#1-Audio_pre_processing_for_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Zyr72Kl00Z7j"
   },
   "source": [
    "# Audio preprocessing \n",
    "**pipeline:**\n",
    "\n",
    "\n",
    "1.   Load a file\n",
    "2.   pad the signal (if necessary)\n",
    "3.   DSP: extracting log spectogram from signal\n",
    "4.   normalize spectogram\n",
    "5.   save the normalized spectogram\n",
    "\n",
    "the previous steps where the preprocessing steps for every audio file, so we will create a class for performing these tasks which is audio **preprocessing**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ee80n8aj1m5S"
   },
   "source": [
    "## Creating loader class\n",
    "for following clean code standards we will separate each function into a class.\n",
    "\n",
    "the first class will be the loader class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "2ByMF5SW2yvV"
   },
   "outputs": [],
   "source": [
    "import librosa\n",
    "import numpy as np\n",
    "import os\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Jtjd4LP_0VG6"
   },
   "outputs": [],
   "source": [
    "#a Loader class is responsible for loading an audio file\n",
    "#built with help of librosa\n",
    "class Loader:\n",
    "  def __init__(self, sample_rate, duration, mono):\n",
    "    self.sample_rate = sample_rate\n",
    "    self.duration = duration\n",
    "    self.mono = mono\n",
    "  \n",
    "  def load(self, file_path):\n",
    "    signal, sample_rate = librosa.load(file_path,sr= self.sample_rate,mono=self.mono, duration=self.duration)\n",
    "    return (signal,sample_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CO5A4GBi3Mh0"
   },
   "source": [
    "## Creating Padder Class\n",
    "built with help of numpy<br> \n",
    "Modes:\n",
    "\n",
    "\n",
    "1.   **constant**: Pads with a constant value default is zero.\n",
    "2.  **edge**:Pads with the edge values  of array.\n",
    "3.  **empty**:Pads with undefined value\n",
    "4.  **mean**:Pads with the mean value of all or part of the vector along each axis.\n",
    "\n",
    "[more](https://numpy.org/doc/stable/reference/generated/numpy.pad.html)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "z1MOyYP63MH1"
   },
   "outputs": [],
   "source": [
    "#a Padding Class is responsible for padding an array\n",
    "class Padder:\n",
    "  def __init__(self, mode=\"constant\"):\n",
    "    self.mode = mode\n",
    "  \n",
    "  def left_pad(self, array, num_missing_item):\n",
    "    padded_array = np.pad(array, (num_missing_item,0),self.mode)\n",
    "    return padded_array\n",
    "\n",
    "  def right_pad(self, array, num_missing_item):\n",
    "    padded_array = np.pad(array, (0,num_missing_item),self.mode)\n",
    "    return padded_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jBlj3pTo9FeJ"
   },
   "source": [
    "## Building LogSpectogramExtractor\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "OvqMa4GH8ClF"
   },
   "outputs": [],
   "source": [
    "#a logspectogramextractor is responsible for extracting log spectogram\n",
    "class LogSpectorgramExtracor:\n",
    "  def __init__(self, frame_size, hop_length):\n",
    "    self.frame_size = frame_size \n",
    "    self.hop_length = hop_length \n",
    "  \n",
    "  def extract(self, signal):\n",
    "    stft = librosa.stft(signal, n_fft = self.frame_size, hop_length=self.hop_length)[:-1]\n",
    "    # ( 1   +frameSize/2*num of frame) reutrns odd number, hence we will ignore last element\n",
    "    spectogram = np.abs(stft)\n",
    "    log_spectogram= librosa.amplitude_to_db(spectogram)\n",
    "    return log_spectogram\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6ubZpoQLAlcy"
   },
   "source": [
    "## Building MinMaxNormalizer\n",
    "Min-max normalization (usually called feature scaling) performs a linear transformation on the original data. This technique gets **all the scaled data in the range (0, 1)**. The formula to achieve this is the following: Min-max normalization preserves the relationships among the original data values.\n",
    "\n",
    "Xscaled= X-Xmin / Xmax- Xmin\n",
    "\n",
    "**at Xscaled = Xmax:**\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;Xscaled = Xmax - Xmin / Xmax - Xmin = 1 (maximum value)\n",
    "\n",
    "**at Xscaled = Xmin:**\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;Xscaled = Xmin - Xmin / Xmax - Xmin = 0 (minimum value)\n",
    "\n",
    "\n",
    "**When should I use min-max normalization? Normalization (Min-Max Scalar)** \n",
    "\n",
    "Normalization is useful in models such as **k-nearest neighbors and artificial neural networks**, or anywhere where the data we are using has **varying scales or precision** \n",
    "\n",
    "**for example:**\n",
    "consider a dataset that has two features **(salary and years worked)**. Both of these features will have high differences between their values. For example, salary might be a thousand times bigger than years worked – normalization makes sure the entire data in the same range.\n",
    "\n",
    "**Note:**\n",
    "\n",
    "Not every dataset needs normalization in Machine Learning. It is only required when data features have varying ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "MT1of-D-AwiN"
   },
   "outputs": [],
   "source": [
    "#a MinMaxNormalizer is responsible for the normalization of an array\n",
    "class MinMaxNormalizer:\n",
    "  def __init__(self, min_val, max_val):\n",
    "    self.min_val = min_val\n",
    "    self.max_val = max_val\n",
    "  \n",
    "  def normlize(self, array):\n",
    "    norm_array = (array - array.min()) / (array.max() -  array.min())\n",
    "    norm_array = norm_array* (self.max_val -  self.min_val) +  self.min_val\n",
    "    #return normalized array between self.max and self.min rather than 0 and 1\n",
    "\n",
    "    return norm_array\n",
    "  \n",
    "  def denormlize(self, norm_array, original_min, original_max):\n",
    "    array = (norm_array - self.min_val)/(self.max_val - self.min_val)\n",
    "    array =  array * (original_max - original_min) + original_min\n",
    "    return array\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "03Ail_YNIYqi"
   },
   "source": [
    "## Building Saver\n",
    "Python pickle module is used for serializing and de-serializing a Python object structure.** Any object in Python can be pickled** so that it can be **saved on disk.**\n",
    "\n",
    "The **serialization** process is a way to convert** a data structure** into a **linear form** that can be stored or transmitted over a network.\n",
    "\n",
    "What pickle does is that it “serializes” the object first before writing it to file. Pickling is a way to convert a python object (list, dict, etc.) into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script.\n",
    "\n",
    "\n",
    "**The Python pickle module basically consists of four methods:**\n",
    "\n",
    "`pickle.dump(obj, file, protocol=None, *, fix_imports=True, buffer_callback=None)`\n",
    "\n",
    "`pickle.dumps(obj, protocol=None, *, fix_imports=True, buffer_callback=None)`\n",
    "\n",
    "`pickle.load(file, *, fix_imports=True, encoding=\"ASCII\", errors=\"strict\", buffers=None)`\n",
    "\n",
    "`pickle.loads(bytes_object, *, fix_imports=True, encoding=\"ASCII\", errors=\"strict\", buffers=None)`\n",
    "\n",
    "The first two methods are used during the **pickling process,** and the other two are used during unpickling. The only difference between dump() and dumps() is that the first creates a file containing the serialization result, whereas the second returns a string.\n",
    "\n",
    "To differentiate dumps() from dump(), it’s helpful to remember that the s at the end of the function name stands for string. The same concept also applies to load() and loads(): The first one reads a file to start the unpickling process, and the second one operates on a string.\n",
    "\n",
    "[more](https://realpython.com/python-pickle-module/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "3DZI64GgIgrz"
   },
   "outputs": [],
   "source": [
    "# a Saver class is responsible to save features and the min, max values\n",
    "class Saver:\n",
    "    def __init__(self,feature_save_dir, min_max_save_dir):\n",
    "      self.feature_save_dir = feature_save_dir\n",
    "      self.min_max_save_dir = min_max_save_dir\n",
    "    \n",
    "    def save_feature(self, feature, file_path):\n",
    "      save_path = self._generate_save_path(file_path)\n",
    "      np.save(save_path, feature)\n",
    "      return save_path\n",
    "    \n",
    "    def _generate_save_path(self, file_path):\n",
    "      file_name = os.path.split(file_path)[1] #the name\n",
    "      save_path = os.path.join(self.feature_save_dir,file_name + \".npy\" )\n",
    "      return save_path\n",
    "    \n",
    "    def save_min_max_values(self,min_max_values):\n",
    "      save_path = os.path.join(self.min_max_save_dir, \"min_max_values.pkl\")\n",
    "      self._save(min_max_values, save_path)\n",
    "    \n",
    "    @staticmethod\n",
    "    def _save(min_max_values, save_path): #serlize the map and save it\n",
    "      with open(save_path, \"wb\") as f :\n",
    "        pickle.dump(min_max_values, f)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3PzZjdGtCt41"
   },
   "source": [
    "## Implementing preprocessing pipeline\n",
    "it will combine the previous classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "RikymSV0DAmi"
   },
   "outputs": [],
   "source": [
    "class PreprocessingPipeline:\n",
    "  def __init__(self):\n",
    "    self.padder =None\n",
    "    self.extractor = None\n",
    "    self.normaliser = None\n",
    "    self.saver = None\n",
    "    self.min_max_values = {}\n",
    "    self._loader =None\n",
    "    self._num_expected_sampled = None\n",
    "  \n",
    "  @property\n",
    "  def loader(self):\n",
    "    return self._loader\n",
    "\n",
    "  @loader.setter\n",
    "  def loader(self, loader):\n",
    "    self._loader = loader\n",
    "    self._num_expected_sampled = int(loader.sample_rate* loader.duration)\n",
    "\n",
    "  def process(self, audio_files_dir):\n",
    "    for root, subroots, files in os.walk(audio_files_dir):\n",
    "      for file in files: #file name\n",
    "        file_path = os.path.join(root, file)\n",
    "        self._process_file(file_path)\n",
    "        print(f\"Proccessed file {file_path}\")\n",
    "    self.saver.save_min_max_values(self.min_max_values)\n",
    "    print(\"saved min max\")\n",
    "  \n",
    "  def _process_file(self, file_path):\n",
    "    signal, sr = self._loader.load(file_path) #load signal\n",
    "    if self._is_padding_necessary(signal):\n",
    "      signal = self._apply_padding(signal)\n",
    "    feature = self.extractor.extract(signal)\n",
    "    norm_feature = self.normaliser.normlize(feature)\n",
    "    save_path = self.saver.save_feature(norm_feature, file_path)\n",
    "    self._store_min_max_values(save_path, feature.min(), feature.max())\n",
    "\n",
    "  def _is_padding_necessary(self, signal):\n",
    "    if len(signal)< self._num_expected_sampled:\n",
    "      return True\n",
    "    return False\n",
    "\n",
    "  def _apply_padding(self, signal):\n",
    "    num_of_padded_sample =self._num_expected_sampled - len(signal)\n",
    "    padded_signal = self.padder.right_pad(signal, num_of_padded_sample)\n",
    "    return padded_signal\n",
    "    \n",
    "  def _store_min_max_values(self, save_path, min_val, max_val):\n",
    "    print(f\"saved at {save_path}\")\n",
    "    self.min_max_values[save_path] = {\n",
    "        \"min\" :min_val,\n",
    "        \"max\": max_val\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wzl4RHCsNsw4"
   },
   "source": [
    "## Runing an object from the class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zHxZL4qZNsCw",
    "outputId": "1d5c503a-71a7-42ad-ead1-4968047d9bb2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/0_lucas_17.wav.npy\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/2_george_30.wav.npy\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/3_jackson_11.wav.npy\n",
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      "saved at ./spectrograms/3_nicolas_0.wav.npy\n",
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      "Proccessed file ./audio/3_nicolas_19.wav\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/3_nicolas_2.wav.npy\n",
      "Proccessed file ./audio/3_nicolas_2.wav\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/3_yweweler_21.wav.npy\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Proccessed file ./audio/4_yweweler_39.wav\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/4_yweweler_4.wav.npy\n",
      "Proccessed file ./audio/4_yweweler_4.wav\n",
      "saved at ./spectrograms/4_yweweler_40.wav.npy\n",
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      "saved at ./spectrograms/5_george_0.wav.npy\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/5_jackson_39.wav.npy\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/5_nicolas_4.wav.npy\n",
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      "Proccessed file ./audio/5_yweweler_41.wav\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/5_yweweler_42.wav.npy\n",
      "Proccessed file ./audio/5_yweweler_42.wav\n",
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      "Proccessed file ./audio/6_jackson_5.wav\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/6_jackson_6.wav.npy\n",
      "Proccessed file ./audio/6_jackson_6.wav\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/6_theo_16.wav.npy\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/7_george_25.wav.npy\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/9_george_6.wav.npy\n",
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      "saved at ./spectrograms/9_lucas_8.wav.npy\n",
      "Proccessed file ./audio/9_lucas_8.wav\n",
      "saved at ./spectrograms/9_lucas_9.wav.npy\n",
      "Proccessed file ./audio/9_lucas_9.wav\n",
      "saved at ./spectrograms/9_nicolas_0.wav.npy\n",
      "Proccessed file ./audio/9_nicolas_0.wav\n",
      "saved at ./spectrograms/9_nicolas_1.wav.npy\n",
      "Proccessed file ./audio/9_nicolas_1.wav\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/9_nicolas_10.wav.npy\n",
      "Proccessed file ./audio/9_nicolas_10.wav\n",
      "saved at ./spectrograms/9_nicolas_11.wav.npy\n",
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      "saved at ./spectrograms/9_nicolas_12.wav.npy\n",
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      "saved at ./spectrograms/9_nicolas_13.wav.npy\n",
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      "saved at ./spectrograms/9_nicolas_14.wav.npy\n",
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      "saved at ./spectrograms/9_nicolas_17.wav.npy\n",
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      "saved at ./spectrograms/9_nicolas_19.wav.npy\n",
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      "saved at ./spectrograms/9_nicolas_31.wav.npy\n",
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      "saved at ./spectrograms/9_nicolas_39.wav.npy\n",
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      "saved at ./spectrograms/9_nicolas_4.wav.npy\n",
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      "saved at ./spectrograms/9_theo_0.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_0.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_18.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_18.wav\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved at ./spectrograms/9_yweweler_19.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_19.wav\n",
      "saved at ./spectrograms/9_yweweler_2.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_2.wav\n",
      "saved at ./spectrograms/9_yweweler_20.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_20.wav\n",
      "saved at ./spectrograms/9_yweweler_21.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_21.wav\n",
      "saved at ./spectrograms/9_yweweler_22.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_23.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_24.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_24.wav\n",
      "saved at ./spectrograms/9_yweweler_25.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_25.wav\n",
      "saved at ./spectrograms/9_yweweler_26.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_27.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_29.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_3.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_3.wav\n",
      "saved at ./spectrograms/9_yweweler_30.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_31.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_32.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_37.wav.npy\n",
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      "saved at ./spectrograms/9_yweweler_38.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_38.wav\n",
      "saved at ./spectrograms/9_yweweler_39.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_39.wav\n",
      "saved at ./spectrograms/9_yweweler_4.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_4.wav\n",
      "saved at ./spectrograms/9_yweweler_40.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_40.wav\n",
      "saved at ./spectrograms/9_yweweler_41.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_41.wav\n",
      "saved at ./spectrograms/9_yweweler_42.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_42.wav\n",
      "saved at ./spectrograms/9_yweweler_43.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_43.wav\n",
      "saved at ./spectrograms/9_yweweler_44.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_44.wav\n",
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      "Proccessed file ./audio/9_yweweler_45.wav\n",
      "saved at ./spectrograms/9_yweweler_46.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_46.wav\n",
      "saved at ./spectrograms/9_yweweler_47.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_47.wav\n",
      "saved at ./spectrograms/9_yweweler_48.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_48.wav\n",
      "saved at ./spectrograms/9_yweweler_49.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_49.wav\n",
      "saved at ./spectrograms/9_yweweler_5.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_5.wav\n",
      "saved at ./spectrograms/9_yweweler_6.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_6.wav\n",
      "saved at ./spectrograms/9_yweweler_7.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_7.wav\n",
      "saved at ./spectrograms/9_yweweler_8.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_8.wav\n",
      "saved at ./spectrograms/9_yweweler_9.wav.npy\n",
      "Proccessed file ./audio/9_yweweler_9.wav\n",
      "saved min max\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    FRAME_SIZE = 512\n",
    "    HOP_LENGTH = 256\n",
    "    DURATION = 0.74  # in seconds\n",
    "    SAMPLE_RATE = 22050\n",
    "    MONO = True\n",
    "\n",
    "    \n",
    "    SPECTROGRAMS_SAVE_DIR = \"./spectrograms/\"\n",
    "    MIN_MAX_VALUES_SAVE_DIR = \"./\"\n",
    "    FILES_DIR = \"./audio/\"\n",
    "    \n",
    "    loader = Loader(SAMPLE_RATE,DURATION, MONO )\n",
    "    padder = Padder()\n",
    "    log_spectogram_extractor = LogSpectorgramExtracor(FRAME_SIZE, HOP_LENGTH)\n",
    "    min_max_normlizer = MinMaxNormalizer(0, 1)\n",
    "    saver = Saver(SPECTROGRAMS_SAVE_DIR, MIN_MAX_VALUES_SAVE_DIR)\n",
    "\n",
    "    preprocessing_pipeline = PreprocessingPipeline()\n",
    "\n",
    "    preprocessing_pipeline.loader = loader\n",
    "    preprocessing_pipeline.padder = padder\n",
    "    preprocessing_pipeline.extractor = log_spectogram_extractor\n",
    "    preprocessing_pipeline.normaliser = min_max_normlizer\n",
    "    preprocessing_pipeline.saver = saver\n",
    "\n",
    "    preprocessing_pipeline.process(FILES_DIR)\n"
   ]
  }
 ],
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