{
  "root": "pretrained_models",
  "structure": {
    "files": [
      {
        "path": "pretrained_models/denoiser_model.onnx",
        "info": "Audio denoising model (DeepFilterNet)"
      }
    ],
    "ckpts": {
      "autoregressive": {
        "path": "pretrained_models/ckpts/autoregressive",
        "description": "Autoregressive neural codec editing model and its dependencies",
        "files": [
          {
            "path": "pretrained_models/ckpts/autoregressive/config.json",
            "info": "Model architecture and hyperparameter configuration for the autoregressive editing model"
          },
          {
            "path": "pretrained_models/ckpts/autoregressive/multilingual_330M.pth",
            "info": "Main weights of the multilingual autoregressive codec editing model (~330M parameters), used when Edit Model=autoregressive"
          },
          {
            "path": "pretrained_models/ckpts/autoregressive/encodec_4cb2048_giga.th",
            "info": "Neural codec encoder (AudioTokenizer) with 4 codebooks × 2048 codewords, used to decode discrete codes into 16kHz waveforms"
          },
          {
            "path": "pretrained_models/ckpts/autoregressive/dac_SR_8codes_2048_hop960_speech.pth",
            "info": "DAC super-resolution model (AudioSR), used to reconstruct audio from 16kHz codec to 48kHz or target sample rate for the AR backend"
          }
        ]
      },
      "multilingual_grl": {
        "path": "pretrained_models/ckpts/multilingual_grl",
        "description": "Main LEMAS-TTS model (multilingual_grl)",
        "files": [
          {
            "path": "pretrained_models/ckpts/multilingual_grl/multilingual_grl.safetensors",
            "info": "Unconditional/text-conditioned non-autoregressive model weights with GRL, supporting multilingual TTS and editing (default Edit Model in Gradio)"
          }
        ]
      },
      "multilingual_prosody": {
        "path": "pretrained_models/ckpts/multilingual_prosody",
        "description": "non-autoregressive variant with an additional prosody encoder",
        "files": [
          {
            "path": "pretrained_models/ckpts/multilingual_prosody/multilingual_prosody.safetensors",
            "info": "Multilingual model weights with global prosody conditioning enabled (selectable in the model menu)"
          }
        ]
      },
      "prosody_encoder": {
        "path": "pretrained_models/ckpts/prosody_encoder",
        "description": "Pretssel / UnitY2-style prosody encoder used by the non-autoregressive prosody backend",
        "files": [
          {
            "path": "pretrained_models/ckpts/prosody_encoder/pretssel_cfg.json",
            "info": "Architecture configuration for the Pretssel/UnitY2 prosody encoder (dimensions, number of layers, etc.)"
          },
          {
            "path": "pretrained_models/ckpts/prosody_encoder/prosody_encoder_UnitY2.pt",
            "info": "Prosody encoder weights, used to extract global prosody embeddings from reference audio"
          }
        ]
      },
      "vocos-mel-24khz": {
        "path": "pretrained_models/ckpts/vocos-mel-24khz",
        "description": "Vocos neural vocoder (mel → 24kHz waveform)",
        "files": [
          {
            "path": "pretrained_models/ckpts/vocos-mel-24khz/config.yaml",
            "info": "Vocos vocoder configuration defining mel feature dimensions and network architecture"
          },
          {
            "path": "pretrained_models/ckpts/vocos-mel-24khz/pytorch_model.bin",
            "info": "Main Vocos vocoder weights, used to decode mel features in the CFM backend"
          },
          {
            "path": "pretrained_models/ckpts/vocos-mel-24khz/README.md",
            "info": "Documentation for the vocoder (origin, usage, and notes)"
          }
        ]
      }
    },
    "data": {
      "multilingual_grl": {
        "path": "pretrained_models/data/multilingual_grl",
        "description": "Text vocabulary for the multilingual_grl model",
        "files": [
          {
            "path": "pretrained_models/data/multilingual_grl/vocab.txt",
            "info": "phone vocabulary corresponding to the text embeddings of the multilingual_grl model"
          }
        ]
      },
      "multilingual_prosody": {
        "path": "pretrained_models/data/multilingual_prosody",
        "description": "Text vocabulary for the multilingual_prosody model",
        "files": [
          {
            "path": "pretrained_models/data/multilingual_prosody/vocab.txt",
            "info": "Shared text vocabulary used by the multilingual_prosody model"
          }
        ]
      }
    },
    "demos": {
      "root": "pretrained_models/demos",
      "files": [
        {
          "path": "pretrained_models/demos/test.wav",
          "info": "Simple test audio used for quick validation of gradio script"
        }
      ],
      "lemas_edit_test": {
        "path": "pretrained_models/demos/lemas_edit_test",
        "description": "Audio samples and alignment annotations for LEMAS-Edit demos",
        "subdirs": {
          "vocals": {
            "path": "pretrained_models/demos/lemas_edit_test/vocals",
            "files": [
              {
                "path": "pretrained_models/demos/lemas_edit_test/vocals/en_AUD0000000214_S0001522.mp3",
                "info": "English demo audio used for AR/NAR editing examples"
              },
              {
                "path": "pretrained_models/demos/lemas_edit_test/vocals/zh_emilia_zh_0008385782.mp3",
                "info": "Chinese demo audio used for multilingual editing examples"
              }
            ]
          },
          "align": {
            "path": "pretrained_models/demos/lemas_edit_test/align",
            "files": [
              {
                "path": "pretrained_models/demos/lemas_edit_test/align/en_AUD0000000214_S0001522.json",
                "info": "MMS alignment JSON for the English demo, including intervals, words, and modified_index used for editing"
              },
              {
                "path": "pretrained_models/demos/lemas_edit_test/align/zh_emilia_zh_0008385782.json",
                "info": "MMS alignment JSON for the Chinese demo"
              }
            ]
          }
        }
      }
    },
    "uvr5": {
      "path": "pretrained_models/uvr5",
      "description": "Kim Vocal UVR5 models and configurations (used for denoising)",
      "files": [
        {
          "path": "pretrained_models/uvr5/Kim_Vocal_1.onnx",
          "info": "Main UVR5 model (ONNX format) for vocal/accompaniment separation and denoising"
        },
        {
          "path": "pretrained_models/uvr5/MDX-Net-Kim-Vocal1.json",
          "info": "UVR5 model architecture and inference configuration (channels, frame length, etc.)"
        },
        {
          "path": "pretrained_models/uvr5/model_data.json",
          "info": "UVR5 metadata including presets, model list, and default parameters"
        },
        {
          "path": "pretrained_models/uvr5/model_name_mapper.json",
          "info": "Mapping from internal UVR5 model names to human-readable names for frontend selection"
        }
      ]
    },
    "whisperx": {
      "path": "pretrained_models/whisperx",
      "description": "WhisperX VAD and segmentation model assets",
      "files": [
        {
          "path": "pretrained_models/whisperx/whisperx-vad-segmentation.bin",
          "info": "WhisperX voice activity detection (VAD) weights used for long-audio segmentation and ASR alignment assistance"
        },
        {
          "path": "pretrained_models/whisperx/whisperx-vad-segmentation.bak",
          "info": "Backup or legacy version of the VAD model, kept for safety"
        }
      ]
    }
  }
}