This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the WAV_SUB-P001 - TR dataset. It achieves the following results on the evaluation set:

Loss: 2.7286
Wer: 0.9843

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: 0.0003
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
No log	0.16	100	3.8990	1.0
No log	0.32	200	2.8678	1.0
No log	0.48	300	2.7795	1.0
No log	0.65	400	2.7316	1.0
4.389	0.81	500	2.6889	0.9784
4.389	0.97	600	3.2727	0.9784
4.389	1.13	700	2.7057	0.9784
4.389	1.29	800	2.8525	0.9964
4.389	1.45	900	2.6685	0.9968
2.5649	1.61	1000	2.7403	1.0
2.5649	1.78	1100	2.7790	1.0
2.5649	1.94	1200	2.8130	0.9786
2.5649	2.1	1300	2.8031	1.0
2.5649	2.26	1400	2.9683	1.0
2.5421	2.42	1500	2.9459	0.9784
2.5421	2.58	1600	2.7052	0.9784
2.5421	2.74	1700	2.7879	0.9786
2.5421	2.91	1800	2.7956	1.0
2.5421	3.07	1900	2.7760	0.9784
2.5357	3.23	2000	2.8594	0.9995
2.5357	3.39	2100	2.9048	0.9796
2.5357	3.55	2200	3.0098	0.9784
2.5357	3.71	2300	2.7079	0.9784
2.5357	3.87	2400	3.2403	1.0
2.5203	4.04	2500	3.0476	0.9784
2.5203	4.2	2600	2.8510	1.0
2.5203	4.36	2700	2.7907	0.9784
2.5203	4.52	2800	2.7486	0.9784
2.5203	4.68	2900	3.1701	1.0
2.5191	4.84	3000	2.9529	0.9784
2.5191	5.0	3100	3.1192	0.9650
2.5191	5.17	3200	2.8596	1.0
2.5191	5.33	3300	2.9193	1.0
2.5191	5.49	3400	3.0367	0.9784
2.5422	5.65	3500	2.9162	0.9784
2.5422	5.81	3600	3.0334	1.0
2.5422	5.97	3700	2.8514	0.9784
2.5422	6.13	3800	2.9654	1.0
2.5422	6.3	3900	3.2616	0.9784
2.5062	6.46	4000	3.3320	0.9793
2.5062	6.62	4100	2.7141	0.9784
2.5062	6.78	4200	3.2108	0.9784
2.5062	6.94	4300	3.0015	0.9784
2.5062	7.1	4400	3.0244	1.0
2.5114	7.26	4500	2.8742	0.9784
2.5114	7.43	4600	3.1471	0.9784
2.5114	7.59	4700	2.7006	0.9773
2.5114	7.75	4800	3.1189	1.0
2.5114	7.91	4900	3.1604	0.9784
2.5065	8.07	5000	2.9297	0.9784
2.5065	8.23	5100	3.0998	0.9784
2.5065	8.39	5200	2.8184	0.9843
2.5065	8.56	5300	2.7133	0.9861
2.5065	8.72	5400	2.7399	0.9811
2.4956	8.88	5500	2.7186	0.9889
2.4956	9.04	5600	2.9872	0.9955
2.4956	9.2	5700	3.0825	0.9993
2.4956	9.36	5800	3.0589	0.9855
2.4956	9.52	5900	2.8177	0.9784
2.4774	9.69	6000	2.8104	0.9993
2.4774	9.85	6100	2.9498	0.9796
2.4774	10.01	6200	3.0006	0.9784
2.4774	10.17	6300	2.8100	0.9784
2.4774	10.33	6400	3.1577	0.9786
2.4689	10.49	6500	2.7814	0.9977
2.4689	10.65	6600	2.7271	0.9836
2.4689	10.82	6700	2.8403	0.9784
2.4689	10.98	6800	2.7257	0.9998
2.4689	11.14	6900	2.6728	0.9898
2.486	11.3	7000	2.7348	0.9809
2.486	11.46	7100	2.7054	0.9982
2.486	11.62	7200	2.7254	0.9948
2.486	11.78	7300	2.7498	0.9891
2.486	11.95	7400	2.7076	0.9898
2.4616	12.11	7500	2.6398	0.9995
2.4616	12.27	7600	2.7626	0.9846
2.4616	12.43	7700	2.6804	0.9814
2.4616	12.59	7800	2.8212	0.9834
2.4616	12.75	7900	2.6535	0.9959
2.4573	12.91	8000	2.7547	0.9993
2.4573	13.08	8100	2.7253	0.9798
2.4573	13.24	8200	2.6851	0.9936
2.4573	13.4	8300	2.7627	0.9907
2.4573	13.56	8400	2.6607	0.9857
2.4487	13.72	8500	2.6645	0.9800
2.4487	13.88	8600	2.7558	0.9973
2.4487	14.04	8700	2.7665	0.9961
2.4487	14.21	8800	2.7697	0.9827
2.4487	14.37	8900	2.8531	0.9918
2.4416	14.53	9000	2.8974	0.9920
2.4416	14.69	9100	2.7308	0.9975
2.4416	14.85	9200	2.7919	0.9816
2.4416	15.01	9300	2.6605	0.9893
2.4416	15.17	9400	2.6058	0.9816
2.4405	15.33	9500	2.6366	0.9911
2.4405	15.5	9600	2.5653	0.9818
2.4405	15.66	9700	2.7026	0.9807
2.4405	15.82	9800	2.7358	0.9796
2.4405	15.98	9900	2.6954	0.9848
2.4352	16.14	10000	2.6610	0.9857
2.4352	16.3	10100	2.7686	0.9811
2.4352	16.46	10200	2.7758	0.9798
2.4352	16.63	10300	2.7515	0.9848
2.4352	16.79	10400	2.7264	0.9911
2.4354	16.95	10500	2.7039	0.9791
2.4354	17.11	10600	2.7580	0.9843
2.4354	17.27	10700	2.7187	0.9855
2.4354	17.43	10800	2.7545	0.9798
2.4354	17.59	10900	2.7452	0.9809
2.4321	17.76	11000	2.6804	0.9836
2.4321	17.92	11100	2.6586	0.9891
2.4321	18.08	11200	2.6805	0.9830
2.4321	18.24	11300	2.6626	0.9871
2.4321	18.4	11400	2.7002	0.9809
2.4193	18.56	11500	2.7054	0.9839
2.4193	18.72	11600	2.7171	0.9900
2.4193	18.89	11700	2.7122	0.9852
2.4193	19.05	11800	2.7058	0.9871
2.4193	19.21	11900	2.7004	0.9839
2.4276	19.37	12000	2.7250	0.9852
2.4276	19.53	12100	2.7126	0.9861
2.4276	19.69	12200	2.7388	0.9834
2.4276	19.85	12300	2.7311	0.9850

Framework versions

Transformers 4.34.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.0

sz-mp/em_ctc

em_ctc

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sz-mp/
em_ctc