README.md

metadata

library_name: transformers
language:
  - ko
license: apache-2.0
base_model: openai/whisper-small
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - GGarri/241113_newdata
metrics:
  - wer
model-index:
  - name: Whisper Small ko
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: customdata
          type: GGarri/241113_newdata
        metrics:
          - name: Wer
            type: wer
            value: 0.8156606851549755

Whisper Small ko

This model is a fine-tuned version of openai/whisper-small on the customdata dataset. It achieves the following results on the evaluation set:

• Loss: 0.0498
• Cer: 1.1070
• Wer: 0.8157

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• 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_steps: 500
• training_steps: 5000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
1.1429	1.5625	100	0.8829	14.7984	14.5304
0.3401	3.125	200	0.2637	2.0625	1.7828
0.0413	4.6875	300	0.0599	1.5498	1.3167
0.0163	6.25	400	0.0462	1.2818	0.9904
0.0127	7.8125	500	0.0517	1.5265	1.1885
0.0065	9.375	600	0.0402	1.5031	1.0487
0.0028	10.9375	700	0.0396	1.7012	1.3167
0.001	12.5	800	0.0406	1.5148	1.1186
0.0004	14.0625	900	0.0405	1.4216	1.0371
0.0005	15.625	1000	0.0424	1.5847	1.1885
0.0001	17.1875	1100	0.0425	1.2701	0.9788
0.0001	18.75	1200	0.0429	1.3051	1.0137
0.0001	20.3125	1300	0.0432	1.2701	0.9788
0.0001	21.875	1400	0.0436	1.2818	0.9904
0.0001	23.4375	1500	0.0439	1.2934	1.0021
0.0001	25.0	1600	0.0441	1.2934	1.0021
0.0001	26.5625	1700	0.0443	1.2934	1.0021
0.0001	28.125	1800	0.0446	1.2934	1.0021
0.0001	29.6875	1900	0.0448	1.2818	0.9904
0.0001	31.25	2000	0.0449	1.2002	0.9089
0.0001	32.8125	2100	0.0454	1.2002	0.9089
0.0001	34.375	2200	0.0458	1.2002	0.9089
0.0	35.9375	2300	0.0461	1.2002	0.9089
0.0	37.5	2400	0.0463	1.1769	0.8856
0.0	39.0625	2500	0.0465	1.1769	0.8856
0.0	40.625	2600	0.0467	1.1536	0.8623
0.0	42.1875	2700	0.0469	1.1303	0.8390
0.0	43.75	2800	0.0471	1.1536	0.8623
0.0	45.3125	2900	0.0473	1.1536	0.8623
0.0	46.875	3000	0.0474	1.1536	0.8623
0.0	48.4375	3100	0.0476	1.1536	0.8623
0.0	50.0	3200	0.0477	1.1303	0.8390
0.0	51.5625	3300	0.0478	1.1419	0.8506
0.0	53.125	3400	0.0479	1.1186	0.8273
0.0	54.6875	3500	0.0481	1.1186	0.8273
0.0	56.25	3600	0.0482	1.1186	0.8273
0.0	57.8125	3700	0.0483	1.1186	0.8273
0.0	59.375	3800	0.0484	1.1070	0.8157
0.0	60.9375	3900	0.0485	1.1070	0.8157
0.0	62.5	4000	0.0487	1.1070	0.8157
0.0	64.0625	4100	0.0490	1.1070	0.8157
0.0	65.625	4200	0.0492	1.1070	0.8157
0.0	67.1875	4300	0.0494	1.1070	0.8157
0.0	68.75	4400	0.0495	1.1070	0.8157
0.0	70.3125	4500	0.0496	1.1070	0.8157
0.0	71.875	4600	0.0497	1.1070	0.8157
0.0	73.4375	4700	0.0497	1.1070	0.8157
0.0	75.0	4800	0.0497	1.1070	0.8157
0.0	76.5625	4900	0.0498	1.1070	0.8157
0.0	78.125	5000	0.0498	1.1070	0.8157

Framework versions

• Transformers 4.46.2
• Pytorch 2.4.0
• Datasets 2.18.0
• Tokenizers 0.20.3