Comments (5)
get error.
Run model on reference(ref) and degraded(deg) Sample rate (fs) - No default. Must select either 8000 or 16000. Note there is narrow band (nb) mode only when sampling rate is 8000Hz.
it seems like the model only can train on 8000hz or 16000hz?
You can train the model on 44100kHz tracks, just comment out the assert line, but due to the self attention computation you need very large GPU memory to train.
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how to inference large wav audio file?when I inference a 60s 44100hz audio file ,it cause too much gpu memory,then it stopped.I set cut_length=44100*1,do you have any idea to solve this problem? Thank you very much.@ruizhecao96
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It should be ok only to infer only a one-second track of 44100kHz, I can infer10 seconds track of 16kHz on a 24GB GPU, how large memory is your GPU?
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my gpu is 24G,I can infer maybe a at most 10s track of 44100.I make every 10s chunks,then conbine them ,but got some audio problems in the connection.if I want to infer 60s,what should i do,do you have any solution?Thank you very much. @ruizhecao96
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I suggest to make the sample size of eahc batch dividable by 400, because the window length of stft is 400, this might solve the connection problem. e.g. for 441000 length track you can cut it to 440000 and reshape it to (11, 40000) and use it as input.
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Related Issues (20)
- RuntimeError HOT 3
- the change of gen_loss during training HOT 1
- RuntimeError
- RuntimeError HOT 2
- About the decreasing of loss HOT 1
- Can not reproduce the results HOT 12
- Training can get stuck HOT 6
- Inferior results trained from scratch HOT 7
- RuntimeeError HOT 1
- Can not reproduce the results HOT 3
- Training GPU requirements HOT 1
- File "pesq/cypesq.pyx", line 1, in init cypesq ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it)
- File "/anaconda3/envs/cmg/lib/python3.9/site-packages/torch/nn/parallel/distributed.py", line 578, in __init__ dist._verify_model_across_ranks(self.process_group, parameters) RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:957, invalid usage, NCCL version 21.0.3 ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc). HOT 2
- How do you resample to 16000? HOT 2
- 时域Loss计算疑惑
- the training speed confusion
- My server has a 3090, but reports that I don't have a gpu HOT 1
- Test set requirements when training
- epochs HOT 1
- 模型训练的采样率以及显卡训练配置咨询 HOT 1
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