minoosh / finetuned_wav2vec2.0-base-on-IEMOCAP_4

huggingface.co
Total runs: 160
24-hour runs: 0
7-day runs: -1
30-day runs: 157
Model's Last Updated: 6월 10 2023
audio-classification

Introduction of finetuned_wav2vec2.0-base-on-IEMOCAP_4

Model Details of finetuned_wav2vec2.0-base-on-IEMOCAP_4

finetuned_wav2vec2.0-base-on-IEMOCAP_4

This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6468
  • Accuracy: 0.6586
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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 15
Training results
Training Loss Epoch Step Validation Loss Accuracy
1.1816 1.0 112 1.1338 0.5177
1.1233 2.0 225 1.0683 0.5355
0.9672 3.0 337 0.9371 0.6031
0.6925 4.0 450 0.8086 0.6818
0.6162 5.0 562 0.7848 0.7051
0.4226 6.0 675 0.7377 0.7428
0.3047 7.0 787 0.7981 0.7251
0.2385 8.0 900 0.8558 0.7428
0.1954 9.0 1012 0.8869 0.7373
0.1454 10.0 1125 0.9470 0.7428
0.116 11.0 1237 1.0448 0.7306
0.1017 12.0 1350 1.1298 0.7295
0.1365 13.0 1462 1.1667 0.7450
0.0995 14.0 1575 1.1252 0.7428
0.0655 14.93 1680 1.1501 0.7395
Framework versions
  • Transformers 4.29.2
  • Pytorch 2.0.0
  • Datasets 2.12.0
  • Tokenizers 0.13.3

Runs of minoosh finetuned_wav2vec2.0-base-on-IEMOCAP_4 on huggingface.co

160
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3-day runs
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7-day runs
157
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finetuned_wav2vec2.0-base-on-IEMOCAP_4 huggingface.co

finetuned_wav2vec2.0-base-on-IEMOCAP_4 huggingface.co is an AI model on huggingface.co that provides finetuned_wav2vec2.0-base-on-IEMOCAP_4's model effect (), which can be used instantly with this minoosh finetuned_wav2vec2.0-base-on-IEMOCAP_4 model. huggingface.co supports a free trial of the finetuned_wav2vec2.0-base-on-IEMOCAP_4 model, and also provides paid use of the finetuned_wav2vec2.0-base-on-IEMOCAP_4. Support call finetuned_wav2vec2.0-base-on-IEMOCAP_4 model through api, including Node.js, Python, http.

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finetuned_wav2vec2.0-base-on-IEMOCAP_4 install

finetuned_wav2vec2.0-base-on-IEMOCAP_4 is an open source model from GitHub that offers a free installation service, and any user can find finetuned_wav2vec2.0-base-on-IEMOCAP_4 on GitHub to install. At the same time, huggingface.co provides the effect of finetuned_wav2vec2.0-base-on-IEMOCAP_4 install, users can directly use finetuned_wav2vec2.0-base-on-IEMOCAP_4 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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Updated: 11월 02 2024