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:
- 6247e6ff66b48db57ec7687ddb654b5a4e0caa2d58a38fdc28f9113992c7d7e3
- Size of remote file:
- 1.26 GB
- SHA256:
- efa7cc1744ff9b2cb159e483c40d35eecfa67d8eae4b1241304e0e5b5c707bef
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