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smutuvi/ndizi-gemma4-e2b-african-asr-merged

sourceHugging Facegemmaupdated 6d agoView on Hugging Face

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Ndizi African ASR — merged weights (Swahili · Amharic · Oromo)

Full merged Transformers checkpoint for GPU / server inference. Built by merging LoRA + embed_audio into Sunbird/Sunflower-Gemma4-E2B.

For phones / edge, use the LiteRT bundle: smutuvi/ndizi-gemma4-e2b-african-asr-litert.

ASR prompts

Use the language-matched prompt (same strings as training / LiteRT on-device):

Language	Prompt
Swahili	`Andika maneno unayosikia katika sauti hii.`
Amharic	`ይህን ንግግር በአማርኛ ጻፍ። ውጤቱ ጽሑፍ ብቻ ይሁን።`
Oromo	`Dubbii kana Afaan Oromootiin barreessi. Barreeffama qofa baasi.`

Training

LoRA + embed_audio on Sunbird/Sunflower-Gemma4-E2B. Shipped checkpoint: artifacts/checkpoints_african_asr/best (v1).

Dataset	Lang	Notes
smutuvi/ndizi-1, smutuvi/ndizi-1-2025	sw	In-domain farm / field Swahili
nickdee96/ALFFA-Swahili-News	sw
hadamard-2/alffa-amharic, snapwre/amharic-speech	am	Capped
google/WaxalNLP `amh_asr` / `orm_asr`	am / om	Capped
turiabu/Sagalee	om	CC BY-NC 4.0 — check commercial use

FLEURS / SALT / Common Voice are eval-only (not in this train mix).

Eval (v1 vs Sunflower, on-device Swahili prompt)

Lower WER is better. Δ = LoRA − Sunflower (negative = improved).

Set	n	Sunflower	Ndizi v1	Δ
smutuvi/ndizi-1:test	690	0.714	0.528	−0.185
smutuvi/ndizi-1-2025:test	351	0.541	0.398	−0.143
Ndizi pooled	1041	0.655	0.484	−0.171
google/fleurs `sw_ke`:test	487	0.255	0.252	−0.003

Amharic / Oromo coverage is present in training; prefer language-matched prompts for those languages. Later adapters (v2+) trade some FLEURS Swahili for stronger Oromo — this Hub ship is v1.

Related repos

Artifact	Repo
Merged full weights	smutuvi/ndizi-gemma4-e2b-african-asr-merged
On-device LiteRT-LM (~2.7 GB)	smutuvi/ndizi-gemma4-e2b-african-asr-litert

Quick start (Transformers)

python

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

repo = "smutuvi/ndizi-gemma4-e2b-african-asr-merged"
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.float16, device_map="auto")
processor = AutoProcessor.from_pretrained(repo)
# Use the Swahili / Amharic / Oromo ASR prompt from the table above with your audio.