Instructions to use esc-benchmark/wav2vec2-ctc-switchboard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-ctc-switchboard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-ctc-switchboard")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-benchmark/wav2vec2-ctc-switchboard") model = AutoModelForCTC.from_pretrained("esc-benchmark/wav2vec2-ctc-switchboard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from esc-benchmark/wav2vec2-ctc-switchboard: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/esc-benchmark/wav2vec2-ctc-switchboard/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://esc-benchmark/wav2vec2-ctc-switchboard/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/esc-benchmark/wav2vec2-ctc-switchboard/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- 6052190ce1ad18f02907a63b2c8d1c885800d634fb20ea0945506174e1f39422
- Size of remote file:
- 1.26 GB
- SHA256:
- de965e57033b5cf6db40ce4bdb9a83d1ddac8e396e33510b226adf5bd94c1ad4
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