Update train_char.py

egs/commonvoice/ASR/zipformer/train_char.py

@@ -74,6 +74,7 @@ from train import (
     add_model_arguments,
     get_adjusted_batch_count,
     get_model,
+    get_params,
     load_checkpoint_if_available,
     save_checkpoint,
     set_batch_count,
@@ -88,7 +89,6 @@ from icefall.checkpoint import (
     update_averaged_model,
 )
 from icefall.dist import cleanup_dist, setup_dist
-from icefall.env import get_env_info
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
 from icefall.utils import (
@@ -320,72 +320,6 @@ def get_parser():
     return parser
 
 
-def get_params() -> AttributeDict:
-    """Return a dict containing training parameters.
-
-    All training related parameters that are not passed from the commandline
-    are saved in the variable `params`.
-
-    Commandline options are merged into `params` after they are parsed, so
-    you can also access them via `params`.
-
-    Explanation of options saved in `params`:
-
-        - best_train_loss: Best training loss so far. It is used to select
-                           the model that has the lowest training loss. It is
-                           updated during the training.
-
-        - best_valid_loss: Best validation loss so far. It is used to select
-                           the model that has the lowest validation loss. It is
-                           updated during the training.
-
-        - best_train_epoch: It is the epoch that has the best training loss.
-
-        - best_valid_epoch: It is the epoch that has the best validation loss.
-
-        - batch_idx_train: Used to writing statistics to tensorboard. It
-                           contains number of batches trained so far across
-                           epochs.
-
-        - log_interval:  Print training loss if batch_idx % log_interval` is 0
-
-        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
-
-        - valid_interval:  Run validation if batch_idx % valid_interval is 0
-
-        - feature_dim: The model input dim. It has to match the one used
-                       in computing features.
-
-        - subsampling_factor:  The subsampling factor for the model.
-
-        - encoder_dim: Hidden dim for multi-head attention model.
-
-        - num_decoder_layers: Number of decoder layer of transformer decoder.
-
-        - warm_step: The warmup period that dictates the decay of the
-              scale on "simple" (un-pruned) loss.
-    """
-    params = AttributeDict(
-        {
-            "best_train_loss": float("inf"),
-            "best_valid_loss": float("inf"),
-            "best_train_epoch": -1,
-            "best_valid_epoch": -1,
-            "batch_idx_train": 0,
-            "log_interval": 50,
-            "reset_interval": 200,
-            "valid_interval": 3000,  # For the 100h subset, use 800
-            # parameters for zipformer
-            "feature_dim": 80,
-            "subsampling_factor": 4,  # not passed in, this is fixed.
-            "warm_step": 2000,
-            "env_info": get_env_info(),
-        }
-    )
-
-    return params
-
-
 def compute_loss(
     params: AttributeDict,
     model: Union[nn.Module, DDP],
@@ -1017,8 +951,8 @@ def main():
         run(rank=0, world_size=1, args=args)
 
 
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
+# torch.set_num_threads(1)
+# torch.set_num_interop_threads(1)
 
 if __name__ == "__main__":
     main()