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:
- 02853cc78884f0290b74226121ee816e9b6867ff159e1e8c07d5f209a7c7d0c7
- Size of remote file:
- 1.26 GB
- SHA256:
- 928d4cb5a441ecd331333a409fb64165c212c6dfef23500633022c379e3612ef
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