Terminology is a bit confusing. What does n_frame_samples mean? Is this the number of samples per frame? or number of frames?

Is the RNN taking in a sequence of frames (ie: a frame per timestep), and the dimension of each frame is the "n_frame_samples"?

  File "samplernn-pytorch/trainer/__init__.py", line 45, in call_plugins
    getattr(plugin, queue_name)(*args)
  File "/usr/local/lib/python3.6/site-packages/torch/utils/trainer/plugins/monitor.py", line 56, in epoch
    stats['epoch_mean'] = epoch_stats[0] / epoch_stats[1]
ZeroDivisionError: division by zero

This is with PyTorch 0.3.0.post4.

Is it correct that the code does not implement images2neibs? I think its unfold in pytorch?

This line in the original code: https://github.com/soroushmehr/sampleRNN_ICLR2017/blob/master/models/three_tier/three_tier.py#L496

In the FrameLevel forward you guys do:

    def forward(self, prev_samples, upper_tier_conditioning, hidden):
        (batch_size, _, _) = prev_samples.size()

        # (batch, seq_len, dim) -> (batch, dim, seq_len)
        input = prev_samples.permute(0, 2, 1)

        # (batch, dim, seq_len)
        # use conv1d instead of FC for speed
        input = self.input_expand(input)

        # (batch, dim, seq_len) -> (batch, seq_len, dim)
        input = input.permute(0, 2, 1)
        
        # add conditioning tier from previous frame 
        if upper_tier_conditioning is not None:
            input += upper_tier_conditioning
        
        # reset hidden state for TBPTT
        reset = hidden is None
        if hidden is None:
            (n_rnn, _) = self.h0.size()
            hidden = self.h0.unsqueeze(1) \
                            .expand(n_rnn, batch_size, self.dim) \
                            .contiguous()
        
        # -
        (output, hidden) = self.rnn(input, hidden)
        
        # permute again so this can upsample for next context
        output = output.permute(0, 2, 1)
        output = self.upsampling(output)
        output = output.permute(0, 2, 1)
        return (output, hidden)

1. are the comments I added correct?

2. I'd like to just use the Linear layer instead of the Conv1d first for understanding purposes. However, the dimensions don't line up when I do it that way. Any thoughts on how to reframe this in terms of a Linear layer?

3. I assume the transposes you do are so that the convolutions work out? is that standard when using Conv1d instead of Linear layer?

Hello,

when training, I get the following error. The module trainer doesn't seem to exist ??

cd .. && python train.py --exp TEST --frame_sizes 16 4 --n_rnn 2 --dataset piano
Traceback (most recent call last):
  File "train.py", line 11, in <module>
    from trainer.plugins import (
  File "/home/vincent/samplernn-pytorch/trainer/plugins.py", line 8, in <module>
    from torch.utils.trainer.plugins.plugin import Plugin
ModuleNotFoundError: No module named 'torch.utils.trainer'

I'm on the pytorch docker, and I'm extremely confused about what I'm doing wrong at this point. Any assistance is appreciated.

root@6b27a1f07b65:~/samplernn-pytorch# pip install -r requirements.txt Collecting librosa==0.5.1 (from -r requirements.txt (line 1)) Using cached librosa-0.5.1.tar.gz Collecting matplotlib==2.1.0 (from -r requirements.txt (line 2)) Using cached matplotlib-2.1.0-cp36-cp36m-manylinux1_x86_64.whl Collecting natsort==5.1.0 (from -r requirements.txt (line 3)) Using cached natsort-5.1.0-py2.py3-none-any.whl Collecting torch==0.2.0.post3 (from -r requirements.txt (line 4)) Could not find a version that satisfies the requirement torch==0.2.0.post3 (from -r requirements.txt (line 4)) (from versions: 0.1.2, 0.1.2.post1) No matching distribution found for torch==0.2.0.post3 (from -r requirements.txt (line 4))

Edit: I can get it working without cuda. At this point is torch==0.2.0.post3 vital?

The quantizer doesn't work with q_levels=512. I'm not sure why but, to me it seems that it should. Maybe the epsilon is too small? For q_levels=512 you get quantized values that are 512 not 511.

I find this variant easier to read and it works :

    samples = (q_levels-1)*samples + 0.1
    samples = samples.long()

RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 705664 and 58368 in dimension 1 at /pytorch/torch/lib/TH/generic/THTensorMath.c:2897

My batch size is 64 instead of the initial 128. It would not run with 128, was getting a divide by 0 error for 128 batch size. I do not know if this could be a factor, but thought it would be worth mentioning.

Has anyone encountered this problem, what fixes are there?

Hi,

Thanks for the good work! I am wondering should https://github.com/deepsound-project/samplernn-pytorch/blob/master/model.py#L49 be:

h0 = torch.zeros(n_rnn, batch_size, dim)?

I noticed the expand function is used later, but it seems using the expand function the values are shared. Is this a bug? 

Many thanks!

Qiuqiang

Hi all,

I have been training the model using google colab. However, the generated sample is noise all the time. Please give me some suggestion;)

Best regards,

Zixun

when I run the code cd datasets ./download-from-youtube.sh "https://www.youtube.com/watch?v=EhO_MrRfftU" 8 piano

"." is not recognized as an internal or external command, operable or batch file shows up.

Anyone can tell me how to deal with this?

Hi, some people have reported that putting torch==0.4.1 in the requirements works for them. For me this produces this error:

/content/samplernn-pytorch/model.py:60: UserWarning: nn.init.kaiming_uniform is now deprecated in favor of nn.init.kaiming_uniform_. init.kaiming_uniform(self.input_expand.weight) /content/samplernn-pytorch/model.py:61: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_. init.constant(self.input_expand.bias, 0) /content/samplernn-pytorch/nn.py:48: UserWarning: nn.init.uniform is now deprecated in favor of nn.init.uniform_. nn.init.uniform(tensor, -math.sqrt(3 / fan_in), math.sqrt(3 / fan_in)) /content/samplernn-pytorch/model.py:76: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_. init.constant(getattr(self.rnn, 'bias_ih_l{}'.format(i)), 0) /content/samplernn-pytorch/nn.py:62: UserWarning: nn.init.orthogonal is now deprecated in favor of nn.init.orthogonal_. init(chunk) /content/samplernn-pytorch/model.py:82: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_. init.constant(getattr(self.rnn, 'bias_hh_l{}'.format(i)), 0) /content/samplernn-pytorch/nn.py:31: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_. nn.init.constant(self.bias, 0) /content/samplernn-pytorch/model.py:90: UserWarning: nn.init.uniform is now deprecated in favor of nn.init.uniform_. self.upsampling.conv_t.weight, -np.sqrt(6 / dim), np.sqrt(6 / dim) /content/samplernn-pytorch/model.py:92: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_. init.constant(self.upsampling.bias, 0) /content/samplernn-pytorch/model.py:141: UserWarning: nn.init.kaiming_uniform is now deprecated in favor of nn.init.kaiming_uniform_. init.kaiming_uniform(self.input.weight) /content/samplernn-pytorch/model.py:150: UserWarning: nn.init.kaiming_uniform is now deprecated in favor of nn.init.kaiming_uniform_. init.kaiming_uniform(self.hidden.weight) /content/samplernn-pytorch/model.py:151: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_. init.constant(self.hidden.bias, 0) /content/samplernn-pytorch/model.py:161: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_. init.constant(self.output.bias, 0) Traceback (most recent call last): File "train.py", line 360, in <module> main(**vars(parser.parse_args())) File "train.py", line 258, in main trainer.run(params['epoch_limit']) File "/content/samplernn-pytorch/trainer/__init__.py", line 56, in run self.train() File "/content/samplernn-pytorch/trainer/__init__.py", line 61, in train enumerate(self.dataset, self.iterations + 1): File "/content/samplernn-pytorch/dataset.py", line 51, in __iter__ for batch in super().__iter__(): File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 314, in __next__ batch = self.collate_fn([self.dataset[i] for i in indices]) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py", line 314, in <listcomp> batch = self.collate_fn([self.dataset[i] for i in indices]) File "/content/samplernn-pytorch/dataset.py", line 34, in __getitem__ torch.from_numpy(seq), self.q_levels RuntimeError: PyTorch was compiled without NumPy support

I'm using this Colab notebook btw: https://drive.google.com/file/d/13tVz73FXyG8Xvidl-SqyxNtBwozAlvth/view?usp=sharing

Any help would be appreciated!

Hi! I have been having a few issues running the code due to older dependencies, the closest I have gotten is running the Colab notebook referenced in the issues comments. The collab notebook but when running the commands on Anaconda I run into this issue: OSError: [WinError 123] The filename, directory name, or volume label syntax is incorrect: Does anyone know what is wrong?

Hi, I manage to setup a Colab for trainging. The trainng occurs, at least the first 100 generated samples do not have sound just clicks. Do you know how many epochs should take? Or maybe is something uncompatible with versions o other thing wrong going on

https://colab.research.google.com/drive/1fRhzNtRmdllD74mLzfyCy8SWuMT7sB3m

Best

Is it possible to run train.py with CUDA 9+?

train.py attempts to import torch.utils.trainer, which seems to have been removed from PyTorch at around version 1.0.0. However, I think 1.0.0 or newer is required for it to run on GPUs with CUDA 9+.

What's the easiest way to resolve this?