Instructions to use BUT-FIT/DiCoW_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BUT-FIT/DiCoW_v2 with Transformers:
# Load model directly from transformers import AutoProcessor, WhisperForConditionalGenerationWithCTC processor = AutoProcessor.from_pretrained("BUT-FIT/DiCoW_v2") model = WhisperForConditionalGenerationWithCTC.from_pretrained("BUT-FIT/DiCoW_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "chunk_length": 30, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 128, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "n_samples": 480000, | |
| "nb_max_frames": 3000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "WhisperProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |