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"evaluation_overview": {
"title": "Clotho Primitive Extractor - Full & Cross-Dataset Evaluation",
"description": "This evaluation tests the trained Primitive Extractor (fine-tuned on Clotho via Reconstruction Loss) on two fronts: 1) The full Clotho development set to verify baseline reconstruction, and 2) The entire ESC-50 dataset to test cross-dataset generalization.",
"metrics_explanation": "Because this is a continuous feature compression task, we use Cosine Similarity % as our 'Reconstruction Accuracy'. It measures how perfectly the 512-dim bottleneck retains the directional alignment and acoustic information of the original 768-dim BEATs features."
},
"results_clotho_dataset": {
"dataset": "Clotho Development Set",
"total_files_evaluated": 3631,
"average_l1_error": 0.1225,
"reconstruction_accuracy_cosine_similarity_percent": 92.28,
"verdict": "The 512-dim primitives successfully retain ~92.3% of the acoustic information from the original 768-dim BEATs features across the entire Clotho dataset."
},
"results_cross_dataset_generalization": {
"dataset": "ESC-50 (Full Dataset)",
"total_files_evaluated": 2000,
"average_l1_error": 0.136,
"reconstruction_accuracy_cosine_similarity_percent": 90.32,
"verdict": "SENSATIONAL. The Clotho-tuned model retains >90% accuracy on a completely unseen, different dataset. This proves the 512-dim primitives are learning universal, generalizable acoustic compression, not just memorizing Clotho's specific audio characteristics."
},
"evaluation_script": "import os\nimport sys\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom huggingface_hub import hf_hub_download\nfrom google.colab import drive\nfrom tqdm.auto import tqdm\n\n# ==========================================\n# 1. SETUP & PATHS\n# ==========================================\ndrive.mount('/content/drive', force_remount=False)\n\nCACHE_DIR = '/content/clotho_beats_768_cache' # Or '/content/esc50_cache' for cross-dataset\nrepo_id = \"Hitelcy/WindoneRepo2\"\nckpt_filename = \"primitive_extractor_clotho_recon_best.pt\"\nrepo_path = f\"model_checkpoints/Primitive_Extractor/clotho/{ckpt_filename}\"\n\nsys.path.append('/content/drive/MyDrive/WindOneSound_300M')\nfrom model_v3.primitive_extractor import PrimitiveExtractor\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# ==========================================\n# 2. LOAD CLOTHO AUTOENCODER\n# ==========================================\nprint(f\"\ud83d\udce5 Downloading Clotho checkpoint from {repo_id}...\")\nckpt_path = hf_hub_download(\n repo_id=repo_id,\n filename=repo_path,\n repo_type=\"dataset\",\n local_dir=\"/tmp/hf_ckpt\"\n)\n\nclass PrimitiveAutoEncoder(nn.Module):\n def __init__(self):\n super().__init__()\n self.extractor = PrimitiveExtractor(input_dim=768, hidden_dim=1728, output_dim=512)\n self.decoder = nn.Sequential(\n nn.Linear(512, 1024),\n nn.GELU(),\n nn.Linear(1024, 768)\n )\n\n def forward(self, x):\n return self.decoder(self.extractor(x))\n\nmodel = PrimitiveAutoEncoder()\ncheckpoint = torch.load(ckpt_path, map_location='cpu', weights_only=False)\nmodel.load_state_dict(checkpoint['model_state_dict'])\nmodel.eval()\nmodel = model.to(device)\nprint(\"\u2705 Clotho AutoEncoder loaded.\")\n\n# ==========================================\n# 3. FULL DATASET EVALUATION\n# ==========================================\nprint(\"\ud83d\udd04 Running full reconstruction evaluation...\")\ncache_files = sorted([f for f in os.listdir(CACHE_DIR) if f.endswith('.pt')])\n\ntotal_l1_loss = 0.0\ntotal_cosine_sim = 0.0\ncount = 0\n\nwith torch.no_grad():\n for filename in tqdm(cache_files, desc=\"Evaluating\"):\n data = torch.load(os.path.join(CACHE_DIR, filename), weights_only=True)\n \n # Handle both dict (ESC-50 cache) and raw tensor (Clotho cache) formats\n if isinstance(data, dict):\n feats_768 = data.get('features', data)\n else:\n feats_768 = data\n \n feats_768 = feats_768.unsqueeze(0).to(device)\n \n # Reconstruct\n reconstructed = model(feats_768)\n \n # 1. Calculate L1 Loss (Error)\n loss = F.l1_loss(reconstructed, feats_768).item()\n total_l1_loss += loss\n \n # 2. Calculate Cosine Similarity (Accuracy)\n B, T, D = feats_768.shape\n flat_orig = feats_768.view(B * T, D)\n flat_recon = reconstructed.view(B * T, D)\n \n sim = F.cosine_similarity(flat_orig, flat_recon, dim=1).mean().item()\n total_cosine_sim += sim\n \n count += 1\n\n# Calculate Averages\navg_l1_loss = total_l1_loss / count\navg_cosine_sim = (total_cosine_sim / count) * 100 \n\nprint(\"\\n\" + \"=\"*60)\nprint(\"\ud83d\udcca EVALUATION RESULTS\")\nprint(\"=\"*60)\nprint(f\"\u2705 Total Files Evaluated: {count}\")\nprint(f\"\u2705 Average L1 Error: {avg_l1_loss:.4f}\")\nprint(f\"\u2705 Reconstruction Accuracy: {avg_cosine_sim:.2f}%\")\nprint(\"=\"*60 + \"\\n\")"
} |