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wataru 2023-06-26 03:13:25 +09:00
parent 15fefdf5c4
commit 1a2220bfe4
4 changed files with 13 additions and 15 deletions
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1
server/voice_changer/RVC/inferencer/InferencerManager.py
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@ -10,6 +10,7 @@ from voice_changer.RVC.inferencer.WebUIInferencer import WebUIInferencer
from voice_changer.RVC.inferencer.WebUIInferencerNono import WebUIInferencerNono
from voice_changer.RVC.inferencer.VorasInferencebeta import VoRASInferencer
class InferencerManager:
currentInferencer: Inferencer | None = None

3
server/voice_changer/RVC/inferencer/rvc_models/infer_pack/commons.py
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@ -94,7 +94,8 @@ def subsequent_mask(length):
return mask
@torch.jit.script
# @torch.jit.script
@torch.jit._script_if_tracing
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b

21
server/voice_changer/RVC/inferencer/voras_beta/commons.py
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@ -23,9 +23,7 @@ def convert_pad_shape(pad_shape):
def kl_divergence(m_p, logs_p, m_q, logs_q):
"""KL(P||Q)"""
kl = (logs_q - logs_p) - 0.5
kl += (
0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
)
kl += 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
return kl
@ -46,7 +44,7 @@ def slice_segments(x, ids_str, segment_size=4):
idx_str = ids_str[i]
idx_end = idx_str + segment_size
r = x[i, :, idx_str:idx_end]
ret[i, :, :r.size(1)] = r
ret[i, :, : r.size(1)] = r
return ret
@ -56,7 +54,7 @@ def slice_segments2(x, ids_str, segment_size=4):
idx_str = ids_str[i]
idx_end = idx_str + segment_size
r = x[i, idx_str:idx_end]
ret[i, :r.size(0)] = r
ret[i, : r.size(0)] = r
return ret
@ -64,7 +62,7 @@ def rand_slice_segments(x, x_lengths, segment_size=4, ids_str=None):
b, d, t = x.size()
if ids_str is None:
ids_str = torch.zeros([b]).to(device=x.device, dtype=x_lengths.dtype)
ids_str_max = torch.maximum(torch.zeros_like(x_lengths).to(device=x_lengths.device ,dtype=x_lengths.dtype), x_lengths - segment_size + 1 - ids_str)
ids_str_max = torch.maximum(torch.zeros_like(x_lengths).to(device=x_lengths.device, dtype=x_lengths.dtype), x_lengths - segment_size + 1 - ids_str)
ids_str += (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
ret = slice_segments(x, ids_str, segment_size)
return ret, ids_str
@ -73,12 +71,8 @@ def rand_slice_segments(x, x_lengths, segment_size=4, ids_str=None):
def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
position = torch.arange(length, dtype=torch.float)
num_timescales = channels // 2
log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
num_timescales - 1
)
inv_timescales = min_timescale * torch.exp(
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
)
log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (num_timescales - 1)
inv_timescales = min_timescale * torch.exp(torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
signal = F.pad(signal, [0, 0, 0, channels % 2])
@ -103,7 +97,8 @@ def subsequent_mask(length):
return mask
@torch.jit.script
# @torch.jit.script
@torch.jit._script_if_tracing
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b

3
server/voice_changer/SoVitsSvc40/models/modules/commons.py
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@ -121,7 +121,8 @@ def subsequent_mask(length):
return mask
@torch.jit.script
#@torch.jit.script
@torch.jit._script_if_tracing
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
n_channels_int = n_channels[0]
in_act = input_a + input_b