Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Yoruba
wav2vec2-bert
Generated from Trainer
Eval Results (legacy)
Instructions to use oyemade/w2v-bert-2.0-yoruba-CV17.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oyemade/w2v-bert-2.0-yoruba-CV17.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="oyemade/w2v-bert-2.0-yoruba-CV17.0")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("oyemade/w2v-bert-2.0-yoruba-CV17.0") model = AutoModelForCTC.from_pretrained("oyemade/w2v-bert-2.0-yoruba-CV17.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download language_model/attrs.json from oyemade/w2v-bert-2.0-yoruba-CV17.0: direct link, hf CLI and curl.
- Browser
- Download file 78 Bytes
-
https://huggingface.co/oyemade/w2v-bert-2.0-yoruba-CV17.0/resolve/main/language_model/attrs.json
- Command line
-
hf download hf://oyemade/w2v-bert-2.0-yoruba-CV17.0/language_model/attrs.json
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curl -L -o attrs.json https://huggingface.co/oyemade/w2v-bert-2.0-yoruba-CV17.0/resolve/main/language_model/attrs.json
78 Bytes
| {"alpha": 0.5, "beta": 1.5, "unk_score_offset": -10.0, "score_boundary": true} |