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singing_voice_conversion
File size: 2,500 Bytes 
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{
  "base_config": "config/vocoder.json",
  "model_type": "GANVocoder",
  // TODO: Choose your needed datasets
  "dataset": [
    "csd",
    "kising",
    "m4singer",
    "nus48e",
    "opencpop",
    "opensinger",
    "opera",
    "pjs",
    "popbutfy",
    "popcs",
    "ljspeech",
    "vctk",
    "libritts",
  ],
  "dataset_path": {
    // TODO: Fill in your dataset path
    "csd": "[dataset path]",
    "kising": "[dataset path]",
    "m4singer": "[dataset path]",
    "nus48e": "[dataset path]",
    "opencpop": "[dataset path]",
    "opensinger": "[dataset path]",
    "opera": "[dataset path]",
    "pjs": "[dataset path]",
    "popbutfy": "[dataset path]",
    "popcs": "[dataset path]",
    "ljspeech": "[dataset path]",
    "vctk": "[dataset path]",
    "libritts": "[dataset path]",
  },
  // TODO: Fill in the output log path
  "log_dir": "ckpts/vocoder",
  "preprocess": {
    // Acoustic features
    "extract_mel": true,
    "extract_audio": true,
    "extract_pitch": false,
    "extract_uv": false,
    "pitch_extractor": "parselmouth",

    // Features used for model training
    "use_mel": true,
    "use_frame_pitch": false,
    "use_uv": false,
    "use_audio": true,

    // TODO: Fill in the output data path
    "processed_dir": "data/",
    "n_mel": 100,
    "sample_rate": 24000
  },
  "model": {
    // TODO: Choose your needed discriminators
    "discriminators": [
      "msd",
      "mpd",
      "msstftd",
      "mssbcqtd",
    ],
    "mpd": {
      "mpd_reshapes": [
        2,
        3,
        5,
        7,
        11
      ],
      "use_spectral_norm": false,
      "discriminator_channel_mult_factor": 1
    },
    "mrd": {
      "resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
      "use_spectral_norm": false,
      "discriminator_channel_mult_factor": 1,
      "mrd_override": false
    },
    "msstftd": {
        "filters": 32
    },
    "mssbcqtd": {
      hop_lengths: [512, 256, 256],
      filters: 32,
      max_filters: 1024,
      filters_scale: 1,
      dilations: [1, 2, 4],
      in_channels: 1,
      out_channels: 1,
      n_octaves: [9, 9, 9],
      bins_per_octaves: [24, 36, 48]
    },
  },
  "train": {
    // TODO: Choose a suitable batch size, training epoch, and save stride
    "batch_size": 32,
    "max_epoch": 1000000,
    "save_checkpoint_stride": [20],
    "adamw": {
        "lr": 2.0e-4,
        "adam_b1": 0.8,
        "adam_b2": 0.99
    },
    "exponential_lr": {
        "lr_decay": 0.999
    },
  }
}