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Ναρουσέ·μ·γιουμεμί·Χινακάννα
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import torch
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from torchaudio.models.wav2vec2.utils import import_fairseq_model
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from fairseq import checkpoint_utils
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from onnxexport.model_onnx_speaker_mix import SynthesizerTrn
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import utils
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def get_hubert_model():
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vec_path = "hubert/checkpoint_best_legacy_500.pt"
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print("load model(s) from {}".format(vec_path))
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[vec_path],
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suffix="",
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)
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model = models[0]
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model.eval()
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return model
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def main(HubertExport, NetExport):
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path = "SoVits4.0"
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'''if HubertExport:
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device = torch.device("cpu")
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vec_path = "hubert/checkpoint_best_legacy_500.pt"
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[vec_path],
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suffix="",
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)
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original = models[0]
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original.eval()
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model = original
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test_input = torch.rand(1, 1, 16000)
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model(test_input)
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torch.onnx.export(model,
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test_input,
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"hubert4.0.onnx",
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export_params=True,
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opset_version=16,
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do_constant_folding=True,
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input_names=['source'],
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output_names=['embed'],
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dynamic_axes={
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'source':
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{
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2: "sample_length"
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},
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}
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)'''
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if NetExport:
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device = torch.device("cpu")
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hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
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SVCVITS = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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**hps.model)
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_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
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_ = SVCVITS.eval().to(device)
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for i in SVCVITS.parameters():
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i.requires_grad = False
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test_hidden_unit = torch.rand(1, 10, SVCVITS.hidden_channels)
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test_pitch = torch.rand(1, 10)
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test_mel2ph = torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0)
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test_uv = torch.ones(1, 10, dtype=torch.float32)
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test_noise = torch.randn(1, 192, 10)
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export_mix = False
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test_sid = torch.LongTensor([0])
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spk_mix = []
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if export_mix:
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n_spk = len(hps.spk)
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for i in range(n_spk):
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spk_mix.append(1.0/float(n_spk))
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test_sid = torch.tensor(spk_mix)
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SVCVITS.export_chara_mix(n_spk)
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input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"]
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output_names = ["audio", ]
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SVCVITS.eval()
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torch.onnx.export(SVCVITS,
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(
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test_hidden_unit.to(device),
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test_pitch.to(device),
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test_mel2ph.to(device),
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test_uv.to(device),
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test_noise.to(device),
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test_sid.to(device)
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),
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f"checkpoints/{path}/model.onnx",
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dynamic_axes={
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"c": [0, 1],
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"f0": [1],
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"mel2ph": [1],
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"uv": [1],
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"noise": [2],
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},
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do_constant_folding=False,
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opset_version=16,
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verbose=False,
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input_names=input_names,
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output_names=output_names)
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if __name__ == '__main__':
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main(False, True)
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import torch
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from torch import nn
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from torch.nn import functional as F
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import cluster
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import modules.attentions as attentions
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import modules.commons as commons
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import modules.modules as modules
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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import utils
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from modules.commons import init_weights, get_padding
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from vdecoder.hifigan.models import Generator
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from utils import f0_to_coarse
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class ResidualCouplingBlock(nn.Module):
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def __init__(self,
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channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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n_flows=4,
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gin_channels=0):
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super().__init__()
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self.channels = channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.n_flows = n_flows
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self.gin_channels = gin_channels
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self.flows = nn.ModuleList()
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for i in range(n_flows):
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self.flows.append(
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modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
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gin_channels=gin_channels, mean_only=True))
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self.flows.append(modules.Flip())
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def forward(self, x, x_mask, g=None, reverse=False):
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if not reverse:
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for flow in self.flows:
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x, _ = flow(x, x_mask, g=g, reverse=reverse)
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else:
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for flow in reversed(self.flows):
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x = flow(x, x_mask, g=g, reverse=reverse)
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return x
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class Encoder(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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hidden_channels,
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kernel_size,
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dilation_rate,
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n_layers,
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gin_channels=0):
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.dilation_rate = dilation_rate
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
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self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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def forward(self, x, x_lengths, g=None):
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# print(x.shape,x_lengths.shape)
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x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
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x = self.pre(x) * x_mask
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x = self.enc(x, x_mask, g=g)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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class TextEncoder(nn.Module):
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def __init__(self,
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out_channels,
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hidden_channels,
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kernel_size,
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n_layers,
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gin_channels=0,
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filter_channels=None,
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n_heads=None,
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p_dropout=None):
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super().__init__()
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.kernel_size = kernel_size
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self.n_layers = n_layers
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self.gin_channels = gin_channels
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self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
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self.f0_emb = nn.Embedding(256, hidden_channels)
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self.enc_ = attentions.Encoder(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout)
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def forward(self, x, x_mask, f0=None, z=None):
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x = x + self.f0_emb(f0).transpose(1, 2)
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x = self.enc_(x * x_mask, x_mask)
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stats = self.proj(x) * x_mask
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m, logs = torch.split(stats, self.out_channels, dim=1)
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z = (m + z * torch.exp(logs)) * x_mask
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return z, m, logs, x_mask
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class DiscriminatorP(torch.nn.Module):
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def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
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super(DiscriminatorP, self).__init__()
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self.period = period
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self.use_spectral_norm = use_spectral_norm
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.convs = nn.ModuleList([
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norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
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])
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self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
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def forward(self, x):
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fmap = []
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class DiscriminatorS(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(DiscriminatorS, self).__init__()
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.convs = nn.ModuleList([
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norm_f(Conv1d(1, 16, 15, 1, padding=7)),
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norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
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norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
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norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
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])
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self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
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def forward(self, x):
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fmap = []
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, modules.LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class F0Decoder(nn.Module):
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def __init__(self,
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out_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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spk_channels=0):
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super().__init__()
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self.out_channels = out_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.spk_channels = spk_channels
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self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
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self.decoder = attentions.FFT(
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout)
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self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
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self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
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self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
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def forward(self, x, norm_f0, x_mask, spk_emb=None):
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x = torch.detach(x)
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if spk_emb is not None:
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x = x + self.cond(spk_emb)
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x += self.f0_prenet(norm_f0)
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x = self.prenet(x) * x_mask
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x = self.decoder(x * x_mask, x_mask)
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x = self.proj(x) * x_mask
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return x
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class SynthesizerTrn(nn.Module):
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"""
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Synthesizer for Training
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"""
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def __init__(self,
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spec_channels,
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segment_size,
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inter_channels,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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resblock,
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resblock_kernel_sizes,
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resblock_dilation_sizes,
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upsample_rates,
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upsample_initial_channel,
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upsample_kernel_sizes,
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gin_channels,
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ssl_dim,
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n_speakers,
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sampling_rate=44100,
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**kwargs):
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super().__init__()
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self.spec_channels = spec_channels
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self.inter_channels = inter_channels
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.resblock = resblock
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self.resblock_kernel_sizes = resblock_kernel_sizes
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self.resblock_dilation_sizes = resblock_dilation_sizes
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self.upsample_rates = upsample_rates
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self.upsample_initial_channel = upsample_initial_channel
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self.upsample_kernel_sizes = upsample_kernel_sizes
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self.segment_size = segment_size
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self.gin_channels = gin_channels
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self.ssl_dim = ssl_dim
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self.emb_g = nn.Embedding(n_speakers, gin_channels)
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self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
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self.enc_p = TextEncoder(
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inter_channels,
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hidden_channels,
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filter_channels=filter_channels,
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n_heads=n_heads,
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n_layers=n_layers,
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kernel_size=kernel_size,
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p_dropout=p_dropout
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)
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hps = {
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"sampling_rate": sampling_rate,
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"inter_channels": inter_channels,
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"resblock": resblock,
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"resblock_kernel_sizes": resblock_kernel_sizes,
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"resblock_dilation_sizes": resblock_dilation_sizes,
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"upsample_rates": upsample_rates,
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"upsample_initial_channel": upsample_initial_channel,
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"upsample_kernel_sizes": upsample_kernel_sizes,
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"gin_channels": gin_channels,
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}
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self.dec = Generator(h=hps)
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self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
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self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
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self.f0_decoder = F0Decoder(
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1,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size,
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p_dropout,
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spk_channels=gin_channels
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)
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self.emb_uv = nn.Embedding(2, hidden_channels)
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self.predict_f0 = False
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cluster_model_path="kmeans_10000.pt"
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if os.path.exists(cluster_model_path):
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self.cluster_model = cluster.get_cluster_model(cluster_model_path)
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else:
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self.cluster_model = None
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self.speaker_map = []
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self.export_mix = False
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def export_chara_mix(self, n_speakers_mix):
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spkmap = []
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for i in range(n_speakers_mix):
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spkmap.append(self.emb_g(torch.LongTensor([[i]])).transpose(1, 2).detach().numpy())
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self.speaker_map = torch.tensor(spkmap)
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self.export_mix = True
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def forward(self, c, f0, mel2ph, uv, noise=None, g=None, cluster_infer_ratio=0.1):
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decoder_inp = F.pad(c, [0, 0, 1, 0])
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mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]])
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c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H]
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if self.cluster_model is not None:
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predict = self.cluster_model[speaker].predict(c.transpose(0, 1))
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model[speaker].cluster_centers_[predict]
|
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cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
|
||||
cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
|
||||
c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
|
||||
|
||||
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
||||
|
||||
if self.export_mix:
|
||||
spk_mix = spk_mix.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
|
||||
g = torch.sum(spk_mix * self.speaker_map, dim=0).transpose(1, 2)
|
||||
else:
|
||||
g = g.unsqueeze(0)
|
||||
g = self.emb_g(g).transpose(1, 2)
|
||||
|
||||
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
|
||||
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
|
||||
|
||||
if self.predict_f0:
|
||||
lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
|
||||
norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
|
||||
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
|
||||
f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
|
||||
|
||||
z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise)
|
||||
z = self.flow(z_p, c_mask, g=g, reverse=True)
|
||||
o = self.dec(z * c_mask, g=g, f0=f0)
|
||||
return o
|
||||
Loading…
Reference in New Issue