Automatic Speech Recognition
Transformers
Safetensors
wav2vec2-bert
Generated from Trainer
Eval Results (legacy)
Instructions to use dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0") model = AutoModelForCTC.from_pretrained("dmusingu/w2v-bert-2.0-luganda-CV-train-validation-7.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6bfef305facab7204a1d6a7a8928b104b5dac01c64c40e8857f91a638ac298df
- Size of remote file:
- 4.98 kB
- SHA256:
- 7ceb05a5d329714d1dbbe4ef15f6db2c1851e2765e2f69dbc7b14b49418a7dbb
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