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https://github.com/storytold/vits-finetuning.git
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Initial commit (move files)
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ZDisket
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"import IPython.display as ipd\n",
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"\n",
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"import os\n",
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"import json\n",
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"import math\n",
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"import torch\n",
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"from torch import nn\n",
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"from torch.nn import functional as F\n",
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"from torch.utils.data import DataLoader\n",
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"\n",
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"import commons\n",
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"import utils\n",
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"from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate\n",
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"from models import SynthesizerTrn\n",
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"from text.symbols import symbols\n",
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"from text import text_to_sequence\n",
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"\n",
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"from scipy.io.wavfile import write\n",
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"\n",
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"\n",
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"def get_text(text, hps):\n",
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" text_norm = text_to_sequence(text, hps.data.text_cleaners)\n",
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" if hps.data.add_blank:\n",
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" text_norm = commons.intersperse(text_norm, 0)\n",
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" text_norm = torch.LongTensor(text_norm)\n",
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" return text_norm"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## LJ Speech"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"hps = utils.get_hparams_from_file(\"./configs/ljs_base.json\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"net_g = SynthesizerTrn(\n",
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" len(symbols),\n",
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" hps.data.filter_length // 2 + 1,\n",
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" hps.train.segment_size // hps.data.hop_length,\n",
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" **hps.model).cuda()\n",
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"_ = net_g.eval()\n",
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"\n",
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"_ = utils.load_checkpoint(\"/path/to/pretrained_ljs.pth\", net_g, None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stn_tst = get_text(\"VITS is Awesome!\", hps)\n",
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"with torch.no_grad():\n",
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" x_tst = stn_tst.cuda().unsqueeze(0)\n",
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" x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
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" 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",
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"ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## VCTK"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"hps = utils.get_hparams_from_file(\"./configs/vctk_base.json\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"net_g = SynthesizerTrn(\n",
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" len(symbols),\n",
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" hps.data.filter_length // 2 + 1,\n",
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" hps.train.segment_size // hps.data.hop_length,\n",
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" n_speakers=hps.data.n_speakers,\n",
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" **hps.model).cuda()\n",
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"_ = net_g.eval()\n",
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"\n",
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"_ = utils.load_checkpoint(\"/path/to/pretrained_vctk.pth\", net_g, None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stn_tst = get_text(\"VITS is Awesome!\", hps)\n",
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"with torch.no_grad():\n",
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" x_tst = stn_tst.cuda().unsqueeze(0)\n",
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" x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()\n",
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" sid = torch.LongTensor([4]).cuda()\n",
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" 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",
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"ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate, normalize=False))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Voice Conversion"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)\n",
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"collate_fn = TextAudioSpeakerCollate()\n",
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"loader = DataLoader(dataset, num_workers=8, shuffle=False,\n",
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" batch_size=1, pin_memory=True,\n",
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" drop_last=True, collate_fn=collate_fn)\n",
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"data_list = list(loader)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with torch.no_grad():\n",
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" x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src = [x.cuda() for x in data_list[0]]\n",
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" sid_tgt1 = torch.LongTensor([1]).cuda()\n",
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" sid_tgt2 = torch.LongTensor([2]).cuda()\n",
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" sid_tgt3 = torch.LongTensor([4]).cuda()\n",
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" audio1 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt1)[0][0,0].data.cpu().float().numpy()\n",
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" audio2 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt2)[0][0,0].data.cpu().float().numpy()\n",
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" audio3 = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt3)[0][0,0].data.cpu().float().numpy()\n",
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"print(\"Original SID: %d\" % sid_src.item())\n",
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"ipd.display(ipd.Audio(y[0].cpu().numpy(), rate=hps.data.sampling_rate, normalize=False))\n",
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"print(\"Converted SID: %d\" % sid_tgt1.item())\n",
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"ipd.display(ipd.Audio(audio1, rate=hps.data.sampling_rate, normalize=False))\n",
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"print(\"Converted SID: %d\" % sid_tgt2.item())\n",
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"ipd.display(ipd.Audio(audio2, rate=hps.data.sampling_rate, normalize=False))\n",
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"print(\"Converted SID: %d\" % sid_tgt3.item())\n",
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"ipd.display(ipd.Audio(audio3, rate=hps.data.sampling_rate, normalize=False))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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