Instructions to use esc-bench/wav2vec2-ctc-librispeech with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-bench/wav2vec2-ctc-librispeech with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-bench/wav2vec2-ctc-librispeech")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-bench/wav2vec2-ctc-librispeech") model = AutoModelForCTC.from_pretrained("esc-bench/wav2vec2-ctc-librispeech", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from esc-bench/wav2vec2-ctc-librispeech: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/esc-bench/wav2vec2-ctc-librispeech/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://esc-bench/wav2vec2-ctc-librispeech/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/esc-bench/wav2vec2-ctc-librispeech/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- 5711c90ce2b3c15e8e53407dfdeb17cda4d9b42ff53206ebffc956fe16e5194e
- Size of remote file:
- 1.26 GB
- SHA256:
- 0d3ed0c5657138e0d9d941c1ad487dd2df01d72585b747b838519ce209c929b4
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