ymoslem/whisper-small-ga2en-v5.5-r

Whisper Small GA-EN Speech Translation

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

• Loss: 1.3690
• Bleu: 32.44
• Chrf: 48.06
• Wer: 63.2598

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	Validation Loss	Bleu	Chrf	Wer
2.3655	0.0438	100	1.7709	8.84	26.21	127.7803
1.8998	0.0876	200	1.5198	14.9	32.89	99.2796
1.5421	0.1313	300	1.3972	16.15	35.77	86.8077
1.3154	0.1751	400	1.3412	20.46	39.48	83.0707
1.1138	0.2189	500	1.3126	23.16	41.28	74.1108
0.9814	0.2627	600	1.3217	25.56	41.67	68.7528
0.8897	0.3065	700	1.2859	27.0	43.54	66.3215
0.7495	0.3503	800	1.2668	21.71	43.03	75.7767
0.7068	0.3940	900	1.2852	17.86	40.88	106.0333
0.6002	0.4378	1000	1.2476	24.0	44.26	78.4331
0.4989	0.4816	1100	1.2756	28.88	45.57	67.2670
0.4464	0.5254	1200	1.2756	27.81	45.53	66.8618
0.3883	0.5692	1300	1.2799	29.84	46.03	64.0702
0.341	0.6130	1400	1.2693	26.51	43.97	75.3715
0.2853	0.6567	1500	1.3310	26.99	45.58	74.0207
0.2611	0.7005	1600	1.3022	25.83	44.79	73.4354
0.2013	0.7443	1700	1.3266	30.78	46.61	63.6650
0.1886	0.7881	1800	1.2943	25.56	45.46	73.7055
0.1517	0.8319	1900	1.3193	28.93	45.09	64.3854
0.1288	0.8757	2000	1.3567	28.22	44.75	67.6722
0.1129	0.9194	2100	1.3431	29.55	46.22	66.2314
0.1	0.9632	2200	1.3365	31.46	48.14	64.9257
0.0505	1.0070	2300	1.3557	30.37	47.16	64.1153
0.0468	1.0508	2400	1.3648	31.57	48.17	62.0891
0.0373	1.0946	2500	1.3661	31.56	47.76	64.7456
0.0297	1.1384	2600	1.3638	31.13	47.74	64.3854
0.0283	1.1821	2700	1.3847	29.98	47.54	65.9613
0.0302	1.2259	2800	1.3730	32.32	48.28	64.0252
0.0229	1.2697	2900	1.3702	31.47	47.55	65.1508
0.0262	1.3135	3000	1.3690	32.44	48.06	63.2598

Framework versions

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