Automatic Speech Recognition
Transformers
PyTorch
JAX
Ganda
wav2vec2
audio
speech
xlsr-fine-tuning-week
Eval Results (legacy)
Instructions to use birgermoell/wav2vec2-luganda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use birgermoell/wav2vec2-luganda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="birgermoell/wav2vec2-luganda")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("birgermoell/wav2vec2-luganda") model = AutoModelForCTC.from_pretrained("birgermoell/wav2vec2-luganda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ad58347e2b3b5967f8f4bffa8400a5cb95881f8dee390bcbc87b7af7813e665
- Size of remote file:
- 2.29 kB
- SHA256:
- 1f4a8c96a480c36d335b5448b1c443ce28e324cd68fc6d09a620a714781974b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.