Model Card for Diva Llama 3

This is an end-to-end Voice Assistant Model which can handle speech and text as inputs. It is trained using distillation loss. More details will be in a paper [COMING SOON]!

See the model in action compared to SALMONN and Qwen-Audio at diva-audio.github.io.

Citation

No Publication As of Yet, But If You Use Please Cite the Below BibTeX:

@misc{DiVA,
      title={{D}istilling an {E}nd-to-{E}nd {V}oice {A}ssistant {W}ithout {I}nstruction {T}raining {D}ata}, 
      author={William Held and Ella Li and Michael Ryan and Weiyan Shi and Yanzhe Zhang and Diyi Yang},
      year={2024},
      eprint={2410.02678},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.02678}, 
}

Model Card for DiVA Llama 3
Citation
Table of Contents
Training Details
- Training Data
- Training Procedure
Environmental Impact
Technical Specifications [optional]
- Model Architecture and Objective
- Compute Infrastructure
  - Hardware
  - Software
Model Card Contact

Training Details

Training Data

This model was trained on the CommonVoice corpus.

Training Procedure

This model was trained for 7k gradient steps with a batch size of 512 Recordings and a linearly decaying learning rate from 5e-5 to zero, with a linear warmup of 70 steps.

Environmental Impact

Hardware Type: V4-32 TPU
Hours used: 8 Hours
Cloud Provider: Google Cloud.
Compute Region: US Central C

Hardware

This model was trained on at V4 TPU on Google Cloud.

Software

This model was trained with Levanter

Model Card Authors [optional]

Will Held

Model Card Contact

[email protected]