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195 lines
6.1 KiB
YAML
195 lines
6.1 KiB
YAML
# This config contains the default values for training 22.05kHz audio codec model which encodes mel spectrogram
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# instead of raw audio.
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# If you want to train model on other dataset, you can change config values according to your dataset.
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# Most dataset-specific arguments are in the head of the config file, see below.
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name: MelCodec
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max_epochs: ???
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# Adjust batch size based on GPU memory
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batch_size: 16
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# When doing weighted sampling with multiple manifests, this defines how many training steps are in an epoch.
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# If null, then weighted sampling is disabled.
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weighted_sampling_steps_per_epoch: null
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# Dataset metadata for each manifest
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# https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/tts/data/vocoder_dataset.py#L39-L41
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train_ds_meta: ???
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val_ds_meta: ???
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log_ds_meta: ???
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log_dir: ???
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# Modify these values based on your sample rate
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sample_rate: 22050
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win_length: 1024
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hop_length: 256
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train_n_samples: 8192 # ~0.37 seconds
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# The product of the up_sample_rates should match the hop_length.
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# For example 8 * 8 * 2 * 2 = 256.
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up_sample_rates: [8, 8, 2, 2]
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model:
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max_epochs: ${max_epochs}
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steps_per_epoch: ${weighted_sampling_steps_per_epoch}
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sample_rate: ${sample_rate}
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samples_per_frame: ${hop_length}
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mel_loss_l1_scale: 1.0
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mel_loss_l2_scale: 0.0
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stft_loss_scale: 20.0
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time_domain_loss_scale: 0.0
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commit_loss_scale: 0.0
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# Probability of updating the discriminator during each training step
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# For example, update the discriminator 1/2 times (1 update for every 2 batches)
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disc_updates_per_period: 1
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disc_update_period: 2
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# All resolutions for mel reconstruction loss, ordered [num_fft, hop_length, window_length]
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loss_resolutions: [
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[32, 8, 32], [64, 16, 64], [128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]
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]
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mel_loss_dims: [5, 10, 20, 40, 80, 160, 320]
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mel_loss_log_guard: 1.0
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stft_loss_log_guard: 1.0
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feature_loss_type: absolute
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train_ds:
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dataset:
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_target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
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dataset_meta: ${train_ds_meta}
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weighted_sampling_steps_per_epoch: ${weighted_sampling_steps_per_epoch}
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sample_rate: ${sample_rate}
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n_samples: ${train_n_samples}
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min_duration: 0.4
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max_duration: null
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dataloader_params:
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batch_size: ${batch_size}
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drop_last: true
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num_workers: 4
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validation_ds:
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dataset:
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_target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
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sample_rate: ${sample_rate}
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n_samples: null
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min_duration: null
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max_duration: null
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trunc_duration: 10.0 # Only use the first 10 seconds of audio for computing validation loss
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dataset_meta: ${val_ds_meta}
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dataloader_params:
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batch_size: 4
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num_workers: 2
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# Configures how audio samples are generated and saved during training.
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# Remove this section to disable logging.
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log_config:
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log_dir: ${log_dir}
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log_epochs: [10, 50, 100, 150, 200]
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epoch_frequency: 100
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log_tensorboard: false
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log_wandb: false
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generators:
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- _target_: nemo.collections.tts.parts.utils.callbacks.AudioCodecArtifactGenerator
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log_audio: true
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log_encoding: false
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log_dequantized: false
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dataset:
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_target_: nemo.collections.tts.data.vocoder_dataset.VocoderDataset
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sample_rate: ${sample_rate}
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n_samples: null
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min_duration: null
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max_duration: null
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trunc_duration: 10.0 # Only log the first 10 seconds of generated audio.
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dataset_meta: ${log_ds_meta}
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dataloader_params:
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batch_size: 4
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num_workers: 2
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audio_encoder:
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_target_: nemo.collections.tts.modules.audio_codec_modules.MultiBandMelEncoder
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mel_bands: [[0, 10], [10, 20], [20, 30], [30, 40], [40, 50], [50, 60], [60, 70], [70, 80]]
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out_channels: 4 # The dimension of each codebook
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hidden_channels: 128
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filters: 256
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mel_processor:
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_target_: nemo.collections.tts.modules.audio_codec_modules.MelSpectrogramProcessor
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mel_dim: 80
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sample_rate: ${sample_rate}
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win_length: ${win_length}
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hop_length: ${hop_length}
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audio_decoder:
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_target_: nemo.collections.tts.modules.audio_codec_modules.HiFiGANDecoder
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up_sample_rates: ${up_sample_rates}
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input_dim: 32 # Should be equal to len(audio_encoder.mel_bands) * audio_encoder.out_channels
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base_channels: 1024 # This is double the base channels of HiFi-GAN V1, making it approximately 4x larger.
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vector_quantizer:
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_target_: nemo.collections.tts.modules.audio_codec_modules.GroupFiniteScalarQuantizer
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num_groups: 8 # Should equal len(audio_encoder.mel_bands)
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num_levels_per_group: [8, 5, 5, 5] # 8 * 5 * 5 * 5 = 1000 entries per codebook
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discriminator:
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_target_: nemo.collections.tts.modules.audio_codec_modules.Discriminator
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discriminators:
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- _target_: nemo.collections.tts.modules.encodec_modules.MultiResolutionDiscriminatorSTFT
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resolutions: [[128, 32, 128], [256, 64, 256], [512, 128, 512], [1024, 256, 1024], [2048, 512, 2048]]
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- _target_: nemo.collections.tts.modules.audio_codec_modules.MultiPeriodDiscriminator
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generator_loss:
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_target_: nemo.collections.tts.losses.audio_codec_loss.GeneratorSquaredLoss
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discriminator_loss:
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_target_: nemo.collections.tts.losses.audio_codec_loss.DiscriminatorSquaredLoss
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optim:
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_target_: torch.optim.Adam
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lr: 2e-4
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betas: [0.8, 0.99]
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sched:
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name: ExponentialLR
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gamma: 0.998
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trainer:
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num_nodes: 1
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devices: 1
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accelerator: gpu
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strategy: ddp_find_unused_parameters_true
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precision: 16
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max_epochs: ${max_epochs}
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accumulate_grad_batches: 1
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enable_checkpointing: False # Provided by exp_manager
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logger: false # Provided by exp_manager
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log_every_n_steps: 100
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check_val_every_n_epoch: 5
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benchmark: false
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exp_manager:
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exp_dir: null
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name: ${name}
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create_tensorboard_logger: false
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create_wandb_logger: false
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wandb_logger_kwargs:
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name: null
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project: null
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create_checkpoint_callback: true
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checkpoint_callback_params:
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monitor: val_loss
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mode: min
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save_top_k: 5
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save_best_model: true
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always_save_nemo: true
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resume_if_exists: false
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resume_ignore_no_checkpoint: false
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