Instructions to use gary109/wav2vec2-base-timit-demo-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gary109/wav2vec2-base-timit-demo-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gary109/wav2vec2-base-timit-demo-colab")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("gary109/wav2vec2-base-timit-demo-colab") model = AutoModelForCTC.from_pretrained("gary109/wav2vec2-base-timit-demo-colab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gary109/wav2vec2-base-timit-demo-colab: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/gary109/wav2vec2-base-timit-demo-colab/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gary109/wav2vec2-base-timit-demo-colab/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gary109/wav2vec2-base-timit-demo-colab/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- ff46e39b988b39e32d659b590d4a28971ea19fd146bd5b8b2b57b6fbf928fb99
- Size of remote file:
- 378 MB
- SHA256:
- 2d2c24c12c7fd856b04070fb6cd9055b51f3c212c0bf885a0218356233a9b0f2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.