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 added_tokens.json from Vkt/model-dataaugmentationpipe: direct link, hf CLI and curl.
- Browser
- Download file 36 Bytes
-
https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/main/added_tokens.json
- Command line
-
hf download hf://Vkt/model-dataaugmentationpipe/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/main/added_tokens.json
36 Bytes
| {"<s>": 37, "</s>": 38, "[PAD]": 39} |