Instructions to use sulaimank/w2vbert-lingala-sd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sulaimank/w2vbert-lingala-sd3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sulaimank/w2vbert-lingala-sd3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sulaimank/w2vbert-lingala-sd3") model = AutoModelForCTC.from_pretrained("sulaimank/w2vbert-lingala-sd3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from sulaimank/w2vbert-lingala-sd3: direct link, hf CLI and curl.
- Browser
- Download file 275 Bytes
-
https://huggingface.co/sulaimank/w2vbert-lingala-sd3/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://sulaimank/w2vbert-lingala-sd3/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/sulaimank/w2vbert-lingala-sd3/resolve/main/preprocessor_config.json
275 Bytes
| { | |
| "feature_extractor_type": "SeamlessM4TFeatureExtractor", | |
| "feature_size": 80, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 1, | |
| "processor_class": "Wav2Vec2BertProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "stride": 2 | |
| } | |