Comments (5)
Json文件的生成规则:整体是一个字典数组,每个Item里有 mix:加噪后的语音路径;clean:干净语音路径;noise:噪声路径。
你如果按照DNS官方脚本生成了数据的话,会得到noisy、clean、noise三个文件夹。他们内部的语音是根据file_id对应起来的。你可以根据这个id来生成对应的json。
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一语惊醒梦中人;原来官方只靠末尾id来一一对应。。。非常感谢。
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作者大大您好,我跑通了您的代码(除了因为版本问题,替换了几个函数;使用的是默认参数训练)。
训练的时候他2个小时一个epoch有点恐怖(1080Ti);我看显存是占满了的,利用率也在有规律浮动。
请问2个小时一个epoch正常吗。
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你的数据量是多少。我当时生成了100小时的数据。用两张P100(16G显存)跑了两周才跑完的。
如果你嫌慢的话,我建议:
1、你可以减小语音长度(比如减小到5s)来增大batch的大小,以加快BatchNorm的收敛。
2、把信噪比控制在 -5 到 20dB,不要像我之前一样去生成 0 到 40dB的数据,我实验发现 -5 到 20dB 50小时的数据和 0 到 40dB 100小时的数据训练出来的模型效果是差不多的。
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明白了,祝周末愉快。
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Related Issues (19)
- how to reduce the cuda memory? HOT 9
- 关于数据集 HOT 1
- JSONDecodeError HOT 1
- 关于模型训练 HOT 1
- 关于训练数据
- training loss
- 关于json文件
- 代码库请教 HOT 2
- 关于模型输出还原过程中的E,C,R选项 HOT 1
- 发错了
- Hello, I have a question about dataset HOT 2
- download DNS dataset HOT 1
- AttributeError: can't set attribute HOT 16
- Inference code HOT 4
- Training & val loss HOT 5
- Question or Bug HOT 3
- 请求数据集 HOT 1
- 模型训练过程中ComplexBatchNorm函数导致内存爆炸 HOT 9
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