Spaces:
Runtime error
Runtime error
File size: 6,344 Bytes
eb9689f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 |
import numpy as np # linear algebra
import pandas as pd
import os
import librosa as lr
import torch
import torch.nn as nn
import pytorch_lightning as pl
import gradio
# HYPERPARAMS
EPOCHS = 200
BATCH_SIZE = 32
NUM_OF_CLASSES = 14
class MFCC_CNN(pl.LightningModule):
def __init__(self, num_of_classes):
super(MFCC_CNN, self).__init__()
self.example_input_array = torch.Tensor(32, 1, 50, 94)
self.train_loss_output = []
self.train_acc_output = []
self.val_acc_output = []
self.val_loss_output = []
self.number_of_classes = num_of_classes
self.conv_1 = nn.Sequential(
nn.Conv2d(in_channels = 1,
out_channels = 64,
kernel_size =3,
padding = 1,
stride = 1),
nn.BatchNorm2d(64),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Dropout(0.1)
)
self.conv_2 = nn.Sequential(
nn.Conv2d(in_channels = 64,
out_channels = 128,
kernel_size = 3,
padding = 1,
stride = 1),
nn.BatchNorm2d(128),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Dropout(0.1)
)
self.conv_3 = nn.Sequential(
nn.Conv2d(in_channels = 128,
out_channels = 256,
kernel_size = 3,
padding = 1,
stride = 1),
nn.BatchNorm2d(256),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Dropout(0.1)
)
self.conv_4 = nn.Sequential(
nn.Conv2d(in_channels = 256,
out_channels = 512,
kernel_size = 3,
padding = 1,
stride = 1),
nn.BatchNorm2d(512),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=2)
)
self.conv_5 = nn.Sequential(
nn.Conv2d(in_channels = 512,
out_channels = 512,
kernel_size = 2,
padding = 0,
stride = 1),
nn.BatchNorm2d(512),
nn.LeakyReLU(),
nn.MaxPool2d(kernel_size=2)
)
self.drop = nn.Dropout(0.1)
self.lin_1 = nn.Linear(1024, 128)
self.lin_2 = nn.Linear(128, 64)
self.lin_3 = nn.Linear(64, num_of_classes)
self.relu = nn.ReLU()
self.softmax = nn.Softmax()
def forward(self, x):
out = self.conv_1(x)
out = self.conv_2(out)
out = self.conv_3(out)
out = self.conv_4(out)
out = self.conv_5(out)
out = torch.flatten(out, start_dim=1)
out = self.drop(self.lin_1(self.relu(out)))
out = self.drop(self.lin_2(self.relu(out)))
out = self.drop(self.lin_3(self.relu(out)))
out = self.softmax(out)
return out
def loss_fn(self, out, target):
return nn.CrossEntropyLoss()(input=out.view(-1, self.number_of_classes),
target=target)
def configure_optimizers(self):
LR=1e-3
optimizer = torch.optim.Adam(self.parameters(), lr=LR, weight_decay=1e-3)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.5,
patience=5,
verbose=True)
return {
'optimizer': optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"monitor": "val_loss",
'interval': 'epoch',
'frequency': 1
},
}
def training_step(self, batch, batch_idx):
mfcc, lable = batch
mfcc = mfcc.view(-1, 1, 50, 94)
lable = lable.view(-1, self.number_of_classes)
out = self(mfcc)
loss = self.loss_fn(out=out, target=lable)
lable = torch.argmax(lable,dim=1)
predictions = torch.argmax(out,dim=1)
accuracy = torch.sum(lable==predictions)/float(len(lable))
self.train_acc_output.append(accuracy.detach().numpy())
self.train_loss_output.append(loss.detach().numpy())
#wandb.log({'train_accuracy_step': accuracy, 'train_loss_step':loss})\
self.log('train_accuracy', accuracy, prog_bar=True, on_epoch=True, on_step=False)
self.log('train_loss', loss, prog_bar=True, on_epoch=True, on_step=False)
return loss
def validation_step(self, batch, batch_idx):
mfcc, lable = batch
mfcc = mfcc.view(-1, 1, 50, 94)
lable = lable.view(-1, self.number_of_classes)
out = self(mfcc)
loss = self.loss_fn(out=out, target=lable)
lable = torch.argmax(lable,dim=1)
predictions = torch.argmax(out,dim=1)
accuracy = torch.sum(lable==predictions)/float(len(lable))
self.val_acc_output.append(accuracy.detach().numpy())
self.val_loss_output.append(loss.detach().numpy())
#wandb.log({'val_accuracy_step': accuracy, 'val_loss_step':loss})
self.log('val_accuracy', accuracy, prog_bar=True, on_epoch=True)
self.log('val_loss', loss, prog_bar=True, on_epoch=True)
return loss
def on_train_epoch_end(self):
train_loss_epoch = self.train_loss_output
train_acc_epoch = self.train_acc_output
#wandb.log({'train_loss_epoch':np.mean(train_loss_epoch),
# 'train_acc_epoch':np.mean(train_acc_epoch)})
self.train_loss_output.clear()
self.train_acc_output.clear()
def on_validation_epoch_end(self):
val_loss_epoch = self.val_loss_output
val_acc_epoch = self.val_acc_output
#wandb.log({'val_loss_epoch':np.mean(val_loss_epoch),
# 'val_acc_epoch':np.mean(val_acc_epoch)})
self.val_acc_output.clear()
self.val_loss_output.clear() |