Instructions to use hadiqa123/XLS-R_53_english with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hadiqa123/XLS-R_53_english with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hadiqa123/XLS-R_53_english")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hadiqa123/XLS-R_53_english") model = AutoModelForCTC.from_pretrained("hadiqa123/XLS-R_53_english", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from hadiqa123/XLS-R_53_english: direct link, hf CLI and curl.
- Browser
- Download file 263 Bytes
-
https://huggingface.co/hadiqa123/XLS-R_53_english/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://hadiqa123/XLS-R_53_english/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/hadiqa123/XLS-R_53_english/resolve/main/preprocessor_config.json
263 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "Wav2Vec2ProcessorWithLM", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |