cahya/wav2vec2-base-turkish-cv8

This model is a fine-tuned version of ./checkpoint-1000 on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - TR dataset. It achieves the following results on the evaluation set:

• Loss: 0.3282
• Wer: 0.2836

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: 96
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 192
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 100.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.0671	2.04	200	0.3079	0.2752
0.6433	4.08	400	0.2728	0.2848
0.5687	6.12	600	0.2882	0.3036
0.5355	8.16	800	0.2778	0.2920
0.5116	10.2	1000	0.2906	0.3014
0.5313	9.16	1200	0.2984	0.3273
0.4996	10.69	1400	0.3170	0.3344
0.4845	12.21	1600	0.3202	0.3634
0.5092	13.74	1800	0.3167	0.3373
0.4777	15.27	2000	0.3292	0.3386
0.4651	16.79	2200	0.3070	0.3427
0.461	18.32	2400	0.3149	0.3561
0.4481	19.85	2600	0.3292	0.3441
0.4479	21.37	2800	0.3142	0.3209
0.4305	22.9	3000	0.3525	0.3547
0.4254	24.43	3200	0.3414	0.3400
0.4066	25.95	3400	0.3118	0.3207
0.4043	27.48	3600	0.3418	0.3483
0.3985	29.01	3800	0.3254	0.3166
0.3982	30.53	4000	0.3306	0.3453
0.3929	32.06	4200	0.3262	0.3229
0.378	33.59	4400	0.3546	0.3336
0.4062	35.11	4600	0.3174	0.3457
0.3648	36.64	4800	0.3377	0.3357
0.3609	38.17	5000	0.3346	0.3520
0.3483	39.69	5200	0.3350	0.3526
0.3548	41.22	5400	0.3330	0.3406
0.3446	42.75	5600	0.3398	0.3372
0.3346	44.27	5800	0.3449	0.3288
0.3309	45.8	6000	0.3320	0.3144
0.326	47.33	6200	0.3400	0.3279
0.3189	48.85	6400	0.3400	0.3150
0.3165	50.38	6600	0.3359	0.2995
0.3132	51.91	6800	0.3343	0.3096
0.3092	53.44	7000	0.3224	0.3029
0.2995	54.96	7200	0.3205	0.2985
0.304	56.49	7400	0.3523	0.3034
0.2952	58.02	7600	0.3289	0.2934
0.2875	59.54	7800	0.3350	0.3008
0.2868	61.07	8000	0.3537	0.3227
0.2875	62.6	8200	0.3389	0.2970
0.2778	64.12	8400	0.3370	0.2960
0.2706	65.65	8600	0.3250	0.2802
0.2669	67.18	8800	0.3351	0.2903
0.2615	68.7	9000	0.3382	0.2989
0.2563	70.23	9200	0.3312	0.2975
0.2546	71.76	9400	0.3212	0.3003
0.2482	73.28	9600	0.3337	0.3091
0.2504	74.81	9800	0.3308	0.3110
0.2456	76.34	10000	0.3157	0.3118
0.2363	77.86	10200	0.3251	0.3144
0.2319	79.39	10400	0.3253	0.3038
0.2266	80.92	10600	0.3374	0.3038
0.2279	82.44	10800	0.3268	0.2964
0.2231	83.97	11000	0.3278	0.2950
0.2185	85.5	11200	0.3462	0.2981
0.2245	87.02	11400	0.3311	0.2895
0.223	88.55	11600	0.3325	0.2877
0.2121	90.08	11800	0.3337	0.2828
0.2126	91.6	12000	0.3325	0.2808
0.2027	93.13	12200	0.3277	0.2820
0.2058	94.66	12400	0.3308	0.2827
0.1991	96.18	12600	0.3279	0.2820
0.1991	97.71	12800	0.3300	0.2822
0.1986	99.24	13000	0.3285	0.2835

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.3
• Tokenizers 0.11.0