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speech-emotion-recognition-wav2vec2 – AI Model by HaniaRuby | AlphaNeural AI
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HaniaRuby
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speech-emotion-recognition-wav2vec2
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transformers
tensorboard
safetensors
wav2vec2
audio-classification
generated_from_trainer
facebook/wav2vec2-base
finetune
apache-2.0
endpoints_compatible
us
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results
This model is a fine-tuned version of
facebook/wav2vec2-base
on the
speech-emotion-recognition-en
dataset. It achieves the following results on the evaluation set:
Loss: 0.7358
Accuracy: 0.8338
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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 6
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.8199
1.0
1161
0.7154
0.7446
0.7633
2.0
2322
0.5800
0.8019
0.5081
3.0
3483
0.5602
0.8084
0.3336
4.0
4644
0.6145
0.8277
0.3169
5.0
5805
0.6933
0.8316
0.1281
6.0
6966
0.7358
0.8338
Framework versions
Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.1
Tokenizers 0.21.1
