Instructions to use VasilisAsim/whisper-finetuned-Ravdess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VasilisAsim/whisper-finetuned-Ravdess with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="VasilisAsim/whisper-finetuned-Ravdess")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("VasilisAsim/whisper-finetuned-Ravdess") model = AutoModelForAudioClassification.from_pretrained("VasilisAsim/whisper-finetuned-Ravdess") - Notebooks
- Google Colab
- Kaggle
whisper-finetuned-Ravdess
This model is a fine-tuned version of BurningFang/finetuned_whisper_for_speech_emotion_recognition_optimised on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1522
- Accuracy: 0.9722
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 |
|---|---|---|---|---|
| 4.8773 | 1.0 | 72 | 0.2190 | 0.9201 |
| 0.8388 | 2.0 | 144 | 0.1605 | 0.9653 |
| 0.1806 | 3.0 | 216 | 0.1867 | 0.9549 |
| 0.0538 | 4.0 | 288 | 0.1933 | 0.9688 |
| 0.0134 | 5.0 | 360 | 0.1522 | 0.9722 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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