Merge branch 'main' into revamp_documentation

examples/asr/speech_to_text_finetune.py

@@ -49,25 +49,25 @@
 For documentation on fine-tuning this model, please visit:
 https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/configs.html#fine-tuning-configurations
 """
-
+import time
 import pytorch_lightning as pl
 from omegaconf import OmegaConf
-from pytorch_lightning.utilities import rank_zero_only
 
 from nemo.collections.asr.models import ASRModel
 from nemo.core.config import hydra_runner
 from nemo.utils import logging, model_utils
 from nemo.utils.exp_manager import exp_manager
+from nemo.utils.get_rank import is_global_rank_zero
 
 
-@rank_zero_only
-def get_base_model(cfg):
+def get_base_model(trainer, cfg):
     """
     Returns the base model to be fine-tuned.
     Currently supports two types of initializations:
     1) `init_from_nemo_model`, and
     2) `init_from_pretrained_model`.
     Args:
+        trainer: PyTorch Lightning Trainer
         cfg: config
     Returns:
         asr_model: ASRModel instance
@@ -84,7 +84,24 @@ def get_base_model(cfg):
     elif nemo_model_path is not None:
         asr_model = ASRModel.restore_from(restore_path=nemo_model_path)
     elif pretrained_name is not None:
-        asr_model = ASRModel.from_pretrained(model_name=pretrained_name)
+        # Due to potential first time download of the model on the cluster, we need to make sure that only one
+        # rank downloads the model and the others wait for the download to finish.
+        num_ranks = trainer.num_devices * trainer.num_devices
+
+        if num_ranks > 1 and is_global_rank_zero():
+            asr_model = ASRModel.from_pretrained(model_name=pretrained_name)
+        else:
+            # Sleep on all ranks for at least 60 seconds
+            wait_time = int(cfg.get('exp_manager', {}).get('seconds_to_sleep', 60))
+            if wait_time < 60:
+                wait_time = 60
+
+            logging.info(f"Sleeping for at least {wait_time} seconds to wait for model download to finish.")
+
+            time.sleep(wait_time)
+
+            # restore model from cached model dir
+            asr_model = ASRModel.from_pretrained(model_name=pretrained_name)
 
     return asr_model
 
@@ -180,7 +197,7 @@ def main(cfg):
             "Currently for simplicity of single script for all model types, we only support `init_from_nemo_model` and `init_from_pretrained_model`"
         )
 
-    asr_model = get_base_model(cfg)
+    asr_model = get_base_model(trainer, cfg)
 
     # Check vocabulary type and update if needed
     asr_model = check_vocabulary(asr_model, cfg)