README.md

metadata

language:
  - ga
  - en
license: apache-2.0
base_model: openai/whisper-medium
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: 28.6
          - name: Wer
            type: wer
            value: 68.52769022962629

Whisper Small GA-EN Speech Translation

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

• Loss: 1.1842
• Bleu: 28.6
• Chrf: 49.54
• 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: 16
• eval_batch_size: 16
• 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: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Bleu	Chrf	Validation Loss	Wer
2.5585	0.0109	100	2.94	16.53	2.1737	176.2269
2.5748	0.0219	200	6.93	24.84	2.0289	101.8460
2.554	0.0328	300	7.16	25.31	1.8861	142.4584
2.276	0.0438	400	8.9	27.72	1.8568	119.7208
2.3077	0.0547	500	14.51	32.58	1.7492	88.3836
2.0852	0.0657	600	16.71	34.1	1.6548	83.6560
2.1602	0.0766	700	14.93	35.2	1.6063	106.4385
1.9556	0.0876	800	20.64	36.74	1.6190	77.5777
1.7516	0.0985	900	15.44	36.67	1.5614	95.0023
1.7502	0.1095	1000	20.65	38.42	1.5317	81.4948
1.6851	0.1204	1100	19.13	37.91	1.5289	87.7533
1.5154	0.1314	1200	19.79	41.21	1.4906	83.3408
1.3658	0.1423	1300	19.58	39.16	1.4623	96.3980
1.3828	0.1533	1400	22.84	42.83	1.4069	77.5777
1.5339	0.1642	1500	20.91	41.62	1.3909	86.5376
1.2441	0.1752	1600	23.35	43.43	1.3726	75.5966
1.1607	0.1861	1700	20.4	42.41	1.3471	85.4120
1.1043	0.1970	1800	21.13	43.4	1.3332	81.5849
1.0698	0.2080	1900	23.84	44.54	1.3413	73.3904
1.0698	0.2189	2000	28.34	47.2	1.2848	66.9068
1.053	0.2299	2100	25.19	46.75	1.2951	73.1652
0.9139	0.2408	2200	28.43	47.11	1.2852	70.7789
0.742	0.2518	2300	30.5	47.62	1.2580	63.6200
0.8627	0.2627	2400	29.97	48.38	1.2308	66.2314
0.7213	0.2737	2500	22.96	46.55	1.2176	83.7010
0.672	0.2846	2600	27.35	48.02	1.2272	71.7695
0.784	0.2956	2700	31.16	50.83	1.2010	65.3760
0.6463	0.3065	2800	30.67	51.24	1.1884	64.9257
0.6028	0.3175	2900	32.07	51.3	1.1866	61.4588
0.6494	0.3284	3000	32.04	50.96	1.1768	63.3048
0.657	0.3394	3100	1.2126	30.55	50.18	66.0964
0.6239	0.3503	3200	1.1836	33.69	52.06	60.2431
0.63	0.3612	3300	1.2201	32.14	51.62	61.7290
0.5155	0.3722	3400	1.1956	32.62	51.99	61.3688
0.5392	0.3831	3500	1.2010	31.13	51.37	63.9802
0.5159	0.3941	3600	1.1831	32.2	51.81	62.4043
0.4535	0.4050	3700	1.1744	31.61	51.77	63.3949
0.3346	0.4160	3800	1.2066	30.67	50.21	65.4660
0.3991	0.4269	3900	1.1870	30.7	50.88	65.2409
0.395	0.4379	4000	1.1842	28.6	49.54	68.5277

Framework versions

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