Instructions to use proseph/ctrlv-wav2vec2-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use proseph/ctrlv-wav2vec2-tokenizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="proseph/ctrlv-wav2vec2-tokenizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("proseph/ctrlv-wav2vec2-tokenizer") model = AutoModelForCTC.from_pretrained("proseph/ctrlv-wav2vec2-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from proseph/ctrlv-wav2vec2-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/proseph/ctrlv-wav2vec2-tokenizer/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://proseph/ctrlv-wav2vec2-tokenizer@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/proseph/ctrlv-wav2vec2-tokenizer/resolve/refs%2Fpr%2F1/pytorch_model.bin
378 MB
- Xet hash:
- 181eeaaad1152365d809ffa698fb143493168008067b6b8cbab59073b8dfd903
- Size of remote file:
- 378 MB
- SHA256:
- 867826ea5a7848712d62ed474d2654d7fe0fd3bbd8ed96a0afa23623c0cebb6f
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