Hubert Finetuned Emotion Audio Dataset
This model is a fine-tuned version of facebook/hubert-base-ls960 on an unknown dataset. It achieves the following results on the evaluation set: Loss: 0.5474 Accuracy: 0.8289
Source du modèle
- Type
- Other
- Taille du fichier
- 361 MB
- Modèle de base
- facebook/hubert-base-ls960
- Version
- vRevision c43aa99
- Mis à jour
- 25 oct. 2026
- Téléchargements
- 14
- Source
- Hugging Face
Description de la source
hubert-finetuned-emotion-audio-dataset
This model is a fine-tuned version of facebook/hubert-base-ls960 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5474
- Accuracy: 0.8289
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 5.1336 | 1.0 | 640 | 1.0654 | 0.6492 |
| 3.4287 | 2.0 | 1280 | 0.7009 | 0.7629 |
| 2.3725 | 3.0 | 1920 | 0.6202 | 0.7965 |
| 2.1159 | 4.0 | 2560 | 0.5752 | 0.8121 |
| 1.7476 | 5.0 | 3200 | 0.5474 | 0.8289 |
Framework versions
- Transformers 5.10.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
Sources
1 source
Sources
1 sourceVérifié 25 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- library_name: transformers license: apache-2.0 base_model: facebook/hubert-base-ls960 tags: - generated_from_trainer metrics: - accuracy model-index: - name: hubert-finetuned-emotion-audio-dataset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hubert-finetuned-emotion-audio-dataset This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5474 - Accuracy: 0.8289 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 5.1336 | 1.0 | 640 | 1.0654 | 0.6492 | | 3.4287 | 2.0 | 1280 | 0.7009 | 0.7629 | | 2.3725 | 3.0 | 1920 | 0.6202 | 0.7965 | | 2.1159 | 4.0 | 2560 | 0.5752 | 0.8121 | | 1.7476 | 5.0 | 3200 | 0.5474 | 0.8289 | ### Framework versions - Transformers 5.10.1 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 14 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo VasilisAsim/hubert-finetuned-emotion-audio-dataset
Source context: 9 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo VasilisAsim/hubert-finetuned-emotion-audio-dataset
