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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4378dcd76ce5ed93fb439386569b5c77a9a6b720d82d264afbdfa1983eeadeb0
- Size of remote file:
- 1.26 GB
- SHA256:
- 5b7184a8b712f7c9a6d87f92e99cef5f6029561db721f4d6395da5333702091f
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