Instructions to use oluwagbotty/mms_eng_yor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oluwagbotty/mms_eng_yor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="oluwagbotty/mms_eng_yor")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("oluwagbotty/mms_eng_yor") model = AutoModelForCTC.from_pretrained("oluwagbotty/mms_eng_yor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-300/preprocessor_config.json from oluwagbotty/mms_eng_yor: direct link, hf CLI and curl.
- Browser
- Download file 214 Bytes
-
https://huggingface.co/oluwagbotty/mms_eng_yor/resolve/864d5d791c390a94462701e845a135a24fa8589f/checkpoint-300/preprocessor_config.json
- Command line
-
hf download hf://oluwagbotty/mms_eng_yor@864d5d791c390a94462701e845a135a24fa8589f/checkpoint-300/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/oluwagbotty/mms_eng_yor/resolve/864d5d791c390a94462701e845a135a24fa8589f/checkpoint-300/preprocessor_config.json
214 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |