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运行报错!求助 #39
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根据issue 7, 需要把 |
谢谢谢谢您!解决了!现在正在运行!感谢!另外我在刚刚关闭的问题里问过您了,就是首先python train.py之后,接下来的步骤是怎样?train.py是在训练模型嘛?我在实验报告里也没有找到详细的运行步骤,烦请回复,谢谢您! |
嗷嗷~谢谢您!我昨天早晨修正错误后就开始运行了,今早发现运行过程中出现了错误,具体如下: During handling of the above exception, another exception occurred: Traceback (most recent call last): systemd[1]: Caught , dumped core as pid 18055. Broadcast message from systemd-journald@dcase-PowerEdge-R730 (Wed 2019-08-14 07:37:30 CST): systemd[1]: Freezing execution. |
我不太清楚。你报错的代码块是写入log的那一段,可能是你的操作系统没有写的权限? |
OKOK |
嗯嗯~学长,这个问题已经解决了,是我自己系统的问题。 |
学长,我在训练到下面这个结果时kill掉了这个程序, |
应该是对的。但你这个是在完整的LibriSpeech数据上跑的还是我这个repo里面的LibriSpeechSamples上跑的? |
是的学长,谢谢您回复,我现在才打算用完整的数据集重新跑一遍。我下载了train-clean-100和train-clean-360,我看实验报告,如果我没有理解错的话,请问是否应该先pre_process.py 得到train-clean-100-npy,然后再train.py? |
|
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果: |
你好,我也想复现这个实验,可以指导一下吗? |
你好,在pre_training.py运行时出现:Found 0000368 files with 00003 different speakers.,一直不动了,请问是怎么回事? |
没有遇到这个问题。从字面理解,你的人数太少了,而且样本也有些少。
…---原始邮件---
发件人: "izhangy"<[email protected]>
发送时间: 2019年12月3日(星期二) 中午11:07
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "Comment"<[email protected]>;"QI ZHANG"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
你好,在pre_training.py运行时出现:Found 0000368 files with 00003 different speakers.,一直不动了,请问是怎么回事?
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非常感谢您的回答!我增加点数据量再试试 |
这个我不清楚,应该是无关的。只要训练了就有模型生成
------------------ 原始邮件 ------------------
发件人: "izhangy"<[email protected]>;
发送时间: 2019年12月3日(星期二) 下午2:44
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "Popcorn"<[email protected]>;"Comment"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
非常感谢您的回答!我增加点数据量再试试
还有一个问题,运行train.py后没有“checkpoints‘文件生成,请问这也是跟数据量有关吗?
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“程序运行两天了还没结束,是怎么回事呢?" |
你就光运行不看代码吗?程序本来就是死循环,我是得到合适的模型之后自己手动结束。
…------------------ 原始邮件 ------------------
发件人: "izhangy"<[email protected]>;
发送时间: 2019年12月6日(星期五) 上午10:13
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "Popcorn"<[email protected]>;"Comment"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
“程序运行两天了还没结束,是怎么回事呢?"
您好,跟楼主一样,我也遇到了这样的问题,请问怎么解决?
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见笑了,我调通后就跑了,也没看------------------ 原始邮件 ------------------
发件人: "QI ZHANG"<[email protected]>
发送时间: 2019年12月6日(星期五) 上午10:19
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "izhangy"<[email protected]>;"Comment"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
你就光运行不看代码吗?程序本来就是死循环,我是得到合适的模型之后自己手动结束。
…------------------ 原始邮件 ------------------
发件人: "izhangy"<[email protected]>;
发送时间: 2019年12月6日(星期五) 上午10:13
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "Popcorn"<[email protected]>;"Comment"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
“程序运行两天了还没结束,是怎么回事呢?"
您好,跟楼主一样,我也遇到了这样的问题,请问怎么解决?
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你好学长,我看了你复现那个Deep-speaker,也出结果了,我想问你几个问题? |
你好学长,我看了你复现那个Deep-speaker,也出结果了,我想问你几个问题? |
util只有函数,你要调用它。训练的久不久,还是要看数据,数据你觉得差不多就行
…---原始邮件---
发件人: "yy835055664"<[email protected]>
发送时间: 2019年12月10日(星期二) 下午4:52
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "Comment"<[email protected]>;"QI ZHANG"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果:
17200,1.8035286664962769
17201,0.8399255871772766
17202,0.7849252820014954
17203,0.20637908577919006
......
200847,0.0009674201137386262
200848,0.0009672052692621946
200849,0.0009669908322393894
......
382718,0.2163151204586029
382719,0.2672955095767975
382720,0.5557892322540283
得到的train_acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17210,0.0,0.9999999999995,0.99999999999998
......
211130,0.0,0.9999999999995,0.99999999999998
211140,0.0,0.9999999999995,0.99999999999998
211000,0.32981049562682213,0.1791044776114538,0.9607142857142851
211000,0.282798833819242,0.1739130434777845,0.9728571428571422
......
382440,0.03024781341107871,0.9152542372876369,0.9964285714285708
382450,0.008746355685131196,0.9285714285709286,0.9971428571428564
得到的acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17200,0.0,0.9999999999995,0.99999999999998
......
210800,0.0,0.9999999999995,0.99999999999998
211000,0.0,0.9999999999995,0.99999999999998
211000,0.3589743589743589,0.12269938650270346,0.9266666666666662
211000,0.358451072736787,0.10714285714243434,0.9743589743589739
......
382200,0.0567765567765568,0.7297297297292311,0.9897435897435893
382400,0.13291470434327576,0.6666666666661832,0.9887179487179483
这时候我就kill掉了train.py,然后python test_model.py得到了结果:
f-measure = 0.5833333333328472, true positive rate = 0.5, accuracy = 0.9857142857142851, equal error rate = 0.08017492711370261
请问学长,我是不是训练得太久了?然后这个结果是这样吗?另外请问demo里面的两张图EER.png和loss.png是怎么得到的?我python utils.py好像什么都没得到?
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果:
17200,1.8035286664962769
17201,0.8399255871772766
17202,0.7849252820014954
17203,0.20637908577919006
......
200847,0.0009674201137386262
200848,0.0009672052692621946
200849,0.0009669908322393894
......
382718,0.2163151204586029
382719,0.2672955095767975
382720,0.5557892322540283
得到的train_acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17210,0.0,0.9999999999995,0.99999999999998
......
211130,0.0,0.9999999999995,0.99999999999998
211140,0.0,0.9999999999995,0.99999999999998
211000,0.32981049562682213,0.1791044776114538,0.9607142857142851
211000,0.282798833819242,0.1739130434777845,0.9728571428571422
......
382440,0.03024781341107871,0.9152542372876369,0.9964285714285708
382450,0.008746355685131196,0.9285714285709286,0.9971428571428564
得到的acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17200,0.0,0.9999999999995,0.99999999999998
......
210800,0.0,0.9999999999995,0.99999999999998
211000,0.0,0.9999999999995,0.99999999999998
211000,0.3589743589743589,0.12269938650270346,0.9266666666666662
211000,0.358451072736787,0.10714285714243434,0.9743589743589739
......
382200,0.0567765567765568,0.7297297297292311,0.9897435897435893
382400,0.13291470434327576,0.6666666666661832,0.9887179487179483
这时候我就kill掉了train.py,然后python test_model.py得到了结果:
f-measure = 0.5833333333328472, true positive rate = 0.5, accuracy = 0.9857142857142851, equal error rate = 0.08017492711370261
请问学长,我是不是训练得太久了?然后这个结果是这样吗?另外请问demo里面的两张图EER.png和loss.png是怎么得到的?我python utils.py好像什么都没得到?
你好学长,我看了你复现那个Deep-speaker,也出结果了,我想问你几个问题?
1.checkpiont里面.h5文件怎么产生的?
2.下载的测试集是直接放到Libri文件里面吗,跟训练语音一块。测试程序需要改动吗?还是训练完了直接就可以测试了?
望学长能给予解答,谢谢!
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你好学长:
我想请教你几个问题?
1.checkpiont里面.h5文件怎么产生的?
2.下载的测试集是直接放到Libri文件里面吗,跟训练语音一块。测试程序需要改动吗?还是训练完了直接就可以测试了?
望学长能给予解答,谢谢!
…------------------ 原始邮件 ------------------
发件人: "QI ZHANG"<[email protected]>;
发送时间: 2019年12月10日(星期二) 下午5:04
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "未、、敢伤"<[email protected]>;"Comment"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
util只有函数,你要调用它。训练的久不久,还是要看数据,数据你觉得差不多就行
---原始邮件---
发件人: "yy835055664"<[email protected]&gt;
发送时间: 2019年12月10日(星期二) 下午4:52
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]&gt;;
抄送: "Comment"<[email protected]&gt;;"QI ZHANG"<[email protected]&gt;;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果:
17200,1.8035286664962769
17201,0.8399255871772766
17202,0.7849252820014954
17203,0.20637908577919006
......
200847,0.0009674201137386262
200848,0.0009672052692621946
200849,0.0009669908322393894
......
382718,0.2163151204586029
382719,0.2672955095767975
382720,0.5557892322540283
得到的train_acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17210,0.0,0.9999999999995,0.99999999999998
......
211130,0.0,0.9999999999995,0.99999999999998
211140,0.0,0.9999999999995,0.99999999999998
211000,0.32981049562682213,0.1791044776114538,0.9607142857142851
211000,0.282798833819242,0.1739130434777845,0.9728571428571422
......
382440,0.03024781341107871,0.9152542372876369,0.9964285714285708
382450,0.008746355685131196,0.9285714285709286,0.9971428571428564
得到的acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17200,0.0,0.9999999999995,0.99999999999998
......
210800,0.0,0.9999999999995,0.99999999999998
211000,0.0,0.9999999999995,0.99999999999998
211000,0.3589743589743589,0.12269938650270346,0.9266666666666662
211000,0.358451072736787,0.10714285714243434,0.9743589743589739
......
382200,0.0567765567765568,0.7297297297292311,0.9897435897435893
382400,0.13291470434327576,0.6666666666661832,0.9887179487179483
这时候我就kill掉了train.py,然后python test_model.py得到了结果:
f-measure = 0.5833333333328472, true positive rate = 0.5, accuracy = 0.9857142857142851, equal error rate = 0.08017492711370261
请问学长,我是不是训练得太久了?然后这个结果是这样吗?另外请问demo里面的两张图EER.png和loss.png是怎么得到的?我python utils.py好像什么都没得到?
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果:
17200,1.8035286664962769
17201,0.8399255871772766
17202,0.7849252820014954
17203,0.20637908577919006
......
200847,0.0009674201137386262
200848,0.0009672052692621946
200849,0.0009669908322393894
......
382718,0.2163151204586029
382719,0.2672955095767975
382720,0.5557892322540283
得到的train_acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17210,0.0,0.9999999999995,0.99999999999998
......
211130,0.0,0.9999999999995,0.99999999999998
211140,0.0,0.9999999999995,0.99999999999998
211000,0.32981049562682213,0.1791044776114538,0.9607142857142851
211000,0.282798833819242,0.1739130434777845,0.9728571428571422
......
382440,0.03024781341107871,0.9152542372876369,0.9964285714285708
382450,0.008746355685131196,0.9285714285709286,0.9971428571428564
得到的acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17200,0.0,0.9999999999995,0.99999999999998
......
210800,0.0,0.9999999999995,0.99999999999998
211000,0.0,0.9999999999995,0.99999999999998
211000,0.3589743589743589,0.12269938650270346,0.9266666666666662
211000,0.358451072736787,0.10714285714243434,0.9743589743589739
......
382200,0.0567765567765568,0.7297297297292311,0.9897435897435893
382400,0.13291470434327576,0.6666666666661832,0.9887179487179483
这时候我就kill掉了train.py,然后python test_model.py得到了结果:
f-measure = 0.5833333333328472, true positive rate = 0.5, accuracy = 0.9857142857142851, equal error rate = 0.08017492711370261
请问学长,我是不是训练得太久了?然后这个结果是这样吗?另外请问demo里面的两张图EER.png和loss.png是怎么得到的?我python utils.py好像什么都没得到?
你好学长,我看了你复现那个Deep-speaker,也出结果了,我想问你几个问题?
1.checkpiont里面.h5文件怎么产生的?
2.下载的测试集是直接放到Libri文件里面吗,跟训练语音一块。测试程序需要改动吗?还是训练完了直接就可以测试了?
望学长能给予解答,谢谢!
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训练就会产生。具体路径和你的代码有关系。代码有一个路径。我没有复现这个,只是看了看。
…---原始邮件---
发件人: "yy835055664"<[email protected]>
发送时间: 2019年12月10日(星期二) 下午5:08
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "Comment"<[email protected]>;"QI ZHANG"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
你好学长:
我想请教你几个问题?
1.checkpiont里面.h5文件怎么产生的?
2.下载的测试集是直接放到Libri文件里面吗,跟训练语音一块。测试程序需要改动吗?还是训练完了直接就可以测试了?
望学长能给予解答,谢谢!
------------------&nbsp;原始邮件&nbsp;------------------
发件人:&nbsp;"QI ZHANG"<[email protected]&gt;;
发送时间:&nbsp;2019年12月10日(星期二) 下午5:04
收件人:&nbsp;"Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]&gt;;
抄送:&nbsp;"未、、敢伤"<[email protected]&gt;;"Comment"<[email protected]&gt;;
主题:&nbsp;Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
util只有函数,你要调用它。训练的久不久,还是要看数据,数据你觉得差不多就行
---原始邮件---
发件人: "yy835055664"<[email protected]&amp;gt;
发送时间: 2019年12月10日(星期二) 下午4:52
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]&amp;gt;;
抄送: "Comment"<[email protected]&amp;gt;;"QI ZHANG"<[email protected]&amp;gt;;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果:
17200,1.8035286664962769
17201,0.8399255871772766
17202,0.7849252820014954
17203,0.20637908577919006
......
200847,0.0009674201137386262
200848,0.0009672052692621946
200849,0.0009669908322393894
......
382718,0.2163151204586029
382719,0.2672955095767975
382720,0.5557892322540283
得到的train_acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17210,0.0,0.9999999999995,0.99999999999998
......
211130,0.0,0.9999999999995,0.99999999999998
211140,0.0,0.9999999999995,0.99999999999998
211000,0.32981049562682213,0.1791044776114538,0.9607142857142851
211000,0.282798833819242,0.1739130434777845,0.9728571428571422
......
382440,0.03024781341107871,0.9152542372876369,0.9964285714285708
382450,0.008746355685131196,0.9285714285709286,0.9971428571428564
得到的acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17200,0.0,0.9999999999995,0.99999999999998
......
210800,0.0,0.9999999999995,0.99999999999998
211000,0.0,0.9999999999995,0.99999999999998
211000,0.3589743589743589,0.12269938650270346,0.9266666666666662
211000,0.358451072736787,0.10714285714243434,0.9743589743589739
......
382200,0.0567765567765568,0.7297297297292311,0.9897435897435893
382400,0.13291470434327576,0.6666666666661832,0.9887179487179483
这时候我就kill掉了train.py,然后python test_model.py得到了结果:
f-measure = 0.5833333333328472, true positive rate = 0.5, accuracy = 0.9857142857142851, equal error rate = 0.08017492711370261
请问学长,我是不是训练得太久了?然后这个结果是这样吗?另外请问demo里面的两张图EER.png和loss.png是怎么得到的?我python utils.py好像什么都没得到?
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果:
17200,1.8035286664962769
17201,0.8399255871772766
17202,0.7849252820014954
17203,0.20637908577919006
......
200847,0.0009674201137386262
200848,0.0009672052692621946
200849,0.0009669908322393894
......
382718,0.2163151204586029
382719,0.2672955095767975
382720,0.5557892322540283
得到的train_acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17210,0.0,0.9999999999995,0.99999999999998
......
211130,0.0,0.9999999999995,0.99999999999998
211140,0.0,0.9999999999995,0.99999999999998
211000,0.32981049562682213,0.1791044776114538,0.9607142857142851
211000,0.282798833819242,0.1739130434777845,0.9728571428571422
......
382440,0.03024781341107871,0.9152542372876369,0.9964285714285708
382450,0.008746355685131196,0.9285714285709286,0.9971428571428564
得到的acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17200,0.0,0.9999999999995,0.99999999999998
......
210800,0.0,0.9999999999995,0.99999999999998
211000,0.0,0.9999999999995,0.99999999999998
211000,0.3589743589743589,0.12269938650270346,0.9266666666666662
211000,0.358451072736787,0.10714285714243434,0.9743589743589739
......
382200,0.0567765567765568,0.7297297297292311,0.9897435897435893
382400,0.13291470434327576,0.6666666666661832,0.9887179487179483
这时候我就kill掉了train.py,然后python test_model.py得到了结果:
f-measure = 0.5833333333328472, true positive rate = 0.5, accuracy = 0.9857142857142851, equal error rate = 0.08017492711370261
请问学长,我是不是训练得太久了?然后这个结果是这样吗?另外请问demo里面的两张图EER.png和loss.png是怎么得到的?我python utils.py好像什么都没得到?
你好学长,我看了你复现那个Deep-speaker,也出结果了,我想问你几个问题?
1.checkpiont里面.h5文件怎么产生的?
2.下载的测试集是直接放到Libri文件里面吗,跟训练语音一块。测试程序需要改动吗?还是训练完了直接就可以测试了?
望学长能给予解答,谢谢!
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学长你好:
想问一下你知道这个问题这么解决吗?训练阶段
…------------------ 原始邮件 ------------------
发件人: "QI ZHANG"<[email protected]>;
发送时间: 2019年12月10日(星期二) 下午5:11
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "未、、敢伤"<[email protected]>;"Comment"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
训练就会产生。具体路径和你的代码有关系。代码有一个路径。我没有复现这个,只是看了看。
---原始邮件---
发件人: "yy835055664"<[email protected]&gt;
发送时间: 2019年12月10日(星期二) 下午5:08
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]&gt;;
抄送: "Comment"<[email protected]&gt;;"QI ZHANG"<[email protected]&gt;;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
你好学长:
我想请教你几个问题?
1.checkpiont里面.h5文件怎么产生的?
2.下载的测试集是直接放到Libri文件里面吗,跟训练语音一块。测试程序需要改动吗?还是训练完了直接就可以测试了?
望学长能给予解答,谢谢!
------------------&amp;nbsp;原始邮件&amp;nbsp;------------------
发件人:&amp;nbsp;"QI ZHANG"<[email protected]&amp;gt;;
发送时间:&amp;nbsp;2019年12月10日(星期二) 下午5:04
收件人:&amp;nbsp;"Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]&amp;gt;;
抄送:&amp;nbsp;"未、、敢伤"<[email protected]&amp;gt;;"Comment"<[email protected]&amp;gt;;
主题:&amp;nbsp;Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
util只有函数,你要调用它。训练的久不久,还是要看数据,数据你觉得差不多就行
---原始邮件---
发件人: "yy835055664"<[email protected]&amp;amp;gt;
发送时间: 2019年12月10日(星期二) 下午4:52
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]&amp;amp;gt;;
抄送: "Comment"<[email protected]&amp;amp;gt;;"QI ZHANG"<[email protected]&amp;amp;gt;;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果:
17200,1.8035286664962769
17201,0.8399255871772766
17202,0.7849252820014954
17203,0.20637908577919006
......
200847,0.0009674201137386262
200848,0.0009672052692621946
200849,0.0009669908322393894
......
382718,0.2163151204586029
382719,0.2672955095767975
382720,0.5557892322540283
得到的train_acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17210,0.0,0.9999999999995,0.99999999999998
......
211130,0.0,0.9999999999995,0.99999999999998
211140,0.0,0.9999999999995,0.99999999999998
211000,0.32981049562682213,0.1791044776114538,0.9607142857142851
211000,0.282798833819242,0.1739130434777845,0.9728571428571422
......
382440,0.03024781341107871,0.9152542372876369,0.9964285714285708
382450,0.008746355685131196,0.9285714285709286,0.9971428571428564
得到的acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17200,0.0,0.9999999999995,0.99999999999998
......
210800,0.0,0.9999999999995,0.99999999999998
211000,0.0,0.9999999999995,0.99999999999998
211000,0.3589743589743589,0.12269938650270346,0.9266666666666662
211000,0.358451072736787,0.10714285714243434,0.9743589743589739
......
382200,0.0567765567765568,0.7297297297292311,0.9897435897435893
382400,0.13291470434327576,0.6666666666661832,0.9887179487179483
这时候我就kill掉了train.py,然后python test_model.py得到了结果:
f-measure = 0.5833333333328472, true positive rate = 0.5, accuracy = 0.9857142857142851, equal error rate = 0.08017492711370261
请问学长,我是不是训练得太久了?然后这个结果是这样吗?另外请问demo里面的两张图EER.png和loss.png是怎么得到的?我python utils.py好像什么都没得到?
学长,抱歉打扰,之前的问题已解决。我用完整的数据集train-clean-100进行了python train.py,好像训练得太久了,得到的losses.txt文件结果:
17200,1.8035286664962769
17201,0.8399255871772766
17202,0.7849252820014954
17203,0.20637908577919006
......
200847,0.0009674201137386262
200848,0.0009672052692621946
200849,0.0009669908322393894
......
382718,0.2163151204586029
382719,0.2672955095767975
382720,0.5557892322540283
得到的train_acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17210,0.0,0.9999999999995,0.99999999999998
......
211130,0.0,0.9999999999995,0.99999999999998
211140,0.0,0.9999999999995,0.99999999999998
211000,0.32981049562682213,0.1791044776114538,0.9607142857142851
211000,0.282798833819242,0.1739130434777845,0.9728571428571422
......
382440,0.03024781341107871,0.9152542372876369,0.9964285714285708
382450,0.008746355685131196,0.9285714285709286,0.9971428571428564
得到的acc_eer.txt结果:
17200,0.0,0.9999999999995,0.99999999999998
17200,0.0,0.9999999999995,0.99999999999998
......
210800,0.0,0.9999999999995,0.99999999999998
211000,0.0,0.9999999999995,0.99999999999998
211000,0.3589743589743589,0.12269938650270346,0.9266666666666662
211000,0.358451072736787,0.10714285714243434,0.9743589743589739
......
382200,0.0567765567765568,0.7297297297292311,0.9897435897435893
382400,0.13291470434327576,0.6666666666661832,0.9887179487179483
这时候我就kill掉了train.py,然后python test_model.py得到了结果:
f-measure = 0.5833333333328472, true positive rate = 0.5, accuracy = 0.9857142857142851, equal error rate = 0.08017492711370261
请问学长,我是不是训练得太久了?然后这个结果是这样吗?另外请问demo里面的两张图EER.png和loss.png是怎么得到的?我python utils.py好像什么都没得到?
你好学长,我看了你复现那个Deep-speaker,也出结果了,我想问你几个问题?
1.checkpiont里面.h5文件怎么产生的?
2.下载的测试集是直接放到Libri文件里面吗,跟训练语音一块。测试程序需要改动吗?还是训练完了直接就可以测试了?
望学长能给予解答,谢谢!
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请问为什么我用编号为1-3的人去训练,然后再用编号为4-5的人去测试,得到的结果却是f-measure = 0.9999999999995, true positive rate = 1.0, accuracy = 0.99999999999998, equal error rate = 0.0,这个结果不就代表着测试的人和训练的人是一样的吗?但实际上并不同啊?你们会这样吗? |
是不是训练太久,你得看一下你的 EER.png 和 Loss.png 如果到后面下降了,就是训练太久了。我觉得你的结果可能没问题。Readme 里的图通过调用
|
|
数据太少了,不能说明问题。还是建议下载完整的LibriSpeech数据再运行程序。 |
师兄用pre_process.py 预处理数据只能处理wav格式的数据吗,我下载了LibriSpeech数据集里面全是flac格式的,是要先把它转换成wav后再运行pre_process.py吗 |
是的,先把falc格式转换为wav,在进行数据预处理
…------------------ 原始邮件 ------------------
发件人: "Walleclipse/Deep_Speaker-speaker_recognition_system" <[email protected]>;
发送时间: 2020年11月25日(星期三) 下午3:38
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: "未、、敢伤"<[email protected]>;"Comment"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
师兄用pre_process.py 预处理数据只能处理wav格式的数据吗,我下载了LibriSpeech数据集里面全是flac格式的,是要先把它转换成wav后再运行pre_process.py吗
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是的,需要先转换下数据格式
Hardin
[email protected]
…------------------ 原始邮件 ------------------
发件人: "yy835055664"<[email protected]>;
发送时间: 2020年11月25日(星期三) 下午3:45
收件人: "Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]>;
抄送: ""<[email protected]>; "Comment"<[email protected]>;
主题: Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
是的,先把falc格式转换为wav,在进行数据预处理
------------------&nbsp;原始邮件&nbsp;------------------
发件人: "Walleclipse/Deep_Speaker-speaker_recognition_system" <[email protected]&gt;;
发送时间:&nbsp;2020年11月25日(星期三) 下午3:38
收件人:&nbsp;"Walleclipse/Deep_Speaker-speaker_recognition_system"<[email protected]&gt;;
抄送:&nbsp;"未、、敢伤"<[email protected]&gt;;"Comment"<[email protected]&gt;;
主题:&nbsp;Re: [Walleclipse/Deep_Speaker-speaker_recognition_system] 运行报错!求助 (#39)
师兄用pre_process.py 预处理数据只能处理wav格式的数据吗,我下载了LibriSpeech数据集里面全是flac格式的,是要先把它转换成wav后再运行pre_process.py吗
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您好,我正在复现您的实验,unbuntu16.04,python3.7,我首先运行了train.py文件,出现了以下错误,请问该如何解决?诚盼回复
model_build_time 5.370615005493164
get batch time 1.98e-05s
forward process time 7.57s
beginning to select..........
select best batch time 0.188s
select_batch_time: 7.82932448387146
Traceback (most recent call last):
File "train.py", line 181, in
main()
File "train.py", line 125, in main
loss = model.train_on_batch(x, y)
File "/home/dcase/miniconda3/lib/python3.7/site-packages/keras/engine/training.py", line 1808, in train_on_batch
check_batch_axis=True)
File "/home/dcase/miniconda3/lib/python3.7/site-packages/keras/engine/training.py", line 1411, in _standardize_user_data
exception_prefix='target')
File "/home/dcase/miniconda3/lib/python3.7/site-packages/keras/engine/training.py", line 153, in _standardize_input_data
str(array.shape))
ValueError: Error when checking target: expected ln to have shape (None, 512) but got array with shape (96, 1)
The text was updated successfully, but these errors were encountered: