Automatic Speech Recognition
Transformers
Safetensors
Oromo
wav2vec2
african-languages
waxal
waxalnet
Instructions to use waxal-benchmarking/mms-300m-waxal-orm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use waxal-benchmarking/mms-300m-waxal-orm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="waxal-benchmarking/mms-300m-waxal-orm")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("waxal-benchmarking/mms-300m-waxal-orm") model = AutoModelForCTC.from_pretrained("waxal-benchmarking/mms-300m-waxal-orm") - Notebooks
- Google Colab
- Kaggle
| { | |
| "feature_extractor": { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| }, | |
| "processor_class": "Wav2Vec2Processor" | |
| } | |