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: 0.5725
• Wer: 0.3413

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
3.508	1.0	500	1.9315	0.9962
0.8832	2.01	1000	0.5552	0.5191
0.4381	3.01	1500	0.4451	0.4574
0.2983	4.02	2000	0.4096	0.4265
0.2232	5.02	2500	0.4280	0.4083
0.1811	6.02	3000	0.4307	0.3942
0.1548	7.03	3500	0.4453	0.3889
0.1367	8.03	4000	0.5043	0.4138
0.1238	9.04	4500	0.4530	0.3807
0.1072	10.04	5000	0.4435	0.3660
0.0978	11.04	5500	0.4739	0.3676
0.0887	12.05	6000	0.5052	0.3761
0.0813	13.05	6500	0.5098	0.3619
0.0741	14.06	7000	0.4666	0.3602
0.0654	15.06	7500	0.5642	0.3657
0.0589	16.06	8000	0.5489	0.3638
0.0559	17.07	8500	0.5260	0.3598
0.0562	18.07	9000	0.5250	0.3640
0.0448	19.08	9500	0.5215	0.3569
0.0436	20.08	10000	0.5117	0.3560
0.0412	21.08	10500	0.4910	0.3570
0.0336	22.09	11000	0.5221	0.3524
0.031	23.09	11500	0.5278	0.3480
0.0339	24.1	12000	0.5353	0.3486
0.0278	25.1	12500	0.5342	0.3462
0.0251	26.1	13000	0.5399	0.3439
0.0242	27.11	13500	0.5626	0.3431
0.0214	28.11	14000	0.5749	0.3408
0.0216	29.12	14500	0.5725	0.3413

Framework versions

• Transformers 4.17.0
• Pytorch 1.12.0+cu113
• Datasets 1.18.3
• Tokenizers 0.12.1