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 tokenizer_config.json from Vkt/model-dataaugmentationpipe: direct link, hf CLI and curl.
- Browser
- Download file 272 Bytes
-
https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/404ca090244635807d2f809aa22029f0835b7fc2/tokenizer_config.json
- Command line
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hf download hf://Vkt/model-dataaugmentationpipe@404ca090244635807d2f809aa22029f0835b7fc2/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/Vkt/model-dataaugmentationpipe/resolve/404ca090244635807d2f809aa22029f0835b7fc2/tokenizer_config.json
272 Bytes
| {"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "replace_word_delimiter_char": " ", "special_tokens_map_file": null, "name_or_path": "./", "tokenizer_class": "Wav2Vec2CTCTokenizer"} |