Comments (4)
Could you provide more information?
from fastspeech.
I dowanload the pretrained model from nvidia. My pytorch version is 1.2. when i run pereprocess.py occurs this problem.
`def forward(self, inputs):
text_inputs, text_lengths, mels, max_len, output_lengths = inputs
text_lengths, output_lengths = text_lengths.data, output_lengths.data
embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
encoder_outputs = self.encoder(embedded_inputs, text_lengths)
mel_outputs, gate_outputs, alignments = self.decoder(
encoder_outputs, mels, memory_lengths=text_lengths)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet`
encoder_outputs has value.
from fastspeech.
I dowanload the pretrained model from nvidia. My pytorch version is 1.2. when i run pereprocess.py occurs this problem.
`def forward(self, inputs):
text_inputs, text_lengths, mels, max_len, output_lengths = inputs
text_lengths, output_lengths = text_lengths.data, output_lengths.dataembedded_inputs = self.embedding(text_inputs).transpose(1, 2) encoder_outputs = self.encoder(embedded_inputs, text_lengths) mel_outputs, gate_outputs, alignments = self.decoder( encoder_outputs, mels, memory_lengths=text_lengths) mel_outputs_postnet = self.postnet(mel_outputs) mel_outputs_postnet = mel_outputs + mel_outputs_postnet`
encoder_outputs has value.
Maybe you should use Pytorch 1.1.0.
from fastspeech.
I solved the problem by modifying the following code.
def get_mask_from_lengths(lengths): max_len = torch.max(lengths).item() ids = torch.arange(0, max_len, out=torch.cuda.LongTensor(max_len)) mask = (ids < lengths.unsqueeze(1)).bool() return mask
from fastspeech.
Related Issues (20)
- Is this still maintained?
- training stops in few seconds and no checkpoint file created HOT 3
- error in new commit HOT 9
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- 请问训练多久得到的pretrain model呢?然后,请问使用了多少GPU呢? HOT 1
- Resume training from checkpoint result in NaN? HOT 1
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from fastspeech.