adding time

script.py

@@ -5,6 +5,7 @@ import tqdm.auto as tqdm
 import os
 import io
 import torch
+import time
 
 # Import your model and anything else you want
 # You can even install other packages included in your repo
@@ -32,28 +33,35 @@ model = Model().to(device)
 out = []
 for el in tqdm.tqdm(dataset_remote):
 
+    start_time = time.time()
+
     # each element is a dict
     # el["id"] id of example and el["audio"] contains the audio file
     # el["audio"]["bytes"] contains bytes from reading the raw audio
     # el["audio"]["path"] containts the filename. This is just for reference and you cant actually load it
 
     # if you are using libraries that expect a file. You can use BytesIO object
-    file_like = io.BytesIO(el["audio"]["bytes"])
-    tensor = preprocess(file_like)
+    try:
+        file_like = io.BytesIO(el["audio"]["bytes"])
+        tensor = preprocess(file_like)
 
-    with torch.no_grad():
-        # soft decision (such as log likelihood score)
-        # positive score correspond to synthetic prediction
-        # negative score correspond to pristine prediction
-        score = model(tensor.to(device)).cpu().item()
+        with torch.no_grad():
+            # soft decision (such as log likelihood score)
+            # positive score correspond to synthetic prediction
+            # negative score correspond to pristine prediction
+            score = model(tensor.to(device)).cpu().item()
 
-        # we require a hard decision to be submited. so you need to pick a threshold
-        pred = "generated" if score > model.threshold else "pristine"
+            # we require a hard decision to be submited. so you need to pick a threshold
+            pred = "generated" if score > model.threshold else "pristine"
 
-    # append your prediction
-    # "id" and "pred" are required. "score" will not be used in scoring but we encourage you to include it. We'll use it for analysis of the results
+        # append your prediction
+        # "id" and "pred" are required. "score" will not be used in scoring but we encourage you to include it. We'll use it for analysis of the results
 
-    out.append(dict(id = el["id"], pred = pred, score = score))
+        out.append(dict(id = el["id"], pred = pred, score = score, time = time.time() - start_time))
+    except Exception as e:
+        print(e)
+        print("failed", el["id"])
+        out.append(dict(id = el["id"]))
 
 # save the final result and that's it
 pd.DataFrame(out).to_csv("submission.csv",index = False)