🚀 wav2vec2-base-timit-demo-colab
此模型是在None數據集上對 facebook/wav2vec2-base 進行微調後的版本。它在評估集上取得了以下結果:
- 損失值:0.6259
- 字錯率(Wer):0.3544
📚 詳細文檔
訓練過程
訓練超參數
訓練期間使用了以下超參數:
- 學習率:0.0001
- 訓練批次大小:4
- 評估批次大小:8
- 隨機種子:42
- 優化器:Adam(β1=0.9,β2=0.999,ε=1e-08)
- 學習率調度器類型:線性
- 學習率調度器熱身步數:1000
- 訓練輪數:30
- 混合精度訓練:原生自動混合精度(Native AMP)
訓練結果
訓練損失 |
輪數 |
步數 |
驗證損失 |
字錯率(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 |
框架版本
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.18.3
- Tokenizers 0.10.3
📄 許可證
本模型採用Apache-2.0許可證。