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Enhancing Real-Time Text-to-Speech Synthesis Efficiency through Consistency Models and Weighted Samplers


Core Concepts
CM-TTS, a novel architecture based on consistency models, achieves high-quality speech synthesis in fewer steps without adversarial training or pre-trained model dependencies. Weighted samplers are introduced to mitigate biases during model training.
Abstract
The paper presents CM-TTS, a novel text-to-speech (TTS) architecture that leverages consistency models to achieve efficient real-time speech synthesis. The key highlights are: CM-TTS is designed to meet the demands of real-time speech synthesis, with its efficient few-step iterative generation process. It can also synthesize speech in a single step, eliminating the need for adversarial training and pre-trained model dependencies. The authors introduce weighted samplers to enhance the model training process. These samplers adjust the weights associated with different sampling points, mitigating biases introduced during training due to the inherent randomness of the sampling process. Comprehensive evaluations covering 12 objective and subjective metrics demonstrate the effectiveness and efficiency of CM-TTS, outperforming existing single-step speech synthesis systems in both fully supervised and zero-shot settings. The proposed architecture comprises four key components: a phoneme encoder, a variance adaptor, a consistency model-based decoder (CM-Decoder), and a vocoder. The CM-Decoder is the core of the system, generating mel-spectrograms through a consistency-based training process. The authors explore three different weighted sampling strategies (uniform, linear, and importance sampling) and find that the importance sampling approach leads to the best performance. Experiments on the VCTK, LJSpeech, and LibriSpeech datasets showcase CM-TTS's superior performance compared to baselines, including FastSpeech2, VITS, DiffSpeech, and DiffGAN-TTS, in both single-step and multi-step synthesis scenarios.
Stats
The proposed CM-TTS model has 28.6 million parameters. The average runtime of training under VCTK, LJSpeech, and LibriSpeech datasets is 34.2 hours, 42.8 hours, and 45.6 hours, respectively. The batch size during training is 32, and the models are trained for 300K steps. The initial learning rate is set to 10e-4, with an exponential decay rate of 0.999.
Quotes
"CM-TTS, a novel architecture grounded in consistency models (CMs), achieves top-quality speech synthesis in fewer steps without adversarial training or pre-trained model dependencies." "We further design weighted samplers to incorporate different sampling positions into model training with dynamic probabilities, ensuring unbiased learning throughout the entire training process."

Key Insights Distilled From

by Xiang Li,Fan... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00569.pdf
CM-TTS

Deeper Inquiries

How can the CM-TTS architecture be extended to other speech-related tasks beyond text-to-speech synthesis, such as speech enhancement or voice conversion

The CM-TTS architecture can be extended to other speech-related tasks beyond text-to-speech synthesis by adapting the model's components and training process to suit the specific requirements of tasks like speech enhancement or voice conversion. For speech enhancement, the consistency model in CM-TTS can be leveraged to improve the quality of noisy speech signals. By incorporating additional modules for noise reduction and signal enhancement, the model can learn to denoise input speech signals effectively. This can involve training the model on paired noisy and clean speech data to learn the mapping between the two and enhance speech quality. In the case of voice conversion, the consistency model can be utilized to capture the unique characteristics of different speakers and facilitate the conversion of one speaker's voice to another. By training the model on paired samples of different speakers, the model can learn to generate speech in the style and characteristics of a target speaker while preserving the linguistic content from the source speaker. By adapting the training data, loss functions, and model architecture to suit the specific requirements of speech enhancement or voice conversion tasks, the CM-TTS architecture can be extended effectively to address a broader range of speech-related applications.

What are the potential limitations of the consistency model approach, and how could they be addressed in future research

The consistency model approach in CM-TTS may have potential limitations that could be addressed in future research to enhance its performance and applicability. Some of these limitations include: Generalization to unseen data: The model's ability to generalize to unseen speakers or speaking styles may be limited by the training data's diversity. To address this, future research could focus on incorporating more diverse datasets to improve the model's generalization capabilities. Robustness to input variations: The model's performance may be affected by variations in input text length, linguistic content, or speaking rate. Future research could explore techniques to improve the model's robustness to such variations, such as dynamic padding strategies or adaptive normalization techniques. Scalability: As the complexity of the model increases, scalability issues may arise, impacting training time and computational resources. Future research could investigate optimization strategies to enhance the model's scalability without compromising performance. By addressing these limitations through advanced training techniques, data augmentation strategies, and model optimizations, the consistency model approach in CM-TTS can be further refined for improved performance and robustness.

Given the importance of speaker diversity in many real-world applications, how could the CM-TTS model be further improved to handle a wider range of speakers and speaking styles

To improve the CM-TTS model's handling of a wider range of speakers and speaking styles, several enhancements can be considered: Multi-speaker training: Training the model on a more extensive multi-speaker dataset can help improve its ability to generalize across different speakers. By exposing the model to a diverse range of voices, it can learn to capture the nuances of various speaking styles and accents. Speaker embeddings: Incorporating speaker embeddings into the model architecture can enable the model to differentiate between speakers more effectively. By encoding speaker-specific information in the embeddings, the model can adapt its synthesis output to match the characteristics of different speakers. Data augmentation: Augmenting the training data with variations in speaking styles, accents, and linguistic content can help the model learn to handle a broader range of inputs. Techniques like speed perturbation, pitch shifting, and text paraphrasing can introduce diversity into the training data. Fine-tuning and transfer learning: Fine-tuning the model on specific speaker datasets or using transfer learning from pre-trained models can enhance its performance on new speakers. By leveraging knowledge from related tasks or domains, the model can adapt more efficiently to unseen speakers. By implementing these enhancements, the CM-TTS model can be further improved to handle a wider range of speakers and speaking styles, making it more versatile and applicable in real-world scenarios.
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