language:
- en
license: cc-by-4.0
multilinguality: monolingual
task_categories:
- sentence-similarity
- feature-extraction
pretty_name: FLiP-data
tags:
- sonar
- speech-embeddings
- text-embeddings
- common-voice
- interpretability
FLiP-data
Preprocessed data for the paper FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings.
FLiP trains a factorized log-linear model to recover lexical content (keywords) from pretrained sentence embeddings via a single linear projection, with no fine-tuning of the encoder. The project code is available on GitHub.
Contents
SONAR embeddings and transcripts for Mozilla Common Voice v15 English (train / dev / test):
| File | Description |
|---|---|
*_speech_embs.npy |
SONAR speech embeddings (float32, shape [N, 1024]) |
*_text_embs.npy |
SONAR text embeddings (float32, shape [N, 1024]) |
*_sim_scores.npy |
Cosine similarity between paired speech and text embeddings |
*_transcript.txt |
Reference transcripts (one utterance per line) |
*_entities_gemini2.5_flash_lite.jsonl |
Named entities extracted with Gemini 2.5 Flash Lite |
Splits: train (~650k utterances), dev, test.
Source data
Embeddings were computed from Mozilla Common Voice v15 English using the SONAR encoder. Audio and transcripts from Common Voice are licensed under CC BY 4.0.
Usage
See the FLiP GitHub repo for full installation instructions and training/evaluation scripts.
Quick start after downloading:
import numpy as np
train_speech = np.load("cv_15/en/sonar_embeddings/train_speech_embs.npy")
train_text = np.load("cv_15/en/sonar_embeddings/train_text_embs.npy")
Citation
@misc{kesiraju2026flip,
title = {{FLiP}: Towards understanding and interpreting multimodal multilingual sentence embeddings},
author = {Kesiraju, Santosh and Yusuf, Bolaji and Sedl{\'{a}}{\v{c}}ek, Simon and Plchot, Old{\v{r}}ich and Schwarz, Petr},
year = {2026},
eprint = {2026.XXXXX},
archivePrefix = {arXiv},
primaryClass = {cs.CL}
}