Instructions to use snehatyagi/wav2vec2_timit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use snehatyagi/wav2vec2_timit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="snehatyagi/wav2vec2_timit")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("snehatyagi/wav2vec2_timit") model = AutoModelForCTC.from_pretrained("snehatyagi/wav2vec2_timit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from snehatyagi/wav2vec2_timit: direct link, hf CLI and curl.
- Browser
- Download file 2.99 kB
-
https://huggingface.co/snehatyagi/wav2vec2_timit/resolve/main/training_args.bin
- Command line
-
hf download hf://snehatyagi/wav2vec2_timit/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/snehatyagi/wav2vec2_timit/resolve/main/training_args.bin
2.99 kB
- Xet hash:
- f648f2e8c23ed2c6be61865c855e7dabce4f85e431b8d56a859acc2b69b1416f
- Size of remote file:
- 2.99 kB
- SHA256:
- 427a7ece5075501d650f487142945c1d07bd7f9d74ae591d3e9524200037893a
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