Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
nigerian-accent
igunosa-labs
unose
Instructions to use igunosalabs/Unose-Huen-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igunosalabs/Unose-Huen-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="igunosalabs/Unose-Huen-Base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("igunosalabs/Unose-Huen-Base") model = AutoModelForSpeechSeq2Seq.from_pretrained("igunosalabs/Unose-Huen-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Unose-Huen-Base (v0.5)
Unose-Huen-Base is a fine-tuned Whisper Base model (74M parameters) optimized for Nigerian English speech recognition, conversational prosody, and colloquial cadence.
Developed by Igunosa Labs as part of the Unose speech suite.
Model Details
- Architecture: Whisper Base (Encoder-Decoder Transformer, 6 layers, 74M params)
- Validation WER: 48.38% on the Nigerian Accented English benchmark
- Inference Targets: CPU / GPU / edge
- Available Formats: PyTorch safetensors, CTranslate2 (, , , )
Ecosystem Links
- Official GitHub Codebase: https://github.com/Igunosa-Labs/unose-huen
- Sister Model (Small 244M): igunosalabs/Unose-Huen-Small
- TTS Model: igunosalabs/Unose-Voice-v1.0
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Model tree for igunosalabs/Unose-Huen-Base
Base model
openai/whisper-base