Medix-AI/coqui-tts
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coqui-tts
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predict stop token from rnn out + mel

This commit is contained in:
Eren Golge 2018-05-14 18:50:35 -07:00
parent d629dafb20
commit 02d72ccbfe
2 changed files with 34 additions and 23 deletions
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18
layers/tacotron.py
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@ -232,11 +232,11 @@ class Decoder(nn.Module):
[nn.GRUCell(256, 256) for _ in range(2)]) [nn.GRUCell(256, 256) for _ in range(2)])
# RNN_state -> |Linear| -> mel_spec # RNN_state -> |Linear| -> mel_spec
self.proj_to_mel = nn.Linear(256, memory_dim * r) self.proj_to_mel = nn.Linear(256, memory_dim * r)
self.stopnet = nn.Sequential(nn.Linear(memory_dim * self.r, memory_dim), self.stopnet = nn.Sequential(nn.Linear(256 + self.r * memory_dim, memory_dim),
nn.ReLU(), nn.ReLU(),
nn.Linear(memory_dim, memory_dim * self.r), nn.Linear(memory_dim, 256 + self.r * memory_dim),
nn.ReLU(), nn.ReLU(),
nn.Linear(memory_dim * self.r, 1), nn.Linear(256 + self.r * memory_dim, 1),
nn.Sigmoid()) nn.Sigmoid())
def forward(self, inputs, memory=None): def forward(self, inputs, memory=None):
@ -301,11 +301,12 @@ class Decoder(nn.Module):
decoder_input, decoder_rnn_hiddens[idx]) decoder_input, decoder_rnn_hiddens[idx])
# Residual connectinon # Residual connectinon
decoder_input = decoder_rnn_hiddens[idx] + decoder_input decoder_input = decoder_rnn_hiddens[idx] + decoder_input
output = decoder_input decoder_output = decoder_input
# predict mel vectors from decoder vectors # predict mel vectors from decoder vectors
output = self.proj_to_mel(output) output = self.proj_to_mel(decoder_output)
stop_input = torch.cat((output, decoder_output), -1)
# predict stop token # predict stop token
stop_token = self.stopnet(output) stop_token = self.stopnet(stop_input)
outputs += [output] outputs += [output]
alignments += [alignment] alignments += [alignment]
stop_tokens += [stop_token] stop_tokens += [stop_token]
@ -326,8 +327,3 @@ class Decoder(nn.Module):
outputs = torch.stack(outputs).transpose(0, 1).contiguous() outputs = torch.stack(outputs).transpose(0, 1).contiguous()
stop_tokens = torch.stack(stop_tokens).transpose(0, 1) stop_tokens = torch.stack(stop_tokens).transpose(0, 1)
return outputs, alignments, stop_tokens return outputs, alignments, stop_tokens
def is_end_of_frames(output, alignment, eps=0.05): # 0.2
return ((output.data <= eps).prod(0) > 0).any() \
and alignment.data[:, int(alignment.shape[1]/2):].sum() > 0.7

37
notebooks/TacotronPlayGround.ipynb
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