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
Training in progress, epoch 1
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +2 -2
config.json
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"torch_dtype": "float32",
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"transformers_version": "4.
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"use_weighted_layer_sum": false,
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"vocab_size": 32,
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"xvector_output_dim": 512
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"torch_dtype": "float32",
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"transformers_version": "4.32.0",
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"use_weighted_layer_sum": false,
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"vocab_size": 32,
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"xvector_output_dim": 512
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pytorch_model.bin
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training_args.bin
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