README.md

metadata

license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - accuracy
model-index:
  - name: my_awesome_mind_model
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.2793103448275862

my_awesome_mind_model

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

• Loss: 2.3078
• Accuracy: 0.2793

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: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.73	2	2.6832	0.1241
No log	1.82	5	2.6800	0.1103
No log	2.91	8	2.6726	0.1207
2.6498	4.0	11	2.6580	0.1483
2.6498	4.73	13	2.6454	0.1379
2.6498	5.82	16	2.6245	0.1379
2.6498	6.91	19	2.6038	0.1414
2.6057	8.0	22	2.5839	0.1552
2.6057	8.73	24	2.5656	0.1655
2.6057	9.82	27	2.5378	0.1552
2.524	10.91	30	2.5192	0.1862
2.524	12.0	33	2.4996	0.1931
2.524	12.73	35	2.4900	0.2069
2.524	13.82	38	2.4663	0.2138
2.4304	14.91	41	2.4498	0.2207
2.4304	16.0	44	2.4309	0.2138
2.4304	16.73	46	2.4291	0.2310
2.4304	17.82	49	2.4106	0.2517
2.3519	18.91	52	2.3944	0.2310
2.3519	20.0	55	2.3949	0.2414
2.3519	20.73	57	2.3807	0.2414
2.2774	21.82	60	2.3661	0.2379
2.2774	22.91	63	2.3600	0.2345
2.2774	24.0	66	2.3572	0.2483
2.2774	24.73	68	2.3430	0.2345
2.2402	25.82	71	2.3369	0.2586
2.2402	26.91	74	2.3365	0.2586
2.2402	28.0	77	2.3301	0.2621
2.2402	28.73	79	2.3274	0.2724
2.1901	29.82	82	2.3266	0.2759
2.1901	30.91	85	2.3207	0.2655
2.1901	32.0	88	2.3115	0.2724
2.148	32.73	90	2.3084	0.2724
2.148	33.82	93	2.3082	0.2724
2.148	34.91	96	2.3094	0.2828
2.148	36.0	99	2.3080	0.2793
2.1303	36.36	100	2.3078	0.2793

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0