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+{
+ "metadata": {
+  "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-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "sns.set()\n",
+    "import librosa\n",
+    "import json\n",
+    "import os\n",
+    "import IPython\n",
+    "%matplotlib inline\n",
+    "from tqdm.notebook import tqdm\n",
+    "import torchaudio\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.pyplot import cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Train Shape: (1216, 7)\nColumns: Index(['recording_id', 'species_id', 'songtype_id', 't_min', 'f_min', 't_max',\n       'f_max'],\n      dtype='object')\nNumber of Species in this dataset: 24\nNumber of SongTypes: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_data = pd.read_csv(\"/home/lustbeast/AudioClass/Dataset/rfcx-species-audio-detection/train_tp.csv\")\n",
+    "print(f\"Train Shape: {train_data.shape}\")\n",
+    "print(f\"Columns: {train_data.columns}\")\n",
+    "print(f\"Number of Species in this dataset: {train_data.species_id.nunique()}\")\n",
+    "print(f\"Number of SongTypes: {train_data.songtype_id.nunique()}\")"
+   ]
+  },
+  {
+   "source": [
+    "### Features:\n",
+    "    1. T_MIN - The starting time of a species sound in an audio file.\n",
+    "    2. T_MAX - The ending time of a species sound in an audio file.\n",
+    "    3. F_MIN - The minimum frequency of the species.\n",
+    "    4. F_MAX -  The maximum frequency of the species."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Null Values"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "recording_id    0\n",
+       "species_id      0\n",
+       "songtype_id     0\n",
+       "t_min           0\n",
+       "f_min           0\n",
+       "t_max           0\n",
+       "f_max           0\n",
+       "dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "train_data.isna().sum()"
+   ]
+  },
+  {
+   "source": [
+    "* There are no null values present in training data."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## EDA"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_count(feature,desc,df):\n",
+    "    fig,ax = plt.subplots(1,1,figsize=(20,5))\n",
+    "    total = len(df)\n",
+    "    img = sns.countplot(df[feature],order = df[feature].value_counts().index[:30])\n",
+    "    img.set_title(desc)\n",
+    "    for p in ax.patches:\n",
+    "        height = p.get_height()\n",
+    "        ax.text(p.get_x()+p.get_width()/2.,\n",
+    "            height+3,\n",
+    "            '{:1.2f}%'.format(100*height/total),\n",
+    "            ha='center'\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1216"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plot_count(feature='species_id',desc='Species Count Plot',df=train_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 1440x360 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"337.411563pt\" version=\"1.1\" viewBox=\"0 0 1183.50875 337.411563\" width=\"1183.50875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-02-02T12:42:37.705256</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.3, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plot_count(feature='songtype_id',desc='SongTypes',df=train_data)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_feature_distribution(data_df, feature, feature2, title, kde_mode=False, hist_mode=True):\n",
+    "    f, ax = plt.subplots(1,1, figsize=(12,6))\n",
+    "    for item in list(data_df[feature2].unique()):\n",
+    "        d_df = data_df.loc[data_df[feature2]==item]\n",
+    "        try:\n",
+    "            sns.distplot(d_df[feature], kde=kde_mode, hist=hist_mode, label=item)\n",
+    "        except:\n",
+    "            pass\n",
+    "    plt.legend(labels=list(data_df[feature2].unique()), bbox_to_anchor=(1, 1), loc='upper right', ncol=2)\n",
+    "    plt.title(title)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"391.771562pt\" version=\"1.1\" viewBox=\"0 0 707.4975 391.771562\" width=\"707.4975pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-02-02T12:42:39.922383</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.3, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plot_feature_distribution(train_data, 'f_min', 'species_id', \"Minimum frequency distribution, TP data, grouped by species id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x432 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plot_feature_distribution(train_data, 'f_max', 'species_id', \"Maximum frequency distribution, TP data, grouped by species id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"391.771562pt\" version=\"1.1\" viewBox=\"0 0 714.49625 391.771562\" width=\"714.49625pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-02-02T12:42:42.367446</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.3, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plot_feature_distribution(train_data, 'f_min', 'songtype_id', \"Maximum frequency distribution, TP data, grouped by species id\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plot_feature_distribution(train_data, 'f_max', 'songtype_id', \"Maximum frequency distribution, TP data, grouped by species id\")"
+   ]
+  },
+  {
+   "source": [
+    "## Grouping the Dataset based on record_id "
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = train_data.recording_id.unique().tolist()\n",
+    "tr_gr = train_data.groupby(['recording_id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Label Generation\n",
+    "bird_dict={}\n",
+    "for f in files:\n",
+    "    lbls = np.zeros(24)\n",
+    "    temp = tr_gr.get_group(f)\n",
+    "    sps = temp.species_id.unique()\n",
+    "    for i in sps:\n",
+    "        lbls[i] = 1\n",
+    "    bird_dict[f] = lbls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1132"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "len(bird_dict)"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index fae084a..8a31443 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,34 @@
-# MacaW
\ No newline at end of file
+# MacaW
+
+## Rainforest Connection Species Sound Classification
+
+---
+
+**NOTE**
+
+This repo is under continious updates, so the pyscripts are being changed continiously. Have a clone if you need
+
+---
+
+### Dataset
+
+The dataset has been provided with this kaggle competitions with guidelines to be followed.
+If you want to use this dataset, please refer kaggle forums, adher to the rules and regulations listed there.
+
+### Exploratory Data Analysis
+
+I have carried out the EDA part of this project using Tableau and the link to the vizs <a href="https://public.tableau.com/profile/thirumalai.kumar#!/vizhome/RFCX/RFCX-DASH">HERE</a>.
+
+If you want the insights and reports from these ongoing EDA, KIT with my <a href="https://docs.google.com/document/d/1jBp9GeHqm9p7PYN3CgWjFSBo1R2oDh7d-kK-olyIUXk/edit">GOOGLE DOC</a>
+
+
+### Models
+
+All my models have been constructed by refering to the <a href="https://arxiv.org/abs/1912.10211">PANN</a> research paper.
+
+
+
+
+- [x] Preprocessing Scripts 
+- [x] Model Construction
+- [x] Training Pipeline
diff --git a/RFCX_kfold.csv b/RFCX_kfold.csv
new file mode 100644
index 0000000..7b1e5c5
--- /dev/null
+++ b/RFCX_kfold.csv
@@ -0,0 +1,1133 @@
+recording_id,species_0,species_1,species_2,species_3,species_4,species_5,species_6,species_7,species_8,species_9,species_10,species_11,species_12,species_13,species_14,species_15,species_16,species_17,species_18,species_19,species_20,species_21,species_22,species_23,kfold,dur_sample,t_min,t_max,duration
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\ No newline at end of file
diff --git a/dataset.py b/dataset.py
new file mode 100644
index 0000000..441efaa
--- /dev/null
+++ b/dataset.py
@@ -0,0 +1,64 @@
+from torch.utils.data import Dataset,DataLoader
+import pandas as pd
+from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
+import numpy as np
+import h5py
+import librosa
+import cv2
+import torch
+from tqdm import tqdm
+import os
+from albumentations import Compose
+from albumentations.pytorch import ToTensorV2
+
+label_cols = ["species_"+str(m) for m in range(24)]
+
+def h5read(path):
+    hfile = h5py.File(path,'r')
+    return np.array(hfile.get('pixels'))
+
+def label_gen():
+    label_dict = {}
+    files = data.recording_id.unique().tolist()
+    for f in files:
+        labels = np.zeros(24)
+        sets = group.get_group(f)
+        tmp = sets.species_id.unique()
+        for i in tmp:
+            labels[i] = 1.
+        
+        label_dict[f] = labels
+    return label_dict
+
+
+class AudioData(Dataset):
+
+    def __init__(self,records,targets,root_dir,transforms=None):
+        
+        self.root_dir = root_dir
+        self.targets = targets
+        self.records = records
+        self.transforms = transforms
+        
+        #print(self.records)
+
+    def __len__(self):
+        return len(self.records)
+
+    def __getitem__(self,idx):
+        
+    
+        img_arr = h5read(os.path.join(self.root_dir,self.records[idx]+'.h5'))
+        label = self.targets[idx]
+
+        assert img_arr.shape[2] == 3
+        if self.transforms is not None:
+            image = self.transforms(image=img_arr)['image']
+        
+        
+
+        return image,torch.Tensor(label)
+
+
+img = h5read("/home/lustbeast/AudioClass/Dataset/rfcx-species-audio-detection/h5/0a350d11c.h5")
+print(img.shape)
\ No newline at end of file
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..f5c15f3
--- /dev/null
+++ b/main.py
@@ -0,0 +1,60 @@
+import argparse
+import librosa
+import os
+import h5py
+from glob import glob
+import numpy as np
+from tqdm import tqdm
+from spectro import wave_to_spec
+import pandas as pd
+
+
+def check_and_create(path):
+    if os.path.isdir(path):
+        print("Path already exists")
+    else:
+        os.makedirs(path)
+
+
+def save_spec(path,offset=None,dur=None,save_format=".npy",save_path=None):
+    check_and_create(save_path)
+    for i in tqdm(range(len(path))):
+        filename = path[i].split("/")[-1].split('.')[0]
+        mels = wave_to_spec(path[i],offset=offset,duration=dur)
+        if save_format == '.npy':
+            np.save(os.path.join(save_path,filename+save_format),mels)
+        elif save_format == '.h5':
+            h5fil = h5py.File(os.path.join(save_path,filename+save_format),'w')
+            h5fil.create_dataset('pixels',data=mels)
+            h5fil.close()
+
+if __name__ == "__main__":
+
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--csv_path", help="The Path to the csv file.")
+    parser.add_argument("--root_dir", help="The root dir of dataset.")
+    parser.add_argument("--format", help="Data Format")
+    parser.add_argument("--save_format",help="data format to save")
+    parser.add_argument("--save_path",help="where to save")
+    
+
+    args = parser.parse_args()
+    paths = args.csv_path
+    save_as = args.save_format
+    save_path = args.save_path
+    root_dir = args.root_dir
+
+    data = pd.read_csv(paths)
+    #print(len(data))
+    check_and_create(save_path)
+    for n in tqdm(range(len(data))):
+        rid = data.recording_id.iloc[n]
+        p = os.path.join(root_dir,rid+args.format)
+        mel = wave_to_spec(p,offset=data.t_min.iloc[n],duration=data.duration.iloc[n])
+        if save_as == ".h5":
+            h5fil = h5py.File(os.path.join(save_path,rid+save_as))
+            h5fil.create_dataset("pixels",data=mel)
+            h5fil.close()
+        elif save_as == ".npy":
+            np.save(os.path.join(root_dir,rid+save_as),arr=mel)
\ No newline at end of file
diff --git a/model.py b/model.py
new file mode 100644
index 0000000..f6eef33
--- /dev/null
+++ b/model.py
@@ -0,0 +1,88 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch
+from torchvision import models
+import numpy as np
+import timm
+import h5py
+from efficientnet_pytorch import EfficientNet
+
+
+
+class custom_effnet(nn.Module):
+    def __init__(self):
+        super(custom_effnet,self).__init__()
+        self.model = EfficientNet.from_pretrained('efficientnet-b3')
+        self.model._conv_stem.in_channels = 1
+        weight = self.model._conv_stem.weight.mean(1,keepdim=True)
+        self.model._conv_stem.weight = torch.nn.Parameter(weight)
+        print(self.model)
+        num_ftrs = self.model.classifier.in_features
+        self.model.classifier = nn.Linear(num_ftrs,24)
+
+    def forward(self,x):
+        return self.model(x)
+
+class custom_resnet(nn.Module):
+    def __init__(self):
+        super(custom_resnet,self).__init__()
+        self.model = models.resnet18(pretrained=True)
+        num_ftrs = self.model.fc.in_features
+        self.model.fc = nn.Linear(num_ftrs,24)
+
+    def forward(self,x):
+        return self.model(x)
+
+class ConvMod(nn.Module):
+    def __init__(self,in_channels,out_channels,kernel_size,act=True,strides=1):
+        super(ConvMod,self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_channels,out_channels=out_channels,kernel_size=kernel_size,stride=strides)
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.act = act
+    
+    def forward(self,x):
+        if self.act is True:
+            x = F.relu(self.bn(self.conv(x)))
+        else:
+            x = self.bn(self.conv(x))
+
+        return x
+
+class CNN_14(nn.Module):
+    def __init__(self,in_channels,out_channels,kernel_size):
+        super(CNN_14,self).__init__()
+        self.conv1 = nn.Sequential(
+            ConvMod(in_channels=in_channels,out_channels=out_channels,kernel_size=kernel_size),
+            ConvMod(64,64,kernel_size=kernel_size)
+        )
+        
+        self.conv2 = nn.Sequential(
+            ConvMod(64,128,kernel_size=kernel_size),
+            ConvMod(128,128,kernel_size=kernel_size)
+        )
+        self.conv3 = nn.Sequential(
+            ConvMod(128,256,kernel_size=kernel_size),
+            ConvMod(256,256,kernel_size=kernel_size)
+
+        )
+        self.conv4 = nn.Sequential(
+            ConvMod(256,512,kernel_size=kernel_size),
+            ConvMod(512,512,kernel_size=kernel_size)
+        )
+        self.pool = nn.AvgPool2d(kernel_size=(2,2))
+        self.linear = nn.Linear(512,512)
+        self.classifier = nn.Linear(512,24)
+    def forward(self,x):
+        x = self.conv1(x)
+        x = self.pool(x)
+        x = self.conv2(x)
+        x = self.pool(x)
+        x = self.conv3(x)
+        x = self.pool(x)
+        x = self.conv4(x)
+        x = F.adaptive_avg_pool2d(x,(1,1))
+        x = x.view(-1,512)
+        x = self.classifier(x)
+        return x
+
+
diff --git a/past.py b/past.py
new file mode 100644
index 0000000..bfb1a30
--- /dev/null
+++ b/past.py
@@ -0,0 +1,30 @@
+df = pd.DataFrame.from_dict(label_gen(),orient='index').reset_index()
+df.columns = ['recording_id']+['species_'+str(n) for n in range(24)]
+label_cols = [f"species_"+str(m) for m in range(24)]
+
+mst = MultilabelStratifiedKFold(n_splits=5)
+X = df.recording_id.values
+y = df[label_cols].values
+df['kfold'] = -1
+for i,(train_ind,val_ind) in enumerate(mst.split(X,y)):
+
+    df.loc[val_ind,'kfold'] = i
+
+df.to_csv("RFCX_kfold.csv",index=False)
+
+
+
+dur_sample = []
+data = pd.read_csv("RFCX_kfold.csv")
+org_data = pd.read_csv("/home/lustbeast/AudioClass/Dataset/rfcx-species-audio-detection/train_tp.csv")
+org_data = org_data.groupby("recording_id").agg({'t_min':lambda x:min(x),'t_max':lambda x:max(x)}).reset_index()
+org_data['duration'] = org_data['t_max'] - org_data["t_min"]
+org_data['duration'] = np.ceil(org_data.duration.values)+5
+data = data.merge(org_data,on="recording_id",how='left')
+data['dur_sample'] = -1
+for i in tqdm(range(len(data))):
+    song_name = os.path.join("/home/lustbeast/AudioClass/Dataset/rfcx-species-audio-detection/train",data.loc[i,"recording_id"]+".flac")
+    song,sr = librosa.load(song_name,sr=None)
+    durs = song.shape[0] / sr
+    data['dur_sample'].iloc[i] = durs
+data.to_csv("RFCX_kfold.csv")
diff --git a/spectro.py b/spectro.py
new file mode 100644
index 0000000..7cb75b7
--- /dev/null
+++ b/spectro.py
@@ -0,0 +1,38 @@
+import h5py
+import librosa
+import cv2
+import numpy as np
+
+class params:
+
+    sampling_rate = 48000
+    mel_bins = 128
+    fmin = 20
+    fmax = sampling_rate // 2
+
+def mono_to_color(x,eps=1e-6):
+   # X = np.stack([x,x,x],axis=-1)
+    mean = x.mean()
+    x = x-mean
+    std = x.std()
+    Xstd = X / std+eps
+    _min,_max = Xstd.min(),Xstd.max()
+    norm_max,norm_min = _max,_min
+    if (_max - _min) > eps:
+        V = Xstd
+        V[V<norm_min] = norm_min
+        V[V>norm_max] = norm_max
+        V = 255 * (V - norm_min) / (norm_max - norm_min)
+        V = V.astype(np.uint8)
+    else:
+        V = np.zeros_like(Xstd,dtype=np.uint8)
+    
+    return V
+
+def wave_to_spec(path,offset=None,duration=None):
+    
+    audio,sr = librosa.load(path,sr=params.sampling_rate,offset=offset,duration=duration)
+    melspec = librosa.feature.melspectrogram(y=audio,sr=sr,n_mels=params.mel_bins,fmin=params.fmin,fmax=params.fmax)
+    melspec = librosa.power_to_db(melspec).astype(np.float32)
+    melspec = mono_to_color(melspec)
+    return melspec
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..8343ee6
--- /dev/null
+++ b/train.py
@@ -0,0 +1,121 @@
+import torch.nn as nn
+import numpy as np
+import os
+import pandas as pd
+import random
+import torch
+import torch.nn.functional as F
+from dataset import AudioData
+from torch.utils.data import DataLoader
+import argparse
+from tqdm import tqdm
+from albumentations import Compose,Resize,Normalize
+from albumentations.pytorch import ToTensorV2
+import torch.optim as optim
+from model import CNN_14,custom_effnet,custom_resnet
+from torch.cuda.amp import autocast,GradScaler
+from sklearn.metrics import label_ranking_average_precision_score
+
+
+scaler = GradScaler()
+loss_tr = nn.BCEWithLogitsLoss()
+loss_val = nn.BCEWithLogitsLoss()
+label_cols = ["species_"+str(m) for m in range(24)]
+
+def seed_everything(seed=2021):
+    np.random.seed(seed)
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    torch.manual_seed(seed)
+    torch.backends.cudnn.benchmark = True
+    torch.backends.cudnn.deterministic = True
+
+
+
+def train_loop(epoch,dataloader,model,optimizer,loss_fn,accum_iter=1,scheduler=None,device="cuda:0"):
+    running_loss = 0.0
+    imbar = tqdm(enumerate(dataloader),total=len(dataloader))
+    for step,(img,label) in imbar:
+        img = img.to(device).float()
+        label = label.to(device).float()
+
+        #Forward Pass
+        with autocast():
+            output = model(img)
+            loss = loss_fn(output,label)
+            if accum_iter>1:
+                loss = loss/accum_iter
+            
+        scaler.scale(loss).backward()
+        running_loss += loss.item()
+            
+        if((step+1) % accum_iter == 0) or((step+1) == len(dataloader)):
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+        
+    description = f"Epoch: {epoch} Loss: {running_loss/len(dataloader):.4f}"
+    print(description)
+    
+    if scheduler is not None:
+        scheduler.step()
+
+
+def val_loop(epoch,dataloader,model,loss_fn):
+    model.eval()
+    val_loss = 0.0
+    val_lwlrap = 0.0
+    imbar = tqdm(enumerate(dataloader),total=len(dataloader))
+    for step,(img,targets) in imbar:
+        img = img.to("cuda:0").float()
+        targets = targets.to("cuda:0").float()
+        with torch.no_grad():
+            outs = model(img)
+            loss = loss_fn(outs,targets)
+        val_loss += loss
+        lwlrap = label_ranking_average_precision_score(y_score=outs.sigmoid().cpu(),y_true=targets.cpu())
+        val_lwlrap += lwlrap
+
+    print(f"Val_Loss: {val_loss/len(dataloader):.4f} Val_LWLRAP: {val_lwlrap/len(dataloader)}")
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--epochs",help="Number of iterations to train the model")
+    parser.add_argument("--data_path",help="Root Dir of the folder containing the data files")
+    parser.add_argument("--csv_path",help="Train csv path")
+    parser.add_argument("--fold",help="Fold number to train")
+    parser.add_argument("--batch_size",help="Batch size to train with")
+    parser.add_argument("--lr",help='Learning Rate')
+    parser.add_argument("--device",help='CPU or CUDA')
+    parser.add_argument("--Grad_Accum",help="Gradient Accumulation")
+
+
+    args = parser.parse_args()
+    num_epochs = int(args.epochs)
+    data_path = args.data_path
+    lr = args.lr
+    device = args.device
+    accum_iter = int(args.Grad_Accum)
+
+    seed_everything()
+
+    data = pd.read_csv(args.csv_path)
+    df_train,df_valid = data[data.kfold != int(args.fold)].reset_index(drop=True),data[data.kfold == int(args.fold)].reset_index(drop=True)
+    #print(df_valid)
+    df_train_tar = df_train[label_cols].values
+    df_valid_tar = df_valid[label_cols].values
+    traindata = AudioData(root_dir=data_path,targets=df_train_tar,records=df_train.recording_id.values,transforms=Compose([Resize(200,600),Normalize(mean=0.485,std=0.229),ToTensorV2()]))
+    valdata = AudioData(root_dir=data_path,targets=df_valid_tar,records=df_valid.recording_id.values,transforms=Compose([Resize(200,600),Normalize(mean=0.485,std=0.229),ToTensorV2()]))
+    trainloader = DataLoader(traindata,batch_size=int(args.batch_size),num_workers=4)
+    valloader = DataLoader(valdata,batch_size=int(args.batch_size),num_workers=4)
+    model = custom_resnet().to(device)
+    opt = optim.Adam(model.parameters(),lr=float(lr),weight_decay=1e-6)
+
+    print("***** Model Training Started *****")
+    for i in range(num_epochs):
+        print(f"Epochs: {i}/{num_epochs}")
+        train_loop(i,trainloader,model,opt,loss_tr,accum_iter,device=device)
+        val_loop(i,valloader,model,loss_val)
+