# This config contains the default values for training HiFi-GAN model on LJSpeech dataset. # If you want to train model on other dataset, you can change config values according to your dataset. # Most dataset-specific arguments are in the head of the config file, see below. name: "HifiGan" train_dataset: ??? validation_datasets: ??? # Default values for dataset with sample_rate=22050 sample_rate: 22050 n_mel_channels: 80 n_window_size: 1024 n_window_stride: 256 n_fft: 1024 lowfreq: 0 highfreq: 8000 window: hann train_n_segments: 8192 train_max_duration: null train_min_duration: 0.75 val_n_segments: 66048 val_max_duration: null val_min_duration: 3 defaults: - model/generator: v1 - model/train_ds: train_ds - model/validation_ds: val_ds model: preprocessor: _target_: nemo.collections.asr.parts.preprocessing.features.FilterbankFeatures nfilt: ${n_mel_channels} lowfreq: ${lowfreq} highfreq: ${highfreq} n_fft: ${n_fft} n_window_size: ${n_window_size} n_window_stride: ${n_window_stride} pad_to: 0 pad_value: -11.52 sample_rate: ${sample_rate} window: ${window} normalize: null preemph: null dither: 0.0 frame_splicing: 1 log: true log_zero_guard_type: clamp log_zero_guard_value: 1e-05 mag_power: 1.0 use_grads: false exact_pad: true optim: _target_: torch.optim.AdamW lr: 0.0002 betas: [0.8, 0.99] sched: name: CosineAnnealing min_lr: 1e-5 warmup_ratio: 0.02 max_steps: 2500000 l1_loss_factor: 45 denoise_strength: 0.0025 trainer: num_nodes: 1 devices: 1 accelerator: gpu strategy: ddp_find_unused_parameters_true precision: 32 max_steps: ${model.max_steps} accumulate_grad_batches: 1 enable_checkpointing: False # Provided by exp_manager logger: false # Provided by exp_manager log_every_n_steps: 100 check_val_every_n_epoch: 10 benchmark: false exp_manager: exp_dir: null name: ${name} create_tensorboard_logger: true create_checkpoint_callback: true checkpoint_callback_params: monitor: val_loss mode: min create_wandb_logger: false wandb_logger_kwargs: name: null project: null entity: null resume_if_exists: false resume_ignore_no_checkpoint: false