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ne-speechembed
MWirelabs/ne-speechembed
ne-speechembed is a machine learning model from MWirelabs. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-4.0.
First speech-text retrieval model for Northeast Indian languages.
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At a glance
- License
- cc-by-4.0
- Access
- Public
- Created
- May 15, 2026
- Updated
- Aug 28, 2026
- SHA
- 22797882
Model card
From the Hugging Face model README
NE-SpeechEmbed
First speech-text retrieval model for Northeast Indian languages.
Architecture
- Speech encoder: Whisper-medium (fine-tuned on NE-ASR, checkpoint-8000)
- Text encoder: xlm-roberta-base
- Projection: Linear(1024→768) speech, Linear(768→768) text
- Loss: InfoNCE with learned temperature
- Embed dim: 768 (L2 normalized)
Languages
Khasi, Garo, Mizo, Nagamese, Kokborok, Assamese, Wancho, Chakma
Retrieval Results (100-sample pool)
| Language | R@1 | R@5 | R@10 |
|---|---|---|---|
| Khasi | 5.0% | 19.0% | 27.0% |
| Garo | 7.0% | 21.0% | 33.0% |
| Mizo | 5.0% | 17.0% | 30.0% |
| Nagamese | 5.0% | 19.0% | 30.0% |
| Kokborok | 8.0% | 24.0% | 35.0% |
| Random | 1.0% | 5.0% | 10.0% |
Training Data
73,476 speech-text pairs from proprietary MWire corpora and Vaani.
Citation
If you use this model, please cite:
@inproceedings{nyalang2026nemultispeech,
title={{NE}-MultiSpeech: A Multilingual Speech Corpus and {ASR} Benchmark for Northeast Indian Languages},
author={Nyalang, Badal and Borah, Angana},
booktitle={The 2026 Conference on Empirical Methods in Natural Language Processing},
year={2026},
url={https://openreview.net/forum?id=cOCsA88Fza}
}
License
CC-BY-4.0 — MWire Labs
- License
- cc-by-4.0
- Languages
- as, kha, grt, multilingual
- Created
- May 15, 2026
- Updated
- Aug 28, 2026