Instructions to use Yehor/w2v-bert-uk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yehor/w2v-bert-uk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Yehor/w2v-bert-uk")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Yehor/w2v-bert-uk") model = AutoModelForCTC.from_pretrained("Yehor/w2v-bert-uk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Yehor/w2v-bert-uk: direct link, hf CLI and curl.
- Browser
- Download file 277 Bytes
-
https://huggingface.co/Yehor/w2v-bert-uk/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Yehor/w2v-bert-uk/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Yehor/w2v-bert-uk/resolve/main/preprocessor_config.json
277 Bytes
| { | |
| "feature_extractor_type": "SeamlessM4TFeatureExtractor", | |
| "feature_size": 80, | |
| "num_mel_bins": 80, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "Wav2Vec2BertProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "stride": 2 | |
| } | |