Input: Text (e.g., "the quick brown fox") transformed into tokens.
Output: Soundwave generated by the model.
Importance of Tokens: Text is split into tokens, which are input into the neural network.
Use of Phones (Phonemes): Characters representing sounds; helpful in generating accurate audio output.
Comparison: Generated soundwaves are compared to actual soundwaves using Mean Squared Error (MSE) to improve the model.
Model Training: Backpropagation of the calculated loss to update the neural network weights.
Model Approaches
1. Transformer
Structure: Multi-layer network with attention mechanisms and multi-layer perceptrons.
Purpose: Generate audio for a given text input segment.
2. Diffuser
Process: Start with a noisy soundwave and gradually reduce noise using neural networks until clean sound is achieved.
Training: Predict noise from clean sound at various noise levels and reduce it iteratively.
3. Generative Adversarial Networks (GANs)
Components: Generator (creates sounds) and Discriminator (differentiates between real and fake sounds).
Training: Improves generator by making it create sounds that the discriminator can't distinguish from real sounds.
Voice Cloning vs Fine-Tuning
Voice Cloning: Use a reference snippet to generate an output; effective if the accent is within the training dataset.
Fine-Tuning: Update model parameters using new data; necessary for unique accents or out-of-dataset voices.
Generating High-Quality Voice Clones
Data Requirements: High-quality audio (ideally 48 kHz, minimal noise), segmented into appropriate lengths (max 512 tokens).
Importance of Alignment: Ensure text and audio are aligned at the phoneme level.
Segmenting Audio: Prune segments at phoneme boundaries, add padding, and avoid overlaps.
Training Data Preparation
Transcription Tools: Whisper X for transcription and phoneme level alignment.
Tokenization: Convert text to tokens that the model can process.
Public Datasets: Uploading datasets to Hugging Face Hub for training.
Fine-Tuning Models
VRAM Requirements: Minimum 48GB, preferably 80GB.
Training Parameters: Setting the number of epochs, batch size, max length of frames, and other hyperparameters.
Model Parameters: Control diffusion and adversarial network training phases.
Scripts for Fine-Tuning: Utilize pre-existing repositories and run fine-tuning scripts in Jupyter notebooks.
Monitoring Training: Use tools like Weights and Biases for logging and visualization of training metrics.
Model Testing: Evaluate the model performance by generating audio and comparing it to reference snippets.
Key Points for Effective TTS Models
Duration of Training: Better results with more data and epochs (e.g., 2 hours of data, higher epochs).
Out-of-Distribution Data: Select appropriate datasets for adversarial training to ensure model generalization.
Optimize VRAM Usage: Balance batch size and efficiency; explore options like gradient accumulation for larger virtual batches.
Conclusion
Essential Tips: Use sufficient and appropriate data, carefully tune hyperparameters, and utilize advanced training setups and monitoring tools to achieve high-quality voice cloning.
Resources: Links to relevant papers, repositories, and tools for practical implementation.