Comments (4)
经过多次实践,多个样本实践,大致摸索出一个规律。如果音频文件有静音的地方,比如电平为0的部分,在一段10秒的音频里,讲话人不说话时,电平为0的部分存在,就会出错无法转换。当一个人讲话第一句低沉沙哑,也有出错的概率,但不是100%出错,感觉这个问题不是项目团队能够解决的,是否是trochaudio的问题。当我把一个不能正常转换的文件通过au剪切分段,一个音频文件中不存在电平为0的停顿后,这个文件就可以被正常的转换了。
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Audio data cannot be converted to 16-bit int format
应该是你输入的音频解码器不支持
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gradio 版本问题
该问题已在gradio-app/gradio#2545 中修复
更新gradio到包含这个改动的版本(3.7)即可解决问题
pip install --upgrade gradio>=3.7
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训练了一个模型大改2400步后我试了一下发现可以用,在转换过程中我发现有些音频转成wav 44100 16bit后可以正常转换,但是有个wav源文件明明自己是16bit,转换就报错ValueError: Audio data cannot be converted to 16-bit int format.哪怕我在au里切一小段下来另存为44100 16bit也是这个报错。wav的参数只有这些,还有什么因素会影响这个源文件无法被转换?
报错中提到的是16-bit int,也就是int_16类型变量,指的是cpu的位数,和音频的比特深度无关
经过多次实践,多个样本实践,大致摸索出一个规律。如果音频文件有静音的地方,比如电平为0的部分,在一段10秒的音频里,讲话人不说话时,电平为0的部分存在,就会出错无法转换。当一个人讲话第一句低沉沙哑,也有出错的概率,但不是100%出错,感觉这个问题不是项目团队能够解决的,是否是trochaudio的问题。当我把一个不能正常转换的文件通过au剪切分段,一个音频文件中不存在电平为0的停顿后,这个文件就可以被正常的转换了。
事实上这个问题是由gradio引起的。在早期版本的gradio中,函数convert_to_16_bit_wav并没有支持np.float64类型的转换,而上传到gradio的音频处理后就是这个类型。所幸这个问题已经在5个月前被修复,而他们修复的方式也很简单——增加了if的判断条件
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Related Issues (20)
- ValueError: math domain error
- [mps] issue with Apple silicon compatibility HOT 1
- [Help]: 特征检索在webui推理中无法使用 HOT 8
- [Help] Where to download the latest weight? error, emb_g.weight is not in the checkpoint HOT 2
- [Help]: subprocess-exited-with-error HOT 1
- [Bug]: KL散度为负数 HOT 3
- [Bug]: ERROR: Failed building wheel for pyworld on Google Colab HOT 3
- issue about speaker embedding
- dlopen: cannot load any more object with static TLS HOT 1
- [Bug]: HOT 2
- [Bug]:ERROR: During reasoning, the final result cannot be output
- [Bug]: ValueError: array is not C-contiguous When using feature_retrieval 使用特征检索时报错 HOT 1
- [Help]: 是否有支持VITS模型ONNX的计划? HOT 1
- [Help]: 为何扩散模型生成的效果要远好於 sovits 的模型? HOT 3
- [Bug]: new Shallow diffusion bug? HOT 3
- [Bug]: Package conflict with numpy
- [Help]: OOM error on 24 GB GPU upon inference HOT 1
- [Bug]: 4.0下如果数据集过多生成F0会导致爆显存 HOT 10
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