Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use dmusingu/luganda_wav2vec2_ctc_train_clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmusingu/luganda_wav2vec2_ctc_train_clean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmusingu/luganda_wav2vec2_ctc_train_clean")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("dmusingu/luganda_wav2vec2_ctc_train_clean") model = AutoModelForCTC.from_pretrained("dmusingu/luganda_wav2vec2_ctc_train_clean", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 26d5bd9bb101205117e612b8aa2e68a53419f0fd92367064aa5b265231a555b0
- Size of remote file:
- 5.05 kB
- SHA256:
- b5f5ced4d5ae3fa6d07760b627408b2d1d526ac0cc22e0126758a110acebc2e8
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