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 training_args.bin from proseph/ctrlv-wav2vec2-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/proseph/ctrlv-wav2vec2-tokenizer/resolve/main/training_args.bin
- Command line
-
hf download hf://proseph/ctrlv-wav2vec2-tokenizer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/proseph/ctrlv-wav2vec2-tokenizer/resolve/main/training_args.bin
2.86 kB
- Xet hash:
- 06d6be54d31c8479167afc0f58aeb8045db603b866c3c13f1821fc63a679891d
- Size of remote file:
- 2.86 kB
- SHA256:
- a49b90909e3db996c141e616aafe642f8ae3e1d950477f6ad29119bac432b9fe
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