# Load model directly
from transformers import AutoProcessor, AutoModel
processor = AutoProcessor.from_pretrained("tinglxn/random-wav2vec2-base")
model = AutoModel.from_pretrained("tinglxn/random-wav2vec2-base")Quick Links
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Check out the documentation for more information.
This is random-wav2vec2-base, an unpretrained version of wav2vec 2.0. The weight of this model is randomly initialized, and can be used for establishing randomized baselines or training a model from scratch. The code used to do so is adapted from: https://huggingface.co/saibo/random-roberta-base.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tinglxn/random-wav2vec2-base")