The model was trained by next-token prediction over a subset of LibriSpeech, Libri-Light and a synthetic data
sTinyStories. It was then trained with DPO over
SpokenSwag.
This is a base SpeechLM and as such can be used to generate continuations for speech segments, or as base for further tuning. See the SlamKitcodebase for more details on usage, and checkout the demo page for some generation examples
Out-of-Scope Use
This model was trained on curated speech datasets which contain mainly audio-books and stories, as such the outputs should not be treated as factual in any way.
How to Get Started with the Model
We refer users to the official repository for full usage explanations - github.
Training Details
We highly encourage users to read the full paper, for full training details, a brief overview is provided below.
Training Data
This model was trained on a subset of LibriSpeech train,
Libri-Light and the synthetic dataset
sTinyStories for the pre-training phase. It was also trained with DPO on the synthetic
dataset SpokenSwag.
Training Procedure
This model was trained by next token prediction over several datasets, and then trained with DPO over SpokenSwag.
Please refer to the paper or code for the full training recipes.
Preprocessing
Speech tokens are extracted from the audio using Hubert-25hz, and quantised using the
official kmeans released with the model in textlesslib. Units are de-duplicated.
We encourage you to explore the official repository for full details - github.
Evaluation
The paper provides full results, we do give here some results and also refer to the demo page to listen to some samples.
This model was trained using only a single Nvidia A5000 GPU, 16 CPU cores and 24 GB of RAM for 24 hours.
Software
The model was trained using the SlamKit codebase which builds upon 🤗transformers extending it to support
easy and efficient training of Speech Language Models.
Citation
BibTeX:
@misc{maimon2025slamming,
title={Slamming: Training a Speech Language Model on One GPU in a Day},
author={Gallil Maimon and Avishai Elmakies and Yossi Adi},
year={2025},
eprint={2502.15814},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.15814},
}
