- The paper introduces a novel toolkit that integrates pretrained audio models with both continuous and discrete representations for improved SVS performance.
- The methodology employs joint training and fine-tuning of acoustic models and vocoders, supported by automatic error correction and dynamic batching.
- Evaluation metrics such as MCD, F0_RMSE, SA, and VUV_E demonstrate the toolkit's capability for high-quality and efficient singing voice synthesis.
Muskits-ESPnet: Enhancing Singing Voice Synthesis with Pretrained Audio Models
The paper "Muskits-ESPnet: A Comprehensive Toolkit for Singing Voice Synthesis in New Paradigm" presents an advanced toolkit aiming to streamline and enhance the Singing Voice Synthesis (SVS) pipeline. This research primarily focuses on leveraging pretrained audio models and exploring both continuous and discrete representations to overcome the existing challenges in SVS.
Introduction and Motivation
Singing Voice Synthesis entails converting music scores into vocal singing that aligns with a specified singer's voice. The task demands precision in lyrics, pitch, and duration while maintaining a realistic and expressive sound. Conventional methods primarily rely on acoustic models to predict feature representations from music scores, followed by vocoders to reconstruct audio. However, these methods often struggle with achieving high standards in pitch accuracy, prosody, and emotional expression due to complex data processing needs.
New Paradigms in SVS
The advent of audio pretraining and discrete representations in large models has introduced new possibilities for SVS. The authors propose two main advancements in this area:
- Enhancing Continuous Representations:
- The paper integrates pretrained audio model features with traditional continuous representations to improve SVS performance. Specifically, hidden embeddings from SSL models trained on extensive corpora are utilized alongside or as substitutes for mel spectrograms. For instance, the new SVS model based on the Variational Auto-Encoder architecture demonstrates superior performance with joint encoding.
- Exploring Discrete Representations:
- The research emphasizes the use of discrete representations derived from audio pretrained models. Semantic tokens from clustering SSL model outputs and acoustic tokens from audio codecs offer efficient data discretization. Incorporating these within the ESPnet framework, the authors developed SVS models that achieve lower spatial costs while maintaining high-quality audio synthesis.
Implementation and Workflow
The paper details the data flow and implementation steps of Muskits-ESPnet, emphasizing its versatility and intelligence. The toolkit supports various file formats and includes modules for automatic error detection and correction, enhancing the accuracy and efficiency of data processing workflows.
- Data Preparation:
- Raw music data is preprocessed into sequences consisting of lyrics, pitch, and duration. The toolkit addresses annotation errors and implements modules for detecting misalignments and correcting metadata, ensuring consistent and accurate annotations.
- Training and Inference:
- The training and inference procedures align with the ESPnet task processing workflow, supporting multi-GPU training and dynamic batching. Enhancements include joint training and fine-tuning paradigms for acoustic models and vocoders, accommodating both continuous and discrete representations.
- Evaluation:
- The toolkit employs objective metrics like Mel Cepstral Distortion (MCD), F0_RMSE, Semitone Accuracy (SA), and Voiced/Unvoiced Error Rate (VUV_E) to evaluate generated audio. Additionally, an innovative perception auto-evaluation module simulates human subjective evaluations, reducing the need for manual scoring.
Conclusion
Muskits-ESPnet represents a significant advancement in the SVS domain by integrating audio pretraining and discrete representations. The toolkit's comprehensive data preprocessing, error correction, and robust model training capabilities position it as a valuable resource for future SVS developments. Its ability to support state-of-the-art SVS models while optimizing data processing workflows demonstrates its potential to set new standards in the field.
The authors' contribution offers both practical and theoretical implications, paving the way for more efficient and accurate SVS processes. Future research could build upon these advancements to explore further optimization of discrete representations and the integration of more sophisticated evaluation metrics to enhance SVS performance continually.