Audio Classification
Transformers
PyTorch
Safetensors
wav2vec2
pretraining
music
audio
speech
audio-representation-learning
arch-benchmark
general-audio
Instructions to use ALM/wav2vec2-large-audioset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ALM/wav2vec2-large-audioset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ALM/wav2vec2-large-audioset")# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("ALM/wav2vec2-large-audioset") model = AutoModelForPreTraining.from_pretrained("ALM/wav2vec2-large-audioset", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d03c9d9405bc3ab835549e34e329b864502bf7e414a45509068a36f157d2127
- Size of remote file:
- 1.27 GB
- SHA256:
- afa441a248197177de941ced743408f8012b8d3b7147780617f57df8fbaa222a
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