Automated evaluation on KWS

EyubogluMerve · Jan 4, 2024 · 4e78ca8 · 4e78ca8
1 parent 7e4cfe1
commit 4e78ca8
Showing 1 changed file with 7 additions and 14 deletions.
diff --git a/notebooks/Automated_Evaluation_KWS.ipynb b/notebooks/Automated_Evaluation_KWS.ipynb
@@ -300,7 +300,7 @@
     "            mixed_signals_loader = torch.utils.data.DataLoader(mixed_signals, batch_size = 256)\n",
     "\n",
     "        else:\n",
-    "            noise_dataset = msnoise.MSnoise(root = '/data_ssd', classes = [noise_kind], d_type = 'test', len = 11005,remove_unknowns=True,\n",
+    "            noise_dataset = msnoise.MSnoise(root = '/data_ssd', classes = [noise_kind], d_type = 'test', len = 11005, remove_unknowns=True,\n",
     "                                        transform=None, quantize=False, download=False)\n",
     "\n",
     "            mixed_signals = signalmixer(test_dataset, snr = db, noise_kind = noise_kind, noise_dataset = noise_dataset)\n",
@@ -337,14 +337,12 @@
     "    if noise == False:\n",
     "        db = None\n",
     "        noise_kind = None\n",
-    "\n",
     "        accuracies = np.zeros(1)\n",
     "        accuracies[0] = evaluate(db, noise, noise_kind)\n",
     "\n",
+    "    # noisy test set evaluation\n",
     "    else:\n",
     "        snr_num = calculate_snr_num(snr_list)\n",
-    "\n",
-    "        # noisy test set evaluation\n",
     "        accuracies = np.zeros(snr_num)\n",
     "\n",
     "        for idx, db in enumerate(snr_list):\n",
@@ -544,15 +542,8 @@
     "\n",
     "        df = pd.DataFrame(csv_list, columns = ['Type', 'SNR (dB)', f'{model_files[model_name]}'])\n",
     "        \n",
-    "    accs.append(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "    accs.append(df)\n",
+    "\n",
     "for i in accs:\n",
     "    df[i.columns[-1]] = i[i.columns[-1]]"
    ]
@@ -859,7 +850,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also create CSV files using the predefined data frames. If the current model file is already in the CSV file, accuracies will not be added to the CSV file. If not, then the current accuracy results will be added to the CSV file as a new column."
+    "You can also create CSV files using the predefined data frames. If the current model file is already in the CSV file, accuracies will not be added to the CSV file. If not, then the current accuracy results will be added to the CSV file as a new column.\n",
+    "\n",
+    "- You have to make sure that you test the same noise kinds for same SNR levels for all of the models."
    ]
   },
   {