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!