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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "ParallelWaveGAN to paddle.ipynb",
"provenance": [],
"collapsed_sections": [],
"private_outputs": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gZNDsJweNp1L"
},
"outputs": [],
"source": [
"!pip install parallel_wavegan paddlepaddle-gpu==2.2.2 \"paddlespeech<1\" pytest-runner"
]
},
{
"cell_type": "code",
"source": [
"!gdown https://drive.google.com/uc?id=1q8oSAzwkqi99oOGXDZyLypCiz0Qzn3Ab\n",
"!unzip -qq Vocoder.zip"
],
"metadata": {
"id": "HqA0VNKEOGfv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# load torch vocoder\n",
"import torch\n",
"from parallel_wavegan.utils import load_model\n",
"\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"\n",
"vocoder_torch = load_model(\"Vocoder/checkpoint-400000steps.pkl\").to(device).eval()\n",
"vocoder_torch.remove_weight_norm()\n",
"_ = vocoder_torch.eval()"
],
"metadata": {
"id": "9F0yA_dyPOVe"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import yaml\n",
"import paddle\n",
"\n",
"from yacs.config import CfgNode\n",
"from paddlespeech.s2t.utils.dynamic_import import dynamic_import\n",
"from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator\n",
"\n",
"with open('Vocoder/config.yml') as f:\n",
" voc_config = CfgNode(yaml.safe_load(f))\n",
"voc_config[\"generator_params\"].pop(\"upsample_net\")\n",
"voc_config[\"generator_params\"][\"upsample_scales\"] = voc_config[\"generator_params\"].pop(\"upsample_params\")[\"upsample_scales\"]\n",
"vocoder_paddle = PWGGenerator(**voc_config[\"generator_params\"])\n",
"vocoder_paddle.remove_weight_norm()\n",
"vocoder_paddle.eval()\n",
"\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",
"convert_weights(vocoder_torch, vocoder_paddle)"
],
"metadata": {
"id": "ch2uVW8OdKN0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"import librosa\n",
"import torchaudio\n",
"import paddleaudio\n",
"import numpy as np\n",
"import IPython.display as ipd\n",
"\n",
"\n",
"to_mel = torchaudio.transforms.MelSpectrogram(\n",
" n_mels=80, n_fft=2048, win_length=1200, hop_length=300)\n",
"fb = to_mel.mel_scale.fb.detach().cpu().numpy().transpose([1,0])\n",
"to_mel = paddleaudio.features.MelSpectrogram(\n",
" n_mels=80, n_fft=2048, win_length=1200, hop_length=300)\n",
"to_mel.fbank_matrix[:] = fb\n",
"mean, std = -4, 4\n",
"\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"\n",
"def preprocess(wave):\n",
" wave_tensor = paddle.to_tensor(wave).astype(paddle.float32)\n",
" mel_tensor = 2*to_mel(wave_tensor)\n",
" mel_tensor = (paddle.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std\n",
" return mel_tensor\n",
"\n",
"if not os.path.exists('p228_023.wav'):\n",
" !wget https://github.com/yl4579/StarGANv2-VC/raw/main/Demo/VCTK-corpus/p228/p228_023.wav\n",
"audio, source_sr = librosa.load('p228_023.wav', sr=24000)\n",
"audio = audio / np.max(np.abs(audio))\n",
"audio.dtype = np.float32\n",
"mel = preprocess(audio)\n",
"\n",
"import numpy as np\n",
"with torch.no_grad():\n",
" with paddle.no_grad():\n",
" c = mel.transpose([0, 2, 1]).squeeze()\n",
" recon_paddle = vocoder_paddle.inference(c)\n",
" recon_paddle = recon_paddle.reshape([-1]).numpy()\n",
" recon_torch = vocoder_torch.inference(torch.from_numpy(c.numpy()).to(device))\n",
" recon_torch = recon_torch.view(-1).cpu().numpy()\n",
" print(np.mean((recon_paddle - recon_torch)**2))\n",
"\n",
"print('Paddle recon:')\n",
"display(ipd.Audio(recon_paddle, rate=24000))\n",
"print('Torch recon:')\n",
"display(ipd.Audio(recon_torch, rate=24000))"
],
"metadata": {
"id": "Q9dK5j1CleJM"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"paddle.save(vocoder_paddle.state_dict(), 'checkpoint-400000steps.pd')\n",
"paddle.save({ 'fbank_matrix': to_mel.fbank_matrix }, 'fbank_matrix.pd')"
],
"metadata": {
"id": "HwaLd_Eq3JrH"
},
"execution_count": null,
"outputs": []
}
]
} |