wav2vec2-base-timit-demo-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: 0.6259
• Wer: 0.3544

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: 4
• 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
3.6744	0.5	500	2.9473	1.0
1.4535	1.01	1000	0.7774	0.6254
0.7376	1.51	1500	0.6923	0.5712
0.5848	2.01	2000	0.5445	0.5023
0.4492	2.51	2500	0.5148	0.4958
0.4006	3.02	3000	0.5283	0.4781
0.3319	3.52	3500	0.5196	0.4628
0.3424	4.02	4000	0.5285	0.4551
0.2772	4.52	4500	0.5060	0.4532
0.2724	5.03	5000	0.5216	0.4422
0.2375	5.53	5500	0.5376	0.4443
0.2279	6.03	6000	0.6051	0.4308
0.2091	6.53	6500	0.5084	0.4423
0.2029	7.04	7000	0.5083	0.4242
0.1784	7.54	7500	0.6123	0.4297
0.1774	8.04	8000	0.5749	0.4339
0.1542	8.54	8500	0.5110	0.4033
0.1638	9.05	9000	0.6324	0.4318
0.1493	9.55	9500	0.6100	0.4152
0.1591	10.05	10000	0.5508	0.4022
0.1304	10.55	10500	0.5090	0.4054
0.1234	11.06	11000	0.6282	0.4093
0.1218	11.56	11500	0.5817	0.3941
0.121	12.06	12000	0.5741	0.3999
0.1073	12.56	12500	0.5818	0.4149
0.104	13.07	13000	0.6492	0.3953
0.0934	13.57	13500	0.5393	0.4083
0.0961	14.07	14000	0.5510	0.3919
0.0965	14.57	14500	0.5896	0.3992
0.0921	15.08	15000	0.5554	0.3947
0.0751	15.58	15500	0.6312	0.3934
0.0805	16.08	16000	0.6732	0.3948
0.0742	16.58	16500	0.5990	0.3884
0.0708	17.09	17000	0.6186	0.3869
0.0679	17.59	17500	0.5837	0.3848
0.072	18.09	18000	0.5831	0.3775
0.0597	18.59	18500	0.6562	0.3843
0.0612	19.1	19000	0.6298	0.3756
0.0514	19.6	19500	0.6746	0.3720
0.061	20.1	20000	0.6236	0.3788
0.054	20.6	20500	0.6012	0.3718
0.0521	21.11	21000	0.6053	0.3778
0.0494	21.61	21500	0.6154	0.3772
0.0468	22.11	22000	0.6052	0.3747
0.0413	22.61	22500	0.5877	0.3716
0.0424	23.12	23000	0.5786	0.3658
0.0403	23.62	23500	0.5828	0.3658
0.0391	24.12	24000	0.5913	0.3685
0.0312	24.62	24500	0.5850	0.3625
0.0316	25.13	25000	0.6029	0.3611
0.0282	25.63	25500	0.6312	0.3624
0.0328	26.13	26000	0.6312	0.3621
0.0258	26.63	26500	0.5891	0.3581
0.0256	27.14	27000	0.6259	0.3546
0.0255	27.64	27500	0.6315	0.3587
0.0249	28.14	28000	0.6547	0.3579
0.025	28.64	28500	0.6237	0.3565
0.0228	29.15	29000	0.6187	0.3559
0.0209	29.65	29500	0.6259	0.3544

Framework versions

• Transformers 4.11.3
• Pytorch 1.10.0+cu102
• Datasets 1.18.3
• Tokenizers 0.10.3