template upload

gitignore

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+__pycache__
+submission.csv

models.py

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+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

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+
+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

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+import pandas as pd
+from datasets import load_dataset
+import numpy as np
+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
+# However, during the evaluation the container will not have access to the internet. 
+# So you must include everything you need in your model repo. Common python libraries will be installed.
+# Feel free to contact us to add dependencies to the requiremnts.txt
+# For testing, this is the docker image that will be used https://github.com/huggingface/competitions/blob/main/Dockerfile 
+# It can be pulled here https://hub.docker.com/r/huggingface/competitions/tags 
+
+from models import Model
+from preprocess import preprocess
+
+
+# load the dataset. dataset will be automatically downloaded to /tmp/data during evaluation
+DATASET_PATH = "/tmp/data"
+dataset_remote = load_dataset(DATASET_PATH,split = "test",streaming = True)
+
+
+# load your model
+device = "cuda:0"
+model = Model().to(device)
+
+
+# iterate over the dataset
+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
+    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()
+
+            # 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
+
+        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"], pred = "none", score = None))
+
+# save the final result and that's it
+pd.DataFrame(out).to_csv("submission.csv",index = False)