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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "starganv2_vc_weights_converter.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CA5i7YAlagUA"
},
"outputs": [],
"source": [
"!git clone https://github.com/yl4579/StarGANv2-VC\n",
"!pip install SoundFile torchaudio munch\n",
"!git clone https://github.com/HighCWu/starganv2vc-paddle\n",
"!cd starganv2vc-paddle && pip install paddlepaddle-gpu==2.2.2 paddleaudio munch pydub\n",
"!cp -r starganv2vc-paddle/starganv2vc_paddle StarGANv2-VC/"
]
},
{
"cell_type": "code",
"source": [
"!gdown https://drive.google.com/uc?id=1nzTyyl-9A1Hmqya2Q_f2bpZkUoRjbZsY"
],
"metadata": {
"id": "ac4g4L1Bbx1t"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!unzip -qq Models.zip\n",
"!rm -rf Models.zip\n",
"!mv Models StarGANv2-VC/Models"
],
"metadata": {
"id": "EJ3vG_RvcOD8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"%cd StarGANv2-VC"
],
"metadata": {
"id": "rKovh1Egi4mJ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"import yaml\n",
"import numpy as np\n",
"import torch\n",
"import warnings\n",
"warnings.simplefilter('ignore')\n",
"\n",
"from munch import Munch\n",
"\n",
"from models import build_model\n",
"\n",
"from Utils.ASR.models import ASRCNN\n",
"from Utils.JDC.model import JDCNet\n",
"\n",
"torch.backends.cudnn.benchmark = True #\n",
"\n",
"def main(config_path):\n",
" config = yaml.safe_load(open(config_path))\n",
" \n",
" device = config.get('device', 'cpu')\n",
"\n",
" # load pretrained ASR model\n",
" ASR_config = config.get('ASR_config', False)\n",
" ASR_path = config.get('ASR_path', False)\n",
" with open(ASR_config) as f:\n",
" ASR_config = yaml.safe_load(f)\n",
" ASR_model_config = ASR_config['model_params']\n",
" ASR_model = ASRCNN(**ASR_model_config)\n",
" params = torch.load(ASR_path, map_location='cpu')['model']\n",
" ASR_model.load_state_dict(params)\n",
" ASR_model.to(device)\n",
" _ = ASR_model.eval()\n",
" \n",
" # load pretrained F0 model\n",
" F0_path = config.get('F0_path', False)\n",
" F0_model = JDCNet(num_class=1, seq_len=192)\n",
" params = torch.load(F0_path, map_location='cpu')['net']\n",
" F0_model.load_state_dict(params)\n",
" F0_model.to(device)\n",
" \n",
" # build model\n",
" _, model_ema = build_model(Munch(config['model_params']), F0_model, ASR_model)\n",
" pretrained_path = 'Models/epoch_00150.pth'# config.get('pretrained_model', False)\n",
" params = torch.load(pretrained_path, map_location='cpu')['model_ema']\n",
" [model_ema[key].load_state_dict(state_dict) for key, state_dict in params.items()]\n",
" _ = [model_ema[key].to(device) for key in model_ema]\n",
"\n",
" return ASR_model, F0_model, model_ema\n",
"\n",
"ASR_model_torch, F0_model_torch, model_ema_torch = main('./Models/config.yml')\n"
],
"metadata": {
"id": "UpMuk5kni67B"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"import yaml\n",
"import numpy as np\n",
"import paddle\n",
"import warnings\n",
"warnings.simplefilter('ignore')\n",
"\n",
"from munch import Munch\n",
"\n",
"from starganv2vc_paddle.models import build_model\n",
"\n",
"from starganv2vc_paddle.Utils.ASR.models import ASRCNN\n",
"from starganv2vc_paddle.Utils.JDC.model import JDCNet\n",
"\n",
"@paddle.no_grad()\n",
"def convert_weights(torch_model, paddle_model):\n",
" _ = torch_model.eval()\n",
" _ = paddle_model.eval()\n",
" dense_layers = []\n",
" for name, layer in torch_model.named_modules():\n",
" if isinstance(layer, torch.nn.Linear):\n",
" dense_layers.append(name)\n",
" torch_state_dict = torch_model.state_dict()\n",
" for name, param in paddle_model.named_parameters():\n",
" name = name.replace('._mean', '.running_mean')\n",
" name = name.replace('._variance', '.running_var')\n",
" name = name.replace('.scale', '.weight')\n",
" target_param = torch_state_dict[name].detach().cpu().numpy()\n",
" if '.'.join(name.split('.')[:-1]) in dense_layers:\n",
" if len(param.shape) == 2:\n",
" target_param = target_param.transpose((1,0))\n",
" param.set_value(paddle.to_tensor(target_param))\n",
"\n",
"@torch.no_grad()\n",
"@paddle.no_grad()\n",
"def main(config_path):\n",
" config = yaml.safe_load(open(config_path))\n",
" \n",
" ASR_config = config.get('ASR_config', False)\n",
" with open(ASR_config) as f:\n",
" ASR_config = yaml.safe_load(f)\n",
" ASR_model_config = ASR_config['model_params']\n",
" ASR_model = ASRCNN(**ASR_model_config)\n",
" _ = ASR_model.eval()\n",
" convert_weights(ASR_model_torch, ASR_model)\n",
"\n",
" F0_model = JDCNet(num_class=1, seq_len=192)\n",
" _ = F0_model.eval()\n",
" convert_weights(F0_model_torch, F0_model)\n",
" \n",
" # build model\n",
" model, model_ema = build_model(Munch(config['model_params']), F0_model, ASR_model)\n",
"\n",
" asr_input = paddle.randn([2, 80, 192])\n",
" asr_output = ASR_model(asr_input)\n",
" asr_output_torch = ASR_model_torch(torch.from_numpy(asr_input.numpy()).cuda())\n",
" print('ASR model input:', asr_input.shape, 'output:', asr_output.shape)\n",
" print('Error:', (asr_output_torch.cpu().numpy() - asr_output.numpy()).mean())\n",
" mel_input = paddle.randn([2, 1, 192, 512])\n",
" f0_output = F0_model(mel_input)\n",
" f0_output_torch = F0_model_torch(torch.from_numpy(mel_input.numpy()).cuda())\n",
" print('F0 model input:', mel_input.shape, 'output:', [t.shape for t in f0_output])\n",
" # print('Error:', (t_dict2['output'].cpu().numpy() - t_dict1['output'].numpy()).mean())\n",
" print('Error:', [(t1.cpu().numpy() - t2.numpy()).mean() for t1, t2 in zip(f0_output_torch, f0_output)])\n",
"\n",
" _ = [convert_weights(model_ema_torch[k], model_ema[k]) for k in model_ema.keys()]\n",
" label = paddle.to_tensor([0,0], dtype=paddle.int64)\n",
" latent_dim = model_ema.mapping_network.shared[0].weight.shape[0]\n",
" latent_style = paddle.randn([2, latent_dim])\n",
" ref = model_ema.mapping_network(latent_style, label)\n",
" ref_torch = model_ema_torch.mapping_network(torch.from_numpy(latent_style.numpy()).cuda(), torch.from_numpy(label.numpy()).cuda())\n",
" print('Error of mapping network:', (ref_torch.cpu().numpy() - ref.numpy()).mean())\n",
" mel_input2 = paddle.randn([2, 1, 192, 512])\n",
" style_ref = model_ema.style_encoder(mel_input2, label)\n",
" style_ref_torch = model_ema_torch.style_encoder(torch.from_numpy(mel_input2.numpy()).cuda(), torch.from_numpy(label.numpy()).cuda())\n",
" print('StyleGANv2-VC encoder inputs:', mel_input2.shape, 'output:', style_ref.shape, 'should has the same shape as the ref:', ref.shape)\n",
" print('Error of style encoder:', (style_ref_torch.cpu().numpy() - style_ref.numpy()).mean())\n",
" f0_feat = F0_model.get_feature_GAN(mel_input)\n",
" f0_feat_torch = F0_model_torch.get_feature_GAN(torch.from_numpy(mel_input.numpy()).cuda())\n",
" print('Error of f0 feat:', (f0_feat_torch.cpu().numpy() - f0_feat.numpy()).mean())\n",
" out = model_ema.generator(mel_input, style_ref, F0=f0_feat)\n",
" out_torch = model_ema_torch.generator(torch.from_numpy(mel_input.numpy()).cuda(), torch.from_numpy(style_ref.numpy()).cuda(), F0=torch.from_numpy(f0_feat.numpy()).cuda())\n",
" print('StyleGANv2-VC inputs:', label.shape, latent_style.shape, mel_input.shape, 'output:', out.shape)\n",
" print('Error:', (out_torch.cpu().numpy() - out.numpy()).mean())\n",
"\n",
" paddle.save({'model': ASR_model.state_dict()}, 'ASR.pd')\n",
" paddle.save({ 'net': F0_model.state_dict()}, 'F0.pd')\n",
" model_ema_dict = {key: model.state_dict() for key, model in model_ema.items()}\n",
" \n",
" paddle.save({ 'model_ema': model_ema_dict }, 'VC.pd')\n",
"\n",
" return 0\n",
"\n",
"main('./Models/config.yml')\n"
],
"metadata": {
"id": "PnuApVuyIIyd"
},
"execution_count": null,
"outputs": []
}
]
} |