Spire is a 7B parameter decoder-only model with strong abilities in machine translation, automatic speech recognition, and speech translation. SpireBase was created by applying speech-centric continued pretraining to TowerBase-7B-v0.1, which was itself created by applying continued pretraining to Llama 2.
Model Checkpoints
We release our checkpoints through Hugging Face. All of our models can be loaded as LlamaForCausalLM instances, allowing inference to be performed with vLLM. For further details on the models, check the paper.
The core of our approach to speech is discretization - continuous speech signals are converted into sequences of tokens, which can then be processed alongside text. Our discretization system consists of a few steps:
HuBERT Large (fairseq download) converts 16kHz .wav files into into a sequence of feature vectors, one for each 20ms frame. We use the representations from layer 22.
Our k-means model (download) maps each frame to one of 5000 clusters.
The sequences of cluster IDs are deduplicated, such that consecutive frames with the same label are collapsed into a single token. This usually shortens the sequence length by about 30%.
The spire package implements this pipeline. Assuming you have downloaded both of these files, you can use it like so:
from datasets import load_dataset
from spire.dsus import Labeler
from spire.utils import fix_fleurs_path
fleurs = load_dataset("google/fleurs", "en_us")
wav = fix_fleurs_path(fleurs["validation"][29], "validation")
labeler = Labeler("hubert_large_ll60k.pt", "kmeans_model")
speech_tokens = labeler.label(wav)
print(speech_tokens)
The output will not be very readable, as it consists of a sequence of Unicode private use area characters. However, these characters are known to the Spire tokenizer and can be combined with text:
TODO: add ASR/ST examples with this sequence
Reproducing our Inference Results
TODO: ducttape example
Reproducing our Training
Citation
If you use Spire, please cite our work:
@misc{spire,
title={From TOWER to SPIRE: Adding the Speech Modality to a Text-Only LLM},
author={Kshitij Ambilduke and Ben Peters and Sonal Sannigrahi and Anil Keshwani and Tsz Kin Lam and Bruno Martins and Marcely Zanon Boito and André F. T. Martins},
year={2025},
eprint={2503.10620},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10620}
}
Funding Information
This is an output of the European Project UTTER (Unified Transcription and Translation for Extended Reality) funded by European Union’s Horizon Europe Research and Innovation programme under grant agreement number 101070631.
