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: 0.589247311827957

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: 1.3338
Accuracy: 0.5892

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
2.7071	0.95	14	2.7063	0.0602
2.7033	1.97	29	2.7006	0.0645
2.6835	2.98	44	2.6793	0.0817
2.6551	4.0	59	2.5549	0.1699
2.5023	4.95	73	2.3970	0.2258
2.4257	5.97	88	2.3068	0.2495
2.2542	6.98	103	2.2121	0.2688
2.2419	8.0	118	2.1736	0.2731
2.1278	8.95	132	2.1675	0.2430
2.0592	9.97	147	2.1207	0.2796
1.9576	10.98	162	2.0662	0.2731
1.9023	12.0	177	1.9738	0.3312
1.8367	12.95	191	2.0420	0.2903
1.7822	13.97	206	2.0161	0.2860
1.6934	14.98	221	2.0215	0.2989
1.7093	16.0	236	1.9287	0.3290
1.6158	16.95	250	1.8138	0.3849
1.5879	17.97	265	1.8043	0.3871
1.5249	18.98	280	1.9117	0.3548
1.4821	20.0	295	1.7242	0.4215
1.4629	20.95	309	1.6981	0.4538
1.3847	21.97	324	1.6701	0.4516
1.3595	22.98	339	1.6891	0.4495
1.298	24.0	354	1.6321	0.4667
1.2479	24.95	368	1.5519	0.4989
1.2135	25.97	383	1.5477	0.4839
1.1833	26.98	398	1.5437	0.5032
1.1298	28.0	413	1.5425	0.5097
1.079	28.95	427	1.5076	0.5247
1.0709	29.97	442	1.5288	0.5140
1.0286	30.98	457	1.4497	0.5419
0.9896	32.0	472	1.4663	0.5355
0.9707	32.95	486	1.4683	0.5333
0.9443	33.97	501	1.4977	0.5226
0.8998	34.98	516	1.4178	0.5505
0.9048	36.0	531	1.4131	0.5462
0.8587	36.95	545	1.3791	0.5634
0.84	37.97	560	1.4036	0.5527
0.8155	38.98	575	1.4139	0.5505
0.8086	40.0	590	1.3993	0.5462
0.808	40.95	604	1.3325	0.5914
0.7929	41.97	619	1.3500	0.5806
0.7635	42.98	634	1.3471	0.5720
0.761	44.0	649	1.3636	0.5634
0.7456	44.95	663	1.3551	0.5828
0.75	45.97	678	1.3431	0.5849
0.7232	46.98	693	1.3338	0.5871
0.7625	47.46	700	1.3338	0.5892

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0