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import os |
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dataset = "kss_elena" |
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data_path = os.path.join("./data", dataset) |
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meta_name = "transcript.v.1.4.txt" |
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textgrid_path="" |
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textgrid_name = "TextGrid.zip" |
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train_visible_devices = "0" |
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synth_visible_devices = "0" |
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text_cleaners = ['korean_cleaners'] |
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sampling_rate = 22050 |
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filter_length = 1024 |
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hop_length = 256 |
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win_length = 1024 |
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max_wav_value = 32768.0 |
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n_mel_channels = 80 |
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mel_fmin = 0 |
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mel_fmax = 8000 |
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f0_min = 71.0 |
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f0_max = 792.8 |
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energy_min = 0.0 |
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energy_max = 283.72 |
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encoder_layer = 4 |
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encoder_head = 2 |
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encoder_hidden = 256 |
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decoder_layer = 4 |
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decoder_head = 2 |
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decoder_hidden = 256 |
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fft_conv1d_filter_size = 1024 |
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fft_conv1d_kernel_size = (9, 1) |
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encoder_dropout = 0.2 |
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decoder_dropout = 0.2 |
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variance_predictor_filter_size = 256 |
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variance_predictor_kernel_size = 3 |
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variance_predictor_dropout = 0.5 |
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max_seq_len = 1000 |
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preprocessed_path = os.path.join("./preprocessed/", dataset) |
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checkpoint_path = os.path.join("./ckpt/", dataset) |
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eval_path = os.path.join("./eval/", dataset) |
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log_path = os.path.join("./log/", dataset) |
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test_path = "./results" |
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batch_size = 4 |
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epochs = 99999 |
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n_warm_up_step = 4000 |
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grad_clip_thresh = 1.0 |
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acc_steps = 1 |
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betas = (0.9, 0.98) |
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eps = 1e-9 |
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weight_decay = 0. |
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vocoder = 'vocgan' |
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vocoder_pretrained_model_name = "kss_elena_2dfbde2_61480.pt" |
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vocoder_pretrained_model_path = os.path.join("./vocoder/pretrained_models/", vocoder_pretrained_model_name) |
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log_offset = 1. |
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save_step = 10000 |
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eval_step = 1000 |
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eval_size = 256 |
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log_step = 1000 |
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clear_Time = 20 |
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