| Modify any of the [Wav2Vec2FeatureExtractor] parameters to create your custom feature extractor: | |
| from transformers import Wav2Vec2FeatureExtractor | |
| w2v2_extractor = Wav2Vec2FeatureExtractor(sampling_rate=8000, do_normalize=False) | |
| print(w2v2_extractor) | |
| Wav2Vec2FeatureExtractor { | |
| "do_normalize": false, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": false, | |
| "sampling_rate": 8000 | |
| } | |
| Processor | |
| For models that support multimodal tasks, 🤗 Transformers offers a processor class that conveniently wraps processing classes such as a feature extractor and a tokenizer into a single object. |