lgris/wavlm-large-CORAA-pt-cv7 - AI Model Zoo

wavlm-large-CORAA-pt-cv7

This model is a fine-tuned version of lgris/WavLM-large-CORAA-pt on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.2546
Wer: 0.2261

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.6029	0.13	100	0.3679	0.3347
0.5297	0.26	200	0.3516	0.3227
0.5134	0.39	300	0.3327	0.3167
0.4941	0.52	400	0.3281	0.3122
0.4816	0.65	500	0.3154	0.3102
0.4649	0.78	600	0.3199	0.3058
0.461	0.91	700	0.3047	0.2974
0.4613	1.04	800	0.3006	0.2900
0.4198	1.17	900	0.2951	0.2891
0.3864	1.3	1000	0.2989	0.2862
0.3963	1.43	1100	0.2932	0.2830
0.3953	1.56	1200	0.2936	0.2829
0.3962	1.69	1300	0.2952	0.2773
0.3811	1.82	1400	0.2915	0.2748
0.3736	1.95	1500	0.2839	0.2684
0.3507	2.08	1600	0.2914	0.2678
0.3277	2.21	1700	0.2895	0.2652
0.3344	2.34	1800	0.2843	0.2673
0.335	2.47	1900	0.2821	0.2635
0.3559	2.6	2000	0.2830	0.2599
0.3254	2.73	2100	0.2711	0.2577
0.3263	2.86	2200	0.2685	0.2546
0.3266	2.99	2300	0.2679	0.2521
0.3066	3.12	2400	0.2727	0.2526
0.2998	3.25	2500	0.2648	0.2537
0.2961	3.38	2600	0.2630	0.2519
0.3046	3.51	2700	0.2684	0.2506
0.3006	3.64	2800	0.2604	0.2492
0.2992	3.77	2900	0.2682	0.2508
0.2775	3.9	3000	0.2732	0.2440
0.2903	4.03	3100	0.2659	0.2427
0.2535	4.16	3200	0.2650	0.2433
0.2714	4.29	3300	0.2588	0.2394
0.2636	4.42	3400	0.2652	0.2434
0.2647	4.55	3500	0.2624	0.2371
0.2796	4.67	3600	0.2611	0.2373
0.2644	4.8	3700	0.2604	0.2341
0.2657	4.93	3800	0.2567	0.2331
0.2423	5.06	3900	0.2594	0.2322
0.2556	5.19	4000	0.2587	0.2323
0.2327	5.32	4100	0.2639	0.2299
0.2613	5.45	4200	0.2569	0.2310
0.2382	5.58	4300	0.2585	0.2298
0.2404	5.71	4400	0.2543	0.2287
0.2368	5.84	4500	0.2553	0.2286
0.2514	5.97	4600	0.2517	0.2279
0.2415	6.1	4700	0.2524	0.2270
0.2338	6.23	4800	0.2540	0.2265
0.219	6.36	4900	0.2549	0.2263
0.2428	6.49	5000	0.2546	0.2261

Framework versions

Transformers 4.16.1
Pytorch 1.10.0+cu111
Datasets 1.18.2
Tokenizers 0.11.0