Instructions to use esb/wav2vec2-ctc-pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esb/wav2vec2-ctc-pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="esb/wav2vec2-ctc-pretrained")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("esb/wav2vec2-ctc-pretrained") model = AutoModel.from_pretrained("esb/wav2vec2-ctc-pretrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from esb/wav2vec2-ctc-pretrained: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/esb/wav2vec2-ctc-pretrained/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://esb/wav2vec2-ctc-pretrained/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/esb/wav2vec2-ctc-pretrained/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- 81a98f84c7e5606f4d14cdf7c3632a4c95d18b314c27343725d38e1306a46b6a
- Size of remote file:
- 1.26 GB
- SHA256:
- 8fc4e342ff9241a5c26ac4a4d62714b182097a3889f9136fbed47ba4b493aff2
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