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 pytorch_model.bin from snehatyagi/wav2vec2_timit: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/snehatyagi/wav2vec2_timit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://snehatyagi/wav2vec2_timit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/snehatyagi/wav2vec2_timit/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 6f272823e1f2db954cde888d70db2b477e1634cd666923f626afe1ede80a09b1
- Size of remote file:
- 378 MB
- SHA256:
- 9376a2245e871125cc690f5f218c0f78b3564faf2643b1dbe8136ab8edf1457e
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