README.md

metadata

library_name: transformers
license: mit
base_model: distil-whisper/distil-large-v2
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - accuracy
  - f1
model-index:
  - name: distil-large-v2_ADReSSo
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7605633802816901
          - name: F1
            type: f1
            value: 0.7733333333333333

distil-large-v2_ADReSSo

This model is a fine-tuned version of distil-whisper/distil-large-v2 on the audiofolder dataset. It achieves the following results on the evaluation set:

• Loss: 1.7772
• Accuracy: 0.7606
• F1: 0.7733

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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.7	1.0	16	0.6865	0.5714	0.25
0.6796	2.0	32	0.6753	0.5	0.0
0.5951	3.0	48	0.5741	0.6905	0.6667
0.3828	4.0	64	1.2909	0.5476	0.24
0.1822	5.0	80	1.0336	0.7857	0.7907
0.2948	6.0	96	0.9771	0.7619	0.7222
0.064	7.0	112	0.9788	0.8333	0.8293
0.0007	8.0	128	1.0079	0.8333	0.8444
0.0003	9.0	144	1.0393	0.8333	0.8444
0.0002	10.0	160	1.0977	0.8571	0.8696
0.0001	11.0	176	1.1214	0.8571	0.8696
0.0001	12.0	192	1.1597	0.8571	0.8696
0.0001	13.0	208	1.1867	0.8571	0.8696
0.0001	14.0	224	1.2124	0.8571	0.8696
0.0001	15.0	240	1.2319	0.8571	0.8696
0.0001	16.0	256	1.2440	0.8571	0.8696
0.0001	17.0	272	1.2629	0.8571	0.8696
0.0001	18.0	288	1.2777	0.8571	0.8696
0.0001	19.0	304	1.2876	0.8571	0.8696
0.0	20.0	320	1.3026	0.8571	0.8696
0.0	21.0	336	1.3156	0.8571	0.8696
0.0	22.0	352	1.3261	0.8571	0.8696
0.0	23.0	368	1.3362	0.8571	0.8696
0.0	24.0	384	1.3445	0.8571	0.8696
0.0	25.0	400	1.3532	0.8571	0.8696
0.0	26.0	416	1.3604	0.8571	0.8696
0.0	27.0	432	1.3673	0.8571	0.8696
0.0	28.0	448	1.3746	0.8571	0.8696
0.0	29.0	464	1.3809	0.8571	0.8696
0.0	30.0	480	1.3878	0.8571	0.8696

Framework versions

• Transformers 4.51.3
• Pytorch 2.5.0+cu118
• Datasets 2.14.6
• Tokenizers 0.21.1