{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "israeli-hometown",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import os\n",
    "import pandas as pd \n",
    "from scipy.io import wavfile\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "waiting-today",
   "metadata": {},
   "outputs": [],
   "source": [
    "samplerate, data = wavfile.read('./7061-6-0-0.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "computational-europe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Lingvo does not support eager execution yet. Please disable eager execution with tf.compat.v1.disable_eager_execution() or proceed at your own risk.\n"
     ]
    }
   ],
   "source": [
    "import leaf_audio.frontend as frontend\n",
    "\n",
    "leaf = frontend.Leaf()\n",
    "melfbanks = frontend.MelFilterbanks()\n",
    "tfbanks = frontend.TimeDomainFilterbanks()\n",
    "sincnet = frontend.SincNet()\n",
    "sincnet_plus = frontend.SincNetPlus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "disciplinary-brain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "compound-diesel",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "sustained-default",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "developmental-lounge",
   "metadata": {},
   "outputs": [],
   "source": [
    "leaf_representation = leaf(data)\n",
    "melfbanks_representation = melfbanks(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fuzzy-helmet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensorflow.python.framework.ops.EagerTensor"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(melfbanks_representation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "funny-emperor",
   "metadata": {},
   "outputs": [],
   "source": [
    "leaf_representation\n",
    "t= melfbanks_representation.numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "raising-stephen",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method _EagerTensorBase.numpy of <tf.Tensor: shape=(99225, 1, 40), dtype=float32, numpy=\n",
       "array([[[-11.512925, -11.512925, -11.512925, ..., -11.512925,\n",
       "         -11.512925, -11.512925]],\n",
       "\n",
       "       [[-11.512925, -11.512925, -11.512925, ..., -11.512925,\n",
       "         -11.512925, -11.512925]],\n",
       "\n",
       "       [[-11.512925, -11.512925, -11.512925, ..., -11.512925,\n",
       "         -11.512925, -11.512925]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-11.512925, -11.512925, -11.512925, ..., -11.512925,\n",
       "         -11.512925, -11.512925]],\n",
       "\n",
       "       [[-11.512393, -11.512265, -11.512299, ..., -11.507828,\n",
       "         -11.507522, -11.507188]],\n",
       "\n",
       "       [[-11.512925, -11.512925, -11.512925, ..., -11.512925,\n",
       "         -11.512925, -11.512925]]], dtype=float32)>>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dressed-present",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}