NaijaVox-V1

Nigeria's Voice in AI.

NaijaVox-V1 is the first open-weight automatic speech recognition model fine-tuned specifically for Nigerian languages — Yoruba (with full diacritics), Hausa, Igbo, Nigerian Pidgin, and Nigerian-accented English. Built on OpenAI Whisper-large-v3 using parameter-efficient LoRA fine-tuning, NaijaVox is designed to be practical, deployable, and accessible to Nigerian developers and researchers.

"Every Nigerian deserves to be heard and understood by AI — in their own language, with their own voice."

→ NaijaVox-2.0 is now available — the improved second generation with 19.1% better average WER, SpecAugment, noise augmentation, and 25,866 training samples across 7 datasets.

🗣️ Languages Supported

Note: <\|ig\|> and <\|pcm\|> are new language tokens added to the Whisper vocabulary. The extended tokenizer is included in this repository.

🚀 Quick Start

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="Axiveri/NaijaVox-V1",
    device=0  # use GPU, or remove for CPU
)

result = pipe("your_audio.wav")
print(result["text"])

Specifying Language

from transformers import WhisperProcessor, WhisperForConditionalGeneration
import torch

model     = WhisperForConditionalGeneration.from_pretrained("Axiveri/NaijaVox-V1")
processor = WhisperProcessor.from_pretrained("Axiveri/NaijaVox-V1")
vocab     = processor.tokenizer.get_vocab()

# Map language to token
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]]
    transcribe = vocab["<|transcribe|>"]
    notimestamps = vocab["<|notimestamps|>"]
    forced_ids = [[1, lang_id], [2, transcribe], [3, notimestamps]]

    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,
            forced_decoder_ids=forced_ids,
            max_new_tokens=448
        )
    return processor.tokenizer.decode(generated[0], skip_special_tokens=True)

📊 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.

Lower WER = better. Human-level transcription is approximately 5–10%.

🎙️ Sample Transcriptions

Real audio samples from FLEURS test, Nigerian English, and Pidgin datasets — data the model never saw during training. Transcriptions generated live by the published model itself.

Yoruba

Hausa

Igbo

Nigerian English

Nigerian Pidgin

🏗️ Model Architecture

Input Audio (16kHz)
        │
        ▼
Whisper-large-v3 Encoder  (frozen during fine-tuning)
        │  1500 × 1280 features
        ▼
Whisper Decoder + LoRA    (r=32, alpha=64, fine-tuned)
  target modules: q_proj, k_proj, v_proj, out_proj
  trainable params: 31,457,280 / 1,574,950,400 (1.997%)
        │
        ▼
Extended Tokenizer         (vocab: 51,868 tokens)
  + <|ig|> Igbo token
  + <|pcm|> Nigerian Pidgin token
        │
        ▼
Transcript

📦 Training Details

Training Datasets

✅ Intended Use

NaijaVox-V1 is designed for:

• 🏦 Fintech & banking — voice-based transactions and customer service in Nigerian languages
• 📱 Mobile apps — voice input for Yoruba, Hausa, Igbo, and Pidgin speakers
• 🎙️ Media & journalism — transcribing interviews and broadcasts in Nigerian languages
• 🏥 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

The following uses are explicitly prohibited under this model's responsible use policy:

• ❌ Non-consensual surveillance — transcribing phone calls, conversations, or audio without the knowledge and consent of all parties
• ❌ Fraud facilitation — using transcription output to generate misleading records, forge spoken statements, or support advance-fee fraud (419 scams)
• ❌ Deepfake pipelines — combining with TTS models to create fake audio-text pairs attributed to real people
• ❌ Discriminatory systems — building applications that deny services based on language or accent identification derived from this model
• ❌ Political disinformation — generating or verifying false transcripts of political speech

While the Apache 2.0 license permits broad commercial use, these prohibited uses apply as a binding behavioral restriction under Axiveri's Responsible AI Use Policy.

👤 Creator

Emmanuel Ariyo (Ememzyvisuals) — Founder, Axiveri

NaijaVox-V1 was conceived, built, and trained by Emmanuel Ariyo, combining ML engineering with a Nigerian cultural design identity to bring open-weight speech recognition to Yoruba, Hausa, Igbo, Nigerian Pidgin, and Nigerian English speakers.

👥 About Axiveri

Axiveri is building Africa's AI infrastructure — open models, open data, and open tools for African languages and developers.

• 🌍 Africlaude Series — African language models
• 🗣️ NaijaVox Collection — NaijaVox-V1 (this model) and NaijaVox-2.0

📄 Citation

@misc{naijavox2026,
  title        = {NaijaVox-V1: Open-Weight Speech Recognition for Nigerian Languages},
  author       = {Ariyo, Emmanuel (Ememzyvisuals)},
  year         = {2026},
  publisher    = {HuggingFace},
  howpublished = {\url{https://huggingface.co/Axiveri/NaijaVox-V1}}
}

📜 License

Apache 2.0 — free for commercial and research use with attribution. Additional behavioral restrictions apply as described in the Prohibited Use section above.