Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use dmusingu/luganda_wav2vec2_ctc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmusingu/luganda_wav2vec2_ctc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmusingu/luganda_wav2vec2_ctc")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("dmusingu/luganda_wav2vec2_ctc") model = AutoModelForCTC.from_pretrained("dmusingu/luganda_wav2vec2_ctc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74ac26e7097b089e07b09cc310936caf773783e037cf8cfa69438b015d90950d
- Size of remote file:
- 4.73 kB
- SHA256:
- 9204dcb9e5f08efbea14f0b587d7c3f6387efbbad2ce9f488dc94c37feec59c1
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