Saving train state of step 1000

checkpoint-1000-epoch-0/model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:5846539ce04532d21ae1018baad4bd0a3389ec6969bfa7327a995a96fe03ff44
+oid sha256:0872c0c697bae76960041656d4e8354cc69b767929e06c0f35615b5e9bc6ed4c
 size 3024943976

checkpoint-1000-epoch-0/optimizer.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c0d86c554f72013a349c5dd47f1d18b5ec93818136e8f7dbd644709a847184cd
+oid sha256:697196fece1aa67a773dc71ee5d622ea128185dbda531c1796ea76634c55d988
 size 955529338

distil-whisper/events.out.tfevents.1705598735.c066756f484e.20967.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08d3c29719c5def41e6b7b23aae8e54d6851e848fa50a028b1940416efea4dee
+size 12458

run_distillation.py

@@ -458,7 +458,7 @@ def log_pred(
 ):
     """Helper function to log target/predicted transcriptions to weights and biases (wandb)."""
     if accelerator.is_main_process:
-        wandb_tracker = accelerator.get_tracker("wandb")
+        # wandb_tracker = accelerator.get_tracker("wandb")
         # pretty name for current step: step 50000 -> step 50k
         cur_step_pretty = f"{int(step // 1000)}k" if step > 1000 else step
         prefix_pretty = prefix.replace("/", "-")
@@ -466,23 +466,23 @@ def log_pred(
         # convert str data to a wandb compatible format
         str_data = [[label_str[i], pred_str[i], norm_label_str[i], norm_pred_str[i]] for i in range(len(pred_str))]
         # log as a table with the appropriate headers
-        wandb_tracker.log_table(
-            table_name=f"predictions/{prefix_pretty}-step-{cur_step_pretty}",
-            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
-            data=str_data[:num_lines],
-            step=step,
-        )
+        # wandb_tracker.log_table(
+        #     table_name=f"predictions/{prefix_pretty}-step-{cur_step_pretty}",
+        #     columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+        #     data=str_data[:num_lines],
+        #     step=step,
+        # )
 
         # log incorrect normalised predictions
         str_data = np.asarray(str_data)
         str_data_incorrect = str_data[str_data[:, -2] != str_data[:, -1]]
         # log as a table with the appropriate headers
-        wandb_tracker.log_table(
-            table_name=f"incorrect_predictions/{prefix_pretty}-step-{cur_step_pretty}",
-            columns=["Target", "Pred", "Norm Target", "Norm Pred"],
-            data=str_data_incorrect[:num_lines],
-            step=step,
-        )
+        # wandb_tracker.log_table(
+        #     table_name=f"incorrect_predictions/{prefix_pretty}-step-{cur_step_pretty}",
+        #     columns=["Target", "Pred", "Norm Target", "Norm Pred"],
+        #     data=str_data_incorrect[:num_lines],
+        #     step=step,
+        # )
 
 
 def convert_dataset_str_to_list(