Ajout training nofold + base test google

TEST_GOOGLE_representation.ipynb

+{
+ "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
+}
+%% Cell type:code id:israeli-hometown tags:
+
+``` python
+import numpy as np
+import os
+import pandas as pd
+from scipy.io import wavfile
+import matplotlib.pyplot as plt
+```
+
+%% Cell type:code id:waiting-today tags:
+
+``` python
+samplerate, data = wavfile.read('./7061-6-0-0.wav')
+```
+
+%% Cell type:code id:computational-europe tags:
+
+``` python
+import leaf_audio.frontend as frontend
+
+leaf = frontend.Leaf()
+melfbanks = frontend.MelFilterbanks()
+tfbanks = frontend.TimeDomainFilterbanks()
+sincnet = frontend.SincNet()
+sincnet_plus = frontend.SincNetPlus()
+```
+
+%%%% Output: stream
+
+    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.
+
+%% Cell type:code id:disciplinary-brain tags:
+
+``` python
+type(data)
+```
+
+%%%% Output: execute_result
+
+    numpy.ndarray
+
+%% Cell type:code id:compound-diesel tags:
+
+``` python
+data = data.astype('float')
+```
+
+%% Cell type:code id:sustained-default tags:
+
+``` python
+data.dtype
+```
+
+%%%% Output: execute_result
+
+    dtype('float64')
+
+%% Cell type:code id:developmental-lounge tags:
+
+``` python
+leaf_representation = leaf(data)
+melfbanks_representation = melfbanks(data)
+```
+
+%% Cell type:code id:fuzzy-helmet tags:
+
+``` python
+type(melfbanks_representation)
+```
+
+%%%% Output: execute_result
+
+    tensorflow.python.framework.ops.EagerTensor
+
+%% Cell type:code id:funny-emperor tags:
+
+``` python
+leaf_representation
+t= melfbanks_representation.numpy
+```
+
+%% Cell type:code id:raising-stephen tags:
+
+``` python
+t
+```
+
+%%%% Output: execute_result
+
+    <bound method _EagerTensorBase.numpy of <tf.Tensor: shape=(99225, 1, 40), dtype=float32, numpy=
+    array([[[-11.512925, -11.512925, -11.512925, ..., -11.512925,
+             -11.512925, -11.512925]],
+    
+           [[-11.512925, -11.512925, -11.512925, ..., -11.512925,
+             -11.512925, -11.512925]],
+    
+           [[-11.512925, -11.512925, -11.512925, ..., -11.512925,
+             -11.512925, -11.512925]],
+    
+           ...,
+    
+           [[-11.512925, -11.512925, -11.512925, ..., -11.512925,
+             -11.512925, -11.512925]],
+    
+           [[-11.512393, -11.512265, -11.512299, ..., -11.507828,
+             -11.507522, -11.507188]],
+    
+           [[-11.512925, -11.512925, -11.512925, ..., -11.512925,
+             -11.512925, -11.512925]]], dtype=float32)>>
+
+%% Cell type:code id:dressed-present tags:
+
+``` python
+```