README.md

metadata

language:
  - ga
  - en
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - ymoslem/IWSLT2023-GA-EN
  - ymoslem/FLEURS-GA-EN
  - ymoslem/BitesizeIrish-GA-EN
  - ymoslem/SpokenWords-GA-EN-MTed
metrics:
  - bleu
  - wer
model-index:
  - name: Whisper Small GA-EN Speech Translation + VAD
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: IWSLT-2023, FLEURS, BiteSize, and SpokenWords
          type: ymoslem/IWSLT2023-GA-EN
        metrics:
          - name: Bleu
            type: bleu
            value: 28.22
          - name: Wer
            type: wer
            value: 68.52769022962629

Whisper Small GA-EN Speech Translation + VAD

This model is a fine-tuned version of openai/whisper-small on the IWSLT-2023, FLEURS, BiteSize, and SpokenWords dataset. It achieves the following results on the evaluation set:

• Loss: 1.7352
• Bleu: 28.22
• Chrf: 44.19
• Wer: 68.5277

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: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 3000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Chrf	Wer
1.9529	0.2188	100	1.7388	12.76	29.03	97.1184
1.5762	0.4376	200	1.5362	15.3	33.31	98.4241
1.2624	0.6565	300	1.4346	17.94	37.2	101.4408
1.0367	0.8753	400	1.4502	21.52	39.13	85.4120
0.4677	1.0941	500	1.4693	23.26	40.49	78.4331
0.4284	1.3129	600	1.5163	21.31	41.41	86.0873
0.4026	1.5317	700	1.4999	24.11	40.59	79.3787
0.4132	1.7505	800	1.5134	27.77	43.01	70.1936
0.3701	1.9694	900	1.5368	27.74	42.61	66.0964
0.1337	2.1882	1000	1.5692	27.96	43.77	64.9257
0.143	2.4070	1100	1.5516	26.06	42.12	71.3192
0.144	2.6258	1200	1.5839	27.55	43.19	69.7434
0.1372	2.8446	1300	1.5510	27.93	43.07	66.1414
0.0573	3.0635	1400	1.6567	26.34	41.69	72.3998
0.0554	3.2823	1500	1.6511	27.98	42.66	68.5277
0.0534	3.5011	1600	1.6732	28.29	43.2	67.1319
0.0588	3.7199	1700	1.6687	27.0	43.31	70.7789
0.0486	3.9387	1800	1.6759	28.02	43.97	66.3665
0.0224	4.1575	1900	1.7597	26.86	41.81	70.5538
0.0264	4.3764	2000	1.7113	27.58	43.38	70.4638
0.0233	4.5952	2100	1.7013	27.83	42.87	68.2575
0.0192	4.8140	2200	1.7351	25.39	42.09	78.0279
0.0149	5.0328	2300	1.7350	27.62	43.99	70.5538
0.0086	5.2516	2400	1.7331	29.37	45.08	68.5277
0.006	5.4705	2500	1.7145	29.04	44.19	66.9968
0.0064	5.6893	2600	1.7322	28.27	43.6	70.2386
0.0053	5.9081	2700	1.7239	27.86	43.78	69.6083
0.0021	6.1269	2800	1.7288	28.14	44.12	68.5727
0.0016	6.3457	2900	1.7375	28.26	44.14	68.7078
0.0023	6.5646	3000	1.7352	28.22	44.19	68.5277

Framework versions

• Transformers 4.41.1
• Pytorch 2.2.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1