index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "from os.path import isdir, join\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "\n",
    "# Math\n",
    "import numpy as np\n",
    "from scipy.fftpack import fft\n",
    "from scipy import signal\n",
    "from scipy.io import wavfile\n",
    "import librosa\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import IPython.display as ipd\n",
    "import librosa.display\n",
    "\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000\n"
     ]
    }
   ],
   "source": [
    "y, sr = librosa.load('./data/New_N_001_cut.wav', sr=None)\n",
    "print(sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mamet/anaconda3/envs/tf/lib/python3.10/site-packages/librosa/core/spectrum.py:256: UserWarning:\n",
      "\n",
      "n_fft=2048 is too large for input signal of length=1600\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make and display a mel-scaled power (energy-squared) spectrogram\n",
    "S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128)\n",
    "\n",
    "# Convert to log scale (dB). We'll use the peak power as reference.\n",
    "log_S = librosa.amplitude_to_db(S, ref=np.max)\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "librosa.display.specshow(log_S, sr=sr, x_axis='time', y_axis='mel')\n",
    "\n",
    "# Put a descriptive title on the plot\n",
    "plt.title('mel power spectrogram')\n",
    "\n",
    "# draw a color bar\n",
    "plt.colorbar(format='%+02.0f dB')\n",
    "\n",
    "# Make the figure layout compact\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "print(len(S))\n",
    "print(len(S[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.io import wavfile\n",
    "\n",
    "def cut_wav_file(input_wav_path, output_wav_path, duration_sec):\n",
    "    # Step 1: Read the WAV file and extract the audio data\n",
    "    sample_rate, audio_data = wavfile.read(input_wav_path)\n",
    "\n",
    "    # Step 2: Compute the number of samples required for the desired duration\n",
    "    samples_required = int(sample_rate * duration_sec)\n",
    "\n",
    "    # Step 3: Ensure the audio data is at least the required duration\n",
    "    if len(audio_data) <= samples_required:\n",
    "        print(\"The input audio is already shorter than the desired duration.\")\n",
    "        return\n",
    "\n",
    "    # Step 4: Cut or reshape the audio data to the desired duration\n",
    "    cut_audio_data = audio_data[:samples_required]\n",
    "\n",
    "    # Step 5: Write the new audio data to a new WAV file\n",
    "    wavfile.write(output_wav_path, sample_rate, cut_audio_data)\n",
    "\n",
    "# Replace 'input_audio.wav' and 'output_audio.wav' with your input and output file paths\n",
    "input_wav_path = './data/New_N_001.wav'\n",
    "output_wav_path = './data/New_N_001_cut.wav'\n",
    "\n",
    "# Set the desired duration in seconds (e.g., 2 seconds)\n",
    "desired_duration_sec = 0.2\n",
    "\n",
    "# Call the function to cut/reshape the audio file\n",
    "cut_wav_file(input_wav_path, output_wav_path, desired_duration_sec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.io import wavfile\n",
    "\n",
    "def plot_amplitude_spectrum(wav_file_path):\n",
    "    # Step 1: Read the WAV file and extract the audio data\n",
    "    sample_rate, audio_data = wavfile.read(wav_file_path)\n",
    "\n",
    "    # Step 2: Compute the Fourier Transform\n",
    "    frequency_domain = np.fft.fft(audio_data)\n",
    "\n",
    "    # Step 3: Compute the amplitude spectrum\n",
    "    amplitude_spectrum = np.abs(frequency_domain)\n",
    "\n",
    "    # Frequency axis in Hertz\n",
    "    frequencies = np.fft.fftfreq(len(amplitude_spectrum), d=1/sample_rate)\n",
    "\n",
    "    # Plotting the amplitude spectrum\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.plot(frequencies[:len(frequencies)//2], amplitude_spectrum[:len(amplitude_spectrum)//2])\n",
    "    plt.xlabel('Frequency (Hz)')\n",
    "    plt.ylabel('Amplitude')\n",
    "    plt.title('Amplitude Spectrum')\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "# Replace 'your_wav_file.wav' with the path to your WAV file\n",
    "wav_file_path = './data/New_N_001_cut.wav'\n",
    "plot_amplitude_spectrum(wav_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000 (16837,)\n"
     ]
    }
   ],
   "source": [
    "train_audio_path = './data/'\n",
    "filename = '/New_N_001.wav'\n",
    "sample_rate, samples = wavfile.read(str(train_audio_path) + filename)\n",
    "print(sample_rate, samples.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_specgram(audio, sample_rate, window_size=20,\n",
    "                 step_size=10, eps=1e-10):\n",
    "    nperseg = int(round(window_size * sample_rate / 1e3))\n",
    "    noverlap = int(round(step_size * sample_rate / 1e3))\n",
    "    freqs, times, spec = signal.spectrogram(audio,\n",
    "                                    fs=sample_rate,\n",
    "                                    window='hann',\n",
    "                                    nperseg=nperseg,\n",
    "                                    noverlap=noverlap,\n",
    "                                    detrend=False)\n",
    "    return freqs, times, np.log(spec.T.astype(np.float32) + eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (8000,) and (16837,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[36], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m ax1\u001b[39m.\u001b[39mset_title(\u001b[39m'\u001b[39m\u001b[39mRaw wave of \u001b[39m\u001b[39m'\u001b[39m \u001b[39m+\u001b[39m filename)\n\u001b[1;32m      6\u001b[0m ax1\u001b[39m.\u001b[39mset_ylabel(\u001b[39m'\u001b[39m\u001b[39mAmplitude\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 7\u001b[0m ax1\u001b[39m.\u001b[39;49mplot(np\u001b[39m.\u001b[39;49mlinspace(\u001b[39m0\u001b[39;49m, sample_rate\u001b[39m/\u001b[39;49m\u001b[39mlen\u001b[39;49m(samples), sample_rate), samples)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1688\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1445\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1446\u001b[0m \u001b[39mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1447\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1685\u001b[0m \u001b[39m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1686\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1687\u001b[0m kwargs \u001b[39m=\u001b[39m cbook\u001b[39m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[39m.\u001b[39mLine2D)\n\u001b[0;32m-> 1688\u001b[0m lines \u001b[39m=\u001b[39m [\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_lines(\u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39mdata, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)]\n\u001b[1;32m   1689\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m lines:\n\u001b[1;32m   1690\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_base.py:311\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m     this \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m args[\u001b[39m0\u001b[39m],\n\u001b[1;32m    310\u001b[0m     args \u001b[39m=\u001b[39m args[\u001b[39m1\u001b[39m:]\n\u001b[0;32m--> 311\u001b[0m \u001b[39myield from\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_plot_args(\n\u001b[1;32m    312\u001b[0m     this, kwargs, ambiguous_fmt_datakey\u001b[39m=\u001b[39;49mambiguous_fmt_datakey)\n",
      "File \u001b[0;32m~/anaconda3/envs/tf/lib/python3.10/site-packages/matplotlib/axes/_base.py:504\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m    501\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes\u001b[39m.\u001b[39myaxis\u001b[39m.\u001b[39mupdate_units(y)\n\u001b[1;32m    503\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m] \u001b[39m!=\u001b[39m y\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]:\n\u001b[0;32m--> 504\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y must have same first dimension, but \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    505\u001b[0m                      \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mhave shapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    506\u001b[0m \u001b[39mif\u001b[39;00m x\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m \u001b[39mor\u001b[39;00m y\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m2\u001b[39m:\n\u001b[1;32m    507\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mx and y can be no greater than 2D, but have \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    508\u001b[0m                      \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mshapes \u001b[39m\u001b[39m{\u001b[39;00mx\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m and \u001b[39m\u001b[39m{\u001b[39;00my\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (8000,) and (16837,)"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freqs, times, spectrogram = log_specgram(samples, sample_rate)\n",
    "\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "ax1.set_title('Raw wave of ' + filename)\n",
    "ax1.set_ylabel('Amplitude')\n",
    "ax1.plot(np.linspace(0, sample_rate/len(samples), sample_rate), samples)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}