Instructions to use midoiv/Wav2Vec2-BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midoiv/Wav2Vec2-BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="midoiv/Wav2Vec2-BERT")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("midoiv/Wav2Vec2-BERT") model = AutoModelForAudioClassification.from_pretrained("midoiv/Wav2Vec2-BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7954c1417efcf6560befb4267ddcb72d14fd54ce8cb06aa3ddeb124e2bb24b0e
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size 1264473240
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