CosyEdit: Unlocking End-to-End Speech Editing Capability from Zero-Shot Text-to-Speech Models
Abstract
Automatic speech editing aims to modify spoken content based on textual instructions, yet traditional cascade systems suffer from complex preprocessing pipelines and a reliance on explicit external temporal alignment. Addressing these limitations, we propose CosyEdit, an end-to-end speech editing model adapted from CosyVoice through task-specific fine-tuning and an optimized inference procedure, which internalizes speech-text alignment while ensuring high consistency between the speech before and after editing. By fine-tuning on only 250 hours of supervised data from our curated GigaEdit dataset, our 400M-parameter model achieves reliable speech editing performance. Experiments on the RealEdit benchmark indicate that CosyEdit not only outperforms several billion-parameter language model baselines but also matches the performance of state-of-the-art cascade approaches. These results demonstrate that, with task-specific fine-tuning and inference optimization, robust and efficient speech editing capabilities can be unlocked from a zero-shot TTS model, yielding a novel and cost-effective end-to-end solution for high-quality speech editing.
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
The main contribution of this paper is the introduction of CosyEdit, an end-to-end speech editing model that effectively utilizes a zero-shot TTS model to achieve high-quality speech editing with minimal fine-tuning. This work represents a significant advancement in the field of speech processing, addressing key challenges in speech editing while demonstrating robust performance against existing methods.
Methodology
The methodology presented in CosyEdit is well-structured, focusing on adapting a zero-shot TTS model for speech editing through task-specific fine-tuning and optimized inference strategies. The authors propose a novel post-training strategy that constructs a supervised dataset from existing speech corpora, showcasing a clear understanding of the challenges in speech editing. The approach effectively internalizes speech-text alignment, which is a significant advancement over traditional cascade systems that rely on external alignment tools. The combination of autoregressive and non-autoregressive models to enhance speech editing capabilities is innovative and demonstrates a thoughtful integration of different modeling techniques.
Results & evaluation
The experimental evaluation is comprehensive, utilizing the RealEdit benchmark to assess the performance of CosyEdit against various baseline models. The results indicate that CosyEdit outperforms several billion-parameter models and matches the performance of state-of-the-art cascade approaches, which is a strong testament to its effectiveness. The use of both objective and subjective metrics, including WER, SpkSIM, and MOS, provides a well-rounded assessment of the model's capabilities. The ablation studies further enhance the understanding of the contributions of different components of the model.
Reproducibility
The paper provides sufficient details regarding the training process, including the dataset used and the training parameters, which aids in reproducibility. However, the absence of a demo or project URL limits the ability for others to directly access the implementation. Clear descriptions of the experimental setup and evaluation metrics enhance the reproducibility of the findings.
Limitations
One limitation of the study is the reliance on a relatively small dataset (250 hours) for fine-tuning, which may affect the generalizability of the model to more diverse speech editing tasks. Additionally, while the results are promising, further exploration of the model's performance in real-world scenarios and with more complex editing tasks would be beneficial. The potential for misuse in generating deepfakes is also acknowledged, suggesting a need for responsible deployment.
Broader impact
The implications of this research are significant, particularly in fields such as multimedia production, intelligent contact centers, and speech data augmentation. By enabling efficient and high-quality speech editing, CosyEdit could enhance the capabilities of various applications that rely on speech processing. The authors also mention plans for open-sourcing their code and datasets, which could foster further research and development in this area. The main contribution of this paper is the introduction of CosyEdit, an end-to-end speech editing model that effectively utilizes a zero-shot TTS model to achieve high-quality speech editing with minimal fine-tuning. This work represents a significant advancement in the field of speech processing, addressing key challenges in speech editing while demonstrating robust performance against existing methods.
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