Instructions to use Vkt/model-dataaugmentationpipe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vkt/model-dataaugmentationpipe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Vkt/model-dataaugmentationpipe")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Vkt/model-dataaugmentationpipe") model = AutoModelForCTC.from_pretrained("Vkt/model-dataaugmentationpipe", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from Vkt/model-dataaugmentationpipe: direct link, hf CLI and curl.
- Browser
- Download file 407 Bytes
-
https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://Vkt/model-dataaugmentationpipe/special_tokens_map.json
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curl -L -o special_tokens_map.json https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/main/special_tokens_map.json
407 Bytes
| {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]", "additional_special_tokens": [{"content": "<s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "</s>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "[PAD]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]} |