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