ketanhdoshi

# ----------------------------
# Inference
# ----------------------------
def inference (model, val_dl):
  correct_prediction = 0
  total_prediction = 0

  # Disable gradient updates
  with torch.no_grad():
    for data in val_dl:

ketanhdoshi

from torch.utils.data import random_split

myds = SoundDS(df, data_path)

# Random split of 80:20 between training and validation
num_items = len(myds)
num_train = round(num_items * 0.8)
num_val = num_items - num_train
train_ds, val_ds = random_split(myds, [num_train, num_val])

ketanhdoshi

# ----------------------------
# Training Loop
# ----------------------------
def training(model, train_dl, num_epochs):
  # Loss Function, Optimizer and Scheduler
  criterion = nn.CrossEntropyLoss()
  optimizer = torch.optim.Adam(model.parameters(),lr=0.001)
  scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.001,
                                                steps_per_epoch=int(len(train_dl)),
                                                epochs=num_epochs,

ketanhdoshi

import torch.nn.functional as F
from torch.nn import init

# ----------------------------
# Audio Classification Model
# ----------------------------
class AudioClassifier (nn.Module):
    # ----------------------------
    # Build the model architecture
    # ----------------------------

ketanhdoshi

# ----------------------------
# Prepare training data from Metadata file
# ----------------------------
import pandas as pd
from pathlib import Path

download_path = Path.cwd()/'UrbanSound8K'

# Read metadata file
metadata_file = download_path/'metadata'/'UrbanSound8K.csv'

ketanhdoshi

from torch.utils.data import DataLoader, Dataset, random_split
import torchaudio

# ----------------------------
# Sound Dataset
# ----------------------------
class SoundDS(Dataset):
  def __init__(self, df, data_path):
    self.df = df
    self.data_path = str(data_path)

ketanhdoshi

  # ----------------------------
  # Augment the Spectrogram by masking out some sections of it in both the frequency
  # dimension (ie. horizontal bars) and the time dimension (vertical bars) to prevent
  # overfitting and to help the model generalise better. The masked sections are
  # replaced with the mean value.
  # ----------------------------
  @staticmethod
  def spectro_augment(spec, max_mask_pct=0.1, n_freq_masks=1, n_time_masks=1):
    _, n_mels, n_steps = spec.shape
    mask_value = spec.mean()

ketanhdoshi

  # ----------------------------
  # Generate a Spectrogram
  # ----------------------------
  @staticmethod
  def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):
    sig,sr = aud
    top_db = 80

    # spec has shape [channel, n_mels, time], where channel is mono, stereo etc
    spec = transforms.MelSpectrogram(sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)

ketanhdoshi

  # ----------------------------
  # Since Resample applies to a single channel, we resample one channel at a time
  # ----------------------------
  @staticmethod
  def resample(aud, newsr):
    sig, sr = aud

    if (sr == newsr):
      # Nothing to do
      return aud

ketanhdoshi

  # ----------------------------
  # Convert the given audio to the desired number of channels
  # ----------------------------
  @staticmethod
  def rechannel(aud, new_channel):
    sig, sr = aud

    if (sig.shape[0] == new_channel):
      # Nothing to do
      return aud