Instructions to use adkta/nep_eng_code-mixed_translit_lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adkta/nep_eng_code-mixed_translit_lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="adkta/nep_eng_code-mixed_translit_lm")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("adkta/nep_eng_code-mixed_translit_lm") model = AutoModelForCTC.from_pretrained("adkta/nep_eng_code-mixed_translit_lm") - Notebooks
- Google Colab
- Kaggle
| { | |
| "feature_extractor": { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| }, | |
| "processor_class": "Wav2Vec2Processor" | |
| } | |