inference_v2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YutY46pes47D"
      },
      "source": [
        "# **Sovits (inference with f0)**\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/Sovits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMIJduVhX2Ge"
      },
      "source": [
        "# Set up"
      ]
    },
    {
      "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\n",
        "!python setup.py build_ext --inplace\n",
        "%cd ..\n",
        "!mkdir results"
      ]
    },
    {
      "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",
        "import numpy as np\n",
        "import librosa\n",
        "\n",
        "from JDC.model import JDCNet\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": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "to_mel = torchaudio.transforms.MelSpectrogram(\n",
        "    n_mels=80, n_fft=2048, win_length=1200, hop_length=300)\n",
        "mean, std = -4, 4\n",
        "\n",
        "def preprocess(wave):\n",
        "    wave_tensor = torch.from_numpy(wave).float()\n",
        "    mel_tensor = to_mel(wave_tensor)\n",
        "    mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std\n",
        "    return mel_tensor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "monWsXd-YUUb"
      },
      "source": [
        "# Load models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EiHW39jFYhou"
      },
      "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"
          ]
        },
        "id": "IDX8lnvdY10R",
        "outputId": "241e7fbf-e064-4fcd-bbf1-a080da8fd1f4"
      },
      "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"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d76dad66cc0a41288f1341b2dabe67db",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/361M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "hubert = torch.hub.load(\"bshall/hubert:main\", \"hubert_soft\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load F0 model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "F0_model = JDCNet(num_class=1, seq_len=192)\n",
        "params = torch.load(\"JDC/bst.t7\", map_location='cpu')['net']\n",
        "F0_model.load_state_dict(params)\n",
        "_ = F0_model.eval()\n",
        "F0_model = F0_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5C5qnjljYoPX"
      },
      "source": [
        "## Load generator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YNtmkYruYVqL"
      },
      "outputs": [],
      "source": [
        "!gdown --id 'your model link' --output generator_accelerator.pth # you may change it\n",
        "\n",
        "hps = utils.get_hparams_from_file(\"path/to/config\")\n",
        "\n",
        "net_g = SynthesizerTrn(\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)\n",
        "_ = net_g.eval()\n",
        "\n",
        "_ = utils.load_checkpoint(\"path/to/generator\", net_g, None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YjdRjrlrbI9c"
      },
      "source": [
        "## Load audio"
      ]
    },
    {
      "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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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "QdP37htOs47R",
        "outputId": "a339b1f5-3455-4201-f0e1-34da68c38124"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6c804192-2371-45b0-b892-9ef5409ea7a7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-6c804192-2371-45b0-b892-9ef5409ea7a7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RhuMgaD2s47S"
      },
      "outputs": [],
      "source": [
        "source, sr = torchaudio.load(\"path/to/wav\")\n",
        "source = source.unsqueeze(0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNe7wm-us47T"
      },
      "source": [
        "## Convert voice\n",
        "\n",
        "Set `f0_feat` as `None` for models trained without f0 feature!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "mKXeJeEVs47T",
        "outputId": "2f32323f-3e0f-4c09-d273-3e527c044aaa"
      },
      "outputs": [],
      "source": [
        "with torch.inference_mode():\n",
        "    # Extract speech units\n",
        "    unit = hubert.units(source)\n",
        "    unit = torch.FloatTensor(unit)\n",
        "    unit_lengths = torch.LongTensor([unit.size(1)])\n",
        "    # Extract f0 feature\n",
        "    f0_feat = F0_model.get_feature_GAN(source_mel.unsqueeze(0))\n",
        "    # f0_feat = None\n",
        "    # Synthesize audio\n",
        "    audio = net_g.infer(unit, unit_lengths, F0=f0_feat, noise_scale=.667, noise_scale_w=0.8, length_scale=0.8)[0][0,0].data.float().numpy()\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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