diff --git "a/vits/inference.ipynb" "b/vits/inference.ipynb"
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/user/lojban/jboselvoha/jboselvoha_env/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "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": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hps = utils.get_hparams_from_file(\"./configs/ljs_base.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "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(\"pretrained_ljs.pth\", net_g, None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "stn_tst = get_text(\"I have no idea.\", 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": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hps = utils.get_hparams_from_file(\"./configs/vctk_base.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "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(\"pretrained_vctk.pth\", net_g, None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "stn_tst = get_text(\"This is the course on the lawzhban logical language.\", 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": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: 'DUMMY2/p234/p234_071.wav'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn [15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dataset \u001b[39m=\u001b[39m TextAudioSpeakerLoader(hps\u001b[39m.\u001b[39;49mdata\u001b[39m.\u001b[39;49mvalidation_files, hps\u001b[39m.\u001b[39;49mdata)\n\u001b[1;32m 2\u001b[0m collate_fn \u001b[39m=\u001b[39m TextAudioSpeakerCollate()\n\u001b[1;32m 3\u001b[0m loader \u001b[39m=\u001b[39m DataLoader(dataset, num_workers\u001b[39m=\u001b[39m\u001b[39m8\u001b[39m, shuffle\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m 4\u001b[0m batch_size\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, pin_memory\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m 5\u001b[0m drop_last\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, collate_fn\u001b[39m=\u001b[39mcollate_fn)\n",
+ "File \u001b[0;32m~/lojban/jboselvoha/vits/data_utils.py:176\u001b[0m, in \u001b[0;36mTextAudioSpeakerLoader.__init__\u001b[0;34m(self, audiopaths_sid_text, hparams)\u001b[0m\n\u001b[1;32m 174\u001b[0m random\u001b[39m.\u001b[39mseed(\u001b[39m1234\u001b[39m)\n\u001b[1;32m 175\u001b[0m random\u001b[39m.\u001b[39mshuffle(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39maudiopaths_sid_text)\n\u001b[0;32m--> 176\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_filter()\n",
+ "File \u001b[0;32m~/lojban/jboselvoha/vits/data_utils.py:191\u001b[0m, in \u001b[0;36mTextAudioSpeakerLoader._filter\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmin_text_len \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(text) \u001b[39mand\u001b[39;00m \u001b[39mlen\u001b[39m(text) \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmax_text_len:\n\u001b[1;32m 190\u001b[0m audiopaths_sid_text_new\u001b[39m.\u001b[39mappend([audiopath, sid, text])\n\u001b[0;32m--> 191\u001b[0m lengths\u001b[39m.\u001b[39mappend(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mgetsize(audiopath) \u001b[39m/\u001b[39m\u001b[39m/\u001b[39m (\u001b[39m2\u001b[39m \u001b[39m*\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhop_length))\n\u001b[1;32m 192\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maudiopaths_sid_text \u001b[39m=\u001b[39m audiopaths_sid_text_new\n\u001b[1;32m 193\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlengths \u001b[39m=\u001b[39m lengths\n",
+ "File \u001b[0;32m~/lojban/jboselvoha/jboselvoha_env/lib/python3.8/genericpath.py:50\u001b[0m, in \u001b[0;36mgetsize\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mgetsize\u001b[39m(filename):\n\u001b[1;32m 49\u001b[0m \u001b[39m\"\"\"Return the size of a file, reported by os.stat().\"\"\"\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m \u001b[39mreturn\u001b[39;00m os\u001b[39m.\u001b[39;49mstat(filename)\u001b[39m.\u001b[39mst_size\n",
+ "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'DUMMY2/p234/p234_071.wav'"
+ ]
+ }
+ ],
+ "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": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'data_list' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn [16], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m----> 2\u001b[0m x, x_lengths, spec, spec_lengths, y, y_lengths, sid_src \u001b[39m=\u001b[39m [x\u001b[39m.\u001b[39mcuda() \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m data_list[\u001b[39m0\u001b[39m]]\n\u001b[1;32m 3\u001b[0m sid_tgt1 \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mLongTensor([\u001b[39m1\u001b[39m])\u001b[39m.\u001b[39mcuda()\n\u001b[1;32m 4\u001b[0m sid_tgt2 \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mLongTensor([\u001b[39m2\u001b[39m])\u001b[39m.\u001b[39mcuda()\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'data_list' is not defined"
+ ]
+ }
+ ],
+ "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 3.10.8 (conda)",
+ "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": "3.8.15"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "e83f87adbbd22850962ce64ca64909ac520dc4a639578a07f0a0cdcfce9beb18"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}