Spaces:
Runtime error
Runtime error
File size: 5,455 Bytes
35aaf1e 9aeb33e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
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) |