Automatic Speech Recognition
Transformers
Safetensors
Hausa
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use EYEDOL/whisper-tiny-hausa2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EYEDOL/whisper-tiny-hausa2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EYEDOL/whisper-tiny-hausa2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EYEDOL/whisper-tiny-hausa2") model = AutoModelForSpeechSeq2Seq.from_pretrained("EYEDOL/whisper-tiny-hausa2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
End of training
Verified signatured50d5b3 -
Model save
Verified signaturee78f49f -
Training in progress, epoch 10
Verified signaturee3329c9 -
Training in progress, epoch 9
Verified signatured2640b1 -
Training in progress, epoch 8
Verified signature17dfc65 -
Training in progress, epoch 7
Verified signature14e03a6 -
Training in progress, epoch 6
Verified signaturea4b073d -
Training in progress, epoch 5
Verified signaturee70a3bd -
Training in progress, epoch 4
Verified signatureddc9019 -
Training in progress, epoch 3
Verified signature56bac9d -
Training in progress, epoch 2
Verified signature296fcd3 -
Training in progress, epoch 1
Verified signatureb80e689 -
initial commit
Verified signature6bfe46b