---
language:
  - en
license: cc-by-4.0
multilinguality: monolingual
task_categories:
  - sentence-similarity
  - feature-extraction
tags:
  - sonar
  - speech-embeddings
  - text-embeddings
  - common-voice
  - interpretability
arxiv: 2604.18109
pretty_name: FLiP-data
---

# FLiP-data

Preprocessed data for the [FLiP](https://github.com/BUTSpeechFIT/FLiP) project — **Factorized Linear Projection for 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.

## 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` (~1M utterances), `dev` (~16k), `test` (~16k).

## Source data

Embeddings were computed from [Mozilla Common Voice v15](https://commonvoice.mozilla.org/) English using the [SONAR](https://github.com/facebookresearch/SONAR) encoder. Audio and transcripts from Common Voice are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).

## Trained checkpoints

| HF repo | Training data | Embedding | Rank | Size |
|---------|--------------|-----------|-----:|-----:|
| [BUT-FIT/FLiP-en-sonar](https://huggingface.co/BUT-FIT/FLiP-en-sonar) → `mcv15/rank-512/` | MCV v15 EN | SONAR | 512 | 207 MB |
| [BUT-FIT/FLiP-en-sonar](https://huggingface.co/BUT-FIT/FLiP-en-sonar) → `mcv15/rank-1024/` | MCV v15 EN | SONAR | 1024 | 414 MB |

## Usage

See the [FLiP GitHub repo](https://github.com/BUTSpeechFIT/FLiP) for full installation instructions and training/evaluation scripts.

Quick start after downloading:

```python
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

```bibtex
@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        = {2604.18109},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2604.18109},
}
```