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 + warmup_ratio=0.01
    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: 29.94
          - name: Wer
            type: wer
            value: 64.34038721296713

Whisper Small GA-EN Speech Translation + VAD + warmup_ratio=0.01

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: 1.7482
• Bleu: 29.94
• Chrf: 45.74
• Wer: 64.3404

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: 0.01
• training_steps: 3000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Bleu	Chrf	Validation Loss	Wer
2.0518	0.2188	100	8.56	25.29	1.8072	123.9982
1.5449	0.4376	200	18.41	34.82	1.5746	83.7461
1.2518	0.6565	300	21.1	36.24	1.5009	83.9712
1.0947	0.8753	400	21.5	41.43	1.4582	89.8694
0.4439	1.0941	500	25.21	41.77	1.4979	72.5799
0.4416	1.3129	600	22.2	40.47	1.5107	79.8739
0.4417	1.5317	700	20.2	40.75	1.5215	88.8789
0.4108	1.7505	800	25.73	41.28	1.5278	67.8073
0.355	1.9694	900	20.6	39.37	1.5436	87.3030
0.1303	2.1882	1000	28.79	42.68	1.5936	68.1675
0.1421	2.4070	1100	27.84	42.58	1.5745	67.5371
0.1341	2.6258	1200	30.52	45.15	1.5953	66.5916
0.1365	2.8446	1300	26.93	43.72	1.6046	74.2909
0.0528	3.0635	1400	29.03	44.12	1.6303	64.8807
0.0519	3.2823	1500	27.75	44.34	1.6774	68.6177
0.0554	3.5011	1600	27.64	45.15	1.6637	71.1842
0.0514	3.7199	1700	30.26	44.62	1.6497	65.4660
0.0503	3.9387	1800	26.88	43.0	1.6780	70.4187
0.0259	4.1575	1900	29.6	44.51	1.6915	64.9707
0.0263	4.3764	2000	25.33	42.51	1.7080	72.3998
0.0254	4.5952	2100	30.59	45.35	1.6884	64.2954
0.0211	4.8140	2200	31.09	46.56	1.6984	64.0252
0.0137	5.0328	2300	28.96	43.67	1.7253	66.3665
0.0075	5.2516	2400	29.77	44.63	1.7112	66.9968
0.0056	5.4705	2500	29.96	45.51	1.7197	64.5655
0.0067	5.6893	2600	29.86	45.25	1.7464	66.0964
0.0064	5.9081	2700	29.47	45.36	1.7440	65.2859
0.0023	6.1269	2800	30.03	46.49	1.7419	64.4755
0.0016	6.3457	2900	29.76	45.64	1.7474	65.0158
0.0019	6.5646	3000	1.7482	29.94	45.74	64.3404

Framework versions

• Transformers 4.41.2
• Pytorch 2.2.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1