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Fix #205
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@ -358,27 +358,6 @@ class Tacotron(nn.Module):
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@r.setter
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def r(self, value):
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self.decoder.r = self.decoder.r.new_tensor(value, requires_grad=False)
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def compute_gst(self, inputs, style_input, speaker_embedding=None):
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""" Compute global style token """
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device = inputs.device
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if isinstance(style_input, dict):
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query = torch.zeros(1, 1, self.gst_embedding_dim//2).to(device)
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if speaker_embedding is not None:
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query = torch.cat([query, speaker_embedding.reshape(1, 1, -1)], dim=-1)
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_GST = torch.tanh(self.gst_layer.style_token_layer.style_tokens)
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gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
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for k_token, v_amplifier in style_input.items():
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key = _GST[int(k_token)].unsqueeze(0).expand(1, -1, -1)
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gst_outputs_att = self.gst_layer.style_token_layer.attention(query, key)
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gst_outputs = gst_outputs + gst_outputs_att * v_amplifier
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elif style_input is None:
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gst_outputs = torch.zeros(1, 1, self.gst_embedding_dim).to(device)
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else:
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gst_outputs = self.gst_layer(style_input, speaker_embedding) # pylint: disable=not-callable
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inputs = self._concat_speaker_embedding(inputs, gst_outputs)
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return inputs
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@staticmethod
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def _concat_speaker_embedding(outputs, speaker_embeddings):
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@ -486,7 +465,7 @@ class Tacotron(nn.Module):
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speaker_embedding_style = (gst_embed[style_idx] * scale).astype(np.float32)
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speaker_embedding_style = torch.from_numpy(np.tile(speaker_embedding_style, (x.shape[0], 1))).to(device)
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else:
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speaker_embedding_style = torch.zeros(2, 1, self.speaker_embedding_size).to(device)
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speaker_embedding_style = torch.zeros(speaker_embedding.size()[0], 1, self.speaker_embedding_size).to(device)
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style_embed = self.gst(speaker_embedding_style, speaker_embedding)
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encoder_seq = self._concat_speaker_embedding(encoder_seq, style_embed)
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# style_embed = style_embed.expand_as(encoder_seq)
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