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Baseline","metadata":{"id":"lmhyn8T-mbrW"}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport torchaudio\nimport os\nfrom tqdm.auto import tqdm\n\nK = 16000\nIN_DIR = \"/kaggle/input/audio-demixing-aicc-round-2/\"\n\ndef load_signal(path, K):\n    wav, _ = torchaudio.load(path)\n    arr = wav[0].numpy()\n    if len(arr) >= K:\n        return arr[:K].astype(np.float32)\n    else:\n        return np.pad(arr, (0, K - len(arr)), mode='constant').astype(np.float32)\n\n# Load CSVs\ntrain = pd.read_csv(\"/kaggle/input/audio-demixing-aicc-round-2/train.csv\")\ntest = pd.read_csv(\"/kaggle/input/audio-demixing-aicc-round-2/test.csv\")\n\n# Load raw training signals\nprint(\"Loading training signals...\")\nX_train = np.vstack([load_signal(os.path.join(IN_DIR, p), K) for p in tqdm(train[\"file\"], desc=\"X_train\")])\nY1_train = np.vstack([load_signal(os.path.join(IN_DIR, p), K) for p in tqdm(train[\"sig_1\"], desc=\"Y1_train\")])\nY2_train = np.vstack([load_signal(os.path.join(IN_DIR, p), K) for p in tqdm(train[\"sig_2\"], desc=\"Y2_train\")])\n\n# Load raw test signals\nprint(\"Loading test signals...\")\nif \"sig_1\" in test.columns:\n    X_test = np.vstack([load_signal(os.path.join(IN_DIR, p), K) for p in tqdm(test[\"sig_1\"], desc=\"X_test\")])\nelse:\n    X_test = np.tile(X_train[0], (len(test), 1))\n\ntest_IDs = test[\"ID\"].astype(str).values","metadata":{"id":"hjWs_FG9usi9","outputId":"7574f3a2-bebd-44c1-c284-591e8d7ff7d7","trusted":true,"execution":{"iopub.status.busy":"2025-12-28T08:23:20.348899Z","iopub.execute_input":"2025-12-28T08:23:20.349317Z","iopub.status.idle":"2025-12-28T08:24:22.743967Z","shell.execute_reply.started":"2025-12-28T08:23:20.349268Z","shell.execute_reply":"2025-12-28T08:24:22.743137Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.linear_model import Ridge\n\n# Flatten for single-step regression\nX_flat = X_train.reshape(-1, 1)\nY1_flat = Y1_train.reshape(-1)\nY2_flat = Y2_train.reshape(-1)\n\n# Train Ridge regressors\nprint(\"Training Ridge models...\")\nm1 = Ridge(alpha=1.0)\nm2 = Ridge(alpha=1.0)\nm1.fit(X_flat, Y1_flat)\nm2.fit(X_flat, Y2_flat)\n\n# Predict on raw test signals\nX_test_flat = X_test.reshape(-1, 1)\npred1_flat = m1.predict(X_test_flat)\npred2_flat = m2.predict(X_test_flat)\n\npred1 = pred1_flat.reshape(len(X_test), K).astype(np.float32)\npred2 = pred2_flat.reshape(len(X_test), K).astype(np.float32)","metadata":{"id":"ZLELSEOjx_p2","outputId":"0d981e4e-a0a9-41ee-8cc9-c12b25a74228","trusted":true,"execution":{"iopub.status.busy":"2025-12-28T08:24:29.559738Z","iopub.execute_input":"2025-12-28T08:24:29.560097Z","iopub.status.idle":"2025-12-28T08:24:30.829957Z","shell.execute_reply.started":"2025-12-28T08:24:29.560065Z","shell.execute_reply":"2025-12-28T08:24:30.829146Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import base64\nimport pandas as pd\nfrom tqdm.auto import tqdm\nimport csv\n\ndef encode_array_base85(arr: np.ndarray) -> str:\n    arr = np.ascontiguousarray(arr.astype(np.float32).ravel())\n    return base64.b85encode(arr.tobytes()).decode(\"ascii\")\n\n# Encode predictions\nrows = []\nfor i in tqdm(range(len(test_IDs)), desc=\"Encoding rows\"):\n    b1 = encode_array_base85(pred1[i])\n    b2 = encode_array_base85(pred2[i])\n    rows.append((str(test_IDs[i]), b1, b2))\n\n# Build submission DataFrame\nsubmission = pd.DataFrame(rows, columns=[\"ID\", \"sig_1\", \"sig_2\"])\nsubmission.to_csv(\"/kaggle/working/submission.csv\", index=False, quoting=csv.QUOTE_ALL)\nprint(\"✓ submission.csv written\")","metadata":{"id":"mq5YckiZTZHv","outputId":"be2684f5-4eb1-4205-e349-96f9d4475e61","trusted":true,"execution":{"iopub.status.busy":"2025-12-28T08:24:53.489285Z","iopub.execute_input":"2025-12-28T08:24:53.489817Z","iopub.status.idle":"2025-12-28T08:24:54.444298Z","shell.execute_reply.started":"2025-12-28T08:24:53.489779Z","shell.execute_reply":"2025-12-28T08:24:54.443398Z"}},"outputs":[],"execution_count":null}]}