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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"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": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Single Speaker"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"hps = utils.get_hparams_from_file(\"configs/XXX.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"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/model.pth\", net_g, None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"stn_tst = get_text(\"γγγ«γ‘γ―\", 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": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"## Multiple Speakers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"hps = utils.get_hparams_from_file(\"./configs/XXX.json\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"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/model.pth\", net_g, None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"stn_tst = get_text(\"γγγ«γ‘γ―\", 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": {
"pycharm": {
"name": "#%% md\n"
}
},
"source": [
"### Voice Conversion"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"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": {
"pycharm": {
"name": "#%%\n"
}
},
"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 3.7.9 64-bit",
"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": "3.7.9"
},
"vscode": {
"interpreter": {
"hash": "c15292341d300295ca9f634d04c483f667a0c1d5ee0c309c2ac4e312cce8b8df"
}
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"nbformat": 4,
"nbformat_minor": 4
}
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