Instructions to use Wiam/wav2vec2-large-robust-ft-libri-960h-finetuned-ravdess-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wiam/wav2vec2-large-robust-ft-libri-960h-finetuned-ravdess-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Wiam/wav2vec2-large-robust-ft-libri-960h-finetuned-ravdess-v3")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Wiam/wav2vec2-large-robust-ft-libri-960h-finetuned-ravdess-v3") model = AutoModelForAudioClassification.from_pretrained("Wiam/wav2vec2-large-robust-ft-libri-960h-finetuned-ravdess-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f0566785ab04505e096e073f091a80e4136727dd69547eb341cb5b6b46ffdca
- Size of remote file:
- 1.26 GB
- SHA256:
- 4148dad44c5642feb16052023296c148fbd2d430eca064a6abe32b19da751739
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