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Export_to_ONNX_TFLite/Export_model_and_testmodel.ipynb +1 -0
Preprocessing/Convert_to_HDF5.ipynb +538 -0
Train_script/EfficientAT_code_train.zip +2 -2
Train_script/Train_guide.ipynb +1117 -0

Export_to_ONNX_TFLite/Export_model_and_testmodel.ipynb ADDED Viewed

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+ {"cells":[{"cell_type":"markdown","source":["#PT to ONNX"],"metadata":{"id":"qZcPxnj6n6kl"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"mRy88TxHd7KE","collapsed":true,"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1747040415700,"user_tz":-420,"elapsed":1754,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}},"outputId":"983ce468-07ee-45ac-9d66-cab24e71691c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'EfficientAT'...\n","remote: Enumerating objects: 395, done.\u001b[K\n","remote: Counting objects: 100% (152/152), done.\u001b[K\n","remote: Compressing objects: 100% (37/37), done.\u001b[K\n","remote: Total 395 (delta 134), reused 115 (delta 115), pack-reused 243 (from 2)\u001b[K\n","Receiving objects: 100% (395/395), 2.64 MiB | 7.12 MiB/s, done.\n","Resolving deltas: 100% (241/241), done.\n"]}],"source":["!git clone https://github.com/fschmid56/EfficientAT.git"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"_SG1Qcgz4zsy","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9d9a9b8c-cd15-4a32-d9cb-b10c91ce4628","executionInfo":{"status":"ok","timestamp":1747040417249,"user_tz":-420,"elapsed":59,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/EfficientAT\n"]}],"source":["%cd EfficientAT"]},{"cell_type":"code","source":["!pip install -r requirements.txt"],"metadata":{"id":"0yBzntF5sRtg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1747040438423,"user_tz":-420,"elapsed":19609,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}},"outputId":"fa2adbab-4d7a-4cb0-9cfb-9a10e45d2310"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting av==10.0.0 (from -r requirements.txt (line 1))\n"," Downloading av-10.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.5 kB)\n","Collecting h5py==3.7.0 (from -r requirements.txt (line 2))\n"," Downloading h5py-3.7.0.tar.gz (392 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m392.4/392.4 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n"," Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n"," Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Collecting librosa==0.9.2 (from -r requirements.txt (line 3))\n"," Downloading librosa-0.9.2-py3-none-any.whl.metadata (8.2 kB)\n","Collecting numpy==1.23.3 (from -r requirements.txt (line 4))\n"," Downloading numpy-1.23.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.3 kB)\n","Collecting scikit_learn==1.1.3 (from -r requirements.txt (line 5))\n"," Downloading scikit_learn-1.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (10 kB)\n","Collecting torch==1.13.0 (from -r requirements.txt (line 6))\n"," Downloading torch-1.13.0-cp311-cp311-manylinux1_x86_64.whl.metadata (24 kB)\n","\u001b[31mERROR: Ignored the following yanked versions: 2.0.0\u001b[0m\u001b[31m\n","\u001b[0m\u001b[31mERROR: Ignored the following versions that require a different python version: 1.21.2 Requires-Python >=3.7,<3.11; 1.21.3 Requires-Python >=3.7,<3.11; 1.21.4 Requires-Python >=3.7,<3.11; 1.21.5 Requires-Python >=3.7,<3.11; 1.21.6 Requires-Python >=3.7,<3.11\u001b[0m\u001b[31m\n","\u001b[0m\u001b[31mERROR: Could not find a version that satisfies the requirement torchaudio==0.13.0 (from versions: 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.4.0, 2.4.1, 2.5.0, 2.5.1, 2.6.0, 2.7.0)\u001b[0m\u001b[31m\n","\u001b[0m\u001b[31mERROR: No matching distribution found for torchaudio==0.13.0\u001b[0m\u001b[31m\n","\u001b[0m"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"szVfWuNA47pi"},"outputs":[],"source":["!python inference.py --model_name=/content/EfficientAT/resources/dymn04_im04_emotion.pt --audio_path=\"/content/EfficientAT/resources/bea_Angry_anger_1-28_0009.mp3\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"01JmuQGb64rW","colab":{"base_uri":"https://localhost:8080/"},"outputId":"09ccc631-5fb5-4226-c523-22fe623d4373"},"outputs":[{"output_type":"stream","name":"stdout","text":["python3: can't open file '/content/EfficientAT/inference_numpy.py': [Errno 2] No such file or directory\n"]}],"source":["!python inference_numpy.py --model_name=dymn04_as --audio_path=\"/content/EfficientAT/resources/recording_1741088005371.wav\""]},{"cell_type":"markdown","source":["#Export to onnx"],"metadata":{"id":"1r5BgfkNA5rc"}},{"cell_type":"code","execution_count":4,"metadata":{"id":"e4GFRBPU7j-b","executionInfo":{"status":"ok","timestamp":1747040464648,"user_tz":-420,"elapsed":12639,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"outputs":[],"source":["import torch\n","from models.dymn.model import get_model as get_dymn\n","from helpers.utils import NAME_TO_WIDTH, labels\n","\n","# model = get_dymn(width_mult=NAME_TO_WIDTH('dymn04_as'), pretrained_name='dymn04_as', strides=[2, 2, 2, 2])\n","# model.to(torch.device('cpu'))\n","model = torch.load('/content/dymn04_im04_acc_0.7408.pt', map_location='cpu', weights_only=False)"]},{"cell_type":"code","source":["!pip install onnx onnxruntime"],"metadata":{"id":"01D0XYhkPfCL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1747040473170,"user_tz":-420,"elapsed":8523,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}},"outputId":"fdf1845d-6c4c-4171-f12f-d55505bff4bd"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting onnx\n"," Downloading onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (16 kB)\n","Collecting onnxruntime\n"," Downloading onnxruntime-1.22.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (4.5 kB)\n","Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.11/dist-packages (from onnx) (2.0.2)\n","Requirement already satisfied: protobuf>=3.20.2 in /usr/local/lib/python3.11/dist-packages (from onnx) (5.29.4)\n","Collecting coloredlogs (from onnxruntime)\n"," Downloading coloredlogs-15.0.1-py2.py3-none-any.whl.metadata (12 kB)\n","Requirement already satisfied: flatbuffers in /usr/local/lib/python3.11/dist-packages (from onnxruntime) (25.2.10)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from onnxruntime) (24.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.11/dist-packages (from onnxruntime) (1.13.1)\n","Collecting humanfriendly>=9.1 (from coloredlogs->onnxruntime)\n"," Downloading humanfriendly-10.0-py2.py3-none-any.whl.metadata (9.2 kB)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy->onnxruntime) (1.3.0)\n","Downloading onnx-1.17.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.0 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.0/16.0 MB\u001b[0m \u001b[31m67.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading onnxruntime-1.22.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (16.4 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.4/16.4 MB\u001b[0m \u001b[31m70.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading coloredlogs-15.0.1-py2.py3-none-any.whl (46 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: onnx, humanfriendly, coloredlogs, onnxruntime\n","Successfully installed coloredlogs-15.0.1 humanfriendly-10.0 onnx-1.17.0 onnxruntime-1.22.0\n"]}]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"z_m2eIxQ3mnx","outputId":"ddc7d438-9d4c-4121-fe23-1e3f3b71a60d","executionInfo":{"status":"ok","timestamp":1747040487470,"user_tz":-420,"elapsed":9851,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/content/EfficientAT/models/dymn/model.py:188: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n"," if return_fmaps:\n","/content/EfficientAT/models/dymn/dy_block.py:173: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n"," assert x.shape[1] == self.channels\n"]}],"source":["dummy_input = torch.randn(1, 1, 128, 400, device=\"cpu\")\n","\n","# Define dynamic axes for the input\n","# Here, we specify that the first dimension (batch size) is dynamic\n","dynamic_axes = {\n"," \"input\": {0: \"batch_size\", 3: \"audio_length\"}, # Input shape: (B, C, H, W)\n","}\n","\n","# Define input and output names\n","input_names = [\"input\"]\n","output_names = [\"output\"]\n","\n","# Export the model\n","torch.onnx.export(\n"," model,\n"," dummy_input,\n"," \"emotion.onnx\",\n"," opset_version=11\n",")"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"mDyu47stDSbg","colab":{"base_uri":"https://localhost:8080/"},"outputId":"2c006904-0ebf-4cbc-c3f5-c6a3cc234076","executionInfo":{"status":"ok","timestamp":1747040650463,"user_tz":-420,"elapsed":19978,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["x shape before (1, 121728)\n","x shape after (1, 121727)\n","spec_x before is (1, 381, 513, 2)\n","spec_x shape is (1, 381, 513)\n","(1, 1, 128, 400)\n","[array([[ 3.432613 , -0.5243076, -0.6463419, -0.5853195, -0.5354723,\n"," -0.5868242, -0.6281568]], dtype=float32)]\n"]}],"source":["import onnxruntime as ort\n","import numpy as np\n","import librosa\n","# from preprocess import MelSTFT\n","\n","# Load the ONNX model\n","session = ort.InferenceSession(\"emotion.onnx\")\n","\n","# Get input and output details\n","input_name = session.get_inputs()[0].name\n","output_name = session.get_outputs()[0].name\n","\n","# preprocess input\n","mel = MelSTFT(n_mels=128, sr=32000, win_length=800, hopsize=320)\n","(waveform, _) = librosa.core.load('/content/03-01-05-01-01-02-05_0.mp3', sr=32000, mono=True)\n","waveform = np.stack([waveform])\n","spec = mel(waveform)\n","if spec.shape[-1] > 400:\n"," spec = spec[:, :, :400]\n","else:\n"," spec = np.pad(spec, ((0, 0), (0, 0), (0, 400 - spec.shape[-1])), mode='constant')\n","spec = np.expand_dims(spec, axis=0)\n","spec = spec.astype(np.float32)\n","print(spec.shape)\n","\n","# Run inference\n","output = session.run([output_name], {input_name: spec})\n","\n","# Print the output\n","print(output)"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1GnlQkWu5_Fc","outputId":"a13d8c8b-896a-431c-a9bf-790226dea5b5","executionInfo":{"status":"ok","timestamp":1747040692360,"user_tz":-420,"elapsed":12,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["0\n"]}],"source":["len(output[0][0])\n","print(np.argmax(output[0][0]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Gf__SsN55_yY"},"outputs":[],"source":["import numpy as np\n","\n","def sigmoid(x):\n"," return 1 / (1 + np.exp(-x))\n","\n","def softmax(x):\n"," exp_x = np.exp(x - np.max(x)) # Subtract max(x) for numerical stability\n"," return exp_x / np.sum(exp_x, axis=-1, keepdims=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RVPMJbnc6roX"},"outputs":[],"source":["preds = softmax(output[0][0])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OfR1epuQ6xPR"},"outputs":[],"source":["sorted_indexes = np.argsort(preds)[::-1]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gCEf2tdJ6x6Q","outputId":"1133b892-d5dd-488e-c987-fc82bcba2812"},"outputs":[{"output_type":"stream","name":"stdout","text":["************* Acoustic Event Detected: *****************\n","Angry: 0.914\n","sad: 0.015\n","Fear: 0.015\n","neutral: 0.014\n","Disgust: 0.014\n","Happy: 0.014\n","surprise: 0.014\n","********************************************************\n"]}],"source":["# Print audio tagging top probabilities\n","print(\"************* Acoustic Event Detected: *****************\")\n","for k in range(7):\n"," print('{}: {:.3f}'.format(labels[sorted_indexes[k]], preds[sorted_indexes[k]]))\n","print(\"********************************************************\")"]},{"cell_type":"markdown","source":["#Load sang onnx fix typepo"],"metadata":{"id":"zBJk6xG7A1e4"}},{"cell_type":"code","execution_count":2,"metadata":{"id":"uHKxnQ-w5ng4","executionInfo":{"status":"ok","timestamp":1747045236944,"user_tz":-420,"elapsed":41,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"outputs":[],"source":["import onnx\n","\n","# Load ONNX model\n","onnx_model = onnx.load(\"/content/emotion.onnx\")"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u1vlWtAF6Le9","outputId":"1a6c6507-7703-4732-9ac8-d4cfc487f813","executionInfo":{"status":"ok","timestamp":1747045239246,"user_tz":-420,"elapsed":129,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Model is valid.\n"]}],"source":["# Rename inputs\n","for i, input_node in enumerate(onnx_model.graph.input):\n"," old_name = input_node.name\n"," new_name = f\"input_{i}\"\n"," input_node.name = new_name\n"," for node in onnx_model.graph.node:\n"," for j, input_name in enumerate(node.input):\n"," if input_name == old_name:\n"," node.input[j] = new_name\n","onnx.checker.check_model(onnx_model)\n","print(\"Model is valid.\")"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"RyYwyJq2LbOz","executionInfo":{"status":"ok","timestamp":1747045251036,"user_tz":-420,"elapsed":88,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"outputs":[],"source":["onnx.save(onnx_model, \"fixed_emotion.onnx\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8_7au43LMHns"},"outputs":[],"source":["import onnxruntime as ort\n","import numpy as np\n","import librosa\n","# from preprocess import MelSTFT\n","\n","# Load the ONNX model\n","session = ort.InferenceSession(\"fixed_dymn04_as_4s.onnx\")\n","\n","# Get input and output details\n","input_name = session.get_inputs()[0].name\n","output_name = session.get_outputs()[0].name\n","\n","# preprocess input\n","mel = MelSTFT(n_mels=128, sr=32000, win_length=800, hopsize=320)\n","(waveform, _) = librosa.core.load('/content/EfficientAT/resources/machine_gun_4_8.wav', sr=32000, mono=True)\n","waveform = np.stack([waveform])\n","spec = mel(waveform)\n","spec = np.expand_dims(spec, axis=0)\n","spec = spec.astype(np.float32)\n","\n","# Run inference\n","output = session.run([output_name], {input_name: spec})"]},{"cell_type":"code","source":["preds = sigmoid(output[0][0])\n","sorted_indexes = np.argsort(preds)[::-1]\n","# Print audio tagging top probabilities\n","print(\"************* Acoustic Event Detected: *****************\")\n","for k in range(10):\n"," print('{}: {:.3f}'.format(labels[sorted_indexes[k]], preds[sorted_indexes[k]]))\n","print(\"********************************************************\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yI4BVCzZu043","outputId":"2e5ba910-2a6e-42b5-ed89-72c6c6fe5d50"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["************* Acoustic Event Detected: *****************\n","Gunshot, gunfire: 0.646\n","Artillery fire: 0.574\n","Machine gun: 0.296\n","Speech: 0.276\n","Music: 0.058\n","Ding: 0.039\n","Outside, rural or natural: 0.029\n","Clang: 0.021\n","Outside, urban or manmade: 0.014\n","Sound effect: 0.013\n","********************************************************\n"]}]},{"cell_type":"code","source":["import numpy as np\n","import tensorflow as tf\n","\n","# Load the TFLite model\n","interpreter = tf.lite.Interpreter(model_path=\"/content/EfficientAT/fixed_dymn04_as_4s.tflite\")\n","interpreter.allocate_tensors()\n","\n","# Get input and output details\n","input_details = interpreter.get_input_details()\n","output_details = interpreter.get_output_details()"],"metadata":{"id":"tYAA2SXg4rAm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["%%time\n","interpreter.set_tensor(input_details[0]['index'], spec)\n","\n","# Run inference\n","interpreter.invoke()\n","output_data = interpreter.get_tensor(output_details[0]['index'])\n","print(output_data)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lCURHCdb50NM","outputId":"b90fd17d-6435-47cd-bce4-d504667ba3a5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[ -0.84756243 -6.042206 -7.31538 -6.587422 -8.137321\n"," -6.8817334 -10.2073965 -7.0277896 -9.497076 -9.076806\n"," -7.9717975 -9.098734 -9.190367 -10.533342 -9.261131\n"," -9.558402 -8.0612755 -9.170078 -9.971562 -9.306829\n"," -10.51197 -9.227371 -8.791254 -7.1896567 -6.929867\n"," -6.8730307 -8.732161 -6.8715105 -7.095218 -9.634821\n"," -7.3266606 -6.496367 -6.867931 -7.2777967 -7.84522\n"," -9.982277 -9.040248 -9.472129 -6.970452 -6.7633314\n"," -8.596198 -8.716958 -11.37014 -9.757639 -9.384382\n"," -11.648908 -9.068704 -9.676451 -9.729941 -10.329362\n"," -11.040093 -7.60864 -9.644373 -7.8703647 -10.20223\n"," -11.044352 -11.488615 -11.821376 -8.239668 -9.515796\n"," -10.249389 -11.395409 -12.753719 -9.719765 -7.815147\n"," -8.667467 -8.783001 -10.783493 -9.077807 -6.502072\n"," -6.641275 -9.622646 -5.2108536 -6.345402 -7.260418\n"," -9.74338 -9.060996 -9.3590765 -9.125456 -9.142018\n"," -8.84309 -7.5067797 -10.2783375 -7.242247 -8.52377\n"," -9.487844 -7.1616087 -8.470291 -7.9428587 -10.482567\n"," -7.625914 -8.034529 -6.576645 -8.566756 -7.922304\n"," -7.995202 -6.649238 -6.5045075 -6.472725 -8.503324\n"," -9.17288 -9.43606 -8.100371 -8.338065 -6.9929743\n"," -6.991773 -7.0988426 -6.6684513 -7.944296 -8.298891\n"," -7.6659217 -6.260497 -7.467764 -7.7779393 -12.417544\n"," -8.141451 -8.614699 -8.081804 -8.368467 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-7.8431225 -10.0173435 -10.403385 -7.2444086 -11.104176\n"," -8.219688 -10.097628 -8.695146 -12.666377 -11.033119\n"," -11.94974 -8.298492 -7.05132 -3.5644822 -4.064981\n"," -11.08213 -12.455801 -7.176717 -10.135718 -7.242347\n"," -10.261881 -9.935315 -8.438743 -8.370187 -10.179698\n"," -7.336482 -8.550757 -12.201432 -9.485798 -11.314145\n"," -7.3142076 -9.465659 -7.8540864 -10.189713 -4.3626466\n"," -12.027302 -6.617275 -5.9098096 -6.6777973 -4.2796392\n"," -3.5423725 -6.6892195 -6.3318806 -11.957001 -9.272463\n"," -9.623184 -11.435066 -9.314948 -10.678869 -9.70005\n"," -10.211389 -10.757169 -8.058382 -9.976052 -8.796942\n"," -10.181307 -9.936882 ]]\n","CPU times: user 62.3 ms, sys: 6.82 ms, total: 69.1 ms\n","Wall time: 74.6 ms\n"]}]},{"cell_type":"code","source":["output_data.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5JbesH4C6E3F","outputId":"56307e6b-e58e-4847-b91a-55a40252e188"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1, 527)"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","source":["sorted_indexes = np.argsort(preds)[::-1]"],"metadata":{"id":"OAd_yDEN6bK7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["sorted_indexes.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RGlx7Tir6iQj","outputId":"dc0dd89b-09fd-4b99-f7f3-714e679eac88"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(527,)"]},"metadata":{},"execution_count":47}]},{"cell_type":"code","source":["preds = sigmoid(output_data[0])\n","sorted_indexes = np.argsort(preds)[::-1]\n","# Print audio tagging top probabilities\n","print(\"************* Acoustic Event Detected: *****************\")\n","for k in range(10):\n"," print('{}: {:.3f}'.format(labels[sorted_indexes[k]], preds[sorted_indexes[k]]))\n","print(\"********************************************************\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PjcLYfAk6Gwj","outputId":"1d42c015-3e73-4f1c-bc83-e03c41175ff7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["************* Acoustic Event Detected: *****************\n","Gunshot, gunfire: 0.589\n","Artillery fire: 0.479\n","Speech: 0.300\n","Machine gun: 0.280\n","Music: 0.055\n","Outside, rural or natural: 0.028\n","Ding: 0.028\n","Clang: 0.017\n","Outside, urban or manmade: 0.014\n","Sound effect: 0.013\n","********************************************************\n"]}]},{"cell_type":"code","source":["!pip install coremltools"],"metadata":{"id":"FYeXiU1FLKP1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import coremltools as ct\n","\n","# Load the ONNX model\n","model_path = \"/content/EfficientAT/resources/dymn04_as.pt\"\n","\n","# Define dynamic input shape\n","inputs = [ct.TensorType(name=\"input_1\", shape=(1, 1, 128, ct.RangeDim(1, 2000)))]\n","\n","# Convert to Core ML\n","coreml_model = ct.convert(model_path, inputs=inputs, source='pytorch')\n","\n","# Save the Core ML model\n","coreml_model.save(\"dymn04_as.mlmodel\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"yKps_yXKLM90","outputId":"aa6b87fb-4d2d-467e-ba28-f65401510e67"},"execution_count":null,"outputs":[{"output_type":"error","ename":"RuntimeError","evalue":"PytorchStreamReader failed locating file constants.pkl: file not found","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-9e995d17096f>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Convert to Core ML\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mcoreml_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mct\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pytorch'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# Save the Core ML model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/coremltools/converters/_converters_entry.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(model, source, inputs, outputs, classifier_config, minimum_deployment_target, convert_to, compute_precision, skip_model_load, compute_units, package_dir, debug)\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[0mspecification_version\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_set_default_specification_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexact_target\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 444\u001b[0;31m mlmodel = mil_convert(\n\u001b[0m\u001b[1;32m 445\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 446\u001b[0m \u001b[0mconvert_from\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexact_source\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36mmil_convert\u001b[0;34m(model, convert_from, convert_to, compute_units, **kwargs)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0mSee\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mcoremltools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \"\"\"\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_mil_convert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_from\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mConverterRegistry\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMLModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute_units\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m_mil_convert\u001b[0;34m(model, convert_from, convert_to, registry, modelClass, compute_units, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"weights_dir\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweights_dir\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m proto, mil_program = mil_convert_to_proto(\n\u001b[0m\u001b[1;32m 212\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0mconvert_from\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36mmil_convert_to_proto\u001b[0;34m(model, convert_from, convert_to, converter_registry, **kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0mfrontend_converter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrontend_converter_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m \u001b[0mprog\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfrontend_converter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mconvert_to\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"neuralnetwork\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/coremltools/converters/mil/converter.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfrontend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtorch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorchscript\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'training'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mtorchscript\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/coremltools/converters/mil/frontend/torch/load.py\u001b[0m in \u001b[0;36m_torchscript_from_model\u001b[0;34m(model_spec)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_spec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmodel_spec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\".pt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m 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torch._C.import_ir_module_from_buffer(\n","\u001b[0;31mRuntimeError\u001b[0m: PytorchStreamReader failed locating file constants.pkl: file not found"]}]},{"cell_type":"code","source":[],"metadata":{"id":"gSMIyJxwg4D-"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#ONNX to tflite\n","Chạy trên kaggle"],"metadata":{"id":"gBGCjfV8n9aW"}},{"cell_type":"code","source":["!pip install onnx_tf tensorflow==2.14.0"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"JDsmanV6vn4n","executionInfo":{"status":"ok","timestamp":1747046031221,"user_tz":-420,"elapsed":67201,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}},"outputId":"d1214964-c566-4766-ef7f-c44850520221"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: onnx_tf in /usr/local/lib/python3.11/dist-packages (1.10.0)\n","Collecting tensorflow==2.14.0\n"," Downloading 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uninstall: tensorflow\n"," Found existing installation: tensorflow 2.12.0\n"," Uninstalling tensorflow-2.12.0:\n"," Successfully uninstalled tensorflow-2.12.0\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","tensorflow-text 2.18.1 requires tensorflow<2.19,>=2.18.0, but you have tensorflow 2.14.0 which is incompatible.\n","tf-keras 2.18.0 requires tensorflow<2.19,>=2.18, but you have tensorflow 2.14.0 which is incompatible.\n","flax 0.10.6 requires jax>=0.5.1, but you have jax 0.4.30 which is incompatible.\n","orbax-checkpoint 0.11.13 requires jax>=0.5.0, but you have jax 0.4.30 which is incompatible.\n","tensorflow-decision-forests 1.11.0 requires tensorflow==2.18.0, but you have tensorflow 2.14.0 which is incompatible.\n","chex 0.1.89 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\n","tensorstore 0.1.74 requires ml_dtypes>=0.3.1, but you have ml-dtypes 0.2.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed keras-2.14.0 ml-dtypes-0.2.0 tensorboard-2.14.1 tensorflow-2.14.0 tensorflow-estimator-2.14.0\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["keras","ml_dtypes","tensorboard","tensorflow"]},"id":"61b023af355f4283aa45d7f6298e8099"}},"metadata":{}}]},{"cell_type":"code","source":["from onnx_tf.backend import prepare\n","import onnx\n","import tensorflow as tf\n","\n","# Load ONNX model\n","onnx_model = onnx.load(\"/content/fixed_emotion.onnx\")\n","\n","# Convert to TensorFlow format\n","tf_rep = prepare(onnx_model)\n","print(tf_rep.inputs)\n","\n","# Export the TensorFlow SavedModel\n","tf_rep.export_graph(\"saved_model_4s_emotion\")\n","\n","# Convert SavedModel to TFLite\n","converter = tf.lite.TFLiteConverter.from_saved_model(\"saved_model_4s_emotion\")\n","converter.optimizations = [tf.lite.Optimize.DEFAULT]\n","tflite_model = converter.convert()\n","\n","# Save the TFLite model\n","with open(\"emotion_model_2025_03_28.tflite\", \"wb\") as f:\n"," f.write(tflite_model)"],"metadata":{"id":"DZDTl5Zqn_Fv","colab":{"base_uri":"https://localhost:8080/","height":547},"executionInfo":{"status":"error","timestamp":1747046056018,"user_tz":-420,"elapsed":11643,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}},"outputId":"509c8a16-d670-4709-c14a-a4c15bb156ee"},"execution_count":1,"outputs":[{"output_type":"error","ename":"ImportError","evalue":"This version of TensorFlow Probability requires TensorFlow version >= 2.18; Detected an installation of version 2.14.0. Please upgrade TensorFlow to proceed.","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-3ba768d958af>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0monnx_tf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprepare\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0monnx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Load ONNX model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/onnx_tf/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/onnx_tf/backend.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0monnx_tf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommon\u001b[0m \u001b[0;32mimport\u001b[0m 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\u001b[0mtfd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0monnx_tf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandlers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend_handler\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBackendHandler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0monnx_tf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandlers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandler\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0monnx_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tensorflow_probability/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# from tensorflow_probability.google import staging # DisableOnExport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;31m# from tensorflow_probability.google import tfp_google # DisableOnExport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;31m# pylint: disable=wildcard-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 23\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tensorflow_probability/python/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;31m# Non-lazy load of packages that register with tensorflow or keras.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpkg_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_maybe_nonlazy_load\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m \u001b[0mdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mglobals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpkg_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Forces loading the package from its lazy loader.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tensorflow_probability/python/internal/lazy_loader.py\u001b[0m in \u001b[0;36m__dir__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0mmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tensorflow_probability/python/internal/lazy_loader.py\u001b[0m in \u001b[0;36m_load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\"\"\"Load the module and insert it into the parent's globals.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_on_first_access\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_on_first_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_on_first_access\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;31m# Import the target module and insert it into the parent's namespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tensorflow_probability/python/__init__.py\u001b[0m in \u001b[0;36m_validate_tf_environment\u001b[0;34m(package)\u001b[0m\n\u001b[1;32m 57\u001b[0m if (distutils.version.LooseVersion(tf.__version__) <\n\u001b[1;32m 58\u001b[0m distutils.version.LooseVersion(required_tensorflow_version)):\n\u001b[0;32m---> 59\u001b[0;31m raise ImportError(\n\u001b[0m\u001b[1;32m 60\u001b[0m \u001b[0;34m'This version of TensorFlow Probability requires TensorFlow '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;34m'version >= {required}; Detected an installation of version {present}. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mImportError\u001b[0m: This version of TensorFlow Probability requires TensorFlow version >= 2.18; Detected an installation of version 2.14.0. Please upgrade TensorFlow to proceed.","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"],"errorDetails":{"actions":[{"action":"open_url","actionText":"Open Examples","url":"/notebooks/snippets/importing_libraries.ipynb"}]}}]},{"cell_type":"markdown","source":["#test cái tflite"],"metadata":{"id":"nnML9fwUoQWg"}},{"cell_type":"markdown","source":["Read label from csv file"],"metadata":{"id":"64ShAXw5M9lS"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"JTLwJstrEbSj"},"outputs":[],"source":["import csv\n","\n","with open('/content/class_labels_indices.csv', 'r') as f:\n"," reader = csv.reader(f, delimiter=',')\n"," lines = list(reader)\n","\n","labels = []\n","ids = [] # Each label has a unique id such as \"/m/068hy\"\n","for i1 in range(1, len(lines)):\n"," id = lines[i1][1]\n"," label = lines[i1][2]\n"," ids.append(id)\n"," labels.append(label)\n","\n","classes_num = len(labels)"]},{"cell_type":"markdown","source":["Preprocess audio"],"metadata":{"id":"ebLOZ2JXNF0w"}},{"cell_type":"code","source":["import numpy as np\n","from numpy.lib.stride_tricks import as_strided\n","from typing import Tuple, Optional, Union\n","\n","def mel_scale_scalar(freq: float) -> float:\n"," \"\"\"Convert frequency to mel scale\"\"\"\n"," return 1127.0 * np.log(1.0 + freq / 700.0)\n","\n","def mel_scale(freq: np.ndarray) -> np.ndarray:\n"," \"\"\"Vector version of mel scale conversion\"\"\"\n"," return 1127.0 * np.log(1.0 + freq / 700.0)\n","\n","def inverse_mel_scale(mel: np.ndarray) -> np.ndarray:\n"," \"\"\"Convert mel scale to frequency\"\"\"\n"," return 700.0 * (np.exp(mel / 1127.0) - 1.0)\n","\n","def vtln_warp_mel_freq(vtln_low: float,\n"," vtln_high: float,\n"," low_freq: float,\n"," high_freq: float,\n"," vtln_warp_factor: float,\n"," mel_freq: np.ndarray) -> np.ndarray:\n"," \"\"\"\n"," Implements VTLN warping for mel frequencies\n"," \"\"\"\n"," return mel_freq # Placeholder - implement if VTLN warping is needed\n","\n","def get_mel_banks(\n"," num_bins: int,\n"," window_length_padded: int,\n"," sample_freq: float,\n"," low_freq: float,\n"," high_freq: float,\n"," vtln_low: float,\n"," vtln_high: float,\n"," vtln_warp_factor: float,\n",") -> Tuple[np.ndarray, np.ndarray]:\n"," \"\"\"\n"," Get mel filterbank matrices following Kaldi's implementation.\n","\n"," Args:\n"," num_bins: Number of triangular bins\n"," window_length_padded: Padded window length\n"," sample_freq: Sampling frequency\n"," low_freq: Lowest frequency to consider\n"," high_freq: Highest frequency to consider\n"," vtln_low: Lower frequency for VTLN\n"," vtln_high: Higher frequency for VTLN\n"," vtln_warp_factor: Warping factor for VTLN\n","\n"," Returns:\n"," Tuple[np.ndarray, np.ndarray]: (bins, center_freqs)\n"," - bins: melbank matrix of shape (num_bins, num_fft_bins)\n"," - center_freqs: center frequencies of bins of shape (num_bins,)\n"," \"\"\"\n"," assert num_bins > 3, \"Must have at least 3 mel bins\"\n"," assert window_length_padded % 2 == 0\n"," num_fft_bins = window_length_padded // 2\n"," nyquist = 0.5 * sample_freq\n","\n"," if high_freq <= 0.0:\n"," high_freq += nyquist\n","\n"," assert (\n"," (0.0 <= low_freq < nyquist) and (0.0 < high_freq <= nyquist) and (low_freq < high_freq)\n"," ), f\"Bad values in options: low-freq {low_freq} and high-freq {high_freq} vs. nyquist {nyquist}\"\n","\n"," # fft-bin width [think of it as Nyquist-freq / half-window-length]\n"," fft_bin_width = sample_freq / window_length_padded\n","\n"," mel_low_freq = mel_scale_scalar(low_freq)\n"," mel_high_freq = mel_scale_scalar(high_freq)\n","\n"," # divide by num_bins+1 in next line because of end-effects where the bins\n"," # spread out to the sides.\n"," mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1)\n","\n"," if vtln_high < 0.0:\n"," vtln_high += nyquist\n","\n"," assert vtln_warp_factor == 1.0 or (\n"," (low_freq < vtln_low < high_freq) and (0.0 < vtln_high < high_freq) and (vtln_low < vtln_high)\n"," ), f\"Bad values in options: vtln-low {vtln_low} and vtln-high {vtln_high}, versus low-freq {low_freq} and high-freq {high_freq}\"\n","\n"," bin = np.arange(num_bins)[:, np.newaxis]\n"," left_mel = mel_low_freq + bin * mel_freq_delta # shape (num_bins, 1)\n"," center_mel = mel_low_freq + (bin + 1.0) * mel_freq_delta # shape (num_bins, 1)\n"," right_mel = mel_low_freq + (bin + 2.0) * mel_freq_delta # shape (num_bins, 1)\n","\n"," if vtln_warp_factor != 1.0:\n"," left_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, left_mel)\n"," center_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, center_mel)\n"," right_mel = vtln_warp_mel_freq(vtln_low, vtln_high, low_freq, high_freq, vtln_warp_factor, right_mel)\n","\n"," center_freqs = inverse_mel_scale(center_mel).squeeze(-1) # shape (num_bins)\n","\n"," # shape (1, num_fft_bins)\n"," mel = mel_scale(fft_bin_width * np.arange(num_fft_bins))[np.newaxis, :]\n","\n"," # shape (num_bins, num_fft_bins)\n"," up_slope = (mel - left_mel) / (center_mel - left_mel)\n"," down_slope = (right_mel - mel) / (right_mel - center_mel)\n","\n"," if vtln_warp_factor == 1.0:\n"," # left_mel < center_mel < right_mel so we can min the two slopes and clamp negative values\n"," bins = np.maximum(0.0, np.minimum(up_slope, down_slope))\n"," else:\n"," # warping can move the order of left_mel, center_mel, right_mel anywhere\n"," bins = np.zeros_like(up_slope)\n"," up_idx = (mel > left_mel) & (mel <= center_mel) # left_mel < mel <= center_mel\n"," down_idx = (mel > center_mel) & (mel < right_mel) # center_mel < mel < right_mel\n"," bins[up_idx] = up_slope[up_idx]\n"," bins[down_idx] = down_slope[down_idx]\n","\n"," return bins, center_freqs\n","\n","def stft(\n"," input: np.ndarray,\n"," n_fft: int,\n"," hop_length: Optional[int] = None,\n"," win_length: Optional[int] = None,\n"," window: Optional[np.ndarray] = None,\n"," center: bool = True,\n"," pad_mode: str = \"reflect\",\n"," normalized: bool = False,\n"," onesided: bool = True,\n"," return_complex: bool = True\n",") -> np.ndarray:\n"," \"\"\"\n"," NumPy implementation of Short-time Fourier transform (STFT).\n","\n"," Args:\n"," input: Input signal (B?, L) where B? is optional batch dimension\n"," n_fft: Size of Fourier transform\n"," hop_length: Distance between neighboring frames (default: n_fft//4)\n"," win_length: Window size (default: n_fft)\n"," window: Window function (1D array)\n"," center: Whether to pad input for centered frames\n"," pad_mode: Padding mode ('reflect', 'constant', 'edge')\n"," normalized: Whether to normalize the STFT\n"," onesided: Whether to return only positive frequencies\n"," return_complex: Whether to return complex array\n"," \"\"\"\n"," # Set default values\n"," if hop_length is None:\n"," hop_length = n_fft // 4\n"," if win_length is None:\n"," win_length = n_fft\n","\n"," # Prepare window\n"," if window is None:\n"," window = np.ones(win_length)\n"," if len(window) < n_fft:\n"," # Pad window to n_fft if needed\n"," pad_width = (n_fft - len(window)) // 2\n"," window = np.pad(window, (pad_width, n_fft - len(window) - pad_width))\n","\n"," # Handle input dimensions\n"," input = np.asarray(input)\n"," if input.ndim == 1:\n"," input = input[np.newaxis, :] # Add batch dimension\n"," squeeze_batch = True\n"," else:\n"," squeeze_batch = False\n","\n"," # Pad signal for centered frames\n"," if center:\n"," pad_width = int(n_fft // 2)\n"," if pad_mode == 'reflect':\n"," input = np.pad(input, ((0, 0), (pad_width, pad_width)), mode='reflect')\n"," elif pad_mode == 'constant':\n"," input = np.pad(input, ((0, 0), (pad_width, pad_width)), mode='constant')\n"," elif pad_mode == 'edge':\n"," input = np.pad(input, ((0, 0), (pad_width, pad_width)), mode='edge')\n","\n"," # Calculate number of frames\n"," n_frames = 1 + (input.shape[-1] - n_fft) // hop_length\n","\n"," # Create frame matrix using stride tricks\n"," frame_length = n_fft\n"," frame_step = hop_length\n"," frame_stride = input.strides[-1]\n","\n"," shape = (input.shape[0], n_frames, frame_length)\n"," strides = (input.strides[0], frame_step * frame_stride, frame_stride)\n","\n"," frames = as_strided(input, shape=shape, strides=strides, writeable=False)\n","\n"," # Apply window\n"," frames = frames * window\n","\n"," # Compute FFT\n"," stft_matrix = np.fft.fft(frames, n=n_fft, axis=-1)\n","\n"," # Normalize if requested\n"," if normalized:\n"," stft_matrix = stft_matrix / np.sqrt(n_fft)\n","\n"," # Handle onesided output\n"," if onesided:\n"," stft_matrix = stft_matrix[..., :(n_fft // 2) + 1]\n","\n"," # Handle return format\n"," if return_complex:\n"," result = stft_matrix\n"," else:\n"," result = np.stack((stft_matrix.real, stft_matrix.imag), axis=-1)\n","\n"," # Remove batch dimension if input was 1D\n"," if squeeze_batch:\n"," result = result[0]\n","\n"," return result\n","\n","class MelSTFT:\n"," def __init__(self, n_mels=128, sr=32000, win_length=800, hopsize=320, n_fft=1024,\n"," fmin=0.0, fmax=None, fmin_aug_range=10, fmax_aug_range=2000):\n"," \"\"\"\n"," Initialize MelSTFT for audio feature extraction using only NumPy.\n"," \"\"\"\n"," self.n_mels = n_mels\n"," self.sr = sr\n"," self.win_length = win_length\n"," self.hopsize = hopsize\n"," self.n_fft = n_fft\n"," self.fmin = fmin\n"," if fmax is None:\n"," fmax = sr // 2 - fmax_aug_range // 2\n"," self.fmax = fmax\n","\n"," # Create Hann window\n"," self.window = np.hanning(win_length)\n","\n"," # Create mel filterbank following Kaldi's implementation\n"," self.mel_basis, _ = get_mel_banks(\n"," self.n_mels,\n"," self.n_fft,\n"," self.sr,\n"," self.fmin,\n"," self.fmax,\n"," 100.0,\n"," -500.,\n"," 1.0\n"," )\n"," self.mel_basis = np.pad(self.mel_basis, ((0, 0), (0, 1)), mode='constant', constant_values=0)\n","\n"," # Pre-emphasis filter coefficients\n"," self.preemphasis_coefficient = np.array([-.97, 1]).reshape(1, 1, 2)\n","\n"," def preemphasis(self, x):\n"," \"\"\"Apply pre-emphasis filter using conv1d equivalent\"\"\"\n"," # Reshape input to match conv1d input shape (batch, channels, length)\n"," x = x.reshape(1, 1, -1)\n","\n"," # Implement conv1d manually\n"," output_size = x.shape[2] - self.preemphasis_coefficient.shape[2] + 1\n"," result = np.zeros((1, 1, output_size))\n","\n"," for i in range(output_size):\n"," result[0, 0, i] = np.sum(x[0, 0, i:i+2] * self.preemphasis_coefficient[0, 0])\n","\n"," return result[0]\n","\n"," def __call__(self, x):\n"," \"\"\"Convert audio to log-Mel spectrogram.\"\"\"\n"," # Apply pre-emphasis\n"," print(f'x shape before {x.shape}')\n"," x = self.preemphasis(x)\n"," print(f'x shape after {x.shape}')\n","\n"," # Compute STFT\n"," spec_x = stft(\n"," input=x,\n"," n_fft=self.n_fft,\n"," hop_length=self.hopsize,\n"," win_length=self.win_length,\n"," window=self.window,\n"," center=True,\n"," pad_mode='reflect',\n"," normalized=False,\n"," return_complex=False\n"," )\n","\n"," # Convert to power spectrogram\n"," print(f'spec_x before is {spec_x.shape}')\n"," spec_x = np.sum(spec_x ** 2, axis=-1)\n","\n"," print(f'spec_x shape is {spec_x.shape}')\n","\n"," # Apply Mel filterbank (ensuring shapes match)\n"," melspec = np.dot(self.mel_basis, spec_x.transpose(0,2,1)).transpose(1,0,2)\n","\n"," # Log-scale and normalize\n"," melspec = np.log(melspec + 1e-5)\n"," melspec = (melspec + 4.5) / 5.\n","\n"," return melspec"],"metadata":{"id":"Ris3ajNhGEkQ","executionInfo":{"status":"ok","timestamp":1747040595424,"user_tz":-420,"elapsed":27,"user":{"displayName":"Nguha Duong","userId":"08400527066055899740"}}},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":["Load model and handle input and output\n","\n","The input shape of model is `[1,1,128, any]`. Note the last axis of input shape is dynamic. It depends on the length of audio.\n","\n","The output shape of model is `[1, 527]`. Currently, we're using the pretrained model and exporting it. Note: this shape might be changed in the future."],"metadata":{"id":"CqXIPms2NMcv"}},{"cell_type":"code","source":["import numpy as np\n","import tensorflow as tf\n","\n","# Load the TFLite model\n","interpreter = tf.lite.Interpreter(model_path=\"/content/emotion_model_2025_03_28.tflite\")\n","interpreter.allocate_tensors()\n","\n","# Get input and output details, can print to see details of input and output\n","input_details = interpreter.get_input_details() # The shape of input is [1, 1, 128, None] the last axis depends on the length of audio, data type = float32\n","output_details = interpreter.get_output_details() # The shape of output is [1, 527], data type = float32"],"metadata":{"id":"VlCUYth6GT0f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# preprocess audio\n","import librosa\n","\n","mel = MelSTFT(n_mels=128, sr=32000, win_length=800, hopsize=320)\n","\n","# convert to waveform\n","(waveform, _) = librosa.core.load('/content/angry_4s.mp3', sr=32000, mono=True)\n","waveform = np.stack([waveform])\n","spec = mel(waveform)\n","if spec.shape[-1] > 400:\n"," spec = spec[:, :, :400]\n","else:\n"," spec = np.pad(spec, ((0, 0), (0, 0), (0, 400 - spec.shape[-1])), mode='constant')\n","spec = np.expand_dims(spec, axis=0)\n","print(f'the shape of spec is {spec.shape}')\n","spec = spec.astype(np.float32)"],"metadata":{"id":"PtexE6_HGlDW","colab":{"base_uri":"https://localhost:8080/"},"outputId":"cde2909e-7b58-4546-8e3a-e6f5779d8d04"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["x shape before (1, 130223)\n","x shape after (1, 130222)\n","spec_x before is (1, 407, 513, 2)\n","spec_x shape is (1, 407, 513)\n","the shape of spec is (1, 1, 128, 400)\n"]}]},{"cell_type":"code","source":["# Run inference\n","interpreter.set_tensor(input_details[0]['index'], spec)\n","interpreter.invoke()\n","output_data = interpreter.get_tensor(output_details[0]['index'])"],"metadata":{"id":"xyy_O-_vHsYy"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Post process output to get the result"],"metadata":{"id":"eeh_JqiyN0zo"}},{"cell_type":"code","source":["# Post processing\n","import numpy as np\n","\n","def sigmoid(x):\n"," return 1 / (1 + np.exp(-x))\n","\n","def softmax(x):\n"," exp_x = np.exp(x - np.max(x)) # Subtract max(x) for numerical stability\n"," return exp_x / np.sum(exp_x, axis=-1, keepdims=True)\n","\n","preds = softmax(output_data[0])\n","sorted_indexes = np.argsort(preds)[::-1]\n","# Print audio tagging top probabilities\n","print(\"* Acoustic Event Detected: *\")\n","for k in range(7):\n"," print('{}: {:.3f}'.format(labels[sorted_indexes[k]], preds[sorted_indexes[k]]))\n","print(\"**\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pPLXt-FYH1Nz","outputId":"f620d201-8bfb-497d-d583-ef73db24cc46"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["* Acoustic Event Detected: *\n","Angry: 0.913\n","sad: 0.015\n","neutral: 0.015\n","Fear: 0.015\n","Happy: 0.014\n","surprise: 0.014\n","Disgust: 0.014\n","**\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"-MMULJzEH3xe"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}

Preprocessing/Convert_to_HDF5.ipynb ADDED Viewed

	@@ -0,0 +1,538 @@

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#convert file audio to mp3 32k"
+      ],
+      "metadata": {
+        "id": "XE8k_JyY5eBb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Wyv972sc5Lb4"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from multiprocessing import Pool, cpu_count\n",
+        "from tqdm import tqdm\n",
+        "import subprocess\n",
+        "\n",
+        "def process_file_ffmpeg(args):\n",
+        "    file_path, input_folder, output_folder = args\n",
+        "    rel_path = os.path.relpath(file_path, input_folder)\n",
+        "    rel_path = os.path.splitext(rel_path)[0] + \".mp3\"  # luôn xuất mp3\n",
+        "    out_path = os.path.join(output_folder, rel_path)\n",
+        "    os.makedirs(os.path.dirname(out_path), exist_ok=True)\n",
+        "\n",
+        "    cmd = [\n",
+        "        \"ffmpeg\",\n",
+        "        \"-y\",  # overwrite nếu đã tồn tại\n",
+        "        \"-i\", file_path,\n",
+        "        \"-ar\", \"32000\",  # sample rate 32kHz\n",
+        "        \"-ac\", \"1\",      # stereo set thành 2 còn mono set thành 1\n",
+        "        \"-b:a\", \"192k\",  # bitrate\n",
+        "        out_path\n",
+        "    ]\n",
+        "\n",
+        "    try:\n",
+        "        subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True)\n",
+        "        return True\n",
+        "    except subprocess.CalledProcessError:\n",
+        "        print(f\"❌ Lỗi khi xử lý {file_path}\")\n",
+        "        return False\n",
+        "\n",
+        "def convert_audio_ffmpeg_multiprocessing(input_folder, output_folder, num_workers=None):\n",
+        "    audio_exts = ('.mp3', '.wav', '.flac', '.m4a', '.ogg')\n",
+        "\n",
+        "    # Lấy danh sách tất cả file audio\n",
+        "    all_files = []\n",
+        "    for root, _, files in os.walk(input_folder):\n",
+        "        for f in files:\n",
+        "            if f.lower().endswith(audio_exts):\n",
+        "                all_files.append(os.path.join(root, f))\n",
+        "\n",
+        "    if num_workers is None:\n",
+        "        num_workers = cpu_count()\n",
+        "\n",
+        "    args_list = [(f, input_folder, output_folder) for f in all_files]\n",
+        "\n",
+        "    # Multiprocessing + tqdm\n",
+        "    with Pool(num_workers) as pool:\n",
+        "        for _ in tqdm(pool.imap_unordered(process_file_ffmpeg, args_list),\n",
+        "                      total=len(args_list), desc=\"Converting to 32kHz MP3\"):\n",
+        "            pass\n",
+        "\n",
+        "# --- Ví dụ sử dụng ---\n",
+        "input_dir = \"/content/dataset\"\n",
+        "output_dir = \"/content/dataset_process\"\n",
+        "convert_audio_ffmpeg_multiprocessing(input_dir, output_dir)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#mp3 to hdf5"
+      ],
+      "metadata": {
+        "id": "kIoHloKr5ky7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Audioset"
+      ],
+      "metadata": {
+        "id": "pctubCgR5sli"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import h5py\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import csv\n",
+        "import os\n",
+        "import io\n",
+        "import av\n",
+        "\n",
+        "def decode_mp3(mp3_arr):\n",
+        "    \"\"\"\n",
+        "    Giải mã một mảng uint8 đại diện cho một file MP3.\n",
+        "    :rtype: np.array\n",
+        "    \"\"\"\n",
+        "    container = av.open(io.BytesIO(mp3_arr.tobytes()))  # Đọc dữ liệu MP3\n",
+        "    stream = next(s for s in container.streams if s.type == 'audio')  # Lấy stream âm thanh\n",
+        "    a = []\n",
+        "    for i, packet in enumerate(container.demux(stream)):  # Demux các gói dữ liệu âm thanh\n",
+        "        for frame in packet.decode():  # Giải mã frame\n",
+        "            a.append(frame.to_ndarray().reshape(-1))  # Chuyển đổi frame thành mảng numpy\n",
+        "    waveform = np.concatenate(a)  # Kết nối tất cả các frame lại\n",
+        "    if waveform.dtype != 'float32':  # Kiểm tra loại dữ liệu\n",
+        "        raise RuntimeError(\"Unexpected wave type\")\n",
+        "    return waveform\n",
+        "\n",
+        "# %%\n",
+        "base_dir = \"/content/output_\"\n",
+        "balanced_csv= '/content/new_updated_balanced_train_segments.csv'\n",
+        "eval_csv= '/content/new_eval_segments.csv'\n",
+        "mp3_path = \"/content/dataset/\"\n",
+        "\n",
+        "\n",
+        "# %%\n",
+        "\n",
+        "\n",
+        "def read_metadata(csv_path, classes_num, id_to_ix):\n",
+        "    \"\"\"Read metadata of AudioSet from a csv file.\"\"\"\n",
+        "\n",
+        "    audio_names = []\n",
+        "    targets = []\n",
+        "\n",
+        "    with open(csv_path, 'r') as fr:\n",
+        "        reader = csv.reader(fr)\n",
+        "        next(reader)  # Skip header line if exists\n",
+        "        next(reader)  # Skip another potential header line\n",
+        "        next(reader)  # Skip another potential header line\n",
+        "\n",
+        "        for line in reader:\n",
+        "            if len(line) < 4:\n",
+        "                continue  # Skip malformed lines\n",
+        "\n",
+        "            audio_name = 'Y{}.mp3'.format(line[0])  # Assumed naming convention\n",
+        "            label_ids = line[3].strip('\"').split(',')\n",
+        "\n",
+        "            audio_names.append(audio_name)\n",
+        "            target = np.zeros(classes_num, dtype=bool)\n",
+        "\n",
+        "            for label_id in label_ids:\n",
+        "                if label_id in id_to_ix:\n",
+        "                    ix = id_to_ix[label_id]\n",
+        "                    target[ix] = 1\n",
+        "                else:\n",
+        "                    print(f\"Warning: Label ID {label_id} not found in id_to_ix.\")\n",
+        "\n",
+        "            targets.append(target)\n",
+        "\n",
+        "    meta_dict = {'audio_name': np.array(audio_names), 'target': np.array(targets)}\n",
+        "    print(meta_dict)\n",
+        "    return meta_dict\n",
+        "\n",
+        "# Load label\n",
+        "with open('/content/new_class_labels_indices_filter_discard.csv', 'r') as f:\n",
+        "    reader = csv.reader(f, delimiter=',')\n",
+        "    lines = list(reader)\n",
+        "\n",
+        "labels = []\n",
+        "ids = []    # Each label has a unique id such as \"/m/068hy\"\n",
+        "for i1 in range(1, len(lines)):\n",
+        "    id = lines[i1][1]\n",
+        "    label = lines[i1][2]\n",
+        "    ids.append(id)\n",
+        "    labels.append(label)\n",
+        "\n",
+        "classes_num = len(labels)\n",
+        "\n",
+        "lb_to_ix = {label : i for i, label in enumerate(labels)}\n",
+        "ix_to_lb = {i : label for i, label in enumerate(labels)}\n",
+        "\n",
+        "id_to_ix = {id : i for i, id in enumerate(ids)}\n",
+        "ix_to_id = {i : id for i, id in enumerate(ids)}\n",
+        "\n",
+        "# %%\n",
+        "\n",
+        "def check_available(balanced_csv,balanced_audio_path,prefix=None):\n",
+        "    meta_csv = read_metadata(balanced_csv,classes_num,id_to_ix)\n",
+        "    #print(meta_csv)\n",
+        "    audios_num = len(meta_csv['audio_name'])\n",
+        "    found=0\n",
+        "    notfound=0\n",
+        "    available_files=[]\n",
+        "    available_targets=[]\n",
+        "    if prefix is None:\n",
+        "        prefix = os.path.basename(balanced_csv)[:-4]\n",
+        "    for n in range(audios_num):\n",
+        "        audio_path =  meta_csv['audio_name'][n]\n",
+        "        #print(balanced_audio_path + f\"{prefix}/{audio_path}\")\n",
+        "        if os.path.isfile(balanced_audio_path + f\"{prefix}/{audio_path}\" ):\n",
+        "            found+=1\n",
+        "            available_files.append(meta_csv['audio_name'][n])\n",
+        "            available_targets.append(meta_csv['target'][n])\n",
+        "        else:\n",
+        "            notfound+=1\n",
+        "    print(f\"Found {found} . not found {notfound}\")\n",
+        "    return available_files,available_targets\n",
+        "# %%\n",
+        "\n",
+        "# %%\n",
+        "\n",
+        "# %%\n",
+        "\n",
+        "\n",
+        "os.makedirs(os.path.dirname(base_dir + \"mp3\"), exist_ok=True)\n",
+        "\n",
+        "for read_file,prefix in [(balanced_csv,\"balanced_train_segments/\"), (eval_csv,\"eval_segments/\"),]:\n",
+        "    print(\"now working on \",read_file,prefix)\n",
+        "    #files, y = torch.load(read_file+\".pth\")\n",
+        "    files, y = check_available(read_file, mp3_path, prefix)\n",
+        "    y = np.packbits(y, axis=-1)\n",
+        "    packed_len = y.shape[1]\n",
+        "    print(files[0], \"classes: \",packed_len, y.dtype)\n",
+        "    available_size = len(files)\n",
+        "    f = files[0][:-3]+\"mp3\"\n",
+        "    a = np.fromfile(mp3_path+prefix + \"/\"+f, dtype='uint8')\n",
+        "\n",
+        "    dt = h5py.vlen_dtype(np.dtype('uint8'))\n",
+        "    save_file = prefix.split(\"/\")[0]\n",
+        "    os.makedirs(os.path.dirname(base_dir + \"mp3/\" ), exist_ok=True)\n",
+        "    with h5py.File(base_dir+ \"mp3/\" + save_file+\"_mp3.hdf\", 'w') as hf:\n",
+        "        audio_name = hf.create_dataset('audio_name', shape=(0,), maxshape=(None,), dtype='S20')\n",
+        "        waveform = hf.create_dataset('mp3', shape=(0,), maxshape=(None,), dtype=dt)\n",
+        "        target = hf.create_dataset('target', shape=(0, packed_len), maxshape=(None, packed_len), dtype=y.dtype)\n",
+        "        for i,file in enumerate(files):\n",
+        "            if i%1000==0:\n",
+        "                print(f\"{i}/{available_size}\")\n",
+        "            f = file[:-3] + \"mp3\"\n",
+        "            a = np.fromfile(mp3_path + prefix  + f, dtype='uint8')\n",
+        "            try:\n",
+        "                # Kiểm tra xem file audio có đọc được không\n",
+        "                decode_mp3(a)  # Dùng hàm decode_mp3 của bạn\n",
+        "\n",
+        "                audio_name.resize((i + 1,))\n",
+        "                waveform.resize((i + 1,))\n",
+        "                target.resize((i + 1, packed_len))\n",
+        "\n",
+        "                audio_name[i]=f\n",
+        "                waveform[i] = a\n",
+        "                target[i] = y[i]\n",
+        "            except Exception as e:\n",
+        "                print(f\"File lỗi tại index {i} với file {file}: {e}\")\n",
+        "\n",
+        "    print(a.shape)\n",
+        "    print(\"Done!\" , prefix)"
+      ],
+      "metadata": {
+        "id": "8oFKEbtb5mzr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##For this structure folder/train/folder(class)/file"
+      ],
+      "metadata": {
+        "id": "mCwcSx8y5v7q"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        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)"
+      ],
+      "metadata": {
+        "id": "cRAr5tkn566K"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import h5py\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import io\n",
+        "import av\n",
+        "from pathlib import Path\n",
+        "from tqdm import tqdm\n",
+        "\n",
+        "def decode_mp3(mp3_arr):\n",
+        "    \"\"\"\n",
+        "    Giải mã một mảng uint8 đại diện cho một file MP3.\n",
+        "    :rtype: np.array\n",
+        "    \"\"\"\n",
+        "    try:\n",
+        "        container = av.open(io.BytesIO(mp3_arr.tobytes()))\n",
+        "        stream = next(s for s in container.streams if s.type == 'audio')\n",
+        "        a = []\n",
+        "        for packet in container.demux(stream):\n",
+        "            for frame in packet.decode():\n",
+        "                a.append(frame.to_ndarray().reshape(-1))\n",
+        "        waveform = np.concatenate(a)\n",
+        "        if waveform.dtype != 'float32':\n",
+        "            raise RuntimeError(\"Unexpected wave type\")\n",
+        "        return waveform\n",
+        "    except Exception as e:\n",
+        "        raise RuntimeError(f\"Cannot decode MP3: {e}\")\n",
+        "\n",
+        "def scan_dataset_structure(dataset_path):\n",
+        "    \"\"\"\n",
+        "    Quét cấu trúc thư mục dataset và tạo mapping cho classes\n",
+        "    Structure: dataset_path/train(or test)/class_name/*.mp3\n",
+        "    \"\"\"\n",
+        "    dataset_path = Path(dataset_path)\n",
+        "\n",
+        "    # Lấy tất cả các class names từ thư mục train\n",
+        "    train_path = dataset_path / \"train\"\n",
+        "    if not train_path.exists():\n",
+        "        raise ValueError(f\"Train folder not found: {train_path}\")\n",
+        "\n",
+        "    classes = sorted([d.name for d in train_path.iterdir() if d.is_dir()])\n",
+        "    classes_num = len(classes)\n",
+        "\n",
+        "    # Tạo mapping\n",
+        "    lb_to_ix = {label: i for i, label in enumerate(classes)}\n",
+        "    ix_to_lb = {i: label for i, label in enumerate(classes)}\n",
+        "\n",
+        "    print(f\"Found {classes_num} classes: {classes[:7]}...\" if len(classes) > 7 else f\"Found {classes_num} classes: {classes}\")\n",
+        "\n",
+        "    return classes, classes_num, lb_to_ix, ix_to_lb\n",
+        "\n",
+        "def collect_audio_files(dataset_path, split='train', shuffle=True, random_seed=42):\n",
+        "    \"\"\"\n",
+        "    Thu thập tất cả audio files từ structure thư mục và shuffle để tránh grouping theo class\n",
+        "    \"\"\"\n",
+        "    dataset_path = Path(dataset_path)\n",
+        "    split_path = dataset_path / split\n",
+        "\n",
+        "    if not split_path.exists():\n",
+        "        raise ValueError(f\"{split} folder not found: {split_path}\")\n",
+        "\n",
+        "    audio_files = []\n",
+        "    labels = []\n",
+        "    class_counts = {}\n",
+        "\n",
+        "    class_dirs = [d for d in split_path.iterdir() if d.is_dir()]\n",
+        "\n",
+        "    print(f\"📁 Scanning {split} folder...\")\n",
+        "    for class_dir in tqdm(class_dirs, desc=f\"Scanning classes\"):\n",
+        "        class_name = class_dir.name\n",
+        "        mp3_files = list(class_dir.glob(\"*.mp3\"))\n",
+        "        class_counts[class_name] = len(mp3_files)\n",
+        "\n",
+        "        for mp3_file in mp3_files:\n",
+        "            audio_files.append(str(mp3_file))\n",
+        "            labels.append(class_name)\n",
+        "\n",
+        "    # Shuffle để tránh việc group theo class trong HDF5\n",
+        "    if shuffle:\n",
+        "        import random\n",
+        "        random.seed(random_seed)\n",
+        "\n",
+        "        # Zip files và labels lại, sau đó shuffle\n",
+        "        combined = list(zip(audio_files, labels))\n",
+        "        random.shuffle(combined)\n",
+        "\n",
+        "        # Unpack lại\n",
+        "        audio_files, labels = zip(*combined)\n",
+        "        audio_files = list(audio_files)\n",
+        "        labels = list(labels)\n",
+        "\n",
+        "        print(f\"🔀 Files shuffled with seed={random_seed}\")\n",
+        "\n",
+        "    # In class distribution\n",
+        "    print(f\"✅ Found {len(audio_files)} audio files in {split} set\")\n",
+        "    print(f\"📊 Class distribution:\")\n",
+        "    for class_name, count in sorted(class_counts.items()):\n",
+        "        percentage = count / len(audio_files) * 100\n",
+        "        print(f\"   {class_name}: {count} files ({percentage:.1f}%)\")\n",
+        "\n",
+        "    return audio_files, labels\n",
+        "\n",
+        "def create_target_array(labels, classes_num, lb_to_ix):\n",
+        "    \"\"\"\n",
+        "    Tạo target array từ danh sách labels\n",
+        "    \"\"\"\n",
+        "    targets = []\n",
+        "    for label in labels:\n",
+        "        target = np.zeros(classes_num, dtype=bool)\n",
+        "        if label in lb_to_ix:\n",
+        "            ix = lb_to_ix[label]\n",
+        "            target[ix] = 1\n",
+        "        targets.append(target)\n",
+        "\n",
+        "    return np.array(targets)\n",
+        "\n",
+        "def convert_to_hdf5(dataset_path, output_dir):\n",
+        "    \"\"\"\n",
+        "    Convert audio dataset to HDF5 format\n",
+        "    \"\"\"\n",
+        "    # Tạo output directory\n",
+        "    os.makedirs(output_dir, exist_ok=True)\n",
+        "\n",
+        "    # Quét cấu trúc dataset\n",
+        "    classes, classes_num, lb_to_ix, ix_to_lb = scan_dataset_structure(dataset_path)\n",
+        "\n",
+        "    # Process both train and test splits\n",
+        "    for split in ['train', 'test']:\n",
+        "        print(f\"\\n=== Processing {split} set ===\")\n",
+        "\n",
+        "        try:\n",
+        "            # Thu thập audio files\n",
+        "            audio_files, labels = collect_audio_files(dataset_path, split)\n",
+        "\n",
+        "            if len(audio_files) == 0:\n",
+        "                print(f\"No audio files found in {split} set, skipping...\")\n",
+        "                continue\n",
+        "\n",
+        "            # Tạo target array\n",
+        "            targets = create_target_array(labels, classes_num, lb_to_ix)\n",
+        "\n",
+        "            # Pack targets để tiết kiệm memory\n",
+        "            packed_targets = np.packbits(targets, axis=-1)\n",
+        "            packed_len = packed_targets.shape[1]\n",
+        "\n",
+        "            print(f\"Target shape: {targets.shape} -> Packed: {packed_targets.shape}\")\n",
+        "\n",
+        "            # Tạo HDF5 file\n",
+        "            dt = h5py.vlen_dtype(np.dtype('uint8'))\n",
+        "            hdf5_path = os.path.join(output_dir, f\"{split}_mp3.hdf5\")\n",
+        "\n",
+        "            with h5py.File(hdf5_path, 'w') as hf:\n",
+        "                # Tạo datasets\n",
+        "                audio_name_ds = hf.create_dataset('audio_name', shape=(0,), maxshape=(None,), dtype='S200')\n",
+        "                waveform_ds = hf.create_dataset('mp3', shape=(0,), maxshape=(None,), dtype=dt)\n",
+        "                target_ds = hf.create_dataset('target', shape=(0, packed_len), maxshape=(None, packed_len), dtype=packed_targets.dtype)\n",
+        "\n",
+        "                # Lưu class info\n",
+        "                hf.attrs['classes'] = [c.encode('utf-8') for c in classes]\n",
+        "                hf.attrs['classes_num'] = classes_num\n",
+        "\n",
+        "                valid_count = 0\n",
+        "                error_count = 0\n",
+        "\n",
+        "                # Process từng file với tqdm\n",
+        "                pbar = tqdm(zip(audio_files, labels),\n",
+        "                           total=len(audio_files),\n",
+        "                           desc=f\"Converting {split}\")\n",
+        "\n",
+        "                for i, (audio_file, label) in enumerate(pbar):\n",
+        "                    try:\n",
+        "                        # Đọc file MP3\n",
+        "                        audio_data = np.fromfile(audio_file, dtype='uint8')\n",
+        "\n",
+        "                        # Kiểm tra tính hợp lệ bằng cách decode\n",
+        "                        decode_mp3(audio_data)\n",
+        "\n",
+        "                        # Resize datasets\n",
+        "                        audio_name_ds.resize((valid_count + 1,))\n",
+        "                        waveform_ds.resize((valid_count + 1,))\n",
+        "                        target_ds.resize((valid_count + 1, packed_len))\n",
+        "\n",
+        "                        # Lưu data\n",
+        "                        filename = os.path.basename(audio_file).encode('utf-8')\n",
+        "                        audio_name_ds[valid_count] = filename\n",
+        "                        waveform_ds[valid_count] = audio_data\n",
+        "                        target_ds[valid_count] = packed_targets[i]\n",
+        "\n",
+        "                        valid_count += 1\n",
+        "\n",
+        "                        # Update progress bar\n",
+        "                        pbar.set_postfix({\n",
+        "                            'Valid': valid_count,\n",
+        "                            'Errors': error_count,\n",
+        "                            'Success Rate': f\"{valid_count/(i+1)*100:.1f}%\"\n",
+        "                        })\n",
+        "\n",
+        "                    except Exception as e:\n",
+        "                        error_count += 1\n",
+        "                        pbar.set_postfix({\n",
+        "                            'Valid': valid_count,\n",
+        "                            'Errors': error_count,\n",
+        "                            'Success Rate': f\"{valid_count/(i+1)*100:.1f}%\"\n",
+        "                        })\n",
+        "                        if error_count <= 5:  # Chỉ show 5 error đầu tiên\n",
+        "                            tqdm.write(f\"❌ Error processing {os.path.basename(audio_file)}: {e}\")\n",
+        "                        continue\n",
+        "\n",
+        "                pbar.close()\n",
+        "\n",
+        "                print(f\"Successfully processed {valid_count}/{len(audio_files)} files\")\n",
+        "                print(f\"Saved to: {hdf5_path}\")\n",
+        "\n",
+        "        except Exception as e:\n",
+        "            print(f\"Error processing {split} set: {e}\")\n",
+        "\n",
+        "def main():\n",
+        "    # Cấu hình paths\n",
+        "    dataset_path = \"/content/dataset\"  # Thay đổi path này\n",
+        "    output_dir = \"/content/dataset_hdf5\"          # Thay đổi path này\n",
+        "\n",
+        "    # Chạy conversion\n",
+        "    convert_to_hdf5(dataset_path, output_dir)\n",
+        "    print(\"\\n=== Conversion completed! ===\")\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    # Example usage:\n",
+        "    # dataset_path = \"/content/audio_dataset\"\n",
+        "    # output_dir = \"/content/output_hdf5\"\n",
+        "    # convert_to_hdf5(dataset_path, output_dir)\n",
+        "    main()"
+      ],
+      "metadata": {
+        "id": "lcdNaKMx59ip"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

Train_script/EfficientAT_code_train.zip CHANGED Viewed

@@ -1,3 +1,3 @@
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-oid sha256:ed1ec9ad0e90da763c5abc4a27f99fad97d073e5f694960c8981deae04705177
-size 22674058

 version https://git-lfs.github.com/spec/v1
+oid sha256:05f83c9fa5de22cc1153c30e0adff06eea1c0a11b6be6cc6ff2a466006b95fe1
+size 22666093

Train_script/Train_guide.ipynb ADDED Viewed

	@@ -0,0 +1,1117 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cd7ca034-54d2-434d-9ba8-9e1877e7a7c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!unzip -q '/workspace/EfficientAT_code_train.zip' -d './train'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2f674532-597d-4471-aef8-53c6f2b3ce7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!unzip -q '/workspace/HDF5_file.zip' -d '/workspace/train/EfficientAT-main/datasets'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7595bbe-a43b-4e08-a7de-1e27206a782b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Please change the dataset_config (filename hdf5) in \"audioset.py\" in \"EfficientAT-main/datasets\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7dd14b84-3451-423f-bc07-16fddddc2a07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting av (from -r /workspace/train/EfficientAT-main/requirements.txt (line 1))\n",
+      "  Downloading av-15.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (4.6 kB)\n",
+      "Collecting h5py (from -r /workspace/train/EfficientAT-main/requirements.txt (line 2))\n",
+      "  Downloading h5py-3.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.7 kB)\n",
+      "Collecting librosa (from -r /workspace/train/EfficientAT-main/requirements.txt (line 3))\n",
+      "  Downloading librosa-0.11.0-py3-none-any.whl.metadata (8.7 kB)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from -r /workspace/train/EfficientAT-main/requirements.txt (line 4)) (1.24.1)\n",
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+      "  Downloading scikit_learn-1.7.1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (11 kB)\n",
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+      "Collecting tqdm (from -r /workspace/train/EfficientAT-main/requirements.txt (line 9))\n",
+      "  Downloading tqdm-4.67.1-py3-none-any.whl.metadata (57 kB)\n",
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+      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
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+      "\u001b[?25hCollecting joblib>=1.0 (from librosa->-r /workspace/train/EfficientAT-main/requirements.txt (line 3))\n",
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+      "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from librosa->-r /workspace/train/EfficientAT-main/requirements.txt (line 3)) (5.1.1)\n",
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+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->-r /workspace/train/EfficientAT-main/requirements.txt (line 6)) (3.9.0)\n",
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+      "Installing collected packages: pytz, tzdata, typing_extensions, tqdm, threadpoolctl, soxr, smmap, sentry-sdk, scipy, protobuf, msgpack, llvmlite, lazy_loader, kiwisolver, joblib, h5py, fonttools, cycler, contourpy, click, av, audioread, annotated-types, typing-inspection, soundfile, scikit_learn, pydantic-core, pooch, pandas, numba, matplotlib, gitdb, seaborn, pydantic, librosa, gitpython, wandb\n",
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+      "    Uninstalling typing_extensions-4.4.0:\n",
+      "      Successfully uninstalled typing_extensions-4.4.0\n",
+      "Successfully installed annotated-types-0.7.0 audioread-3.0.1 av-15.0.0 click-8.2.1 contourpy-1.3.2 cycler-0.12.1 fonttools-4.59.1 gitdb-4.0.12 gitpython-3.1.45 h5py-3.14.0 joblib-1.5.1 kiwisolver-1.4.9 lazy_loader-0.4 librosa-0.11.0 llvmlite-0.44.0 matplotlib-3.10.5 msgpack-1.1.1 numba-0.61.2 pandas-2.3.1 pooch-1.8.2 protobuf-6.32.0 pydantic-2.11.7 pydantic-core-2.33.2 pytz-2025.2 scikit_learn-1.7.1 scipy-1.15.3 seaborn-0.13.2 sentry-sdk-2.35.0 smmap-5.0.2 soundfile-0.13.1 soxr-0.5.0.post1 threadpoolctl-3.6.0 tqdm-4.67.1 typing-inspection-0.4.1 typing_extensions-4.14.1 tzdata-2025.2 wandb-0.21.1\n",
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install -r /workspace/train/EfficientAT-main/requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5a7a42e9-3f79-4f63-a6ea-98db2206ca96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/workspace/train/EfficientAT-main\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.\n",
+      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd /workspace/train/EfficientAT-main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0abf14b-46a1-4f92-9f70-e0de40dff46a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: FMAX is None, setting to 15000 \n",
+      "The number of classes in the loaded state dict (=527) and the current model (=7) is not the same. Dropping final fully-connected layer and loading weights in non-strict mode!\n",
+      "DyMN(\n",
+      "  (layers): ModuleList(\n",
+      "    (0): DY_Block(\n",
+      "      (exp_conv): DynamicWrapper(\n",
+      "        (module): Identity()\n",
+      "      )\n",
+      "      (exp_norm): Identity()\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): Identity()\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(8, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=32, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(8, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(16, 8, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(16, 8, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): Sequential()\n",
+      "        (pool_t): Sequential()\n",
+      "      )\n",
+      "    )\n",
+      "    (1): DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): ReLU(inplace=True)\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=96, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(8, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(16, 24, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(16, 24, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): AvgPool2d(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0))\n",
+      "        (pool_t): AvgPool2d(kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
+      "      )\n",
+      "    )\n",
+      "    (2): DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): ReLU(inplace=True)\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=128, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(16, 32, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(16, 32, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): Sequential()\n",
+      "        (pool_t): Sequential()\n",
+      "      )\n",
+      "    )\n",
+      "    (3): DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): ReLU(inplace=True)\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=128, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(16, 32, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(16, 32, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): AvgPool2d(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0))\n",
+      "        (pool_t): AvgPool2d(kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
+      "      )\n",
+      "    )\n",
+      "    (4-5): 2 x DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): ReLU(inplace=True)\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=192, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=16, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(16, 48, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(16, 48, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): Sequential()\n",
+      "        (pool_t): Sequential()\n",
+      "      )\n",
+      "    )\n",
+      "    (6): DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): Hardswish()\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(96, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=384, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(16, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(24, 96, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(24, 96, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): AvgPool2d(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0))\n",
+      "        (pool_t): AvgPool2d(kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
+      "      )\n",
+      "    )\n",
+      "    (7): DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): Hardswish()\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(80, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=320, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(32, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(24, 80, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(24, 80, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): Sequential()\n",
+      "        (pool_t): Sequential()\n",
+      "      )\n",
+      "    )\n",
+      "    (8-9): 2 x DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): Hardswish()\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(72, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=288, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=24, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(32, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(32, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(24, 72, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): Sequential()\n",
+      "        (pool_t): Sequential()\n",
+      "      )\n",
+      "    )\n",
+      "    (10): DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(192, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): Hardswish()\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(192, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=768, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(32, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): Sequential()\n",
+      "        (pool_t): Sequential()\n",
+      "      )\n",
+      "    )\n",
+      "    (11): DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(272, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): Hardswish()\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(272, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=1088, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(48, 272, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(48, 272, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): Sequential()\n",
+      "        (pool_t): Sequential()\n",
+      "      )\n",
+      "    )\n",
+      "    (12): DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(272, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): Hardswish()\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(272, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=1088, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(48, 272, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(48, 272, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): AvgPool2d(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0))\n",
+      "        (pool_t): AvgPool2d(kernel_size=(1, 3), stride=(1, 2), padding=(0, 1))\n",
+      "      )\n",
+      "    )\n",
+      "    (13-14): 2 x DY_Block(\n",
+      "      (exp_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (exp_norm): BatchNorm2d(384, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (exp_act): DynamicWrapper(\n",
+      "        (module): Hardswish()\n",
+      "      )\n",
+      "      (depth_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (depth_norm): BatchNorm2d(384, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (depth_act): DyReLUB(\n",
+      "        (coef_net): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=1536, bias=True)\n",
+      "        )\n",
+      "        (sigmoid): Sigmoid()\n",
+      "      )\n",
+      "      (ca): CoordAtt()\n",
+      "      (proj_conv): DynamicConv(\n",
+      "        (residuals): Sequential(\n",
+      "          (0): Linear(in_features=48, out_features=4, bias=True)\n",
+      "        )\n",
+      "      )\n",
+      "      (proj_norm): BatchNorm2d(64, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "      (context_gen): ContextGen(\n",
+      "        (joint_conv): Conv2d(64, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (joint_norm): BatchNorm2d(48, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "        (joint_act): Hardswish()\n",
+      "        (conv_f): Conv2d(48, 384, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (conv_t): Conv2d(48, 384, kernel_size=(1, 1), stride=(1, 1))\n",
+      "        (pool_f): Sequential()\n",
+      "        (pool_t): Sequential()\n",
+      "      )\n",
+      "    )\n",
+      "  )\n",
+      "  (in_c): Conv2dNormActivation(\n",
+      "    (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "    (1): BatchNorm2d(8, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "    (2): Hardswish()\n",
+      "  )\n",
+      "  (out_c): Conv2dNormActivation(\n",
+      "    (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "    (1): BatchNorm2d(384, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)\n",
+      "    (2): Hardswish()\n",
+      "  )\n",
+      "  (classifier): Sequential(\n",
+      "    (0): AdaptiveAvgPool2d(output_size=1)\n",
+      "    (1): Flatten(start_dim=1, end_dim=-1)\n",
+      "    (2): Linear(in_features=384, out_features=512, bias=True)\n",
+      "    (3): Hardswish()\n",
+      "    (4): Dropout(p=0.2, inplace=True)\n",
+      "    (5): Linear(in_features=512, out_features=7, bias=True)\n",
+      "  )\n",
+      ")\n",
+      "Dataset from ./datasets/train_mp3.hdf5 with length 36878.\n",
+      "Warning: sample_weight_offset=100 minnow=1355.0\n",
+      "Dataset from ./datasets/test_mp3.hdf5 with length 9261.\n",
+      "Epoch 1/200:   0%|                                      | 0/145 [00:00<?, ?it/s]Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Setting temperature for attention over kernels to 30.0\n",
+      "Epoch 1/200: 100%|████████████| 145/145 [01:15<00:00,  1.93it/s, train_loss=1.8]\n",
+      "Validating: 100%|███████████████████████████████| 37/37 [00:09<00:00,  3.87it/s]\n",
+      "Confusion Matrix:\n",
+      "[[ 529    0    0   76  656   19    0]\n",
+      " [  70    2    0    6  799    5    0]\n",
+      " [  99    0    0    1  311   11    0]\n",
+      " [ 440    0    0  104 1255   22    0]\n",
+      " [ 115    0    1   34 2903   12    0]\n",
+      " [ 195    0    2   51 1063  150    0]\n",
+      " [  69    0    0    4  239   18    0]]\n",
+      "Epoch 1/200, Train Loss: 1.8010, Validation Loss: 1.7283, Validation Accuracy: 0.3982, LR: 0.000012, Time: 84.82s\n",
+      "Epoch 2/200:   0%|                                      | 0/145 [00:00<?, ?it/s]Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Setting temperature for attention over kernels to 29.0\n",
+      "Epoch 2/200: 100%|███████████| 145/145 [01:14<00:00,  1.95it/s, train_loss=1.52]\n",
+      "Validating: 100%|███████████████████████████████| 37/37 [00:09<00:00,  3.87it/s]\n",
+      "Confusion Matrix:\n",
+      "[[ 438    3    0  324  484   31    0]\n",
+      " [  41   38    0   51  744    8    0]\n",
+      " [  80    7    0   26  294   15    0]\n",
+      " [ 158    5    0  519 1084   55    0]\n",
+      " [  30    4    0   54 2936   41    0]\n",
+      " [  52   10    0  140  956  303    0]\n",
+      " [  37   11    0   21  232   29    0]]\n",
+      "Epoch 2/200, Train Loss: 1.5245, Validation Loss: 1.6068, Validation Accuracy: 0.4572, LR: 0.000022, Time: 84.03s\n",
+      "Epoch 3/200:   0%|                                      | 0/145 [00:00<?, ?it/s]Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Setting temperature for attention over kernels to 28.0\n",
+      "Epoch 3/200: 100%|███████████| 145/145 [01:14<00:00,  1.95it/s, train_loss=1.36]\n",
+      "Validating: 100%|███████████████████████████████| 37/37 [00:09<00:00,  3.88it/s]\n",
+      "Confusion Matrix:\n",
+      "[[ 112    0    0  723  324  121    0]\n",
+      " [  13  139    0  160  518   52    0]\n",
+      " [   9    0    0  148  199   66    0]\n",
+      " [   5    1    0  950  765  100    0]\n",
+      " [   6    0    0  222 2709  128    0]\n",
+      " [   5    1    0  224  567  664    0]\n",
+      " [   5   14    0   68  149   94    0]]\n",
+      "Epoch 3/200, Train Loss: 1.3576, Validation Loss: 1.6087, Validation Accuracy: 0.4939, LR: 0.000060, Time: 83.90s\n",
+      "Epoch 4/200:   0%|                                      | 0/145 [00:00<?, ?it/s]Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Setting temperature for attention over kernels to 27.0\n",
+      "Epoch 4/200: 100%|███████████| 145/145 [01:14<00:00,  1.95it/s, train_loss=1.26]\n",
+      "Validating: 100%|███████████████████████████████| 37/37 [00:09<00:00,  3.85it/s]\n",
+      "Confusion Matrix:\n",
+      "[[  28    2    0  472  590  188    0]\n",
+      " [   0  198    0   60  603   21    0]\n",
+      " [   0    0    0   37  338   47    0]\n",
+      " [   0    1    0  659 1077   84    0]\n",
+      " [   0    0    0   21 3000   44    0]\n",
+      " [   0    0    0   42 1005  414    0]\n",
+      " [   0   15    0    7  213   95    0]]\n",
+      "Epoch 4/200, Train Loss: 1.2573, Validation Loss: 1.8212, Validation Accuracy: 0.4642, LR: 0.000142, Time: 84.06s\n",
+      "Epoch 5/200:   0%|                                      | 0/145 [00:00<?, ?it/s]Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Setting temperature for attention over kernels to 26.0\n",
+      "Epoch 5/200:  73%|████████   | 106/145 [00:55<00:19,  2.01it/s, train_loss=1.27]"
+     ]
+    }
+   ],
+   "source": [
+    "!python ex_train_audio_classification_mode.py --cuda --train --pretrained --gain_augment=5 --n_epochs=200 --model_name=dymn04_im --batch_size=256 --max_lr=0.001 --pretrain_final_temp=30 --adamw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d3b0a027-1774-4725-9e29-0112001369fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm -rf '/workspace/train/EfficientAT-main/training_results_dymn04_im_20250816_031533'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe6df804-8e48-41fc-9dab-5c409d591379",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!python inference_classification.py --cuda --pre_trained_model_path='/workspace/main/EfficientAT-main/output_model/best_checkpoint/dymn04_im04_acc_0.8923.pt' --audio_path=\"/workspace/main/EfficientAT-main/03-01-01-01-02-01-24.mp3\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c8074fe-eedf-4cf6-8ab3-1815e733e6b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "\n",
+    "# Mount Google Drive nếu cần\n",
+    "\n",
+    "# Đường dẫn tới thư mục cần zip\n",
+    "folder_to_zip = '/workspace/main/EfficientAT-main'\n",
+    "output_zip = '/workspace/main/EfficientAT-main.zip'\n",
+    "\n",
+    "# Tạo file zip\n",
+    "shutil.make_archive(output_zip.replace('.zip', ''), 'zip', folder_to_zip)\n",
+    "\n",
+    "print(f'Thư mục đã được nén thành công thành file zip: {output_zip}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3c819525-cde6-4c3b-a24d-0bfb5e5c2b01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3edf2d9-07d7-4472-86ec-81b800dcecbd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}