diff --git a/advanced_training_notebook.ipynb b/advanced_training_notebook.ipynb
index fe129aa..df9d4e2 100644
--- a/advanced_training_notebook.ipynb
+++ b/advanced_training_notebook.ipynb
@@ -246,12 +246,13 @@
     "    fname = \"fma_xs.zip\"\n",
     "    link = \"https://huggingface.co/datasets/mchl914/fma_xsmall/resolve/main/\" + fname\n",
     "    out_dir = os.path.join(output_dir, fname)\n",
-    "    os.system(f\"wget -O {out_dir} {link}\")\n",
-    "    os.system(f\"cd {output_dir} && unzip -q {fname}\")\n",
+    "    print(f\"Downloading {fname}...\")\n",
+    "    os.system(f\"wget -q -O {out_dir} {link}\")\n",
+    "    print(f\"Extracting {fname}...\")\n",
+    "    os.system(f\"unzip -q -o {out_dir} -d {output_dir}\")\n",
     "\n",
     "output_dir = \"./fma_16k\"\n",
-    "if not os.path.exists(output_dir):\n",
-    "    os.mkdir(output_dir)\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
     "\n",
     "# Save clips to 16-bit PCM wav files\n",
     "fma_files = list(Path(\"fma/fma_small\").glob(\"**/*.mp3\"))\n",
@@ -262,25 +263,32 @@
     "\n",
     "    corrupted_files = []\n",
     "    print(\"Converting FMA files to 16kHz WAV...\")\n",
-    "    for row in tqdm(fma_dataset):\n",
+    "    for row in tqdm(fma_dataset, desc=\"Processing FMA files\", unit=\"file\"):\n",
     "        try:\n",
-    "            name = row[\"audio\"][\"path\"].split(\"/\")[-1].replace(\".mp3\", \".wav\")\n",
+    "            name = Path(row[\"audio\"][\"path\"]).stem + \".wav\"\n",
+    "            output_path = Path(output_dir) / name\n",
+    "\n",
+    "            # Check if audio data is valid before writing\n",
+    "            if row[\"audio\"][\"array\"] is None or len(row[\"audio\"][\"array\"]) == 0:\n",
+    "                raise ValueError(\"Empty or invalid audio data\")\n",
+    "\n",
     "            scipy.io.wavfile.write(\n",
-    "                os.path.join(output_dir, name), \n",
-    "                16000, \n",
-    "                (row[\"audio\"][\"array\"] * 32767).astype(np.int16)\n",
+    "                output_path,\n",
+    "                16000,\n",
+    "                (row[\"audio\"][\"array\"] * 32767).astype(np.int16),\n",
     "            )\n",
     "        except Exception as e:\n",
-    "            print(f\"Error converting {row['audio']['path']}: {e}\")\n",
     "            corrupted_files.append(row[\"audio\"][\"path\"])\n",
     "\n",
     "    if corrupted_files:\n",
-    "        with open(\"fma_corrupted_files.log\", \"w\") as log_file:\n",
+    "        log_path = Path(output_dir) / \"fma_corrupted_files.log\"\n",
+    "        with open(log_path, \"w\") as log_file:\n",
     "            log_file.writelines(f\"{file}\\n\" for file in corrupted_files)\n",
+    "        print(f\"Logged {len(corrupted_files)} corrupted files to {log_path}\")\n",
     "else:\n",
     "    print(\"No MP3 files found in FMA.\")\n",
     "\n",
-    "print(\"Dataset preparation complete!\")"
+    "print(\"FMA dataset preparation complete!\")"
    ]
   },
   {
@@ -551,7 +559,7 @@
     "    },\n",
     "    {\n",
     "        \"features_dir\": \"negative_datasets/no_speech\",\n",
-    "        \"sampling_weight\": 5.0,  # Balanced\n",
+    "        \"sampling_weight\": 7.0,  # Balanced\n",
     "        \"penalty_weight\": 1.0,\n",
     "        \"truth\": False,\n",
     "        \"truncation_strategy\": \"random\",\n",
@@ -559,7 +567,7 @@
     "    },\n",
     "    {\n",
     "        \"features_dir\": \"negative_datasets/dinner_party_eval\",\n",
-    "        \"sampling_weight\": 0.0,\n",
+    "        \"sampling_weight\": 8.0,\n",
     "        \"penalty_weight\": 1.0,\n",
     "        \"truth\": False,\n",
     "        \"truncation_strategy\": \"split\",\n",
@@ -567,18 +575,18 @@
     "    },\n",
     "]\n",
     "\n",
-    "config[\"training_steps\"] = [20000]  # Increased\n",
-    "config[\"positive_class_weight\"] = [1]\n",
-    "config[\"negative_class_weight\"] = [20]  # Adjusted\n",
-    "config[\"learning_rates\"] = [0.001]  # Adjusted\n",
-    "config[\"batch_size\"] = 128\n",
+    "config[\"training_steps\"] = [30000]  # Increased\n",
+    "config[\"positive_class_weight\"] = [2]\n",
+    "config[\"negative_class_weight\"] = [15]  # Adjusted\n",
+    "config[\"learning_rates\"] = [0.001, 0.0001]  # Adjusted\n",
+    "config[\"batch_size\"] = 256\n",
     "\n",
-    "config[\"time_mask_max_size\"] = [0]  # Enabled SpecAugment\n",
-    "config[\"time_mask_count\"] = [0]\n",
-    "config[\"freq_mask_max_size\"] = [0]\n",
-    "config[\"freq_mask_count\"] = [0]\n",
+    "config[\"time_mask_max_size\"] = [50]  # Enabled SpecAugment\n",
+    "config[\"time_mask_count\"] = [2]\n",
+    "config[\"freq_mask_max_size\"] = [5]\n",
+    "config[\"freq_mask_count\"] = [2]\n",
     "\n",
-    "config[\"eval_step_interval\"] = 500  # Adjusted\n",
+    "config[\"eval_step_interval\"] = 250  # Adjusted\n",
     "config[\"clip_duration_ms\"] = 1500  # Increased\n",
     "\n",
     "config[\"target_minimization\"] = 0.9\n",
@@ -640,7 +648,7 @@
     "id": "ex_UIWvwtjAN"
    },
    "outputs": [],
-    "source": [
+   "source": [
     "import shutil\n",
     "import json\n",
     "from IPython.display import FileLink\n",