DiffSinger_multidict_colab_notebook.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "MP5rRkbTpnG8",
        "Wv0gfI5feBSc",
        "eexZl_OCDmQ3",
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        "4sbU1aH5kGFE"
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      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MLo7Ghinsan/DiffSinger_colab_notebook_MLo7/blob/main/DiffSinger_multidict_colab_notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MP5rRkbTpnG8"
      },
      "source": [
        "# _**[DiffSinger](https://github.com/openvpi/DiffSinger)**_\n",
        "_Singing Voice Synthesis via Shallow Diffusion Mechanism (SVS & TTS)_\n",
        "\n",
        "\\\n",
        "____\n",
        "\n",
        "Note:\n",
        "- This notebook is an alternative to the main notebook for multi-dictionary training\n",
        "\n",
        "```We refer \"variance\" as \"parameters\" to avoid the confusion```\n",
        "\n",
        "\\\n",
        "____\n",
        "\n",
        "####**This notebook is maintained by MLo7**\n",
        "___"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Setup**"
      ],
      "metadata": {
        "id": "Wv0gfI5feBSc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pK8aicf8A2sj",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4dbf211f-4af1-428c-c948-8799125dd588"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "setup complete!\n",
            "|\n",
            "|\n",
            "|\n"
          ]
        },
        {
          "output_type": "display_data",
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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "#@title # Mount Google Drive and Setup\n",
        "\n",
        "\n",
        "from IPython.display import clear_output\n",
        "from IPython.display import Audio, display, HTML\n",
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n",
        "\n",
        "if not os.path.exists(\"/content/pretrain_models\"):\n",
        "    os.makedirs(\"/content/pretrain_models\")\n",
        "\n",
        "%cd /content\n",
        "!wget https://github.com/MLo7Ghinsan/DiffSinger_colab_notebook_MLo7/releases/download/OU_files/jpn_dict.txt\n",
        "\n",
        "!rm -rf /content/sample_data\n",
        "!apt-get install aria2\n",
        "clear_output()\n",
        "\n",
        "!git clone https://github.com/UtaUtaUtau/nnsvs-db-converter\n",
        "!git clone --branch multi-dict https://github.com/openvpi/DiffSinger.git\n",
        "!git clone https://github.com/openvpi/MakeDiffSinger #for ds training\n",
        "!git clone https://github.com/MLo7Ghinsan/ghin_shenanigans #for ds segmenting <3 ig and for setup complete wav\n",
        "!git clone https://github.com/openvpi/SOME #yass some midi estimation\n",
        "\n",
        "clear_output()\n",
        "!pip install torch torchvision torchaudio\n",
        "clear_output()\n",
        "!pip install -r /content/DiffSinger/requirements.txt\n",
        "!pip install -r /content/SOME/requirements.txt\n",
        "!pip install mido einops\n",
        "clear_output()\n",
        "!aria2c https://github.com/openvpi/vocoders/releases/download/nsf-hifigan-44.1k-hop512-128bin-2024.02/nsf_hifigan_44.1k_hop512_128bin_2024.02.zip\n",
        "!aria2c https://github.com/openvpi/DiffSinger/releases/download/v2.1.0/rmvpe.zip\n",
        "!aria2c https://github.com/openvpi/SOME/releases/download/v1.0.0-baseline/0119_continuous128_5spk.zip\n",
        "!wget https://github.com/yxlllc/vocal-remover/releases/download/hnsep_240512/hnsep_240512.zip #using wget cus aria2c takes a lot longer for some reason\n",
        "!unzip -q /content/hnsep_240512.zip -d /content/DiffSinger/checkpoints\n",
        "!unzip -q 0119_continuous128_5spk.zip -d /content/DiffSinger/checkpoints/SOME\n",
        "!unzip -q nsf_hifigan_44.1k_hop512_128bin_2024.02.zip -d /content/DiffSinger/checkpoints\n",
        "!unzip -q /content/rmvpe.zip -d /content/DiffSinger/checkpoints\n",
        "!unzip -q /content/rmvpe.zip -d /content/MakeDiffSinger/variance-temp-solution/assets\n",
        "!rm /content/nsf_hifigan_44.1k_hop512_128bin_2024.02.zip\n",
        "!rm /content/rmvpe.zip\n",
        "!rm /content/0119_continuous128_5spk.zip\n",
        "\n",
        "# pretrain stuff\n",
        "!aria2c -d /content/pretrain_models -o acoustic_pretrain.ckpt https://github.com/haru0l/diffsinger_models/releases/download/acoustic/model_ckpt_steps_49000.ckpt\n",
        "!aria2c -d /content/pretrain_models -o variance_pretrain.ckpt https://github.com/haru0l/diffsinger_models/releases/download/variance/model_ckpt_steps_51000.ckpt\n",
        "\n",
        "clear_output()\n",
        "!pip install --upgrade tensorboard\n",
        "clear_output()\n",
        "!pip install protobuf\n",
        "clear_output()\n",
        "!pip install onnxruntime\n",
        "clear_output()\n",
        "!pip install pydub\n",
        "clear_output()\n",
        "#shit tons of clear output cus i dont wanna see anything <3\n",
        "\n",
        "print(\"setup complete!\")\n",
        "print(\"|\")\n",
        "print(\"|\")\n",
        "print(\"|\")\n",
        "\n",
        "chika_dance = '<img src=\"https://raw.githack.com/MLo7Ghinsan/ghin_shenanigans/main/image_and_gif/chika_dance.gif\"/>'\n",
        "display(HTML(chika_dance))\n",
        "\n",
        "with open(\"/content/ghin_shenanigans/audio/setup_complete.wav\", \"rb\") as f:\n",
        "    setup_complete_sound = f.read()\n",
        "Audio(data=setup_complete_sound, autoplay=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Preprocess data for training**"
      ],
      "metadata": {
        "id": "eexZl_OCDmQ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cp /content/DiffSinger/configs/acoustic.yaml /content/DiffSinger/configs/acoustic_og.yaml"
      ],
      "metadata": {
        "id": "vKmLYHagsnU4"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /content/DiffSinger/dictionaries/*"
      ],
      "metadata": {
        "id": "QXrU3KkA070K"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title #Extract Data\n",
        "#@markdown ___\n",
        "%cd /content\n",
        "#@markdown this cell will create a folder name [raw_data] in the root folder of colab (/content) and extract your data into it\n",
        "\n",
        "data_type = \"lab + wav (NNSVS format)\" # @param [\"lab + wav (NNSVS format)\", \"csv + wav (DiffSinger format)\", \"ds (DiffSinger format)\"]\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> The path to your data zip file\n",
        "\n",
        "data_zip_path = \"/content/drive/MyDrive/multi_lang_test.zip\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ___\n",
        "\n",
        "#@markdown nnsvs-db-converter settings (lab + wav ONLY)\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> _These values can exceed the amount that's in your data to maximize the segment length or to keep the data as is_\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> This option is necessary for variance's pitch training\n",
        "estimate_midi_option = \"False\" # @param [\"False\", \"True | parselmouth\", \"True | harvest\", \"True | SOME\"]\n",
        "if estimate_midi_option == \"True | parselmouth\":\n",
        "    estimate_midi = True\n",
        "    midi_pitch_ext = \"parselmouth\"\n",
        "elif estimate_midi_option == \"True | harvest\":\n",
        "    estimate_midi = True\n",
        "    midi_pitch_ext = \"harvest\"\n",
        "else:\n",
        "    estimate_midi = False\n",
        "    midi_pitch_ext = None\n",
        "#@markdown <font size=\"-1.5\"> Determine how long it will segment your data to based on silence phoneme placement (seconds)\n",
        "segment_length = 15 #@param {type:\"slider\", min:5, max:35, step:1}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> Determine how many silence phoneme is allowed in the middle of each segment\n",
        "max_silence_phoneme_amount = 2 #@param {type:\"slider\", min:0, max:50, step:1}\n",
        "\n",
        "# leaving -S at 60 so max silence can be 60 seconds that exceeds the segment legnth cap idk why///\n",
        "# making the segment length cap at 35 secs because any longer than that would make training goes really slow\n",
        "\n",
        "# my ass dont remember why i made two... i think one is unnecessary extra but mehhh\n",
        "all_shits = \"/content/raw_data\"\n",
        "all_shits_not_wav_n_lab = \"/content/raw_data/diffsinger_db\"\n",
        "\n",
        "import os\n",
        "import csv\n",
        "import json\n",
        "import shutil\n",
        "from pydub import AudioSegment\n",
        "import yaml\n",
        "\n",
        "if os.path.exists(\"/content/raw_data\"):\n",
        "    shutil.rmtree(\"/content/raw_data\")\n",
        "\n",
        "if not os.path.exists(all_shits_not_wav_n_lab):\n",
        "  os.makedirs(all_shits_not_wav_n_lab)\n",
        "\n",
        "# using 'if not' bc i edited the wrong section which im also too lazy to fix it <3\n",
        "if not data_type == \"lab + wav (NNSVS format)\":\n",
        "    #changed to 7zip to support more compression types\n",
        "    !7z x \"$data_zip_path\" -o{all_shits_not_wav_n_lab}\n",
        "    for root, dirs, files in os.walk(all_shits):\n",
        "        for filename in files:\n",
        "            if filename.endswith(\".lab\"):\n",
        "                file_path = os.path.join(root, filename)\n",
        "                with open(file_path, \"r\") as file:\n",
        "                    file_data = file.read()\n",
        "                file_data = file_data.replace(\"SP\", \"pau\")\n",
        "                file_data = file_data.replace(\"br\", \"AP\")\n",
        "                with open(file_path, \"w\") as file:\n",
        "                    file.write(file_data)\n",
        "\n",
        "else:\n",
        "    !7z x \"$data_zip_path\" -o{all_shits_not_wav_n_lab}\n",
        "\n",
        "\n",
        "# for funny auto dict generator lmao\n",
        "out = \"/content/DiffSinger/dictionaries\"\n",
        "dictionary_files = []\n",
        "dictionary_conf_lines = []\n",
        "\n",
        "def is_excluded(phoneme):\n",
        "    return phoneme in [\"pau\", \"AP\", \"SP\", \"sil\"]\n",
        "\n",
        "lang_config_path = all_shits_not_wav_n_lab +\"/lang_config.yaml\"\n",
        "\n",
        "with open(lang_config_path, \"r\") as yaml_file:\n",
        "    lang_config = yaml.safe_load(yaml_file)\n",
        "\n",
        "languages = lang_config.get(\"languages\", [])\n",
        "extra_phonemes = lang_config.get(\"extra_phonemes\", [])\n",
        "merged_phoneme_groups = lang_config.get(\"merged_phoneme_groups\", [])\n",
        "\n",
        "for folder in os.listdir(all_shits_not_wav_n_lab):\n",
        "    if '.' in folder:\n",
        "        _, lang_code = folder.rsplit('.', 1)\n",
        "        if lang_code not in languages:\n",
        "            continue\n",
        "\n",
        "        phonemes = set()\n",
        "        phoneme_folder_path = os.path.join(all_shits_not_wav_n_lab, folder)\n",
        "\n",
        "        for root, dirs, files in os.walk(phoneme_folder_path):\n",
        "            for file in files:\n",
        "                fpath = os.path.join(root, file)\n",
        "\n",
        "                if data_type == \"lab + wav (NNSVS format)\":\n",
        "                    if file.endswith(\".lab\"):\n",
        "                        with open(fpath, \"r\") as lab_file:\n",
        "                            for line in lab_file:\n",
        "                                line = line.strip()\n",
        "                                if line:\n",
        "                                    phoneme = line.split()[2]\n",
        "                                    if not is_excluded(phoneme):\n",
        "                                        phonemes.add(phoneme)\n",
        "\n",
        "                elif data_type == \"csv + wav (DiffSinger format)\":\n",
        "                    if file.endswith(\".csv\"):\n",
        "                        with open(fpath, \"r\", newline=\"\") as csv_file:\n",
        "                            csv_reader = csv.DictReader(csv_file)\n",
        "                            for row in csv_reader:\n",
        "                                if \"ph_seq\" in row:\n",
        "                                    ph_seq = row[\"ph_seq\"].strip()\n",
        "                                    for phoneme in ph_seq.split():\n",
        "                                        if not is_excluded(phoneme):\n",
        "                                            phonemes.add(phoneme)\n",
        "\n",
        "                else:\n",
        "                    if file.endswith(\".ds\"):\n",
        "                        with open(fpath, \"r\") as json_file:\n",
        "                            data = json.load(json_file)\n",
        "                            for entry in data:\n",
        "                                if \"ph_seq\" in entry:\n",
        "                                    ph_seq = entry[\"ph_seq\"].strip()\n",
        "                                    for phoneme in ph_seq.split():\n",
        "                                        if not is_excluded(phoneme):\n",
        "                                            phonemes.add(phoneme)\n",
        "\n",
        "        output_path = os.path.join(out, f\"{lang_code}.txt\")\n",
        "        dictionary_files.append(output_path)\n",
        "        dictionary_conf_lines.append(f\"{lang_code}: '{output_path}'\")\n",
        "        with open(output_path, \"w\", encoding=\"utf-8\") as out_file:\n",
        "            for phoneme in sorted(phonemes):\n",
        "                out_file.write(f\"{phoneme}\\t{phoneme}\\n\")\n",
        "\n",
        "# used this for check runs\n",
        "#for dicks in dictionary_files:\n",
        "#    print(dicks)\n",
        "\n",
        "# for vowels and consonants.txt.... well adding luquid type for uta's script\n",
        "dict_path = out\n",
        "vowel_types = {\"a\", \"i\", \"u\", \"e\", \"o\", \"N\", \"M\", \"NG\"}\n",
        "liquid_types = {\"y\", \"w\", \"l\", \"r\"} # r for english labels, it should be fine with jp too\n",
        "vowel_data = []\n",
        "consonant_data = []\n",
        "liquid_data = []\n",
        "\n",
        "for dict_path in dictionary_files:\n",
        "    with open(dict_path, \"r\") as f:\n",
        "        for line in f:\n",
        "            phoneme, _ = line.strip().split(\"\\t\")\n",
        "            if phoneme[0] in vowel_types:\n",
        "                vowel_data.append(phoneme)\n",
        "            elif phoneme[0] in liquid_types:\n",
        "                liquid_data.append(phoneme)\n",
        "            else:\n",
        "                consonant_data.append(phoneme)\n",
        "\n",
        "vowel_data.sort()\n",
        "liquid_data.sort()\n",
        "consonant_data.sort()\n",
        "directory = os.path.dirname(dict_path)\n",
        "\n",
        "# make txt for language json file\n",
        "vowel_txt_path = os.path.join(directory, \"vowels.txt\")\n",
        "with open(vowel_txt_path, \"w\") as f:\n",
        "    f.write(\" \".join(vowel_data))\n",
        "liquid_txt_path = os.path.join(directory, \"liquids.txt\")\n",
        "with open(liquid_txt_path, \"w\") as f:\n",
        "    f.write(\" \".join(liquid_data))\n",
        "consonant_txt_path = os.path.join(directory, \"consonants.txt\")\n",
        "with open(consonant_txt_path, \"w\") as f:\n",
        "    f.write(\" \".join(consonant_data))\n",
        "\n",
        "\n",
        "# here's a funny json append\n",
        "with open(vowel_txt_path, \"r\") as f:\n",
        "    vowel_data = f.read().split()\n",
        "with open(liquid_txt_path, \"r\") as f:\n",
        "    liquid_data = f.read().split()\n",
        "with open(consonant_txt_path, \"r\") as f:\n",
        "    consonant_data = f.read().split()\n",
        "liquid_list = {liquid: True for liquid in liquid_data} #temp fix, might need more research about the push in timing'''\n",
        "phones4json = {\"vowels\": vowel_data, \"liquids\": liquid_list}\n",
        "with open(\"/content/nnsvs-db-converter/lang.sample.json\", \"w\") as rawr:\n",
        "    json.dump(phones4json, rawr, indent=4)\n",
        "\n",
        "\n",
        "if data_type == \"lab + wav (NNSVS format)\":\n",
        "    db_converter_script = \"/content/nnsvs-db-converter/db_converter.py\"\n",
        "    for raw_folder_name in os.listdir(all_shits_not_wav_n_lab):\n",
        "        raw_folder_path = os.path.join(all_shits_not_wav_n_lab, raw_folder_name)\n",
        "        if os.path.isdir(raw_folder_path):\n",
        "            if estimate_midi:\n",
        "                !python {db_converter_script} -s {max_silence_phoneme_amount} -l {segment_length} -m -c -L \"/content/nnsvs-db-converter/lang.sample.json\" {raw_folder_path}\n",
        "            else:\n",
        "                !python {db_converter_script} -s {max_silence_phoneme_amount} -l {segment_length} -L \"/content/nnsvs-db-converter/lang.sample.json\" {raw_folder_path}\n",
        "            !rm -rf {raw_folder_path}/*.wav {raw_folder_path}/*.lab\n",
        "            !mv {raw_folder_path}/diffsinger_db/* {raw_folder_path} 2> /dev/null\n",
        "            !rm -rf {raw_folder_path}/diffsinger_db\n",
        "            if estimate_midi_option == \"True | SOME\":\n",
        "                !python /content/SOME/batch_infer.py --model \"/content/DiffSinger/checkpoints/SOME/0119_continuous256_5spk/model_ckpt_steps_100000_simplified.ckpt\" --dataset {raw_folder_path} --overwrite\n",
        "\n",
        "elif data_type == \"ds (DiffSinger format)\":\n",
        "    ds_segment_script = \"/content/ghin_shenanigans/scripts/ds_segmentor.py\"\n",
        "    ds2csv_script = \"/content/MakeDiffSinger/variance-temp-solution/convert_ds.py\"\n",
        "    for raw_folder_name in os.listdir(all_shits_not_wav_n_lab):\n",
        "        raw_folder_path = os.path.join(all_shits_not_wav_n_lab, raw_folder_name)\n",
        "        if os.path.isdir(raw_folder_path):\n",
        "            ds_exp_path = os.path.join(raw_folder_path, \"ds\")\n",
        "            csv_exp_path = os.path.join(raw_folder_path, \"transcriptions.csv\")\n",
        "            !python {ds_segment_script} {raw_folder_path} --export_path {ds_exp_path}\n",
        "            !rm -rf {raw_folder_path}/*.ds #clean it cus why not\n",
        "            !python {ds2csv_script} ds2csv {ds_exp_path} {csv_exp_path}\n",
        "else:\n",
        "    pass\n",
        "\n",
        "# make it replace the first SP to AP cus it seems like people always forgot about it\n",
        "for root, _, files in os.walk(all_shits_not_wav_n_lab):\n",
        "    for file in files:\n",
        "        if file.endswith(\".csv\"):\n",
        "            file_path = os.path.join(root, file)\n",
        "            with open(file_path, \"r\", newline=\"\") as input_file:\n",
        "                csv_reader = csv.reader(input_file)\n",
        "                data = [row for row in csv_reader]\n",
        "                header = data[0]\n",
        "                if \"ph_seq\" in header:\n",
        "                    ph_seq_index = header.index(\"ph_seq\")\n",
        "                    if len(data) > 1 and len(data[1]) > ph_seq_index:\n",
        "                        data[1][ph_seq_index] = data[1][ph_seq_index].replace(\"SP\", \"AP\", 1)\n",
        "            with open(file_path, \"w\", newline=\"\") as output_file:\n",
        "                csv_writer = csv.writer(output_file)\n",
        "                csv_writer.writerows(data)\n",
        "\n",
        "print(\"extraction complete!\")\n",
        "print(\"|\")\n",
        "print(\"|\")\n",
        "print(\"|\")\n",
        "print(\"I'm also nice enough to convert your data and also write your dictionaries lmao. You are welcome :)\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "JsP1TGg2F1g3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1dc0ba0-8b40-459e-b9ac-8d5609ca7961"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "\n",
            "7-Zip [64] 16.02 : Copyright (c) 1999-2016 Igor Pavlov : 2016-05-21\n",
            "p7zip Version 16.02 (locale=en_US.UTF-8,Utf16=on,HugeFiles=on,64 bits,2 CPUs Intel(R) Xeon(R) CPU @ 2.20GHz (406F0),ASM,AES-NI)\n",
            "\n",
            "Scanning the drive for archives:\n",
            "  0M Scan /content/drive/MyDrive/\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b                                 \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b1 file, 12747075 bytes (13 MiB)\n",
            "\n",
            "Extracting archive: /content/drive/MyDrive/multi_lang_test.zip\n",
            "--\n",
            "Path = /content/drive/MyDrive/multi_lang_test.zip\n",
            "Type = zip\n",
            "Physical Size = 12747075\n",
            "\n",
            "  0%\b\b\b\b    \b\b\b\bEverything is Ok\n",
            "\n",
            "Folders: 3\n",
            "Files: 27\n",
            "Size:       15785798\n",
            "Compressed: 12747075\n",
            "11/18/24 Mon 06:33:44 - INFO: Finding all labels.\n",
            "11/18/24 Mon 06:33:44 - INFO: Found 3 labels.\n",
            "11/18/24 Mon 06:33:44 - INFO: Making directories and files.\n",
            "11/18/24 Mon 06:33:44 - INFO: Reading /content/raw_data/diffsinger_db/Printo.th/21_TH.wav.\n",
            "11/18/24 Mon 06:33:44 - INFO: Segmenting /content/raw_data/diffsinger_db/Printo.th/21_TH.lab.\n",
            "11/18/24 Mon 06:33:44 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:44 - INFO: Segment 1 / 3\n",
            "11/18/24 Mon 06:33:44 - INFO: Segment 2 / 3\n",
            "11/18/24 Mon 06:33:44 - INFO: Segment 3 / 3\n",
            "11/18/24 Mon 06:33:44 - INFO: Reading /content/raw_data/diffsinger_db/Printo.th/22_TH.wav.\n",
            "11/18/24 Mon 06:33:44 - INFO: Segmenting /content/raw_data/diffsinger_db/Printo.th/22_TH.lab.\n",
            "11/18/24 Mon 06:33:44 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:44 - INFO: Segment 1 / 2\n",
            "11/18/24 Mon 06:33:44 - INFO: Segment 2 / 2\n",
            "11/18/24 Mon 06:33:44 - INFO: Reading /content/raw_data/diffsinger_db/Printo.th/23_TH.wav.\n",
            "11/18/24 Mon 06:33:44 - INFO: Segmenting /content/raw_data/diffsinger_db/Printo.th/23_TH.lab.\n",
            "11/18/24 Mon 06:33:44 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:44 - INFO: Segment 1 / 3\n",
            "11/18/24 Mon 06:33:44 - INFO: Segment 2 / 3\n",
            "11/18/24 Mon 06:33:44 - INFO: Segment 3 / 3\n",
            "11/18/24 Mon 06:33:44 - INFO: Writing all transcripts.\n",
            "11/18/24 Mon 06:33:44 - INFO: Took 0.12374267399991368 seconds\n",
            "11/18/24 Mon 06:33:46 - INFO: Finding all labels.\n",
            "11/18/24 Mon 06:33:46 - INFO: Found 5 labels.\n",
            "11/18/24 Mon 06:33:46 - INFO: Making directories and files.\n",
            "11/18/24 Mon 06:33:46 - INFO: Reading /content/raw_data/diffsinger_db/Neiro.jp/dawn_jp_try.wav.\n",
            "11/18/24 Mon 06:33:48 - INFO: Segmenting /content/raw_data/diffsinger_db/Neiro.jp/dawn_jp_try.lab.\n",
            "11/18/24 Mon 06:33:48 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:48 - INFO: Segment 1 / 2\n",
            "11/18/24 Mon 06:33:48 - INFO: Segment 2 / 2\n",
            "11/18/24 Mon 06:33:48 - INFO: Reading /content/raw_data/diffsinger_db/Neiro.jp/encore-01.wav.\n",
            "11/18/24 Mon 06:33:49 - INFO: Segmenting /content/raw_data/diffsinger_db/Neiro.jp/encore-01.lab.\n",
            "11/18/24 Mon 06:33:49 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:49 - INFO: Segment 1 / 1\n",
            "11/18/24 Mon 06:33:49 - INFO: Reading /content/raw_data/diffsinger_db/Neiro.jp/encore-02.wav.\n",
            "11/18/24 Mon 06:33:49 - INFO: Segmenting /content/raw_data/diffsinger_db/Neiro.jp/encore-02.lab.\n",
            "11/18/24 Mon 06:33:49 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:49 - INFO: Segment 1 / 1\n",
            "11/18/24 Mon 06:33:49 - INFO: Reading /content/raw_data/diffsinger_db/Neiro.jp/encore-03.wav.\n",
            "11/18/24 Mon 06:33:50 - INFO: Segmenting /content/raw_data/diffsinger_db/Neiro.jp/encore-03.lab.\n",
            "11/18/24 Mon 06:33:50 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:50 - INFO: Segment 1 / 3\n",
            "11/18/24 Mon 06:33:50 - INFO: Segment 2 / 3\n",
            "11/18/24 Mon 06:33:50 - INFO: Segment 3 / 3\n",
            "11/18/24 Mon 06:33:50 - INFO: Reading /content/raw_data/diffsinger_db/Neiro.jp/first_1.wav.\n",
            "11/18/24 Mon 06:33:51 - INFO: Segmenting /content/raw_data/diffsinger_db/Neiro.jp/first_1.lab.\n",
            "11/18/24 Mon 06:33:51 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:51 - INFO: Segment 1 / 3\n",
            "11/18/24 Mon 06:33:51 - INFO: Segment 2 / 3\n",
            "11/18/24 Mon 06:33:51 - INFO: Segment 3 / 3\n",
            "11/18/24 Mon 06:33:51 - INFO: Writing all transcripts.\n",
            "11/18/24 Mon 06:33:51 - INFO: Took 4.673207490000095 seconds\n",
            "11/18/24 Mon 06:33:53 - INFO: Finding all labels.\n",
            "11/18/24 Mon 06:33:53 - INFO: Found 5 labels.\n",
            "11/18/24 Mon 06:33:53 - INFO: Making directories and files.\n",
            "11/18/24 Mon 06:33:53 - INFO: Reading /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg1.wav.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segmenting /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg1.lab.\n",
            "11/18/24 Mon 06:33:53 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segment 1 / 1\n",
            "11/18/24 Mon 06:33:53 - INFO: Reading /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg2.wav.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segmenting /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg2.lab.\n",
            "11/18/24 Mon 06:33:53 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segment 1 / 1\n",
            "11/18/24 Mon 06:33:53 - INFO: Reading /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg3.wav.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segmenting /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg3.lab.\n",
            "11/18/24 Mon 06:33:53 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segment 1 / 2\n",
            "11/18/24 Mon 06:33:53 - INFO: Segment 2 / 2\n",
            "11/18/24 Mon 06:33:53 - INFO: Reading /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg4.wav.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segmenting /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg4.lab.\n",
            "11/18/24 Mon 06:33:53 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segment 1 / 2\n",
            "11/18/24 Mon 06:33:53 - INFO: Segment 2 / 2\n",
            "11/18/24 Mon 06:33:53 - INFO: Reading /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg5.wav.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segmenting /content/raw_data/diffsinger_db/AI_dol.eng/30_Minutes_seg5.lab.\n",
            "11/18/24 Mon 06:33:53 - INFO: Splitting wave file and preparing transcription lines.\n",
            "11/18/24 Mon 06:33:53 - INFO: Segment 1 / 2\n",
            "11/18/24 Mon 06:33:53 - INFO: Segment 2 / 2\n",
            "11/18/24 Mon 06:33:53 - INFO: Writing all transcripts.\n",
            "11/18/24 Mon 06:33:53 - INFO: Took 0.07138197799940826 seconds\n",
            "extraction complete!\n",
            "|\n",
            "|\n",
            "|\n",
            "I'm also nice enough to convert your data and also write your dictionaries lmao. You are welcome :)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title #Edit Config\n",
        "#@markdown ___\n",
        "\n",
        "import re\n",
        "import os\n",
        "import yaml\n",
        "import random #for the random test files lmaoz\n",
        "\n",
        "%cd /content\n",
        "clear_output()\n",
        "#@markdown The model type user is training\n",
        "model_type = \"acoustic\" # @param [\"acoustic\", \"variance\"]\n",
        "config_cap = model_type.upper()\n",
        "diffusion_type = \"reflow\" # @param [\"ddpm\", \"reflow\"]\n",
        "diff_accelerator = \"ddim\" # @param [\"ddim\", \"pndm\", \"dpm-solver\", \"unipc\"]\n",
        "loss_type = \"l2\" # @param [\"l1\", \"l2\"]\n",
        "\n",
        "spk_name = [folder_name for folder_name in os.listdir(all_shits_not_wav_n_lab) if os.path.isdir(os.path.join(all_shits_not_wav_n_lab, folder_name))]\n",
        "# i used spk_name for something else cus i forgor now imma just copy and paste it\n",
        "spk_names = [folder_name for folder_name in os.listdir(all_shits_not_wav_n_lab) if os.path.isdir(os.path.join(all_shits_not_wav_n_lab, folder_name))]\n",
        "num_spk = len(spk_name)\n",
        "num_lang = len(dictionary_files)\n",
        "raw_dir = []\n",
        "for folder_name in spk_name:\n",
        "    folder_path = os.path.join(all_shits_not_wav_n_lab, folder_name)\n",
        "    raw_dir.append(folder_path)\n",
        "if num_spk == 1:\n",
        "    singer_type = \"SINGLE-SPEAKER\"\n",
        "    use_spk_id = False\n",
        "    all_wav_files = []\n",
        "    for root, dirs, files in os.walk(\"/content/raw_data/diffsinger_db\"):\n",
        "        for file in files:\n",
        "            if file.endswith((\".wav\", \".ds\")):\n",
        "                full_path = os.path.join(root, file)\n",
        "                all_wav_files.append(full_path)\n",
        "    random.shuffle(all_wav_files)\n",
        "    random_ass_wavs = all_wav_files[:3]\n",
        "    random_ass_test_files = [os.path.splitext(os.path.basename(file))[0] for file in random_ass_wavs]\n",
        "\n",
        "else:\n",
        "    singer_type = \"MULTI-SPEAKER\"\n",
        "    use_spk_id = True\n",
        "    use_lang_id = True\n",
        "    folder_to_id = {folder_name: i for i, folder_name in enumerate(spk_name)}\n",
        "    datasets = []\n",
        "\n",
        "    for spk_id, (folder_path, speaker_name) in enumerate(zip(raw_dir, spk_name)):\n",
        "        if data_type == \"ds (DiffSinger format)\":\n",
        "            audio_files = [f[:-4] for f in os.listdir(folder_path) if f.endswith(\".ds\")]\n",
        "        else:\n",
        "            audio_files = [f[:-4] for f in os.listdir(folder_path + \"/wavs\") if f.endswith(\".wav\")]\n",
        "        folder_id = folder_to_id.get(speaker_name, -1)\n",
        "        prefixed_audio_files = [f\"{audio_file}\" for audio_file in audio_files]\n",
        "\n",
        "        random_ass_test_files = prefixed_audio_files[:3]\n",
        "\n",
        "        datasets.append({\n",
        "            \"raw_data_dir\": folder_path,\n",
        "            \"speaker\": speaker_name,\n",
        "            \"spk_id\": spk_id,\n",
        "            \"language\": speaker_name.split(\".\")[-1],\n",
        "            \"test_prefixes\": random_ass_test_files\n",
        "        })\n",
        "\n",
        "dictionaries = {}\n",
        "for line in dictionary_conf_lines:\n",
        "    key, value = line.split(\": \", 1)\n",
        "    dictionaries[key] = value.strip(\"'\")\n",
        "\n",
        "#@markdown Shallow Diffusion training\n",
        "use_shallow_diffusion = \"true | gt_val\" # @param [\"false\", \"true | aux_val\", \"true | gt_val\"]\n",
        "if use_shallow_diffusion == \"false\":\n",
        "    shallow = False\n",
        "    gt_shallow = False\n",
        "elif use_shallow_diffusion == \"true | aux_val\":\n",
        "    shallow = True\n",
        "    gt_shallow = False\n",
        "else:\n",
        "    shallow = True\n",
        "    gt_shallow = True\n",
        "\n",
        "#@markdown Half precision, or mixed precision can result in improved performance, achieving speedups on training (from [doc](https://lightning.ai/docs/pytorch/stable/common/trainer.html#precision))\n",
        "# the reason why i dont add 64 is because colab is already dreadfully slow at 32 so yes im leaving it out\n",
        "precision = \"16-mixed\" # @param [\"32-true\", \"bf16-mixed\", \"16-mixed\", \"bf16\", \"16\"]\n",
        "\n",
        "#@markdown User model save path\n",
        "save_dir = \"/content/test\" #@param {type:\"string\"}\n",
        "\n",
        "binary_save_dir = save_dir + \"/binary\"\n",
        "\n",
        "conf_dir = save_dir\n",
        "\n",
        "#@markdown ....................................................................................................................................................................................................................................................................................................................................................................................................................................\n",
        "\n",
        "#@markdown Option to use base model for finetuning\n",
        "\n",
        "enable_finetuning = False # @param {type:\"boolean\"}\n",
        "\n",
        "\n",
        "#@markdown Path to custom base model, leave blank to use [default](https://github.com/haru0l/diffsinger_models) models\n",
        "#wtf haru i just looked at your readme\"\"\"\"\"\n",
        "\n",
        "base_model_path = \"\" # @param {type:\"string\"}\n",
        "\n",
        "if enable_finetuning:\n",
        "    pretrain = True\n",
        "    if base_model_path:\n",
        "        pretrain_ckpt = base_model_path\n",
        "    else:\n",
        "        pretrain_ckpt = f\"/content/pretrain_models/{model_type}_pretrain.ckpt\"\n",
        "    finetune_strict_shapes = False\n",
        "    finetune_ckpt_path = pretrain_ckpt\n",
        "else:\n",
        "    pretrain = False\n",
        "    finetune_strict_shapes = True #default value\n",
        "    finetune_ckpt_path = None #default value\n",
        "\n",
        "#@markdown ....................................................................................................................................................................................................................................................................................................................................................................................................................................\n",
        "\n",
        "#@markdown Model embeds check; Tension, Energy, Breathiness, Voicing | for both <font color = \"yellow\">acoustic and variance\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> we limited the pair up choice to prevent the quality and usage issue, if user wish to enable option(s) outside of these choices then please keep in mind that most of these embeds do not work well together except for [energy + breathiness]\n",
        "\n",
        "selected_param = \"tension\" # @param [\"energy + breathiness\", \"tension\", \"voicing\", \"none\"]\n",
        "if selected_param ==  \"energy + breathiness\":\n",
        "    tension_training = False\n",
        "    energy_training = True\n",
        "    breathiness_training = True\n",
        "    voicing_training = True\n",
        "elif selected_param == \"tension\":\n",
        "    tension_training = True\n",
        "    energy_training = False\n",
        "    breathiness_training = False\n",
        "    voicing_training = False\n",
        "elif selected_param == \"voicing\":\n",
        "    tension_training = False\n",
        "    energy_training = False\n",
        "    breathiness_training = False\n",
        "    voicing_training = True\n",
        "else:\n",
        "    tension_training = False\n",
        "    energy_training = False\n",
        "    breathiness_training = False\n",
        "    voicing_training = False\n",
        "\n",
        "parameter_extraction_method = \"vr\" # @param [\"vr\", \"world\"]\n",
        "\n",
        "### forcing data aug to be true by default cus i dont think anyone would disable it and its good to be on by default\n",
        "data_aug = True #param {type:\"boolean\"}\n",
        "\n",
        "#@markdown ....................................................................................................................................................................................................................................................................................................................................................................................................................................\n",
        "\n",
        "#@markdown Model training check | for <font color = \"yellow\">variance </font>only\n",
        "\n",
        "\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> due to skill issues, if user wish to train with glide embed, please enable it manually in the config\n",
        "pitch_training = \"False\" # @param [\"False\", \"True | Standard\", \"True | MelodyEncoder\"]\n",
        "if pitch_training == \"False\":\n",
        "    pitch_training = False\n",
        "    use_melody_encoder = False\n",
        "    use_glide_embed = False\n",
        "elif pitch_training == \"True | Standard\":\n",
        "    pitch_training = True\n",
        "    use_melody_encoder = False\n",
        "    use_glide_embed = False\n",
        "else:\n",
        "    pitch_training = True\n",
        "    use_melody_encoder = True\n",
        "    use_glide_embed = False\n",
        "\n",
        "duration_training = True #@param {type: \"boolean\"}\n",
        "\n",
        "#@markdown ....................................................................................................................................................................................................................................................................................................................................................................................................................................\n",
        "\n",
        "#@markdown Pitch extractor algorithm\n",
        "\n",
        "f0_ext = \"parselmouth\" # @param [\"parselmouth\", \"rmvpe\", \"harvest\"]\n",
        "if f0_ext == \"rmvpe\":\n",
        "    pe_ckpt_pth = \"checkpoints/rmvpe/model.pt\"\n",
        "else:\n",
        "    pe_ckpt_pth = None\n",
        "\n",
        "#@markdown ....................................................................................................................................................................................................................................................................................................................................................................................................................................\n",
        "\n",
        "#@markdown Proceeding sections are the parameters that will greatly affect the model's final quality and size. Read about them [here](https://github.com/openvpi/DiffSinger/blob/main/docs/ConfigurationSchemas.md)\n",
        "\n",
        "#@markdown So if you don't know what they do then please <font color = \"red\">leave these options at default </font>, otherwise it could affect your model badly\n",
        "\n",
        "#@markdown <font size=\"-2.5\">anyone is welcome to experiment though\n",
        "\n",
        "#@markdown <font size=\"-1.5\">model_hidden_size: hidden layers for FS2 and token param embeds\n",
        "\n",
        "#@markdown <font size=\"-1.5\">model_residual_layers | model_residual_channels: the model's main layers and channels\n",
        "\n",
        "#@markdown ....................................................................................................................................................................................................................................................................................................................................................................................................................................\n",
        "#@markdown Model's network/layer size for <font color = \"yellow\">acoustic\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> The quality of samplig_algorithm is in order, range from euler being the LEAST accurate to rk5 being the MOST accurate.... Though euler works fine on most cases\n",
        "sampling_algorithm = \"euler\" # @param [\"euler\", \"rk2\", \"rk4\", \"rk5\"]\n",
        "\n",
        "acoustic_hidden_size = 256 # @param {type:\"slider\", min:2, max:1024, step:2}\n",
        "\n",
        "acoustic_num_layers = 6 # @param {type:\"slider\", min:2, max:42, step:2}\n",
        "acoustic_num_channels = 1024 # @param {type:\"slider\", min:2, max:2048, step:2}\n",
        "\n",
        "#@markdown Model's network/layer size for <font color = \"yellow\">variance\n",
        "variance_hidden_size = 256 # @param {type:\"slider\", min:2, max:1024, step:2}\n",
        "duration_hidden_size = 512 # @param {type:\"slider\", min:2, max:1024, step:2}\n",
        "melody_encoder_hidden_size = 128 # @param {type:\"slider\", min:2, max:1024, step:2}\n",
        "\n",
        "pitch_num_layers = 6 # @param {type:\"slider\", min:2, max:100, step:2}\n",
        "pitch_num_channels = 512 # @param {type:\"slider\", min:2, max:1024, step:2}\n",
        "variance_num_layers = 6 # @param {type:\"slider\", min:2, max:100, step:2}\n",
        "variance_num_channels = 384 # @param {type:\"slider\", min:2, max:1024, step:2}\n",
        "\n",
        "\n",
        "\n",
        "with open(\"/content/DiffSinger/configs/base.yaml\", \"r\") as config:\n",
        "    mother = yaml.safe_load(config)\n",
        "mother[\"pl_trainer_precision\"] = precision\n",
        "with open(\"/content/DiffSinger/configs/base.yaml\", \"w\") as config:\n",
        "    yaml.dump(mother, config)\n",
        "\n",
        "if  data_type == \"ds (DiffSinger format)\":\n",
        "    prefer_ds = True\n",
        "else:\n",
        "    prefer_ds = False\n",
        "\n",
        "if model_type == \"acoustic\":\n",
        "    with open(\"/content/DiffSinger/configs/acoustic.yaml\", \"r\") as config:\n",
        "        bitch_ass_config = yaml.safe_load(config)\n",
        "    bitch_ass_config[\"datasets\"] = datasets\n",
        "    bitch_ass_config[\"num_spk\"] = num_spk\n",
        "    bitch_ass_config[\"use_spk_id\"] = use_spk_id\n",
        "    bitch_ass_config[\"extra_phonemes\"] = extra_phonemes\n",
        "    bitch_ass_config[\"merged_phoneme_groups\"] = merged_phoneme_groups\n",
        "    bitch_ass_config[\"num_lang\"] = num_lang\n",
        "    bitch_ass_config[\"pretrain\"] = pretrain\n",
        "    bitch_ass_config[\"diffusion_type\"] = diffusion_type\n",
        "    bitch_ass_config[\"diff_accelerator\"] = diff_accelerator\n",
        "    bitch_ass_config[\"main_loss_type\"] = loss_type\n",
        "    bitch_ass_config[\"binary_data_dir\"] = binary_save_dir\n",
        "    bitch_ass_config[\"dictionaries\"] = dictionaries\n",
        "    bitch_ass_config[\"augmentation_args\"][\"random_pitch_shifting\"][\"enabled\"] = data_aug\n",
        "    bitch_ass_config[\"augmentation_args\"][\"random_time_stretching\"][\"enabled\"] = data_aug\n",
        "    bitch_ass_config[\"use_key_shift_embed\"] = data_aug\n",
        "    bitch_ass_config[\"use_speed_embed\"] = data_aug\n",
        "    bitch_ass_config[\"pe\"] = f0_ext\n",
        "    bitch_ass_config[\"use_energy_embed\"] = energy_training\n",
        "    bitch_ass_config[\"use_breathiness_embed\"] = breathiness_training\n",
        "    bitch_ass_config[\"use_tension_embed\"] = tension_training\n",
        "    bitch_ass_config[\"use_voicing_embed\"] = voicing_training\n",
        "\n",
        "    bitch_ass_config[\"pe_ckpt\"] = pe_ckpt_pth\n",
        "    #shallow diff stuff\n",
        "    bitch_ass_config[\"use_shallow_diffusion\"] = shallow\n",
        "    bitch_ass_config[\"shallow_diffusion_args\"][\"val_gt_start\"] = gt_shallow\n",
        "    #finetue stuff\n",
        "    bitch_ass_config[\"finetune_enabled\"] = enable_finetuning\n",
        "    bitch_ass_config[\"finetune_ckpt_path\"] = finetune_ckpt_path\n",
        "    bitch_ass_config[\"finetune_strict_shapes\"] = finetune_strict_shapes\n",
        "    #vr\n",
        "    bitch_ass_config[\"hnsep\"] = parameter_extraction_method\n",
        "    #layers\n",
        "    bitch_ass_config[\"sampling_algorithm\"] = sampling_algorithm\n",
        "    bitch_ass_config[\"hidden_size\"] = acoustic_hidden_size\n",
        "    bitch_ass_config[\"backbone_type\"] = \"lynxnet\"\n",
        "    bitch_ass_config[\"backbone_args\"][\"num_layers\"] = acoustic_num_layers\n",
        "    bitch_ass_config[\"backbone_args\"][\"num_channels\"] = acoustic_num_channels\n",
        "\n",
        "    with open(\"/content/DiffSinger/configs/acoustic.yaml\", \"w\") as config:\n",
        "        yaml.dump(bitch_ass_config, config)\n",
        "else:\n",
        "    with open(\"/content/DiffSinger/configs/variance.yaml\", \"r\") as config:\n",
        "        bitch_ass_config = yaml.safe_load(config)\n",
        "    bitch_ass_config[\"datasets\"] = datasets\n",
        "    bitch_ass_config[\"num_spk\"] = num_spk\n",
        "    bitch_ass_config[\"use_spk_id\"] = use_spk_id\n",
        "    bitch_ass_config[\"extra_phonemes\"] = extra_phonemes\n",
        "    bitch_ass_config[\"merged_phoneme_groups\"] = merged_phoneme_groups\n",
        "    bitch_ass_config[\"use_lang_id\"] = use_lang_id\n",
        "    bitch_ass_config[\"num_lang\"] = num_lang\n",
        "    bitch_ass_config[\"main_loss_type\"] = loss_type\n",
        "    bitch_ass_config[\"diffusion_type\"] = diffusion_type\n",
        "    bitch_ass_config[\"diff_accelerator\"] = diff_accelerator\n",
        "    bitch_ass_config[\"binary_data_dir\"] = binary_save_dir\n",
        "    bitch_ass_config[\"dictionaries\"] = dictionaries\n",
        "    bitch_ass_config[\"pe\"] = f0_ext # i think variance uses it for pitch ref as ground-truth for pitch training soooo\n",
        "    bitch_ass_config[\"pe_ckpt\"] = pe_ckpt_pth #same goes to this one\n",
        "\n",
        "    bitch_ass_config[\"predict_energy\"] = energy_training\n",
        "    bitch_ass_config[\"predict_breathiness\"] = breathiness_training\n",
        "    bitch_ass_config[\"predict_tension\"] = tension_training\n",
        "    bitch_ass_config[\"predict_pitch\"] = pitch_training\n",
        "    bitch_ass_config[\"predict_voicing\"] = voicing_training\n",
        "\n",
        "    bitch_ass_config[\"use_melody_encoder\"] = use_melody_encoder\n",
        "    bitch_ass_config[\"use_glide_embed\"] = use_glide_embed\n",
        "    bitch_ass_config[\"predict_dur\"] = duration_training\n",
        "    bitch_ass_config[\"binarization_args\"][\"prefer_ds\"] = prefer_ds\n",
        "    #finetune stuff\n",
        "    bitch_ass_config[\"finetune_enabled\"] = enable_finetuning\n",
        "    bitch_ass_config[\"finetune_ckpt_path\"] = finetune_ckpt_path\n",
        "    bitch_ass_config[\"finetune_strict_shapes\"] = finetune_strict_shapes\n",
        "    #vr\n",
        "    bitch_ass_config[\"hnsep\"] = parameter_extraction_method\n",
        "    bitch_ass_config[\"hnsep_ckpt\"] = \"checkpoints/vr/model.pt\"\n",
        "    #layers\n",
        "    bitch_ass_config[\"hidden_size\"] = variance_hidden_size\n",
        "    bitch_ass_config[\"dur_prediction_args\"][\"hidden_size\"] = duration_hidden_size\n",
        "    bitch_ass_config[\"melody_encoder_args\"][\"hidden_size\"] = melody_encoder_hidden_size\n",
        "    bitch_ass_config[\"variances_prediction_args\"][\"backbone_type\"] = \"lynxnet\"\n",
        "    bitch_ass_config[\"variances_prediction_args\"][\"backbone_args\"][\"num_layers\"] = variance_num_layers\n",
        "    bitch_ass_config[\"variances_prediction_args\"][\"backbone_args\"][\"num_channels\"] = variance_num_channels\n",
        "    bitch_ass_config[\"pitch_prediction_args\"][\"backbone_type\"] = \"lynxnet\"\n",
        "    bitch_ass_config[\"pitch_prediction_args\"][\"backbone_args\"][\"num_layers\"] = pitch_num_layers\n",
        "    bitch_ass_config[\"pitch_prediction_args\"][\"backbone_args\"][\"num_channels\"] = pitch_num_channels\n",
        "\n",
        "    with open(\"/content/DiffSinger/configs/variance.yaml\", \"w\") as config:\n",
        "        yaml.dump(bitch_ass_config, config)\n",
        "\n",
        "os.makedirs(save_dir, exist_ok=True)\n",
        "\n",
        "\n",
        "with open(\"/content/DiffSinger/utils/hparams.py\", \"r\") as f:\n",
        "    hparams_py_read = f.read()\n",
        "hparams_py_read = re.sub(r\"args_work_dir\\s*=\\s*.*\", f\"args_work_dir = '{save_dir}'\", hparams_py_read)\n",
        "with open(\"/content/DiffSinger/utils/hparams.py\", \"w\") as f:\n",
        "    f.write(hparams_py_read)\n",
        "\n",
        "with open(\"/content/DiffSinger/utils/training_utils.py\", \"r\") as f:\n",
        "    training_utils_stuff = f.read()\n",
        "training_utils_stuff = re.sub(\"relative_path\\s*=\\s*.*\", \"relative_path = filepath.relative_to(Path('/content').resolve())\", training_utils_stuff)\n",
        "with open(\"/content/DiffSinger/utils/training_utils.py\", \"w\") as f:\n",
        "    f.write(training_utils_stuff)\n",
        "\n",
        "print(\"config updated! see below for config's information\")\n",
        "print(\"|\")\n",
        "print(\"|\")\n",
        "print(\"|\")\n",
        "print(f\"+++---{config_cap} {singer_type} TRAINING---+++\")\n",
        "print(\"|\")\n",
        "print(\"|\")\n",
        "print(\"|\")\n",
        "print(\"+++---user's settings---+++\")\n",
        "print(\"\\n\")\n",
        "print(f\"speaker name: {spk_names}\")\n",
        "print(\"\\n\")\n",
        "print(f\"data augmentation: {data_aug}\")\n",
        "print(\"\\n\")\n",
        "print(f\"pitch extractor: {f0_ext}\")\n",
        "print(\"\\n\")\n",
        "print(f\"binary data save directory: {binary_save_dir}\")\n",
        "print(\"\\n\")\n",
        "print(f\"your model will be saved to: {save_dir}\")\n",
        "print(\"\\n\")\n",
        "print(\"==========================================================================================\")\n",
        "print(\"\\n\")\n",
        "print(\"+++---other auto-defined settings---+++\")\n",
        "print(\"\\n\")\n",
        "print(f\"test files (auto selected): {random_ass_test_files}\")\n",
        "print(\"\\n\")\n",
        "print(\"dictionary (auto generated): custom_dict.txt\")\n",
        "print(\"\\n\")\n",
        "print(\"==========================================================================================\")\n",
        "print(\"\\n\")\n",
        "print(\"if you don't like or disagree with any of these options,\")\n",
        "print(f\"you can go and edit the config at [/content/DiffSinger/configs/{model_type}.yaml]\")\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "nI3dzDv_Mr9Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e706905b-1439-48e2-fa8b-ec3e09c463be"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "config updated! see below for config's information\n",
            "|\n",
            "|\n",
            "|\n",
            "+++---ACOUSTIC MULTI-SPEAKER TRAINING---+++\n",
            "|\n",
            "|\n",
            "|\n",
            "+++---user's settings---+++\n",
            "\n",
            "\n",
            "speaker name: ['Printo.th', 'Neiro.jp', 'AI_dol.eng']\n",
            "\n",
            "\n",
            "data augmentation: True\n",
            "\n",
            "\n",
            "pitch extractor: parselmouth\n",
            "\n",
            "\n",
            "binary data save directory: /content/test/binary\n",
            "\n",
            "\n",
            "your model will be saved to: /content/test\n",
            "\n",
            "\n",
            "==========================================================================================\n",
            "\n",
            "\n",
            "+++---other auto-defined settings---+++\n",
            "\n",
            "\n",
            "test files (auto selected): ['30_Minutes_seg5_seg001', '30_Minutes_seg4_seg001', '30_Minutes_seg1_seg000']\n",
            "\n",
            "\n",
            "dictionary (auto generated): custom_dict.txt\n",
            "\n",
            "\n",
            "==========================================================================================\n",
            "\n",
            "\n",
            "if you don't like or disagree with any of these options,\n",
            "you can go and edit the config at [/content/DiffSinger/configs/acoustic.yaml]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown # Preprocess data\n",
        "import os\n",
        "#we dont need that old f0 limit change anymore <3\n",
        "training_config = f\"/content/DiffSinger/configs/{model_type}.yaml\"\n",
        "%cd /content/DiffSinger\n",
        "os.environ['PYTHONPATH']='.'\n",
        "!CUDA_VISIBLE_DEVICES=0 python /content/DiffSinger/scripts/binarize.py --config {training_config} --reset"
      ],
      "metadata": {
        "cellView": "form",
        "id": "76NvDR1cXlDM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "455a1501-ffe8-4f09-d625-234c457b314e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/DiffSinger\n",
            "| Hparams chains:  ['configs/base.yaml', '/content/DiffSinger/configs/acoustic.yaml']\n",
            "| Hparams: \n",
            "\u001b[0;33mK_step\u001b[0m: 400, \u001b[0;33mK_step_infer\u001b[0m: 400, \u001b[0;33mT_start\u001b[0m: 0.4, \u001b[0;33mT_start_infer\u001b[0m: 0.4, \u001b[0;33maccumulate_grad_batches\u001b[0m: 1, \n",
            "\u001b[0;33maudio_num_mel_bins\u001b[0m: 128, \u001b[0;33maudio_sample_rate\u001b[0m: 44100, \u001b[0;33maugmentation_args\u001b[0m: {'fixed_pitch_shifting': {'enabled': False, 'scale': 0.5, 'targets': [-5.0, 5.0]}, 'random_pitch_shifting': {'enabled': True, 'range': [-5.0, 5.0], 'scale': 0.75}, 'random_time_stretching': {'enabled': True, 'range': [0.5, 2.0], 'scale': 0.75}}, \u001b[0;33mbackbone_args\u001b[0m: {'dropout_rate': 0.0, 'kernel_size': 31, 'num_channels': 1024, 'num_layers': 6}, \u001b[0;33mbackbone_type\u001b[0m: lynxnet, \n",
            "\u001b[0;33mbase_config\u001b[0m: ['configs/base.yaml'], \u001b[0;33mbinarization_args\u001b[0m: {'num_workers': 0, 'shuffle': True}, \u001b[0;33mbinarizer_cls\u001b[0m: preprocessing.acoustic_binarizer.AcousticBinarizer, \u001b[0;33mbinary_data_dir\u001b[0m: /content/test/binary, \u001b[0;33mbreathiness_smooth_width\u001b[0m: 0.12, \n",
            "\u001b[0;33mclip_grad_norm\u001b[0m: 1, \u001b[0;33mdataloader_prefetch_factor\u001b[0m: 2, \u001b[0;33mdataset_size_key\u001b[0m: lengths, \u001b[0;33mdatasets\u001b[0m: [{'language': 'th', 'raw_data_dir': '/content/raw_data/diffsinger_db/Printo.th', 'speaker': 'Printo.th', 'spk_id': 0, 'test_prefixes': ['21_TH_seg001', '22_TH_seg000', '21_TH_seg000']}, {'language': 'jp', 'raw_data_dir': '/content/raw_data/diffsinger_db/Neiro.jp', 'speaker': 'Neiro.jp', 'spk_id': 1, 'test_prefixes': ['encore-03_seg002', 'encore-01_seg000', 'first_1_seg000']}, {'language': 'eng', 'raw_data_dir': '/content/raw_data/diffsinger_db/AI_dol.eng', 'speaker': 'AI_dol.eng', 'spk_id': 2, 'test_prefixes': ['30_Minutes_seg5_seg001', '30_Minutes_seg4_seg001', '30_Minutes_seg1_seg000']}], \u001b[0;33mdictionaries\u001b[0m: {'eng': '/content/DiffSinger/dictionaries/eng.txt', 'jp': '/content/DiffSinger/dictionaries/jp.txt', 'th': '/content/DiffSinger/dictionaries/th.txt'}, \n",
            "\u001b[0;33mdiff_accelerator\u001b[0m: ddim, \u001b[0;33mdiff_speedup\u001b[0m: 10, \u001b[0;33mdiffusion_type\u001b[0m: reflow, \u001b[0;33mdropout\u001b[0m: 0.1, \u001b[0;33mds_workers\u001b[0m: 4, \n",
            "\u001b[0;33menc_ffn_kernel_size\u001b[0m: 9, \u001b[0;33menc_layers\u001b[0m: 4, \u001b[0;33menergy_smooth_width\u001b[0m: 0.12, \u001b[0;33mexp_name\u001b[0m: , \u001b[0;33mextra_phonemes\u001b[0m: ['AP', 'SP', 'vf'], \n",
            "\u001b[0;33mf0_max\u001b[0m: 1100, \u001b[0;33mf0_min\u001b[0m: 65, \u001b[0;33mffn_act\u001b[0m: gelu, \u001b[0;33mfft_size\u001b[0m: 2048, \u001b[0;33mfinetune_ckpt_path\u001b[0m: None, \n",
            "\u001b[0;33mfinetune_enabled\u001b[0m: False, \u001b[0;33mfinetune_ignored_params\u001b[0m: ['model.fs2.encoder.embed_tokens', 'model.fs2.txt_embed', 'model.fs2.spk_embed'], \u001b[0;33mfinetune_strict_shapes\u001b[0m: True, \u001b[0;33mfmax\u001b[0m: 16000, \u001b[0;33mfmin\u001b[0m: 40, \n",
            "\u001b[0;33mfreezing_enabled\u001b[0m: False, \u001b[0;33mfrozen_params\u001b[0m: [], \u001b[0;33mhidden_size\u001b[0m: 256, \u001b[0;33mhnsep\u001b[0m: vr, \u001b[0;33mhnsep_ckpt\u001b[0m: checkpoints/vr/model.pt, \n",
            "\u001b[0;33mhop_size\u001b[0m: 512, \u001b[0;33minfer\u001b[0m: False, \u001b[0;33mlambda_aux_mel_loss\u001b[0m: 0.2, \u001b[0;33mlog_interval\u001b[0m: 100, \u001b[0;33mlr_scheduler_args\u001b[0m: {'gamma': 0.75, 'scheduler_cls': 'torch.optim.lr_scheduler.StepLR', 'step_size': 10000}, \n",
            "\u001b[0;33mmain_loss_log_norm\u001b[0m: False, \u001b[0;33mmain_loss_type\u001b[0m: l2, \u001b[0;33mmax_batch_frames\u001b[0m: 50000, \u001b[0;33mmax_batch_size\u001b[0m: 64, \u001b[0;33mmax_beta\u001b[0m: 0.02, \n",
            "\u001b[0;33mmax_updates\u001b[0m: 160000, \u001b[0;33mmax_val_batch_frames\u001b[0m: 60000, \u001b[0;33mmax_val_batch_size\u001b[0m: 1, \u001b[0;33mmel_base\u001b[0m: e, \u001b[0;33mmel_vmax\u001b[0m: 4.0, \n",
            "\u001b[0;33mmel_vmin\u001b[0m: -14.0, \u001b[0;33mmerged_phoneme_groups\u001b[0m: [], \u001b[0;33mnccl_p2p\u001b[0m: True, \u001b[0;33mnum_ckpt_keep\u001b[0m: 5, \u001b[0;33mnum_heads\u001b[0m: 2, \n",
            "\u001b[0;33mnum_lang\u001b[0m: 3, \u001b[0;33mnum_sanity_val_steps\u001b[0m: 1, \u001b[0;33mnum_spk\u001b[0m: 3, \u001b[0;33mnum_valid_plots\u001b[0m: 10, \u001b[0;33moptimizer_args\u001b[0m: {'beta1': 0.9, 'beta2': 0.98, 'lr': 0.0006, 'optimizer_cls': 'torch.optim.AdamW', 'weight_decay': 0}, \n",
            "\u001b[0;33mpe\u001b[0m: parselmouth, \u001b[0;33mpe_ckpt\u001b[0m: None, \u001b[0;33mpermanent_ckpt_interval\u001b[0m: 20000, \u001b[0;33mpermanent_ckpt_start\u001b[0m: 80000, \u001b[0;33mpl_trainer_accelerator\u001b[0m: auto, \n",
            "\u001b[0;33mpl_trainer_devices\u001b[0m: auto, \u001b[0;33mpl_trainer_num_nodes\u001b[0m: 1, \u001b[0;33mpl_trainer_precision\u001b[0m: 16-mixed, \u001b[0;33mpl_trainer_strategy\u001b[0m: {'find_unused_parameters': False, 'name': 'auto', 'process_group_backend': 'nccl'}, \u001b[0;33mpretrain\u001b[0m: False, \n",
            "\u001b[0;33mrel_pos\u001b[0m: True, \u001b[0;33msampler_frame_count_grid\u001b[0m: 6, \u001b[0;33msampling_algorithm\u001b[0m: euler, \u001b[0;33msampling_steps\u001b[0m: 20, \u001b[0;33mschedule_type\u001b[0m: linear, \n",
            "\u001b[0;33mshallow_diffusion_args\u001b[0m: {'aux_decoder_arch': 'convnext', 'aux_decoder_args': {'dropout_rate': 0.1, 'kernel_size': 7, 'num_channels': 512, 'num_layers': 6}, 'aux_decoder_grad': 0.1, 'train_aux_decoder': True, 'train_diffusion': True, 'val_gt_start': True}, \u001b[0;33msort_by_len\u001b[0m: True, \u001b[0;33mspec_max\u001b[0m: [0], \u001b[0;33mspec_min\u001b[0m: [-12], \u001b[0;33mtask_cls\u001b[0m: training.acoustic_task.AcousticTask, \n",
            "\u001b[0;33mtension_smooth_width\u001b[0m: 0.12, \u001b[0;33mtime_scale_factor\u001b[0m: 1000, \u001b[0;33mtimesteps\u001b[0m: 1000, \u001b[0;33muse_breathiness_embed\u001b[0m: False, \u001b[0;33muse_energy_embed\u001b[0m: False, \n",
            "\u001b[0;33muse_key_shift_embed\u001b[0m: True, \u001b[0;33muse_lang_id\u001b[0m: False, \u001b[0;33muse_pos_embed\u001b[0m: True, \u001b[0;33muse_shallow_diffusion\u001b[0m: True, \u001b[0;33muse_speed_embed\u001b[0m: True, \n",
            "\u001b[0;33muse_spk_id\u001b[0m: True, \u001b[0;33muse_tension_embed\u001b[0m: True, \u001b[0;33muse_voicing_embed\u001b[0m: False, \u001b[0;33mval_check_interval\u001b[0m: 2000, \u001b[0;33mval_with_vocoder\u001b[0m: True, \n",
            "\u001b[0;33mvocoder\u001b[0m: NsfHifiGAN, \u001b[0;33mvocoder_ckpt\u001b[0m: checkpoints/nsf_hifigan_44.1k_hop512_128bin_2024.02/model.ckpt, \u001b[0;33mvoicing_smooth_width\u001b[0m: 0.12, \u001b[0;33mwin_size\u001b[0m: 2048, \u001b[0;33mwork_dir\u001b[0m: /content/test, \n",
            "\n",
            "| Binarizer:  <class 'preprocessing.acoustic_binarizer.AcousticBinarizer'>\n",
            "| spk_map:  {'Printo.th': 0, 'Neiro.jp': 1, 'AI_dol.eng': 2}\n",
            "| lang_map:  {'eng': 1, 'jp': 2, 'th': 3}\n",
            "===== Phoneme Distribution Summary =====\n",
            "AP: 25, SP: 87, eng/ae: 1, eng/ah: 8, eng/ao: 1, eng/aw: 3, eng/ay: 10, eng/d: 9, eng/dh: 2, eng/eh: 1,\n",
            "eng/ey: 3, eng/f: 4, eng/hh: 1, eng/ih: 3, eng/iy: 4, eng/k: 4, eng/l: 4, eng/m: 5, eng/n: 5, eng/ow: 1,\n",
            "eng/r: 3, eng/s: 5, eng/sh: 1, eng/t: 11, eng/uh: 3, eng/uw: 4, eng/v: 4, eng/w: 4, eng/z: 1, jp/M: 1,\n",
            "jp/N: 5, jp/a: 47, jp/ai: 2, jp/b: 3, jp/ch: 2, jp/d: 9, jp/e: 27, jp/exh: 1, jp/g: 8, jp/gx: 3,\n",
            "jp/h: 7, jp/hf: 1, jp/i: 36, jp/k: 14, jp/ky: 1, jp/l: 2, jp/m: 12, jp/n: 12, jp/o: 42, jp/ow: 1,\n",
            "jp/r: 23, jp/s: 10, jp/sh: 1, jp/t: 16, jp/ts: 3, jp/tt: 3, jp/u: 21, jp/vb: 2, jp/w: 7, jp/y: 8,\n",
            "jp/z: 3, th/A: 4, th/B: 3, th/D: 3, th/E: 13, th/I: 23, th/K: 19, th/O: 12, th/U: 2, th/Ua: 2,\n",
            "th/W: 1, th/Y: 7, th/a: 32, th/au: 7, th/b: 2, th/ch: 5, th/d: 9, th/e: 11, th/f: 3, th/h: 4,\n",
            "th/i: 39, th/j: 3, th/k: 3, th/kh: 12, th/l: 4, th/m: 33, th/n: 31, th/ng: 4, th/o: 7, th/p: 2,\n",
            "th/ph: 3, th/r: 20, th/s: 6, th/t: 2, th/th: 24, th/u: 4, th/v: 1, th/w: 6, th/y: 8, vf: 2\n",
            "| save summary to '/content/test/binary/phoneme_distribution.jpg'\n",
            "  0% 0/9 [00:00<?, ?it/s]/content/DiffSinger/modules/hnsep/vr/__init__.py:33: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  model.load_state_dict(torch.load(model_path, map_location='cpu'))\n",
            "100% 9/9 [00:03<00:00,  2.65it/s]\n",
            "| valid total duration: 64.55s\n",
            "| valid respective duration: Printo.th=27.09s, Neiro.jp=25.91s, AI_dol.eng=11.55s\n",
            "100% 17/17 [00:06<00:00,  2.53it/s]\n",
            "| train total duration (before augmentation): 113.19s\n",
            "| train respective duration (before augmentation): Printo.th=36.15s, Neiro.jp=59.34s, AI_dol.eng=17.69s\n",
            "| train total duration (after augmentation): 248.17s (2.19x)\n",
            "| train respective duration (after augmentation): Printo.th=69.82s, Neiro.jp=116.97s, AI_dol.eng=61.39s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Training**"
      ],
      "metadata": {
        "id": "0J3b18EKdzMC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown #Train your model\n",
        "%cd /content/DiffSinger\n",
        "import re\n",
        "import os\n",
        "import yaml\n",
        "#@markdown ___\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> Step interval of when your model will be validate and save\n",
        "save_interval = 2000 #@param {type:\"slider\", min:100, max:10000, step:100}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> batch size setting, too low can cause bottleneck, too high can cause oom\n",
        "batch_size = 9 # @param {type:\"slider\", min:1, max:100, step:1}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> step interval of when your model will stop training automatically\n",
        "max_updates = 160000 # @param {type:\"slider\", min:100, max:2000000, step:100}\n",
        "\n",
        "#@markdown ___\n",
        "\n",
        "#@markdown ###**Only edit this section if you want to resume training**\n",
        "resume_training = False #@param {type:\"boolean\"}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> select this option if you locally binarized your data | this option will only append your binary data path in your config | \"binary\" folder must be in the same directory as config.yaml\n",
        "local_data = False #@param {type:\"boolean\"}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> path to the config you got from training\n",
        "re_config_path = \"\" #@param {type:\"string\"}\n",
        "model_dir = os.path.dirname(re_config_path)\n",
        "save_dir = model_dir\n",
        "if resume_training:\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"r\") as f:\n",
        "        hparams_py_read = f.read()\n",
        "    hparams_py_read = re.sub(r\"args_work_dir\\s*=\\s*.*\", f\"args_work_dir = '{save_dir}'\", hparams_py_read)\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"w\") as f:\n",
        "        f.write(hparams_py_read)\n",
        "    with open(\"/content/DiffSinger/utils/training_utils.py\", \"r\") as f:\n",
        "        training_utils_stuff = f.read()\n",
        "    training_utils_stuff = re.sub(\"relative_path\\s*=\\s*.*\", \"relative_path = filepath.relative_to(Path('/content').resolve())\", training_utils_stuff)\n",
        "    with open(\"/content/DiffSinger/utils/training_utils.py\", \"w\") as f:\n",
        "        f.write(training_utils_stuff)\n",
        "\n",
        "    config_path = re_config_path\n",
        "    log_dir = save_dir\n",
        "\n",
        "    !cp {model_dir}/dictionary.txt /content/DiffSinger/dictionaries/custom_dict.txt\n",
        "\n",
        "else:\n",
        "    config_path = training_config\n",
        "    log_dir = conf_dir\n",
        "\n",
        "with open(config_path, \"r\") as config:\n",
        "    ehe = yaml.safe_load(config)\n",
        "config_dir = os.path.dirname(config_path)\n",
        "yuh = os.path.join(config_dir, \"binary\")\n",
        "\n",
        "ehe[\"val_check_interval\"] = save_interval\n",
        "ehe[\"max_batch_size\"] = batch_size\n",
        "ehe[\"max_updates\"] = max_updates\n",
        "if local_data:\n",
        "    ehe[\"binary_data_dir\"] = yuh\n",
        "with open(config_path, \"w\") as config:\n",
        "    yaml.dump(ehe, config)\n",
        "\n",
        "logs = log_dir\n",
        "%reload_ext tensorboard\n",
        "%tensorboard --logdir {logs}/lightning_logs\n",
        "\n",
        "!python /content/DiffSinger/scripts/train.py --config {config_path} --exp_name ${save_dir} --reset"
      ],
      "metadata": {
        "cellView": "form",
        "id": "Lu5w72UWgccC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Convert model to ONNX format**"
      ],
      "metadata": {
        "id": "FY40fGHEg9_i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown # Drop Speakers from Model (Optional)\n",
        "#@markdown ___\n",
        "#@markdown <font size=\"-1.5\"> Use this to drop speakers from your model for distribution. You will need to do it for both acoustic and variance models.\n",
        "\n",
        "drop_model_path = '' #@param {type: \"string\"}\n",
        "#@markdown <font size=\"-1.5\"> Type the ID of speakers you'd like to KEEP separated by commas. Ex: \"0,3,4\" <br>\n",
        "#@markdown <font size=\"-1.5\"> Note: You can find the ID of speakers in the model by opening the ```spk_map.json``` file in the model folder.<br>\n",
        "#@markdown <font size=\"-1.5\"> If you see ```{\"natural\": 0, \"power\": 1, \"silly\": 2}``` but only want to keep \"natural\" and \"power\", type ```0,1``` below.\n",
        "retain_speakers = '' #@param {type: \"string\"}\n",
        "#@markdown <font size=\"-1.5\"> If you don't know what this means, don't change it.\n",
        "fill_embed = 'zeros' #@param ['zeros', 'random', 'mean', 'cyclic']\n",
        "\n",
        "drop_out_path = drop_model_path[:-5] + '_spk-dropped.ckpt'\n",
        "\n",
        "!python /content/DiffSinger/scripts/drop_spk.py {drop_model_path} {drop_out_path} --retain {retain_speakers} --fill {fill_embed}\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "21ILzW4OEnh4",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown # Export ONNX\n",
        "#@markdown ___\n",
        "%cd /content\n",
        "from IPython.display import clear_output\n",
        "clear_output()\n",
        "import os\n",
        "import zipfile\n",
        "import shutil\n",
        "\n",
        "#move this here so users doesnt have to wait for this to install for training\n",
        "print(\"installing dependencies for onnx conversion...\")\n",
        "!pip install -r /content/DiffSinger/requirements-onnx.txt -q -q -q 2>/dev/null\n",
        "\n",
        "# to counter IF the user is to re-run this cell <3\n",
        "if os.path.exists(\"/content/OU_compatible_files\"):\n",
        "    shutil.rmtree(\"/content/OU_compatible_files\")\n",
        "    os.remove(\"/content/jpn_dict.txt\")\n",
        "else:\n",
        "    pass\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> select this if you don't want to see the onnx converter's output\n",
        "no_output = True # @param {type:\"boolean\"}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> path to your **ACOUSTIC CHECKPOINT** (leave blank if you don't have any): automatically use latest checkpoint that is in the same folder\n",
        "acoustic_checkpoint_path = \"\" #@param{type:\"string'}\n",
        "acoustic_folder_name = os.path.basename(os.path.dirname(acoustic_checkpoint_path)) + \"_acoustic\"\n",
        "acoustic_folder_path = os.path.dirname(acoustic_checkpoint_path)\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> path to your **VARIANCE CHECKPOINT** (leave blank if you don't have any): automatically use latest checkpoint that is in the same folder\n",
        "variance_checkpoint_path = \"\" #@param{type:\"string'}\n",
        "variance_folder_name = os.path.basename(os.path.dirname(variance_checkpoint_path)) + \"_variance\"\n",
        "variance_folder_path = os.path.dirname(variance_checkpoint_path)\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> path to where you want to save your ONNX files (it will create a folder named \"onnx\" in this path)\n",
        "exp_folder = \"\" #@param{type:\"string\"}\n",
        "\n",
        "acoustic_onnx_exp = exp_folder + \"/onnx/acoustic\"\n",
        "variance_onnx_exp = exp_folder + \"/onnx/variance\"\n",
        "\n",
        "if not acoustic_checkpoint_path:\n",
        "    print(\"\\n\")\n",
        "    print(\"acoustic ckeckpoint path not specified, not exporting acoustic ONNX...\")\n",
        "else:\n",
        "    print(\"\\n\")\n",
        "    print(\"converting acoustic to onnx...\")\n",
        "    #cp stuff cus apparently exporter doesnt work without it\n",
        "    !cp {acoustic_folder_path}/config.yaml -r /content/DiffSinger/checkpoints/{acoustic_folder_name}\n",
        "    search_text = \"        args_work_dir = os.path.join(\"\n",
        "    replacement = f\"        args_work_dir = '{acoustic_folder_path}'\"\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"r\") as file:\n",
        "        lines = file.readlines()\n",
        "    for i, line in enumerate(lines):\n",
        "        if search_text in line:\n",
        "            lines[i] = replacement + \"\\n\"\n",
        "            break\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"w\") as file:\n",
        "            file.writelines(lines)\n",
        "    #incase if anyone wanna change it lmao\n",
        "    search_text_alt = \"        args_work_dir = '\"\n",
        "    replacement_alt = f\"        args_work_dir = '{acoustic_folder_path}'\"\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"r\") as file:\n",
        "        lines = file.readlines()\n",
        "    for i, line in enumerate(lines):\n",
        "        if search_text_alt in line:\n",
        "            lines[i] = replacement_alt + \"\\n\"\n",
        "            break\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"w\") as file:\n",
        "            file.writelines(lines)\n",
        "\n",
        "    if no_output:\n",
        "        !python /content/DiffSinger/scripts/export.py acoustic --exp {acoustic_folder_name} --out {exp_folder}/onnx/acoustic >/dev/null 2>&1\n",
        "    else:\n",
        "        !python /content/DiffSinger/scripts/export.py acoustic --exp {acoustic_folder_name} --out {exp_folder}/onnx/acoustic\n",
        "\n",
        "\n",
        "if not variance_checkpoint_path:\n",
        "    print(\"\\n\")\n",
        "    print(\"variance ckeckpoint path not specified, not exporting variance ONNX...\")\n",
        "else:\n",
        "    print(\"\\n\")\n",
        "    print(\"converting variance to onnx...\")\n",
        "    #cp stuff cus apparently exporter doesnt work without it\n",
        "    !cp {variance_folder_path}/config.yaml -r /content/DiffSinger/checkpoints/{variance_folder_name}\n",
        "    search_text = \"        args_work_dir = os.path.join(\"\n",
        "    replacement = f\"        args_work_dir = '{variance_folder_path}'\"\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"r\") as file:\n",
        "        lines = file.readlines()\n",
        "    for i, line in enumerate(lines):\n",
        "        if search_text in line:\n",
        "            lines[i] = replacement + \"\\n\"\n",
        "            break\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"w\") as file:\n",
        "            file.writelines(lines)\n",
        "    #incase if anyone wanna change it lmao\n",
        "    search_text_alt = \"        args_work_dir = '\"\n",
        "    replacement_alt = f\"        args_work_dir = '{variance_folder_path}'\"\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"r\") as file:\n",
        "        lines = file.readlines()\n",
        "    for i, line in enumerate(lines):\n",
        "        if search_text_alt in line:\n",
        "            lines[i] = replacement_alt + \"\\n\"\n",
        "            break\n",
        "    with open(\"/content/DiffSinger/utils/hparams.py\", \"w\") as file:\n",
        "            file.writelines(lines)\n",
        "    if no_output:\n",
        "        !python /content/DiffSinger/scripts/export.py variance --exp {variance_folder_name} --out {exp_folder}/onnx/variance >/dev/null 2>&1\n",
        "    else:\n",
        "        !python /content/DiffSinger/scripts/export.py variance --exp {variance_folder_name} --out {exp_folder}/onnx/variance\n",
        "\n",
        "\n",
        "if not variance_checkpoint_path:\n",
        "    folder_paths = [acoustic_onnx_exp]\n",
        "elif not acoustic_checkpoint_path:\n",
        "    folder_paths = [variance_onnx_exp]\n",
        "else:\n",
        "    folder_paths = [acoustic_onnx_exp, variance_onnx_exp]\n",
        "\n",
        "patterns = {\"acoustic.onnx\": \"acoustic.onnx\", \"dur.onnx\": \"dur.onnx\", \"linguistic.onnx\": \"linguistic.onnx\", \"pitch.onnx\": \"pitch.onnx\", \"variance.onnx\": \"variance.onnx\", \"phonemes.txt\": \"phonemes.txt\"}\n",
        "\n",
        "for folder_path in folder_paths:\n",
        "    for filename in os.listdir(folder_path):\n",
        "        for pattern, new_name in patterns.items():\n",
        "            if pattern in filename:\n",
        "                old_path = os.path.join(folder_path, filename)\n",
        "                new_path = os.path.join(folder_path, new_name)\n",
        "                if os.path.exists(old_path):\n",
        "                    os.rename(old_path, new_path)\n",
        "for folder_path in folder_paths:\n",
        "    for filename in os.listdir(folder_path):\n",
        "        if \"acoustic_acoustic.\" in filename:\n",
        "            new_filename = filename.replace(\"acoustic_acoustic.\", \"acoustic_\")\n",
        "        elif \"variance_variance.\" in filename:\n",
        "            new_filename = filename.replace(\"variance_variance.\", \"variance_\")\n",
        "        else:\n",
        "            new_filename = filename\n",
        "        old_path = os.path.join(folder_path, filename)\n",
        "        new_path = os.path.join(folder_path, new_filename)\n",
        "        os.rename(old_path, new_path)\n",
        "print(\"\\n\")\n",
        "print(\"ONNX export complete! Please refer to https://github.com/xunmengshe/OpenUtau/wiki/Voicebank-Development to make your model OU compatible\")\n",
        "print(\"\\n\")\n",
        "print(\"Or use the 'Build OpenUtau VB' cell to have things set up for you\")\n"
      ],
      "metadata": {
        "id": "x33iZhZchEMW",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Miscellaneous**"
      ],
      "metadata": {
        "id": "4sbU1aH5kGFE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title #Raw data conversion\n",
        "#@markdown ___\n",
        "%cd /content\n",
        "#@markdown This cell will export .lab and .ds files along with your data\n",
        "\n",
        "data_type = \"lab + wav (NNSVS format)\" # @param [\"lab + wav (NNSVS format)\"]\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> The path to your data zip file\n",
        "\n",
        "data_zip_path = \"\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> The path you will be saving the data to\n",
        "\n",
        "data_save_path = \"\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ___\n",
        "\n",
        "export_ds = True\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> _These values can exceed the amount that's in your data to maximize the segment length or to keep the data as is_\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> Determine how long it will segment your data to based on silence phoneme placement (seconds)\n",
        "segment_length = 15 #@param {type:\"slider\", min:5, max:35, step:1}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> Determine how many silence phoneme is allowed in the middle of each segment\n",
        "max_silence_phoneme_amount = 2 #@param {type:\"slider\", min:0, max:50, step:1}\n",
        "\n",
        "# leaving -S at 60 so max silence can be 60 seconds that exceeds the segment legnth cap idk why///\n",
        "# making the segment length cap at 35 secs because any longer than that would make training goes really slow\n",
        "\n",
        "# my ass dont remember why i made two... i think one is unnecessary extra but mehhh\n",
        "all_shits = \"/content/raw_data\"\n",
        "all_shits_not_wav_n_lab = \"/content/raw_data/diffsinger_db\"\n",
        "\n",
        "import os\n",
        "import csv\n",
        "import json\n",
        "import shutil\n",
        "from pydub import AudioSegment\n",
        "\n",
        "if os.path.exists(\"/content/raw_data\"):\n",
        "    shutil.rmtree(\"/content/raw_data\")\n",
        "\n",
        "if not os.path.exists(all_shits_not_wav_n_lab):\n",
        "  os.makedirs(all_shits_not_wav_n_lab)\n",
        "\n",
        "# using 'if not' bc i edited the wrong section which im also too lazy to fix it <3\n",
        "if not data_type == \"lab + wav (NNSVS format)\":\n",
        "    #changed to 7zip to support more compression types\n",
        "    !7z x \"$data_zip_path\" -o{all_shits_not_wav_n_lab}\n",
        "    for root, dirs, files in os.walk(all_shits):\n",
        "        for filename in files:\n",
        "            if filename.endswith(\".lab\"):\n",
        "                file_path = os.path.join(root, filename)\n",
        "                with open(file_path, \"r\") as file:\n",
        "                    file_data = file.read()\n",
        "                file_data = file_data.replace(\"SP\", \"pau\")\n",
        "                file_data = file_data.replace(\"br\", \"AP\")\n",
        "                with open(file_path, \"w\") as file:\n",
        "                    file.write(file_data)\n",
        "\n",
        "else:\n",
        "    !7z x \"$data_zip_path\" -o{all_shits_not_wav_n_lab}\n",
        "\n",
        "\n",
        "# for funny auto dict generator lmao\n",
        "out = \"/content/raw_data/custom_dict.txt\"\n",
        "\n",
        "phonemes = set()\n",
        "\n",
        "def is_excluded(phoneme):\n",
        "    return phoneme in [\"pau\", \"AP\", \"SP\"]\n",
        "\n",
        "if data_type == \"lab + wav (NNSVS format)\":\n",
        "    phoneme_folder_path = all_shits\n",
        "    for root, dirs, files in os.walk(phoneme_folder_path):\n",
        "        for file in files:\n",
        "            if file.endswith(\".lab\"):\n",
        "                fpath = os.path.join(root, file)\n",
        "                with open(fpath, \"r\") as lab_file:\n",
        "                    for line in lab_file:\n",
        "                        line = line.strip()\n",
        "                        if line:\n",
        "                            phoneme = line.split()[2]\n",
        "                            if not is_excluded(phoneme):\n",
        "                                phonemes.add(phoneme)\n",
        "\n",
        "with open(out, \"w\") as f:\n",
        "    for phoneme in sorted(phonemes):\n",
        "        f.write(phoneme + \"\t\" + phoneme + \"\\n\")\n",
        "\n",
        "# for vowels and consonants.txt.... well adding liquid type for uta's script\n",
        "dict_path = out\n",
        "vowel_types = {\"a\", \"i\", \"u\", \"e\", \"o\", \"N\", \"M\", \"NG\"}\n",
        "liquid_types = {\"y\", \"w\", \"l\", \"r\"} # r for english labels, it should be fine with jp too\n",
        "vowel_data = []\n",
        "consonant_data = []\n",
        "liquid_data = []\n",
        "\n",
        "with open(dict_path, \"r\") as f:\n",
        "    for line in f:\n",
        "        phoneme, _ = line.strip().split(\"\\t\")\n",
        "        if phoneme[0] in vowel_types:\n",
        "            vowel_data.append(phoneme)\n",
        "        elif phoneme[0] in liquid_types:\n",
        "            liquid_data.append(phoneme)\n",
        "        else:\n",
        "            consonant_data.append(phoneme)\n",
        "\n",
        "vowel_data.sort()\n",
        "liquid_data.sort()\n",
        "consonant_data.sort()\n",
        "directory = os.path.dirname(dict_path)\n",
        "\n",
        "# make txt for language json file\n",
        "vowel_txt_path = os.path.join(directory, \"vowels.txt\")\n",
        "with open(vowel_txt_path, \"w\") as f:\n",
        "    f.write(\" \".join(vowel_data))\n",
        "liquid_txt_path = os.path.join(directory, \"liquids.txt\")\n",
        "with open(liquid_txt_path, \"w\") as f:\n",
        "    f.write(\" \".join(liquid_data))\n",
        "consonant_txt_path = os.path.join(directory, \"consonants.txt\")\n",
        "with open(consonant_txt_path, \"w\") as f:\n",
        "    f.write(\" \".join(consonant_data))\n",
        "\n",
        "\n",
        "# here's a funny json append\n",
        "with open(vowel_txt_path, \"r\") as f:\n",
        "    vowel_data = f.read().split()\n",
        "with open(liquid_txt_path, \"r\") as f:\n",
        "    liquid_data = f.read().split()\n",
        "with open(consonant_txt_path, \"r\") as f:\n",
        "    consonant_data = f.read().split()\n",
        "phones4json = {\"vowels\": vowel_data, \"liquids\": liquid_data}\n",
        "with open(\"/content/nnsvs-db-converter/lang.sample.json\", \"w\") as rawr:\n",
        "    json.dump(phones4json, rawr, indent=4)\n",
        "\n",
        "\n",
        "if data_type == \"lab + wav (NNSVS format)\":\n",
        "    db_converter_script = \"/content/nnsvs-db-converter/db_converter.py\"\n",
        "    for raw_folder_name in os.listdir(all_shits_not_wav_n_lab):\n",
        "        raw_folder_path = os.path.join(all_shits_not_wav_n_lab, raw_folder_name)\n",
        "        if os.path.isdir(raw_folder_path):\n",
        "            !python {db_converter_script} -s {max_silence_phoneme_amount} -S 60 -l {segment_length} ${export_lab} -mD -c -L \"/content/nnsvs-db-converter/lang.sample.json\" -w htk --folder {raw_folder_path}\n",
        "\n",
        "if data_type == \"lab + wav (NNSVS format)\":\n",
        "    for raw_folder_name in os.listdir(all_shits_not_wav_n_lab):\n",
        "        raw_folder_path = os.path.join(all_shits_not_wav_n_lab, raw_folder_name)\n",
        "        !rm -rf {raw_folder_path}/*.wav {raw_folder_path}/*.lab\n",
        "        !mv {raw_folder_path}/diffsinger_db/* {raw_folder_path} 2> /dev/null\n",
        "        !rm -rf {raw_folder_path}/diffsinger_db\n",
        "        #!cp {raw_folder_path}/wavs/*.wav {raw_folder_path}\n",
        "\n",
        "# make it replace the first SP to AP cus it seems like people always forgot about it\n",
        "for root, _, files in os.walk(all_shits_not_wav_n_lab):\n",
        "    for file in files:\n",
        "        if file.endswith(\".csv\"):\n",
        "            file_path = os.path.join(root, file)\n",
        "            with open(file_path, \"r\", newline=\"\") as input_file:\n",
        "                csv_reader = csv.reader(input_file)\n",
        "                data = [row for row in csv_reader]\n",
        "                header = data[0]\n",
        "                if \"ph_seq\" in header:\n",
        "                    ph_seq_index = header.index(\"ph_seq\")\n",
        "                    if len(data) > 1 and len(data[1]) > ph_seq_index:\n",
        "                        data[1][ph_seq_index] = data[1][ph_seq_index].replace(\"SP\", \"AP\", 1)\n",
        "            with open(file_path, \"w\", newline=\"\") as output_file:\n",
        "                csv_writer = csv.writer(output_file)\n",
        "                csv_writer.writerows(data)\n",
        "\n",
        "print(\"extraction complete!\")\n",
        "print(\"\\n\")\n",
        "print(\"zipping up files...\")\n",
        "!zip -q -9 -r {data_save_path}/data.zip /content/raw_data/*"
      ],
      "metadata": {
        "cellView": "form",
        "id": "AI7EQ2jQkGEq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown # Build OpenUtau VB\n",
        "#@markdown ___\n",
        "#i need to clean this up it seems\n",
        "#plan: add a build ou section here by inserting onnx paths (or just the folder containing the folders to the onnx files) to build ou\n",
        "# ill have a config read function too so i dont have to add checkmark of if people train with embeds or shallow diff or not <3\n",
        "# yes im lazy rawr x3\n",
        "%cd /content\n",
        "import os\n",
        "import shutil\n",
        "import yaml\n",
        "\n",
        "constr_folder = \"/content/OU_voicebank\"\n",
        "if not os.path.exists(constr_folder):\n",
        "    os.makedirs(constr_folder)\n",
        "else:\n",
        "    shutil.rmtree(constr_folder)\n",
        "    os.makedirs(constr_folder)\n",
        "\n",
        "clear_output()\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> path to your **ACOUSTIC ONNX FOLDER**\n",
        "acoustic_onnx_folder = \"\" #@param{type:\"string'}\n",
        "#@markdown <font size=\"-1.5\"> path to the config.yaml of acoustic model\n",
        "acoustic_config = \"\" #@param{type:\"string'}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> path to your **VARIANCE ONNX FOLDER**\n",
        "variance_onnx_folder = \"\" #@param{type:\"string'}\n",
        "#@markdown <font size=\"-1.5\"> path to the config.yaml of variance model\n",
        "variance_config = \"\" #@param{type:\"string'}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> path to your word to phoneme dict (leave blank to use default Japanese dict)\n",
        "dictionary_path = \"\" #@param{type:\"string\"}\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> path to the folder you want to save the zip file to\n",
        "save_path = \"\" #@param{type:\"string\"}\n",
        "\n",
        "#@markdown ___\n",
        "\n",
        "#@markdown ## Character Configuration | character.txt and character.yaml\n",
        "\n",
        "#@markdown <font size=\"-1.5\"> your character display name| **required**\n",
        "name = \"\" #@param{type:\"string\"}\n",
        "\n",
        "print(\"copying files...\")\n",
        "main_stuff = f\"{constr_folder}/{name}\"\n",
        "if not os.path.exists(main_stuff):\n",
        "    os.makedirs(main_stuff)\n",
        "if not os.path.exists(f\"{main_stuff}/dsmain\"):\n",
        "    os.makedirs(f\"{main_stuff}/dsmain/embeds/acoustic\")\n",
        "    os.makedirs(f\"{main_stuff}/dsmain/embeds/variance\")\n",
        "!cp {acoustic_onnx_folder}/acoustic.onnx {main_stuff}/dsmain\n",
        "!cp {acoustic_onnx_folder}/phonemes.txt {main_stuff}/dsmain\n",
        "!cp {acoustic_onnx_folder}/*.emb {main_stuff}/dsmain/embeds/acoustic >/dev/null 2>&1\n",
        "!cp {variance_onnx_folder}/*.emb {main_stuff}/dsmain/embeds/variance >/dev/null 2>&1\n",
        "\n",
        "if variance_onnx_folder:\n",
        "    !cp {variance_onnx_folder}/linguistic.onnx {main_stuff}/dsmain\n",
        "else:\n",
        "    pass\n",
        "\n",
        "print(\"\\n\")\n",
        "print(\"writing character.txt...\")\n",
        "with open(f\"{main_stuff}/character.txt\", \"w\") as file:\n",
        "    file.write(f\"name={name}\\n\")\n",
        "    file.write(\"image=\\n\")\n",
        "    file.write(\"author=\\n\")\n",
        "    file.write(\"voice=\\n\")\n",
        "    file.write(\"web=\\n\")\n",
        "\n",
        "print(\"\\n\")\n",
        "print(\"writing character.yaml...\")\n",
        "with open(f\"{main_stuff}/character.yaml\", \"w\") as file:\n",
        "    file.write(\"text_file_encoding: utf-8\\n\")\n",
        "    file.write(\"portrait:\\n\")\n",
        "    file.write(\"portrait_opacity: 0.45\\n\")\n",
        "    file.write(\"default_phonemizer: OpenUtau.Core.DiffSinger.DiffSingerPhonemizer\\n\")\n",
        "    file.write(\"singer_type: diffsinger\\n\")\n",
        "acoustic_emb_files = os.listdir(acoustic_onnx_folder)\n",
        "acoustic_embeds = []\n",
        "acoustic_color_suffix = []\n",
        "for file in acoustic_emb_files:\n",
        "    if file.endswith(\".emb\"):\n",
        "        acoustic_emb = os.path.splitext(file)[0]\n",
        "        acoustic_embeds.append(\"dsmain/embeds/acoustic/\" + acoustic_emb)\n",
        "        acoustic_color_suffix.append(acoustic_emb)\n",
        "subbanks = []\n",
        "for i, (acoustic_embed_color, acoustic_embed_suffix) in enumerate(zip(acoustic_color_suffix, acoustic_embeds), start=1):\n",
        "    color = f\"{i:02}: {acoustic_embed_color}\"\n",
        "    suffix = f\"{acoustic_embed_suffix}\"\n",
        "    subbanks.append({\"color\": color, \"suffix\": suffix})\n",
        "if subbanks:\n",
        "    with open(f\"{main_stuff}/character.yaml\", \"r\") as config:\n",
        "        i_wanna_die_slash_j = yaml.safe_load(config)\n",
        "    i_wanna_die_slash_j[\"subbanks\"] = subbanks\n",
        "    with open(f\"{main_stuff}/character.yaml\", \"w\") as config:\n",
        "        yaml.dump(i_wanna_die_slash_j, config)\n",
        "\n",
        "print(\"\\n\")\n",
        "print(\"writing dsconfig.yaml for acoustic...\")\n",
        "with open(f\"{main_stuff}/dsconfig.yaml\", \"w\") as file:\n",
        "    file.write(\"phonemes: dsmain/phonemes.txt\\n\")\n",
        "    file.write(\"acoustic: dsmain/acoustic.onnx\\n\")\n",
        "    file.write(\"vocoder: nsf_hifigan\\n\")\n",
        "    file.write(\"singer_type: diffsinger\\n\")\n",
        "with open(acoustic_config, \"r\") as config:\n",
        "    mfking_config = yaml.safe_load(config)\n",
        "use_energy_embed = mfking_config.get(\"use_energy_embed\")\n",
        "use_breathiness_embed = mfking_config.get(\"use_breathiness_embed\")\n",
        "use_shallow_diffusion = mfking_config.get(\"use_shallow_diffusion\")\n",
        "max_depth = mfking_config.get(\"T_start\")\n",
        "speakers = mfking_config.get(\"speakers\") #looking back here, why is this even here lmao cus i used acoustic_embeds instead of speakers\n",
        "augmentation_arg = mfking_config.get(\"augmentation_args\")\n",
        "pitch_aug = mfking_config.get(\"use_key_shift_embed\")\n",
        "time_aug = mfking_config.get(\"use_speed_embed\")\n",
        "voicing = mfking_config.get(\"use_voicing_embed\")\n",
        "tension = mfking_config.get(\"use_tension_embed\")\n",
        "sample_rate = mfking_config.get(\"audio_sample_rate\")\n",
        "hop_size = mfking_config.get(\"hop_size\")\n",
        "win_size = mfking_config.get(\"win_size\")\n",
        "fft_size = mfking_config.get(\"fft_size\")\n",
        "num_mel_bins = mfking_config.get(\"audio_num_mel_bins\")\n",
        "mel_fmin = mfking_config.get(\"fmin\")\n",
        "mel_fmax = mfking_config.get(\"fmax\")\n",
        "mel_base = mfking_config.get(\"mel_base\")\n",
        "\n",
        "with open(f\"{main_stuff}/dsconfig.yaml\", \"r\") as config:\n",
        "    why_are_there_so_many_i_could_prob_make_it_one = yaml.safe_load(config)\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"use_energy_embed\"] = use_energy_embed\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"use_breathiness_embed\"] = use_breathiness_embed\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"use_variable_depth\"] = use_shallow_diffusion\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"max_depth\"] = max_depth\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"augmentation_args\"] = augmentation_arg\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"use_key_shift_embed\"] = pitch_aug\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"use_speed_embed\"] = time_aug\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"use_voicing_embed\"] = voicing\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"use_tension_embed\"] = tension\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"use_continuous_acceleration\"] = True\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"sample_rate\"] = sample_rate\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"hop_size\"] = hop_size\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"win_size\"] = win_size\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"fft_size\"] = fft_size\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"num_mel_bins\"] = num_mel_bins\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"fmin\"] = mel_fmin\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"fmax\"] = mel_fmax\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"mel_base\"] = mel_base\n",
        "why_are_there_so_many_i_could_prob_make_it_one[\"mel_scale\"] = \"slaney\"\n",
        "\n",
        "\n",
        "if subbanks:\n",
        "    why_are_there_so_many_i_could_prob_make_it_one[\"speakers\"] = acoustic_embeds\n",
        "with open(f\"{main_stuff}/dsconfig.yaml\", \"w\") as config:\n",
        "    yaml.dump(why_are_there_so_many_i_could_prob_make_it_one, config)\n",
        "\n",
        "\n",
        "variance_emb_files = os.listdir(variance_onnx_folder)\n",
        "variance_embeds = []\n",
        "for file in variance_emb_files:\n",
        "    if file.endswith(\".emb\"):\n",
        "        variance_emb = os.path.splitext(file)[0]\n",
        "        variance_embeds.append(\"../dsmain/embeds/variance/\" + variance_emb)\n",
        "\n",
        "print(\"\\n\")\n",
        "print(\"writing dsdict.yaml...\")\n",
        "if not dictionary_path:\n",
        "    dict_path = \"/content/jpn_dict.txt\"\n",
        "else:\n",
        "    dict_path = dictionary_path\n",
        "\n",
        "# for symbols list\n",
        "phoneme_dict_path = f\"{acoustic_onnx_folder}/dictionary.txt\"\n",
        "\n",
        "dsdict = \"dsdict.yaml\"\n",
        "\n",
        "def parse_phonemes(phonemes_str):\n",
        "    return phonemes_str.split()\n",
        "\n",
        "entries = []\n",
        "vowel_types = {\"a\", \"i\", \"u\", \"e\", \"o\", \"N\", \"M\", \"NG\", \"cl\", \"vf\"}\n",
        "vowel_data = []\n",
        "stop_data = []\n",
        "\n",
        "# Process the specified dictionary\n",
        "with open(dict_path, \"r\") as f:\n",
        "    for line in f:\n",
        "        word, phonemes_str = line.strip().split(\"\\t\")\n",
        "        phonemes = parse_phonemes(phonemes_str)\n",
        "        if len(phonemes) == 1:\n",
        "            entries.append({\"grapheme\": word, \"phonemes\": phonemes})\n",
        "        else:\n",
        "            entries.append({\"grapheme\": word, \"phonemes\": phonemes})\n",
        "\n",
        "with open(phoneme_dict_path, \"r\") as f:\n",
        "    for line in f:\n",
        "        phoneme, _ = line.strip().split(\"\\t\")\n",
        "        phoneme_type = \"vowel\" if phoneme[0] in vowel_types else \"stop\"\n",
        "        entry = {\"symbol\": phoneme, \"type\": phoneme_type}\n",
        "        if phoneme_type == \"vowel\":\n",
        "            vowel_data.append(entry)\n",
        "        else:\n",
        "            stop_data.append(entry)\n",
        "\n",
        "vowel_data.sort(key=lambda x: x[\"symbol\"])\n",
        "stop_data.sort(key=lambda x: x[\"symbol\"])\n",
        "\n",
        "dsdict_path = os.path.join(constr_folder, dsdict)\n",
        "with open(dsdict_path, \"w\") as f:\n",
        "    f.write(\"entries:\\n\")\n",
        "    for entry in entries:\n",
        "        f.write(f\"- grapheme: {entry['grapheme']}\\n\")\n",
        "        f.write(\"  phonemes:\\n\")\n",
        "        for phoneme in entry[\"phonemes\"]:\n",
        "            f.write(f\"  - {phoneme}\\n\")\n",
        "\n",
        "    f.write(\"\\nsymbols:\\n\")\n",
        "    for entry in vowel_data + stop_data:\n",
        "        f.write(f\"- symbol: {entry['symbol']}\\n\")\n",
        "        f.write(f\"  type: {entry['type']}\\n\")\n",
        "\n",
        "with open(variance_config, \"r\") as config:\n",
        "    mfking_config = yaml.safe_load(config)\n",
        "sample_rate = mfking_config.get(\"audio_sample_rate\")\n",
        "hop_size = mfking_config.get(\"hop_size\")\n",
        "predict_dur = mfking_config.get(\"predict_dur\")\n",
        "predict_pitch = mfking_config.get(\"predict_pitch\")\n",
        "use_melody_encoder = mfking_config.get(\"use_melody_encoder\")\n",
        "predict_voicing = mfking_config.get(\"predict_voicing\")\n",
        "predict_tension = mfking_config.get(\"predict_tension\")\n",
        "predict_energy = mfking_config.get(\"predict_energy\")\n",
        "predict_breathiness = mfking_config.get(\"predict_breathiness\")\n",
        "\n",
        "dur_onnx_path = variance_onnx_folder + \"/dur.onnx\"\n",
        "if os.path.exists(dur_onnx_path):\n",
        "    print(\"\\n\")\n",
        "    print(\"making dsdur directory and necessary files...\")\n",
        "    os.makedirs(f\"{main_stuff}/dsdur\")\n",
        "    !cp {dur_onnx_path} {main_stuff}/dsdur\n",
        "    !cp {dsdict_path} {main_stuff}/dsdur\n",
        "    with open(f\"{main_stuff}/dsdur/dsconfig.yaml\", \"w\") as file:\n",
        "        file.write(\"phonemes: ../dsmain/phonemes.txt\\n\")\n",
        "        file.write(\"linguistic: ../dsmain/linguistic.onnx\\n\")\n",
        "        file.write(\"dur: dur.onnx\\n\")\n",
        "    with open(f\"{main_stuff}/dsdur/dsconfig.yaml\", \"r\") as config:\n",
        "        dsdur_config = yaml.safe_load(config)\n",
        "    dsdur_config[\"use_continuous_acceleration\"] = True\n",
        "    dsdur_config[\"sample_rate\"] = sample_rate\n",
        "    dsdur_config[\"hop_size\"] = hop_size\n",
        "    dsdur_config[\"predict_dur\"] = predict_dur\n",
        "    if subbanks:\n",
        "        dsdur_config[\"speakers\"] = variance_embeds\n",
        "    with open(f\"{main_stuff}/dsdur/dsconfig.yaml\", \"w\") as config:\n",
        "        yaml.dump(dsdur_config, config)\n",
        "else:\n",
        "    print(\"\\n\")\n",
        "    print(\"dur.onnx not found, skipping on making dsdur folder...\")\n",
        "\n",
        "pitch_onnx_path = variance_onnx_folder + \"/pitch.onnx\"\n",
        "if os.path.exists(pitch_onnx_path):\n",
        "    print(\"\\n\")\n",
        "    print(\"making dspitch directory and necessary files...\")\n",
        "    os.makedirs(f\"{main_stuff}/dspitch\")\n",
        "    !cp {pitch_onnx_path} {main_stuff}/dspitch\n",
        "    !cp {dsdict_path} {main_stuff}/dspitch\n",
        "    with open(f\"{main_stuff}/dspitch/dsconfig.yaml\", \"w\") as file:\n",
        "        file.write(\"phonemes: ../dsmain/phonemes.txt\\n\")\n",
        "        file.write(\"linguistic: ../dsmain/linguistic.onnx\\n\")\n",
        "        file.write(\"pitch: pitch.onnx\\n\")\n",
        "        file.write(\"use_expr: true\\n\")\n",
        "    with open(f\"{main_stuff}/dspitch/dsconfig.yaml\", \"r\") as config:\n",
        "        dspitch_config = yaml.safe_load(config)\n",
        "    dspitch_config[\"use_continuous_acceleration\"] = True\n",
        "    dspitch_config[\"sample_rate\"] = sample_rate\n",
        "    dspitch_config[\"hop_size\"] = hop_size\n",
        "    dspitch_config[\"predict_dur\"] = predict_pitch\n",
        "    if subbanks:\n",
        "        dspitch_config[\"speakers\"] = variance_embeds\n",
        "    dspitch_config[\"use_note_rest\"] = use_melody_encoder\n",
        "    with open(f\"{main_stuff}/dspitch/dsconfig.yaml\", \"w\") as config:\n",
        "        yaml.dump(dspitch_config, config)\n",
        "else:\n",
        "    print(\"\\n\")\n",
        "    print(\"pitch.onnx not found, skipping on making dspitch folder...\")\n",
        "\n",
        "variance_onnx_path = variance_onnx_folder + \"/variance.onnx\"\n",
        "if os.path.exists(variance_onnx_path):\n",
        "    print(\"\\n\")\n",
        "    print(\"making dsvariance directory and necessary files...\")\n",
        "    os.makedirs(f\"{main_stuff}/dsvariance\")\n",
        "    !cp {variance_onnx_path} {main_stuff}/dsvariance\n",
        "    !cp {dsdict_path} {main_stuff}/dsvariance\n",
        "    with open(f\"{main_stuff}/dsvariance/dsconfig.yaml\", \"w\") as file:\n",
        "        file.write(\"phonemes: ../dsmain/phonemes.txt\\n\")\n",
        "        file.write(\"linguistic: ../dsmain/linguistic.onnx\\n\")\n",
        "        file.write(\"variance: variance.onnx\\n\")\n",
        "    with open(f\"{main_stuff}/dsvariance/dsconfig.yaml\", \"r\") as config:\n",
        "        dsvariance_config = yaml.safe_load(config)\n",
        "    dsvariance_config[\"use_continuous_acceleration\"] = True\n",
        "    dsvariance_config[\"sample_rate\"] = sample_rate\n",
        "    dsvariance_config[\"hop_size\"] = hop_size\n",
        "    dsvariance_config[\"predict_dur\"] = True #this one will always be true cus if there's no variance model, it shouldnt make this folder in the first place\n",
        "    dsvariance_config[\"predict_voicing\"] = predict_voicing\n",
        "    dsvariance_config[\"predict_tension\"] = predict_tension\n",
        "    dsvariance_config[\"predict_energy\"] = predict_energy\n",
        "    dsvariance_config[\"predict_breathiness\"] = predict_breathiness\n",
        "    if subbanks:\n",
        "        dsvariance_config[\"speakers\"] = variance_embeds\n",
        "    with open(f\"{main_stuff}/dsvariance/dsconfig.yaml\", \"w\") as config:\n",
        "        yaml.dump(dsvariance_config, config)\n",
        "else:\n",
        "    print(\"\\n\")\n",
        "    print(\"variance.onnx not found, skipping on making dsvariance folder...\")\n",
        "\n",
        "!rm -rf {dsdict_path}\n",
        "#im too lazy to write codes so ill just do this, itll only remove those folders if they're empty anyway\n",
        "!rm -d {main_stuff}/dsmain/embeds/* >/dev/null 2>&1\n",
        "!rm -d {main_stuff}/dsmain/embeds >/dev/null 2>&1\n",
        "\n",
        "print(\"\\n\")\n",
        "print(\"zipping up files...\")\n",
        "!zip -q -9 -r {save_path}/{name}.zip {main_stuff}/*\n",
        "\n",
        "print(\"\\n\")\n",
        "print(\"done!\")\n",
        "\n",
        "print(\"\\n\")\n",
        "print(\"You can download your model zip and use it in OpenUtau! If anything needed to be edit in the config then please do so\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "A70Sc3Hbmxh0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}