README.md

metadata

language:
  - ga
  - en
license: apache-2.0
base_model: openai/whisper-large
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 Large 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: 30.16
          - name: Wer
            type: wer
            value: 65.60108059432687

Whisper Large GA-EN Speech Translation

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

• Loss: 1.1716
• Bleu: 31.61
• Chrf: 50.4
• Wer: 63.8001

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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.03
• training_steps: 3000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Bleu	Chrf	Validation Loss	Wer
3.1547	0.03	100	3.75	18.71	2.4173	124.0882
2.6996	0.07	200	8.16	25.45	2.1329	114.1378
2.4841	0.1	300	6.4	23.6	2.0262	158.1720
2.4706	0.13	400	9.16	27.67	1.9688	120.0810
2.3575	0.16	500	13.66	31.5	1.8284	100.8555
2.1916	0.2	600	12.97	31.8	1.7486	110.1756
2.1353	0.23	700	16.7	33.52	1.7568	86.8528
1.9885	0.26	800	19.34	35.35	1.6395	78.7033
1.9126	0.3	900	20.21	36.28	1.5658	78.2080
1.6418	0.33	1000	18.61	38.49	1.4998	86.8528
1.5782	0.36	1100	22.91	40.04	1.4716	71.0941
1.4899	0.39	1200	21.55	40.92	1.4444	78.7933
1.3155	0.43	1300	24.95	42.05	1.3934	70.9140
1.4144	0.46	1400	28.38	46.18	1.2791	65.8262
1.1949	0.49	1500	26.95	45.84	1.2879	70.6889
1.0179	0.53	1600	26.12	46.4	1.2624	69.6983
1.0935	0.56	1700	28.51	48.24	1.2076	67.4021
1.061	0.59	1800	27.42	48.83	1.1812	71.4543
1.0955	0.62	1900	31.32	49.91	1.1503	62.9896
1.0607	0.66	2000	31.26	50.41	1.1318	62.3143
1.1135	0.6897	2100	1.2135	26.57	46.18	69.7884
0.9819	0.7225	2200	1.2252	26.95	49.47	65.0158
0.9909	0.7553	2300	1.2072	30.35	46.49	63.3048
0.9521	0.7882	2400	1.2130	24.76	46.44	70.6889
0.8245	0.8210	2500	1.1724	24.84	47.05	78.1630
0.8303	0.8539	2600	1.1812	27.56	47.48	70.1036
0.6934	0.8867	2700	1.1716	31.61	50.4	63.8001
0.7117	0.9195	2800	1.1650	30.82	49.95	65.0158
0.6944	0.9524	2900	1.1516	31.21	49.8	63.5750
0.7132	0.9852	3000	1.1390	30.16	49.77	65.6011

Framework versions

• Transformers 4.40.0
• Pytorch 2.0.1+cu118
• Datasets 2.18.0
• Tokenizers 0.19.1