Instructions to use Chars/AI-baki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chars/AI-baki with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Chars/AI-baki", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,325 Bytes
3f9bd99 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | # 常见错误和解决办法
错误反馈:(https://github.com/YYuX-1145/Bert-VITS2-Integration-package/issues)
# whisper转写文本时的错误
```
Warning: no short audios found
```
* ffmpeg没有被正确安装。
* 文件结构摆放错误。
# 训练时的报错
## 数据集问题
```
ERROR:models:emb_g.weight is not in the checkpoint
```
加载底膜时,是正常的。无需担心
```
RuntimeError: expand(torch.FloatTensor{[2, 1025, 278]}, size=[2, 1025]): the number of sizes provided (2) must be greater or equal to the number of dimensions in the tensor (3)
```
* 原因:音频为双声道,没有重采样。
```
RuntimeError: The expanded size of the tensor (32) must match the existing size (0) at non-singleton dimension 1. Target sizes: [192, 32]. Tensor sizes: [192, 0]
```
* 可能的原因:数据集过短
```
UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`.
0it [00:00, ?it/s]
INFO:OUTPUT_MODEL:====> Epoch: 2
0it [00:00, ?it/s]
INFO:OUTPUT_MODEL:====> Epoch: 3
0it [00:00, ?it/s]
INFO:OUTPUT_MODEL:====> Epoch: 4
0it [00:00, ?it/s]
```
* 可能原因:空跑了,可能是音频过少或者其他原因导致没法正常分配训练音频。
## 硬件和软件问题(CUDA,CUDNN ERROR)
```
CUDNN_XXX_ERROR
```
* 原因:显卡硬件或驱动故障,或者爆显存
* 解决方法:不要超频,更新(或回滚到稳定的版本)驱动。
* 爆显存解决方法:降低批大小。
```
OSError: [WinError 1455] 页面文件太小,无法完成操作。
```
* 原因:(虚拟)内存不足
* 解决方法:确保硬盘空间充足(通常是C盘),并让Windows自动分配虚拟内存,并降低训练全局配置里的num_workers。
```
RuntimeError: PytorchStreamReader failed reading zip archive: invalid header or archive is corrupted
```
* 在极个别情况下出现过,可能在下载时数据损坏。
* 重新下载相关文件。
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