Kernel size error when rave.decode(z)

[robinmeier]

i get the following error when i try generating from the prior

Traceback (most recent call last): File "/home/syrinx/RAVE/ravezeke1-generate.py", line 43, in <module> y = rave.decode(z) File "/home/syrinx/RAVE/rave/model.py", line 582, in decode y = self.decoder(z, add_noise=True) File "/home/syrinx/miniconda2/envs/rave/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/syrinx/RAVE/rave/model.py", line 235, in forward x = self.net(x) File "/home/syrinx/miniconda2/envs/rave/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl return forward_call(*input, **kwargs) File "/home/syrinx/miniconda2/envs/rave/lib/python3.9/site-packages/torch/nn/modules/container.py", line 141, in forward input = module(input) File "/home/syrinx/miniconda2/envs/rave/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1120, in _call_impl result = forward_call(*input, **kwargs) File "/home/syrinx/miniconda2/envs/rave/lib/python3.9/site-packages/cached_conv/convs.py", line 74, in forward return nn.functional.conv1d( RuntimeError: Calculated padded input size per channel: (6). Kernel size: (7). Kernel size can't be greater than actual input size

this is the code

`################ PRIOR GENERATION ################

STEP 1: CREATE DUMMY INPUT TENSOR

generation_length = 2**18 # approximately 6s at 48kHz
x = torch.randn(1, 1, generation_length) # dummy input
z = rave.encode(x) # dummy latent representation
z = torch.zeros_like(z)

STEP 2: AUTOREGRESSIVE GENERATION

z = prior.quantized_normal.encode(prior.diagonal_shift(z))
z = prior.generate(z)
z = prior.diagonal_shift.inverse(prior.quantized_normal.decode(z))

STEP 3: SYNTHESIS AND EXPORT

y = rave.decode(z)
sf.write("output_audio.wav", y.reshape(-1).numpy(), sr)
`

when i change generation_length to a smaller size, i get the error

RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1] because the unspecified dimension size -1 can be any value and is ambiguous

my audio is in 44.1kHz

[robinmeier]

ps: reconstruction is working though

[caillonantoine]

Can you check the size of z before the decoding ?

[robinmeier]

when i do z.size() i get
[1, 128, 0]

[caillonantoine]

So your tensor is empty... what does generation_length equals to ?

[robinmeier]

generation_length = 2**18

[caillonantoine]

Didn't you lower it ?

[robinmeier]

with
generation_lenght = 2**17 i get

i = i // self.groups Traceback (most recent call last): File "/home/syrinx/RAVE/ravezeke1-generate.py", line 37, in <module> z = prior.quantized_normal.encode(prior.diagonal_shift(z)) File "/home/syrinx/RAVE/prior/core.py", line 24, in encode return self.to_stack_one_hot(x.long()) File "/home/syrinx/RAVE/prior/core.py", line 29, in to_stack_one_hot x = x.reshape(x.shape[0], x.shape[1], -1) RuntimeError: cannot reshape tensor of 0 elements into shape [1, 0, -1] because the unspecified dimension size -1 can be any value and is ambiguous

and in any case i get the following warning

/home/syrinx/RAVE/prior/core.py:52: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').

[caillonantoine]

Mhhh.. how many dimensions inside the latent space ? I'm guessing the problem comes from the diagonal shift which requires at least 2xlatent_dim time steps to operate !

[robinmeier]

sorry, how do i see the number of dims inside the latent space?
i was using the default values of the cli helper:

choose a name for the training: starling2
path to the .wav files: /home/syrinx/robin-voizo/zekestar/b1159/wavs/
temporary folder (fast drive): /home/syrinx/RAVE/
sampling rate (defaults to 48000): 44100
multiband number (defaults to 16):
training example duration (defaults to 65536 samples):
model capacity (defaults to 6):
number of steps for stage 1 (defaults to 1000000):
prior resolution (defaults to 32):
reconstruction fidelity (defaults to 0.95):
latency compensation (defaults to false):

[caillonantoine]

It's the size of the dummy latent representation

[robinmeier]

wiith generation_length 2**17 z.size() is torch.Size([1, 128, 64]) with generation_length 2**18 it's torch.Size([1, 128, 128])

i'm getting my checkpoints from here, in case this could be relevant

rave = RAVE.load_from_checkpoint("/home/syrinx/RAVE/runs/starling1/rave/version_0/checkpoints/best.ckpt", strict=False).eval()
prior = Prior.load_from_checkpoint("/home/syrinx/RAVE/runs/starling1/prior/version_0/checkpoints/best.ckpt", strict=False).eval()

[caillonantoine]

Ok I see the problem ! The README is quite wrong, I'm about to fix it asap

[robinmeier]

awesome. thanks.
can i continue to run training sets or should i wait for the fix?

[caillonantoine]

You can, and I've modified the instructions in the readme :)

[robinmeier]

ah yes of course - helps to load the model :)
thank you!