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