Automatic Speech Recognition
Transformers
Hausa
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Cdial/hausa-asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cdial/hausa-asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Cdial/hausa-asr")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Cdial/hausa-asr") model = AutoModelForCTC.from_pretrained("Cdial/hausa-asr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0cfb694094893408fdc5c63d40659f6c7f894ac073f53c49b6895ea6fe7c979
- Size of remote file:
- 1.26 GB
- SHA256:
- 7be87e0d4d5617e9068e57ef8f6b00e4cb98c93ac21b4ed18d1cc7d5570872e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.