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common.yaml

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+path: ./logs/${hydra.job.config_name}/${now:%Y-%m-%d}/${now:%H-%M-%S}
+
+log_level: INFO
+seed: 1
+tb_log_dir: tensorboard
+tqdm: true
+
+hydra:
+  run:
+    dir: ${path}
+  job_logging:
+    formatters:
+      colorlog:
+        format: '[%(cyan)s%(asctime)s%(reset)s][%(blue)s%(name)s:%(lineno)s:%(funcName)s()%(reset)s][%(log_color)s%(levelname)s%(reset)s]
+          - %(message)s'
+    handlers:
+      file:
+        filename: ${hydra.run.dir}/${hydra.job.name}_${now:%Y-%m-%d}_${now:%H-%M-%S}.log
+
+defaults:
+  - override hydra/job_logging: colorlog
+  - override hydra/hydra_logging: colorlog

config.yaml

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+defaults:
+- common
+
+train:
+  batch_size: 128
+  betas: [0.8, 0.99]
+  c_kl: 1.0
+  c_mel: 45
+  distributed: false  # BUG: multi-gpu is not working
+  use_multiprocessing: false # BUG: multi-gpu is not working
+  epochs: 20
+  eps: 1e-9
+  fp16_run: false
+  init_lr_ratio: 1
+  raise_error: false
+  learning_rate: 2e-4
+  log_interval: 10
+  log_level: ${log_level}
+  lr_decay: 0.98
+  max_speclen: 128
+  port: 8005
+  resume_training: false  # set to false to finetune from a model
+  seed: 1234
+  segment_size: 8960
+  use_sr: false
+  valid_epoch_interval: 1
+  valid_steps_interval: 1000
+  save_epoch_interval: 10
+  save_steps_interval: 1000
+  warmup_epochs: 0
+  # weighted_batch_speaker_sampling : false
+  # weighted_batch_lang_sampling : false
+  weighted_batch_speaker_sampling : 0.5
+  weighted_batch_lang_sampling : 0.5
+
+data:
+  dataset_dir: /raid/lucasgris/free-svc/data
+  filter_length: 1280
+  hop_length: 320
+  max_wav_value: 32768.0
+  mel_fmax: null
+  mel_fmin: 0.0
+  n_mel_channels: 80
+  num_workers: 64
+  # For pitch extraction, set the pitch_predictor (will compute in dataloader) or pitch_features_dir (will load from disk)
+  pitch_predictor: rmvpe # pm | crepe | harvest | dio | rmvpe | fcpe
+  pitch_features_dir: ${data.dataset_dir}/pitch_features/
+  sampling_rate: 24000
+  spectrogram_dir: null #${data.dataset_dir}/spectrograms # it is recommended NOT to use if you have small disk space
+  # For speaker embedding extraction, set the use_spk_emb to True and spk_embeddings_dir (will load from disk) or configure the model to compute it on forward
+  use_spk_emb: true
+  spk_embeddings_dir: ${data.dataset_dir}/spk_embeddings
+  # SR augmentation is deprecated, set use_sr to False
+  sr_min_max: [68, 92]
+  # For content feature extraction, set the content_feature_dir (will load from disk) or configure the model to compute it on forward
+  content_feature_dir: null
+  training_files: data/train.csv
+  validation_files: data/valid.csv
+  win_length: 1280
+
+model:
+  save_dir: null
+  filter_channels: 768
+  finetune_from_model:
+    discriminator: /raid/lucasgris/free-svc/D-freevc-24.pth
+    generator: /raid/lucasgris/free-svc/freevc-24.pth
+  hidden_channels: 192
+  inter_channels: 192
+  kernel_size: 3
+  n_heads: 2
+  n_layers_q: 3
+  n_layers: 6
+  p_dropout: 0.1
+  resblock_dilation_sizes: [[1,3,5], [1,3,5], [1,3,5]]
+  resblock_kernel_sizes: [3,7,11]
+  resblock: 1
+  c_dim: 768
+  upsample_initial_channel: 512
+  upsample_kernel_sizes: [16,16,4,4]
+  upsample_rates: [10,8,2,2]
+  use_spectral_norm: false
+  freeze_external_spk: true
+  device: cuda
+  # For online speaker embedding extraction, set the use_spk_emb to True and spk_encoder_type
+  use_spk_emb: false
+  gin_channels: null # gin_channels = spk_encoder.embedding_dim
+  spk_encoder_type: null # ECAPA2SpeakerEncoder16k |
+  # For content feature extraction, set the content_encoder_type and content_encoder_ckpt
+  content_encoder_type: null # load from disk (data) - hubert | wavlm
+  content_encoder_ckpt: null # load from disk (data) - [path] | models/wavlm/WavLM-Large.pt | lengyue233/content-vec-best
+  post_content_encoder_type: vits-encoder-with-uv-emb # or freevc-bottleneck
+  coarse_f0: true
+  cond_f0_on_flow: false