---
## Nigeria's Voice in AI. Now Sharper.
**NaijaVox-2.0** is the second generation of Axiveri's open-weight automatic speech recognition model for Nigerian languages — Yoruba (with full diacritics), Hausa, Igbo, Nigerian Pidgin, and Nigerian-accented English. Built on OpenAI Whisper-large-v3 with PEFT LoRA fine-tuning, NaijaVox-2.0 delivers significant accuracy gains over V1 through a larger and more diverse training corpus (25,866 samples across 7 datasets), deeper LoRA adaptation (r=64 targeting attention and feed-forward layers), SpecAugment, and realistic noise augmentation for real-world robustness.
> *"Every Nigerian deserves to be heard and understood by AI — in their own language, with their own voice."*
**[← NaijaVox-V1](https://huggingface.co/Axiveri/NaijaVox-V1)** — the original model
---
## 📈 V1 → V2 Improvement
Evaluated on identical test sets with identical methodology (50 samples/language, strict WER, no normalization):
| Language | V1 WER | V2 WER | Absolute Δ | Relative Gain |
|---|---|---|---|---|
| 🇳🇬 Yoruba | 28.8% | **22.3%** | −6.5pp | **+22.6%** |
| 🇳🇬 Hausa | 31.0% | **25.8%** | −5.2pp | **+16.8%** |
| 🇳🇬 Igbo | 41.9% | **30.5%** | −11.4pp | **+27.2%** |
| 🇳🇬 Nigerian English | 21.1% | **19.6%** | −1.5pp | **+7.1%** |
| 🇳🇬 Nigerian Pidgin | 16.8% | **14.7%** | −2.1pp | **+12.5%** |
| **Average** | **27.9%** | **22.58%** | **−5.3pp** | **+19.1%** |
> Igbo sees the largest jump (+27.2% relative) — driven by WaxalNLP Igbo TTS data and Nigerian Common Voice Igbo samples, combined with SpecAugment frequency masking.
---
## 🗣️ Languages Supported
| Language | ISO Code | Script | Token |
|---|---|---|---|
| Yoruba | `yo` | Latin + full diacritics (ẹ, ọ, ṣ, à, á, etc.) | `<\|yo\|>` |
| Hausa | `ha` | Latin + special chars (ƙ, ƴ, ɗ, etc.) | `<\|ha\|>` |
| Igbo | `ig` | Latin + diacritics | `<\|ig\|>` |
| Nigerian Pidgin | `pcm` | Latin | `<\|pcm\|>` |
| Nigerian English | `en` | Latin | `<\|en\|>` |
> **Note:** `<\|ig\|>` and `<\|pcm\|>` are custom language tokens added to the Whisper vocabulary. The extended tokenizer is included in this repository.
---
## 🚀 Quick Start
```python
from transformers import pipeline
pipe = pipeline(
"automatic-speech-recognition",
model="Axiveri/NaijaVox-2.0",
device=0 # use GPU, or remove for CPU
)
result = pipe("your_audio.wav")
print(result["text"])
```
### Specifying Language
```python
from transformers import (
WhisperForConditionalGeneration, WhisperFeatureExtractor,
WhisperProcessor, PreTrainedTokenizerFast,
)
from huggingface_hub import hf_hub_download
import torch
MODEL_ID = "Axiveri/NaijaVox-2.0"
model = WhisperForConditionalGeneration.from_pretrained(MODEL_ID)
# Standard load first; this model's custom <|pcm|> / <|ig|> tokens don't
# always come through cleanly this way, so fall back to manually rebuilding
# the tokenizer from tokenizer.json if they're missing.
try:
processor = WhisperProcessor.from_pretrained(MODEL_ID)
vocab = processor.tokenizer.get_vocab()
assert "<|pcm|>" in vocab and "<|ig|>" in vocab
except Exception:
fe = WhisperFeatureExtractor.from_pretrained(MODEL_ID)
tok_file = hf_hub_download(repo_id=MODEL_ID, filename="tokenizer.json")
tokenizer = PreTrainedTokenizerFast(tokenizer_file=tok_file)
tokenizer.add_special_tokens({
"additional_special_tokens": [
t for t in [
"<|startoftranscript|>", "<|endoftext|>", "<|transcribe|>",
"<|notimestamps|>", "<|en|>", "<|yo|>", "<|ha|>", "<|ig|>", "<|pcm|>",
]
if t not in tokenizer.get_vocab()
]
})
processor = WhisperProcessor(feature_extractor=fe, tokenizer=tokenizer)
vocab = processor.tokenizer.get_vocab()
LANG_TOKENS = {
"yoruba": "<|yo|>",
"hausa": "<|ha|>",
"igbo": "<|ig|>",
"nigerian_english": "<|en|>",
"pidgin": "<|pcm|>",
}
def transcribe(audio_array, sampling_rate, language="yoruba"):
lang_id = vocab[LANG_TOKENS[language]]
start = vocab["<|startoftranscript|>"]
trans = vocab["<|transcribe|>"]
nots = vocab["<|notimestamps|>"]
decoder_input_ids = torch.tensor([[start, lang_id, trans, nots]])
inputs = processor.feature_extractor(
audio_array, sampling_rate=sampling_rate, return_tensors="pt"
).input_features
with torch.no_grad():
generated = model.generate(
input_features=inputs,
decoder_input_ids=decoder_input_ids,
max_new_tokens=448
)
return processor.tokenizer.decode(generated[0], skip_special_tokens=True).strip()
```
---
## 📊 Benchmark Results
Evaluated on FLEURS test splits (Yoruba, Hausa, Igbo), Nigerian Pidgin ASR test set, and Nigerian Accented English dataset. 50 samples per language, greedy decoding, strict WER via `jiwer` (no text normalization). **Identical methodology to V1 for direct comparison.**
| Language | WER (%) | Accuracy (%) | Test Set | Samples |
|---|---|---|---|---|
| 🇳🇬 Nigerian Pidgin | **14.7** | **85.3** | asr-nigerian-pidgin/nigerian-pidgin-1.0 | 50 |
| 🇳🇬 Nigerian English | **19.6** | **80.4** | benjaminogbonna/nigerian_accented_english | 50 |
| 🇳🇬 Yoruba | **22.3** | **77.7** | google/fleurs yo_ng | 50 |
| 🇳🇬 Hausa | **25.8** | **74.2** | google/fleurs ha_ng | 50 |
| 🇳🇬 Igbo | **30.5** | **70.5** | google/fleurs ig_ng | 50 |
| **Average** | **22.58** | **77.62** | — | 250 |
> Lower WER = better. Human-level transcription ≈ 5–10%.
---
## 🛡️ Robustness Improvements over V1
### SpecAugment
Frequency masking (up to 27 mel bins) and time masking (up to 100 time steps) applied to mel spectrograms during training. This prevents over-reliance on specific frequency bands or time positions, improving generalization to real-world recordings.
### Noise Augmentation
30% of training samples received realistic background noise injection at random SNR levels before mel extraction. This directly trains the model for common Nigerian recording conditions — market noise, phone compression artifacts, outdoor ambient sound, and crowd audio.
### Code-Switching Robustness
Trained on Nigerian Pidgin and Nigerian English together with Yoruba, Hausa, and Igbo — all of which contain natural code-switching patterns present in everyday Nigerian speech, media, and social content.
---
## 🎙️ Sample Transcriptions
> Real audio samples from FLEURS test, Nigerian English, and Pidgin datasets — data the model **never saw during training**. Transcriptions generated by the published merged model.
### Yoruba
| Reference | Audio | NaijaVox-2.0 Output |
|---|:---:|---|
| *àwọn èyàn ti mọ̀ nípa àwọn kemika pepe bí wúrà fàdákà àti kọ́pa àtijọ́ torípé a lè rí wọn* | | àwọn èèyàn ti mọ̀ nípa àwọn kẹmíkà pèèpèé bí wúrà fàdákà àti kọpa àtijọ́ torí pé a lè rí wọn |
| *àwọn ara ìrano lo kọ́kọ́ bẹ̀rẹ̀ si ni sin ewure ní bíi ọdún 15,0000 sẹ́yìn ní oke sagrosi* | | àwọn ará ìrà náà ló kọ́kọ́ bẹ̀rẹ̀ sí ní sin ewúrẹ́ ní bí ọdún 1500 sẹ́yìn ní òkè sagrosi |
### Hausa
| Reference | Audio | NaijaVox-2.0 Output |
|---|:---:|---|
| *an kwatanta faretin gine-ginen da ke yin sararin samaniyar hong kong da ginshiƙi mai walƙi* | | an kwatanta feretin gine-ginen da ke yin sararin samaniya hong kong da ginshiki mai walƙiy |
| *aristotle masanin falsafa ne yayi tunanin cewa komai ya kunshi cakuda daya ko fiye daga ab* | | aristotle masanin falsafani ya yi tunanin cewa kome ya kunshi ca kuda daya ko fiye daga ab |
### Igbo
| Reference | Audio | NaijaVox-2.0 Output |
|---|:---:|---|
| *ka akara rossby na-adị obere karịa ka arụmarụ na-adịkwu obere nke kpakpando n'ikwanye ugwu* | | akara rossby na-adị obere karịa ka arụmarụ na-adịkwa obere nke kpakpando n'ịkwà nye monto |
| *ka agha dara mba britenị jiri ndị agha elu mmiri gbochie ndị jamani inweta enyemaka* | | ka agha adara mba briten jiri ndị agha elu mmiri gbochie ndị jamanị inweta enyemaka |
### Nigerian English
| Reference | Audio | NaijaVox-2.0 Output |
|---|:---:|---|
| *Did it change plain? Yes. yes. Ok that means he was correct so this is if he's right that* | | Did it change green? Yes. Ok that means she was correct. So this is if its red then its no |
| *Ebube Nwagbo studied Mass Communication at Nnamdi Azikiwe University.* | | Ebube Nwagbo studied Mass Communication at Nnamdi Azikiwe University. |
### Nigerian Pidgin
| Reference | Audio | NaijaVox-2.0 Output |
|---|:---:|---|
| *on top di injury her uncle no even carry her go hospital for treatment* | | on top di injury and her uncle no even carry her go hospital for treatment |
| *she tell don jazzy for december 2016 say as she be* | | she tell don jazzy for december 2016 say i should be |
---
## 🏗️ Model Architecture
```
Input Audio (16kHz)
│
▼
Whisper-large-v3 Encoder (frozen during fine-tuning)
│ 1500 × 1280 features
▼
Whisper Decoder + LoRA (r=64, alpha=128, fine-tuned)
target modules: q_proj, k_proj, v_proj, out_proj, fc1, fc2
V1: attention only (q/k/v/out) — V2: adds feed-forward (fc1/fc2)
│
▼
Extended Tokenizer (vocab: 51,868 tokens)
+ <|ig|> Igbo token
+ <|pcm|> Nigerian Pidgin token
│
▼
Transcript
```
> V2 publishes a **fully merged standalone model** — no PEFT dependency required. Load directly with `transformers`.
---
## 📦 Training Details
| Parameter | V1 | V2 |
|---|---|---|
| Base model | openai/whisper-large-v3 | openai/whisper-large-v3 |
| Fine-tuning method | LoRA (PEFT) | LoRA (PEFT) |
| LoRA rank | 32 | **64** |
| LoRA alpha | 64 | **128** |
| Target modules | q/k/v/out_proj | **q/k/v/out_proj + fc1/fc2** |
| LoRA dropout | 0.05 | 0.05 |
| Training precision | fp16 | fp16 |
| Effective batch size | 16 | **32** |
| Learning rate | 1e-3 | **5e-4** |
| Warmup steps | 50 | **200** |
| Epochs (best) | 2 | **3 of 5** |
| SpecAugment | ❌ | **✅** |
| Noise augmentation | ❌ | **✅ (30% of samples)** |
| Total training samples | 13,866 | **25,866** |
| GPU | Tesla T4 × 2 (Kaggle) | Tesla T4 × 2 (Kaggle) |
| Total training time | ~20 hours | ~40 hours |
### Training Datasets
| Dataset | Language(s) | Samples | New in V2 |
|---|---|---|---|
| google/fleurs (yo_ng, ha_ng, ig_ng) | Yoruba, Hausa, Igbo | 8,437 | — |
| benjaminogbonna/nigerian_accented_english_dataset | Nigerian English | 2,721 | — |
| asr-nigerian-pidgin/nigerian-pidgin-1.0 | Nigerian Pidgin | 2,708 | — |
| Tundragoon/IroyinSpeech | Yoruba | 2,500 | ✅ |
| google/WaxalNLP (ha/ig/yo/pcm) | Hausa, Igbo, Yoruba, Pidgin | 6,000 | ✅ |
| benjaminogbonna/nigerian_common_voice_dataset | en/ha/ig/yo | 2,000 | ✅ |
| vpetukhov/bible_tts_hausa | Hausa | 1,500 | ✅ |
| **Total** | **5 languages** | **25,866** | |
---
## ✅ Intended Use
- 🏦 **Fintech & banking** — voice transactions and customer service in Nigerian languages
- 📱 **Mobile apps** — voice input for Yoruba, Hausa, Igbo, and Pidgin speakers
- 🎙️ **Media & journalism** — transcribing interviews and broadcasts
- 🏥 **Healthcare** — patient intake and medical documentation
- 📚 **Education** — language learning tools and accessibility
- 🔬 **Research** — low-resource ASR study for West African languages
- ♿ **Accessibility** — assistive technology for Nigerians with disabilities
---
## 🚫 Prohibited Use
- ❌ **Non-consensual surveillance** — transcribing calls without consent of all parties
- ❌ **Fraud facilitation** — forging spoken statements or supporting advance-fee fraud
- ❌ **Deepfake pipelines** — combining with TTS to fake audio attributed to real people
- ❌ **Discriminatory systems** — denying services based on language or accent identification
- ❌ **Political disinformation** — generating or verifying false transcripts of political speech
---
## 👤 Creator
**Emmanuel Ariyo (Ememzyvisuals)** — Founder, Axiveri
NaijaVox is conceived, built, and trained by Emmanuel Ariyo — combining ML engineering with a Nigerian cultural design identity to bring open-weight speech recognition to Nigerian language speakers.
---
## 👥 About Axiveri
**Axiveri** is building Africa's AI infrastructure — open models, open data, and open tools for African languages and developers.
- 🌍 [Axiveri on HuggingFace](https://huggingface.co/Axiveri)
- 🗣️ [NaijaVox Collection](https://huggingface.co/collections/Axiveri/naijavox)
---
## 📄 Citation
```bibtex
@misc{naijavox2026,
title = {NaijaVox-2.0: Open-Weight Speech Recognition for Nigerian Languages},
author = {Ariyo, Emmanuel (Ememzyvisuals)},
year = {2026},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/Axiveri/NaijaVox-2.0}}
}
```
---
## 📜 License
The model weights in this repository are released under the **Apache License 2.0**.
### Training Data Notice
NaijaVox-2.0 was fine-tuned using multiple publicly available datasets obtained from their respective publishers and repositories. Each dataset remains subject to its own original license, attribution requirements, and terms of use.
This repository does not claim ownership of the underlying training datasets and does not modify or supersede the licenses governing those datasets. Users are responsible for reviewing and complying with the applicable terms of any datasets used during training.
If any dataset attribution or licensing information requires correction or clarification, please open an issue or contact the maintainers.
### Responsible Use
NaijaVox-2.0 is intended for lawful and ethical automatic speech recognition applications. Users are expected to comply with all applicable laws, regulations, and the licenses governing both this repository and any underlying datasets.
---
