Open LittleMaWen opened 5 years ago
根据issue 7, 需要把 train.py
中的 121行改成以下形式:
y = np.random.uniform(size=(x.shape[0], 512))
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: OSError: [Errno 30] Read-only file system
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 181, in
systemd[1]: Caught
Broadcast message from systemd-journald@dcase-PowerEdge-R730 (Wed 2019-08-14 07:37:30 CST):
systemd[1]: Freezing execution. 请问您知道是什么原因么?
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? 你可以查看 https://github.com/pypa/virtualenv/issues/209 或者 https://askubuntu.com/questions/1082876/oserror-errno-30-read-only-file-system 或者 https://forums.fast.ai/t/how-can-i-load-a-pretrained-model-on-kaggle-using-fastai/13941/4
OKOK~谢谢您!非常感谢您不厌其烦为我解答~谢谢您!
嗯嗯~学长,这个问题已经解决了,是我自己系统的问题。 现在我已经python train.py两天多了,程序还在运行, 2019-08-19 09:07:35,157 [INFO] train.py/main | == Presenting step #132313 2019-08-19 09:07:35,351 [INFO] train.py/main | == Processed in 0.19s by the network, training loss = 0.004015110433101654. get batch time 4.53e-06s forward process time 0.317s beginning to select.......... select best batch time 0.71s select_batch_time: 1.0472040176391602 这是最近一条显示结果,我看到您在其他问题中回答train.py是个无限循环的,需要自己kill掉,请问我现在这个结果是可以kill掉了吗?我不是很明白
学长,我在训练到下面这个结果时kill掉了这个程序, 2019-08-20 12:24:52,587 [INFO] train.py/main | == Presenting step #211143 2019-08-20 12:24:52,776 [INFO] train.py/main | == Processed in 0.19s by the network, training loss = 0.0009784128051251173. 然后按照学长说的test_model.py了一下,得到了这个结果, Found checkpoint [checkpoints/model_211000_0.00100.h5]. Resume from here... Found 0000368 files with 00003 different speakers. f-measure = 0.9999999999995, true positive rate = 1.0, accuracy = 0.99999999999998, equal error rate = 0.0 学长,请问是这样吗?
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? 你需要用完整的数据集。
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? 另外就是实验报告中提到的test-clean数据集,请问这是作为测试集的吗?它在哪一部分使用?在跑LibriSpeechSamples时,我也只看到用了train-clean-100中的一部分音频,没有找到test-clean的部分。
1) 是先通过 pre_process.py 来预处理数据,随后再train 2) test_clean 数据集也是从 Librispeech 这里下载的,test_clean是专门用来测试的数据
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,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"<notifications@github.com> 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "Comment"<comment@noreply.github.com>;"QI ZHANG"<826794289@qq.com>; 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 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?
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"<notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "Popcorn"<826794289@qq.com>;"Comment"<comment@noreply.github.com>; 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?
— You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.
“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?
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"<notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "Popcorn"<826794289@qq.com>;"Comment"<comment@noreply.github.com>; 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"notifications@github.com 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"Deep_Speaker-speaker_recognition_system@noreply.github.com; 抄送: "izhangy"475603468@qq.com;"Comment"comment@noreply.github.com; 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"<notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "Popcorn"<826794289@qq.com>;"Comment"<comment@noreply.github.com>; 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?
— You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe. — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.
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!
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!
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"<notifications@github.com> 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "Comment"<comment@noreply.github.com>;"QI ZHANG"<826794289@qq.com>; 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"<notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "未、、敢伤"<835055664@qq.com>;"Comment"<comment@noreply.github.com>; 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"<notifications@github.com> 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "Comment"<comment@noreply.github.com>;"QI ZHANG"<826794289@qq.com>; 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!
— You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe. — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.
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"<notifications@github.com> 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "Comment"<comment@noreply.github.com>;"QI ZHANG"<826794289@qq.com>; 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!
------------------ 原始邮件 ------------------ 发件人: "QI ZHANG"<notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "未、、敢伤"<835055664@qq.com>;"Comment"<comment@noreply.github.com>; 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"<notifications@github.com&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"<Deep_Speaker-speaker_recognition_system@noreply.github.com&gt;;
抄送: "Comment"<comment@noreply.github.com&gt;;"QI ZHANG"<826794289@qq.com&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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You are receiving this because you commented.
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You are receiving this because you commented.
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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"<notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "未、、敢伤"<835055664@qq.com>;"Comment"<comment@noreply.github.com>; 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"<notifications@github.com> 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "Comment"<comment@noreply.github.com>;"QI ZHANG"<826794289@qq.com>; 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"<notifications@github.com&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"<Deep_Speaker-speaker_recognition_system@noreply.github.com&gt;;
抄送:&nbsp;"未、、敢伤"<835055664@qq.com&gt;;"Comment"<comment@noreply.github.com&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"<notifications@github.com&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"<Deep_Speaker-speaker_recognition_system@noreply.github.com&amp;gt;;
抄送: "Comment"<comment@noreply.github.com&amp;gt;;"QI ZHANG"<826794289@qq.com&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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You are receiving this because you commented.
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You are receiving this because you commented.
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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?
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?
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 utils.py
里面的 def plot_loss
来画 e.g.
import constants as c
from utils import plot_loss,plot_acc
loss_file=c.CHECKPOINT_FOLDER+'/losses.txt' # loss log file path
plot_loss(loss_file)
acc_file=c.CHECKPOINT_FOLDER+'/acc_eer.txt'# acc log file path
plot_acc(acc_file)
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!
train.py
以后每过 SAVE_PER_EPOCHS
步就会保存 checkpiont 里面.h5文件。默认情况下SAVE_PER_EPOCHS=200
, 你可以在constants.py
里修改 SAVE_PER_EPOCHS
。import constants as c
from pre_process import preprocess_and_save
test_wav_dir = "audio/LibriSpeechSamples/test-clean-100"
preprocess_and_save(wav_dir=test_wav_dir,out_dir=c.TEST_DIR )
train.py 就会在训练的过程中测试
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?
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" <notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "未、、敢伤"<835055664@qq.com>;"Comment"<comment@noreply.github.com>; 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 wodeshamaomao@qq.com
------------------ 原始邮件 ------------------ 发件人: "yy835055664"<notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: ""<475603468@qq.com>; "Comment"<comment@noreply.github.com>; 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
------------------ 原始邮件 ------------------ 发件人: "Walleclipse/Deep_Speaker-speaker_recognition_system" <notifications@github.com>; 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"<Deep_Speaker-speaker_recognition_system@noreply.github.com>; 抄送: "未、、敢伤"<835055664@qq.com>;"Comment"<comment@noreply.github.com>; 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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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)