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Whisper Medium GA-EN Speech Translation

Name: whisper-medium-ga2en-v6.3.1-r
Rating: 2.5 (7 reviews)

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

Loss: 1.0885
Bleu: 30.86
Chrf: 54.11
Wer: 67.0419

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: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Bleu	Chrf	Validation Loss	Wer
2.5374	0.0138	100	2.56	18.92	2.1201	222.4674
2.446	0.0276	200	3.07	20.56	2.1960	170.5088
2.2819	0.0414	300	5.87	25.17	1.9811	114.5880
2.1904	0.0552	400	8.41	25.65	1.9974	99.1896
2.026	0.0690	500	7.99	27.64	1.8961	130.7069
2.0448	0.0828	600	9.15	27.78	1.9410	104.9077
1.8606	0.0966	700	9.57	29.34	1.8451	110.4908
1.9887	0.1103	800	13.44	32.32	1.7419	84.3314
1.8633	0.1241	900	13.43	31.58	1.7376	102.1162
1.7576	0.1379	1000	11.9	32.68	1.6879	106.6186
1.7142	0.1517	1100	12.4	33.66	1.7571	102.6114
1.7168	0.1655	1200	17.35	36.55	1.6003	87.9784
1.6741	0.1793	1300	15.41	35.46	1.5883	92.8411
1.6534	0.1931	1400	17.12	37.24	1.5366	90.2296
1.58	0.2069	1500	17.49	38.5	1.5141	92.1207
1.403	0.2207	1600	16.78	39.13	1.4606	88.9689
1.3806	0.2345	1700	19.26	40.02	1.4263	86.7177
1.5111	0.2483	1800	18.4	39.47	1.4060	92.2557
1.4261	0.2621	1900	21.19	42.13	1.3911	78.7033
1.2974	0.2759	2000	15.6	38.66	1.3871	100.3152
1.2694	0.2897	2100	16.21	39.99	1.3527	91.2652
1.204	0.3034	2200	20.2	41.18	1.3232	86.8978
1.1922	0.3172	2300	16.44	40.85	1.3338	103.1968
1.1237	0.3310	2400	19.29	43.73	1.2830	94.4620
1.0989	0.3448	2500	25.11	46.84	1.2844	75.0563
1.0766	0.3586	2600	23.87	46.1	1.2578	74.5160
1.0432	0.3724	2700	22.31	44.91	1.2414	86.9878
1.1588	0.3862	2800	23.32	45.94	1.2051	77.1724
1.0062	0.4	2900	26.15	48.27	1.2059	69.4282
0.9178	0.4138	3000	29.13	48.92	1.1756	64.7456
0.9108	0.4276	3100	28.34	48.9	1.1665	67.2220
0.9868	0.4414	3200	25.64	48.93	1.1489	75.3264
0.9563	0.4552	3300	27.58	49.67	1.1181	71.8145
0.9138	0.4690	3400	28.37	50.96	1.1247	71.4543
0.8508	0.4828	3500	29.75	51.41	1.1007	68.3476
0.836	0.4966	3600	30.99	52.2	1.1114	66.5916
0.8435	0.5103	3700	30.64	52.77	1.0782	68.2125
0.8323	0.5241	3800	29.78	52.94	1.0744	68.9779
0.818	0.5379	3900	31.23	53.21	1.0639	67.7623
0.8095	0.5517	4000	31.02	53.51	1.0576	68.5277
0.922	0.5655	4100	1.2445	25.47	46.16	74.2909
1.0387	0.5793	4200	1.2634	25.44	46.19	71.0491
0.9386	0.5931	4300	1.2457	22.36	45.4	76.8122
0.9297	0.6069	4400	1.2502	28.65	46.48	65.7362
0.9837	0.6207	4500	1.2503	26.81	46.53	68.9779
1.0226	0.6345	4600	1.2282	19.37	44.1	86.4926
0.9896	0.6483	4700	1.2568	26.06	46.46	70.8240
0.9805	0.6621	4800	1.2364	19.29	42.56	82.0351
0.8982	0.6759	4900	1.2346	28.58	47.84	64.6556
0.8303	0.6897	5000	1.2136	27.25	48.15	68.3476
0.905	0.7034	5100	1.1808	27.99	50.31	67.2220
0.8125	0.7172	5200	1.1971	28.91	47.63	65.4660
0.7965	0.7310	5300	1.1789	25.96	47.21	69.5633
0.8244	0.7448	5400	1.2237	28.65	48.63	66.6367
0.7637	0.7586	5500	1.1765	30.4	50.24	66.6817
0.7333	0.7724	5600	1.1295	29.94	51.34	68.8879
0.8141	0.7862	5700	1.1238	27.51	50.61	74.7861
0.6969	0.8	5800	1.1350	23.95	48.76	87.6632
0.7162	0.8138	5900	1.1493	26.34	48.65	74.0207
0.7421	0.8276	6000	1.0976	28.69	52.23	68.5727
0.593	0.8414	6100	1.1163	34.96	53.13	59.3426
0.678	0.8552	6200	1.1072	34.14	53.2	61.6839
0.6018	0.8690	6300	1.0959	31.8	53.33	64.1153
0.6038	0.8828	6400	1.0959	24.77	50.61	84.2413
0.6174	0.8966	6500	1.0891	25.48	50.6	81.6749
0.595	0.9103	6600	1.1037	23.83	48.07	83.3859
0.6114	0.9241	6700	1.0723	28.03	52.18	70.7789
0.6257	0.9379	6800	1.0797	33.13	52.95	61.5038
0.6689	0.9517	6900	1.0803	30.53	52.41	68.4376
0.4908	0.9655	7000	1.0901	30.1	51.71	69.1130
0.5439	0.9793	7100	1.0672	25.81	49.36	76.5871
0.5994	0.9931	7200	1.0705	31.56	52.51	66.1414
0.2451	1.0069	7300	1.1069	33.0	53.29	64.7006
0.2609	1.0207	7400	1.0877	31.68	54.3	64.9257
0.2813	1.0345	7500	1.0910	34.93	54.74	60.1531
0.2367	1.0483	7600	1.0999	30.87	53.09	65.9163
0.2018	1.0621	7700	1.0917	35.53	54.42	58.7573
0.2407	1.0759	7800	1.0859	34.38	54.5	60.9185
0.2385	1.0897	7900	1.0866	31.27	54.12	65.3309
0.2074	1.1034	8000	1.0885	30.86	54.11	67.0419

Framework versions

Transformers 4.41.2
Pytorch 2.2.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

ymoslem/whisper-medium-ga2en-v6.3.1-r

Whisper Medium GA-EN Speech Translation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ymoslem/
whisper-medium-ga2en-v6.3.1-r