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
    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: 27.66
          - name: Wer
            type: wer
            value: 72.0396217919856

Whisper Small GA-EN Speech Translation

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: 1.7786
• Bleu: 27.66
• Chrf: 43.04
• Wer: 72.0396

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
1.9416	0.2155	100	1.7899	13.09	26.48	104.4575
1.5186	0.4310	200	1.5696	18.6	35.75	87.5732
1.2884	0.6466	300	1.4751	17.57	37.19	87.2580
1.0729	0.8621	400	1.4345	17.92	38.23	99.2346
0.4574	1.0776	500	1.5585	22.48	39.17	83.1607
0.4517	1.2931	600	1.5763	22.53	38.38	81.7650
0.4385	1.5086	700	1.5852	20.05	39.46	96.8483
0.3934	1.7241	800	1.5332	26.89	42.67	70.6889
0.3587	1.9397	900	1.5025	28.95	44.16	64.9707
0.1528	2.1552	1000	1.5882	28.32	42.36	65.8712
0.1425	2.3707	1100	1.6056	25.5	42.42	75.0113
0.1389	2.5862	1200	1.6236	26.52	42.11	70.6439
0.1532	2.8017	1300	1.6196	25.78	41.61	75.9118
0.1138	3.0172	1400	1.7185	26.01	40.88	69.6983
0.0661	3.2328	1500	1.6626	28.74	43.16	71.2292
0.0625	3.4483	1600	1.6835	29.16	43.6	66.3215
0.0615	3.6638	1700	1.6756	28.93	44.08	68.3476
0.0611	3.8793	1800	1.6648	27.77	43.67	72.1747
0.0344	4.0948	1900	1.7351	28.33	44.18	68.1225
0.0339	4.3103	2000	1.7715	28.9	42.98	67.0869
0.0369	4.5259	2100	1.7200	29.83	44.87	64.8807
0.0326	4.7414	2200	1.7232	28.23	43.75	69.3832
0.0346	4.9569	2300	1.7688	27.72	43.1	72.8050
0.0167	5.1724	2400	1.8072	28.73	43.26	67.4471
0.0146	5.3879	2500	1.7801	29.91	44.24	66.4566
0.0165	5.6034	2600	1.7782	29.34	44.33	68.2125
0.0143	5.8190	2700	1.7675	27.78	43.07	72.5799
0.0106	6.0345	2800	1.7660	29.45	43.31	67.5371
0.0098	6.25	2900	1.7803	27.89	42.67	71.6344
0.0087	6.4655	3000	1.7786	27.66	43.04	72.0396

Framework versions

• Transformers 4.40.2
• Pytorch 2.2.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1