updates

.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+submission.csv

models.py

@@ -0,0 +1,11 @@
+import torch
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.fc1 = torch.nn.Linear(10, 5)
+        self.threshold = 0.
+
+    def forward(self, x):
+       ## generates a random float the same size as x
+       return torch.randn(x.shape[0]).to(x.device)

preprocess.py

@@ -0,0 +1,10 @@
+
+import librosa
+import torch
+
+def preprocess(audio_file):
+    # Load the audio file
+    y, sr = librosa.load(audio_file, sr=None)
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    tensor  = torch.from_numpy(mfccs)[None]
+    return tensor

script.py

@@ -4,6 +4,7 @@ import numpy as np
 import tqdm.auto as tqdm
 import os
 import io
+import torch
 
 # Import your model and anything else you want
 # You can even install other packages included in your repo
@@ -14,7 +15,7 @@ import io
 # It can be pulled here https://hub.docker.com/r/huggingface/competitions/tags 
 
 from models import Model
-from preprocess import preproccess
+from preprocess import preprocess
 
 
 # load the dataset. dataset will be automatically downloaded to /tmp/data during evaluation
@@ -38,7 +39,7 @@ for el in tqdm.tqdm(dataset_remote):
 
     # if you are using libraries that expect a file. You can use BytesIO object
     file_like = io.BytesIO(el["audio"]["bytes"])
-    tensor = preproces(file_like)
+    tensor = preprocess(file_like)
 
     with torch.no_grad():
         # soft decision (such as log likelihood score)
@@ -50,9 +51,9 @@ for el in tqdm.tqdm(dataset_remote):
         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 for analysis of the results
+    # "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))
 
 # save the final result and that's it
 pd.DataFrame(out).to_csv("submission.csv",index = False)