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metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: speech-emotion-recognition-with-openai-whisper-large-v3
    results: []

speech-emotion-recognition-with-openai-whisper-large-v3

This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5008
  • Accuracy: 0.9199
  • Precision: 0.9230
  • Recall: 0.9199
  • F1: 0.9198

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: 5e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 5
  • total_train_batch_size: 10
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
0.4948 0.9995 394 0.4911 0.8286 0.8449 0.8286 0.8302
0.6271 1.9990 788 0.5307 0.8225 0.8559 0.8225 0.8277
0.2364 2.9985 1182 0.5076 0.8692 0.8727 0.8692 0.8684
0.0156 3.9980 1576 0.5669 0.8732 0.8868 0.8732 0.8745
0.2305 5.0 1971 0.4578 0.9108 0.9142 0.9108 0.9114
0.0112 5.9995 2365 0.4701 0.9108 0.9159 0.9108 0.9114
0.0013 6.9990 2759 0.5232 0.9138 0.9204 0.9138 0.9137
0.1894 7.9985 3153 0.5008 0.9199 0.9230 0.9199 0.9198
0.0877 8.9980 3547 0.5517 0.9138 0.9152 0.9138 0.9138
0.1471 10.0 3942 0.5856 0.8895 0.9002 0.8895 0.8915
0.0026 10.9995 4336 0.8334 0.8773 0.8949 0.8773 0.8770

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.0
  • Tokenizers 0.19.1