ymoslem/whisper-small-ga2en-v3.3-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: 1.4107
• Bleu: 27.85
• Chrf: 46.91
• Wer: 73.4354

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
2.3549	0.1312	100	1.8335	7.17	24.71	135.7497
1.8906	0.2625	200	1.5173	15.56	34.19	91.5353
1.653	0.3937	300	1.3530	17.17	36.35	103.7371
1.4901	0.5249	400	1.3334	24.65	43.44	78.2530
1.3551	0.6562	500	1.2763	27.04	43.88	67.4471
1.2187	0.7874	600	1.2618	27.08	43.98	69.2031
1.0359	0.9186	700	1.2644	20.82	40.76	96.8483
0.5364	1.0499	800	1.3258	24.9	42.9	65.8262
0.4892	1.1811	900	1.3296	23.82	42.86	72.3098
0.4504	1.3123	1000	1.3001	25.78	43.72	75.5065
0.4161	1.4436	1100	1.2948	27.16	44.31	67.3120
0.3953	1.5748	1200	1.3261	29.14	44.65	65.5110
0.3509	1.7060	1300	1.3398	22.75	44.32	80.1441
0.2955	1.8373	1400	1.3077	26.29	42.89	74.8762
0.2801	1.9685	1500	1.3206	25.51	43.39	76.5871
0.1084	2.0997	1600	1.3609	28.01	45.59	68.1225
0.1003	2.2310	1700	1.3722	26.4	42.69	72.8501
0.1083	2.3622	1800	1.3776	3.81	19.2	396.1279
0.0939	2.4934	1900	1.3729	28.43	45.61	69.2031
0.0909	2.6247	2000	1.3834	27.12	43.39	67.4921
0.0772	2.7559	2100	1.4094	28.44	44.15	65.5110
0.0753	2.8871	2200	1.3825	30.5	46.21	64.9257
0.0438	3.0184	2300	1.4198	30.44	46.18	62.5844
0.0257	3.1496	2400	1.4033	31.03	46.67	63.6650
0.0252	3.2808	2500	1.4045	31.2	46.44	62.4043
0.0241	3.4121	2600	1.3971	32.42	48.21	61.1436
0.0208	3.5433	2700	1.4129	30.36	46.28	65.7362
0.0186	3.6745	2800	1.4076	31.14	47.73	64.4304
0.018	3.8058	2900	1.4151	27.67	45.87	73.5254
0.0193	3.9370	3000	1.4107	27.85	46.91	73.4354

Framework versions

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