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ValueError: Cannot load file containing pickled data when allow_pickle=False

Open GCVillager opened this issue 2 years ago • 1 comments

环境miniconda+python3.9，win10
最下面同时出现以下错误：
RuntimeError: The expanded size of the tensor (12800) must match the existing size (0) at non-singleton dimension 1. Target sizes: [1, 12800]. Tensor sizes: [0]
ValueError: Cannot load file containing pickled data when allow_pickle=False

PS F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI>  & 'D:\miniconda3\envs\rvc\python.exe' 'c:\Users\GCVil\.vscode\extensions\ms-python.python-2023.12.0\pythonFiles\lib\python\debugpy\adapter/../..\debugpy\launcher' '55248' '--' 'F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI\infer-web.py' 
PS F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI>  f:; cd 'f:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI'; & 'D:\miniconda3\envs\rvc\python.exe' 'c:\Users\GCVil\.vscode\extensions\ms-python.python-2023.12.0\pythonFiles\lib\python\debugpy\adapter/../..\debugpy\launcher' '55260' '--' 'F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI\infer-web.py' 
Found GPU NVIDIA GeForce RTX 3060
Use Language: zh_CN
Running on local URL:  http://0.0.0.0:7865
start preprocess
['trainset_preprocess_pipeline_print.py', 'F:\\AI\\ganyu_wav', '40000', '8', 'F:\\AI\\rvc\\Retrieval-based-Voice-Conversion-WebUI/logs/ganyu', 'False']    
F:\AI\ganyu_wav/GI000100003.wav->Suc.
F:\AI\ganyu_wav/GI000100006.wav->Suc.
F:\AI\ganyu_wav/GI000100005.wav->Suc.
F:\AI\ganyu_wav/GI000100007.wav->Suc.
F:\AI\ganyu_wav/GI000100011.wav->Suc.
F:\AI\ganyu_wav/GI000100004.wav->Suc.
F:\AI\ganyu_wav/GI000100014.wav->Suc.
F:\AI\ganyu_wav/GI000100013.wav->Suc.
F:\AI\ganyu_wav/GI000100019.wav->Suc.
F:\AI\ganyu_wav/GI000100002.wav->Suc.
F:\AI\ganyu_wav/GI000100001.wav->Suc.
F:\AI\ganyu_wav/GI000100008.wav->Suc.
F:\AI\ganyu_wav/GI000100022.wav->Suc.
F:\AI\ganyu_wav/GI000100021.wav->Suc.
F:\AI\ganyu_wav/GI000100027.wav->Suc.
F:\AI\ganyu_wav/GI000100016.wav->Suc.
F:\AI\ganyu_wav/GI000100012.wav->Suc.
F:\AI\ganyu_wav/GI000100010.wav->Suc.
F:\AI\ganyu_wav/GI000100015.wav->Suc.
F:\AI\ganyu_wav/GI000100018.wav->Suc.
F:\AI\ganyu_wav/GI000100023.wav->Suc.
F:\AI\ganyu_wav/GI000100024.wav->Suc.
F:\AI\ganyu_wav/GI000100035.wav->Suc.
F:\AI\ganyu_wav/GI000100026.wav->Suc.
F:\AI\ganyu_wav/GI000100009.wav->Suc.
F:\AI\ganyu_wav/GI000100031.wav->Suc.
F:\AI\ganyu_wav/GI000100020.wav->Suc.
F:\AI\ganyu_wav/GI000100017.wav->Suc.
F:\AI\ganyu_wav/GI000100029.wav->Suc.
F:\AI\ganyu_wav/GI000100030.wav->Suc.
F:\AI\ganyu_wav/GI000100037.wav->Suc.
F:\AI\ganyu_wav/GI000100043.wav->Suc.
F:\AI\ganyu_wav/GI000100032.wav->Suc.
F:\AI\ganyu_wav/GI000100028.wav->Suc.
F:\AI\ganyu_wav/GI000100039.wav->Suc.
F:\AI\ganyu_wav/GI000100040.wav->Suc.
F:\AI\ganyu_wav/GI000100038.wav->Suc.
F:\AI\ganyu_wav/GI000100034.wav->Suc.
F:\AI\ganyu_wav/GI000100045.wav->Suc.
F:\AI\ganyu_wav/GI000100025.wav->Suc.
F:\AI\ganyu_wav/GI000100048.wav->Suc.
F:\AI\ganyu_wav/GI000100053.wav->Suc.
F:\AI\ganyu_wav/GI000100051.wav->Suc.
F:\AI\ganyu_wav/GI000100033.wav->Suc.
F:\AI\ganyu_wav/GI000100046.wav->Suc.
F:\AI\ganyu_wav/GI000100042.wav->Suc.
F:\AI\ganyu_wav/GI000100061.wav->Suc.
F:\AI\ganyu_wav/GI000100056.wav->Suc.
F:\AI\ganyu_wav/GI000100041.wav->Suc.
F:\AI\ganyu_wav/GI000100054.wav->Suc.
F:\AI\ganyu_wav/GI000100069.wav->Suc.
F:\AI\ganyu_wav/GI000100036.wav->Suc.
F:\AI\ganyu_wav/GI000100047.wav->Suc.
F:\AI\ganyu_wav/GI000100050.wav->Suc.
F:\AI\ganyu_wav/GI000100062.wav->Suc.
F:\AI\ganyu_wav/GI000100055.wav->Suc.
F:\AI\ganyu_wav/GI000100058.wav->Suc.
F:\AI\ganyu_wav/GI000100049.wav->Suc.
F:\AI\ganyu_wav/GI000100077.wav->Suc.
F:\AI\ganyu_wav/GI000100044.wav->Suc.
F:\AI\ganyu_wav/GI000100066.wav->Suc.
F:\AI\ganyu_wav/GI000100064.wav->Suc.
F:\AI\ganyu_wav/GI000100052.wav->Suc.
F:\AI\ganyu_wav/GI000100059.wav->Suc.
F:\AI\ganyu_wav/GI000100074.wav->Suc.
F:\AI\ganyu_wav/GI000100060.wav->Suc.
F:\AI\ganyu_wav/GI000100089.wav->Suc.
F:\AI\ganyu_wav/GI000100068.wav->Suc.
F:\AI\ganyu_wav/GI000100085.wav->Suc.
F:\AI\ganyu_wav/GI000100057.wav->Suc.
F:\AI\ganyu_wav/GI000100103.wav->Suc.
F:\AI\ganyu_wav/GI000100070.wav->Suc.
F:\AI\ganyu_wav/GI000100115.wav->Suc.
F:\AI\ganyu_wav/GI000100063.wav->Suc.
F:\AI\ganyu_wav/GI000100065.wav->Suc.
F:\AI\ganyu_wav/GI000100079.wav->Suc.
F:\AI\ganyu_wav/GI000100071.wav->Suc.
F:\AI\ganyu_wav/GI000100130.wav->Suc.
F:\AI\ganyu_wav/GI000100072.wav->Suc.
F:\AI\ganyu_wav/GI000100090.wav->Suc.
F:\AI\ganyu_wav/GI000100067.wav->Suc.
F:\AI\ganyu_wav/GI000100073.wav->Suc.
F:\AI\ganyu_wav/GI000100095.wav->Suc.
F:\AI\ganyu_wav/GI000100105.wav->Suc.
F:\AI\ganyu_wav/GI000100117.wav->Suc.
F:\AI\ganyu_wav/GI000100076.wav->Suc.
F:\AI\ganyu_wav/GI000100082.wav->Suc.
F:\AI\ganyu_wav/GI000100110.wav->Suc.
F:\AI\ganyu_wav/GI000100075.wav->Suc.
F:\AI\ganyu_wav/GI000100148.wav->Suc.
F:\AI\ganyu_wav/GI000100081.wav->Suc.
F:\AI\ganyu_wav/GI000100125.wav->Suc.
F:\AI\ganyu_wav/GI000100087.wav->Suc.
F:\AI\ganyu_wav/GI000100131.wav->Suc.
F:\AI\ganyu_wav/GI000100164.wav->Suc.
F:\AI\ganyu_wav/GI000100100.wav->Suc.
F:\AI\ganyu_wav/GI000100083.wav->Suc.
F:\AI\ganyu_wav/GI000100140.wav->Suc.
F:\AI\ganyu_wav/GI000100189.wav->Suc.
F:\AI\ganyu_wav/GI000100150.wav->Suc.
F:\AI\ganyu_wav/GI000100158.wav->Suc.
F:\AI\ganyu_wav/GI000100202.wav->Suc.
F:\AI\ganyu_wav/GI000100112.wav->Suc.
F:\AI\ganyu_wav/GI000100174.wav->Suc.
F:\AI\ganyu_wav/GI000100091.wav->Suc.
F:\AI\ganyu_wav/GI000100094.wav->Suc.F:\AI\ganyu_wav/GI000100086.wav->Suc.

F:\AI\ganyu_wav/GI000100093.wav->Suc.
F:\AI\ganyu_wav/GI000100182.wav->Suc.
F:\AI\ganyu_wav/GI000100129.wav->Suc.
F:\AI\ganyu_wav/GI000100210.wav->Suc.
F:\AI\ganyu_wav/GI000100193.wav->Suc.
F:\AI\ganyu_wav/GI000100106.wav->Suc.
F:\AI\ganyu_wav/GI000100108.wav->Suc.
F:\AI\ganyu_wav/GI000100199.wav->Suc.
F:\AI\ganyu_wav/GI000100109.wav->Suc.
F:\AI\ganyu_wav/GI000100098.wav->Suc.
F:\AI\ganyu_wav/GI000100146.wav->Suc.
F:\AI\ganyu_wav/GI000100207.wav->Suc.
F:\AI\ganyu_wav/GI000100218.wav->Suc.
F:\AI\ganyu_wav/GI000100119.wav->Suc.
F:\AI\ganyu_wav/GI000100203.wav->Suc.
F:\AI\ganyu_wav/GI000100123.wav->Suc.
F:\AI\ganyu_wav/GI000100111.wav->Suc.
F:\AI\ganyu_wav/GI000100163.wav->Suc.
F:\AI\ganyu_wav/GI000100122.wav->Suc.
F:\AI\ganyu_wav/GI000100211.wav->Suc.
F:\AI\ganyu_wav/GI000100127.wav->Suc.
F:\AI\ganyu_wav/GI000100215.wav->Suc.
F:\AI\ganyu_wav/GI000100139.wav->Suc.
F:\AI\ganyu_wav/GI000100219.wav->Suc.
F:\AI\ganyu_wav/GI000100138.wav->Suc.
F:\AI\ganyu_wav/GI000100226.wav->Suc.
F:\AI\ganyu_wav/GI000100132.wav->Suc.
F:\AI\ganyu_wav/GI000100187.wav->Suc.
F:\AI\ganyu_wav/GI000100141.wav->Suc.
F:\AI\ganyu_wav/GI000100223.wav->Suc.
F:\AI\ganyu_wav/GI000100157.wav->Suc.
F:\AI\ganyu_wav/GI000100152.wav->Suc.
F:\AI\ganyu_wav/GI000100156.wav->Suc.
F:\AI\ganyu_wav/GI000100227.wav->Suc.
F:\AI\ganyu_wav/GI000100161.wav->Suc.
F:\AI\ganyu_wav/GI000100234.wav->Suc.
F:\AI\ganyu_wav/GI000100201.wav->Suc.
F:\AI\ganyu_wav/GI000100180.wav->Suc.
F:\AI\ganyu_wav/GI000100235.wav->Suc.
F:\AI\ganyu_wav/GI000100231.wav->Suc.
F:\AI\ganyu_wav/GI000100176.wav->Suc.
F:\AI\ganyu_wav/GI000100179.wav->Suc.
F:\AI\ganyu_wav/GI000100198.wav->Suc.
F:\AI\ganyu_wav/GI000100196.wav->Suc.
F:\AI\ganyu_wav/GI000100183.wav->Suc.
F:\AI\ganyu_wav/GI000100243.wav->Suc.
F:\AI\ganyu_wav/GI000100239.wav->Suc.
F:\AI\ganyu_wav/GI000100209.wav->Suc.
F:\AI\ganyu_wav/GI000100242.wav->Suc.
F:\AI\ganyu_wav/GI000100197.wav->Suc.
F:\AI\ganyu_wav/GI000100206.wav->Suc.
F:\AI\ganyu_wav/GI000100204.wav->Suc.
F:\AI\ganyu_wav/GI000100200.wav->Suc.
F:\AI\ganyu_wav/GI000100247.wav->Suc.
F:\AI\ganyu_wav/GI000100205.wav->Suc.
F:\AI\ganyu_wav/GI000100250.wav->Suc.
F:\AI\ganyu_wav/GI000100251.wav->Suc.
F:\AI\ganyu_wav/GI000100214.wav->Suc.
F:\AI\ganyu_wav/GI000100208.wav->Suc.
F:\AI\ganyu_wav/GI000100217.wav->Suc.
F:\AI\ganyu_wav/GI000100212.wav->Suc.
F:\AI\ganyu_wav/GI000100213.wav->Suc.
F:\AI\ganyu_wav/GI000100225.wav->Suc.
F:\AI\ganyu_wav/GI000100222.wav->Suc.
F:\AI\ganyu_wav/GI000100255.wav->Suc.
F:\AI\ganyu_wav/GI000100259.wav->Suc.
F:\AI\ganyu_wav/GI000100216.wav->Suc.
F:\AI\ganyu_wav/GI000100258.wav->Suc.
F:\AI\ganyu_wav/GI000100220.wav->Suc.
F:\AI\ganyu_wav/GI000100221.wav->Suc.
F:\AI\ganyu_wav/GI000100263.wav->Suc.
F:\AI\ganyu_wav/GI000100224.wav->Suc.
F:\AI\ganyu_wav/GI000100229.wav->Suc.
F:\AI\ganyu_wav/GI000100230.wav->Suc.
F:\AI\ganyu_wav/GI000100228.wav->Suc.
F:\AI\ganyu_wav/GI000100233.wav->Suc.
F:\AI\ganyu_wav/GI000100271.wav->Suc.
F:\AI\ganyu_wav/GI000100267.wav->Suc.
F:\AI\ganyu_wav/GI000100232.wav->Suc.
F:\AI\ganyu_wav/GI000100238.wav->Suc.
F:\AI\ganyu_wav/GI000100266.wav->Suc.
F:\AI\ganyu_wav/GI000100241.wav->Suc.
F:\AI\ganyu_wav/GI000100237.wav->Suc.
F:\AI\ganyu_wav/GI000100274.wav->Suc.
F:\AI\ganyu_wav/GI000100275.wav->Suc.
F:\AI\ganyu_wav/GI000100246.wav->Suc.
F:\AI\ganyu_wav/GI000100236.wav->Suc.
F:\AI\ganyu_wav/GI000100279.wav->Suc.
F:\AI\ganyu_wav/GI000100249.wav->Suc.
F:\AI\ganyu_wav/GI000100240.wav->Suc.
F:\AI\ganyu_wav/GI000100254.wav->Suc.
F:\AI\ganyu_wav/GI000100245.wav->Suc.
F:\AI\ganyu_wav/GI000100244.wav->Suc.
F:\AI\ganyu_wav/GI000100287.wav->Suc.
F:\AI\ganyu_wav/GI000100257.wav->Suc.
F:\AI\ganyu_wav/GI000100283.wav->Suc.
F:\AI\ganyu_wav/GI000100282.wav->Suc.
F:\AI\ganyu_wav/GI000100248.wav->Suc.
F:\AI\ganyu_wav/GI000100253.wav->Suc.
F:\AI\ganyu_wav/GI000100262.wav->Suc.
F:\AI\ganyu_wav/GI000100295.wav->Suc.
F:\AI\ganyu_wav/GI000100252.wav->Suc.
F:\AI\ganyu_wav/GI000100291.wav->Suc.
F:\AI\ganyu_wav/GI000100265.wav->Suc.
F:\AI\ganyu_wav/GI000100256.wav->Suc.
F:\AI\ganyu_wav/GI000100290.wav->Suc.
F:\AI\ganyu_wav/GI000100299.wav->Suc.
F:\AI\ganyu_wav/GI000100261.wav->Suc.F:\AI\ganyu_wav/GI000100260.wav->Suc.

F:\AI\ganyu_wav/GI000100270.wav->Suc.
F:\AI\ganyu_wav/GI000100298.wav->Suc.
F:\AI\ganyu_wav/GI000100264.wav->Suc.
F:\AI\ganyu_wav/GI000100278.wav->Suc.
F:\AI\ganyu_wav/GI000100273.wav->Suc.
F:\AI\ganyu_wav/GI000100269.wav->Suc.
F:\AI\ganyu_wav/GI000100268.wav->Suc.
F:\AI\ganyu_wav/GI000100272.wav->Suc.
F:\AI\ganyu_wav/GI000100286.wav->Suc.
F:\AI\ganyu_wav/GI000100276.wav->Suc.
F:\AI\ganyu_wav/GI000100281.wav->Suc.
F:\AI\ganyu_wav/GI000100277.wav->Suc.
F:\AI\ganyu_wav/GI000100280.wav->Suc.
F:\AI\ganyu_wav/GI000100294.wav->Suc.
F:\AI\ganyu_wav/GI000100284.wav->Suc.
F:\AI\ganyu_wav/GI000100289.wav->Suc.
F:\AI\ganyu_wav/GI000100285.wav->Suc.
F:\AI\ganyu_wav/GI000100288.wav->Suc.
F:\AI\ganyu_wav/GI000100297.wav->Suc.
F:\AI\ganyu_wav/GI000100293.wav->Suc.
F:\AI\ganyu_wav/GI000100292.wav->Suc.
F:\AI\ganyu_wav/GI000100296.wav->Suc.
F:\AI\ganyu_wav/GI000100300.wav->Suc.
end preprocess
start preprocess
['trainset_preprocess_pipeline_print.py', 'F:\\AI\\ganyu_wav', '40000', '8', 'F:\\AI\\rvc\\Retrieval-based-Voice-Conversion-WebUI/logs/ganyu', 'False']    
F:\AI\ganyu_wav/GI000100003.wav->Suc.
F:\AI\ganyu_wav/GI000100006.wav->Suc.
F:\AI\ganyu_wav/GI000100005.wav->Suc.
F:\AI\ganyu_wav/GI000100007.wav->Suc.
F:\AI\ganyu_wav/GI000100011.wav->Suc.
F:\AI\ganyu_wav/GI000100004.wav->Suc.
F:\AI\ganyu_wav/GI000100014.wav->Suc.
F:\AI\ganyu_wav/GI000100013.wav->Suc.
F:\AI\ganyu_wav/GI000100019.wav->Suc.
F:\AI\ganyu_wav/GI000100002.wav->Suc.
F:\AI\ganyu_wav/GI000100001.wav->Suc.
F:\AI\ganyu_wav/GI000100008.wav->Suc.
F:\AI\ganyu_wav/GI000100022.wav->Suc.
F:\AI\ganyu_wav/GI000100021.wav->Suc.
F:\AI\ganyu_wav/GI000100027.wav->Suc.
F:\AI\ganyu_wav/GI000100016.wav->Suc.
F:\AI\ganyu_wav/GI000100012.wav->Suc.
F:\AI\ganyu_wav/GI000100010.wav->Suc.
F:\AI\ganyu_wav/GI000100015.wav->Suc.
F:\AI\ganyu_wav/GI000100018.wav->Suc.
F:\AI\ganyu_wav/GI000100023.wav->Suc.
F:\AI\ganyu_wav/GI000100024.wav->Suc.
F:\AI\ganyu_wav/GI000100035.wav->Suc.
F:\AI\ganyu_wav/GI000100026.wav->Suc.
F:\AI\ganyu_wav/GI000100009.wav->Suc.
F:\AI\ganyu_wav/GI000100031.wav->Suc.
F:\AI\ganyu_wav/GI000100020.wav->Suc.
F:\AI\ganyu_wav/GI000100017.wav->Suc.
F:\AI\ganyu_wav/GI000100029.wav->Suc.
F:\AI\ganyu_wav/GI000100030.wav->Suc.
F:\AI\ganyu_wav/GI000100037.wav->Suc.
F:\AI\ganyu_wav/GI000100043.wav->Suc.
F:\AI\ganyu_wav/GI000100032.wav->Suc.
F:\AI\ganyu_wav/GI000100028.wav->Suc.
F:\AI\ganyu_wav/GI000100039.wav->Suc.
F:\AI\ganyu_wav/GI000100040.wav->Suc.
F:\AI\ganyu_wav/GI000100038.wav->Suc.
F:\AI\ganyu_wav/GI000100034.wav->Suc.
F:\AI\ganyu_wav/GI000100045.wav->Suc.
F:\AI\ganyu_wav/GI000100025.wav->Suc.
F:\AI\ganyu_wav/GI000100048.wav->Suc.
F:\AI\ganyu_wav/GI000100053.wav->Suc.
F:\AI\ganyu_wav/GI000100051.wav->Suc.
F:\AI\ganyu_wav/GI000100033.wav->Suc.
F:\AI\ganyu_wav/GI000100046.wav->Suc.
F:\AI\ganyu_wav/GI000100042.wav->Suc.
F:\AI\ganyu_wav/GI000100061.wav->Suc.
F:\AI\ganyu_wav/GI000100056.wav->Suc.
F:\AI\ganyu_wav/GI000100041.wav->Suc.
F:\AI\ganyu_wav/GI000100054.wav->Suc.
F:\AI\ganyu_wav/GI000100069.wav->Suc.
F:\AI\ganyu_wav/GI000100036.wav->Suc.
F:\AI\ganyu_wav/GI000100047.wav->Suc.
F:\AI\ganyu_wav/GI000100050.wav->Suc.
F:\AI\ganyu_wav/GI000100062.wav->Suc.
F:\AI\ganyu_wav/GI000100055.wav->Suc.
F:\AI\ganyu_wav/GI000100058.wav->Suc.
F:\AI\ganyu_wav/GI000100049.wav->Suc.
F:\AI\ganyu_wav/GI000100077.wav->Suc.
F:\AI\ganyu_wav/GI000100044.wav->Suc.
F:\AI\ganyu_wav/GI000100066.wav->Suc.
F:\AI\ganyu_wav/GI000100064.wav->Suc.
F:\AI\ganyu_wav/GI000100052.wav->Suc.
F:\AI\ganyu_wav/GI000100059.wav->Suc.
F:\AI\ganyu_wav/GI000100074.wav->Suc.
F:\AI\ganyu_wav/GI000100060.wav->Suc.
F:\AI\ganyu_wav/GI000100089.wav->Suc.
F:\AI\ganyu_wav/GI000100068.wav->Suc.
F:\AI\ganyu_wav/GI000100085.wav->Suc.
F:\AI\ganyu_wav/GI000100057.wav->Suc.
F:\AI\ganyu_wav/GI000100103.wav->Suc.
F:\AI\ganyu_wav/GI000100070.wav->Suc.
F:\AI\ganyu_wav/GI000100115.wav->Suc.
F:\AI\ganyu_wav/GI000100063.wav->Suc.
F:\AI\ganyu_wav/GI000100065.wav->Suc.
F:\AI\ganyu_wav/GI000100079.wav->Suc.
F:\AI\ganyu_wav/GI000100071.wav->Suc.
F:\AI\ganyu_wav/GI000100130.wav->Suc.
F:\AI\ganyu_wav/GI000100072.wav->Suc.
F:\AI\ganyu_wav/GI000100090.wav->Suc.
F:\AI\ganyu_wav/GI000100067.wav->Suc.
F:\AI\ganyu_wav/GI000100073.wav->Suc.
F:\AI\ganyu_wav/GI000100095.wav->Suc.
F:\AI\ganyu_wav/GI000100105.wav->Suc.
F:\AI\ganyu_wav/GI000100117.wav->Suc.
F:\AI\ganyu_wav/GI000100076.wav->Suc.
F:\AI\ganyu_wav/GI000100082.wav->Suc.
F:\AI\ganyu_wav/GI000100110.wav->Suc.
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F:\AI\ganyu_wav/GI000100245.wav->Suc.
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F:\AI\ganyu_wav/GI000100287.wav->Suc.
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F:\AI\ganyu_wav/GI000100283.wav->Suc.
F:\AI\ganyu_wav/GI000100282.wav->Suc.
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F:\AI\ganyu_wav/GI000100253.wav->Suc.
F:\AI\ganyu_wav/GI000100262.wav->Suc.
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F:\AI\ganyu_wav/GI000100252.wav->Suc.
F:\AI\ganyu_wav/GI000100291.wav->Suc.
F:\AI\ganyu_wav/GI000100265.wav->Suc.
F:\AI\ganyu_wav/GI000100256.wav->Suc.
F:\AI\ganyu_wav/GI000100290.wav->Suc.
F:\AI\ganyu_wav/GI000100299.wav->Suc.
F:\AI\ganyu_wav/GI000100260.wav->Suc.
F:\AI\ganyu_wav/GI000100261.wav->Suc.
F:\AI\ganyu_wav/GI000100270.wav->Suc.
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F:\AI\ganyu_wav/GI000100264.wav->Suc.
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F:\AI\ganyu_wav/GI000100273.wav->Suc.
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F:\AI\ganyu_wav/GI000100296.wav->Suc.
F:\AI\ganyu_wav/GI000100300.wav->Suc.
end preprocess

['extract_feature_print.py', 'cuda:0', '1', '0', '0', 'F:\\AI\\rvc\\Retrieval-based-Voice-Conversion-WebUI/logs/ganyu', 'v1']
F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI/logs/ganyu
load model(s) from hubert_base.pt
2023-07-11 19:06:33 | INFO | fairseq.tasks.hubert_pretraining | current directory is F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI
2023-07-11 19:06:33 | INFO | fairseq.tasks.hubert_pretraining | HubertPretrainingTask Config {'_name': 'hubert_pretraining', 'data': 'metadata', 'fine_tuning': False, 'labels': ['km'], 'label_dir': 'label', 'label_rate': 50.0, 'sample_rate': 16000, 'normalize': False, 'enable_padding': False, 'max_keep_size': None, 'max_sample_size': 250000, 'min_sample_size': 32000, 'single_target': False, 'random_crop': True, 'pad_audio': False}
2023-07-11 19:06:33 | INFO | fairseq.models.hubert.hubert | HubertModel Config: {'_name': 'hubert', 'label_rate': 50.0, 'extractor_mode': default, 'encoder_layers': 12, 'encoder_embed_dim': 768, 'encoder_ffn_embed_dim': 3072, 'encoder_attention_heads': 12, 'activation_fn': gelu, 'layer_type': transformer, 'dropout': 0.1, 'attention_dropout': 0.1, 'activation_dropout': 0.0, 'encoder_layerdrop': 0.05, 'dropout_input': 0.1, 'dropout_features': 0.1, 'final_dim': 256, 'untie_final_proj': True, 'layer_norm_first': False, 'conv_feature_layers': '[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2', 'conv_bias': False, 'logit_temp': 0.1, 'target_glu': False, 'feature_grad_mult': 0.1, 'mask_length': 10, 'mask_prob': 0.8, 'mask_selection': static, 'mask_other': 0.0, 'no_mask_overlap': False, 'mask_min_space': 1, 'mask_channel_length': 10, 'mask_channel_prob': 0.0, 'mask_channel_selection': static, 'mask_channel_other': 0.0, 'no_mask_channel_overlap': False, 'mask_channel_min_space': 1, 'conv_pos': 128, 'conv_pos_groups': 16, 'latent_temp': [2.0, 0.5, 0.999995], 'skip_masked': False, 'skip_nomask': False, 'checkpoint_activations': False, 'required_seq_len_multiple': 2, 'depthwise_conv_kernel_size': 31, 'attn_type': '', 'pos_enc_type': 'abs', 'fp16': False}
move model to cuda
all-feature-1030
all-feature-done
['extract_feature_print.py', 'cuda:0', '1', '0', '0', 'F:\\AI\\rvc\\Retrieval-based-Voice-Conversion-WebUI/logs/ganyu', 'v1']
F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI/logs/ganyu
load model(s) from hubert_base.pt
move model to cuda
all-feature-1030
all-feature-done

INFO:ganyu:{'train': {'log_interval': 200, 'seed': 1234, 'epochs': 20000, 'learning_rate': 0.0001, 'betas': [0.8, 0.99], 'eps': 1e-09, 'batch_size': 6, 'fp16_run': False, 'lr_decay': 0.999875, 'segment_size': 12800, 'init_lr_ratio': 1, 'warmup_epochs': 0, 'c_mel': 45, 'c_kl': 1.0}, 'data': {'max_wav_value': 32768.0, 'sampling_rate': 40000, 'filter_length': 2048, 'hop_length': 400, 'win_length': 2048, 'n_mel_channels': 125, 'mel_fmin': 0.0, 'mel_fmax': None, 'training_files': './logs\\ganyu/filelist.txt'}, 'model': {'inter_channels': 192, 'hidden_channels': 192, 'filter_channels': 768, 'n_heads': 2, 'n_layers': 6, 'kernel_size': 3, 'p_dropout': 0, 'resblock': '1', 'resblock_kernel_sizes': [3, 7, 11], 'resblock_dilation_sizes': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'upsample_rates': [10, 10, 2, 2], 'upsample_initial_channel': 512, 'upsample_kernel_sizes': [16, 16, 4, 4], 'use_spectral_norm': False, 'gin_channels': 256, 'spk_embed_dim': 109}, 'model_dir': './logs\\ganyu', 'experiment_dir': './logs\\ganyu', 'save_every_epoch': 5, 'name': 'ganyu', 'total_epoch': 20, 'pretrainG': 'pretrained/G40k.pth', 'pretrainD': 'pretrained/D40k.pth', 'version': 'v1', 'gpus': '0', 'sample_rate': '40k', 'if_f0': 0, 'if_latest': 0, 'save_every_weights': '0', 'if_cache_data_in_gpu': 0}
INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:1 to store for rank: 0
INFO:torch.distributed.distributed_c10d:Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 1 nodes.
gin_channels: 256 self.spk_embed_dim: 109
INFO:ganyu:loaded pretrained pretrained/G40k.pth
<All keys matched successfully>
INFO:ganyu:loaded pretrained pretrained/D40k.pth
<All keys matched successfully>
D:\miniconda3\envs\rvc\lib\site-packages\torch\functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\SpectralOps.cpp:867.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
D:\miniconda3\envs\rvc\lib\site-packages\torch\functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\SpectralOps.cpp:867.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
D:\miniconda3\envs\rvc\lib\site-packages\torch\functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\SpectralOps.cpp:867.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
D:\miniconda3\envs\rvc\lib\site-packages\torch\functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\SpectralOps.cpp:867.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
D:\miniconda3\envs\rvc\lib\site-packages\torch\functional.py:641: UserWarning: stft with return_complex=False is deprecated. In a future pytorch release, stft will return complex tensors for all inputs, and return_complex=False will raise an error.
Note: you can still call torch.view_as_real on the complex output to recover the old return format. (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\SpectralOps.cpp:867.)
  return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]
INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration.
D:\miniconda3\envs\rvc\lib\site-packages\torch\autograd\__init__.py:200: UserWarning: Grad strides do not match bucket view strides. This may indicate grad was not created according to the gradient layout contract, or that the param's strides changed since DDP was constructed.  This is not an error, but may impair performance.
grad.sizes() = [1, 21, 96], strides() = [62112, 96, 1]
bucket_view.sizes() = [1, 21, 96], strides() = [2016, 96, 1] (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\distributed\c10d\reducer.cpp:337.)
  Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
INFO:ganyu:Train Epoch: 1 [0%]
INFO:ganyu:[0, 0.0001]
INFO:ganyu:loss_disc=3.251, loss_gen=4.721, loss_fm=13.295,loss_mel=37.414, loss_kl=9.000
DEBUG:matplotlib:matplotlib data path: D:\miniconda3\envs\rvc\lib\site-packages\matplotlib\mpl-data
DEBUG:matplotlib:CONFIGDIR=C:\Users\GCVil\.matplotlib
DEBUG:matplotlib:interactive is False
DEBUG:matplotlib:platform is win32
INFO:torch.nn.parallel.distributed:Reducer buckets have been rebuilt in this iteration.
Process Process-1:
Traceback (most recent call last):
  File "D:\miniconda3\envs\rvc\lib\multiprocessing\process.py", line 315, in _bootstrap
    self.run()
  File "D:\miniconda3\envs\rvc\lib\multiprocessing\process.py", line 108, in run
    self._target(*self._args, **self._kwargs)
  File "F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI\train_nsf_sim_cache_sid_load_pretrain.py", line 223, in run
    train_and_evaluate(
  File "F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI\train_nsf_sim_cache_sid_load_pretrain.py", line 425, in train_and_evaluate
    wave = commons.slice_segments(
  File "F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI\lib\infer_pack\commons.py", line 49, in slice_segments
    ret[i] = x[i, :, idx_str:idx_end]
RuntimeError: The expanded size of the tensor (12800) must match the existing size (0) at non-singleton dimension 1.  Target sizes: [1, 12800].  Tensor sizes: [0]
Traceback (most recent call last):
  File "D:\miniconda3\envs\rvc\lib\site-packages\gradio\routes.py", line 321, in run_predict
    output = await app.blocks.process_api(
  File "D:\miniconda3\envs\rvc\lib\site-packages\gradio\blocks.py", line 1006, in process_api
    result = await self.call_function(fn_index, inputs, iterator, request)
  File "D:\miniconda3\envs\rvc\lib\site-packages\gradio\blocks.py", line 859, in call_function
    prediction = await anyio.to_thread.run_sync(
  File "D:\miniconda3\envs\rvc\lib\site-packages\anyio\to_thread.py", line 33, in run_sync
    return await get_asynclib().run_sync_in_worker_thread(
  File "D:\miniconda3\envs\rvc\lib\site-packages\anyio\_backends\_asyncio.py", line 877, in run_sync_in_worker_thread
    return await future
  File "D:\miniconda3\envs\rvc\lib\site-packages\anyio\_backends\_asyncio.py", line 807, in run
    result = context.run(func, *args)
  File "D:\miniconda3\envs\rvc\lib\site-packages\gradio\utils.py", line 408, in async_iteration
    return next(iterator)
  File "F:\AI\rvc\Retrieval-based-Voice-Conversion-WebUI\infer-web.py", line 1177, in train1key
    phone = np.load("%s/%s" % (feature_dir, name))
  File "D:\miniconda3\envs\rvc\lib\site-packages\numpy\lib\npyio.py", line 438, in load
    raise ValueError("Cannot load file containing pickled data "
ValueError: Cannot load file containing pickled data when allow_pickle=False

GCVillager avatar Jul 11 '23 11:07 GCVillager

我怀疑log目录下3_feature256中存在空白的.pt文件是导致出错的原因，因此我将出错的文件对应的音频找出来之后，删除log文件夹下的内容再次生成，没有出现此问题。之后把那个音频文件放回去，也无法复现此问题。

GCVillager avatar Jul 12 '23 12:07 GCVillager