Instructions to use Rafeq/wav2vec2-base-cry-classification__ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rafeq/wav2vec2-base-cry-classification__ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Rafeq/wav2vec2-base-cry-classification__")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Rafeq/wav2vec2-base-cry-classification__") model = AutoModelForAudioClassification.from_pretrained("Rafeq/wav2vec2-base-cry-classification__") - 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 sha256:72ef359e6a53293837ab23e6c98811246d6b9711b90cd3d8537122deadc7dc83
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size 378306424
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