revise conf/*.yaml

deepspeech/utils/checkpoint.py

@@ -24,7 +24,6 @@ from paddle.optimizer import Optimizer
 
 from deepspeech.utils import mp_tools
 from deepspeech.utils.log import Log
-# import operator
 
 logger = Log(__name__).getlog()
 
@@ -38,7 +37,7 @@ class Checkpoint(object):
         self.kbest_n = kbest_n
         self.latest_n = latest_n
         self._save_all = (kbest_n == -1)
-    
+
     def add_checkpoint(self,
                        checkpoint_dir,
                        tag_or_iteration,
@@ -64,10 +63,10 @@ class Checkpoint(object):
             self._save_checkpoint_record(checkpoint_dir, tag_or_iteration)
 
     def load_latest_parameters(self,
-                         model,
-                         optimizer=None,
-                         checkpoint_dir=None,
-                         checkpoint_path=None):
+                               model,
+                               optimizer=None,
+                               checkpoint_dir=None,
+                               checkpoint_path=None):
         """Load a last model checkpoint from disk. 
         Args:
             model (Layer): model to load parameters.
@@ -80,14 +79,14 @@ class Checkpoint(object):
         Returns:
             configs (dict): epoch or step, lr and other meta info should be saved.
         """
-        return self._load_parameters(model, optimizer, checkpoint_dir, checkpoint_path, 
-                                "checkpoint_latest")
+        return self._load_parameters(model, optimizer, checkpoint_dir,
+                                     checkpoint_path, "checkpoint_latest")
 
     def load_best_parameters(self,
-                         model,
-                         optimizer=None,
-                         checkpoint_dir=None,
-                         checkpoint_path=None):
+                             model,
+                             optimizer=None,
+                             checkpoint_dir=None,
+                             checkpoint_path=None):
         """Load a last model checkpoint from disk. 
         Args:
             model (Layer): model to load parameters.
@@ -100,8 +99,8 @@ class Checkpoint(object):
         Returns:
             configs (dict): epoch or step, lr and other meta info should be saved.
         """
-        return self._load_parameters(model, optimizer, checkpoint_dir, checkpoint_path, 
-                                "checkpoint_best")
+        return self._load_parameters(model, optimizer, checkpoint_dir,
+                                     checkpoint_path, "checkpoint_best")
 
     def _should_save_best(self, metric: float) -> bool:
         if not self._best_full():
@@ -248,7 +247,6 @@ class Checkpoint(object):
                 configs = json.load(fin)
         return configs
 
-    
     @mp_tools.rank_zero_only
     def _save_parameters(self,
                          checkpoint_dir: str,

examples/aishell/s0/conf/deepspeech2.yaml

@@ -48,6 +48,9 @@ training:
   weight_decay: 1e-06
   global_grad_clip: 3.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
   
 decoding:
   batch_size: 128

examples/aishell/s1/conf/chunk_conformer.yaml

@@ -90,6 +90,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/aishell/s1/conf/conformer.yaml

@@ -88,6 +88,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/librispeech/s0/conf/deepspeech2.yaml

@@ -43,6 +43,9 @@ training:
   weight_decay: 1e-06
   global_grad_clip: 5.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 decoding:
   batch_size: 128

examples/librispeech/s1/conf/chunk_confermer.yaml

@@ -91,6 +91,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/librispeech/s1/conf/chunk_transformer.yaml

@@ -84,6 +84,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/librispeech/s1/conf/conformer.yaml

@@ -87,6 +87,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/librispeech/s1/conf/transformer.yaml

@@ -82,6 +82,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/tiny/s1/conf/chunk_confermer.yaml

@@ -91,6 +91,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 10
+    latest_n: 1
 
 
 decoding:

examples/tiny/s1/conf/chunk_transformer.yaml

@@ -84,6 +84,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 10
+    latest_n: 1
 
 
 decoding:

examples/tiny/s1/conf/conformer.yaml

@@ -87,6 +87,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 10
+    latest_n: 1
 
 
 decoding:

examples/tiny/s1/conf/transformer.yaml

@@ -84,6 +84,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 10
+    latest_n: 1
 
 
 decoding: