wac2vec-lllfantomlll

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

• Loss: 0.5560
• Wer: 0.3417

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.5768	1.0	500	2.0283	1.0238
0.9219	2.01	1000	0.5103	0.5022
0.4497	3.01	1500	0.4746	0.4669
0.3163	4.02	2000	0.4144	0.4229
0.2374	5.02	2500	0.4186	0.4161
0.2033	6.02	3000	0.4115	0.3975
0.1603	7.03	3500	0.4424	0.3817
0.1455	8.03	4000	0.4151	0.3918
0.1276	9.04	4500	0.4940	0.3798
0.108	10.04	5000	0.4580	0.3688
0.1053	11.04	5500	0.4243	0.3700
0.0929	12.05	6000	0.4999	0.3727
0.0896	13.05	6500	0.4991	0.3624
0.0748	14.06	7000	0.4924	0.3602
0.0681	15.06	7500	0.4908	0.3544
0.0619	16.06	8000	0.5021	0.3559
0.0569	17.07	8500	0.5448	0.3518
0.0549	18.07	9000	0.4919	0.3508
0.0478	19.08	9500	0.4704	0.3513
0.0437	20.08	10000	0.5058	0.3555
0.0421	21.08	10500	0.5127	0.3489
0.0362	22.09	11000	0.5439	0.3527
0.0322	23.09	11500	0.5418	0.3469
0.0327	24.1	12000	0.5298	0.3422
0.0292	25.1	12500	0.5511	0.3426
0.0246	26.1	13000	0.5349	0.3472
0.0251	27.11	13500	0.5646	0.3391
0.0214	28.11	14000	0.5821	0.3424
0.0217	29.12	14500	0.5560	0.3417

Framework versions

• Transformers 4.17.0
• Pytorch 1.11.0+cu113
• Datasets 1.18.3
• Tokenizers 0.12.1