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| import torch | |
| import commons | |
| import models | |
| import math | |
| from torch import nn | |
| from torch.nn import functional as F | |
| import modules | |
| import attentions | |
| from torch.nn import Conv1d, ConvTranspose1d, Conv2d | |
| from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm | |
| from commons import init_weights, get_padding | |
| class TextEncoder(nn.Module): | |
| def __init__(self, | |
| n_vocab, | |
| out_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| emotion_embedding): | |
| super().__init__() | |
| self.n_vocab = n_vocab | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.filter_channels = filter_channels | |
| self.n_heads = n_heads | |
| self.n_layers = n_layers | |
| self.kernel_size = kernel_size | |
| self.p_dropout = p_dropout | |
| self.emotion_embedding = emotion_embedding | |
| if self.n_vocab!=0: | |
| self.emb = nn.Embedding(n_vocab, hidden_channels) | |
| if emotion_embedding: | |
| self.emo_proj = nn.Linear(1024, hidden_channels) | |
| nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) | |
| self.encoder = attentions.Encoder( | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout) | |
| self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, x, x_lengths, emotion_embedding=None): | |
| if self.n_vocab!=0: | |
| x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] | |
| if emotion_embedding is not None: | |
| print("emotion added") | |
| x = x + self.emo_proj(emotion_embedding.unsqueeze(1)) | |
| x = torch.transpose(x, 1, -1) # [b, h, t] | |
| x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) | |
| x = self.encoder(x * x_mask, x_mask) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| return x, m, logs, x_mask | |
| class PosteriorEncoder(nn.Module): | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| hidden_channels, | |
| kernel_size, | |
| dilation_rate, | |
| n_layers, | |
| gin_channels=0): | |
| super().__init__() | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.hidden_channels = hidden_channels | |
| self.kernel_size = kernel_size | |
| self.dilation_rate = dilation_rate | |
| self.n_layers = n_layers | |
| self.gin_channels = gin_channels | |
| self.pre = nn.Conv1d(in_channels, hidden_channels, 1) | |
| self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) | |
| self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) | |
| def forward(self, x, x_lengths, g=None): | |
| x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) | |
| x = self.pre(x) * x_mask | |
| x = self.enc(x, x_mask, g=g) | |
| stats = self.proj(x) * x_mask | |
| m, logs = torch.split(stats, self.out_channels, dim=1) | |
| z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask | |
| return z, m, logs, x_mask | |
| 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, | |
| emotion_embedding=False, | |
| **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 | |
| ) | |
| self.enc_p = TextEncoder(n_vocab, | |
| inter_channels, | |
| hidden_channels, | |
| filter_channels, | |
| n_heads, | |
| n_layers, | |
| kernel_size, | |
| p_dropout, | |
| emotion_embedding) | |
| self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) | |
| def infer(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, emotion_embedding) | |
| 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) | |
| def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): | |
| from ONNXVITS_utils import runonnx | |
| assert self.n_speakers > 0, "n_speakers have to be larger than 0." | |
| g_src = self.emb_g(sid_src).unsqueeze(-1) | |
| g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) | |
| z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) | |
| # z_p = self.flow(z, y_mask, g=g_src) | |
| z_p = runonnx("ONNX_net/flow.onnx", z_p=z.numpy(), y_mask=y_mask.numpy(), g=g_src.numpy()) | |
| z_p = torch.from_numpy(z_p[0]) | |
| # z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) | |
| z_hat = runonnx("ONNX_net/flow.onnx", z_p=z_p.numpy(), y_mask=y_mask.numpy(), g=g_tgt.numpy()) | |
| z_hat = torch.from_numpy(z_hat[0]) | |
| # o_hat = self.dec(z_hat * y_mask, g=g_tgt) | |
| o_hat = runonnx("ONNX_net/dec.onnx", z_in=(z_hat * y_mask).numpy(), g=g_tgt.numpy()) | |
| o_hat = torch.from_numpy(o_hat[0]) | |
| return o_hat, y_mask, (z, z_p, z_hat) |