Instructions to use sharpcoder/wav2vec2_bjorn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharpcoder/wav2vec2_bjorn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sharpcoder/wav2vec2_bjorn")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sharpcoder/wav2vec2_bjorn") model = AutoModelForCTC.from_pretrained("sharpcoder/wav2vec2_bjorn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from sharpcoder/wav2vec2_bjorn: direct link, hf CLI and curl.
- Browser
- Download file 143 Bytes
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https://huggingface.co/sharpcoder/wav2vec2_bjorn/resolve/main/README.md
- Command line
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hf download hf://sharpcoder/wav2vec2_bjorn/README.md
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curl -L -o README.md https://huggingface.co/sharpcoder/wav2vec2_bjorn/resolve/main/README.md
143 Bytes
This project is meant to fine-tune the facebook/wav2vec2 speech-to-text library using my voice specifically for my own speech to text purposes.