Automatic Speech Recognition
Transformers
PyTorch
Ganda
wav2vec2
audio
common_voice
hf-asr-leaderboard
robust-speech-event
speech
Eval Results (legacy)
Instructions to use cahya/wav2vec2-luganda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/wav2vec2-luganda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="cahya/wav2vec2-luganda")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("cahya/wav2vec2-luganda") model = AutoModelForCTC.from_pretrained("cahya/wav2vec2-luganda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64603f91326e980e699e670c4cce803a648881c750f2251748f50c48e07012cc
- Size of remote file:
- 2.99 kB
- SHA256:
- f6686eb761c60d4a768aedfe2f313d0901efa07932ef0df79b0ebfac5a88d1e5
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