Instructions to use wbcmthh42/wavlm_handcrafted_feat3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wbcmthh42/wavlm_handcrafted_feat3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="wbcmthh42/wavlm_handcrafted_feat3")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("wbcmthh42/wavlm_handcrafted_feat3") model = AutoModelForAudioClassification.from_pretrained("wbcmthh42/wavlm_handcrafted_feat3") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +5 -5
- model.safetensors +1 -1
README.md
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This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the None dataset.
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It achieves the following results on the evaluation set:
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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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### Framework versions
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This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6121
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- F1: 0.7785
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | F1 |
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| 0.5362 | 1.0 | 3777 | 0.6599 | 0.7557 |
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| 0.485 | 2.0 | 7555 | 0.6121 | 0.7785 |
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| 0.5009 | 3.0 | 11331 | 0.6301 | 0.7743 |
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### Framework versions
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model.safetensors
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