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:
- 171587a5c1142169fa53a0acf50690847ef6024f7178f0adabe3444d76016d99
- Size of remote file:
- 4.16 kB
- SHA256:
- 7cb8b24fe0e7e9a9fcb20d19d910e1a64067c2fc0b80ed4901f2c218882ef562
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