High-Fidelity Singing Voice Generation via a Novel Neural Vocoder with Accelerated Training

핵심 개념

InstructSing, a novel neural vocoder, can generate high-quality 48kHz singing voices while converging much faster compared to other state-of-the-art neural vocoders.

초록

The paper proposes a novel neural vocoder called InstructSing for singing voice synthesis (SVS) tasks. InstructSing aims to achieve a balanced trade-off between training time and high-quality voice generation.

The key components of InstructSing are:

1. InstructNet: A DDSP-based module that generates an 8kHz harmonic and noise sequence as an instructive signal to guide the training of the subsequent modules.

2. BridgeNet: A UNet-based module that refines the harmonic and noise sequences from InstructNet into a latent variable sequence containing enriched periodic and aperiodic information.

3. Extended WaveNet (ExWaveNet): Responsible for generating the final 48kHz high-fidelity singing voice waveform using the mel-spectrogram and the latent variable sequence from BridgeNet.

4. Multi-Period Discriminator (MPD) and Multi-Resolution Multi-Band STFT Discriminator (MR-MBSD): Two discriminators that enhance the audio quality through adversarial training.

The authors show that InstructSing can converge within 20,000 training steps, which is only one-tenth of the training steps required by other strong baseline systems. InstructSing also outperforms the baselines in both training speed and voice quality, while maintaining acceptable inference speed.

통계

InstructSing can converge within 20,000 training steps, which is only one-tenth of the training steps required by other strong baseline systems.
InstructSing achieves the highest Mean Opinion Score (MOS) among all evaluated vocoders, including the ground truth.
InstructSing has the smallest MOS difference between seen and unseen singers, indicating better generalization ability.
InstructSing has the best STOI and PESQ scores compared to other vocoders.

인용구

"InstructSing offers several advantages in achieving a balance between training time and high-quality voices."
"The DDSP-based InstructNet, serving as one of the generator components, can generate harmonic and noise sequences. These sequences not only produce low-resolution 8kHz audio as instructive signals to accelerate model convergence but also provide enriched periodic and aperiodic knowledge as guidance."

핵심 통찰 요약

InstructSing: High-Fidelity Singing Voice Generation via Instructing Yourself

by Chang Zeng, ... 게시일 arxiv.org 09-11-2024

https://arxiv.org/pdf/2409.06330.pdf

더 깊은 질문

How can the inference speed of InstructSing be further optimized for deployment on CPU-only machines?

To optimize the inference speed of InstructSing for deployment on CPU-only machines, several strategies can be employed:

Model Quantization: Reducing the precision of the model weights from floating-point to lower bit representations (e.g., INT8) can significantly decrease the model size and improve inference speed without a substantial loss in audio quality. This technique is particularly effective for neural networks and can be implemented using frameworks like TensorFlow Lite or PyTorch's quantization tools.

Pruning: This involves removing less significant weights from the model, which can lead to a sparser network that requires fewer computations during inference. Techniques such as weight pruning or neuron pruning can be applied to the InstructSing architecture to enhance efficiency.

Optimized Libraries: Utilizing optimized libraries such as Intel MKL-DNN or ONNX Runtime can leverage CPU-specific optimizations, including multi-threading and SIMD (Single Instruction, Multiple Data) operations, to accelerate inference.

Batch Processing: Implementing batch processing during inference can improve throughput by allowing multiple audio samples to be processed simultaneously. This is particularly useful in scenarios where multiple audio generations are required in real-time.

Model Distillation: Training a smaller, more efficient model (student model) to replicate the performance of the larger InstructSing model (teacher model) can lead to faster inference times. This process involves transferring knowledge from the teacher to the student, resulting in a compact model that retains much of the original's performance.

Asynchronous Processing: Implementing asynchronous processing techniques can help in overlapping computation and I/O operations, thus improving the overall responsiveness of the system when generating audio.

By integrating these strategies, the inference speed of InstructSing can be significantly enhanced, making it more suitable for deployment in environments with limited computational resources, such as CPU-only machines.

What are the potential limitations or drawbacks of the proposed approach, and how could they be addressed in future work?

While InstructSing presents a novel approach to high-fidelity singing voice synthesis, several limitations and drawbacks can be identified:

Dependency on High-Quality Data: The performance of InstructSing heavily relies on the availability of extensive high-quality singing data for training. In scenarios where such data is scarce, the model may not generalize well. Future work could explore data augmentation techniques or semi-supervised learning to enhance the model's robustness in low-data environments.

Generalization to Diverse Vocal Styles: Although InstructSing shows promise in generating high-fidelity voices, it may struggle with diverse vocal styles or accents not represented in the training dataset. To address this, future research could focus on incorporating multi-singer training datasets or employing transfer learning techniques to adapt the model to new vocal characteristics.

Real-Time Performance: While the model achieves acceptable inference speeds, real-time performance may still be a challenge in certain applications. Future iterations could prioritize optimizing the architecture further or exploring lightweight alternatives that maintain quality while enhancing speed.

Complexity of the Model: The architecture of InstructSing, which combines multiple components (InstructNet, BridgeNet, and ExWaveNet), may introduce complexity in deployment and maintenance. Simplifying the model or modularizing components for easier updates and improvements could be beneficial.

Ethical Considerations: The potential for misuse of voice synthesis technology raises ethical concerns, particularly regarding consent and the creation of deepfakes. Future work should emphasize developing guidelines and safeguards to ensure responsible use of the technology, including watermarking synthesized audio to indicate its artificial nature.

By addressing these limitations through targeted research and development, the InstructSing framework can be further refined and made more versatile for a broader range of applications.

How can the InstructSing framework be extended or adapted to other audio generation tasks beyond singing voice synthesis?

The InstructSing framework, with its innovative architecture and training methodologies, can be extended or adapted to various audio generation tasks beyond singing voice synthesis in several ways:

Speech Synthesis: The principles of InstructSing can be applied to text-to-speech (TTS) systems. By modifying the input features to include phonemes and linguistic information, the framework can generate natural-sounding speech. The adversarial training approach can help improve the quality and expressiveness of the synthesized speech.

Music Generation: InstructSing can be adapted for generating instrumental music by incorporating MIDI data as input features. The model can learn to synthesize various musical instruments, leveraging the harmonic-plus-noise approach to create rich and complex audio textures.

Sound Effects Generation: The framework can be utilized to generate sound effects for multimedia applications, such as video games or films. By training on diverse sound effect datasets, InstructSing can learn to produce realistic environmental sounds, impacts, and other audio cues.

Audio Restoration: The architecture can be repurposed for audio restoration tasks, such as denoising or inpainting missing audio segments. By training the model on degraded audio samples, it can learn to reconstruct high-quality audio from noisy or incomplete inputs.

Interactive Audio Applications: InstructSing can be integrated into interactive applications, such as virtual reality (VR) or augmented reality (AR), where real-time audio generation is crucial. The framework can be adapted to respond to user inputs dynamically, generating audio that enhances the immersive experience.

Cross-Modal Audio Generation: The framework can be extended to generate audio from visual inputs, such as generating sound effects based on video content. This cross-modal approach can open new avenues for creative applications in multimedia content creation.

By leveraging the core components of InstructSing and adapting them to these diverse audio generation tasks, the framework can significantly contribute to advancements in various fields, enhancing the quality and creativity of audio content across applications.