license: apache-2.0
datasets:
- Ijwi-ry-Ikirundi-AI/Kirundi_Open_Speech_Dataset
language:
- rn
- fr
- en
metrics:
- bleu
base_model:
- facebook/nllb-200-distilled-600M
pipeline_tag: translation
library_name: transformers
tags:
- nllb
- fine-tuned
- kirundi
- multilingual
- umuseke-w-ijwi
- ijwi-ry-ikirundi
- burundi
model-index:
- name: nllb-kirundi-multi
results:
- task:
type: translation
dataset:
name: Kirundi Open Speech Dataset
type: Ijwi-ry-Ikirundi-AI/Kirundi_Open_Speech_Dataset
metrics:
- name: BLEU
type: bleu
value: 9.94
nllb-kirundi-multi (Umuseke w'Ijwi) 🇧🇮
Ikirundi cacu, Ijwi ryacu!
(Our language, our voice!)
Fine-tuned NLLB-200-600M model for multidirectional translation.
Part of the Umuseke w'Ijwi initiative for African Linguistic Sovereignty.
🌍 Vision
Our mission is to bridge the "Digital Linguistic Divide" for the 30 million Kirundi speakers in East Africa. By creating open-source AI infrastructure, we ensure that Kirundi is a first-class citizen in the global digital revolution.
This model serves as a foundational "engine" for:
- 🎙️ Speech-to-Text (ASR)
- 🗣️ Text-to-Speech (TTS)
- 🌐 Machine Translation (MT)
🚀 Key Features
This is a multidirectional translation model (Seq2Seq) capable of translating:
- Kirundi (run_Latn) ↔ French (fra_Latn)
- Kirundi (run_Latn) ↔ English (eng_Latn)
📊 Training & Performance
- Base Model: Meta NLLB-200-distilled-600M.
- Dataset: Kirundi Open Speech Dataset (approx. 11k pairs in this version).
- Current Score: 9.94 BLEU (Iteration V1). Performance is expected to increase as the community adds more validated "Gold" phrases.
🛠️ Usage (Python)
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Ijwi-ry-Ikirundi-AI/nllb-kirundi-multi")
model = AutoModelForSeq2SeqLM.from_pretrained("Ijwi-ry-Ikirundi-AI/nllb-kirundi-multi")
# Example: Kirundi -> French
tokenizer.src_lang = "run_Latn"
text = "Amahoro y'Imana abane nawe."
inputs = tokenizer(text, return_tensors="pt")
output = model.generate(
**inputs,
forced_bos_token_id=tokenizer.convert_tokens_to_ids("fra_Latn")
)
print(tokenizer.decode(output[0], skip_special_tokens=True))
🫱🏽🫲🏻 Community & Open Source
This project is dedicated to the Open Source community. We believe that linguistic data and AI models should be public goods to foster innovation and inclusion across Africa.
👥 Contributors
- Samandari: github.com/Sama-ndari
- Rugwe: github.com/Irumva17
Built by Ijwi ry'Ikirundi AI.