Gender_Determination_Using_Voice.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3C8SF9wWayrI","outputId":"b29b68bd-ae26-413d-b097-0bde80e9fc33","executionInfo":{"status":"ok","timestamp":1690070011162,"user_tz":-330,"elapsed":27664,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"0arIuPsWmxXx"},"source":["# New section"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QzB6J0hebPve","outputId":"22734060-7fe2-4229-b55f-c9bbd16d20a9","executionInfo":{"status":"ok","timestamp":1690070051305,"user_tz":-330,"elapsed":35476,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n","  inflating: /content/gender-recognition-by-voice-master/data/cv-valid-train/sample-167756.npy  \n","  inflating: /content/gender-recognition-by-voice-master/data/cv-valid-train/sample-167762.npy  \n","  inflating: /content/gender-recognition-by-voice-master/data/cv-valid-train/sample-167767.npy  \n","  inflating: /content/gender-recognition-by-voice-master/data/cv-valid-train/sample-167768.npy  \n","  inflating: /content/gender-recognition-by-voice-master/data/cv-valid-train/sample-167773.npy  \n","  inflating: /content/gender-recognition-by-voice-master/data/cv-valid-train/sample-167776.npy  \n","  inflating: /content/gender-recognition-by-voice-master/data/cv-valid-train/sample-167787.npy  \n","  inflating: /content/gender-recognition-by-voice-master/data/cv-valid-train/sample-167789.npy  \n","  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inflating: /content/gender-recognition-by-voice-master/train.py  \n","  inflating: /content/gender-recognition-by-voice-master/utils.py  \n"]}],"source":["!unzip \"/content/drive/MyDrive/gender-recognition-by-voice-master.zip\" -d \"/content/\""]},{"cell_type":"code","execution_count":3,"metadata":{"id":"zp4y4iQpb3hk","executionInfo":{"status":"ok","timestamp":1690070056790,"user_tz":-330,"elapsed":476,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[],"source":["!mv \"/content/gender-recognition-by-voice-master/data\" \"/content/\""]},{"cell_type":"code","source":["for i in range(2,11):\n","  print(i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YYsdVvqjGCYh","executionInfo":{"status":"ok","timestamp":1690088055155,"user_tz":-330,"elapsed":411,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}},"outputId":"b5dc6f44-190e-4d45-eb17-54978a59a4f8"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["2\n","3\n","4\n","5\n","6\n","7\n","8\n","9\n","10\n"]}]},{"cell_type":"markdown","source":[],"metadata":{"id":"nXVWFc1XGAaJ"}},{"cell_type":"code","execution_count":4,"metadata":{"id":"63mLxPLQb9H_","executionInfo":{"status":"ok","timestamp":1690070060478,"user_tz":-330,"elapsed":479,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[],"source":["!mv \"/content/gender-recognition-by-voice-master/balanced-all.csv\" \"/content/\""]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WvnUxBFEcE99","outputId":"f14f03d5-91cf-4380-a737-b4b74b901938","executionInfo":{"status":"ok","timestamp":1690070073651,"user_tz":-330,"elapsed":7038,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Reading package lists... Done\n","Building dependency tree... Done\n","Reading state information... Done\n","libasound2-dev is already the newest version (1.2.6.1-1ubuntu1).\n","ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n","Suggested packages:\n","  portaudio19-doc\n","The following NEW packages will be installed:\n","  libportaudio2 libportaudiocpp0 portaudio19-dev\n","0 upgraded, 3 newly installed, 0 to remove and 8 not upgraded.\n","Need to get 188 kB of archives.\n","After this operation, 927 kB of additional disk space will be used.\n","Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libportaudio2 amd64 19.6.0-1.1 [65.3 kB]\n","Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 libportaudiocpp0 amd64 19.6.0-1.1 [16.1 kB]\n","Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 portaudio19-dev amd64 19.6.0-1.1 [106 kB]\n","Fetched 188 kB in 1s (292 kB/s)\n","Selecting previously unselected package libportaudio2:amd64.\n","(Reading database ... 129824 files and directories currently installed.)\n","Preparing to unpack .../libportaudio2_19.6.0-1.1_amd64.deb ...\n","Unpacking libportaudio2:amd64 (19.6.0-1.1) ...\n","Selecting previously unselected package libportaudiocpp0:amd64.\n","Preparing to unpack .../libportaudiocpp0_19.6.0-1.1_amd64.deb ...\n","Unpacking libportaudiocpp0:amd64 (19.6.0-1.1) ...\n","Selecting previously unselected package portaudio19-dev:amd64.\n","Preparing to unpack .../portaudio19-dev_19.6.0-1.1_amd64.deb ...\n","Unpacking portaudio19-dev:amd64 (19.6.0-1.1) ...\n","Setting up libportaudio2:amd64 (19.6.0-1.1) ...\n","Setting up libportaudiocpp0:amd64 (19.6.0-1.1) ...\n","Setting up portaudio19-dev:amd64 (19.6.0-1.1) ...\n","Processing triggers for libc-bin (2.35-0ubuntu3.1) ...\n"]}],"source":["!apt install libasound2-dev portaudio19-dev libportaudio2 libportaudiocpp0 ffmpeg"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9nDi68MicGHb","outputId":"875cb5b0-6d34-4eb1-ff02-b8f126ad0f8a","executionInfo":{"status":"ok","timestamp":1690070089488,"user_tz":-330,"elapsed":12756,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting pyaudio\n","  Downloading PyAudio-0.2.13.tar.gz (46 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/46.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.8/46.8 kB\u001b[0m \u001b[31m1.4 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","Building wheels for collected packages: pyaudio\n","  Building wheel for pyaudio (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for pyaudio: filename=PyAudio-0.2.13-cp310-cp310-linux_x86_64.whl size=63861 sha256=f11170b42cd0a47a5777b772f92d366b2bdb0aad365cd1fd2f47e82d1c73f7ed\n","  Stored in directory: /root/.cache/pip/wheels/14/f1/c2/d102b4765a82c5a7bb273998dca7e4a53fc58e9a1a516fda81\n","Successfully built pyaudio\n","Installing collected packages: pyaudio\n","Successfully installed pyaudio-0.2.13\n"]}],"source":["!pip install pyaudio"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"pCmdY8TZcJrb","executionInfo":{"status":"ok","timestamp":1690070098465,"user_tz":-330,"elapsed":4716,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import os\n","import tqdm\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.layers import Dense, LSTM, Dropout\n","from sklearn.model_selection import train_test_split\n","import pyaudio\n","import os\n","import wave\n","import librosa\n","import numpy as np\n","from sys import byteorder\n","from array import array\n","from struct import pack\n","import os\n","from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"F83nnu_GcMqK","executionInfo":{"status":"ok","timestamp":1690070102365,"user_tz":-330,"elapsed":410,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[],"source":["label2int = {\n","    \"male\": 1,\n","    \"female\": 0\n","}"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"mZDEi7CVcPvq","executionInfo":{"status":"ok","timestamp":1690070106963,"user_tz":-330,"elapsed":409,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[],"source":["def load_data(vector_length=128):\n","    \"\"\"A function to load gender recognition dataset from `data` folder\n","    After the second run, this will load from results/features.npy and results/labels.npy files\n","    as it is much faster!\"\"\"\n","    # make sure results folder exists\n","    if not os.path.isdir(\"results\"):\n","        os.mkdir(\"results\")\n","    # if features & labels already loaded individually and bundled, load them from there instead\n","    if os.path.isfile(\"results/features.npy\") and os.path.isfile(\"results/labels.npy\"):\n","        X = np.load(\"results/features.npy\")\n","        y = np.load(\"results/labels.npy\")\n","        return X, y\n","    # read dataframe\n","    df = pd.read_csv(\"/content/balanced-all.csv\")\n","    # get total samples\n","    n_samples = len(df)\n","    # get total male samples\n","    n_male_samples = len(df[df['gender'] == 'male'])\n","    # get total female samples\n","    n_female_samples = len(df[df['gender'] == 'female'])\n","    print(\"Total samples:\", n_samples)\n","    print(\"Total male samples:\", n_male_samples)\n","    print(\"Total female samples:\", n_female_samples)\n","    # initialize an empty array for all audio features\n","    X = np.zeros((n_samples, vector_length))\n","    # initialize an empty array for all audio labels (1 for male and 0 for female)\n","    y = np.zeros((n_samples, 1))\n","    for i, (filename, gender) in tqdm.tqdm(enumerate(zip(df['filename'], df['gender'])), \"Loading data\", total=n_samples):\n","        features = np.load(filename)\n","        X[i] = features\n","        y[i] = label2int[gender]\n","    # save the audio features and labels into files\n","    # so we won't load each one of them next run\n","    np.save(\"results/features\", X)\n","    np.save(\"results/labels\", y)\n","    return X, y\n","\n","\n","def split_data(X, y, test_size=0.1, valid_size=0.1):\n","    # split training set and testing set\n","    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=7)\n","    # split training set and validation set\n","    X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=valid_size, random_state=7)\n","    # return a dictionary of values\n","    return {\n","        \"X_train\": X_train,\n","        \"X_valid\": X_valid,\n","        \"X_test\": X_test,\n","        \"y_train\": y_train,\n","        \"y_valid\": y_valid,\n","        \"y_test\": y_test\n","    }\n","\n"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"E4ArX81ScUu2","executionInfo":{"status":"ok","timestamp":1690070127918,"user_tz":-330,"elapsed":413,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[],"source":["def create_model(vector_length=128):\n","    \"\"\"5 hidden dense layers from 256 units to 64, not the best model, but not bad.\"\"\"\n","    model = Sequential()\n","    model.add(Dense(256, input_shape=(vector_length,)))\n","    model.add(Dropout(0.3))\n","    model.add(Dense(256, activation=\"relu\"))\n","    model.add(Dropout(0.3))\n","    model.add(Dense(128, activation=\"relu\"))\n","    model.add(Dropout(0.3))\n","    model.add(Dense(128, activation=\"relu\"))\n","    model.add(Dropout(0.3))\n","    model.add(Dense(64, activation=\"relu\"))\n","    model.add(Dropout(0.3))\n","    # one output neuron with sigmoid activation function, 0 means female, 1 means male\n","    model.add(Dense(1, activation=\"sigmoid\"))\n","    # using binary crossentropy as it's male/female classification (binary)\n","    model.compile(loss=\"binary_crossentropy\", metrics=[\"accuracy\"], optimizer=\"adam\")\n","    # print summary of the model\n","    model.summary()\n","    return model"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DiXCDPcmcbXd","outputId":"a1dc078b-61dc-4a50-bf75-af015e441982","executionInfo":{"status":"ok","timestamp":1690070296536,"user_tz":-330,"elapsed":164130,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Total samples: 66938\n","Total male samples: 33469\n","Total female samples: 33469\n"]},{"output_type":"stream","name":"stderr","text":["Loading data: 100%|██████████| 66938/66938 [00:17<00:00, 3915.31it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," dense (Dense)               (None, 256)               33024     \n","                                                                 \n"," dropout (Dropout)           (None, 256)               0         \n","                                                                 \n"," dense_1 (Dense)             (None, 256)               65792     \n","                                                                 \n"," dropout_1 (Dropout)         (None, 256)               0         \n","                                                                 \n"," dense_2 (Dense)             (None, 128)               32896     \n","                                                                 \n"," dropout_2 (Dropout)         (None, 128)               0         \n","                                                                 \n"," dense_3 (Dense)             (None, 128)               16512     \n","                                                                 \n"," dropout_3 (Dropout)         (None, 128)               0         \n","                                                                 \n"," dense_4 (Dense)             (None, 64)                8256      \n","                                                                 \n"," dropout_4 (Dropout)         (None, 64)                0         \n","                                                                 \n"," dense_5 (Dense)             (None, 1)                 65        \n","                                                                 \n","=================================================================\n","Total params: 156,545\n","Trainable params: 156,545\n","Non-trainable params: 0\n","_________________________________________________________________\n","Epoch 1/50\n","848/848 [==============================] - 11s 5ms/step - loss: 0.5674 - accuracy: 0.7670 - val_loss: 0.3993 - val_accuracy: 0.8515\n","Epoch 2/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.4164 - accuracy: 0.8358 - val_loss: 0.3182 - val_accuracy: 0.8740\n","Epoch 3/50\n","848/848 [==============================] - 5s 6ms/step - loss: 0.3804 - accuracy: 0.8528 - val_loss: 0.3085 - val_accuracy: 0.8790\n","Epoch 4/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.3613 - accuracy: 0.8612 - val_loss: 0.2937 - val_accuracy: 0.8876\n","Epoch 5/50\n","848/848 [==============================] - 6s 7ms/step - loss: 0.3458 - accuracy: 0.8662 - val_loss: 0.2995 - val_accuracy: 0.8837\n","Epoch 6/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.3322 - accuracy: 0.8724 - val_loss: 0.2807 - val_accuracy: 0.8933\n","Epoch 7/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.3257 - accuracy: 0.8747 - val_loss: 0.2647 - val_accuracy: 0.8983\n","Epoch 8/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.3231 - accuracy: 0.8767 - val_loss: 0.2633 - val_accuracy: 0.8976\n","Epoch 9/50\n","848/848 [==============================] - 5s 6ms/step - loss: 0.3118 - accuracy: 0.8808 - val_loss: 0.2831 - val_accuracy: 0.8893\n","Epoch 10/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.3074 - accuracy: 0.8834 - val_loss: 0.2564 - val_accuracy: 0.9004\n","Epoch 11/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.3036 - accuracy: 0.8837 - val_loss: 0.2699 - val_accuracy: 0.8933\n","Epoch 12/50\n","848/848 [==============================] - 5s 6ms/step - loss: 0.2986 - accuracy: 0.8858 - val_loss: 0.2691 - val_accuracy: 0.8994\n","Epoch 13/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.2972 - accuracy: 0.8893 - val_loss: 0.2710 - val_accuracy: 0.8973\n","Epoch 14/50\n","848/848 [==============================] - 4s 4ms/step - loss: 0.2929 - accuracy: 0.8893 - val_loss: 0.2421 - val_accuracy: 0.9059\n","Epoch 15/50\n","848/848 [==============================] - 5s 6ms/step - loss: 0.2827 - accuracy: 0.8925 - val_loss: 0.2516 - val_accuracy: 0.9031\n","Epoch 16/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.2815 - accuracy: 0.8940 - val_loss: 0.2543 - val_accuracy: 0.9064\n","Epoch 17/50\n","848/848 [==============================] - 4s 5ms/step - loss: 0.2838 - accuracy: 0.8931 - val_loss: 0.2464 - val_accuracy: 0.9102\n","Epoch 18/50\n","848/848 [==============================] - 5s 5ms/step - loss: 0.2807 - accuracy: 0.8940 - val_loss: 0.2430 - val_accuracy: 0.9080\n","Epoch 19/50\n","848/848 [==============================] - 5s 6ms/step - loss: 0.2798 - accuracy: 0.8948 - val_loss: 0.2448 - val_accuracy: 0.9089\n","Evaluating the model using 6694 samples...\n","Loss: 0.2432\n","Accuracy: 91.16%\n"]}],"source":["# load the dataset\n","X, y = load_data()\n","# split the data into training, validation and testing sets\n","data = split_data(X, y, test_size=0.1, valid_size=0.1)\n","# construct the model\n","model = create_model()\n","\n","# use tensorboard to view metrics\n","tensorboard = TensorBoard(log_dir=\"logs\")\n","# define early stopping to stop training after 5 epochs of not improving\n","early_stopping = EarlyStopping(mode=\"min\", patience=5, restore_best_weights=True)\n","\n","batch_size = 64\n","epochs = 50\n","\n","# train the model using the training set and validating using validation set\n","model.fit(data[\"X_train\"], data[\"y_train\"], epochs=epochs, batch_size=batch_size, validation_data=(data[\"X_valid\"], data[\"y_valid\"]),\n","          callbacks=[tensorboard, early_stopping])\n","\n","# save the model to a file\n","model.save(\"results/model.h5\")\n","\n","# evaluating the model using the testing set\n","print(f\"Evaluating the model using {len(data['X_test'])} samples...\")\n","loss, accuracy = model.evaluate(data[\"X_test\"], data[\"y_test\"], verbose=0)\n","print(f\"Loss: {loss:.4f}\")\n","print(f\"Accuracy: {accuracy*100:.2f}%\")"]},{"cell_type":"code","source":["!pip install librosa==0.9.1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":696},"id":"bQTVQ_FY1n6c","executionInfo":{"status":"ok","timestamp":1690070380613,"user_tz":-330,"elapsed":4642,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}},"outputId":"9001ca21-7f14-4a01-8878-884c85f7c307"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting librosa==0.9.1\n","  Downloading librosa-0.9.1-py3-none-any.whl (213 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/213.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/213.1 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.1/213.1 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta 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(1.4.4)\n","Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from pooch>=1.0->librosa==0.9.1) (2.27.1)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->librosa==0.9.1) (3.2.0)\n","Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.10/dist-packages (from soundfile>=0.10.2->librosa==0.9.1) (1.15.1)\n","Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa==0.9.1) (2.21)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.9.1) (1.26.16)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.9.1) (2023.5.7)\n","Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.9.1) (2.0.12)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->pooch>=1.0->librosa==0.9.1) (3.4)\n","Installing collected packages: resampy, librosa\n","  Attempting uninstall: librosa\n","    Found existing installation: librosa 0.10.0.post2\n","    Uninstalling librosa-0.10.0.post2:\n","      Successfully uninstalled librosa-0.10.0.post2\n","Successfully installed librosa-0.9.1 resampy-0.4.2\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["librosa"]}}},"metadata":{}}]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"s9UNM654ewC_","outputId":"c17b633b-4b54-4bdf-bf0a-e1130cbe2750","executionInfo":{"status":"ok","timestamp":1690070451858,"user_tz":-330,"elapsed":3074,"user":{"displayName":"RIYA RAJ SAXENA","userId":"11960982247536150433"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," dense_12 (Dense)            (None, 256)               33024     \n","                                                                 \n"," dropout_10 (Dropout)        (None, 256)               0         \n","                                                                 \n"," dense_13 (Dense)            (None, 256)               65792     \n","                                                                 \n"," dropout_11 (Dropout)        (None, 256)               0         \n","                                                                 \n"," dense_14 (Dense)            (None, 128)               32896     \n","                                                                 \n"," dropout_12 (Dropout)        (None, 128)               0         \n","                                                                 \n"," dense_15 (Dense)            (None, 128)               16512     \n","                                                                 \n"," dropout_13 (Dropout)        (None, 128)               0         \n","                                                                 \n"," dense_16 (Dense)            (None, 64)                8256      \n","                                                                 \n"," dropout_14 (Dropout)        (None, 64)                0         \n","                                                                 \n"," dense_17 (Dense)            (None, 1)                 65        \n","                                                                 \n","=================================================================\n","Total params: 156,545\n","Trainable params: 156,545\n","Non-trainable params: 0\n","_________________________________________________________________\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-14-8de44f0d0e73>:141: FutureWarning: Pass y=[-4.9940022e-12 -1.6038110e-11 -3.8427977e-13 ...  0.0000000e+00\n","  0.0000000e+00  0.0000000e+00] as keyword args. From version 0.10 passing these as positional arguments will result in an error\n","  mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n"]},{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 210ms/step\n","Result: female\n","Probabilities:     Male: 9.15%    Female: 90.85%\n"]}],"source":["THRESHOLD = 500\n","CHUNK_SIZE = 1024\n","FORMAT = pyaudio.paInt16\n","RATE = 16000\n","\n","SILENCE = 30\n","\n","def is_silent(snd_data):\n","    \"Returns 'True' if below the 'silent' threshold\"\n","    return max(snd_data) < THRESHOLD\n","\n","def normalize(snd_data):\n","    \"Average the volume out\"\n","    MAXIMUM = 16384\n","    times = float(MAXIMUM)/max(abs(i) for i in snd_data)\n","\n","    r = array('h')\n","    for i in snd_data:\n","        r.append(int(i*times))\n","    return r\n","\n","def trim(snd_data):\n","    \"Trim the blank spots at the start and end\"\n","    def _trim(snd_data):\n","        snd_started = False\n","        r = array('h')\n","\n","        for i in snd_data:\n","            if not snd_started and abs(i)>THRESHOLD:\n","                snd_started = True\n","                r.append(i)\n","\n","            elif snd_started:\n","                r.append(i)\n","        return r\n","\n","    # Trim to the left\n","    snd_data = _trim(snd_data)\n","\n","    # Trim to the right\n","    snd_data.reverse()\n","    snd_data = _trim(snd_data)\n","    snd_data.reverse()\n","    return snd_data\n","\n","def add_silence(snd_data, seconds):\n","    \"Add silence to the start and end of 'snd_data' of length 'seconds' (float)\"\n","    r = array('h', [0 for i in range(int(seconds*RATE))])\n","    r.extend(snd_data)\n","    r.extend([0 for i in range(int(seconds*RATE))])\n","    return r\n","\n","def record():\n","    \"\"\"\n","    Record a word or words from the microphone and\n","    return the data as an array of signed shorts.\n","    Normalizes the audio, trims silence from the\n","    start and end, and pads with 0.5 seconds of\n","    blank sound to make sure VLC et al can play\n","    it without getting chopped off.\n","    \"\"\"\n","    p = pyaudio.PyAudio()\n","    stream = p.open(format=FORMAT, channels=1, rate=RATE,\n","        input=True, output=True,\n","        frames_per_buffer=CHUNK_SIZE)\n","\n","    num_silent = 0\n","    snd_started = False\n","\n","    r = array('h')\n","\n","    while 1:\n","        # little endian, signed short\n","        snd_data = array('h', stream.read(CHUNK_SIZE))\n","        if byteorder == 'big':\n","            snd_data.byteswap()\n","        r.extend(snd_data)\n","\n","        silent = is_silent(snd_data)\n","\n","        if silent and snd_started:\n","            num_silent += 1\n","        elif not silent and not snd_started:\n","            snd_started = True\n","\n","        if snd_started and num_silent > SILENCE:\n","            break\n","\n","    sample_width = p.get_sample_size(FORMAT)\n","    stream.stop_stream()\n","    stream.close()\n","    p.terminate()\n","\n","    r = normalize(r)\n","    r = trim(r)\n","    r = add_silence(r, 0.5)\n","    return sample_width, r\n","\n","def record_to_file(path):\n","    \"Records from the microphone and outputs the resulting data to 'path'\"\n","    sample_width, data = record()\n","    data = pack('<' + ('h'*len(data)), *data)\n","\n","    wf = wave.open(path, 'wb')\n","    wf.setnchannels(1)\n","    wf.setsampwidth(sample_width)\n","    wf.setframerate(RATE)\n","    wf.writeframes(data)\n","    wf.close()\n","\n","\n","\n","def extract_feature(file_name, **kwargs):\n","    \"\"\"\n","    Extract feature from audio file `file_name`\n","        Features supported:\n","            - MFCC (mfcc)\n","            - Chroma (chroma)\n","            - MEL Spectrogram Frequency (mel)\n","            - Contrast (contrast)\n","            - Tonnetz (tonnetz)\n","        e.g:\n","        `features = extract_feature(path, mel=True, mfcc=True)`\n","    \"\"\"\n","    mfcc = kwargs.get(\"mfcc\")\n","    chroma = kwargs.get(\"chroma\")\n","    mel = kwargs.get(\"mel\")\n","    contrast = kwargs.get(\"contrast\")\n","    tonnetz = kwargs.get(\"tonnetz\")\n","    X, sample_rate = librosa.core.load(file_name)\n","    if chroma or contrast:\n","        stft = np.abs(librosa.stft(X))\n","    result = np.array([])\n","    if mfcc:\n","        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\n","        result = np.hstack((result, mfccs))\n","    if chroma:\n","        chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n","        result = np.hstack((result, chroma))\n","    if mel:\n","        mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n","        result = np.hstack((result, mel))\n","    if contrast:\n","        contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n","        result = np.hstack((result, contrast))\n","    if tonnetz:\n","        tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)\n","        result = np.hstack((result, tonnetz))\n","    return result\n","\n","\n","if __name__ == \"__main__\":\n","    # load the saved model (after training)\n","    # model = pickle.load(open(\"result/mlp_classifier.model\", \"rb\"))\n","    import argparse\n","    parser = argparse.ArgumentParser(description=\"\"\"Gender recognition script, this will load the model you trained,\n","                                    and perform inference on a sample you provide (either using your voice or a file)\"\"\")\n","    parser.add_argument(\"-f\", \"--file\", help=\"The path to the file, preferred to be in WAV format\")\n","    args = parser.parse_args()\n","    file = \"/content/sisters-voices-103432.mp3\"\n","    # construct the model\n","    model = create_model()\n","    # load the saved/trained weights\n","    model.load_weights(\"results/model.h5\")\n","    if not file or not os.path.isfile(file):\n","        # if file not provided, or it doesn't exist, use your voice\n","        print(\"Please talk\")\n","        # put the file name here\n","        file = \"test.wav\"\n","        # record the file (start talking)\n","        record_to_file(file)\n","    # extract features and reshape it\n","    features = extract_feature(file, mel=True).reshape(1, -1)\n","    # predict the gender!\n","    male_prob = model.predict(features)[0][0]\n","    female_prob = 1 - male_prob\n","    gender = \"male\" if male_prob > female_prob else \"female\"\n","    # show the result!\n","    print(\"Result:\", gender)\n","    print(f\"Probabilities:     Male: {male_prob*100:.2f}%    Female: {female_prob*100:.2f}%\")"]},{"cell_type":"markdown","source":["# New section"],"metadata":{"id":"qP0o53LXoJfD"}}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}