98 lines
2.2 KiB
YAML
98 lines
2.2 KiB
YAML
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#### general settings
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name: train_tacotron2_lj
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use_tb_logger: true
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gpu_ids: [0]
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start_step: -1
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fp16: false
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checkpointing_enabled: true
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wandb: false
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datasets:
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train:
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name: lj
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n_workers: 1
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batch_size: 72
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mode: nv_tacotron
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path: E:\4k6k\datasets\audio\LJSpeech-1.1\ljs_audio_text_train_filelist.txt
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networks:
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mel_gen:
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type: generator
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which_model_G: nv_tacotron2
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args:
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encoder_kernel_size: 5
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encoder_n_convolutions: 3
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encoder_embedding_dim: 512
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decoder_rnn_dim: 1024
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prenet_dim: 256
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max_decoder_steps: 1000
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attention_rnn_dim: 1024
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attention_dim: 128
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attention_location_n_filters: 32
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attention_location_kernel_size: 31
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postnet_embedding_dim: 512
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postnet_kernel_size: 5
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postnet_n_convolutions: 5
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#### path
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path:
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#pretrain_model_generator: ../experiments/diffusion_unet_128_imageset_22000.pt
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strict_load: true
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#resume_state: ../experiments/train_imgset_unet_diffusion/training_state/54000.state
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steps:
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generator:
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training: mel_gen
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optimizer: adamw
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optimizer_params:
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lr: !!float 1.2e-3
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weight_decay: !!float 1e-6
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beta1: 0.9
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beta2: 0.9999
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clip_grad_eps: 1.0
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injectors:
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mel:
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type: generator
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generator: mel_gen
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in: [padded_text, input_lengths, padded_mel, output_lengths]
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out: [mel_outputs, mel_outputs_postnet, gate_outputs, alignments]
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losses:
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tacotron_loss:
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type: nv_tacotron2_loss
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weight: 1
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mel_target_key: padded_mel
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mel_output_key: mel_outputs
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mel_output_postnet_key: mel_outputs_postnet
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gate_target_key: padded_gate
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gate_output_key: gate_outputs
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train:
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niter: 500000
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warmup_iter: -1
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mega_batch_factor: 3
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ema_rate: .999
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val_freq: 500
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default_lr_scheme: MultiStepLR
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gen_lr_steps: [ 50000, 100000, 150000 ]
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lr_gamma: 0.5
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eval:
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evaluators:
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val:
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type: mel
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for: mel_gen
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batch_size: 16
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dataset:
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mode: nv_tacotron
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path: E:\4k6k\datasets\audio\LJSpeech-1.1\ljs_audio_text_val_filelist.txt
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logger:
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print_freq: 30
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save_checkpoint_freq: 500
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visuals: [mel_outputs, padded_mel]
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is_mel_spectrogram: true
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visual_debug_rate: 100
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