Update files

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1.log

@@ -0,0 +1,1152 @@
+# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.1.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.1.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 
+# Started at Fri Dec  1 15:58:34 UTC 2023
+#
+/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.1.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.1.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000
+[wieling-3-a100] 2023-12-01 15:58:40,398 (gan_tts:293) INFO: Vocabulary size: 46
+[wieling-3-a100] 2023-12-01 15:58:40,545 (encoder:174) INFO: encoder self-attention layer type = relative self-attention
+/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace`
+  warnings.warn(
+[wieling-3-a100] 2023-12-01 15:58:41,774 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False
+[wieling-3-a100] 2023-12-01 15:58:41,789 (abs_task:1269) INFO: Model structure:
+ESPnetGANTTSModel(
+  (feats_extract): LogMelFbank(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True)
+    (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False)
+  )
+  (tts): VITS(
+    (generator): VITSGenerator(
+      (text_encoder): TextEncoder(
+        (emb): Embedding(46, 192)
+        (encoder): Encoder(
+          (embed): Sequential(
+            (0): RelPositionalEncoding(
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+          )
+          (encoders): MultiSequential(
+            (0): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (1): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (2): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (3): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (4): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (5): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+        )
+        (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
+      )
+      (decoder): HiFiGANGenerator(
+        (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,))
+        (upsamples): ModuleList(
+          (0): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,))
+          )
+          (1): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,))
+          )
+          (2): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,))
+          )
+          (3): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,))
+          )
+        )
+        (blocks): ModuleList(
+          (0): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (1): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (2): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (3): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (4): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (5): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (6): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (7): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (8): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (9): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (10): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (11): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+        )
+        (output_conv): Sequential(
+          (0): LeakyReLU(negative_slope=0.01)
+          (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,))
+          (2): Tanh()
+        )
+        (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,))
+      )
+      (posterior_encoder): PosteriorEncoder(
+        (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,))
+        (encoder): WaveNet(
+          (conv_layers): ModuleList(
+            (0-15): 16 x ResidualBlock(
+              (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+              (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+              (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+            )
+          )
+        )
+        (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
+      )
+      (flow): ResidualAffineCouplingBlock(
+        (flows): ModuleList(
+          (0): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (1): FlipFlow()
+          (2): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (3): FlipFlow()
+          (4): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (5): FlipFlow()
+          (6): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (7): FlipFlow()
+        )
+      )
+      (duration_predictor): StochasticDurationPredictor(
+        (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (dds): DilatedDepthSeparableConv(
+          (convs): ModuleList(
+            (0): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (1): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (2): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+          )
+        )
+        (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (log_flow): LogFlow()
+        (flows): ModuleList(
+          (0): ElementwiseAffineFlow()
+          (1): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (2): FlipFlow()
+          (3): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (4): FlipFlow()
+          (5): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (6): FlipFlow()
+          (7): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (8): FlipFlow()
+        )
+        (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+        (post_dds): DilatedDepthSeparableConv(
+          (convs): ModuleList(
+            (0): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (1): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (2): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+          )
+        )
+        (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (post_flows): ModuleList(
+          (0): ElementwiseAffineFlow()
+          (1): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (2): FlipFlow()
+          (3): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (4): FlipFlow()
+          (5): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (6): FlipFlow()
+          (7): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (8): FlipFlow()
+        )
+        (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,))
+      )
+      (global_emb): Embedding(4, 256)
+    )
+    (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator(
+      (msd): HiFiGANMultiScaleDiscriminator(
+        (discriminators): ModuleList(
+          (0): HiFiGANScaleDiscriminator(
+            (layers): ModuleList(
+              (0): Sequential(
+                (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (1): Sequential(
+                (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (2): Sequential(
+                (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (3): Sequential(
+                (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (4): Sequential(
+                (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (5): Sequential(
+                (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (6): Sequential(
+                (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,))
+            )
+          )
+        )
+      )
+      (mpd): HiFiGANMultiPeriodDiscriminator(
+        (discriminators): ModuleList(
+          (0-4): 5 x HiFiGANPeriodDiscriminator(
+            (convs): ModuleList(
+              (0): Sequential(
+                (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (1): Sequential(
+                (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (2): Sequential(
+                (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (3): Sequential(
+                (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (4): Sequential(
+                (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+            )
+            (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0))
+          )
+        )
+      )
+    )
+    (generator_adv_loss): GeneratorAdversarialLoss()
+    (discriminator_adv_loss): DiscriminatorAdversarialLoss()
+    (feat_match_loss): FeatureMatchLoss()
+    (mel_loss): MelSpectrogramLoss(
+      (wav_to_mel): LogMelFbank(
+        (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True)
+        (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False)
+      )
+    )
+    (kl_loss): KLDivergenceLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetGANTTSModel
+    Total Number of model parameters: 96.24 M
+    Number of trainable parameters: 96.24 M (100.0%)
+    Size: 384.96 MB
+    Type: torch.float32
+[wieling-3-a100] 2023-12-01 15:58:41,789 (abs_task:1272) INFO: Optimizer:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.8, 0.99]
+    capturable: False
+    differentiable: False
+    eps: 1e-09
+    foreach: None
+    fused: None
+    initial_lr: 0.0003
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0.0
+)
+[wieling-3-a100] 2023-12-01 15:58:41,789 (abs_task:1273) INFO: Scheduler: <torch.optim.lr_scheduler.ExponentialLR object at 0x7ff08e5c38b0>
+[wieling-3-a100] 2023-12-01 15:58:41,790 (abs_task:1272) INFO: Optimizer2:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.8, 0.99]
+    capturable: False
+    differentiable: False
+    eps: 1e-09
+    foreach: None
+    fused: None
+    initial_lr: 0.0003
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0.0
+)
+[wieling-3-a100] 2023-12-01 15:58:41,790 (abs_task:1273) INFO: Scheduler2: <torch.optim.lr_scheduler.ExponentialLR object at 0x7ff08e5c3850>
+[wieling-3-a100] 2023-12-01 15:58:41,790 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/config.yaml
+[wieling-3-a100] 2023-12-01 15:58:41,807 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.1.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.1.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['<blank>', '<unk>', '<space>', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', '<sos/eos>'], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False)
+# Accounting: time=16 threads=1
+# Ended (code 0) at Fri Dec  1 15:58:50 UTC 2023, elapsed time 16 seconds

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/config.yaml

@@ -0,0 +1,383 @@
+config: conf/train_vits.yaml
+print_config: false
+log_level: INFO
+drop_last_iter: false
+dry_run: false
+iterator_type: sequence
+valid_iterator_type: null
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+    - max
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+    - text
+    - text
+-   - dump/raw/train_nodev/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_nodev/utt2sid
+    - sids
+    - text_int
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+    - text
+    - text
+-   - dump/raw/train_dev/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_dev/utt2sid
+    - sids
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+required:
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+version: '202310'
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exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/stats_keys

@@ -0,0 +1,2 @@
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+oid sha256:92b775aaa5d2948c527ad29e192999a8388c2e6fd81fad2475ee7da577f3594a
+size 778

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+size 1402

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exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10.log

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+# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.10.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.10.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 
+# Started at Fri Dec  1 15:58:34 UTC 2023
+#
+/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.10.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.10.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000
+[wieling-3-a100] 2023-12-01 15:58:40,202 (gan_tts:293) INFO: Vocabulary size: 46
+[wieling-3-a100] 2023-12-01 15:58:40,315 (encoder:174) INFO: encoder self-attention layer type = relative self-attention
+/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace`
+  warnings.warn(
+[wieling-3-a100] 2023-12-01 15:58:41,727 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False
+[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1269) INFO: Model structure:
+ESPnetGANTTSModel(
+  (feats_extract): LogMelFbank(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True)
+    (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False)
+  )
+  (tts): VITS(
+    (generator): VITSGenerator(
+      (text_encoder): TextEncoder(
+        (emb): Embedding(46, 192)
+        (encoder): Encoder(
+          (embed): Sequential(
+            (0): RelPositionalEncoding(
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+          )
+          (encoders): MultiSequential(
+            (0): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (1): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (2): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (3): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (4): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (5): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+        )
+        (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
+      )
+      (decoder): HiFiGANGenerator(
+        (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,))
+        (upsamples): ModuleList(
+          (0): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,))
+          )
+          (1): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,))
+          )
+          (2): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,))
+          )
+          (3): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,))
+          )
+        )
+        (blocks): ModuleList(
+          (0): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (1): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (2): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (3): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (4): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (5): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (6): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (7): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (8): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (9): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (10): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (11): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+        )
+        (output_conv): Sequential(
+          (0): LeakyReLU(negative_slope=0.01)
+          (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,))
+          (2): Tanh()
+        )
+        (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,))
+      )
+      (posterior_encoder): PosteriorEncoder(
+        (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,))
+        (encoder): WaveNet(
+          (conv_layers): ModuleList(
+            (0-15): 16 x ResidualBlock(
+              (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+              (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+              (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+            )
+          )
+        )
+        (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
+      )
+      (flow): ResidualAffineCouplingBlock(
+        (flows): ModuleList(
+          (0): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (1): FlipFlow()
+          (2): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (3): FlipFlow()
+          (4): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (5): FlipFlow()
+          (6): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (7): FlipFlow()
+        )
+      )
+      (duration_predictor): StochasticDurationPredictor(
+        (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (dds): DilatedDepthSeparableConv(
+          (convs): ModuleList(
+            (0): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (1): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (2): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+          )
+        )
+        (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (log_flow): LogFlow()
+        (flows): ModuleList(
+          (0): ElementwiseAffineFlow()
+          (1): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (2): FlipFlow()
+          (3): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (4): FlipFlow()
+          (5): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (6): FlipFlow()
+          (7): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (8): FlipFlow()
+        )
+        (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+        (post_dds): DilatedDepthSeparableConv(
+          (convs): ModuleList(
+            (0): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (1): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (2): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+          )
+        )
+        (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (post_flows): ModuleList(
+          (0): ElementwiseAffineFlow()
+          (1): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (2): FlipFlow()
+          (3): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (4): FlipFlow()
+          (5): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (6): FlipFlow()
+          (7): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (8): FlipFlow()
+        )
+        (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,))
+      )
+      (global_emb): Embedding(4, 256)
+    )
+    (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator(
+      (msd): HiFiGANMultiScaleDiscriminator(
+        (discriminators): ModuleList(
+          (0): HiFiGANScaleDiscriminator(
+            (layers): ModuleList(
+              (0): Sequential(
+                (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (1): Sequential(
+                (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (2): Sequential(
+                (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (3): Sequential(
+                (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (4): Sequential(
+                (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (5): Sequential(
+                (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (6): Sequential(
+                (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,))
+            )
+          )
+        )
+      )
+      (mpd): HiFiGANMultiPeriodDiscriminator(
+        (discriminators): ModuleList(
+          (0-4): 5 x HiFiGANPeriodDiscriminator(
+            (convs): ModuleList(
+              (0): Sequential(
+                (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (1): Sequential(
+                (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (2): Sequential(
+                (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (3): Sequential(
+                (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (4): Sequential(
+                (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+            )
+            (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0))
+          )
+        )
+      )
+    )
+    (generator_adv_loss): GeneratorAdversarialLoss()
+    (discriminator_adv_loss): DiscriminatorAdversarialLoss()
+    (feat_match_loss): FeatureMatchLoss()
+    (mel_loss): MelSpectrogramLoss(
+      (wav_to_mel): LogMelFbank(
+        (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True)
+        (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False)
+      )
+    )
+    (kl_loss): KLDivergenceLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetGANTTSModel
+    Total Number of model parameters: 96.24 M
+    Number of trainable parameters: 96.24 M (100.0%)
+    Size: 384.96 MB
+    Type: torch.float32
+[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1272) INFO: Optimizer:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.8, 0.99]
+    capturable: False
+    differentiable: False
+    eps: 1e-09
+    foreach: None
+    fused: None
+    initial_lr: 0.0003
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0.0
+)
+[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1273) INFO: Scheduler: <torch.optim.lr_scheduler.ExponentialLR object at 0x7ffa7c9b4880>
+[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1272) INFO: Optimizer2:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.8, 0.99]
+    capturable: False
+    differentiable: False
+    eps: 1e-09
+    foreach: None
+    fused: None
+    initial_lr: 0.0003
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0.0
+)
+[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1273) INFO: Scheduler2: <torch.optim.lr_scheduler.ExponentialLR object at 0x7ffa7c9b4820>
+[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/config.yaml
+[wieling-3-a100] 2023-12-01 15:58:41,790 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.10.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.10.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['<blank>', '<unk>', '<space>', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', '<sos/eos>'], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False)
+# Accounting: time=18 threads=1
+# Ended (code 0) at Fri Dec  1 15:58:52 UTC 2023, elapsed time 18 seconds

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/config.yaml

@@ -0,0 +1,383 @@
+config: conf/train_vits.yaml
+print_config: false
+log_level: INFO
+drop_last_iter: false
+dry_run: false
+iterator_type: sequence
+valid_iterator_type: null
+output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10
+ngpu: 0
+seed: 67823
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: true
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: false
+collect_stats: true
+write_collected_feats: false
+max_epoch: 1000
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - train
+    - total_count
+    - max
+keep_nbest_models: 10
+nbest_averaging_interval: 0
+grad_clip: -1
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 1
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+log_interval: 50
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: true
+wandb_project: GROTTS
+wandb_id: null
+wandb_entity: null
+wandb_name: VITS_lr_3.0e-4
+wandb_model_log_interval: -1
+detect_anomaly: false
+use_lora: false
+save_lora_only: true
+lora_conf: {}
+pretrain_path: null
+init_param:
+- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 1000
+batch_size: 40
+valid_batch_size: null
+batch_bins: 10000000
+valid_batch_bins: null
+train_shape_file:
+- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.10.scp
+valid_shape_file:
+- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.10.scp
+batch_type: numel
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+shuffle_within_batch: false
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+chunk_excluded_key_prefixes: []
+chunk_default_fs: null
+train_data_path_and_name_and_type:
+-   - dump/raw/train_nodev/text
+    - text
+    - text
+-   - dump/raw/train_nodev/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_nodev/utt2sid
+    - sids
+    - text_int
+valid_data_path_and_name_and_type:
+-   - dump/raw/train_dev/text
+    - text
+    - text
+-   - dump/raw/train_dev/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_dev/utt2sid
+    - sids
+    - text_int
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+allow_multi_rates: false
+valid_max_cache_size: null
+exclude_weight_decay: false
+exclude_weight_decay_conf: {}
+optim: adamw
+optim_conf:
+    lr: 0.0003
+    betas:
+    - 0.8
+    - 0.99
+    eps: 1.0e-09
+    weight_decay: 0.0
+scheduler: exponentiallr
+scheduler_conf:
+    gamma: 0.999875
+optim2: adamw
+optim2_conf:
+    lr: 0.0003
+    betas:
+    - 0.8
+    - 0.99
+    eps: 1.0e-09
+    weight_decay: 0.0
+scheduler2: exponentiallr
+scheduler2_conf:
+    gamma: 0.999875
+generator_first: false
+token_list:
+- <blank>
+- <unk>
+- <space>
+- e
+- n
+- a
+- o
+- t
+- i
+- r
+- d
+- s
+- k
+- l
+- m
+- u
+- g
+- h
+- w
+- v
+- .
+- z
+- b
+- p
+- ','
+- j
+- c
+- f
+- ‘
+- ’
+- ':'
+- '?'
+- ö
+- ''''
+- '!'
+- '-'
+- ;
+- ò
+- è
+- ì
+- é
+- y
+- ë
+- x
+- q
+- <sos/eos>
+odim: null
+model_conf: {}
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: null
+cleaner: null
+g2p: null
+feats_extract: fbank
+feats_extract_conf:
+    n_fft: 1024
+    hop_length: 256
+    win_length: null
+    fs: 22050
+    fmin: 80
+    fmax: 7600
+    n_mels: 80
+normalize: null
+normalize_conf: {}
+tts: vits
+tts_conf:
+    generator_type: vits_generator
+    generator_params:
+        hidden_channels: 192
+        spks: 4
+        global_channels: 256
+        segment_size: 32
+        text_encoder_attention_heads: 2
+        text_encoder_ffn_expand: 4
+        text_encoder_blocks: 6
+        text_encoder_positionwise_layer_type: conv1d
+        text_encoder_positionwise_conv_kernel_size: 3
+        text_encoder_positional_encoding_layer_type: rel_pos
+        text_encoder_self_attention_layer_type: rel_selfattn
+        text_encoder_activation_type: swish
+        text_encoder_normalize_before: true
+        text_encoder_dropout_rate: 0.1
+        text_encoder_positional_dropout_rate: 0.0
+        text_encoder_attention_dropout_rate: 0.1
+        use_macaron_style_in_text_encoder: true
+        use_conformer_conv_in_text_encoder: false
+        text_encoder_conformer_kernel_size: -1
+        decoder_kernel_size: 7
+        decoder_channels: 512
+        decoder_upsample_scales:
+        - 8
+        - 8
+        - 2
+        - 2
+        decoder_upsample_kernel_sizes:
+        - 16
+        - 16
+        - 4
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exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11.log

@@ -0,0 +1,1152 @@
+# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.11.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.11.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 
+# Started at Fri Dec  1 15:58:34 UTC 2023
+#
+/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.11.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.11.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000
+[wieling-3-a100] 2023-12-01 15:58:40,493 (gan_tts:293) INFO: Vocabulary size: 46
+[wieling-3-a100] 2023-12-01 15:58:40,627 (encoder:174) INFO: encoder self-attention layer type = relative self-attention
+/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace`
+  warnings.warn(
+[wieling-3-a100] 2023-12-01 15:58:41,832 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False
+[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1269) INFO: Model structure:
+ESPnetGANTTSModel(
+  (feats_extract): LogMelFbank(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True)
+    (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False)
+  )
+  (tts): VITS(
+    (generator): VITSGenerator(
+      (text_encoder): TextEncoder(
+        (emb): Embedding(46, 192)
+        (encoder): Encoder(
+          (embed): Sequential(
+            (0): RelPositionalEncoding(
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+          )
+          (encoders): MultiSequential(
+            (0): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (1): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (2): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (3): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (4): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (5): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+        )
+        (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
+      )
+      (decoder): HiFiGANGenerator(
+        (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,))
+        (upsamples): ModuleList(
+          (0): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,))
+          )
+          (1): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,))
+          )
+          (2): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,))
+          )
+          (3): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,))
+          )
+        )
+        (blocks): ModuleList(
+          (0): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (1): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (2): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (3): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (4): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (5): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (6): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (7): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (8): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (9): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (10): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (11): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+        )
+        (output_conv): Sequential(
+          (0): LeakyReLU(negative_slope=0.01)
+          (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,))
+          (2): Tanh()
+        )
+        (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,))
+      )
+      (posterior_encoder): PosteriorEncoder(
+        (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,))
+        (encoder): WaveNet(
+          (conv_layers): ModuleList(
+            (0-15): 16 x ResidualBlock(
+              (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+              (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+              (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+            )
+          )
+        )
+        (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
+      )
+      (flow): ResidualAffineCouplingBlock(
+        (flows): ModuleList(
+          (0): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (1): FlipFlow()
+          (2): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (3): FlipFlow()
+          (4): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (5): FlipFlow()
+          (6): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (7): FlipFlow()
+        )
+      )
+      (duration_predictor): StochasticDurationPredictor(
+        (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (dds): DilatedDepthSeparableConv(
+          (convs): ModuleList(
+            (0): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (1): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (2): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+          )
+        )
+        (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (log_flow): LogFlow()
+        (flows): ModuleList(
+          (0): ElementwiseAffineFlow()
+          (1): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (2): FlipFlow()
+          (3): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (4): FlipFlow()
+          (5): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (6): FlipFlow()
+          (7): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (8): FlipFlow()
+        )
+        (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+        (post_dds): DilatedDepthSeparableConv(
+          (convs): ModuleList(
+            (0): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (1): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (2): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+          )
+        )
+        (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (post_flows): ModuleList(
+          (0): ElementwiseAffineFlow()
+          (1): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (2): FlipFlow()
+          (3): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (4): FlipFlow()
+          (5): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (6): FlipFlow()
+          (7): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (8): FlipFlow()
+        )
+        (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,))
+      )
+      (global_emb): Embedding(4, 256)
+    )
+    (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator(
+      (msd): HiFiGANMultiScaleDiscriminator(
+        (discriminators): ModuleList(
+          (0): HiFiGANScaleDiscriminator(
+            (layers): ModuleList(
+              (0): Sequential(
+                (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (1): Sequential(
+                (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (2): Sequential(
+                (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (3): Sequential(
+                (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (4): Sequential(
+                (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (5): Sequential(
+                (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (6): Sequential(
+                (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,))
+            )
+          )
+        )
+      )
+      (mpd): HiFiGANMultiPeriodDiscriminator(
+        (discriminators): ModuleList(
+          (0-4): 5 x HiFiGANPeriodDiscriminator(
+            (convs): ModuleList(
+              (0): Sequential(
+                (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (1): Sequential(
+                (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (2): Sequential(
+                (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (3): Sequential(
+                (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (4): Sequential(
+                (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+            )
+            (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0))
+          )
+        )
+      )
+    )
+    (generator_adv_loss): GeneratorAdversarialLoss()
+    (discriminator_adv_loss): DiscriminatorAdversarialLoss()
+    (feat_match_loss): FeatureMatchLoss()
+    (mel_loss): MelSpectrogramLoss(
+      (wav_to_mel): LogMelFbank(
+        (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True)
+        (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False)
+      )
+    )
+    (kl_loss): KLDivergenceLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetGANTTSModel
+    Total Number of model parameters: 96.24 M
+    Number of trainable parameters: 96.24 M (100.0%)
+    Size: 384.96 MB
+    Type: torch.float32
+[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1272) INFO: Optimizer:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.8, 0.99]
+    capturable: False
+    differentiable: False
+    eps: 1e-09
+    foreach: None
+    fused: None
+    initial_lr: 0.0003
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0.0
+)
+[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1273) INFO: Scheduler: <torch.optim.lr_scheduler.ExponentialLR object at 0x7f9de23eb8b0>
+[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1272) INFO: Optimizer2:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.8, 0.99]
+    capturable: False
+    differentiable: False
+    eps: 1e-09
+    foreach: None
+    fused: None
+    initial_lr: 0.0003
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0.0
+)
+[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1273) INFO: Scheduler2: <torch.optim.lr_scheduler.ExponentialLR object at 0x7f9de23eb850>
+[wieling-3-a100] 2023-12-01 15:58:41,848 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/config.yaml
+[wieling-3-a100] 2023-12-01 15:58:41,866 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.11.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.11.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['<blank>', '<unk>', '<space>', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', '<sos/eos>'], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False)
+# Accounting: time=18 threads=1
+# Ended (code 0) at Fri Dec  1 15:58:52 UTC 2023, elapsed time 18 seconds

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/config.yaml

@@ -0,0 +1,383 @@
+config: conf/train_vits.yaml
+print_config: false
+log_level: INFO
+drop_last_iter: false
+dry_run: false
+iterator_type: sequence
+valid_iterator_type: null
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+    - speech
+    - sound
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+    - text
+-   - dump/raw/train_dev/wav.scp
+    - speech
+    - sound
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exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/speech_shape

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exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/stats_keys

@@ -0,0 +1,2 @@
+feats
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exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/text_shape

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+Hoogelaandsters-2738-MoanMorn 102
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+Hoogelaandsters-2751-MoanMorn 34
+Hoogelaandsters-2755-MoanMorn 170
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+Hoogelaandsters-2763-MoanMorn 170
+Hoogelaandsters-2768-MoanMorn 57
+Hoogelaandsters-2772-MoanMorn 82
+Hoogelaandsters-2776-MoanMorn 108
+Hoogelaandsters-2780-MoanMorn 203
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+Hoogelaandsters-2788-MoanMorn 86
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+Hoogelaandsters-2796-MoanMorn 72
+Hoogelaandsters-2801-MoanMorn 75
+Hoogelaandsters-2809-MoanMorn 82
+Hoogelaandsters-2813-MoanMorn 56

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/batch_keys

@@ -0,0 +1,3 @@
+text
+speech
+sids

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/feats_lengths_stats.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d91f08590d0800f915c76c97f826494001a888019e15e8cb5906b40df85e42f5
+size 778

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/feats_stats.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2bcc2334a15eb36d64c9c834c251b257c11f2f3609d48e3b645a451272efd35f
+size 1402

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/sids_shape

@@ -0,0 +1,5 @@
+Oldambsters-0001-AigenOardegheden 1
+Oldambsters-0215-AigenOardegheden 1
+Oldambsters-0054-AigenOardegheden 1
+Oldambsters-0106-AigenOardegheden 1
+Oldambsters-0160-AigenOardegheden 1

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/speech_shape

@@ -0,0 +1,5 @@
+Oldambsters-0001-AigenOardegheden 115718
+Oldambsters-0215-AigenOardegheden 156715
+Oldambsters-0054-AigenOardegheden 129824
+Oldambsters-0106-AigenOardegheden 70560
+Oldambsters-0160-AigenOardegheden 50803

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/stats_keys

@@ -0,0 +1,2 @@
+feats
+feats_lengths

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/text_shape

@@ -0,0 +1,5 @@
+Oldambsters-0001-AigenOardegheden 89
+Oldambsters-0215-AigenOardegheden 110
+Oldambsters-0054-AigenOardegheden 7
+Oldambsters-0106-AigenOardegheden 44
+Oldambsters-0160-AigenOardegheden 34

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12.log

@@ -0,0 +1,1152 @@
+# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.12.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.12.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 
+# Started at Fri Dec  1 15:58:34 UTC 2023
+#
+/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.12.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.12.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000
+[wieling-3-a100] 2023-12-01 15:58:40,886 (gan_tts:293) INFO: Vocabulary size: 46
+[wieling-3-a100] 2023-12-01 15:58:41,003 (encoder:174) INFO: encoder self-attention layer type = relative self-attention
+/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.
+  warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.")
+/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace`
+  warnings.warn(
+[wieling-3-a100] 2023-12-01 15:58:42,381 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False
+[wieling-3-a100] 2023-12-01 15:58:42,453 (abs_task:1269) INFO: Model structure:
+ESPnetGANTTSModel(
+  (feats_extract): LogMelFbank(
+    (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True)
+    (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False)
+  )
+  (tts): VITS(
+    (generator): VITSGenerator(
+      (text_encoder): TextEncoder(
+        (emb): Embedding(46, 192)
+        (encoder): Encoder(
+          (embed): Sequential(
+            (0): RelPositionalEncoding(
+              (dropout): Dropout(p=0.0, inplace=False)
+            )
+          )
+          (encoders): MultiSequential(
+            (0): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (1): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (2): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (3): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (4): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+            (5): EncoderLayer(
+              (self_attn): RelPositionMultiHeadedAttention(
+                (linear_q): Linear(in_features=192, out_features=192, bias=True)
+                (linear_k): Linear(in_features=192, out_features=192, bias=True)
+                (linear_v): Linear(in_features=192, out_features=192, bias=True)
+                (linear_out): Linear(in_features=192, out_features=192, bias=True)
+                (dropout): Dropout(p=0.1, inplace=False)
+                (linear_pos): Linear(in_features=192, out_features=192, bias=False)
+              )
+              (feed_forward): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (feed_forward_macaron): MultiLayeredConv1d(
+                (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,))
+                (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,))
+                (dropout): Dropout(p=0.1, inplace=False)
+              )
+              (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+              (dropout): Dropout(p=0.1, inplace=False)
+            )
+          )
+          (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True)
+        )
+        (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
+      )
+      (decoder): HiFiGANGenerator(
+        (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,))
+        (upsamples): ModuleList(
+          (0): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,))
+          )
+          (1): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,))
+          )
+          (2): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,))
+          )
+          (3): Sequential(
+            (0): LeakyReLU(negative_slope=0.1)
+            (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,))
+          )
+        )
+        (blocks): ModuleList(
+          (0): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (1): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (2): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (3): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (4): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (5): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (6): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (7): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (8): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+          (9): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))
+              )
+            )
+          )
+          (10): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,))
+              )
+            )
+          )
+          (11): ResidualBlock(
+            (convs1): ModuleList(
+              (0): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+              (1): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,))
+              )
+              (2): Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,))
+              )
+            )
+            (convs2): ModuleList(
+              (0-2): 3 x Sequential(
+                (0): LeakyReLU(negative_slope=0.1)
+                (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,))
+              )
+            )
+          )
+        )
+        (output_conv): Sequential(
+          (0): LeakyReLU(negative_slope=0.01)
+          (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,))
+          (2): Tanh()
+        )
+        (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,))
+      )
+      (posterior_encoder): PosteriorEncoder(
+        (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,))
+        (encoder): WaveNet(
+          (conv_layers): ModuleList(
+            (0-15): 16 x ResidualBlock(
+              (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+              (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+              (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+            )
+          )
+        )
+        (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,))
+      )
+      (flow): ResidualAffineCouplingBlock(
+        (flows): ModuleList(
+          (0): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (1): FlipFlow()
+          (2): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (3): FlipFlow()
+          (4): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (5): FlipFlow()
+          (6): ResidualAffineCouplingLayer(
+            (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,))
+            (encoder): WaveNet(
+              (conv_layers): ModuleList(
+                (0-3): 4 x ResidualBlock(
+                  (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,))
+                  (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False)
+                  (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,))
+                )
+              )
+            )
+            (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,))
+          )
+          (7): FlipFlow()
+        )
+      )
+      (duration_predictor): StochasticDurationPredictor(
+        (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (dds): DilatedDepthSeparableConv(
+          (convs): ModuleList(
+            (0): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (1): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (2): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+          )
+        )
+        (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (log_flow): LogFlow()
+        (flows): ModuleList(
+          (0): ElementwiseAffineFlow()
+          (1): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (2): FlipFlow()
+          (3): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (4): FlipFlow()
+          (5): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (6): FlipFlow()
+          (7): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (8): FlipFlow()
+        )
+        (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+        (post_dds): DilatedDepthSeparableConv(
+          (convs): ModuleList(
+            (0): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (1): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+            (2): Sequential(
+              (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+              (1): Transpose()
+              (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (3): Transpose()
+              (4): GELU(approximate='none')
+              (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+              (6): Transpose()
+              (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+              (8): Transpose()
+              (9): GELU(approximate='none')
+              (10): Dropout(p=0.5, inplace=False)
+            )
+          )
+        )
+        (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+        (post_flows): ModuleList(
+          (0): ElementwiseAffineFlow()
+          (1): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (2): FlipFlow()
+          (3): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (4): FlipFlow()
+          (5): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (6): FlipFlow()
+          (7): ConvFlow(
+            (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,))
+            (dds_conv): DilatedDepthSeparableConv(
+              (convs): ModuleList(
+                (0): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (1): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+                (2): Sequential(
+                  (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192)
+                  (1): Transpose()
+                  (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (3): Transpose()
+                  (4): GELU(approximate='none')
+                  (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,))
+                  (6): Transpose()
+                  (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True)
+                  (8): Transpose()
+                  (9): GELU(approximate='none')
+                  (10): Dropout(p=0.0, inplace=False)
+                )
+              )
+            )
+            (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,))
+          )
+          (8): FlipFlow()
+        )
+        (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,))
+      )
+      (global_emb): Embedding(4, 256)
+    )
+    (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator(
+      (msd): HiFiGANMultiScaleDiscriminator(
+        (discriminators): ModuleList(
+          (0): HiFiGANScaleDiscriminator(
+            (layers): ModuleList(
+              (0): Sequential(
+                (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (1): Sequential(
+                (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (2): Sequential(
+                (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (3): Sequential(
+                (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (4): Sequential(
+                (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (5): Sequential(
+                (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16)
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (6): Sequential(
+                (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,))
+            )
+          )
+        )
+      )
+      (mpd): HiFiGANMultiPeriodDiscriminator(
+        (discriminators): ModuleList(
+          (0-4): 5 x HiFiGANPeriodDiscriminator(
+            (convs): ModuleList(
+              (0): Sequential(
+                (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (1): Sequential(
+                (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (2): Sequential(
+                (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (3): Sequential(
+                (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+              (4): Sequential(
+                (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0))
+                (1): LeakyReLU(negative_slope=0.1)
+              )
+            )
+            (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0))
+          )
+        )
+      )
+    )
+    (generator_adv_loss): GeneratorAdversarialLoss()
+    (discriminator_adv_loss): DiscriminatorAdversarialLoss()
+    (feat_match_loss): FeatureMatchLoss()
+    (mel_loss): MelSpectrogramLoss(
+      (wav_to_mel): LogMelFbank(
+        (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True)
+        (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False)
+      )
+    )
+    (kl_loss): KLDivergenceLoss()
+  )
+)
+
+Model summary:
+    Class Name: ESPnetGANTTSModel
+    Total Number of model parameters: 96.24 M
+    Number of trainable parameters: 96.24 M (100.0%)
+    Size: 384.96 MB
+    Type: torch.float32
+[wieling-3-a100] 2023-12-01 15:58:42,453 (abs_task:1272) INFO: Optimizer:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.8, 0.99]
+    capturable: False
+    differentiable: False
+    eps: 1e-09
+    foreach: None
+    fused: None
+    initial_lr: 0.0003
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0.0
+)
+[wieling-3-a100] 2023-12-01 15:58:42,453 (abs_task:1273) INFO: Scheduler: <torch.optim.lr_scheduler.ExponentialLR object at 0x7f03bc341880>
+[wieling-3-a100] 2023-12-01 15:58:42,453 (abs_task:1272) INFO: Optimizer2:
+AdamW (
+Parameter Group 0
+    amsgrad: False
+    betas: [0.8, 0.99]
+    capturable: False
+    differentiable: False
+    eps: 1e-09
+    foreach: None
+    fused: None
+    initial_lr: 0.0003
+    lr: 0.0003
+    maximize: False
+    weight_decay: 0.0
+)
+[wieling-3-a100] 2023-12-01 15:58:42,454 (abs_task:1273) INFO: Scheduler2: <torch.optim.lr_scheduler.ExponentialLR object at 0x7f03bc341820>
+[wieling-3-a100] 2023-12-01 15:58:42,454 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/config.yaml
+[wieling-3-a100] 2023-12-01 15:58:42,480 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.12.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.12.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['<blank>', '<unk>', '<space>', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', '<sos/eos>'], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False)
+# Accounting: time=18 threads=1
+# Ended (code 0) at Fri Dec  1 15:58:52 UTC 2023, elapsed time 18 seconds

exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/config.yaml

@@ -0,0 +1,383 @@
+config: conf/train_vits.yaml
+print_config: false
+log_level: INFO
+drop_last_iter: false
+dry_run: false
+iterator_type: sequence
+valid_iterator_type: null
+output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12
+ngpu: 0
+seed: 67823
+num_workers: 4
+num_att_plot: 3
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: null
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: false
+unused_parameters: true
+sharded_ddp: false
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: false
+collect_stats: true
+write_collected_feats: false
+max_epoch: 1000
+patience: null
+val_scheduler_criterion:
+- valid
+- loss
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+-   - train
+    - total_count
+    - max
+keep_nbest_models: 10
+nbest_averaging_interval: 0
+grad_clip: -1
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 1
+no_forward_run: false
+resume: false
+train_dtype: float32
+use_amp: false
+log_interval: 50
+use_matplotlib: true
+use_tensorboard: true
+create_graph_in_tensorboard: false
+use_wandb: true
+wandb_project: GROTTS
+wandb_id: null
+wandb_entity: null
+wandb_name: VITS_lr_3.0e-4
+wandb_model_log_interval: -1
+detect_anomaly: false
+use_lora: false
+save_lora_only: true
+lora_conf: {}
+pretrain_path: null
+init_param:
+- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: 1000
+batch_size: 40
+valid_batch_size: null
+batch_bins: 10000000
+valid_batch_bins: null
+train_shape_file:
+- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.12.scp
+valid_shape_file:
+- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.12.scp
+batch_type: numel
+valid_batch_type: null
+fold_length: []
+sort_in_batch: descending
+shuffle_within_batch: false
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+chunk_excluded_key_prefixes: []
+chunk_default_fs: null
+train_data_path_and_name_and_type:
+-   - dump/raw/train_nodev/text
+    - text
+    - text
+-   - dump/raw/train_nodev/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_nodev/utt2sid
+    - sids
+    - text_int
+valid_data_path_and_name_and_type:
+-   - dump/raw/train_dev/text
+    - text
+    - text
+-   - dump/raw/train_dev/wav.scp
+    - speech
+    - sound
+-   - dump/raw/train_dev/utt2sid
+    - sids
+    - text_int
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+allow_multi_rates: false
+valid_max_cache_size: null
+exclude_weight_decay: false
+exclude_weight_decay_conf: {}
+optim: adamw
+optim_conf:
+    lr: 0.0003
+    betas:
+    - 0.8
+    - 0.99
+    eps: 1.0e-09
+    weight_decay: 0.0
+scheduler: exponentiallr
+scheduler_conf:
+    gamma: 0.999875
+optim2: adamw
+optim2_conf:
+    lr: 0.0003
+    betas:
+    - 0.8
+    - 0.99
+    eps: 1.0e-09
+    weight_decay: 0.0
+scheduler2: exponentiallr
+scheduler2_conf:
+    gamma: 0.999875
+generator_first: false
+token_list:
+- <blank>
+- <unk>
+- <space>
+- e
+- n
+- a
+- o
+- t
+- i
+- r
+- d
+- s
+- k
+- l
+- m
+- u
+- g
+- h
+- w
+- v
+- .
+- z
+- b
+- p
+- ','
+- j
+- c
+- f
+- ‘
+- ’
+- ':'
+- '?'
+- ö
+- ''''
+- '!'
+- '-'
+- ;
+- ò
+- è
+- ì
+- é
+- y
+- ë
+- x
+- q
+- <sos/eos>
+odim: null
+model_conf: {}
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: null
+cleaner: null
+g2p: null
+feats_extract: fbank
+feats_extract_conf:
+    n_fft: 1024
+    hop_length: 256
+    win_length: null
+    fs: 22050
+    fmin: 80
+    fmax: 7600
+    n_mels: 80
+normalize: null
+normalize_conf: {}
+tts: vits
+tts_conf:
+    generator_type: vits_generator
+    generator_params:
+        hidden_channels: 192
+        spks: 4
+        global_channels: 256
+        segment_size: 32
+        text_encoder_attention_heads: 2
+        text_encoder_ffn_expand: 4
+        text_encoder_blocks: 6
+        text_encoder_positionwise_layer_type: conv1d
+        text_encoder_positionwise_conv_kernel_size: 3
+        text_encoder_positional_encoding_layer_type: rel_pos
+        text_encoder_self_attention_layer_type: rel_selfattn
+        text_encoder_activation_type: swish
+        text_encoder_normalize_before: true
+        text_encoder_dropout_rate: 0.1
+        text_encoder_positional_dropout_rate: 0.0
+        text_encoder_attention_dropout_rate: 0.1
+        use_macaron_style_in_text_encoder: true
+        use_conformer_conv_in_text_encoder: false
+        text_encoder_conformer_kernel_size: -1
+        decoder_kernel_size: 7
+        decoder_channels: 512
+        decoder_upsample_scales:
+        - 8
+        - 8
+        - 2
+        - 2
+        decoder_upsample_kernel_sizes:
+        - 16
+        - 16
+        - 4
+        - 4
+        decoder_resblock_kernel_sizes:
+        - 3
+        - 7
+        - 11
+        decoder_resblock_dilations:
+        -   - 1
+            - 3
+            - 5
+        -   - 1
+            - 3
+            - 5
+        -   - 1
+            - 3
+            - 5
+        use_weight_norm_in_decoder: true
+        posterior_encoder_kernel_size: 5
+        posterior_encoder_layers: 16
+        posterior_encoder_stacks: 1
+        posterior_encoder_base_dilation: 1
+        posterior_encoder_dropout_rate: 0.0
+        use_weight_norm_in_posterior_encoder: true
+        flow_flows: 4
+        flow_kernel_size: 5
+        flow_base_dilation: 1
+        flow_layers: 4
+        flow_dropout_rate: 0.0
+        use_weight_norm_in_flow: true
+        use_only_mean_in_flow: true
+        stochastic_duration_predictor_kernel_size: 3
+        stochastic_duration_predictor_dropout_rate: 0.5
+        stochastic_duration_predictor_flows: 4
+        stochastic_duration_predictor_dds_conv_layers: 3
+        vocabs: 46
+        aux_channels: 80
+    discriminator_type: hifigan_multi_scale_multi_period_discriminator
+    discriminator_params:
+        scales: 1
+        scale_downsample_pooling: AvgPool1d
+        scale_downsample_pooling_params:
+            kernel_size: 4
+            stride: 2
+            padding: 2
+        scale_discriminator_params:
+            in_channels: 1
+            out_channels: 1
+            kernel_sizes:
+            - 15
+            - 41
+            - 5
+            - 3
+            channels: 128
+            max_downsample_channels: 1024
+            max_groups: 16
+            bias: true
+            downsample_scales:
+            - 2
+            - 2
+            - 4
+            - 4
+            - 1
+            nonlinear_activation: LeakyReLU
+            nonlinear_activation_params:
+                negative_slope: 0.1
+            use_weight_norm: false
+            use_spectral_norm: false
+        follow_official_norm: false
+        periods:
+        - 2
+        - 3
+        - 5
+        - 7
+        - 11
+        period_discriminator_params:
+            in_channels: 1
+            out_channels: 1
+            kernel_sizes:
+            - 5
+            - 3
+            channels: 32
+            downsample_scales:
+            - 3
+            - 3
+            - 3
+            - 3
+            - 1
+            max_downsample_channels: 1024
+            bias: true
+            nonlinear_activation: LeakyReLU
+            nonlinear_activation_params:
+                negative_slope: 0.1
+            use_weight_norm: true
+            use_spectral_norm: false
+    generator_adv_loss_params:
+        average_by_discriminators: false
+        loss_type: mse
+    discriminator_adv_loss_params:
+        average_by_discriminators: false
+        loss_type: mse
+    feat_match_loss_params:
+        average_by_discriminators: false
+        average_by_layers: false
+        include_final_outputs: true
+    mel_loss_params:
+        fs: 22050
+        n_fft: 1024
+        hop_length: 256
+        win_length: null
+        window: hann
+        n_mels: 80
+        fmin: 0
+        fmax: null
+        log_base: null
+    lambda_adv: 1.0
+    lambda_mel: 45.0
+    lambda_feat_match: 2.0
+    lambda_dur: 1.0
+    lambda_kl: 1.0
+    sampling_rate: 22050
+    cache_generator_outputs: true
+pitch_extract: null
+pitch_extract_conf:
+    fs: 22050
+    n_fft: 1024
+    hop_length: 256
+    f0max: 400
+    f0min: 80
+pitch_normalize: null
+pitch_normalize_conf: {}
+energy_extract: null
+energy_extract_conf:
+    fs: 22050
+    n_fft: 1024
+    hop_length: 256
+    win_length: null
+energy_normalize: null
+energy_normalize_conf: {}
+required:
+- output_dir
+- token_list
+version: '202310'
+distributed: false