Instructions to use hf-internal-testing/tiny-random-wav2vec2-conformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-wav2vec2-conformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-internal-testing/tiny-random-wav2vec2-conformer")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-conformer") model = AutoModelForCTC.from_pretrained("hf-internal-testing/tiny-random-wav2vec2-conformer") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
#2
by Xenova HF Staff - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:10bcf001bf54f2596e5637e45e0ddef3a9ab7506a5217b3c3236b3279cf4ab7f
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size 1017864
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