Spaces:
Sleeping
Sleeping
File size: 6,103 Bytes
7bcf8d7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import IPython.display as ipd\n",
"\n",
"import os\n",
"import json\n",
"import math\n",
"import torch\n",
"from torch import nn\n",
"from torch.nn import functional as F\n",
"from torch.utils.data import DataLoader\n",
"\n",
"import commons\n",
"import utils\n",
"from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate\n",
"from models import SynthesizerTrn\n",
"from text.symbols import symbols\n",
"from text import text_to_sequence\n",
"\n",
"from scipy.io.wavfile import write\n",
"\n",
"\n",
"def get_text(text, hps):\n",
" text_norm = text_to_sequence(text, hps.data.text_cleaners)\n",
" if hps.data.add_blank:\n",
" text_norm = commons.intersperse(text_norm, 0)\n",
" text_norm = torch.LongTensor(text_norm)\n",
" return text_norm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LJ Speech"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hps = utils.get_hparams_from_file(\"./configs/ljs_base.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"net_g = SynthesizerTrn(\n",
" len(symbols),\n",
" hps.data.filter_length // 2 + 1,\n",
" hps.train.segment_size // hps.data.hop_length,\n",
" **hps.model).cuda()\n",
"_ = net_g.eval()\n",
"\n",
"_ = utils.load_checkpoint(\"/path/to/pretrained_ljs.pth\", net_g, None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"stn_tst = get_text(\"VITS is Awesome!\", hps)\n",
"with torch.no_grad():\n",
" x_tst = stn_tst.cuda().unsqueeze(0)\n",
" x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
" audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()\n",
"ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## VCTK"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"hps = utils.get_hparams_from_file(\"./configs/vctk_base.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"net_g = SynthesizerTrn(\n",
" len(symbols),\n",
" hps.data.filter_length // 2 + 1,\n",
" hps.train.segment_size // hps.data.hop_length,\n",
" n_speakers=hps.data.n_speakers,\n",
" **hps.model).cuda()\n",
"_ = net_g.eval()\n",
"\n",
"_ = utils.load_checkpoint(\"/path/to/pretrained_vctk.pth\", net_g, None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"stn_tst = get_text(\"VITS is Awesome!\", hps)\n",
"with torch.no_grad():\n",
" x_tst = stn_tst.cuda().unsqueeze(0)\n",
" x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
" sid = torch.LongTensor([4]).cuda()\n",
" audio = net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()\n",
"ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Voice Conversion"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)\n",
"collate_fn = TextAudioSpeakerCollate()\n",
"loader = DataLoader(dataset, num_workers=8, shuffle=False,\n",
" batch_size=1, pin_memory=True,\n",
" drop_last=True, collate_fn=collate_fn)\n",
"data_list = list(loader)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with torch.no_grad():\n",
" x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda() for x in data_list[0]]\n",
" sid_tgt1 = torch.LongTensor([1]).cuda()\n",
" sid_tgt2 = torch.LongTensor([2]).cuda()\n",
" sid_tgt3 = torch.LongTensor([4]).cuda()\n",
" audio1 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0,0].data.cpu().float().numpy()\n",
" audio2 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt2)[0][0,0].data.cpu().float().numpy()\n",
" audio3 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt3)[0][0,0].data.cpu().float().numpy()\n",
"print(\"Original SID: %d\" % sid_src.item())\n",
"ipd.display(ipd.Audio(y[0].cpu().numpy(), rate=hps.data.sampling_rate, normalize=False))\n",
"print(\"Converted SID: %d\" % sid_tgt1.item())\n",
"ipd.display(ipd.Audio(audio1, rate=hps.data.sampling_rate, normalize=False))\n",
"print(\"Converted SID: %d\" % sid_tgt2.item())\n",
"ipd.display(ipd.Audio(audio2, rate=hps.data.sampling_rate, normalize=False))\n",
"print(\"Converted SID: %d\" % sid_tgt3.item())\n",
"ipd.display(ipd.Audio(audio3, rate=hps.data.sampling_rate, normalize=False))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|