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 Medium 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: 29.54
          - name: Wer
            type: wer
            value: 62.40432237730752

Whisper Medium 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: 1.1929
Bleu: 29.54
Chrf: 51.58
Wer: 62.4043

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	Validation Loss	Bleu	Chrf	Wer
2.4382	0.0109	100	2.1114	3.07	16.85	171.0491
2.6151	0.0219	200	2.0207	6.25	23.02	126.9698
2.5699	0.0328	300	1.8660	5.71	24.03	155.5606
2.3084	0.0438	400	1.8084	9.87	28.45	129.0860
2.3327	0.0547	500	1.7823	12.01	31.92	102.7915
2.1495	0.0657	600	1.7238	13.97	32.4	98.6042
2.2164	0.0766	700	1.6538	11.21	33.19	146.0153
2.0071	0.0876	800	1.7038	14.34	35.72	96.9383
1.8334	0.0985	900	1.6329	16.51	37.23	96.8032
1.8359	0.1095	1000	1.6637	17.87	35.94	84.4665
1.7703	0.1204	1100	1.5626	19.54	39.02	79.7839
1.5805	0.1314	1200	1.5618	20.19	40.4	77.8028
1.4545	0.1423	1300	1.5599	13.88	35.53	112.5619
1.5177	0.1533	1400	1.4880	18.79	40.11	84.6916
1.6335	0.1642	1500	1.4996	16.41	38.64	96.9833
1.3809	0.1752	1600	1.4739	18.3	40.17	101.8910
1.2694	0.1861	1700	1.4498	22.53	43.15	76.9923
1.2321	0.1970	1800	1.4163	19.92	42.59	84.6015
1.1969	0.2080	1900	1.4137	21.63	44.92	85.3670
1.2023	0.2189	2000	1.3530	20.42	41.57	82.8906
1.1676	0.2299	2100	1.3723	22.82	44.23	78.1180
1.0332	0.2408	2200	1.3641	26.73	44.75	70.2386
0.8589	0.2518	2300	1.3344	26.94	46.89	72.7600
0.9829	0.2627	2400	1.3181	28.15	47.21	69.1130
0.8228	0.2737	2500	1.3049	26.98	47.41	74.0207
0.7667	0.2846	2600	1.2698	30.0	49.42	65.1058
0.8749	0.2956	2700	1.2878	27.91	47.67	66.9518
0.7504	0.3065	2800	1.2670	32.03	50.35	63.6650
0.7069	0.3175	2900	1.2771	30.7	49.53	64.4304
0.7199	0.3284	3000	1.2658	30.21	48.93	65.5561
0.6207	0.3394	3100	1.2687	30.82	49.11	66.0063
0.5995	0.3503	3200	1.2207	31.99	50.94	62.9446
0.6294	0.3612	3300	1.2422	31.05	50.85	64.7006
0.4612	0.3722	3400	1.2203	33.1	51.82	61.9090
0.5138	0.3831	3500	1.2007	32.08	51.86	63.0797
0.5059	0.3941	3600	1.2130	31.8	51.19	63.9352
0.417	0.4050	3700	1.1975	32.45	51.41	62.2692
0.2958	0.4160	3800	1.2046	29.29	51.39	62.7645
0.393	0.4269	3900	1.1968	28.95	51.45	63.1697
0.3858	0.4379	4000	1.1929	29.54	51.58	62.4043

Framework versions

Transformers 4.41.2
Pytorch 2.2.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1