Where to Get Data for Machine Learning and Deep Learning Model Creation

1. Primary Data Sources for Machine Learning and Deep Learning

2. Data Collection Techniques for Custom and Specialized Models

3. Data Types by Model Requirements

4. How Data is Categorized on the Internet

5. Best Practices for Data Collection and Usage

Creating a Simple Deep Learning Model for Voice Tone Change Using PyTorch

Step 1: Collecting and Preprocessing Audio Data

For this example, we use voice recordings from datasets like LibriSpeech or Mozilla’s Common Voice. After downloading the audio data, we convert it to spectrograms using Librosa, making it suitable for our CNN model.

import librosa
import torch
import numpy as np

# Load an audio sample
audio_path = 'path_to_audio_file.wav'
audio_data, sample_rate = librosa.load(audio_path, sr=16000)

# Convert to a Mel-spectrogram
spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sample_rate, n_mels=128)
log_spectrogram = librosa.power_to_db(spectrogram, ref=np.max)

# Convert to torch tensor for model input
input_data = torch.tensor(log_spectrogram, dtype=torch.float32).unsqueeze(0).unsqueeze(0)

Step 2: Building a Simple CNN for Voice Tone Change

We use a simple Convolutional Neural Network (CNN) model to process spectrograms. The model adjusts audio properties based on patterns in pitch and frequency.

import torch.nn as nn

class SimpleToneChangeModel(nn.Module):
    def __init__(self):
        super(SimpleToneChangeModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(32 * 128 * 128, 128)
        self.fc2 = nn.Linear(128, 128)

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.relu(self.conv2(x))
        x = x.view(x.size(0), -1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Instantiate the model
model = SimpleToneChangeModel()

Step 3: Training the Model

For training, we define a loss function and optimizer. The model learns by comparing original and pitch-shifted spectrograms, adjusting its weights to alter audio pitch effectively.

import torch.optim as optim

# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Example dataset class for audio spectrograms
class AudioDataset(torch.utils.data.Dataset):
    def __init__(self, audio_data_list):
        self.audio_data_list = audio_data_list

    def __len__(self):
        return len(self.audio_data_list)

    def __getitem__(self, idx):
        original_spectrogram = self.audio_data_list[idx]
        pitch_shifted_audio = librosa.effects.pitch_shift(original_spectrogram, sr=16000, n_steps=2)
        target_spectrogram = librosa.feature.melspectrogram(y=pitch_shifted_audio, sr=16000, n_mels=128)
        
        return torch.tensor(original_spectrogram, dtype=torch.float32), torch.tensor(target_spectrogram, dtype=torch.float32)

# DataLoader setup
audio_data = [input_data]
audio_dataset = AudioDataset(audio_data)
data_loader = torch.utils.data.DataLoader(audio_dataset, batch_size=1, shuffle=True)

# Training loop
for epoch in range(10):
    for inputs, targets in data_loader:
        inputs = inputs.unsqueeze(1)
        targets = targets.unsqueeze(1)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
    
    print(f"Epoch {epoch + 1}, Loss: {loss.item()}")

Step 4: Testing the Model

After training, we use the model to alter the tone of a new audio sample, creating a pitch-shifted version of the input audio.

with torch.no_grad():
    test_audio, _ = librosa.load('test_audio.wav', sr=16000)
    test_spectrogram = librosa.feature.melspectrogram(y=test_audio, sr=16000, n_mels=128)
    test_input = torch.tensor(test_spectrogram, dtype=torch.float32).unsqueeze(0).unsqueeze(0)

    output_spectrogram = model(test_input).squeeze().numpy()
    output_audio = librosa.feature.inverse.mel_to_audio(output_spectrogram)

# Save or play the modified audio
librosa.output.write_wav('output_audio.wav', output_audio, sr=16000)

Summary

In this example, we covered data gathering, model selection, training, and testing to create a simple tone-changing model. This basic CNN model can be expanded for more sophisticated audio processing tasks by increasing its complexity or adding recurrent layers.

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