SynthVC: Leveraging Synthetic Data for End-to-End Low Latency Streaming Voice Conversion
Abstract
Voice Conversion (VC) aims to modify a speaker's timbre while preserving linguistic content. While recent VC models achieve strong performance, most struggle in real-time streaming scenarios due to high latency, dependence on ASR modules, or complex speaker disentanglement, which often results in timbre leakage or degraded naturalness. We present SynthVC, a streaming end-to-end VC framework that directly learns speaker timbre transformation from synthetic parallel data generated by a pre-trained zero-shot VC model. This design eliminates the need for explicit content-speaker separation or recognition modules. Built upon a neural audio codec architecture, SynthVC supports low-latency streaming inference with high output fidelity. Experimental results show that SynthVC outperforms baseline streaming VC systems in both naturalness and speaker similarity, achieving an end-to-end latency of just 77.1 ms.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
SynthVC represents a notable advancement in the field of voice conversion by effectively addressing the challenges of real-time processing and speaker timbre transformation through innovative use of synthetic data and neural audio codecs. The comprehensive methodology and robust experimental validation highlight its potential impact on both academic research and practical applications in audio processing.
Methodology
The methodology presented in SynthVC is innovative in its approach to voice conversion by leveraging synthetic data generated from a pre-trained zero-shot VC model. This circumvents the need for traditional ASR models and disentanglement strategies, which are often prone to latency issues and timbre leakage. The architecture is built on a neural audio codec, allowing for low-latency streaming while maintaining high fidelity. The introduction of a dedicated speaker transformation module in the latent space is a significant improvement over previous methods, enhancing the model's ability to capture speaker-specific characteristics without compromising audio quality.
Results & evaluation
The experimental evaluation is thorough, utilizing both subjective and objective metrics to assess the performance of SynthVC against established baselines. The results demonstrate that SynthVC outperforms other models in terms of naturalness and speaker similarity, achieving a competitive end-to-end latency of 77.1 ms. The use of a diverse dataset and the implementation of a two-stage training strategy further bolster the reliability of the findings. However, the paper could benefit from a more extensive discussion on the statistical significance of the results.
Reproducibility
The paper provides sufficient details about the training configurations, datasets, and evaluation metrics, which are crucial for reproducibility. The use of open-source models like Seed-VC as a data generator is a positive aspect, as it allows other researchers to replicate the synthetic data generation process. However, the specific hyperparameters and training settings could be more explicitly detailed to facilitate exact replication.
Limitations
One limitation of the study is the reliance on synthetic data, which may not fully capture the complexities of real-world voice conversion scenarios. Additionally, while the model shows promise in terms of latency and quality, the subjective evaluation scores, particularly for the smaller models, suggest that there may still be trade-offs in performance that need to be addressed. The paper does not explore the potential impact of different languages or dialects on the model's performance, which could be an important consideration for broader applications.
Broader impact
The implications of SynthVC are significant, particularly in real-time applications such as live broadcasting, video conferencing, and interactive voice response systems. The ability to convert voices with low latency while maintaining high fidelity opens up new possibilities in entertainment, accessibility, and privacy. Moreover, the approach could inspire further research into the use of synthetic data in other areas of machine learning, potentially leading to advancements in various domains. SynthVC represents a notable advancement in the field of voice conversion by effectively addressing the challenges of real-time processing and speaker timbre transformation through innovative use of synthetic data and neural audio codecs. The comprehensive methodology and robust experimental validation highlight its potential impact on both academic research and practical applications in audio processing.
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