Siyong/MC

wav2vec-base-All

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: 3.0545
• Wer: 0.8861
• Cer: 0.5014

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: 120
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
No log	3.33	500	4.0654	1.0	0.9823
No log	6.67	1000	3.4532	1.0	0.9823
No log	10.0	1500	3.0707	0.9992	0.9781
No log	13.33	2000	2.7335	1.0017	0.9027
No log	16.67	2500	2.5896	1.0690	0.7302
No log	20.0	3000	2.3315	1.0690	0.6677
No log	23.33	3500	2.2217	1.0150	0.5966
No log	26.67	4000	2.3802	1.0549	0.5948
No log	30.0	4500	2.2208	0.9975	0.5681
2.4224	33.33	5000	2.2687	0.9800	0.5537
2.4224	36.67	5500	2.3169	0.9476	0.5493
2.4224	40.0	6000	2.5196	0.9900	0.5509
2.4224	43.33	6500	2.4816	0.9501	0.5272
2.4224	46.67	7000	2.4894	0.9485	0.5276
2.4224	50.0	7500	2.4555	0.9418	0.5305
2.4224	53.33	8000	2.7326	0.9559	0.5255
2.4224	56.67	8500	2.5514	0.9227	0.5209
2.4224	60.0	9000	2.9135	0.9717	0.5455
2.4224	63.33	9500	3.0465	0.8346	0.5002
0.8569	66.67	10000	2.8177	0.9302	0.5216
0.8569	70.0	10500	2.9908	0.9310	0.5128
0.8569	73.33	11000	3.1752	0.9235	0.5284
0.8569	76.67	11500	2.7412	0.8886	0.5
0.8569	80.0	12000	2.7362	0.9127	0.5040
0.8569	83.33	12500	2.9636	0.9152	0.5093
0.8569	86.67	13000	3.0139	0.9011	0.5097
0.8569	90.0	13500	2.8325	0.8853	0.5032
0.8569	93.33	14000	3.0383	0.8845	0.5056
0.8569	96.67	14500	2.7931	0.8795	0.4965
0.3881	100.0	15000	2.8972	0.8928	0.5012
0.3881	103.33	15500	2.7780	0.8736	0.4947
0.3881	106.67	16000	3.1081	0.9036	0.5109
0.3881	110.0	16500	3.0078	0.8928	0.5032
0.3881	113.33	17000	3.0245	0.8886	0.5009
0.3881	116.67	17500	3.0739	0.8928	0.5065
0.3881	120.0	18000	3.0545	0.8861	0.5014

Framework versions

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