_librispeech
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- fixie-ai/wenetspeech
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- fixie-ai/covost2
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metrics:
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- bleu
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# Model Card for Ultravox
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Ultravox is a multimodal Speech LLM built around a pretrained [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) and [whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) backbone.
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See https://ultravox.ai for the GitHub repo and more information.
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## Model Details
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### Model Description
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Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message).
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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.
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Using the merged embeddings as input, the model will then generate output text as usual.
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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.
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No preference tuning has been applied to this revision of the model.
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- **Developed by:** Fixie.ai
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- **License:** MIT
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### Model Sources
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- **Repository:** https://ultravox.ai
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- **Demo:** See repo
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## Usage
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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.
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To use the model, try the following:
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```python
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# pip install transformers peft librosa
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import transformers
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import numpy as np
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import librosa
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pipe = transformers.pipeline(model='fixie-ai/ultravox-v0_4', trust_remote_code=True)
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path = "<path-to-input-audio>" # TODO: pass the audio here
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audio, sr = librosa.load(path, sr=16000)
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turns = [
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{
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"role": "system",
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"content": "You are a friendly and helpful character. You love to answer questions for people."
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},
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]
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pipe({'audio': audio, 'turns': turns, 'sampling_rate': sr}, max_new_tokens=30)
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```
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## Training Details
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The model uses a pre-trained [Llama3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) backbone as well as the encoder part of [whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo).
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Only the multi-modal adapter is trained, while Whisper encoder and Llama are kept frozen.
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We use a knowledge-distillation loss where Ultravox is trying to match the logits of the text-based Llama backbone.
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### Training Data
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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.
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### Training Procedure
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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).
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#### Training Hyperparameters
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- **Training regime:** BF16 mixed precision training
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- **Hardward used:** 8x H100 GPUs
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#### Speeds, Sizes, Times
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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 3.1 8B backbone.
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Check out the audio tab on [TheFastest.ai](https://thefastest.ai/?m=audio) for daily benchmarks and a comparison with other existing models.
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## Evaluation
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| | Ultravox 0.4 8B | **Ultravox 0.4.1 8B** |
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| **en_ar** | 11.17 | 12.28 |
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| **en_de** | 25.47 | 27.13 |
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| **es_en** | 37.11 | 39.16 |
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| **ru_en** | 38.96 | 39.65 |
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| **en_ca** | 27.46 | 29.94 |
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| **zh_en** | 10.08 | 14.55 |
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