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
  - ymoslem/Tatoeba-Speech-Irish
  - ymoslem/Wikimedia-Speech-Irish
metrics:
  - bleu
  - wer
model-index:
  - name: Whisper Small GA-EN Speech Translation
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia
          type: ymoslem/IWSLT2023-GA-EN
        metrics:
          - name: Bleu
            type: bleu
            value: 31.78
          - name: Wer
            type: wer
            value: 62.31427285006754

Whisper Small GA-EN Speech Translation

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

• Loss: 1.3732
• Bleu: 31.78
• Chrf: 47.41
• Wer: 62.3143

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
• lr_scheduler_warmup_ratio: 0.03
• training_steps: 3000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Bleu	Chrf	Validation Loss	Wer
2.4081	0.0438	100	9.25	24.92	1.8707	101.1706
1.9316	0.0876	200	13.88	32.67	1.5419	101.5308
1.5533	0.1313	300	17.52	36.68	1.4046	83.1607
1.3403	0.1751	400	17.71	37.97	1.3565	93.5615
1.1303	0.2189	500	19.24	40.02	1.3224	82.0801
0.9892	0.2627	600	26.07	43.16	1.3077	72.8050
0.9005	0.3065	700	27.37	44.24	1.2918	64.1603
0.7547	0.3503	800	27.68	44.28	1.2754	66.0964
0.7199	0.3940	900	23.99	42.99	1.2895	78.0729
0.6095	0.4378	1000	1.2716	16.56	41.2	116.5691
0.5072	0.4816	1100	1.2901	25.39	44.04	75.2364
0.4599	0.5254	1200	1.2634	28.45	45.71	67.0419
0.3987	0.5692	1300	1.3004	25.84	45.63	75.1013
0.3443	0.6130	1400	1.2871	29.09	46.25	65.4210
0.2882	0.6567	1500	1.3242	29.14	44.4	66.0063
0.2687	0.7005	1600	1.3135	22.9	43.76	92.2557
0.2059	0.7443	1700	1.3160	31.13	47.45	63.6650
0.1991	0.7881	1800	1.2960	31.45	47.47	63.6650
0.1523	0.8319	1900	1.3215	31.21	47.38	64.1153
0.1349	0.8757	2000	1.3402	30.58	46.32	63.7551
0.111	0.9194	2100	1.3311	30.92	48.17	62.2242
0.1055	0.9632	2200	1.3548	30.56	46.56	65.2409
0.0525	1.0070	2300	1.3754	31.28	48.1	64.2954
0.0498	1.0508	2400	1.3729	31.16	47.8	61.7290
0.0372	1.0946	2500	1.3498	32.13	48.77	61.4588
0.029	1.1384	2600	1.3723	32.04	48.32	61.8640
0.0285	1.1821	2700	1.3748	31.91	47.58	61.8640
0.0292	1.2259	2800	1.3764	31.92	47.96	61.1887
0.025	1.2697	2900	1.3799	31.64	47.47	62.2242
0.0253	1.3135	3000	1.3732	31.78	47.41	62.3143

Framework versions

• Transformers 4.41.2
• Pytorch 2.2.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1