许可证: mit
基础模型: microsoft/speecht5_tts
标签:
- 训练生成
数据集:
- fleurs
模型索引:
- 名称: speecht5_finetuned_fleurs_zh_4000
结果: []
speecht5_finetuned_fleurs_zh_4000
该模型是基于microsoft/speecht5_tts在fleurs数据集上微调的版本。
在评估集上取得了以下结果:
模型描述
需补充更多信息
预期用途与限制
需补充更多信息
训练与评估数据
需补充更多信息
训练过程
训练超参数
训练过程中使用了以下超参数:
- 学习率: 1e-05
- 训练批次大小: 4
- 评估批次大小: 2
- 随机种子: 42
- 梯度累积步数: 8
- 总训练批次大小: 32
- 优化器: Adam (beta1=0.9, beta2=0.999, epsilon=1e-08)
- 学习率调度器类型: 线性
- 学习率预热步数: 500
- 训练总步数: 4000
训练结果
| 训练损失 |
周期 |
步数 |
验证损失 |
| 0.7366 |
1.09 |
100 |
0.6059 |
| 0.5892 |
2.19 |
200 |
0.5104 |
| 0.5436 |
3.28 |
300 |
0.4585 |
| 0.4848 |
4.38 |
400 |
0.4333 |
| 0.4733 |
5.47 |
500 |
0.4276 |
| 0.4534 |
6.57 |
600 |
0.4194 |
| 0.454 |
7.66 |
700 |
0.4172 |
| 0.4489 |
8.76 |
800 |
0.4111 |
| 0.4401 |
9.85 |
900 |
0.4108 |
| 0.441 |
10.94 |
1000 |
0.4136 |
| 0.437 |
12.04 |
1100 |
0.4078 |
| 0.4333 |
13.13 |
1200 |
0.4067 |
| 0.4328 |
14.23 |
1300 |
0.4002 |
| 0.4289 |
15.32 |
1400 |
0.4015 |
| 0.4254 |
16.42 |
1500 |
0.4012 |
| 0.427 |
17.51 |
1600 |
0.4020 |
| 0.4273 |
18.6 |
1700 |
0.4008 |
| 0.4222 |
19.7 |
1800 |
0.3966 |
| 0.4305 |
20.79 |
1900 |
0.3998 |
| 0.4198 |
21.89 |
2000 |
0.3954 |
| 0.4225 |
22.98 |
2100 |
0.3961 |
| 0.4223 |
24.08 |
2200 |
0.3965 |
| 0.4201 |
25.17 |
2300 |
0.3922 |
| 0.4234 |
26.27 |
2400 |
0.3939 |
| 0.4213 |
27.36 |
2500 |
0.3930 |
| 0.4182 |
28.45 |
2600 |
0.3934 |
| 0.4119 |
29.55 |
2700 |
0.3925 |
| 0.4113 |
30.64 |
2800 |
0.3907 |
| 0.4131 |
31.74 |
2900 |
0.3907 |
| 0.4135 |
32.83 |
3000 |
0.3933 |
| 0.4142 |
33.93 |
3100 |
0.3909 |
| 0.4144 |
35.02 |
3200 |
0.3919 |
| 0.414 |
36.11 |
3300 |
0.3919 |
| 0.418 |
37.21 |
3400 |
0.3899 |
| 0.4094 |
38.3 |
3500 |
0.3897 |
| 0.4149 |
39.4 |
3600 |
0.3924 |
| 0.4105 |
40.49 |
3700 |
0.3905 |
| 0.413 |
41.59 |
3800 |
0.3895 |
| 0.4117 |
42.68 |
3900 |
0.3900 |
| 0.4096 |
43.78 |
4000 |
0.3888 |
框架版本
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
Safetensors
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