From 080b9dd525d732abbd5a68bd75468ddfce6e164e Mon Sep 17 00:00:00 2001
From: Francis-Hu <87693204+Francis-Komizu@users.noreply.github.com>
Date: Thu, 1 Sep 2022 10:00:04 +0800
Subject: [PATCH] support multi-speaker inference
---
inference.ipynb | 1421 ++++++++++++++++++++++++-----------------------
1 file changed, 711 insertions(+), 710 deletions(-)
diff --git a/inference.ipynb b/inference.ipynb
index b6673a3..dd58a86 100644
--- a/inference.ipynb
+++ b/inference.ipynb
@@ -1,742 +1,743 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YutY46pes47D"
- },
- "source": [
- "# **Sovits (inference)**\n",
- "\n",
- "Author：Francis Hu\n",
- "\n",
- "E-mail：francishr@whu.edu.cn\n",
- "\n",
- "QQ：2235306122\n",
- "\n",
- "BILIBILI：https://space.bilibili.com/636704927\n",
- "\n",
- "Repository：https://github.com/Francis-Komizu/VITS-Yosuga "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "CMIJduVhX2Ge"
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YutY46pes47D"
+ },
+ "source": [
+ "# **Sovits (inference)**\n",
+ "\n",
+ "Author：Francis Hu\n",
+ "\n",
+ "E-mail：francishr@whu.edu.cn\n",
+ "\n",
+ "QQ：2235306122\n",
+ "\n",
+ "BILIBILI：https://space.bilibili.com/636704927\n",
+ "\n",
+ "Repository：https://github.com/Francis-Komizu/VITS-Yosuga "
+ ]
},
- "source": [
- "# Set up"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "BfuRLaqys47I"
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CMIJduVhX2Ge"
+ },
+ "source": [
+ "# Set up"
+ ]
},
- "outputs": [],
- "source": [
- "!git clone https://github.com/Francis-Komizu/Sovits\n",
- "%cd Sovits\n",
- "!pip install -r requirements.txt\n",
- "%cd monotonic_align",
- "!python setup.py build_ext --inplace",
- "%cd ..",
- "!mkdir results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "RMUmOmIiX873"
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "BfuRLaqys47I"
+ },
+ "outputs": [],
+ "source": [
+ "!git clone https://github.com/Francis-Komizu/Sovits\n",
+ "%cd Sovits\n",
+ "!pip install -r requirements.txt\n",
+ "%cd monotonic_align",
+ "!python setup.py build_ext --inplace",
+ "%cd ..",
+ "!mkdir results"
+ ]
},
- "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",
- "import torchaudio\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 UnitAudioLoader, UnitAudioCollate\n",
- "from models import SynthesizerTrn\n",
- "import requests\n",
- "\n",
- "from scipy.io.wavfile import write"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "monWsXd-YUUb"
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "RMUmOmIiX873"
+ },
+ "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",
+ "import torchaudio\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 UnitAudioLoader, UnitAudioCollate\n",
+ "from models import SynthesizerTrn\n",
+ "import requests\n",
+ "\n",
+ "from scipy.io.wavfile import write"
+ ]
},
- "source": [
- "# Load models"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EiHW39jFYhou"
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "monWsXd-YUUb"
+ },
+ "source": [
+ "# Load models"
+ ]
},
- "source": [
- "## Load content encoder"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 138,
- "referenced_widgets": [
- "d76dad66cc0a41288f1341b2dabe67db",
- "bc6dd2acce994ade8eb00eec140b06f1",
- "968fb5bd8ade4d00900ae7518d34134a",
- "af016572edeb46798d63a35c5075ceaf",
- "8919c90dedaa426d88145e23ac41dfd3",
- "37804d7e11f3447b9e554ad343d4d529",
- "f7b4f789dffc438f9f4261c514bd208c",
- "e234895efc6e481a818bb2f3f6d01c5d",
- "ddcce2e399ad4399923caef9d78d14ee",
- "68250d5ef1404960ba51d2e1d02578d2",
- "792ef83da5294d2d92e99936e0976a87"
- ]
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EiHW39jFYhou"
},
- "id": "IDX8lnvdY10R",
- "outputId": "241e7fbf-e064-4fcd-bbf1-a080da8fd1f4"
+ "source": [
+ "## Load content encoder"
+ ]
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/usr/local/lib/python3.7/dist-packages/torch/hub.py:267: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour\n",
- " \"You are about to download and run code from an untrusted repository. In a future release, this won't \"\n",
- "Downloading: \"https://github.com/bshall/hubert/zipball/main\" to /root/.cache/torch/hub/main.zip\n",
- "Downloading: \"https://github.com/bshall/hubert/releases/download/v0.1/hubert-soft-0d54a1f4.pt\" to /root/.cache/torch/hub/checkpoints/hubert-soft-0d54a1f4.pt\n"
- ]
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 138,
+ "referenced_widgets": [
+ "d76dad66cc0a41288f1341b2dabe67db",
+ "bc6dd2acce994ade8eb00eec140b06f1",
+ "968fb5bd8ade4d00900ae7518d34134a",
+ "af016572edeb46798d63a35c5075ceaf",
+ "8919c90dedaa426d88145e23ac41dfd3",
+ "37804d7e11f3447b9e554ad343d4d529",
+ "f7b4f789dffc438f9f4261c514bd208c",
+ "e234895efc6e481a818bb2f3f6d01c5d",
+ "ddcce2e399ad4399923caef9d78d14ee",
+ "68250d5ef1404960ba51d2e1d02578d2",
+ "792ef83da5294d2d92e99936e0976a87"
+ ]
+ },
+ "id": "IDX8lnvdY10R",
+ "outputId": "241e7fbf-e064-4fcd-bbf1-a080da8fd1f4"
},
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "d76dad66cc0a41288f1341b2dabe67db",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- " 0%| | 0.00/361M [00:00\n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
- ]
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 75,
+ "resources": {
+ "http://localhost:8080/nbextensions/google.colab/files.js": {
+ "data": 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+ "status_text": ""
+ }
+ }
},
- "metadata": {},
- "output_type": "display_data"
+ "id": "QdP37htOs47R",
+ "outputId": "a339b1f5-3455-4201-f0e1-34da68c38124"
},
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saving natsume_0030.wav to natsume_0030.wav\n"
- ]
- }
- ],
- "source": [
- "from google.colab import files\n",
- "\n",
- "uploaded = files.upload()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "RhuMgaD2s47S"
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Saving natsume_0030.wav to natsume_0030.wav\n"
+ ]
+ }
+ ],
+ "source": [
+ "from google.colab import files\n",
+ "\n",
+ "uploaded = files.upload()"
+ ]
},
- "outputs": [],
- "source": [
- "source, sr = torchaudio.load(\"path/to/wav\")\n",
- "source = source.unsqueeze(0)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hNe7wm-us47T"
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "RhuMgaD2s47S"
+ },
+ "outputs": [],
+ "source": [
+ "source, sr = torchaudio.load(\"path/to/wav\")\n",
+ "source = source.unsqueeze(0)"
+ ]
},
- "source": [
- "## Convert voice"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 202
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hNe7wm-us47T"
},
- "id": "mKXeJeEVs47T",
- "outputId": "2f32323f-3e0f-4c09-d273-3e527c044aaa"
+ "source": [
+ "## Convert voice"
+ ]
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "torch.Size([1, 269, 256])\n",
- "tensor([269])\n",
- "Source:\n"
- ]
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 202
+ },
+ "id": "mKXeJeEVs47T",
+ "outputId": "2f32323f-3e0f-4c09-d273-3e527c044aaa"
},
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([1, 269, 256])\n",
+ "tensor([269])\n",
+ "Source:\n"
]
},
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Converted:\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
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+ " "
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Converted:\n"
]
},
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "with torch.inference_mode():\n",
- " # Extract speech units\n",
- " unit = hubert.units(source)\n",
- " unit_lengths = torch.LongTensor([unit.size(1)])\n",
- " # for multi-speaker inference\n",
- " # sid = torch.LongTensor([4])\n",
- "\n",
- " # Synthesize audio\n",
- " audio = net_g.infer(unit, unit_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1.0)[0][0,0].data.float().numpy()\n",
- " # for multi-speaker inference\m",
- " # audio = net_g.infer(unit, unit_lengths, sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1.0)[0][0,0].data.float().numpy()\n",
- "\n",
- "\n",
- "print(\"Source:\")\n",
- "ipd.display(ipd.Audio(source.squeeze(), rate=hps.data.sampling_rate))\n",
- "print(\"Converted:\")\n",
- "ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# References\n",
- "\n",
- "https://github.com/bshall/acoustic-model\n",
- "\n",
- "https://github.com/jaywalnut310/vits"
- ]
- }
- ],
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}
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+ "source": [
+ "with torch.inference_mode():\n",
+ " # Extract speech units\n",
+ " unit = hubert.units(source)\n",
+ " unit_lengths = torch.LongTensor([unit.size(1)])\n",
+ " # for multi-speaker inference\n",
+ " # sid = torch.LongTensor([4])\n",
+ "\n",
+ " # Synthesize audio\n",
+ " audio = net_g.infer(unit, unit_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1.0)[0][0,0].data.float().numpy()\n",
+ " # for multi-speaker inference\n",
+ " # audio = net_g.infer(unit, unit_lengths, sid, noise_scale=.667, noise_scale_w=0.8, length_scale=1.0)[0][0,0].data.float().numpy()\n",
+ "\n",
+ "\n",
+ "print(\"Source:\")\n",
+ "ipd.display(ipd.Audio(source.squeeze(), rate=hps.data.sampling_rate))\n",
+ "print(\"Converted:\")\n",
+ "ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# References\n",
+ "\n",
+ "https://github.com/bshall/acoustic-model\n",
+ "\n",
+ "https://github.com/jaywalnut310/vits"
+ ]
+ }
+ ],
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