In this research project, the authors introduce F5-TTS, a fully non-autoregressive text-to-speech system that is based on flow matching with Diffusion Transformer (DiT). Unlike traditional models that require complex designs such as duration models, text encoders, and phoneme alignment, F5-TTS simplifies the process by padding the text input with filler tokens to match the length of the input speech. This denoising technique for speech generation was initially proven feasible by E2 TTS; however, E2 TTS faced challenges with slow convergence and low robustness. To address these issues, the authors first refine the input text representation using ConvNeXt to improve alignment with speech. They also propose an inference-time Sway Sampling strategy to enhance model performance and efficiency. This sampling strategy can be easily applied to existing flow matching-based models without requiring retraining. As a result of these enhancements, F5-TTS achieves faster training and an improved inference RTF of 0.15 compared to state-of-the-art diffusion-based TTS models. Trained on a public 100K hours multilingual dataset, F5-TTS demonstrates highly natural and expressive zero-shot ability, seamless code-switching capability, and efficient speed control. The model produces fluent and faithful speech with Flow matching (F5-TTS), showcasing its versatility and effectiveness in synthesizing high-quality speech outputs. The authors provide demo samples at https://SWivid.github.io/F5-TTS and release all code and checkpoints to encourage further community development. Additionally, the authors acknowledge Tianrui Wang, Xiaofei Wang, Yakun Song, Yifan Yang, Yiwei Guo, and Yunchong Xiao for their valuable discussions during the project. In terms of ethics considerations, given the potential risks associated with misuse of the model such as voice identification spoofing, it is recommended to implement watermarks and develop detection mechanisms for audio outputs. Furthermore, detailed ablation studies were conducted to evaluate F5-TTS's efficiency compared to E2 TTS. Small models were trained on a Mandarin dataset using different configurations to assess alignment learning capabilities. The experiments highlighted the importance of refining input representations for better alignment with speech modalities. Overall, this paper presents a comprehensive analysis of F5-TTS's architecture and performance improvements over existing models in the field of text-to-speech synthesis.
Summary- F5-TTS is a special way to turn written words into spoken words using a system called DiT.
- It makes things easier by adding extra words to the written text so it matches the length of the spoken words, without needing complicated tools like duration models or phoneme alignment.
- The system uses special techniques to make sure it works well and sounds good, even when dealing with challenges like slow learning and not being very strong.
- F5-TTS learns quickly and speaks at a speed of 0.15 times faster than other similar systems, showing off its natural sound, ability to switch between languages smoothly, and control over how fast it talks.
- The system can talk in a clear and accurate way, proving that it is good at making high-quality speech.
Definitions
- Non-autoregressive: A method where each part of a process can happen independently without waiting for previous parts to finish first.
- Diffusion Transformer (DiT): A type of technology used in F5-TTS for converting text into speech by matching patterns smoothly.
- Robustness: How well something can handle difficult situations or changes without breaking down.
- Inference-time: The period when the system processes information and generates output based on what it has learned.
- Zero-shot ability: The skill to perform tasks accurately without prior training or specific examples.
In recent years, there has been a growing interest in developing text-to-speech (TTS) systems that can generate natural and expressive speech outputs. Traditional TTS models often require complex designs such as duration models, text encoders, and phoneme alignment, making the process of synthesizing speech time-consuming and resource-intensive. In response to these challenges, a team of researchers from Southwest University in China have introduced F5-TTS - a fully non-autoregressive TTS system based on flow matching with Diffusion Transformer (DiT).
The paper titled "F5-TTS: A Fully Non-Autoregressive Text-to-Speech System Based on Flow Matching with Diffusion Transformer" presents an innovative approach to TTS synthesis that simplifies the process by padding the input text with filler tokens to match the length of the input speech. This denoising technique was initially proven feasible by E2 TTS; however, it faced challenges with slow convergence and low robustness. To address these issues, the authors first refine the input text representation using ConvNeXt to improve alignment with speech.
One of the key contributions of this research is the proposed inference-time Sway Sampling strategy which enhances model performance and efficiency. This sampling strategy can be easily applied to existing flow matching-based models without requiring retraining. As a result of these enhancements, F5-TTS achieves faster training and an improved inference RTF (real-time factor) of 0.15 compared to state-of-the-art diffusion-based TTS models.
To evaluate F5-TTS's capabilities, the authors trained small models on a Mandarin dataset using different configurations for alignment learning. The experiments highlighted the importance of refining input representations for better alignment with speech modalities.
One notable aspect of F5-TTS is its versatility in handling multilingual datasets. Trained on a public 100K hours multilingual dataset, F5-TTS demonstrates highly natural and expressive zero-shot ability, seamless code-switching capability, and efficient speed control. The model produces fluent and faithful speech outputs, showcasing its effectiveness in synthesizing high-quality speech.
To make their research accessible to the community, the authors have provided demo samples at https://SWivid.github.io/F5-TTS and released all code and checkpoints for further development. They also acknowledge Tianrui Wang, Xiaofei Wang, Yakun Song, Yifan Yang, Yiwei Guo, and Yunchong Xiao for their valuable discussions during the project.
While F5-TTS shows promising results in TTS synthesis, it is important to consider potential ethical implications associated with its use. Given the risk of misuse such as voice identification spoofing, it is recommended to implement watermarks and develop detection mechanisms for audio outputs.
In conclusion, this paper presents a comprehensive analysis of F5-TTS's architecture and performance improvements over existing models in the field of text-to-speech synthesis. Its innovative approach simplifies the process of TTS synthesis while achieving faster training times and improved inference RTF. With its versatility in handling multilingual datasets and producing natural speech outputs with zero-shot ability and code-switching capability, F5-TTS has great potential for real-world applications. The authors' release of demo samples and code encourages further community development in this area.
This paper introduces F5-TTS, a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT). Without requiring complex designs such as duration model, text encoder, and phoneme alignment, the text input is simply padded with filler tokens to the same length as input speech, and then the denoising is performed for speech generation, which was originally proved feasible by E2 TTS. However, the original design of E2 TTS makes it hard to follow due to its slow convergence and low robustness. To address these issues, we first model the input with ConvNeXt to refine the text representation, making it easy to align with the speech. We further propose an inference-time Sway Sampling strategy, which significantly improves our model's performance and efficiency. This sampling strategy for flow step can be easily applied to existing flow matching based models without retraining. Our design allows faster training and achieves an inference RTF of 0.15, which is greatly improved compared to state-of-the-art diffusion-based TTS models. Trained on a public 100K hours multilingual dataset, our Fairytaler Fakes Fluent and Faithful speech with Flow matching (F5-TTS) exhibits highly natural and expressive zero-shot ability, seamless code-switching capability, and speed control efficiency. Demo samples can be found at https://SWivid.github.io/F5-TTS. We release all code and checkpoints to promote community development.
