wav2vec2-base-timit-demo-google-colab

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: 0.4770
• Wer: 0.3360

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
3.6401	1.0	500	2.4138	1.0
0.9717	2.01	1000	0.6175	0.5531
0.4393	3.01	1500	0.4309	0.4414
0.2976	4.02	2000	0.4167	0.4162
0.2345	5.02	2500	0.4273	0.3927
0.1919	6.02	3000	0.3983	0.3886
0.1565	7.03	3500	0.5581	0.3928
0.1439	8.03	4000	0.4509	0.3821
0.1266	9.04	4500	0.4733	0.3774
0.1091	10.04	5000	0.4755	0.3808
0.1001	11.04	5500	0.4435	0.3689
0.0911	12.05	6000	0.4962	0.3897
0.0813	13.05	6500	0.5031	0.3622
0.0729	14.06	7000	0.4853	0.3597
0.0651	15.06	7500	0.5180	0.3577
0.0608	16.06	8000	0.5251	0.3630
0.0592	17.07	8500	0.4915	0.3591
0.0577	18.07	9000	0.4724	0.3656
0.0463	19.08	9500	0.4536	0.3546
0.0475	20.08	10000	0.5107	0.3546
0.0464	21.08	10500	0.4829	0.3464
0.0369	22.09	11000	0.4844	0.3448
0.0327	23.09	11500	0.4865	0.3437
0.0337	24.1	12000	0.4825	0.3488
0.0271	25.1	12500	0.4824	0.3445
0.0236	26.1	13000	0.4747	0.3397
0.0243	27.11	13500	0.4840	0.3397
0.0226	28.11	14000	0.4716	0.3354
0.0235	29.12	14500	0.4770	0.3360

Framework versions

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