refactor(config): change default log dir from logs to logs_and_checkp…

…oints

fixes #39

.gitignore

@@ -2,6 +2,7 @@
 __pycache__
 .coverage
 logs
+logs_and_checkpoints
 *.ckpt
 .vscode
 *egg-info

docs/guides/custom.md

@@ -80,7 +80,7 @@ Which would use the GPU accelerator and specify 1 device/chip.
 
 To generate audio when you train your feature prediction network, you need to add your vocoder checkpoint to the `config/feature_prediction.yaml`
 
-At the bottom of that file you'll find a key called vocoder_path. Add the absolute path to your trained vocder (here it would be `/path/to/test/logs/VocoderExperiment/base/checkpoints/last.ckpt` where `/path/to` would be the actual path to it on your computer.)
+At the bottom of that file you'll find a key called vocoder_path. Add the absolute path to your trained vocder (here it would be `/path/to/test/logs_and_checkpoints/VocoderExperiment/base/checkpoints/last.ckpt` where `/path/to` would be the actual path to it on your computer.)
 
 Once you've replaced the vocoder_path key, you can train your feature prediction network:
 
@@ -93,7 +93,7 @@ everyvoice fs2 train -p config/feature_prediction.yaml
 You can synthesize by pointing the CLI to your trained feature prediction network and passing in the text. You can export to wav, npy, or pt files.
 
 ```bash
-everyvoice fs2 synthesize logs/FeaturePredictionExperiment/base/checkpoints/last.ckpt -t "මෙදා සැරේ සාකච්ඡාවක් විදියට නෙවෙයි නේද පල කරල තියෙන්නෙ" -a gpu -d 1 -O wav
+everyvoice fs2 synthesize logs_and_checkpoints/FeaturePredictionExperiment/base/checkpoints/last.ckpt -t "මෙදා සැරේ සාකච්ඡාවක් විදියට නෙවෙයි නේද පල කරල තියෙන්නෙ" -a gpu -d 1 -O wav
 ```
 
 <!-- % Step 10 (optional): Finetune your vocoder

everyvoice/config/base/base_shared.yaml

@@ -23,7 +23,7 @@ aligner:
     filelist_loader: "everyvoice.utils.generic_dict_loader"
     logger:
       name: "BaseExperiment"
-      save_dir: "./logs"
+      save_dir: "./logs_and_checkpoints"
       sub_dir: "everyvoice.utils.get_current_time"
       version: "base"
     val_data_workers: 0
@@ -135,7 +135,7 @@ feature_prediction:
     filelist_loader: "everyvoice.utils.generic_dict_loader"
     logger:
       name: "BaseExperiment"
-      save_dir: "./logs"
+      save_dir: "./logs_and_checkpoints"
       sub_dir: "everyvoice.utils.get_current_time"
       version: "base"
     val_data_workers: 0
@@ -176,7 +176,7 @@ vocoder:
     filelist_loader: "everyvoice.utils.generic_dict_loader"
     logger:
       name: "BaseExperiment"
-      save_dir: "./logs"
+      save_dir: "./logs_and_checkpoints"
       sub_dir: "everyvoice.utils.get_current_time"
       version: "base"
     val_data_workers: 0

everyvoice/config/default/aligner.yaml

@@ -22,7 +22,7 @@ training:
   filelist_loader: "everyvoice.utils.generic_dict_loader"
   logger:
     name: "BaseExperiment"
-    save_dir: "./logs"
+    save_dir: "./logs_and_checkpoints"
     sub_dir: "everyvoice.utils.get_current_time"
     version: "base"
   val_data_workers: 0

everyvoice/config/default/fastspeech2.yaml

@@ -102,7 +102,7 @@ training:
   filelist_loader: "everyvoice.utils.generic_dict_loader"
   logger:
     name: "BaseExperiment"
-    save_dir: "./logs"
+    save_dir: "./logs_and_checkpoints"
     sub_dir: "everyvoice.utils.get_current_time"
     version: "base"
   val_data_workers: 0

everyvoice/config/default/hifigan.yaml

@@ -31,7 +31,7 @@ training:
   filelist_loader: "everyvoice.utils.generic_dict_loader"
   logger:
     name: "BaseExperiment"
-    save_dir: "./logs"
+    save_dir: "./logs_and_checkpoints"
     sub_dir: "everyvoice.utils.get_current_time"
     version: "base"
   val_data_workers: 0

everyvoice/config/default/istft.yaml

@@ -31,7 +31,7 @@ training:
   filelist_loader: "everyvoice.utils.generic_dict_loader"
   logger:
     name: "Base-iSTFT-Experiment"
-    save_dir: "./logs"
+    save_dir: "./logs_and_checkpoints"
     sub_dir: "everyvoice.utils.get_current_time"
     version: "base"
   val_data_workers: 0

everyvoice/config/shared_types.py

@@ -99,7 +99,7 @@ def __init__(self, **data) -> None:
 
 class LoggerConfig(ConfigModel):
     name: str = "BaseExperiment"
-    save_dir: DirectoryPath = Path("./logs")
+    save_dir: DirectoryPath = Path("./logs_and_checkpoints")
     sub_dir: str = "everyvoice.utils.get_current_time"
     version: str = "base"
 

everyvoice/model/e2e/config/lj_training.yaml

@@ -5,13 +5,13 @@ ckpt_epochs: 1
 max_epochs: 1000
 seed: 1234
 finetune_checkpoint: null
-vocoder_checkpoint: "./logs/LJ/base-vocoder/checkpoints/last.ckpt"
-feature_prediction_checkpoint: "./logs/LJ/fastspeech2-base/checkpoints/last.ckpt"
+vocoder_checkpoint: "./logs_and_checkpoints/LJ/base-vocoder/checkpoints/last.ckpt"
+feature_prediction_checkpoint: "./logs_and_checkpoints/LJ/fastspeech2-base/checkpoints/last.ckpt"
 filelist: "./preprocessed/LJ/processed_filelist.psv"
 filelist_loader: "everyvoice.utils.generic_dict_loader"
 logger:
   name: "LJ"
-  save_dir: "./logs"
+  save_dir: "./logs_and_checkpoints"
   sub_dir: "everyvoice.utils.get_current_time"
   version: "e2e"
 val_data_workers: 0

everyvoice/model/e2e/config/training.yaml

@@ -11,7 +11,7 @@ filelist: "./preprocessed/LJ/processed_filelist.psv"
 filelist_loader: "everyvoice.utils.generic_dict_loader"
 logger:
   name: "BaseExperiment"
-  save_dir: "./logs"
+  save_dir: "./logs_and_checkpoints"
   sub_dir: "everyvoice.utils.get_current_time"
   version: "e2e"
 val_data_workers: 0

everyvoice/tests/test_wizard.py

@@ -55,6 +55,9 @@ def test_config_format_effect(self):
             ]
             config_step.state["dataset_test"]["sox_effects"] = []
             config_step.effect()
+            self.assertTrue(
+                (Path(tmpdirname) / config_step.name / "logs_and_checkpoints").exists()
+            )
 
     def test_access_response(self):
         root_step = Step(

everyvoice/wizard/basic.py

@@ -97,7 +97,8 @@ def effect(self):
         config_dir = (output_path / "config").absolute()
         config_dir.mkdir(exist_ok=True, parents=True)
         # log dir
-        log_dir = (output_path / "logs").absolute()
+        log_dir = LoggerConfig().save_dir.stem
+        log_dir = (output_path / log_dir).absolute()
         log_dir.mkdir(parents=True, exist_ok=True)
         datasets = []
         # Text Configuration