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: 1.6820
Bleu: 30.14
Chrf: 44.97
Wer: 68.7528

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
2.0448	0.2155	100	1.7532	10.55	28.24	117.7848
1.5028	0.4310	200	1.5587	17.12	35.11	93.2913
1.3161	0.6466	300	1.5073	17.41	37.84	104.0973
1.0537	0.8621	400	1.4560	17.49	38.19	103.8721
0.4704	1.0776	500	1.4946	16.44	36.79	102.9266
0.4589	1.2931	600	1.5117	21.55	38.66	82.6204
0.4343	1.5086	700	1.5146	24.5	41.43	75.5966
0.4068	1.7241	800	1.4547	28.37	45.27	65.6011
0.3757	1.9397	900	1.4957	27.0	43.66	67.4471
0.1388	2.1552	1000	1.5642	26.72	41.74	66.5016
0.1472	2.3707	1100	1.5845	27.74	42.67	68.1675
0.1408	2.5862	1200	1.5932	28.95	44.07	65.4660
0.1436	2.8017	1300	1.5808	27.66	42.71	68.3476
0.1094	3.0172	1400	1.5684	27.1	43.29	71.3643
0.0677	3.2328	1500	1.6287	26.92	42.33	68.4376
0.0567	3.4483	1600	1.6431	23.58	42.85	81.7199
0.0594	3.6638	1700	1.6084	26.54	42.45	77.4426
0.0623	3.8793	1800	1.5817	29.29	45.85	67.8523
0.0323	4.0948	1900	1.6630	29.24	43.47	67.5822
0.0317	4.3103	2000	1.6494	26.43	43.4	73.3904
0.0325	4.5259	2100	1.6968	27.06	42.4	68.5277
0.025	4.7414	2200	1.6436	29.16	44.24	67.5371
0.0316	4.9569	2300	1.6412	29.9	46.15	66.5016
0.0159	5.1724	2400	1.6714	29.56	44.68	66.7717
0.0158	5.3879	2500	1.6458	29.56	45.46	65.8262
0.015	5.6034	2600	1.6595	29.97	44.9	68.0774
0.0145	5.8190	2700	1.6545	31.15	46.35	65.6461
0.0106	6.0345	2800	1.6724	30.24	45.36	66.8618
0.0076	6.25	2900	1.6834	30.25	45.13	67.9424
0.0049	6.4655	3000	1.6820	30.14	44.97	68.7528

Framework versions

Transformers 4.41.2
Pytorch 2.2.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1

ymoslem/whisper-small-ga2en-v1.7-r

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ymoslem/
whisper-small-ga2en-v1.7-r