Instructions to use esc-benchmark/wav2vec2-ctc-ami with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-ctc-ami with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-ctc-ami")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-benchmark/wav2vec2-ctc-ami") model = AutoModelForCTC.from_pretrained("esc-benchmark/wav2vec2-ctc-ami", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from esc-benchmark/wav2vec2-ctc-ami: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/esc-benchmark/wav2vec2-ctc-ami/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://esc-benchmark/wav2vec2-ctc-ami/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/esc-benchmark/wav2vec2-ctc-ami/resolve/main/flax_model.msgpack
1.26 GB
- Xet hash:
- 41a161ea040ddbc11b18ad2a5567139a0816487e728d720b60c3b06b4f594105
- Size of remote file:
- 1.26 GB
- SHA256:
- 7173b3850cb7f212e8fc37640c9f519f42f90704e4eae106d3e562f6e66267d9
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