wav2vec2-burak-new-300-v2-4

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3402
• Wer: 0.2237

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: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 131

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
7.7711	2.45	500	3.1768	1.0
3.1194	4.9	1000	2.6401	1.0
1.4593	7.35	1500	0.5243	0.5960
0.7581	9.8	2000	0.3534	0.4432
0.5843	12.25	2500	0.3159	0.4157
0.4703	14.71	3000	0.3003	0.3668
0.4045	17.16	3500	0.2891	0.3414
0.3581	19.61	4000	0.2609	0.3207
0.3268	22.06	4500	0.2622	0.3207
0.3063	24.51	5000	0.2805	0.3193
0.2729	26.96	5500	0.2674	0.2884
0.249	29.41	6000	0.2740	0.2953
0.2275	31.86	6500	0.2729	0.2753
0.2295	34.31	7000	0.2801	0.2691
0.2105	36.76	7500	0.2992	0.2801
0.1905	39.22	8000	0.2967	0.2663
0.1884	41.67	8500	0.2911	0.2691
0.1773	44.12	9000	0.2966	0.2753
0.1672	46.57	9500	0.3051	0.2505
0.1632	49.02	10000	0.2872	0.2491
0.1553	51.47	10500	0.3121	0.2629
0.1619	53.92	11000	0.3044	0.2581
0.1444	56.37	11500	0.3135	0.2567
0.1451	58.82	12000	0.3033	0.2519
0.1386	61.27	12500	0.3079	0.2622
0.1261	63.73	13000	0.3037	0.2395
0.1287	66.18	13500	0.3221	0.2409
0.1236	68.63	14000	0.3179	0.2464
0.1215	71.08	14500	0.3521	0.2429
0.1208	73.53	15000	0.3481	0.2540
0.1128	75.98	15500	0.3288	0.2402
0.1108	78.43	16000	0.3238	0.2450
0.1074	80.88	16500	0.3178	0.2416
0.1086	83.33	17000	0.3461	0.2361
0.1059	85.78	17500	0.3342	0.2457
0.0981	88.24	18000	0.3382	0.2354
0.0995	90.69	18500	0.3466	0.2416
0.0995	93.14	19000	0.3326	0.2271
0.0929	95.59	19500	0.3526	0.2237
0.0944	98.04	20000	0.3516	0.2347
0.089	100.49	20500	0.3504	0.2271
0.0915	102.94	21000	0.3425	0.2285
0.0845	105.39	21500	0.3309	0.2306
0.0887	107.84	22000	0.3196	0.2264
0.0812	110.29	22500	0.3285	0.2264
0.0856	112.75	23000	0.3347	0.2251
0.0778	115.2	23500	0.3403	0.2271
0.0748	117.65	24000	0.3427	0.2278
0.0803	120.1	24500	0.3380	0.2223
0.0768	122.55	25000	0.3392	0.2189
0.0764	125.0	25500	0.3423	0.2278
0.0786	127.45	26000	0.3423	0.2230
0.0766	129.9	26500	0.3402	0.2237

Framework versions

• Transformers 4.22.2
• Pytorch 1.12.1+cu113
• Datasets 2.5.2
• Tokenizers 0.12.1