Creating a custom ML model for integration into a Blender add-on
Creating a custom ML model for integration into a Blender add-on is a comprehensive process. Below are detailed step-by-step instructions, starting from installation to deployment and integration.
1. Environment Setup
- Install Required Tools
- Python: Install Python (>=3.8). Recommended version: Python 3.9.
- Libraries: Install key libraries:
pip install numpy pandas matplotlib torch torchvision tensorflow librosa phonemizer
Install Blender's Python Dependencies
Blender uses its own Python version. Add required libraries:
1. Locate Blender's Python path:
Example: C:\Program Files\Blender Foundation\Blender 3.x\3.x\python\bin
2. Open a terminal in this directory:
./python.exe -m ensurepip
./python.exe -m pip install numpy pandas torch librosa phonemizer
Install Jupyter Notebook (Optional)
For an interactive data preparation and training environment:
pip install notebook
2. Collect Data
Dataset Example
For lip-syncing, use datasets like:
LibriSpeech: Speech audio with transcripts.
TIMIT: Phoneme-labeled audio.
CMU Pronouncing Dictionary: Phoneme mapping for English words.
Download Dataset
Example: Download LibriSpeech:
wget http://www.openslr.org/resources/12/train-clean-100.tar.gz
tar -xvzf train-clean-100.tar.gz
Organize Dataset
Audio files (.wav or .mp3).
Phoneme labels (.txt or .json):
example.wav: ["A", "M", "E"]
3. Preprocess Data
Convert raw audio and labels into a numerical format.
Extract Features from Audio
Use librosa to extract MFCCs (Mel Frequency Cepstral Coefficients):
import librosa
import numpy as np
def extract_features(audio_path):
audio, sr = librosa.load(audio_path, sr=16000) # Load audio at 16kHz
mfccs = librosa.feature.mfcc(audio, sr=sr, n_mfcc=13) # Extract 13 MFCCs
return np.mean(mfccs.T, axis=0)
features = extract_features("example.wav")
print(features)
Prepare Dataset
Organize features and labels:
import os
import json
def prepare_dataset(audio_dir, label_file):
dataset = []
with open(label_file, 'r') as file:
labels = json.load(file)
for audio_file, phonemes in labels.items():
features = extract_features(os.path.join(audio_dir, audio_file))
dataset.append((features, phonemes))
return dataset
# Example usage
dataset = prepare_dataset("audio_files/", "labels.json")
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4. Train Model
Use PyTorch for training:
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
class PhonemeDataset(Dataset):
def __init__(self, dataset):
self.dataset = dataset
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
features, label = self.dataset[idx]
return torch.tensor(features, dtype=torch.float32), torch.tensor(label, dtype=torch.long)
# Define model
class LipSyncModel(nn.Module):
def __init__(self, input_size, output_size):
super(LipSyncModel, self).__init__()
self.fc1 = nn.Linear(input_size, 128)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(128, output_size)
def forward(self, x):
x = self.relu(self.fc1(x))
return self.fc2(x)
# Prepare data
train_dataset = PhonemeDataset(dataset)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
# Train model
model = LipSyncModel(input_size=13, output_size=10) # Adjust output size to number of phonemes
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(10): # Number of epochs
for features, labels in train_loader:
optimizer.zero_grad()
outputs = model(features)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print(f"Epoch {epoch+1}, Loss: {loss.item()}")
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5. Evaluate Model
Evaluate the model on a test set:
test_loss = 0
correct = 0
total = 0
model.eval()
with torch.no_grad():
for features, labels in test_loader:
outputs = model(features)
loss = criterion(outputs, labels)
test_loss += loss.item()
_, predicted = torch.max(outputs, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f"Test Loss: {test_loss / len(test_loader)}, Accuracy: {100 * correct / total}%")
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6. Save the Model
Save the trained model:
torch.save(model.state_dict(), "lip_sync_model.pth")
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7. Deploy Model in Blender Add-on
Load the Model
In your Blender add-on:
import torch
from your_model_file import LipSyncModel
model = LipSyncModel(input_size=13, output_size=10)
model.load_state_dict(torch.load("lip_sync_model.pth"))
model.eval()
Integrate with Blender
Add a function to analyze audio and generate phoneme timings:
def predict_phonemes(audio_path):
features = extract_features(audio_path)
features = torch.tensor(features, dtype=torch.float32).unsqueeze(0)
with torch.no_grad():
outputs = model(features)
phoneme = torch.argmax(outputs, dim=1).item()
return phoneme
Use the predicted phoneme data to keyframe Blender shape keys.
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8. Automate Integration
Add UI in Blender for users to select audio, predict phonemes, and apply shape keys. Update your lip_sync_operator.py:
class LipSyncOperator(bpy.types.Operator):
bl_idname = "object.lip_sync"
bl_label = "Lip Sync with Audio"
def execute(self, context):
audio_path = context.scene.audio_file_path
phonemes = predict_phonemes(audio_path)
# Apply phoneme data as shape key animations in Blender
for phoneme, timings in phonemes:
# Add keyframes to shape keys
pass
return {'FINISHED'}
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9. Test and Debug
Test the add-on with different audio inputs.
Debug and fine-tune phoneme detection and shape key animations.
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10. Package and
Distribute
Zip the add-on folder.
Share it on Blender Market, GitHub, or other platforms.
This workflow will help you create a fully functional custom ML model and integrate it seamlessly into Blender. Let me know if you'd like help with any specific part!
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