| 89 | |
| 90 | |
| 91 | class ProsodyPredictor(nn.Module): |
| 92 | def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1): |
| 93 | super().__init__() |
| 94 | self.text_encoder = DurationEncoder(sty_dim=style_dim, d_model=d_hid,nlayers=nlayers, dropout=dropout) |
| 95 | self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True) |
| 96 | self.duration_proj = LinearNorm(d_hid, max_dur) |
| 97 | self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True) |
| 98 | self.F0 = nn.ModuleList() |
| 99 | self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout)) |
| 100 | self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout)) |
| 101 | self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)) |
| 102 | self.N = nn.ModuleList() |
| 103 | self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout)) |
| 104 | self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout)) |
| 105 | self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout)) |
| 106 | self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0) |
| 107 | self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0) |
| 108 | |
| 109 | def forward(self, texts, style, text_lengths, alignment, m): |
| 110 | d = self.text_encoder(texts, style, text_lengths, m) |
| 111 | m = m.unsqueeze(1) |
| 112 | lengths = text_lengths if text_lengths.device == torch.device('cpu') else text_lengths.to('cpu') |
| 113 | x = nn.utils.rnn.pack_padded_sequence(d, lengths, batch_first=True, enforce_sorted=False) |
| 114 | self.lstm.flatten_parameters() |
| 115 | x, _ = self.lstm(x) |
| 116 | x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True) |
| 117 | x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]], device=x.device) |
| 118 | x_pad[:, :x.shape[1], :] = x |
| 119 | x = x_pad |
| 120 | duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=False)) |
| 121 | en = (d.transpose(-1, -2) @ alignment) |
| 122 | return duration.squeeze(-1), en |
| 123 | |
| 124 | def F0Ntrain(self, x, s): |
| 125 | x, _ = self.shared(x.transpose(-1, -2)) |
| 126 | F0 = x.transpose(-1, -2) |
| 127 | for block in self.F0: |
| 128 | F0 = block(F0, s) |
| 129 | F0 = self.F0_proj(F0) |
| 130 | N = x.transpose(-1, -2) |
| 131 | for block in self.N: |
| 132 | N = block(N, s) |
| 133 | N = self.N_proj(N) |
| 134 | return F0.squeeze(1), N.squeeze(1) |
| 135 | |
| 136 | |
| 137 | class DurationEncoder(nn.Module): |