VERSE V2 — Nepali, Maithili and English Speech Model
VERSE V2 is a multilingual LoRA/PEFT adapter fine-tuned from Gemma 4 E4B for speech transcription and speech-to-text translation across English, Nepali, and Maithili.
This repository contains the final V2 adapter from training step 12761.
Model details
Developed by: Aashish Thakuri and Team Northlight
Model type: Multimodal speech-to-text LoRA/PEFT adapter
Base model:unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
Languages: English, Nepali, and Maithili
Fine-tuning method: LoRA / PEFT
LoRA rank: 8
LoRA alpha: 16
LoRA dropout: 0
Trainable parameters: 18,350,080
Approximate trainable share: 0.23%
Final checkpoint:checkpoint-12761
Funding: Not publicly disclosed
License: This adapter is distributed subject to the applicable Gemma terms and the licenses of the source dataset and dependencies.
Supported tasks
Transcription
English audio → English text
Nepali audio → Nepali text
Maithili audio → Maithili text
Speech-to-text translation
English audio → Nepali text
English audio → Maithili text
Nepali audio → English text
Nepali audio → Maithili text
Maithili audio → English text
Maithili audio → Nepali text
This gives nine total language directions:
text
1en → en, ne, mai
2ne → ne, en, mai
3mai → mai, en, ne
Training data
The source audio dataset was:
Firoj112/chatterbox-multilingual-data
Clean training source clips
Language
Clips
English
21,846
Nepali
25,184
Maithili
19,784
Total
66,814
Each source clip produced one same-language transcription task and two speech-translation tasks.
Final V2 multitask data
Task type
Examples
Transcription
66,814
Speech translation
133,628
Total
200,442
Validation data:
Validation item
Count
Source clips
3,712
Multitask examples
11,136
Why IndicTrans2 was used
The original dataset contained audio and same-language transcripts, but it did not contain every required cross-language translation target.
IndicTrans2 was used only to generate translated text targets for training:
AI4Bharat/indictrans2-en-indic-200M
AI4Bharat/indictrans2-indic-en-200M
AI4Bharat/indictrans2-indic-indic-320M
It was used for English ↔ Nepali, English ↔ Maithili, and Nepali ↔ Maithili target generation. IndicTrans2 did not generate the source audio and is not used as the final inference model.
Training setup
Cloud platform: RunPod
Project path:/workspace/verse-v2
Training method: LoRA / PEFT
Epochs: 1
Per-device batch size: 16
Final optimizer step: 12,761
Final checkpoint path:/workspace/verse-v2/gemma4-e4b-verse-v2-multitask/checkpoint-12761
Quick validation results
The current results are a small validation estimate using five clips per language.
Language
Samples
WER
CER
English
5
6.72%
3.05%
Nepali
5
8.20%
1.57%
Maithili
5
30.36%
6.45%
Combined
15
12.35%
3.44%
Lower WER and CER are better. These values are preliminary and should not be treated as a final benchmark or state-of-the-art claim.
VERSE V2 is intended for research and development involving live captions, subtitle generation, podcast transcription, multilingual speech accessibility, Nepali and Maithili speech recognition, and speech-to-text translation.
Limitations
Maithili transcription currently has higher WER than English and Nepali.
The reported validation sample contains only 15 clips.
Translation targets partly depend on IndicTrans2-generated labels.
Noise, overlapping speakers, music, long audio, rare dialects, and code-switching may reduce accuracy.
The adapter requires the compatible Gemma 4 E4B base model.
Independent evaluation is recommended before production or high-stakes use.
Ethical considerations
This model may produce incorrect transcriptions or translations. Outputs should be reviewed before use in legal, medical, financial, safety-critical, or other high-stakes settings. Users are responsible for respecting privacy, consent, copyright, dataset terms, and applicable laws when processing audio.
Model sources
Base model:unsloth/gemma-4-e4b-it-unsloth-bnb-4bit
VERSE V2 was built using Gemma 4 by Google DeepMind, Unsloth, Hugging Face Transformers and PEFT, IndicTrans2 by AI4Bharat, RunPod cloud infrastructure, and Firoj112/chatterbox-multilingual-data.
Citation
A formal paper citation is not available yet. Until one is published, cite the model repository:
text
1Aashish Thakuri and Team Northlight.
2VERSE V2: Nepali, Maithili and English Speech Model.
3Hugging Face, 2026.
4https://huggingface.co/Aashishhhhhhhh/verse-v2-nepali-maithili
Disclaimer
This repository contains a LoRA/PEFT adapter, not the full merged Gemma base model. To use VERSE V2, load the compatible Gemma 4 E4B base model and then attach this adapter.
