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Wav2Vec2_Fine_tuned_on_RAVDESS_2_Speech_Emotion_Recognition

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metadata
license: apache-2.0
base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: Wav2Vec2_Fine_tuned_on_RAVDESS_2_Speech_Emotion_Recognition
    results: []

Wav2Vec2_Fine_tuned_on_RAVDESS_2_Speech_Emotion_Recognition

This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-english.

The dataset used to fine-tune the original pre-trained model is the RAVDESS dataset. This dataset provides 7442 samples of recordings from actors performing on 6 different emotions in English, which are:

emotions = ['angry', 'calm', 'disgust', 'fearful', 'happy', 'neutral', 'sad', 'surprised']

It achieves the following results on the evaluation set:

  • Loss: 0.5638
  • Accuracy: 0.8125

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: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.1085 0.0694 10 2.0715 0.1701
2.043 0.1389 20 2.0531 0.1944
2.0038 0.2083 30 1.9162 0.3056
1.9217 0.2778 40 1.8085 0.3264
1.7814 0.3472 50 1.6440 0.3611
1.5997 0.4167 60 1.5428 0.3681
1.5293 0.4861 70 1.4812 0.4062
1.5473 0.5556 80 1.3423 0.4826
1.5098 0.625 90 1.3632 0.4653
1.1967 0.6944 100 1.3762 0.4618
1.2255 0.7639 110 1.3456 0.4618
1.6152 0.8333 120 1.3206 0.4826
1.1365 0.9028 130 1.3343 0.4792
1.1254 0.9722 140 1.2481 0.4792
1.3486 1.0417 150 1.4024 0.4688
1.2029 1.1111 160 1.1053 0.5556
1.0734 1.1806 170 1.1238 0.6181
1.029 1.25 180 1.3111 0.5347
1.0955 1.3194 190 1.0256 0.6146
0.8893 1.3889 200 0.9970 0.6389
0.8874 1.4583 210 0.9895 0.6389
0.9227 1.5278 220 0.8335 0.6667
0.7566 1.5972 230 0.8839 0.6944
0.8062 1.6667 240 0.8070 0.7118
0.6773 1.7361 250 0.7592 0.7222
0.7874 1.8056 260 1.1098 0.6285
0.8262 1.875 270 0.6952 0.7569
0.568 1.9444 280 0.7635 0.7326
0.6914 2.0139 290 0.6607 0.7917
0.6838 2.0833 300 0.8466 0.7049
0.6318 2.1528 310 0.6612 0.8056
0.604 2.2222 320 0.9257 0.6667
0.5321 2.2917 330 0.6067 0.7986
0.3421 2.3611 340 0.6594 0.7535
0.3536 2.4306 350 0.6525 0.7812
0.3087 2.5 360 0.6412 0.7812
0.4236 2.5694 370 0.6560 0.7812
0.5134 2.6389 380 0.6614 0.7882
0.5709 2.7083 390 0.5989 0.8021
0.2912 2.7778 400 0.6142 0.7951
0.516 2.8472 410 0.5926 0.7986
0.3835 2.9167 420 0.5797 0.8125
0.4055 2.9861 430 0.5638 0.8125

Framework versions

  • Transformers 4.41.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.1.dev0
  • Tokenizers 0.19.1