Instructions to use Akaash1/wav2vec-khmer-english-high with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akaash1/wav2vec-khmer-english-high with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Akaash1/wav2vec-khmer-english-high")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Akaash1/wav2vec-khmer-english-high") model = AutoModelForCTC.from_pretrained("Akaash1/wav2vec-khmer-english-high") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "Wav2Vec2Processor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |