update configs

examples/clarinet/configs/clarinet_ljspeech.yaml

 data:
-  batch_size: 4
+  batch_size: 8
   train_clip_seconds: 0.5
   sample_rate: 22050
   hop_length: 256

examples/deepvoice3/README.md

@@ -23,7 +23,7 @@ The model consists of an encoder, a decoder and a converter (and a speaker embed
 
 ```text
 ├── data.py          data_processing
-├── ljspeech.yaml    (example) configuration file
+├── configs/    (example) configuration files
 ├── sentences.txt    sample sentences
 ├── synthesis.py     script to synthesize waveform from text
 ├── train.py         script to train a model
@@ -72,7 +72,7 @@ optional arguments:
 example script:
 
 ```bash
-python train.py --config=./ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
+python train.py --config=configs/ljspeech.yaml --data=./LJSpeech-1.1/ --output=experiment --device=0
 ```
 
 You can monitor training log via tensorboard, using the script below.
@@ -110,5 +110,5 @@ optional arguments:
 example script:
 
 ```bash
-python synthesis.py --config=./ljspeech.yaml --device=0 experiment/checkpoints/model_step_005000000 sentences.txt generated
+python synthesis.py --config=configs/ljspeech.yaml --device=0 experiment/checkpoints/model_step_005000000 sentences.txt generated
 ```

examples/wavenet/configs/wavenet_mixture_of_gaussians.yaml

@@ -30,7 +30,7 @@ train:
   snap_interval: 10000
   eval_interval: 10000
   
-  max_iterations: 200000
+  max_iterations: 2000000
 
 
 

examples/wavenet/configs/wavenet_single_gaussian.yaml

 data:
-  batch_size: 4
+  batch_size: 32
   train_clip_seconds: 0.5
   sample_rate: 22050
   hop_length: 256
@@ -30,7 +30,7 @@ train:
   snap_interval: 10000
   eval_interval: 10000
   
-  max_iterations: 200000
+  max_iterations: 2000000
 
 
 

examples/wavenet/configs/wavenet_softmax.yaml

@@ -30,7 +30,7 @@ train:
   snap_interval: 10000
   eval_interval: 10000
   
-  max_iterations: 200000
+  max_iterations: 2000000