Closed kamileqq closed 1 year ago
ffmpeg the sounds back to wav. Also don't use pm for training, use harvest or any crepe variant
hello now it started throwing "Exception: Each element of kernel_size should be odd. Skipping" many times
that might be an issue with your filter radius
yeah setting the filter radius to 0 or 1 fixed the "kernel size should be odd" issue but i still get the NaN
I also have this problem, no matter what is set in the train interface, an error is reported. (NaN)
windows10 conda Python3.8.10
I can't really reproduce it, do you have ffmpeg installed, and can you check the files in the
data/training/RVC/Test/0_16k/
folder? these should be playable wav files, if not, something went wrong earlier down the line.
also check if you have ffmpeg installed, that could also cause this
Hello! The bug persist here too!
Has it a debug mode or CLI mode to test?
There is no debug mode or CLI mode. The error is printed here: https://github.com/gitmylo/audio-webui/blob/559bd7ebfab0162a353e585188a3bae9d309320b/webui/ui/tabs/training/training/rvc_workspace.py#L302 Which implies at least one of the features contains NaN.
You could try a different dataset, see if that makes a difference.
I changed the files into WAV 16000 in Audacity, but the problem persist.
The line wich create the bug is "logits = model.extract_features(**inputs)".
Follow bellow the variables before and after this line:
model variable:
HubertModel( (feature_extractor): ConvFeatureExtractionModel( (conv_layers): ModuleList( (0): Sequential( (0): Conv1d(1, 512, kernel_size=(10,), stride=(5,), bias=False) (1): Dropout(p=0.0, inplace=False) (2): Fp32GroupNorm(512, 512, eps=1e-05, affine=True) (3): GELU(approximate='none') ) (1-4): 4 x Sequential( (0): Conv1d(512, 512, kernel_size=(3,), stride=(2,), bias=False) (1): Dropout(p=0.0, inplace=False) (2): GELU(approximate='none') ) (5-6): 2 x Sequential( (0): Conv1d(512, 512, kernel_size=(2,), stride=(2,), bias=False) (1): Dropout(p=0.0, inplace=False) (2): GELU(approximate='none') ) ) ) (post_extract_proj): Linear(in_features=512, out_features=768, bias=True) (dropout_input): Dropout(p=0.1, inplace=False) (dropout_features): Dropout(p=0.1, inplace=False) (encoder): TransformerEncoder( (pos_conv): Sequential( (0): Conv1d(768, 768, kernel_size=(128,), stride=(1,), padding=(64,), groups=16) (1): SamePad() (2): GELU(approximate='none') ) (layers): ModuleList( (0-11): 12 x TransformerSentenceEncoderLayer( (self_attn): MultiheadAttention( (dropout_module): FairseqDropout() (k_proj): Linear(in_features=768, out_features=768, bias=True) (v_proj): Linear(in_features=768, out_features=768, bias=True) (q_proj): Linear(in_features=768, out_features=768, bias=True) (out_proj): Linear(in_features=768, out_features=768, bias=True) ) (dropout1): Dropout(p=0.1, inplace=False) (dropout2): Dropout(p=0.0, inplace=False) (dropout3): Dropout(p=0.1, inplace=False) (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (fc1): Linear(in_features=768, out_features=3072, bias=True) (fc2): Linear(in_features=3072, out_features=768, bias=True) (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) ) (layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True) (final_proj): Linear(in_features=768, out_features=256, bias=True) )
Variable input: {'source': tensor([[ 1.3542e-04, 3.9577e-04, 8.7976e-05, ..., -2.2034e-02, -3.8208e-02, -4.8920e-02]], device='cuda:0', dtype=torch.float16), 'padding_mask': tensor([[False, False, False, ..., False, False, False]], device='cuda:0'), 'output_layer': 12}
Variable logits: (tensor([[[nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], ..., [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan], [nan, nan, nan, ..., nan, nan, nan]]], device='cuda:0', dtype=torch.float16), tensor([[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]], device='cuda:0'))
I'm still not sure if it's a hardware/software difference, or something with the audio files.
I find the bug! Change in "def pitch_extract():" the line 263 "model = model.half()" to "model = model.float()" and the line 285 ""source": features.half().to(device)" to ""source": features.float().to(device)". This occur due 16xx gpu.
Source: https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/issues/204
I find the bug! Change in "def pitch_extract():" the line 263 "model = model.half()" to "model = model.float()" and the line 285 ""source": features.half().to(device)" to ""source": features.float().to(device)". This occur due 16xx gpu.
Source: RVC-Project/Retrieval-based-Voice-Conversion-WebUI#204
Thank you so much, I'll fix this immediately.
It will use a bit more memory, but training uses more than it anyways, so that won't be a problem.
When i'm clicking extract pitches it shows that "1.wav contains NaN" and like this for 59 other files, these files have sound in them. I tried doing it on pm and harvest extraction methods
Expected behavior webui should extract pitches succesfully
Screenshots
![image](https://github.com/gitmylo/audio-webui/assets/58258293/c09ac48a-7b1b-4afd-9c66-ed1cee80797b)