Flamed-TTS: Flow Matching Attention-Free Models for Efficient Generating and Dynamic Pacing Zero-shot Text-to-Speech
Abstract
Zero-shot Text-to-Speech (TTS) has recently advanced significantly, enabling models to synthesize speech from text using short, limited-context prompts. These prompts serve as voice exemplars, allowing the model to mimic speaker identity, prosody, and other traits without extensive speaker-specific data. Although recent approaches incorporating language models, diffusion, and flow matching have proven their effectiveness in zero-shot TTS, they still encounter challenges such as unreliable synthesis caused by token repetition or unexpected content transfer, along with slow inference and substantial computational overhead. Moreover, temporal diversity-crucial for enhancing the naturalness of synthesized speech-remains largely underexplored. To address these challenges, we propose Flamed-TTS, a novel zero-shot TTS framework that emphasizes low computational cost, low latency, and high speech fidelity alongside rich temporal diversity. To achieve this, we reformulate the flow matching training paradigm and incorporate both discrete and continuous representations corresponding to different attributes of speech. Experimental results demonstrate that Flamed-TTS surpasses state-of-the-art models in terms of intelligibility, naturalness, speaker similarity, acoustic characteristics preservation, and dynamic pace. Notably, Flamed-TTS achieves the best WER of 4% compared to the leading zero-shot TTS baselines, while maintaining low latency in inference and high fidelity in generated speech. Code and audio samples are available at our demo page https://flamed-tts.github.io.
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 Flamed-TTS, a novel zero-shot TTS framework that effectively addresses challenges in speech synthesis while maintaining high fidelity and low computational costs. This work represents a meaningful step forward in the field of audio machine learning, particularly in enhancing the capabilities of TTS systems.
Methodology
The proposed Flamed-TTS framework innovatively reformulates the flow matching training paradigm by integrating both discrete and continuous representations to enhance the synthesis of speech attributes. This approach addresses key challenges in zero-shot TTS, such as token repetition and content transfer issues, while also focusing on computational efficiency and temporal diversity. The methodology appears well-structured, leveraging existing techniques while introducing novel elements that contribute to the overall effectiveness of the model.
Results & evaluation
The experimental section is robust, benchmarking Flamed-TTS against several state-of-the-art zero-shot TTS systems. The metrics used for evaluation, including WER, RTF, and speaker similarity, are comprehensive and relevant for assessing the performance of TTS systems. The reported results indicate that Flamed-TTS outperforms existing models across multiple dimensions, which strengthens the claims of the paper. However, the paper could benefit from more detailed descriptions of the datasets used for training and evaluation.
Reproducibility
The paper provides sufficient implementation details and mentions the use of publicly available models and datasets for benchmarking, which aids in reproducibility. However, the lack of specific institutional affiliations and citations may hinder others from fully replicating the study without additional context.
Limitations
While the paper presents significant advancements, it does not thoroughly address potential limitations such as the generalizability of the model across diverse languages or dialects, or the scalability of the approach to larger datasets. Additionally, the reliance on specific hardware for performance metrics may limit the applicability of results in different environments.
Broader impact
The advancements in zero-shot TTS have the potential to significantly impact various applications, including virtual assistants, gaming, and content creation, where personalized and natural-sounding speech synthesis is crucial. The focus on low latency and high fidelity makes this work particularly relevant in real-time applications, enhancing user experience. The main contribution of this paper is the introduction of Flamed-TTS, a novel zero-shot TTS framework that effectively addresses challenges in speech synthesis while maintaining high fidelity and low computational costs. This work represents a meaningful step forward in the field of audio machine learning, particularly in enhancing the capabilities of TTS systems.
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