audioldm_train/config/mos_as_token/qa_mdt.yaml

log_directory: "./log/latent_diffusion"
project: "audioldm"
precision: "high"

# TODO: change this with your project path
base_root: "./qa-mdt"

# TODO: change this with your pretrained path
# TODO: pretrained path is also needed in "base_root/offset_pretrained_checkpoints.json"
pretrained:
 clap_music: "./qa-mdt/checkpoints/clap_music"
 flan_t5: "./qa-mdt/checkpoints/flant5"
 hifi-gan: "./qa-mdt/checkpoints/hifi-gan/checkpoints"
 roberta-base: "./qa-mdt/checkpoints/robertabase"

# TODO: lmdb dataset that stores pMOS of the training dataset
# while in inference, we don't need it !!!
# while in inference, we don't need it !!!
# while in inference, we don't need it !!!
mos_path: "" 

train_path:
  train_lmdb_path: [] # path list of training lmdb folders

val_path:
  val_lmdb_path: [] # path list of training lmdb folders
  val_key_path: [] #  path list of training lmdb key files

variables:
  sampling_rate: &sampling_rate 16000 
  mel_bins: &mel_bins 64
  latent_embed_dim: &latent_embed_dim 8
  latent_t_size: &latent_t_size 256 # TODO might need to change
  latent_f_size: &latent_f_size 16 # TODO might need to change
  in_channels: &unet_in_channels 8 # TODO might need to change
  optimize_ddpm_parameter: &optimize_ddpm_parameter true
  optimize_gpt: &optimize_gpt true
  warmup_steps: &warmup_steps 2000

# we rewrite the dataset so it may not be needed
data: 
  train: ["audiocaps"]
  val: "audiocaps"
  test: "audiocaps"
  class_label_indices: "audioset_eval_subset"
  dataloader_add_ons: ["waveform_rs_48k"] 

step:
  validation_every_n_epochs: 10000
  save_checkpoint_every_n_steps: 1000
  # limit_val_batches: 2
  max_steps: 8000000
  save_top_k: 1000

preprocessing:
  audio:
    sampling_rate: *sampling_rate
    max_wav_value: 32768.0
    duration: 10.24
  stft:
    filter_length: 1024
    hop_length: 160
    win_length: 1024
  mel:
    n_mel_channels: *mel_bins
    mel_fmin: 0
    mel_fmax: 8000 

augmentation:
  mixup: 0.0

model:
  target: qa_mdt.audioldm_train.modules.latent_diffusion.ddpm.LatentDiffusion
  params: 
    # Autoencoder
    first_stage_config:
      base_learning_rate: 8.0e-06
      target: qa_mdt.audioldm_train.modules.latent_encoder.autoencoder.AutoencoderKL
      params: 
        # TODO: change it with your VAE checkpoint
        reload_from_ckpt: "./qa-mdt/checkpoints/hifi-gan/checkpoints/vae_mel_16k_64bins.ckpt"
        sampling_rate: *sampling_rate
        batchsize: 1
        monitor: val/rec_loss
        image_key: fbank
        subband: 1
        embed_dim: *latent_embed_dim
        time_shuffle: 1
        lossconfig:
          target: qa_mdt.audioldm_train.losses.LPIPSWithDiscriminator
          params:
            disc_start: 50001
            kl_weight: 1000.0
            disc_weight: 0.5
            disc_in_channels: 1
        ddconfig: 
          double_z: true
          mel_bins: *mel_bins
          z_channels: 8
          resolution: 256
          downsample_time: false
          in_channels: 1
          out_ch: 1
          ch: 128 
          ch_mult:
          - 1
          - 2
          - 4
          num_res_blocks: 2
          attn_resolutions: []
          dropout: 0.0
    
    # Other parameters
    base_learning_rate: 8.0e-5
    warmup_steps: *warmup_steps
    optimize_ddpm_parameter: *optimize_ddpm_parameter
    sampling_rate: *sampling_rate
    batchsize: 16
    linear_start: 0.0015
    linear_end: 0.0195
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    unconditional_prob_cfg: 0.1
    parameterization: eps # [eps, x0, v]
    first_stage_key: fbank
    latent_t_size: *latent_t_size
    latent_f_size: *latent_f_size
    channels: *latent_embed_dim
    monitor: val/loss_simple_ema
    scale_by_std: true

    unet_config:
      # TODO: choose your class, Default: MDT_MOS_AS_TOKEN 
      # (Noted: the 2D-Rope, SwiGLU and the MDT are in two classes, when training with all of them, they should be changed and merged)
      target: qa_mdt.audioldm_train.modules.diffusionmodules.PixArt.PixArt_MDT_MOS_AS_TOKEN
      params:
        input_size : [256, 16]
      # patch_size: [16,4]
        patch_size : [4, 1]
        overlap_size: [0, 0]
        in_channels : 8
        hidden_size : 1152
        depth : 28
        num_heads : 16
        mlp_ratio : 4.0
        class_dropout_prob : 0.1
        pred_sigma : True
        drop_path : 0.
        window_size : 0
        window_block_indexes : None
        use_rel_pos : False
        cond_dim : 1024
        lewei_scale : 1.0
        overlap: [0, 0]
        use_cfg: true
        mask_ratio: 0.30
        decode_layer: 8
    
    cond_stage_config:
      crossattn_flan_t5:
        cond_stage_key: text
        conditioning_key: crossattn
        target: qa_mdt.audioldm_train.conditional_models.FlanT5HiddenState

    evaluation_params:
      unconditional_guidance_scale: 3.5
      ddim_sampling_steps: 200
      n_candidates_per_samples: 3