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from diffusion_onnx import GaussianDiffusion
import os
import yaml
import torch
import torch.nn as nn
import numpy as np
from wavenet import WaveNet
import torch.nn.functional as F
import diffusion
class DotDict(dict):
def __getattr__(*args):
val = dict.get(*args)
return DotDict(val) if type(val) is dict else val
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
def load_model_vocoder(
model_path,
device='cpu'):
config_file = os.path.join(os.path.split(model_path)[0], 'config.yaml')
with open(config_file, "r") as config:
args = yaml.safe_load(config)
args = DotDict(args)
# load model
model = Unit2Mel(
args.data.encoder_out_channels,
args.model.n_spk,
args.model.use_pitch_aug,
128,
args.model.n_layers,
args.model.n_chans,
args.model.n_hidden)
print(' [Loading] ' + model_path)
ckpt = torch.load(model_path, map_location=torch.device(device))
model.to(device)
model.load_state_dict(ckpt['model'])
model.eval()
return model, args
class Unit2Mel(nn.Module):
def __init__(
self,
input_channel,
n_spk,
use_pitch_aug=False,
out_dims=128,
n_layers=20,
n_chans=384,
n_hidden=256):
super().__init__()
self.unit_embed = nn.Linear(input_channel, n_hidden)
self.f0_embed = nn.Linear(1, n_hidden)
self.volume_embed = nn.Linear(1, n_hidden)
if use_pitch_aug:
self.aug_shift_embed = nn.Linear(1, n_hidden, bias=False)
else:
self.aug_shift_embed = None
self.n_spk = n_spk
if n_spk is not None and n_spk > 1:
self.spk_embed = nn.Embedding(n_spk, n_hidden)
# diffusion
self.decoder = GaussianDiffusion(out_dims, n_layers, n_chans, n_hidden)
self.hidden_size = n_hidden
self.speaker_map = torch.zeros((self.n_spk,1,1,n_hidden))
def forward(self, units, mel2ph, f0, volume, g = None):
'''
input:
B x n_frames x n_unit
return:
dict of B x n_frames x feat
'''
decoder_inp = F.pad(units, [0, 0, 1, 0])
mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, units.shape[-1]])
units = torch.gather(decoder_inp, 1, mel2ph_) # [B, T, H]
x = self.unit_embed(units) + self.f0_embed((1 + f0.unsqueeze(-1) / 700).log()) + self.volume_embed(volume.unsqueeze(-1))
if self.n_spk is not None and self.n_spk > 1: # [N, S] * [S, B, 1, H]
g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1)) # [N, S, B, 1, 1]
g = g * self.speaker_map # [N, S, B, 1, H]
g = torch.sum(g, dim=1) # [N, 1, B, 1, H]
g = g.transpose(0, -1).transpose(0, -2).squeeze(0) # [B, H, N]
x = x.transpose(1, 2) + g
return x
else:
return x.transpose(1, 2)
def init_spkembed(self, units, f0, volume, spk_id = None, spk_mix_dict = None, aug_shift = None,
gt_spec=None, infer=True, infer_speedup=10, method='dpm-solver', k_step=300, use_tqdm=True):
'''
input:
B x n_frames x n_unit
return:
dict of B x n_frames x feat
'''
x = self.unit_embed(units) + self.f0_embed((1+ f0 / 700).log()) + self.volume_embed(volume)
if self.n_spk is not None and self.n_spk > 1:
if spk_mix_dict is not None:
spk_embed_mix = torch.zeros((1,1,self.hidden_size))
for k, v in spk_mix_dict.items():
spk_id_torch = torch.LongTensor(np.array([[k]])).to(units.device)
spk_embeddd = self.spk_embed(spk_id_torch)
self.speaker_map[k] = spk_embeddd
spk_embed_mix = spk_embed_mix + v * spk_embeddd
x = x + spk_embed_mix
else:
x = x + self.spk_embed(spk_id - 1)
self.speaker_map = self.speaker_map.unsqueeze(0)
self.speaker_map = self.speaker_map.detach()
return x.transpose(1, 2)
def OnnxExport(self, project_name=None, init_noise=None, export_encoder=True, export_denoise=True, export_pred=True, export_after=True):
hubert_hidden_size = 768
n_frames = 100
hubert = torch.randn((1, n_frames, hubert_hidden_size))
mel2ph = torch.arange(end=n_frames).unsqueeze(0).long()
f0 = torch.randn((1, n_frames))
volume = torch.randn((1, n_frames))
spk_mix = []
spks = {}
if self.n_spk is not None and self.n_spk > 1:
for i in range(self.n_spk):
spk_mix.append(1.0/float(self.n_spk))
spks.update({i:1.0/float(self.n_spk)})
spk_mix = torch.tensor(spk_mix)
spk_mix = spk_mix.repeat(n_frames, 1)
orgouttt = self.init_spkembed(hubert, f0.unsqueeze(-1), volume.unsqueeze(-1), spk_mix_dict=spks)
outtt = self.forward(hubert, mel2ph, f0, volume, spk_mix)
if export_encoder:
torch.onnx.export(
self,
(hubert, mel2ph, f0, volume, spk_mix),
f"{project_name}_encoder.onnx",
input_names=["hubert", "mel2ph", "f0", "volume", "spk_mix"],
output_names=["mel_pred"],
dynamic_axes={
"hubert": [1],
"f0": [1],
"volume": [1],
"mel2ph": [1],
"spk_mix": [0],
},
opset_version=16
)
self.decoder.OnnxExport(project_name, init_noise=init_noise, export_denoise=export_denoise, export_pred=export_pred, export_after=export_after)
def ExportOnnx(self, project_name=None):
hubert_hidden_size = 768
n_frames = 100
hubert = torch.randn((1, n_frames, hubert_hidden_size))
mel2ph = torch.arange(end=n_frames).unsqueeze(0).long()
f0 = torch.randn((1, n_frames))
volume = torch.randn((1, n_frames))
spk_mix = []
spks = {}
if self.n_spk is not None and self.n_spk > 1:
for i in range(self.n_spk):
spk_mix.append(1.0/float(self.n_spk))
spks.update({i:1.0/float(self.n_spk)})
spk_mix = torch.tensor(spk_mix)
orgouttt = self.orgforward(hubert, f0.unsqueeze(-1), volume.unsqueeze(-1), spk_mix_dict=spks)
outtt = self.forward(hubert, mel2ph, f0, volume, spk_mix)
torch.onnx.export(
self,
(hubert, mel2ph, f0, volume, spk_mix),
f"{project_name}_encoder.onnx",
input_names=["hubert", "mel2ph", "f0", "volume", "spk_mix"],
output_names=["mel_pred"],
dynamic_axes={
"hubert": [1],
"f0": [1],
"volume": [1],
"mel2ph": [1]
},
opset_version=16
)
condition = torch.randn(1,self.decoder.n_hidden,n_frames)
noise = torch.randn((1, 1, self.decoder.mel_bins, condition.shape[2]), dtype=torch.float32)
pndm_speedup = torch.LongTensor([100])
K_steps = torch.LongTensor([1000])
self.decoder = torch.jit.script(self.decoder)
self.decoder(condition, noise, pndm_speedup, K_steps)
torch.onnx.export(
self.decoder,
(condition, noise, pndm_speedup, K_steps),
f"{project_name}_diffusion.onnx",
input_names=["condition", "noise", "pndm_speedup", "K_steps"],
output_names=["mel"],
dynamic_axes={
"condition": [2],
"noise": [3],
},
opset_version=16
)
if __name__ == "__main__":
project_name = "dddsp"
model_path = f'{project_name}/model_500000.pt'
model, _ = load_model_vocoder(model_path)
# 分开Diffusion导出（需要使用MoeSS/MoeVoiceStudio或者自己编写Pndm/Dpm采样）
model.OnnxExport(project_name, export_encoder=True, export_denoise=True, export_pred=True, export_after=True)
# 合并Diffusion导出（Encoder和Diffusion分开，直接将Encoder的结果和初始噪声输入Diffusion即可）
# model.ExportOnnx(project_name)
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