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| import torch | |
| import commons | |
| import models | |
| class SynthesizerTrn(models.SynthesizerTrn): | |
| """ | |
| Synthesizer for Training | |
| """ | |
| def __init__(self, | |
| n_vocab, | |
| spec_channels, | |
| segment_size, | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| n_speakers=0, | |
| gin_channels=0, | |
| use_sdp=True, | |
| **kwargs): | |
| super().__init__( | |
| n_vocab, | |
| spec_channels, | |
| segment_size, | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| resblock, | |
| resblock_kernel_sizes, | |
| resblock_dilation_sizes, | |
| upsample_rates, | |
| upsample_initial_channel, | |
| upsample_kernel_sizes, | |
| n_speakers=n_speakers, | |
| gin_channels=gin_channels, | |
| use_sdp=use_sdp, | |
| **kwargs | |
| ) | |
| def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): | |
| from ONNXVITS_utils import runonnx | |
| #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) | |
| x, m_p, logs_p, x_mask = runonnx("ONNX_net/enc_p.onnx", x=x.numpy(), x_lengths=x_lengths.numpy()) | |
| x = torch.from_numpy(x) | |
| m_p = torch.from_numpy(m_p) | |
| logs_p = torch.from_numpy(logs_p) | |
| x_mask = torch.from_numpy(x_mask) | |
| if self.n_speakers > 0: | |
| g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
| else: | |
| g = None | |
| #logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) | |
| logw = runonnx("ONNX_net/dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy()) | |
| logw = torch.from_numpy(logw[0]) | |
| w = torch.exp(logw) * x_mask * length_scale | |
| w_ceil = torch.ceil(w) | |
| y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) | |
| attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) | |
| attn = commons.generate_path(w_ceil, attn_mask) | |
| m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] | |
| logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] | |
| z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale | |
| #z = self.flow(z_p, y_mask, g=g, reverse=True) | |
| z = runonnx("ONNX_net/flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy()) | |
| z = torch.from_numpy(z[0]) | |
| #o = self.dec((z * y_mask)[:,:,:max_len], g=g) | |
| o = runonnx("ONNX_net/dec.onnx", z_in=(z * y_mask)[:,:,:max_len].numpy(), g=g.numpy()) | |
| o = torch.from_numpy(o[0]) | |
| return o, attn, y_mask, (z, z_p, m_p, logs_p) | |
| def predict_duration(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None, | |
| emotion_embedding=None): | |
| from ONNXVITS_utils import runonnx | |
| #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) | |
| x, m_p, logs_p, x_mask = runonnx("ONNX_net/enc_p.onnx", x=x.numpy(), x_lengths=x_lengths.numpy()) | |
| x = torch.from_numpy(x) | |
| m_p = torch.from_numpy(m_p) | |
| logs_p = torch.from_numpy(logs_p) | |
| x_mask = torch.from_numpy(x_mask) | |
| if self.n_speakers > 0: | |
| g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
| else: | |
| g = None | |
| #logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) | |
| logw = runonnx("ONNX_net/dp.onnx", x=x.numpy(), x_mask=x_mask.numpy(), g=g.numpy()) | |
| logw = torch.from_numpy(logw[0]) | |
| w = torch.exp(logw) * x_mask * length_scale | |
| w_ceil = torch.ceil(w) | |
| return list(w_ceil.squeeze()) | |
| def infer_with_duration(self, x, x_lengths, w_ceil, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None, | |
| emotion_embedding=None): | |
| from ONNXVITS_utils import runonnx | |
| #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths) | |
| x, m_p, logs_p, x_mask = runonnx("ONNX_net/enc_p.onnx", x=x.numpy(), x_lengths=x_lengths.numpy()) | |
| x = torch.from_numpy(x) | |
| m_p = torch.from_numpy(m_p) | |
| logs_p = torch.from_numpy(logs_p) | |
| x_mask = torch.from_numpy(x_mask) | |
| if self.n_speakers > 0: | |
| g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] | |
| else: | |
| g = None | |
| assert len(w_ceil) == x.shape[2] | |
| w_ceil = torch.FloatTensor(w_ceil).reshape(1, 1, -1) | |
| y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() | |
| y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) | |
| attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) | |
| attn = commons.generate_path(w_ceil, attn_mask) | |
| m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] | |
| logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] | |
| z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale | |
| #z = self.flow(z_p, y_mask, g=g, reverse=True) | |
| z = runonnx("ONNX_net/flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g.numpy()) | |
| z = torch.from_numpy(z[0]) | |
| #o = self.dec((z * y_mask)[:,:,:max_len], g=g) | |
| o = runonnx("ONNX_net/dec.onnx", z_in=(z * y_mask)[:,:,:max_len].numpy(), g=g.numpy()) | |
| o = torch.from_numpy(o[0]) | |
| return o, attn, y_mask, (z, z_p, m_p, logs_p) |