I’m using the GTZAN dataset and am trying to make a CNN music genre classifier. My .wav files are stores in a subdirectory called ‘genres_original’, which is within the root directory, ‘Data’. I’m having two issues: my .wav files aren’t being recognized although I’m certain they are in the right format for PyTorch (.wav) and I have a tensor mismatch issue which I think is related to the wav file problem as well?
<code>import os
import glob
import random
import torch
import torchaudio
import pandas as pd
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.model_selection import train_test_split
# Path to the directory containing .wav files
data_dir = 'Data/genres_original'
# List all .wav files
wav_files = glob.glob(os.path.join(data_dir, '*.wav'))
# print(wav_files)
# Extract labels and file paths
data = []
for root, dirs, files in os.walk(data_dir):
for file in files:
if file.endswith('.wav'):
wav_file = os.path.join(root, file)
genre, _ = os.path.splitext(file)
genre = genre.split('.')[0]
data.append((file, genre))
print(data)
# Convert to DataFrame
df = pd.DataFrame(data, columns=['file_path', 'label'])
# Mapping of labels to indices
label_to_index = {label: idx for idx, label in enumerate(sorted(df['label'].unique()))}
index_to_label = {idx: label for label, idx in label_to_index.items()}
# Convert labels to indices
df['label'] = df['label'].map(label_to_index)
# print("Genre to int mapping:")
# print(label_to_index)
class AudioUtil():
@staticmethod
def open(audio_file):
sig, sr = torchaudio.load(str(audio_file))
return (sig, sr)
@staticmethod
def rechannel(aud, new_channel):
sig, sr = aud
if sig.shape[0] == new_channel:
return aud
if new_channel == 1:
sig = sig.mean(dim=0, keepdim=True)
else:
sig = sig.expand(new_channel, -1)
return (sig, sr)
@staticmethod
def resample(aud, new_sr):
sig, sr = aud
if sr == new_sr:
return aud
num_channels = sig.shape[0]
resig = torchaudio.transforms.Resample(sr, new_sr)(sig[:1, :])
if num_channels > 1:
retwo = torchaudio.transforms.Resample(sr, new_sr)(sig[1:, :])
resig = torch.cat([resig, retwo])
return (resig, new_sr)
@staticmethod
def pad_trunc(aud, max_ms):
sig, sr = aud
num_rows, sig_len = sig.shape
max_len = sr // 1000 * max_ms
if sig_len > max_len:
sig = sig[:, :max_len]
elif sig_len < max_len:
pad_begin_len = random.randint(0, max_len - sig_len)
pad_end_len = max_len - sig_len - pad_begin_len
pad_begin = torch.zeros((num_rows, pad_begin_len))
pad_end = torch.zeros((num_rows, pad_end_len))
sig = torch.cat((pad_begin, sig, pad_end), 1)
return (sig, sr)
@staticmethod
def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):
sig, sr = aud
top_db = 80
sgram = torchaudio.transforms.MelSpectrogram(
sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)
sgram = torchaudio.transforms.AmplitudeToDB(top_db=top_db)(sgram)
return sgram
class GenreDataset(Dataset):
def __init__(self, df, duration=5000, sr=22050, transform=None):
self.df = df
self.duration = duration
self.sr = sr
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
file_path = self.df.iloc[idx, 0]
label = self.df.iloc[idx, 1]
# Convert the label to an integer if it's a string
if isinstance(label, str):
label = genre_to_int[label]
# Split the file path to get the file name and extension separately
file_dir, file_name = os.path.split(file_path)
file_name_parts = file_name.split('.')
# Assume the last part is the extension
file_ext = file_name_parts[-1]
# Reconstruct the file path with the correct extension
corrected_file_path = os.path.join(file_dir, '.'.join(file_name_parts[:-1]) + '.' + file_ext)
try:
aud = AudioUtil.open(corrected_file_path)
except Exception as e:
print(f"Error opening file {corrected_file_path}: {e}")
return None, None
if aud is None:
return None, None
aud = AudioUtil.resample(aud, self.sr)
aud = AudioUtil.rechannel(aud, 1)
aud = AudioUtil.pad_trunc(aud, self.duration)
sgram = AudioUtil.spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None)
if self.transform:
sgram = self.transform(sgram)
return sgram, torch.tensor(label, dtype=torch.long)
# Ensure reproducibility
random.seed(42)
df = df.sample(frac=1, random_state=42).reset_index(drop=True)
# Split into train and validation sets (80% train, 20% validation)
train_df, val_df = train_test_split(df, test_size=0.2, stratify=df['label'], random_state=42)
train_dataset = GenreDataset(train_df)
val_dataset = GenreDataset(val_df)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, drop_last=True)
val_loader = DataLoader(val_dataset, batch_size=32, drop_last=True)
class AudioClassifier(nn.Module):
def __init__(self):
super(AudioClassifier, self).__init__()
self.conv1 = nn.Conv2d(1, 8, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(8, 16, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(64 * 4 * 4, 128)
self.fc2 = nn.Linear(128, 10)
self.dropout = nn.Dropout(0.3)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = self.pool(F.relu(self.conv4(x)))
x = x.view(-1, 64 * 4 * 4)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AudioClassifier().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20):
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
if outputs.size(0) != labels.size(0):
print(f"Mismatch in batch sizes: outputs={outputs.size(0)}, labels={labels.size(0)}")
print(f"Outputs shape: {outputs.shape}")
print(f"Labels shape: {labels.shape}")
continue
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
epoch_loss = running_loss / len(train_loader.dataset)
print(f'Epoch {epoch}/{num_epochs - 1}, Loss: {epoch_loss:.4f}')
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)
total += labels.size(0)
val_acc = correct.double() / total
print(f'Validation Accuracy: {val_acc:.4f}')
train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20)
</code>
<code>import os
import glob
import random
import torch
import torchaudio
import pandas as pd
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.model_selection import train_test_split
# Path to the directory containing .wav files
data_dir = 'Data/genres_original'
# List all .wav files
wav_files = glob.glob(os.path.join(data_dir, '*.wav'))
# print(wav_files)
# Extract labels and file paths
data = []
for root, dirs, files in os.walk(data_dir):
for file in files:
if file.endswith('.wav'):
wav_file = os.path.join(root, file)
genre, _ = os.path.splitext(file)
genre = genre.split('.')[0]
data.append((file, genre))
print(data)
# Convert to DataFrame
df = pd.DataFrame(data, columns=['file_path', 'label'])
# Mapping of labels to indices
label_to_index = {label: idx for idx, label in enumerate(sorted(df['label'].unique()))}
index_to_label = {idx: label for label, idx in label_to_index.items()}
# Convert labels to indices
df['label'] = df['label'].map(label_to_index)
# print("Genre to int mapping:")
# print(label_to_index)
class AudioUtil():
@staticmethod
def open(audio_file):
sig, sr = torchaudio.load(str(audio_file))
return (sig, sr)
@staticmethod
def rechannel(aud, new_channel):
sig, sr = aud
if sig.shape[0] == new_channel:
return aud
if new_channel == 1:
sig = sig.mean(dim=0, keepdim=True)
else:
sig = sig.expand(new_channel, -1)
return (sig, sr)
@staticmethod
def resample(aud, new_sr):
sig, sr = aud
if sr == new_sr:
return aud
num_channels = sig.shape[0]
resig = torchaudio.transforms.Resample(sr, new_sr)(sig[:1, :])
if num_channels > 1:
retwo = torchaudio.transforms.Resample(sr, new_sr)(sig[1:, :])
resig = torch.cat([resig, retwo])
return (resig, new_sr)
@staticmethod
def pad_trunc(aud, max_ms):
sig, sr = aud
num_rows, sig_len = sig.shape
max_len = sr // 1000 * max_ms
if sig_len > max_len:
sig = sig[:, :max_len]
elif sig_len < max_len:
pad_begin_len = random.randint(0, max_len - sig_len)
pad_end_len = max_len - sig_len - pad_begin_len
pad_begin = torch.zeros((num_rows, pad_begin_len))
pad_end = torch.zeros((num_rows, pad_end_len))
sig = torch.cat((pad_begin, sig, pad_end), 1)
return (sig, sr)
@staticmethod
def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):
sig, sr = aud
top_db = 80
sgram = torchaudio.transforms.MelSpectrogram(
sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)
sgram = torchaudio.transforms.AmplitudeToDB(top_db=top_db)(sgram)
return sgram
class GenreDataset(Dataset):
def __init__(self, df, duration=5000, sr=22050, transform=None):
self.df = df
self.duration = duration
self.sr = sr
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
file_path = self.df.iloc[idx, 0]
label = self.df.iloc[idx, 1]
# Convert the label to an integer if it's a string
if isinstance(label, str):
label = genre_to_int[label]
# Split the file path to get the file name and extension separately
file_dir, file_name = os.path.split(file_path)
file_name_parts = file_name.split('.')
# Assume the last part is the extension
file_ext = file_name_parts[-1]
# Reconstruct the file path with the correct extension
corrected_file_path = os.path.join(file_dir, '.'.join(file_name_parts[:-1]) + '.' + file_ext)
try:
aud = AudioUtil.open(corrected_file_path)
except Exception as e:
print(f"Error opening file {corrected_file_path}: {e}")
return None, None
if aud is None:
return None, None
aud = AudioUtil.resample(aud, self.sr)
aud = AudioUtil.rechannel(aud, 1)
aud = AudioUtil.pad_trunc(aud, self.duration)
sgram = AudioUtil.spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None)
if self.transform:
sgram = self.transform(sgram)
return sgram, torch.tensor(label, dtype=torch.long)
# Ensure reproducibility
random.seed(42)
df = df.sample(frac=1, random_state=42).reset_index(drop=True)
# Split into train and validation sets (80% train, 20% validation)
train_df, val_df = train_test_split(df, test_size=0.2, stratify=df['label'], random_state=42)
train_dataset = GenreDataset(train_df)
val_dataset = GenreDataset(val_df)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, drop_last=True)
val_loader = DataLoader(val_dataset, batch_size=32, drop_last=True)
class AudioClassifier(nn.Module):
def __init__(self):
super(AudioClassifier, self).__init__()
self.conv1 = nn.Conv2d(1, 8, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(8, 16, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(64 * 4 * 4, 128)
self.fc2 = nn.Linear(128, 10)
self.dropout = nn.Dropout(0.3)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = self.pool(F.relu(self.conv4(x)))
x = x.view(-1, 64 * 4 * 4)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AudioClassifier().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20):
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
if outputs.size(0) != labels.size(0):
print(f"Mismatch in batch sizes: outputs={outputs.size(0)}, labels={labels.size(0)}")
print(f"Outputs shape: {outputs.shape}")
print(f"Labels shape: {labels.shape}")
continue
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
epoch_loss = running_loss / len(train_loader.dataset)
print(f'Epoch {epoch}/{num_epochs - 1}, Loss: {epoch_loss:.4f}')
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)
total += labels.size(0)
val_acc = correct.double() / total
print(f'Validation Accuracy: {val_acc:.4f}')
train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20)
</code>
import os
import glob
import random
import torch
import torchaudio
import pandas as pd
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.model_selection import train_test_split
# Path to the directory containing .wav files
data_dir = 'Data/genres_original'
# List all .wav files
wav_files = glob.glob(os.path.join(data_dir, '*.wav'))
# print(wav_files)
# Extract labels and file paths
data = []
for root, dirs, files in os.walk(data_dir):
for file in files:
if file.endswith('.wav'):
wav_file = os.path.join(root, file)
genre, _ = os.path.splitext(file)
genre = genre.split('.')[0]
data.append((file, genre))
print(data)
# Convert to DataFrame
df = pd.DataFrame(data, columns=['file_path', 'label'])
# Mapping of labels to indices
label_to_index = {label: idx for idx, label in enumerate(sorted(df['label'].unique()))}
index_to_label = {idx: label for label, idx in label_to_index.items()}
# Convert labels to indices
df['label'] = df['label'].map(label_to_index)
# print("Genre to int mapping:")
# print(label_to_index)
class AudioUtil():
@staticmethod
def open(audio_file):
sig, sr = torchaudio.load(str(audio_file))
return (sig, sr)
@staticmethod
def rechannel(aud, new_channel):
sig, sr = aud
if sig.shape[0] == new_channel:
return aud
if new_channel == 1:
sig = sig.mean(dim=0, keepdim=True)
else:
sig = sig.expand(new_channel, -1)
return (sig, sr)
@staticmethod
def resample(aud, new_sr):
sig, sr = aud
if sr == new_sr:
return aud
num_channels = sig.shape[0]
resig = torchaudio.transforms.Resample(sr, new_sr)(sig[:1, :])
if num_channels > 1:
retwo = torchaudio.transforms.Resample(sr, new_sr)(sig[1:, :])
resig = torch.cat([resig, retwo])
return (resig, new_sr)
@staticmethod
def pad_trunc(aud, max_ms):
sig, sr = aud
num_rows, sig_len = sig.shape
max_len = sr // 1000 * max_ms
if sig_len > max_len:
sig = sig[:, :max_len]
elif sig_len < max_len:
pad_begin_len = random.randint(0, max_len - sig_len)
pad_end_len = max_len - sig_len - pad_begin_len
pad_begin = torch.zeros((num_rows, pad_begin_len))
pad_end = torch.zeros((num_rows, pad_end_len))
sig = torch.cat((pad_begin, sig, pad_end), 1)
return (sig, sr)
@staticmethod
def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):
sig, sr = aud
top_db = 80
sgram = torchaudio.transforms.MelSpectrogram(
sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)
sgram = torchaudio.transforms.AmplitudeToDB(top_db=top_db)(sgram)
return sgram
class GenreDataset(Dataset):
def __init__(self, df, duration=5000, sr=22050, transform=None):
self.df = df
self.duration = duration
self.sr = sr
self.transform = transform
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
file_path = self.df.iloc[idx, 0]
label = self.df.iloc[idx, 1]
# Convert the label to an integer if it's a string
if isinstance(label, str):
label = genre_to_int[label]
# Split the file path to get the file name and extension separately
file_dir, file_name = os.path.split(file_path)
file_name_parts = file_name.split('.')
# Assume the last part is the extension
file_ext = file_name_parts[-1]
# Reconstruct the file path with the correct extension
corrected_file_path = os.path.join(file_dir, '.'.join(file_name_parts[:-1]) + '.' + file_ext)
try:
aud = AudioUtil.open(corrected_file_path)
except Exception as e:
print(f"Error opening file {corrected_file_path}: {e}")
return None, None
if aud is None:
return None, None
aud = AudioUtil.resample(aud, self.sr)
aud = AudioUtil.rechannel(aud, 1)
aud = AudioUtil.pad_trunc(aud, self.duration)
sgram = AudioUtil.spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None)
if self.transform:
sgram = self.transform(sgram)
return sgram, torch.tensor(label, dtype=torch.long)
# Ensure reproducibility
random.seed(42)
df = df.sample(frac=1, random_state=42).reset_index(drop=True)
# Split into train and validation sets (80% train, 20% validation)
train_df, val_df = train_test_split(df, test_size=0.2, stratify=df['label'], random_state=42)
train_dataset = GenreDataset(train_df)
val_dataset = GenreDataset(val_df)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, drop_last=True)
val_loader = DataLoader(val_dataset, batch_size=32, drop_last=True)
class AudioClassifier(nn.Module):
def __init__(self):
super(AudioClassifier, self).__init__()
self.conv1 = nn.Conv2d(1, 8, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(8, 16, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(64 * 4 * 4, 128)
self.fc2 = nn.Linear(128, 10)
self.dropout = nn.Dropout(0.3)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = self.pool(F.relu(self.conv4(x)))
x = x.view(-1, 64 * 4 * 4)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AudioClassifier().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20):
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
if outputs.size(0) != labels.size(0):
print(f"Mismatch in batch sizes: outputs={outputs.size(0)}, labels={labels.size(0)}")
print(f"Outputs shape: {outputs.shape}")
print(f"Labels shape: {labels.shape}")
continue
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
epoch_loss = running_loss / len(train_loader.dataset)
print(f'Epoch {epoch}/{num_epochs - 1}, Loss: {epoch_loss:.4f}')
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in val_loader:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
correct += torch.sum(preds == labels.data)
total += labels.size(0)
val_acc = correct.double() / total
print(f'Validation Accuracy: {val_acc:.4f}')
train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20)
When I print the file names, I get
<code>Data/genres_original/blues_00067.wav
</code>
<code>Data/genres_original/blues_00067.wav
</code>
Data/genres_original/blues_00067.wav
and so on instead of just
<code>blues_00067.wav,
</code>
<code>blues_00067.wav,
</code>
blues_00067.wav,
which I think is stopping torchaudio from recognizing the format although they are .wav files. I tried the following to solve this:
<code># Extract labels and file paths
data = []
for root, dirs, files in os.walk(data_dir):
for file in files:
if file.endswith('.wav'):
wav_file = os.path.join(root, file)
genre, _ = os.path.splitext(file)
genre = genre.split('.')[0]
data.append((file, genre))
print(data)
</code>
<code># Extract labels and file paths
data = []
for root, dirs, files in os.walk(data_dir):
for file in files:
if file.endswith('.wav'):
wav_file = os.path.join(root, file)
genre, _ = os.path.splitext(file)
genre = genre.split('.')[0]
data.append((file, genre))
print(data)
</code>
# Extract labels and file paths
data = []
for root, dirs, files in os.walk(data_dir):
for file in files:
if file.endswith('.wav'):
wav_file = os.path.join(root, file)
genre, _ = os.path.splitext(file)
genre = genre.split('.')[0]
data.append((file, genre))
print(data)
This prints [(blues.00067.wav, blues),(country.00054.wav, country)….] which is what I am expecting.
That still did not solve the unrecognized file format issue. So I created the corrected_file_path var in getitem:
<code> def __getitem__(self, idx):
file_path = self.df.iloc[idx, 0]
label = self.df.iloc[idx, 1]
# Convert the label to an integer if it's a string
if isinstance(label, str):
label = genre_to_int[label]
# Split the file path to get the file name and extension separately
file_dir, file_name = os.path.split(file_path)
file_name_parts = file_name.split('.')
# Assume the last part is the extension
file_ext = file_name_parts[-1]
# Reconstruct the file path with the correct extension
corrected_file_path = os.path.join(file_dir, '.'.join(file_name_parts[:-1]) + '.' + file_ext)
try:
aud = AudioUtil.open(corrected_file_path)
except Exception as e:
print(f"Error opening file {corrected_file_path}: {e}")
return None, None
if aud is None:
return None, None
aud = AudioUtil.resample(aud, self.sr)
aud = AudioUtil.rechannel(aud, 1)
aud = AudioUtil.pad_trunc(aud, self.duration)
sgram = AudioUtil.spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None)
if self.transform:
sgram = self.transform(sgram)
return sgram, torch.tensor(label, dtype=torch.long)
</code>
<code> def __getitem__(self, idx):
file_path = self.df.iloc[idx, 0]
label = self.df.iloc[idx, 1]
# Convert the label to an integer if it's a string
if isinstance(label, str):
label = genre_to_int[label]
# Split the file path to get the file name and extension separately
file_dir, file_name = os.path.split(file_path)
file_name_parts = file_name.split('.')
# Assume the last part is the extension
file_ext = file_name_parts[-1]
# Reconstruct the file path with the correct extension
corrected_file_path = os.path.join(file_dir, '.'.join(file_name_parts[:-1]) + '.' + file_ext)
try:
aud = AudioUtil.open(corrected_file_path)
except Exception as e:
print(f"Error opening file {corrected_file_path}: {e}")
return None, None
if aud is None:
return None, None
aud = AudioUtil.resample(aud, self.sr)
aud = AudioUtil.rechannel(aud, 1)
aud = AudioUtil.pad_trunc(aud, self.duration)
sgram = AudioUtil.spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None)
if self.transform:
sgram = self.transform(sgram)
return sgram, torch.tensor(label, dtype=torch.long)
</code>
def __getitem__(self, idx):
file_path = self.df.iloc[idx, 0]
label = self.df.iloc[idx, 1]
# Convert the label to an integer if it's a string
if isinstance(label, str):
label = genre_to_int[label]
# Split the file path to get the file name and extension separately
file_dir, file_name = os.path.split(file_path)
file_name_parts = file_name.split('.')
# Assume the last part is the extension
file_ext = file_name_parts[-1]
# Reconstruct the file path with the correct extension
corrected_file_path = os.path.join(file_dir, '.'.join(file_name_parts[:-1]) + '.' + file_ext)
try:
aud = AudioUtil.open(corrected_file_path)
except Exception as e:
print(f"Error opening file {corrected_file_path}: {e}")
return None, None
if aud is None:
return None, None
aud = AudioUtil.resample(aud, self.sr)
aud = AudioUtil.rechannel(aud, 1)
aud = AudioUtil.pad_trunc(aud, self.duration)
sgram = AudioUtil.spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None)
if self.transform:
sgram = self.transform(sgram)
return sgram, torch.tensor(label, dtype=torch.long)
But that did not fix it either. I reinstalled soundfile too. Here is the exact error:
<code>Error opening file disco.00069.wav: Error opening 'disco.00069.wav': System error.
Error opening file country.00053.wav: Error opening 'country.00053.wav': System error.
Error opening file reggae.00050.wav: Error opening 'reggae.00050.wav': System error.
Error opening file reggae.00095.wav: Error opening 'reggae.00095.wav': System error.
Error opening file metal.00057.wav: Error opening 'metal.00057.wav': System error.
(and so on for every file)
</code>
<code>Error opening file disco.00069.wav: Error opening 'disco.00069.wav': System error.
Error opening file country.00053.wav: Error opening 'country.00053.wav': System error.
Error opening file reggae.00050.wav: Error opening 'reggae.00050.wav': System error.
Error opening file reggae.00095.wav: Error opening 'reggae.00095.wav': System error.
Error opening file metal.00057.wav: Error opening 'metal.00057.wav': System error.
(and so on for every file)
</code>
Error opening file disco.00069.wav: Error opening 'disco.00069.wav': System error.
Error opening file country.00053.wav: Error opening 'country.00053.wav': System error.
Error opening file reggae.00050.wav: Error opening 'reggae.00050.wav': System error.
Error opening file reggae.00095.wav: Error opening 'reggae.00095.wav': System error.
Error opening file metal.00057.wav: Error opening 'metal.00057.wav': System error.
(and so on for every file)
This leads to another error:
<code>Traceback (most recent call last):
File "HIDDEN_PATH/music.py", line 212, in <module>
train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20)
File "HIDDEN_PATH/music.py", line 182, in train_model
for inputs, labels in train_loader:
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 675, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
return self.collate_fn(data)
^^^^^^^^^^^^^^^^^^^^^
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 316, in default_collate
return collate(batch, collate_fn_map=default_collate_fn_map)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 173, in collate
return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 173, in <listcomp>
return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 191, in collate
raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>
</code>
<code>Traceback (most recent call last):
File "HIDDEN_PATH/music.py", line 212, in <module>
train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20)
File "HIDDEN_PATH/music.py", line 182, in train_model
for inputs, labels in train_loader:
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 675, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
return self.collate_fn(data)
^^^^^^^^^^^^^^^^^^^^^
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 316, in default_collate
return collate(batch, collate_fn_map=default_collate_fn_map)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 173, in collate
return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 173, in <listcomp>
return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 191, in collate
raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>
</code>
Traceback (most recent call last):
File "HIDDEN_PATH/music.py", line 212, in <module>
train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=20)
File "HIDDEN_PATH/music.py", line 182, in train_model
for inputs, labels in train_loader:
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/dataloader.py", line 675, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 54, in fetch
return self.collate_fn(data)
^^^^^^^^^^^^^^^^^^^^^
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 316, in default_collate
return collate(batch, collate_fn_map=default_collate_fn_map)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 173, in collate
return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 173, in <listcomp>
return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "HIDDEN_PATH/anaconda3/lib/python3.11/site-packages/torch/utils/data/_utils/collate.py", line 191, in collate
raise TypeError(default_collate_err_msg_format.format(elem_type))
TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'NoneType'>
Any help is much appreciated!