🚀 wav2vec2-large-xls-r-300m-br-d10
该模型是在MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - BR数据集上对[facebook/wav2vec2 - xls - r - 300m](https://huggingface.co/facebook/wav2vec2 - xls - r - 300m)进行微调后的版本。它在语音识别任务中表现出色,能够将音频准确地转换为文本。
🚀 快速开始
本模型是在MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - BR数据集上对[facebook/wav2vec2 - xls - r - 300m](https://huggingface.co/facebook/wav2vec2 - xls - r - 300m)进行微调得到的。在评估集上取得了如下结果:
- 损失值:1.1382
- 词错误率(Wer):0.4895
📚 详细文档
评估命令
- 在mozilla - foundation/common_voice_8_0的测试分割集上进行评估
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d10 --dataset mozilla-foundation/common_voice_8_0 --config br --split test --log_outputs
- 在speech - recognition - community - v2/dev_data上进行评估
布列塔尼语在speech - recognition - community - v2/dev_data中不可用。
训练超参数
训练过程中使用了以下超参数:
属性 |
详情 |
学习率 |
0.0004 |
训练批次大小 |
16 |
评估批次大小 |
8 |
随机种子 |
42 |
梯度累积步数 |
2 |
总训练批次大小 |
32 |
优化器 |
Adam(β1 = 0.9,β2 = 0.999,ε = 1e - 08) |
学习率调度器类型 |
线性 |
学习率调度器热身步数 |
800 |
训练轮数 |
50 |
混合精度训练 |
原生自动混合精度(Native AMP) |
训练结果
训练损失 |
轮数 |
步数 |
验证损失 |
词错误率(Wer) |
13.611 |
0.68 |
100 |
5.8492 |
1.0 |
3.8176 |
1.35 |
200 |
3.2181 |
1.0 |
3.0457 |
2.03 |
300 |
3.0902 |
1.0 |
2.2632 |
2.7 |
400 |
1.4882 |
0.9426 |
1.1965 |
3.38 |
500 |
1.1396 |
0.7950 |
0.984 |
4.05 |
600 |
1.0216 |
0.7583 |
0.8036 |
4.73 |
700 |
1.0258 |
0.7202 |
0.7061 |
5.41 |
800 |
0.9710 |
0.6820 |
0.689 |
6.08 |
900 |
0.9731 |
0.6488 |
0.6063 |
6.76 |
1000 |
0.9442 |
0.6569 |
0.5215 |
7.43 |
1100 |
1.0221 |
0.6671 |
0.4965 |
8.11 |
1200 |
0.9266 |
0.6181 |
0.4321 |
8.78 |
1300 |
0.9050 |
0.5991 |
0.3762 |
9.46 |
1400 |
0.9801 |
0.6134 |
0.3747 |
10.14 |
1500 |
0.9210 |
0.5747 |
0.3554 |
10.81 |
1600 |
0.9720 |
0.6051 |
0.3148 |
11.49 |
1700 |
0.9672 |
0.6099 |
0.3176 |
12.16 |
1800 |
1.0120 |
0.5966 |
0.2915 |
12.84 |
1900 |
0.9490 |
0.5653 |
0.2696 |
13.51 |
2000 |
0.9394 |
0.5819 |
0.2569 |
14.19 |
2100 |
1.0197 |
0.5667 |
0.2395 |
14.86 |
2200 |
0.9771 |
0.5608 |
0.2367 |
15.54 |
2300 |
1.0516 |
0.5678 |
0.2153 |
16.22 |
2400 |
1.0097 |
0.5679 |
0.2092 |
16.89 |
2500 |
1.0143 |
0.5430 |
0.2046 |
17.57 |
2600 |
1.0884 |
0.5631 |
0.1937 |
18.24 |
2700 |
1.0113 |
0.5648 |
0.1752 |
18.92 |
2800 |
1.0056 |
0.5470 |
0.164 |
19.59 |
2900 |
1.0340 |
0.5508 |
0.1723 |
20.27 |
3000 |
1.0743 |
0.5615 |
0.1535 |
20.95 |
3100 |
1.0495 |
0.5465 |
0.1432 |
21.62 |
3200 |
1.0390 |
0.5333 |
0.1561 |
22.3 |
3300 |
1.0798 |
0.5590 |
0.1384 |
22.97 |
3400 |
1.1716 |
0.5449 |
0.1359 |
23.65 |
3500 |
1.1154 |
0.5420 |
0.1356 |
24.32 |
3600 |
1.0883 |
0.5387 |
0.1355 |
25.0 |
3700 |
1.1114 |
0.5504 |
0.1158 |
25.68 |
3800 |
1.1171 |
0.5388 |
0.1166 |
26.35 |
3900 |
1.1335 |
0.5403 |
0.1165 |
27.03 |
4000 |
1.1374 |
0.5248 |
0.1064 |
27.7 |
4100 |
1.0336 |
0.5298 |
0.0987 |
28.38 |
4200 |
1.0407 |
0.5216 |
0.104 |
29.05 |
4300 |
1.1012 |
0.5350 |
0.0894 |
29.73 |
4400 |
1.1016 |
0.5310 |
0.0912 |
30.41 |
4500 |
1.1383 |
0.5302 |
0.0972 |
31.08 |
4600 |
1.0851 |
0.5214 |
0.0832 |
31.76 |
4700 |
1.1705 |
0.5311 |
0.0859 |
32.43 |
4800 |
1.0750 |
0.5192 |
0.0811 |
33.11 |
4900 |
1.0900 |
0.5180 |
0.0825 |
33.78 |
5000 |
1.1271 |
0.5196 |
0.07 |
34.46 |
5100 |
1.1289 |
0.5141 |
0.0689 |
35.14 |
5200 |
1.0960 |
0.5101 |
0.068 |
35.81 |
5300 |
1.1377 |
0.5050 |
0.0776 |
36.49 |
5400 |
1.0880 |
0.5194 |
0.0642 |
37.16 |
5500 |
1.1027 |
0.5076 |
0.0607 |
37.84 |
5600 |
1.1293 |
0.5119 |
0.0607 |
38.51 |
5700 |
1.1229 |
0.5103 |
0.0545 |
39.19 |
5800 |
1.1168 |
0.5103 |
0.0562 |
39.86 |
5900 |
1.1206 |
0.5073 |
0.0484 |
40.54 |
6000 |
1.1710 |
0.5019 |
0.0499 |
41.22 |
6100 |
1.1511 |
0.5100 |
0.0455 |
41.89 |
6200 |
1.1488 |
0.5009 |
0.0475 |
42.57 |
6300 |
1.1196 |
0.4944 |
0.0413 |
43.24 |
6400 |
1.1654 |
0.4996 |
0.0389 |
43.92 |
6500 |
1.0961 |
0.4930 |
0.0428 |
44.59 |
6600 |
1.0955 |
0.4938 |
0.039 |
45.27 |
6700 |
1.1323 |
0.4955 |
0.0352 |
45.95 |
6800 |
1.1040 |
0.4930 |
0.0334 |
46.62 |
6900 |
1.1382 |
0.4942 |
0.0338 |
47.3 |
7000 |
1.1264 |
0.4911 |
0.0307 |
47.97 |
7100 |
1.1216 |
0.4881 |
0.0286 |
48.65 |
7200 |
1.1459 |
0.4894 |
0.0348 |
49.32 |
7300 |
1.1419 |
0.4906 |
0.0329 |
50.0 |
7400 |
1.1382 |
0.4895 |
框架版本
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
📄 许可证
本模型采用Apache 2.0许可证。